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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

DEPARTMENT OF TRANSPORTATION
National Highway Traffic Safety
Administration
49 CFR Parts 523, 531, 533, 536, and
537
ENVIRONMENTAL PROTECTION
AGENCY
40 CFR Parts 85 and 86
[NHTSA–2018–0067; EPA–HQ–OAR–2018–
0283; FRL–9981–74–OAR]
RIN 2127–AL76; RIN 2060–AU09

The Safer Affordable Fuel-Efficient
(SAFE) Vehicles Rule for Model Years
2021–2026 Passenger Cars and Light
Trucks
Environmental Protection
Agency and National Highway Traffic
Safety Administration.
ACTION: Notice of proposed rulemaking.
AGENCY:

The National Highway Traffic
Safety Administration (NHTSA) and the
Environmental Protection Agency (EPA)
are proposing the ‘‘Safer Affordable
Fuel-Efficient (SAFE) Vehicles Rule for
Model Years 2021–2026 Passenger Cars
and Light Trucks’’ (SAFE Vehicles
Rule). The SAFE Vehicles Rule, if
finalized, would amend certain existing
Corporate Average Fuel Economy
(CAFE) and tailpipe carbon dioxide
emissions standards for passenger cars
and light trucks and establish new
standards, all covering model years
2021 through 2026. More specifically,
NHTSA is proposing new CAFE
standards for model years 2022 through
2026 and amending its 2021 model year
CAFE standards because they are no
longer maximum feasible standards, and
EPA is proposing to amend its carbon
dioxide emissions standards for model
years 2021 through 2025 because they
are no longer appropriate and
reasonable in addition to establishing
new standards for model year 2026. The
preferred alternative is to retain the
model year 2020 standards (specifically,
the footprint target curves for passenger
cars and light trucks) for both programs
through model year 2026, but comment
is sought on a range of alternatives
discussed throughout this document.
Compared to maintaining the post-2020
standards set forth in 2012, current
estimates indicate that the proposed
SAFE Vehicles Rule would save over
500 billion dollars in societal costs and
reduce highway fatalities by 12,700
lives (over the lifetimes of vehicles
through MY 2029). U.S. fuel
consumption would increase by about

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SUMMARY:

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half a million barrels per day (2–3
percent of total daily consumption,
according to the Energy Information
Administration) and would impact the
global climate by 3/1000th of one degree
Celsius by 2100, also when compared to
the standards set forth in 2012.
DATES: Comments: Comments are
requested on or before October 23, 2018.
Under the Paperwork Reduction Act,
comments on the information collection
provisions must be received by the
Office of Management and Budget
(OMB) on or before October 23, 2018.
See the SUPPLEMENTARY INFORMATION
section on ‘‘Public Participation,’’
below, for more information about
written comments.
Public Hearings: NHTSA and EPA
will jointly hold three public hearings
in Washington, DC; the Detroit, MI area;
and in the Los Angeles, CA area. The
agencies will announce the specific
dates and addresses for each hearing
location in a supplemental Federal
Register notice. The agencies will
accept oral and written comments to the
rulemaking documents, and NHTSA
will also accept comments to the Draft
Environmental Impact Statement (DEIS)
at these hearings. The hearings will start
at 10 a.m. local time and continue until
everyone has had a chance to speak. See
the SUPPLEMENTARY INFORMATION section
on ‘‘Public Participation,’’ below, for
more information about the public
hearings.
You may send comments,
identified by Docket No. EPA–HQ–
OAR–2018–0283 and/or NHTSA–2018–
0067, by any of the following methods:
• Federal eRulemaking Portal: http://
www.regulations.gov. Follow the
instructions for sending comments.
• Fax: EPA: (202) 566–9744; NHTSA:
(202) 493–2251.
• Mail:
Æ EPA: Environmental Protection
Agency, EPA Docket Center (EPA/DC),
Air and Radiation Docket, Mail Code
28221T, 1200 Pennsylvania Avenue
NW, Washington, DC 20460, Attention
Docket ID No. EPA–HQ–OAR–2018–
0283. In addition, please mail a copy of
your comments on the information
collection provisions for the EPA
proposal to the Office of Information
and Regulatory Affairs, Office of
Management and Budget (OMB), Attn:
Desk Officer for EPA, 725 17th St. NW,
Washington, DC 20503.
Æ NHTSA: Docket Management
Facility, M–30, U.S. Department of
Transportation, West Building, Ground
Floor, Rm. W12–140, 1200 New Jersey
Avenue SE, Washington, DC 20590.
• Hand Delivery:

ADDRESSES:

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Æ EPA: Docket Center (EPA/DC), EPA
West, Room B102, 1301 Constitution
Avenue NW, Washington, DC, Attention
Docket ID No. EPA–HQ–OAR–2018–
0283. Such deliveries are only accepted
during the Docket’s normal hours of
operation, and special arrangements
should be made for deliveries of boxed
information.
Æ NHTSA: West Building, Ground
Floor, Rm. W12–140, 1200 New Jersey
Avenue SE, Washington, DC 20590,
between 9 a.m. and 4 p.m. Eastern Time,
Monday through Friday, except Federal
holidays.
Instructions: All submissions received
must include the agency name and
docket number or Regulatory
Information Number (RIN) for this
rulemaking. All comments received will
be posted without change to http://
www.regulations.gov, including any
personal information provided. For
detailed instructions on sending
comments and additional information
on the rulemaking process, see the
‘‘Public Participation’’ heading of the
SUPPLEMENTARY INFORMATION section of
this document.
Docket: For access to the dockets to
read background documents or
comments received, go to http://
www.regulations.gov, and/or:
• For EPA: EPA Docket Center (EPA/
DC), EPA West, Room 3334, 1301
Constitution Avenue NW, Washington,
DC 20460. The Public Reading Room is
open from 8:30 a.m. to 4:30 p.m.,
Monday through Friday, excluding legal
holidays. The telephone number for the
Public Reading Room is (202) 566–1744.
• For NHTSA: Docket Management
Facility, M–30, U.S. Department of
Transportation, West Building, Ground
Floor, Rm. W12–140, 1200 New Jersey
Avenue SE, Washington, DC 20590. The
Docket Management Facility is open
between 9 a.m. and 4 p.m. Eastern Time,
Monday through Friday, except Federal
holidays.
FOR FURTHER INFORMATION CONTACT:
EPA: Christopher Lieske, Office of
Transportation and Air Quality,
Assessment and Standards Division,
Environmental Protection Agency, 2000
Traverwood Drive, Ann Arbor, MI
48105; telephone number: (734) 214–
4584; fax number: (734) 214–4816;
email address: lieske.christopher@
epa.gov, or contact the Assessment and
Standards Division, email address:
[email protected]. NHTSA: James
Tamm, Office of Rulemaking, Fuel
Economy Division, National Highway
Traffic Safety Administration, 1200 New
Jersey Avenue SE, Washington, DC
20590; telephone number: (202) 493–
0515.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
SUPPLEMENTARY INFORMATION:

I. Overview of Joint NHTSA/EPA Proposal
II. Technical Foundation for NPRM Analysis
III. Proposed CAFE and CO2 Standards for
MYs 2021–2026
IV. Alternative CAFE and GHG Standards
Considered for MYs 2021/22–2026
V. Proposed Standards, the Agencies’
Statutory Obligations, and Why the
Agencies Propose To Choose Them Over
the Alternatives
VI. Preemption of State and Local Laws
VII. Impacts of the Proposed CAFE and CO2
Standards
VIII. Impacts of Alternative CAFE and CO2
Standards Considered for MYs 2021/22–
2026
IX. Vehicle Classification
X. Compliance and Enforcement
XI. Public Participation
XII. Regulatory Notices and Analyses

I. Overview of Joint NHTSA/EPA
Proposal

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A. Executive Summary
In this notice, the National Highway
Traffic Safety Administration (NHTSA)
and the Environmental Protection
Agency (EPA) (collectively, ‘‘the
agencies’’) are proposing the ‘‘Safer
Affordable Fuel-Efficient (SAFE)
Vehicles Rule for Model Years 2021–
2026 Passenger Cars and Light Trucks’’
(SAFE Vehicles Rule). The proposed
SAFE Vehicles Rule would set
Corporate Average Fuel Economy
(CAFE) and carbon dioxide (CO2)
emissions standards, respectively, for
passenger cars and light trucks
manufactured for sale in the United
States in model years (MYs) 2021
through 2026.1 CAFE and CO2 standards
have the power to transform the vehicle
fleet and affect Americans’ lives in
significant, if not always immediately
obvious, ways. The proposed SAFE
Vehicles Rule seeks to ensure that
government action on these standards is
appropriate, reasonable, consistent with
law, consistent with current and
foreseeable future economic realities,
and supported by a transparent
assessment of current facts and data.
The agencies must act to propose and
finalize these standards and do not have
discretion to decline to regulate.
Congress requires NHTSA to set CAFE
standards for each model year.2
Congress also requires EPA to set
emissions standards for light-duty
vehicles if EPA has made an
‘‘endangerment finding’’ that the
pollutant in question—in this case,
1 NHTSA sets CAFE standards under the Energy
Policy and Conservation Act of 1975 (EPCA), as
amended by the Energy Independence and Security
Act of 2007 (EISA). EPA sets CO2 standards under
the Clean Air Act (CAA).
2 49 U.S.C. 32902.

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CO2—‘‘cause[s] or contribute[s] to air
pollution which may reasonably be
anticipated to endanger public health or
welfare.’’ 3 NHTSA and EPA are
proposing these standards concurrently
because tailpipe CO2 emissions
standards are directly and inherently
related to fuel economy standards,4 and
if finalized, these rules would apply
concurrently to the same fleet of
vehicles. By working together to
develop these proposals, the agencies
reduce regulatory burden on industry
and improve administrative efficiency.
Consistent with both agencies’
statutes, this proposal is entirely de
novo, based on an entirely new analysis
reflecting the best and most up-to-date
information available to the agencies at
the time of this rulemaking. The
agencies worked together in 2012 to
develop CAFE and CO2 standards for
MYs 2017 and beyond; in that
rulemaking action, EPA set CO2
standards for MYs 2017–2025, while
NHTSA set final CAFE standards for
MYs 2017–2021 and also put forth
‘‘augural’’ CAFE standards for MYs
2022–2025, consistent with EPA’s CO2
standards for those model years. EPA’s
CO2 standards for MYs 2022–2025 were
subject to a ‘‘mid-term evaluation,’’ by
which EPA bound itself through
regulation to re-evaluate the CO2
standards for those model years and to
undertake to develop new CO2
standards through a regulatory process
if it concluded that the previously
finalized standards were no longer
appropriate. EPA regulations on the
mid-term evaluation process required
EPA to issue a Final Determination no
later than April 1, 2018 on whether the
GHG standards for MY 2022–2025 lightduty vehicles remain appropriate under
3 42 U.S.C. 7521, see also 74 FR 66495 (Dec. 15,
2009) (‘‘Endangerment and Cause or Contribute
Findings for Greenhouse Gases under Section
202(a) of the Clean Air Act’’).
4 See, e.g., 75 FR 25324, at 25327 (May 7, 2010)
(‘‘The National Program is both needed and
possible because the relationship between
improving fuel economy and reducing tailpipe CO2
emissions is a very direct and close one. The
amount of those CO2 emissions is essentially
constant per gallon combusted of a given type of
fuel. Thus, the more fuel efficient a vehicle is, the
less fuel it burns to travel a given distance. The less
fuel it burns, the less CO2 it emits in traveling that
distance. [citation omitted] While there are
emission control technologies that reduce the
pollutants (e.g., carbon monoxide) produced by
imperfect combustion of fuel by capturing or
converting them to other compounds, there is no
such technology for CO2. Further, while some of
those pollutants can also be reduced by achieving
a more complete combustion of fuel, doing so only
increases the tailpipe emissions of CO2. Thus, there
is a single pool of technologies for addressing these
twin problems, i.e., those that reduce fuel
consumption and thereby reduce CO2 emissions as
well.’’)

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section 202(a) of the Clean Air Act.5 The
regulations also required the issuance of
a draft Technical Assessment Report
(TAR) by November 15, 2017, an
opportunity for public comment on the
draft TAR, and, before making a Final
Determination, an opportunity for
public comment on whether the GHG
standards for MY 2022–2025 remain
appropriate. In July 2016, the draft TAR
was issued for public comment jointly
by the EPA, NHTSA, and the California
Air Resources Board (CARB).6
Following the draft TAR, EPA published
a Proposed Determination for public
comment on December 6, 2016 and
provided less than 30 days for public
comments over major holidays.7 EPA
published the January 2017
Determination on EPA’s website and
regulations.gov finding that the MY
2022–2025 standards remained
appropriate.8
On March 15, 2017, President Trump
announced a restoration of the original
mid-term review timeline. The
President made clear in his remarks,
‘‘[i]f the standards threatened auto jobs,
then commonsense changes’’ would be
made in order to protect the economic
viability of the U.S. automotive
industry.’’ 9 In response to the
President’s direction, EPA announced in
a March 22, 2017, Federal Register
notice, its intention to reconsider the
Final Determination of the mid-term
evaluation of GHGs emissions standards
for MY 2022–2025 light-duty vehicles.10
The Administrator stated that EPA
would coordinate its reconsideration
with the rulemaking process to be
undertaken by NHTSA regarding CAFE
standards for cars and light trucks for
the same model years.
On August 21, 2017, EPA published a
notice in the Federal Register
announcing the opening of a 45-day
public comment period and inviting
stakeholders to submit any additional
comments, data, and information they
believed were relevant to the
Administrator’s reconsideration of the
5 40 CFR 86.1818–12(h)(1); see also 77 FR 62624
(Oct. 15, 2012).
6 81 FR 49217 (Jul. 27, 2016).
7 81 FR 87927 (Dec. 6, 2016).
8 Docket item EPA–HQ–OAR–2015–0827–6270
(EPA–420–R–17–001). This conclusion generated a
significant amount of public concern. See, e.g.,
Letter from Auto Alliance to Scott Pruitt,
Administrator, Environmental Protection Agency
(Feb. 21, 2017); Letter from Global Automakers to
Scott Pruitt, Administrator, Environmental
Protection Agency (Feb. 21, 2017).
9 See https://www.whitehouse.gov/briefingsstatements/remarks-president-trump-americancenter-mobility-detroit-mi/.
10 82 FR 14671 (Mar. 22, 2017).

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

January 2017 Determination.11 EPA held
a public hearing in Washington DC on
September 6, 2017.12 EPA received
more than 290,000 comments in
response to the August 21, 2017
notice.13
EPA has since concluded, based on
more recent information, that those
standards are no longer appropriate.14
NHTSA’s ‘‘augural’’ CAFE standards for
MYs 2022–2025 were not final in 2012
because Congress prohibits NHTSA
from finalizing new CAFE standards for
more than five model years in a single
rulemaking.15 NHTSA was therefore
obligated from the beginning to
undertake a new rulemaking to set
CAFE standards for MYs 2022–2025.
The proposed SAFE Vehicles Rule
begins the rulemaking process for both
agencies to establish new standards for
MYs 2022–2025 passenger cars and light
trucks. Standards are concurrently being
proposed for MY 2026 in order to
provide regulatory stability for as many
years as is legally permissible for both
agencies together.
Separately, the proposed SAFE
Vehicles Rule includes revised
standards for MY 2021 passenger cars
and light trucks. The information now
available and the current analysis
11 82

FR 39551 (Aug. 21, 2017).
FR 39976 (Aug. 23, 2017).
13 The public comments, public hearing
transcript, and other information relevant to the
Mid-term Evaluation are available in docket EPA–
HQ–OAR–2015–0827.
14 83 FR 16077 (Apr. 2, 2018).
15 49 U.S.C. 32902.

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suggest that the CAFE standards
previously set for MY 2021 are no
longer maximum feasible, and the CO2
standards previously set for MY 2021
are no longer appropriate. Agencies
always have authority under the
Administrative Procedure Act to revisit
previous decisions in light of new facts,
as long as they provide notice and an
opportunity for comment, and it is
plainly the best practice to do so when
changed circumstances so warrant.16
Thus, the proposed SAFE Vehicles
Rule would maintain the CAFE and CO2
standards applicable in MY 2020 for
MYs 2021–2026, while taking comment
on a wide range of alternatives,
including different stringencies and
retaining existing CO2 standards and the
augural CAFE standards.17 Table I–4
16 See

FCC v. Fox Television, 556 U.S. 502 (2009).
This does not mean that the miles per
gallon and grams per mile levels that were
estimated for the MY 2020 fleet in 2012 would be
the ‘‘standards’’ going forward into MYs 2021–2026.
Both NHTSA and EPA set CAFE and CO2 standards,
respectively, as mathematical functions based on
vehicle footprint. These mathematical functions
that are the actual standards are defined as ‘‘curves’’
that are separate for passenger cars and light trucks,
under which each vehicle manufacturer’s
compliance obligation varies depending on the
footprints of the cars and trucks that it ultimately
produces for sale in a given model year. It is the
MY 2020 CAFE and CO2 curves which we propose
would continue to apply to the passenger car and
light truck fleets for MYs 2021–2026. The mpg and
g/mi values which those curves would eventually
require of the fleets in those model years would be
known for certain only at the ends of each of those
model years. While it is convenient to discuss
CAFE and CO2 standards as a set ‘‘mpg,’’ ‘‘g/mi,’’
or ‘‘mpg-e’’ number, attempting to define those
values today will end up being inaccurate.
17 Note:

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below presents those alternatives. We
note further that prior to MY 2021, CO2
targets include adjustments reflecting
the use of automotive refrigerants with
reduced global warming potential
(GWP) and/or the use of technologies
that reduce the refrigerant leaks, and
optionally offsets for nitrous oxide and
methane emissions. In the interests of
harmonizing with the CAFE program,
EPA is proposing to exclude air
conditioning refrigerants and leakage,
and nitrous oxide and methane
emissions for compliance with CO2
standards after model year 2020 but
seeks comment on whether to retain
these element, and reinsert A/C leakage
offsets, and remain disharmonized with
the CAFE program. EPA also seeks
comment on whether to change existing
methane and nitrous oxide standards
that were finalized in the 2012 rule.
Specifically, EPA seeks information
from the public on whether those
existing standards are appropriate, or
whether they should be revised to be
less stringent or more stringent based on
any updated data.
While actual requirements will
ultimately vary for automakers
depending upon their individual fleet
mix of vehicles, many stakeholders will
likely be interested in the current
estimate of what the MY 2020 CAFE and
CO2 curves would translate to, in terms
of miles per gallon (mpg) and grams per
mile (g/mi), in MYs 2021–2026. These
estimates are shown in the following
tables.
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Table 1-1- Average of OEM'
s CAFE an d CO2 E sf 1mat ed R eqmreme nts for Passenger Cars
Model Year

Avg. ofOEMs' Est.
Requirements
CAFE (mpg)
C02 (g/mi)

2017
2018
2019
2020
2021
2022
2023
2024
2025
2026

39.1
40.5
42.0
43.7
43.7
43.7
43.7
43.7
43.7
43.7

220
210
201
191
204
204
204
204
204
204

Table 1-2- Average of OEM'
.
d R eqmrem ents for Light Trucks
s CAFE an d CO 2 E st1mate
Model Year

Avg. ofOEMs' Est.
Requirements
CAFE (mpg)
C02 (g/mi)

2017
2018
2019
2020
2021
2022
2023
2024
2025
2026

29.5
30.1
30.6
31.3
31.3
31.3
31.3
31.3
31.3
31.3

294
284
277
269
284
284
284
284
284
284

Table 1-3- Average of OEMs' Estimated CAFE and C02 Requirements (Passenger Cars
and Li~ht Trucks)

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34.0
34.9
35.8
36.9
36.9
36.9
36.9
37.0
37.0
37.0

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254
244
236
227
241
241
241
241
240
240

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EP24AU18.001

2017
2018
2019
2020
2021
2022
2023
2024
2025
2026

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Model Year

Avg. ofOEMs' Est.
Requirements
CAFE (mpg)
C0 2 (g/mi)

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In the tables above, estimated
required CO2 increases between MY
2020 and MY 2021 because, again, EPA
is proposing to exclude CO2-equivalent
emission improvements associated with

air conditioning refrigerants and leakage
(and, optionally, offsets for nitrous
oxide and methane emissions) after
model year 2020.
As explained above, the agencies are
taking comment on a wide range of

Ta bl e I- 4 - R egu atory AI ternat1ves

c urrently un der c ons1

eratwn

Alternative

Change in stringency

A/C
efficiency
and offcycle
..
provisiOns

Baseline/
No-Action

MY 2021 standards remain in place; MYs 2022-2025 augural
CAFE standards are finalized and GHG standards remain
unchanged; MY 2026 standards are set at MY 2025 levels
Existing standards through MY 2020, then 0%/year increases
for both passenger cars and light trucks, for MY s 2021-2026
Existing standards through MY 2020, then 0.5%/year increases
for both passenger cars and light trucks, for MY s 2021-2026
Existing standards through MY 2020, then 0.5%/year increases
for both passenger cars and light trucks, for MY s 2021-2026

No change

1
(Proposed)
2
3

4

5

6

7

8

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alternatives and have specifically
modeled eight alternatives (including
the proposed alternative) and the
current requirements (i.e., baseline/noaction). The modeled alternatives are
provided below:

Existing standards through MY 2020, then 1%/year increases
for passenger cars and 2%/year increases for light trucks, for
MYs 2021-2026
Existing standards through MY 2021, then 1%/year increases
for passenger cars and 2%/year increases for light trucks, for
MY s 2022-2026
Existing standards through MY 2020, then 2%/year increases
for passenger cars and 3 %/year increases for light trucks, for
MYs 2021-2026
Existing standards through MY 2020, then 2%/year increases
for passenger cars and 3 %/year increases for light trucks, for
MYs 2021-2026

Existing standards through MY 2021, then 2%/year increases
for passenger cars and 3 %/year increases for light trucks, for
MY s 2022-2026

No change
No change
Phase out
these
adjustments
overMYs
2022-2026
No change

C02 Equivalent
AC Refrigerant
Leakage,
Nitrous Oxide
and Methane
Emissions
Included for
Compliance?
Yes, for all
MYs 18
No, beginning
19
in MY 2021
No, beginning
in MY 2021
No, beginning
in MY 2021

No, beginning
in MY 2021

No change

No, beginning
in MY 2022

No change

No, beginning
in MY 2021

Phase out
these
adjustments
overMYs
2022-2026
No change

No, beginning
in MY 2021

No, beginning
in MY 2022

Summary of Rationale
Since finalizing the agencies’ previous
joint rulemaking in 2012 titled ‘‘Final
Rule for Model Year 2017 and Later

Light-Duty Vehicle Greenhouse Gas
Emission and Corporate Average Fuel
Economy Standards,’’ and even since
EPA’s 2016 and early 2017 ‘‘mid-term

evaluation’’ process, the agencies have
gathered new information, and have
performed new analysis. That new
information and analysis has led the

18 Carbon dioxide equivalent of air conditioning
refrigerant leakage, nitrous oxide and methane
emissions are included for compliance with the
EPA standards for all MYs under the baseline/no
action alternative. Carbon dioxide equivalent is

calculated using the Global Warming Potential
(GWP) of each of the emissions.
19 Beginning in MY 2021, the proposal provides
that the GWP equivalents of air conditioning

refrigerant leakage, nitrous oxide and methane
emissions would no longer be able to be included
with the tailpipe CO2 for compliance with tailpipe
CO2 standards.

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agencies to the tentative conclusion that
holding standards constant at MY 2020
levels through MY 2026 is maximum
feasible, for CAFE purposes, and
appropriate, for CO2 purposes.
Technologies have played out
differently in the fleet from what the
agencies assumed in 2012.
The technology to improve fuel
economy and reduce CO2 emissions has
not changed dramatically since prior
analyses were conducted: A wide
variety of technologies are still available
to accomplish the goals of the programs,
and a wide variety of technologies
would likely be used by industry to
accomplish these goals. There remains
no single technology that the majority of
vehicles made by the majority of
manufacturers can implement at low
cost without affecting other vehicle
attributes that consumers value more
than fuel economy and CO2 emissions.
Even when used in combination,
technologies that can improve fuel
economy and reduce CO2 emissions still
need to (1) actually work together and
(2) be acceptable to consumers and
avoid sacrificing other vehicle attributes
while also avoiding undue increases in
vehicle cost. Optimism about the costs
and effectiveness of many individual
technologies, as compared to recent
prior rounds of rulemaking, is
somewhat tempered; a clearer
understanding of what technologies are
already on vehicles in the fleet and how
they are being used, again as compared
to recent prior rounds of rulemaking,
means that technologies that previously
appeared to offer significant ‘‘bang for
the buck’’ may no longer do so.
Additionally, in light of the reality that
vehicle manufacturers may choose the
relatively cost-effective technology
option of vehicle lightweighting for a
wide array of vehicles and not just the
largest and heaviest, it is now
recognized that as the stringency of
standards increases, so does the
likelihood that higher stringency will
increase on-road fatalities. As it turns
out, there is no such thing as a free
lunch.20
Technology that can improve both
fuel economy and/or performance may
not be dedicated solely to fuel economy.
As fleet-wide fuel efficiency has
improved over time, additional
improvements have become both more
complicated and more costly. There are
two primary reasons for this

phenomenon. First, as discussed, there
is a known pool of technologies for
improving fuel economy and reducing
CO2 emissions. Many of these
technologies, when actually
implemented on vehicles, can be used
to improve other vehicle attributes such
as ‘‘zero to 60’’ performance, towing,
and hauling, etc., either instead of or in
addition to improving fuel economy and
reducing CO2 emissions. As one
example, a V6 engine can be
turbocharged and downsized so that it
consumes only as much fuel as an inline
4-cylinder engine, or it can be
turbocharged and downsized so that it
consumes less fuel than it would
originally have consumed (but more
than the inline 4-cylinder would) while
also providing more low-end torque. As
another example, a vehicle can be
lightweighted so that it consumes less
fuel than it would originally have
consumed, or so that it consumes the
same amount of fuel it would originally
have consumed but can carry more
content, like additional safety or
infotainment equipment. Manufacturers
employing ‘‘fuel-saving/emissionsreducing’’ technologies in the real world
make decisions regarding how to
employ that technology such that fewer
than 100% of the possible fuel-saving/
emissions-reducing benefits result. They
do this because this is what consumers
want, and more so than exclusively fuel
economy improvements.
This makes actual fuel economy gains
more expensive.
Thus, even though the technologies
may be largely the same, previous
assumptions about how much fuel can
be saved or how much emissions can be
reduced by employing various
technologies may not have played out as
prior analyses suggested, meaning that
previous assumptions about how much
it would cost to save that much fuel or
reduce that much in emissions fall
correspondingly short. For example, the
agencies assumed in the 2010 final rule
that dual clutch transmissions would be
widely used to improve fuel economy
due to expectations of strong
effectiveness and very low cost: In
practice, dual clutch transmissions had
significant customer acceptance issues,
and few manufacturers employ them in
the U.S. market today.21 The agencies
included some ‘‘technologies’’ in the
2012 final rule analysis that were
defined ambiguously and/or in ways

that precluded observation in the
known (MYs 2008 and 2010) fleets,
likely leading to double counting in
cases where the known vehicles already
reflected the assumed efficiency
improvement. For example, the agencies
assumed that transmission ‘‘shift
optimizers’’ would be available and
fairly widely used in MYs 2017–2025,
but involving software controls, a
‘‘technology’’ not defined in a way that
would be observed in the fleet (unlike,
for example, a dual clutch
transmission).
To be clear, this is no one’s ‘‘fault’’—
the CAFE and CO2 standards do not
require manufacturers to use particular
technologies in particular ways, and
both agencies’ past analyses generally
sought to illustrate technology paths to
compliance that were assumed to be as
cost-effective as possible. If
manufacturers choose different paths for
reasons not accounted for in regulatory
analysis, or choose to use technologies
differently from what the agencies
previously assumed, it does not
necessarily mean that the analyses were
unreasonable when performed. It does
mean, however, that the fleet ought to
be reflected as it stands today, with the
technology it has and as that technology
has been used, and consider what
technology remains on the table at this
point, whether and when it can
realistically be available for widespread
use in production, and how much it
would cost to implement.
Incremental additional fuel economy
benefits are subject to diminishing
returns.
As fleet-wide fuel efficiency improves
and CO2 emissions are reduced, the
incremental benefit of continuing to
improve/reduce inevitably decreases.
This is because, as the base level of fuel
economy improves, fewer gallons are
saved from subsequent incremental
improvements. Put simply, a one mpg
increase for vehicles with low fuel
economy will result in far greater
savings than an identical 1 mpg increase
for vehicles with higher fuel economy,
and the cost for achieving a one-mpg
increase for low fuel economy vehicles
is far less than for higher fuel economy
vehicles. This means that improving
fuel economy is subject to diminishing
returns. Annual fuel consumption can
be calculated as follows:

20 Mankiw, N. Gregory, Principles of
Macroeconomics, Sixth Edition, 2012, at 4.
21 In fact, one manufacturer saw enough customer
pushback that it launched a buyback program. See,

e.g., Steve Lehto, ‘‘What you need to know about
the settlement for Ford Powershift owners,’’ Road
and Track, Oct. 19, 2017. Available at https://
www.roadandtrack.com/car-culture/a10316276/

what-you-need-to-know-about-the-proposedsettlement-for-ford-powershift-owners/ (last
accessed Jul. 2, 2018).

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For purposes of illustration, assume a
vehicle owner who drives a light vehicle
15,000 miles per year (a typical
assumption for analytical purposes).22 If
that owner trades in a vehicle with fuel
economy of 15 mpg for one with fuel
economy of 20 mpg, the owner’s annual
fuel consumption would drop from
1,000 gallons to 750 gallons—saving 250
gallons annually. If, however, that
owner were to trade in a vehicle with
fuel economy of 30 mpg for one with
fuel economy of 40 mpg, the owner’s
annual gasoline consumption would
drop from 500 gallons/year to 375
gallons/year—only 125 gallons even
though the mpg improvement is twice

as large. Going from 40 to 50 mpg would
save only 75 gallons/year. Yet, each
additional fuel economy improvement
becomes much more expensive as the
low-hanging fruit of low-cost
technological improvement options are
picked.23 Automakers, who must
nonetheless continue adding technology
to improve fuel economy and reduce
CO2 emissions, will either sacrifice
other performance attributes or raise the
price of vehicles—neither of which is
attractive to most consumers.
If fuel prices are high, the value of
those gallons may be enough to offset
the cost of further fuel economy
improvements, but (1) the most recent

reference case projections in the Energy
Information Administration’s (EIA’s)
Annual Energy Outlook (AEO 2017 and
AEO 2018) do not indicate particularly
high fuel prices in the foreseeable
future, given underlying assumptions,24
and (2) as the baseline level of fuel
economy continues to increase, the
marginal cost of the next gallon saved
similarly increases with the cost of the
technologies required to meet the
savings. The following figure illustrates
the fact that fuel savings and
corresponding avoided costs diminish
with increasing fuel economy, showing
the same basic pattern as a 2014
illustration developed by EIA.25

22 A different vehicle-miles-traveled (VMT)
assumption would change the absolute numbers in
the example, but would not change the
mathematical principles. Today’s analysis uses
mileage accumulation schedules that average about
15,000 miles annually over the first six years of
vehicle operation.
23 The examples in the text above are presented
in mpg because that is a metric which should be
readily understandable to most readers, but the
example would hold true for grams of CO2 per mile
as well. If a vehicle emits 300 g/mi CO2, a 20

percent improvement is 60 g/mi, so that the vehicle
would emit 240 g/mi. At 180 g/mi, a 20%
improvement is 36 g/mi, so the vehicle would get
144 g/mi. In order to continue achieving similarly
large (on an absolute basis) emissions reductions,
mathematics require the percentage reduction to
continue increasing.
24 The U.S. Energy Information Administration
(EIA) is the statistical and analytical agency within
the U.S. Department of Energy (DOE). EIA is the
nation’s premiere source of energy information, and
every fuel economy rulemaking since 2002 (and

every joint CAFE and CO2 rulemaking since 2009)
has applied fuel price projections from EIA’s
Annual Energy Outlook (AEO). AEO projections,
documentation, and underlying data and estimates
are available at https://www.eia.gov/outlooks/aeo/.
25 Today in Energy: Fuel economy improvements
show diminishing returns in fuel savings, U.S.
Energy Information Administration (Jul. 11, 2014),
https://www.eia.gov/todayinenergy/detail.php?id=
17071.

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This effect is mathematical in nature
and long-established, but when
combined with relatively low fuel prices
potentially through 2050, and the
likelihood that a large majority of
American consumers could
consequently continue to place a higher
value on vehicle attributes other than
fuel economy, it makes manufacturers’
ability to sell light vehicles with everhigher fuel economy and ever-lower
carbon dioxide emissions increasingly
difficult. Put more simply, if gas is
cheap and each additional improvement
saves less gas anyway, most consumers
would rather spend their money on
attributes other than fuel economy when
they are considering a new vehicle
purchase, whether that is more safety
technology, a better infotainment
package, a more powerful powertrain, or
other features (or, indeed, they may
prefer to spend the savings on
something other than automobiles).
Manufacturers trying to sell consumers
more fuel economy in such
circumstances may convince consumers
who place weight on efficiency and
reduced carbon emissions, but
consumers decide for themselves what
attributes are worth to them. And while
some contend that consumers do not
sufficiently consider or value future fuel
savings when making vehicle
purchasing decisions,26 information
regarding the benefits of higher fuel
economy has never been made more
readily available than today, with a host
of online tools and mandatory
prominent disclosures on new vehicles
on the Monroney label showing fuel
savings compared to average vehicles.
This is not a question of ‘‘if you build
it, they will come.’’ Despite the
widespread availability of fuel economy
information, and despite manufacturers
building and marketing vehicles with
higher fuel economy and increasing
their offerings of hybrid and electric
vehicles, in the past several years as gas
prices have remained low, consumer
preferences have shifted markedly away
from higher-fuel-economy smaller and
midsize passenger vehicles toward
crossovers and truck-based utility
vehicles.27 Some consumers plainly
26 In docket numbers EPA–HQ–OAR–2015–0827
and NHTSA–2016–0068, see comments submitted
by, e.g., Consumer Federation of America (NHTSA–
2016–0068–0054, at p. 57, et seq.) and the
Environmental Defense Fund (EPA–HQ–OAR–
2015–0827–4086, at p. 18, et seq.).
27 Carey, N. Lured by rising SUV sales,
automakers flood market with models, Reuters
(Mar. 29, 2018), available at https://
www.reuters.com/article/us-autoshow-new-yorksuvs/lured-by-rising-suv-sales-automakers-floodmarket-with-models-idUSKBN1H50KI (last accessed
Jun. 13, 2018). Many commentators have recently
argued that manufacturers are deliberately

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value fuel economy and low CO2
emissions above other attributes, and
thanks in part to CAFE and CO2
standards, they have a plentiful
selection of high-fuel economy and low
CO2-emitting vehicles to choose from,
but those consumers represent a
relatively small percentage of buyers.
Changed petroleum market has
supported a shift in consumer
preferences.
In 2012, the agencies projected fuel
prices would rise significantly, and the
United States would continue to rely
heavily upon imports of oil, subjecting
the country to heightened risk of price
shocks.28 Things have changed
significantly since 2012, with fuel prices
significantly lower than anticipated, and
projected to remain low through 2050.
Furthermore, the global petroleum
market has shifted dramatically with the
United States taking advantage of its
own oil supplies through technological
advances that allow for cost-effective
extraction of shale oil. The U.S. is now
the world’s largest oil producer and
expected to become a net petroleum
exporter in the next decade.29
At least partially in response to lower
fuel prices, consumers have moved
more heavily into crossovers, sport
utility vehicles and pickup trucks, than
anticipated at the time of the last
rulemaking. Because standards are
based on footprint and specified
separately for passenger cars and light
trucks, these shifts do not necessarily
pose compliance challenges by
themselves, but they tend to reduce the
overall average fuel economy rates and
increasing vehicle footprint size in order to get
‘‘easier’’ CAFE and CO2 standards. This
misunderstands, somewhat, how the footprintbased standards work. While it is correct that largerfootprint vehicles have less stringent ‘‘targets,’’ the
difficulty of compliance rests in how far above or
below those vehicles are as compared to their
targets, and more specifically, whether the
manufacturer is selling so many vehicles that are far
short of their targets that they cannot average out
to compliant levels through other vehicles sold that
beat their targets. For example, under the CAFE
program, a manufacturer building a fleet of largerfootprint vehicles may have an objectively lower
mpg-value compliance obligation than a
manufacturer building a more mixed fleet, but it
may still be more challenging for the first
manufacturer to reach its compliance obligation if
it is selling only very-low-mpg variants at any given
footprint. There is only so much that increasing
footprint makes it ‘‘easier’’ for a manufacturer to
reach compliance.
28 The 2012 final rule analysis relied on the
Energy Information Administration’s Annual
Energy Outlook 2012 Early Release, which assumed
significantly higher fuel prices than the AEO 2017
(or AEO 2018) currently available. See 77 FR 62624,
62715 (Oct. 15, 2012) for the 2012 final rule’s
description of the fuel price estimates used.
29 Annual Energy Outlook 2018, U.S. Energy
Information Administration, at 53 (Feb. 6, 2018),
https://www.eia.gov/outlooks/aeo/pdf/
AEO2018.pdf.

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increase the overall average CO2
emission rates of the new vehicle fleet.
Consumers are also demonstrating a
preference for more powerful engines
and vehicles with higher seating
positions and ride height (and
accompanying mass increase relative to
footprint) 30—all of which present
challenges for achieving increased fuel
economy levels and lower CO2 emission
rates.
The Consequence of Unreasonable
Fuel Economy and CO2 Standards:
Increased vehicle prices keep consumers
in older, dirtier, and less safe vehicles.
Consumers tend to avoid purchasing
things that they neither want or need.
The analysis in today’s proposal moves
closer to being able to represent this fact
through an improved model for vehicle
scrappage rates. While neither this nor
a sales response model, also included in
today’s analysis, nor the combination of
the two, are consumer choice models,
today’s analysis illustrates market-wide
impacts on the sale of new vehicles and
the retention of used vehicles. Higher
vehicle prices, which result from morestringent fuel economy standards, have
an effect on consumer purchasing
decisions. As prices increase, the
market-wide incentive to extract
additional travel from used vehicles
increases. The average age of the inservice fleet has been increasing, and
when fleet turnover slows, not only
does it take longer for fleet-wide fuel
economy and CO2 emissions to improve,
but also safety improvements, criteria
pollutant emissions improvements,
many other vehicle attributes that also
provide societal benefits take longer to
be reflected in the overall U.S. fleet as
well because of reduced turnover.
Raising vehicle prices too far, too fast,
such as through very stringent fuel
economy and CO2 emissions standards
(especially considering that, on a fleetwide basis, new vehicle sales and
turnover do not appear strongly
responsive to fuel economy), has effects
beyond simply a slowdown in sales.
Improvements over time have better
longer-term effects simply by not
alienating consumers, as compared to
great leaps forward that drive people out
of the new car market or into vehicles
that do not meet their needs. The
industry has achieved tremendous gains
in fuel economy over the past decade,
and these increases will continue at
least through 2020.
Along with these gains, there have
also been tremendous increases in
vehicle prices, as new vehicles become
increasingly unaffordable—with the
average new vehicle transaction price
30 See

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recently exceeding $36,000—up by
more than $3,000 since 2014 alone.31 In
fact, a recent independent study
indicated that the average new car price
is unaffordable to median-income
families in every metropolitan region in
the United States except one:
Washington, DC.32 That analysis used
the historically accepted approach that
consumers should make a downpayment of at least 20% of a vehicle’s
purchase price, finance for no longer
than four years, and make payments of
10% or less of the consumer’s annual
income to car payments and insurance.
But the market looks nothing like that
these days, with average financing terms
of 68 months, and an increasing
proportion exceeding 72 or even 84
months.33 Longer financing terms may

sradovich on DSK3GMQ082PROD with PROPOSALS2

31 See, e.g., Average New-Car Prices Rise Nearly
4 Percent for January 2018 On Shifting Sales Mix,
According To Kelley Blue Book, Kelley Blue Book,
https://mediaroom.kbb.com/2018-02-01-AverageNew-Car-Prices-Rise-Nearly-4-Percent-For-January2018-On-Shifting-Sales-Mix-According-To-KelleyBlue-Book (last accessed Jun. 15, 2018).
32 Bell, C. What’s an ‘affordable’ car where you
live? The answer may surprise you, Bankrate.com
(Jun. 28, 2017), available at https://
www.bankrate.com/auto/new-car-affordabilitysurvey/ (last accessed Jun. 15, 2018).
33 Average Auto Loan Interest Rates: 2018 Facts
and Figures, ValuePenguin, available at https://

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allow a consumer to keep their monthly
payment affordable but can have serious
potential financial consequences.
Longer-term financing leads (generally)
to higher interest rates, larger finance
charges and total consumer costs, and a
longer period of time with negative
equity. In 2012, the agencies expected
prices to increase under the standards
announced at that time. The agencies
estimated that, compared to a
continuation of the model year 2016
standards, the standards issued through
model year 2025 would eventually
increase average prices by about $1,500–
$1,800.34 35 36 Circumstances have
www.valuepenguin.com/auto-loans/average-autoloan-interest-rates (last accessed Jun. 15, 2018).
34 77 FR 62624, 62666 (Oct. 15, 2012).
35 The $1,500 figure reported in 2012 by NHTSA
reflected application of carried-forward credits in
model year 2025, rather than an achieved CAFE
level that could be sustainably compliant beyond
2025 (with standards remaining at 2025 levels). As
for the 2016 draft TAR, NHTSA has since updated
its modeling approach to extend far enough into the
future that any unsustainable credit deficits are
eliminated. Like analyses published by EPA in
2016, 2017, and early 2018, the $1,800 figure
reported in 2012 by EPA did not reflect either
simulation of manufacturers’ multiyear plans to
progress from the initial MY 2008 fleet to the MY
2025 fleet or any accounting for manufacturers’
potential application of banked credits. Today’s
analysis of both CAFE and CO2 standards accounts

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changed, the analytical methods and
inputs have been updated (including
updates to address issues still present in
analyses published in 2016, 2017, and
early 2018), and today, the analysis
suggests that, compared to the proposed
standards today, the previously-issued
standards would increase average
vehicle prices by about $2,100. While
today’s estimate is similar in magnitude
to the 2012 estimate, it is relative to a
baseline that includes increases in
stringency between MY 2016 and MY
2020. Compared to leaving vehicle
technology at MY 2016 levels, today’s
analysis shows the previously-issued
standards through model year 2025
could eventually increase average
vehicle prices by approximately $2,700.
A pause in continued increases in fuel
economy standards, and cost increases
attributable thereto, is appropriate.

explicitly for multiyear planning and credit
banking.
36 While EPA did not refer to the reported $1,800
as an estimate of the increase in average prices,
because EPA did not assume that manufacturers
would reduce profit margins, the $1,800 estimate is
appropriately interpreted as an estimate of the
average increase in vehicle prices.

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Energy Conservation
EPCA requires that NHTSA, when
determining the maximum feasible
levels of CAFE standards, consider the
need of the Nation to conserve energy.
However, EPCA also requires that
NHTSA consider other factors, such as
37 Data on new vehicle prices are from U.S.
Bureau of Economic Analysis, National Income and
Product Accounts, Supplemental Table 7.2.5S, Auto
and Truck Unit Sales, Production, Inventories,
Expenditures, and Price (https://www.bea.gov/
iTable/iTable.cfm?reqid=19&step=2#reqid=
19&step=3&isuri=1&1921=underlying&1903=2055,
last accessed Jul. 20, 2018). Median Household
Income data are from U.S. Census Bureau, Table A–
1, Households by Total Money Income, Race, and
Hispanic Origin of Householder: 1967 to 2016
(https://www.census.gov/data/tables/2017/demo/
income-poverty/p60-259.html, last accessed Jul. 20,
2018).

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technological feasibility and economic
practicability. The analysis suggests
that, compared to the standards issued
previously for MYs 2021–2025, today’s
proposed rule will eventually (by the
early 2030s) increase U.S. petroleum
consumption by about 0.5 million
barrels per day—about two to three
percent of projected total U.S.
consumption. While significant, this
additional petroleum consumption is,
from an economic perspective, dwarfed
by the cost savings also projected to
result from today’s proposal, as
indicated by the consideration of net
benefits appearing below.
Safety Benefits From Preferred
Alternative
Today’s proposed rule is anticipated
to prevent more than 12,700 on-road
fatalities 38 and significantly more
injuries as compared to the standards
set forth in the 2012 final rule over the
lifetimes of vehicles as more new, safer
vehicles are purchased than the current
(and augural) standards. A large portion
of these safety benefits will come from
38 Over

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improved fleet turnover as more
consumers will be able to afford newer
and safer vehicles.
Recent NHTSA analysis shows that
the proportion of passengers killed in a
vehicle 18 or more model years old is
nearly double that of a vehicle three
model years old or newer.39 As the
average car on the road is approaching
12 years old, apparently the oldest in
our history,40 major safety benefits will
occur by reducing fleet age. Other safety
benefits will occur from other areas
such as avoiding the increased driving
39 Passenger Vehicle Occupant Injury Severity by
Vehicle Age and Model Year in Fatal Crashes,
Traffic Safety Facts Research Note, DOT HS 812
528. Washington, DC: National Highway Traffic
Safety Administration. April 2018.
40 See, e.g., IHS Markit, Vehicles Getting Older:
Average Age of Light Cars and Trucks in U.S. Rises
Again in 2016 to 11.5 years, IHS Markit Says, IHS
Markit (Nov. 22, 2016), http://news.ihsmarkit.com/
press-release/automotive/vehicles-getting-olderaverage-age-light-cars-and-trucks-us-rises-again-201
(‘‘. . . consumers are continuing the trend of
holding onto their vehicles longer than ever. As of
the end of 2015, the average length of ownership
measured a record 79.3 months, more than 1.5
months longer than reported in the previous year.
For used vehicles, it is nearly 66 months. Both are
significantly longer lengths of ownership since the
same measure a decade ago.’’).

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Preferred Alternative
For all of these reasons, the agencies
are proposing to maintain the MY 2020
fuel economy and CO2 emissions
standards for MYs 2021–2026. Our goal
is to establish standards that promote
both energy conservation and safety, in
light of what is technologically feasible
and economically practicable, as
directed by Congress.

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that would otherwise result from higher
fuel efficiency (known as the rebound
effect) and avoiding the mass reductions
in passenger cars that might otherwise
be required to meet the standards
established in 2012.41 Together these
and other factors lead to estimated
annual fatalities under the proposed
standards that are significantly
reduced 42 relative to those that would
occur under current (and augural)
standards.

The Preferred Alternative Would Have
Negligible Environmental Impacts on
Air Quality
Improving fleet turnover will result in
consumers getting into newer and
cleaner vehicles, accelerating the rate at
which older, more-polluting vehicles
are removed from the roadways. Also,
reducing fuel economy (relative to
levels that would occur under
previously-issued standards) would
increase the marginal cost of driving
newer vehicles, reducing mileage
accumulated by those vehicles, and

reducing corresponding emissions. On
the other hand, increasing fuel
consumption would increase emissions
resulting from petroleum refining and
related ‘‘upstream’’ processes. Our
analysis shows that none of the
regulatory alternatives considered in
this proposal would noticeably impact
net emissions of smog-forming or other
‘‘criteria’’ or toxic air pollutants, as
illustrated by the following graph. That
said, the resultant tailpipe emissions
reductions should be especially
beneficial to highly trafficked corridors.

Climate Change Impacts From Preferred
Alternative
The estimated effects of this proposal
in terms of fuel savings and CO2
emissions, again perhaps somewhat
counter-intuitively, is relatively small as
compared to the 2012 final rule.43

NHTSA’s Environmental Impact
Statement performed for this
rulemaking shows that the preferred
alternative would result in 3/1,000ths of
a degree Celsius increase in global
average temperatures by 2100, relative
to the standards finalized in 2012. On a
net CO2 basis, the results are similarly

minimal. The following graph compares
the estimated atmospheric CO2
concentration (789.76 ppm) in 2100
under the proposed standards to the
estimated level (789.11 ppm) under the
standards set forth in 2012—or an 8/
100ths of a percentage increase:

41 The agencies are specifically requesting
comment on the appropriateness and level of the
effects of the rebound effect. The agencies also seek
comment on changes as compared to the 2012
modeling relating to mass reduction assumptions.
During that rulemaking, the analysis limited the
amount of mass reduction assumed for certain
vehicles, which impacted the results regarding
potential for adverse safety effects, even while
acknowledging that manufacturers would not

necessarily choose to avoid mass reductions in the
ways that the agencies assumed. See, 77 FR 623624,
62763 (Oct. 15, 2012). By choosing where and how
to limit assumed mass reduction, the 2012 rule’s
safety analysis reduced the projected apparent risk
to safety associated with aggressive fuel economy
and CO2 targets. That specific assumption has been
removed for today’s analysis.
42 The reduction in annual fatalities varies each
calendar year, averaging 894 fewer fatalities

annually for the CAFE program and 1,150 fewer
fatalities for the CO2 program over calendar years
2036–2045.
43 Counter-intuitiveness is relative, however. The
estimated effects of the 2012 final rule on climate
were similarly small in magnitude, as shown in the
Final EIS accompanying that rule and available on
NHTSA’s website.

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Maintaining the MY 2020 curves for
MYs 2021–2026 will save American
consumers, the auto industry, and the
public a considerable amount of money

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as compared to if EPA retained the
previously-set CO2 standards and
NHTSA finalized the augural standards.
This was identified as the preferred
alternative, in part, because it
maximizes net benefits compared to the

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other alternatives analyzed, recognizing
the statutory considerations for both
agencies. Comment is sought on
whether this is an appropriate basis for
selection.

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Net Benefits From Preferred Alternative

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These estimates, reported as changes
relative to impacts under the standards
issued in 2012, account for impacts on
vehicles produced during model years
2016–2029, as well as (through changes
in utilization) vehicles produced in
earlier model years, throughout those
vehicles’ useful lives. Reported values
are in 2016 dollars, and reflect threepercent and seven-percent discount
rates. Under CAFE standards, costs are
estimated to decrease by $502 billion
overall at a three-percent discount rate
($335 billion at a seven-percent
discount rate); benefits are estimated to
decrease by $326 billion at a threepercent discount rate ($204 billion at a
seven-percent discount rate). Thus, net
benefits are estimated to increase by
$176 billion at a three-percent discount
rate and $132 billion at a seven-percent
discount rate. The estimated impacts
under CO2 standards are similar, with
net benefits estimated to increase by
$201 billion at a three-percent discount
rate and $141 billion at a seven-percent
discount rate.

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Compliance Flexibilities
This proposal also seeks comment on
a variety of changes to NHTSA’s and
EPA’s compliance programs for CAFE
and CO2 as well as related programs.
Compliance flexibilities can generally
be grouped into two categories. The first
category are those compliance
flexibilities that reduce unnecessary
compliance costs and provide for a more
efficient program. The second category
of compliance flexibilities are those that
distort the market—such as by
incentivizing the implementation of one
type of technology by providing credit
for compliance in excess of real-world
fuel savings.
Both programs provide for the
generation of credits based upon fleetwide over-compliance, provide for
adjustments to the test measured value
of each individual vehicle based upon
the implementation of certain fuel
saving technologies, and provide
additional incentives for the
implementation of certain preferred
technologies (regardless of actual fuel
savings). Auto manufacturers and others
have petitioned for a host of additional

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adjustment- and incentive-type
flexibilities, where there is not always
consumer interest in the technologies to
be incentivized nor is there necessarily
clear fuel-saving and emissionsreducing benefit to be derived from that
incentivization. The agencies seek
comment on all of those requests as part
of this proposal.
Over-compliance credits, which can
be built up in part through use of the
above-described per-vehicle
adjustments and incentives, can be
saved and either applied retroactively to
accounts for previous non-compliance,
or carried forward to mitigate future
non-compliance. Such credits can also
be traded to other automakers for cash
or for other credits for different fleets.
But such trading is not pursued openly.
Under the CAFE program, the public is
not made aware of inter-automaker
trades, nor are shareholders. And even
the agencies are not informed of the
price of credits. With the exception of
statutorily-mandated credits, the
agencies seek comment on all aspects of
the current system. The agencies are
particularly interested in comments on
flexibilities that may distort the market.

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The agencies seek comment as to
whether some adjustments and nonstatutory incentives and other
provisions should be eliminated and
stringency levels adjusted accordingly.
In general, well-functioning banking
and trading provisions increase market
efficiency and reduce the overall costs
of compliance with regulatory
objectives. The agencies request
comment on whether the current system
as implemented might need
improvements to achieve greater
efficiencies. We seek comment on
specific programmatic changes that
could improve compliance with current
standards in the most efficient way,
ranging from requiring public disclosure
of some or all aspects of credit trades,
to potentially eliminating credit trading
in the CAFE program. We request
commenters to provide any data,
evidence, or existing literature to help
agency decision-making.

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One National Standard
Setting appropriate and maximum
feasible fuel economy and tailpipe CO2
emissions standards requires regulatory
efficiency. This proposal addresses a
fundamental and unnecessary
complication in the currently-existing
regulatory framework, which is the
regulation of GHG emissions from
passenger cars and light trucks by the
State of California through its GHG
standards and Zero Emission Vehicle
(ZEV) mandate and subsequent
adoption of these standards by other
States. Both EPCA and the CAA
preempt State regulation of motor
vehicle emissions (in EPCA’s case,
standards that are related to fuel
economy standards). The CAA gives
EPA the authority to waive preemption
for California under certain
circumstances. EPCA does not provide
for a waiver of preemption under any
circumstances. In short, the agencies
propose to maintain one national
standard—a standard that is set
exclusively by the Federal government.
Proposed Withdrawal of California’s
Clean Air Act Preemption Waiver
EPA granted a waiver of preemption
to California in 2013 for its ‘‘Advanced
Clean Car’’ regulations, composed of its
GHG standards, its ‘‘Low Emission
Vehicle (LEV)’’ program and the ZEV
program,44 and, as allowed under the
CAA, a number of other States adopted
California’s standards.45 The CAA states
that EPA shall not grant a waiver of
preemption if EPA finds that
California’s determination that its
44 78

FR 2112 (Jan. 9, 2013).
Section 177, 42 U.S.C. 7507.

45 CAA

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standards are, in the aggregate, at least
as protective of public health and
welfare as applicable Federal standards,
is arbitrary and capricious; that
California does not need its own
standards to meet compelling or
extraordinary conditions; or that such
California standards and accompanying
enforcement procedures are not
consistent with Section 202(a) of the
CAA. In this proposal, EPA is proposing
to withdraw the waiver granted to
California in 2013 for the GHG and ZEV
requirements of its Advanced Clean
Cars program, in light of all of these
factors.
Attempting to solve climate change,
even in part, through the Section 209
waiver provision is fundamentally
different from that section’s original
purpose of addressing smog-related air
quality problems. When California was
merely trying to solve its air quality
issues, there was a relativelystraightforward technology solution to
the problems, implementation of which
did not affect how consumers lived and
drove. Section 209 allowed California to
pursue additional reductions to address
its notorious smog problems by
requiring more stringent standards, and
allowed California and other States that
failed to comply with Federal air quality
standards to make progress toward
compliance. Trying to reduce carbon
emissions from motor vehicles in any
significant way involves changes to the
entire vehicle, not simply the addition
of a single or a handful of control
technologies. The greater the emissions
reductions are sought, the greater the
likelihood that the characteristics and
capabilities of the vehicle currently
sought by most American consumers
will have to change significantly. Yet,
even decades later, California continues
to be in widespread non-attainment
with Federal air quality standards.46 In
the past decade, California has
disproportionately focused on GHG
emissions. Parts of California have a real
and significant local air pollution
problem, but CO2 is not part of that local
problem.
California’s Tailpipe CO2 Emissions
Standards and ZEV Mandate Conflict
With EPCA
Moreover, California regulation of
tailpipe CO2 emissions, both through its
GHG standards and ZEV program,
conflicts directly and indirectly with
EPCA and the CAFE program. EPCA
expressly preempts State standards
46 See California Nonattainment/Maintenance
Status for Each County by Year for All Criteria
Pollutants, current as of May 31, 2018, at https://
www3.epa.gov/airquality/greenbook/anayo_ca.html
(last accessed June 15, 2018).

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related to fuel economy. Tailpipe CO2
standards, whether in the form of fleetwide CO2 limits or in the form of
requirements that manufacturers selling
vehicles in California sell a certain
number of low- and no-tailpipe-CO2
emissions vehicles as part of their
overall sales, are unquestionably related
to fuel economy standards. Standards
that control tailpipe CO2 emissions are
de facto fuel economy standards
because CO2 is a direct and inevitable
byproduct of the combustion of carbonbased fuels to make energy, and the vast
majority of the energy that powers
passenger cars and light trucks comes
from carbon-based fuels.
Improving fuel economy means
getting the vehicle to go farther on a
gallon of gas; a vehicle that goes farther
on a gallon of gas produces less CO2 per
unit of distance; therefore, improving
fuel economy necessarily reduces
tailpipe CO2 emissions, and reducing
CO2 emissions necessarily improves fuel
economy. EPCA therefore necessarily
preempts California’s Advanced Clean
Cars program to the extent that it
regulates or prohibits tailpipe CO2
emissions. Section VI of this proposal,
below, discusses the CAA waiver and
EPCA preemption in more detail.
Eliminating California’s regulation of
fuel economy pursuant to Congressional
direction will provide benefits to the
American public. The automotive
industry will, appropriately, deal with
fuel economy standards on a national
basis—eliminating duplicative
regulatory requirements. Further,
elimination of California’s ZEV program
will allow automakers to develop such
vehicles in response to consumer
demand instead of regulatory mandate.
This regulatory mandate has required
automakers to spend tens of billions of
dollars to develop products that a
significant majority of consumers have
not adopted, and consequently to sell
such products at a loss. All of this is
paid for through cross subsidization by
increasing prices of other vehicles not
just in California and other States that
have adopted California’s ZEV mandate,
but throughout the country.
Request for Comment
The agencies look forward to all
comments on this proposal, and wish to
emphasize that obtaining public input is
extremely important to us in selecting
from among the alternatives in a final
rule. While the agencies and the
Administration met with a variety of
stakeholders prior to issuance of this
proposal, those meetings have not
resulted in a predetermined final rule
outcome. The Administrative Procedure
Act requires that agencies provide the

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public with adequate notice of a
proposed rule followed by a meaningful
opportunity to comment on the rule’s
content. The agencies are committed to
following that directive.
II. Technical Foundation for NPRM
Analysis

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A. Basics of CAFE and CO2 Standards
Analysis
The agencies’ analysis of CAFE and
CO2 standards involves two basic
elements: first, estimating ways each
manufacturer could potentially respond
to a given set of standards in a manner
that considers potential consumer
response; and second, estimating
various impacts of those responses.
Estimating manufacturers’ potential
responses involves simulating
manufacturers’ decision-making
processes regarding the year-by-year
application of fuel-saving technologies
to specific vehicles. Estimating impacts
involves calculating resultant changes
in new vehicle costs, estimating a
variety of costs (e.g., for fuel) and effects
(e.g., CO2 emissions from fuel
combustion) occurring as vehicles are
driven over their lifetimes before
eventually being scrapped, and
estimating the monetary value of these
effects. Estimating impacts also involves
consideration of the response of
consumers—e.g., whether consumers
will purchase the vehicles and in what
quantities. Both of these basic analytical
elements involve the application of
many analytical inputs.
The agencies’ analysis uses the CAFE
model to estimate manufacturers’
potential responses to new CAFE and
CO2 standards and to estimate various
impacts of those responses. The model
makes use of many inputs, values of
which are developed outside of the
model and not by the model. For
example, the model applies fuel prices;
it does not estimate fuel prices. The
model does not determine the form or
stringency of the standards; instead, the
model applies inputs specifying the
form and stringency of standards to be
analyzed and produces outputs showing
effects of manufacturers working to
meet those standards, which become the
basis for comparing between different
potential stringencies.
DOT’s Volpe National Transportation
Systems Center (often simply referred to
as the ‘‘Volpe Center’’) develops,
maintains, and applies the model for
NHTSA. NHTSA has used the CAFE
model to perform analyses supporting
every CAFE rulemaking since 2001, and
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and GHG emissions also used the CAFE
model for analysis.47
DOT recently arranged for a formal
peer review of the model. In general,
reviewers’ comments strongly supported
the model’s conceptual basis and
implementation, and commenters
provided several specific
recommendations. DOT staff agreed
with many of these recommendations
and have worked to implement them
wherever practicable. Implementing
some of them would require
considerable further research,
development, and testing, and will be
considered going forward. For a handful
of other recommendations, DOT staff
disagreed, often finding the
recommendations involved
considerations (e.g., other policies, such
as those involving fuel taxation) beyond
the model itself or were based on
concerns with inputs rather than how
the model itself functioned. A report
available in the docket for this
rulemaking presents peer reviewers’
detailed comments and
recommendations, and provides DOT’s
detailed responses.48
The agencies also use four DOE and
DOE-sponsored models to develop
inputs to the CAFE model, including
three developed and maintained by
DOE’s Argonne National Laboratory.
The agencies use the DOE Energy
Information Administration’s (EIA’s)
National Energy Modeling System
(NEMS) to estimate fuel prices,49 and
used Argonne’s Greenhouse gases,
Regulated Emissions, and Energy use in
Transportation (GREET) model to
estimate emissions rates from fuel
production and distribution processes.50
DOT also sponsored DOE/Argonne to
use their Autonomie full-vehicle
simulation system to estimate the fuel
economy impacts for roughly a million
combinations of technologies and
vehicle types.51 52
47 While this rulemaking employed the CAFE
model for analysis, EPA and DOT used different
versions of the CAFE model for establishing their
respective standards, and EPA also used the EPA
MOVES model. See 81 FR 73478, 73743 (Oct. 25,
2016).
48 Docket No. NHTSA–2018–0067.
49 See https://www.eia.gov/outlooks/aeo/info_
nems_archive.php. Today’s notice uses fuel prices
estimated using the Annual Energy Outlook (AEO)
2017 version of NEMS (see https://www.eia.gov/
outlooks/archive/aeo17/ and https://www.eia.gov/
outlooks/aeo/data/browser/#/?id=3-AEO2017
&cases=ref2017&sourcekey=0).
50 Information regarding GREET is available at
https://greet.es.anl.gov/index.php. Availability of
NEMS is discussed at https://www.eia.gov/
outlooks/aeo/info_nems_archive.php. Today’s
notice uses fuel prices estimated using the AEO
2017 version of NEMS.
51 As part of the Argonne simulation effort,
individual technology combinations simulated in
Autonomie were paired with Argonne’s BatPAC

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EPA developed two models after
2009, referred to as the ‘‘ALPHA’’ and
‘‘OMEGA’’ models, which provide some
of the same capabilities as the
Autonomie and CAFE models. EPA
applied the OMEGA model to conduct
analysis of GHG standards promulgated
in 2010 and 2012, and the ALPHA and
OMEGA models to conduct analysis
discussed in the above-mentioned 2016
Draft TAR and Proposed and Final
Determinations regarding standards
beyond 2021. In an August 2017 notice,
the agencies requested comments on,
among other things, whether EPA
should use alternative methodologies
and modeling, including DOE/
Argonne’s Autonomie full-vehicle
simulation tool and DOT’s CAFE
model.53
Having reviewed comments on the
subject and having considered the
matter fully, the agencies have
determined it is reasonable and
appropriate to use DOE/Argonne’s
model for full-vehicle simulation, and to
use DOT’s CAFE model for analysis of
regulatory alternatives. EPA interprets
Section 202(a) of the CAA as giving the
agency broad discretion in how it
develops and sets GHG standards for
light-duty vehicles. Nothing in Section
202(a) mandates that EPA use any
specific model or set of models for
analysis of potential CO2 standards for
light-duty vehicles. EPA weighs many
factors when determining appropriate
levels for CO2 standards, including the
cost of compliance (see Section
202(a)(2)), lead time necessary for
compliance (also Section 202(a)(2)),
safety (see NRDC v. EPA, 655 F.2d 318,
336 n. 31 (D.C. Cir. 1981) and other
impacts on consumers,54 and energy
impacts associated with use of the
technology.55 Using the CAFE model
model to estimate the battery cost associated with
each technology combination based on
characteristics of the simulated vehicle and its level
of electrification. Information regarding Argonne’s
BatPAC model is available at http://
www.cse.anl.gov/batpac/.
52 Additionally, the impact of engine technologies
on fuel consumption, torque, and other metrics was
characterized using GT POWER simulation
modeling in combination with other engine
modeling that was conducted by IAV Automotive
Engineering, Inc. (IAV). The engine characterization
‘‘maps’’ resulting from this analysis were used as
inputs for the Autonomie full-vehicle simulation
modeling. Information regarding GT Power is
available at https://www.gtisoft.com/gt-suiteapplications/propulsion-systems/gt-power-enginesimulation-software.
53 82 FR 39533 (Aug. 21, 2017).
54 Since its earliest Title II regulations, EPA has
considered the safety of pollution control
technologies. See 45 FR 14496, 14503 (1980).
55 See George E. Warren Corp. v. EPA, 159 F.3d
616, 623–624 (D.C. Cir. 1998) (ordinarily
permissible for EPA to consider factors not
specifically enumerated in the Act).

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allows consideration of the following
factors: the CAFE model explicitly
evaluates the cost of compliance for
each manufacturer, each fleet, and each
model year; it accounts for lead time
necessary for compliance by directly
incorporating estimated manufacturer
production cycles for every vehicle in
the fleet, ensuring that the analysis does
not assume vehicles can be redesigned
to incorporate more technology without
regard to lead time considerations; it
provides information on safety effects
associated with different levels of
standards and information about many
other impacts on consumers, and it
calculates energy impacts (i.e., fuel
saved or consumed) as a primary
function, besides being capable of
providing information about many other
factors within EPA’s broad CAA
discretion to consider.
Because the CAFE model simulates a
wide range of actual constraints and
practices related to automotive
engineering, planning, and production,
such as common vehicle platforms,
sharing of engines among different
vehicle models, and timing of major
vehicle redesigns, the analysis produced
by the CAFE model provides a
transparent and realistic basis to show
pathways manufacturers could follow
over time in applying new technologies,
which helps better assess impacts of
potential future standards. Furthermore,
because the CAFE model also accounts
fully for regulatory compliance
provisions (now including CO2
compliance provisions), such as
adjustments for reduced refrigerant
leakage, production ‘‘multipliers’’ for
some specific types of vehicles (e.g.,
PHEVs), and carried-forward (i.e.,
banked) credits, the CAFE model
provides a transparent and realistic
basis to estimate how such technologies
might be applied over time in response
to CAFE or CO2 standards.
There are sound reasons for the
agencies to use the CAFE model going
forward in this rulemaking. First, the
CAFE and CO2 fact analyses are
inextricably linked. Furthermore, the
analysis produced by the CAFE model
and DOE/Argonne’s Autonomie
addresses several analytical needs. The
CAFE model provides an explicit yearby-year simulation of manufacturers’
application of technology to their
products in response to a year-by-year
progression of CAFE standards and
accounts for sharing of technologies and
the implications for timing, scope, and
limits on the potential to optimize
powertrains for fuel economy. In the
real world, standards actually are
specified on a year-by-year basis, not
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future, and manufacturers’ year-by-year
plans involve some vehicles ‘‘carrying
forward’’ technology from prior model
years and some other vehicles possibly
applying ‘‘extra’’ technology in
anticipation of standards in ensuing
model years, and manufacturers’
planning also involves applying credits
carried forward between model years.
Furthermore, manufacturers cannot
optimize the powertrain for fuel
economy on every vehicle model
configuration—for example, a given
engine shared among multiple vehicle
models cannot practicably be split into
different versions for each configuration
of each model, each with a slightly
different displacement. The CAFE
model is designed to account for these
real-world factors.
Considering the technological
heterogeneity of manufacturers’ current
product offerings, and the wide range of
ways in which the many fuel economyimproving/CO2 emissions-reducing
technologies included in the analysis
can be combined, the CAFE model has
been designed to use inputs that provide
an estimate of the fuel economy
achieved for many tens of thousands of
different potential combinations of fuelsaving technologies. Across the range of
technology classes encompassed by the
analysis fleet, today’s analysis involves
more than a million such estimates.
While the CAFE model requires no
specific approach to developing these
inputs, the National Academy of
Sciences (NAS) has recommended, and
stakeholders have commented, that fullvehicle simulation provides the best
balance between realism and
practicality. DOE/Argonne has spent
several years developing, applying, and
expanding means to use distributed
computing to exercise its Autonomie
full-vehicle simulation tool over the
scale necessary for realistic analysis of
CAFE or average CO2 standards. This
scalability and related flexibility (in
terms of expanding the set of
technologies to be simulated) makes
Autonomie well-suited for developing
inputs to the CAFE model.
Additionally, DOE/Argonne’s
Autonomie also has a long history of
development and widespread
application by a much wider range of
users in government, academia, and
industry. Many of these users apply
Autonomie to inform funding and
design decisions. These real-world
exercises have contributed significantly
to aspects of Autonomie important to
producing realistic estimates of fuel
economy levels and CO2 emission rates,
such as estimation and consideration of
performance, utility, and driveability
metrics (e.g., towing capability, shift

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business, frequency of engine on/off
transitions). This steadily increasing
realism has, in turn, steadily increased
confidence in the appropriateness of
using Autonomie to make significant
investment decisions. Notably, DOE
uses Autonomie for analysis supporting
budget priorities and plans for programs
managed by its Vehicle Technologies
Office (VTO). Considering the
advantages of DOE/Argonne’s
Autonomie model, it is reasonable and
appropriate to use Autonomie to
estimate fuel economy levels and CO2
emission rates for different
combinations of technologies as applied
to different types of vehicles.
Commenters have also suggested that
the CAFE model’s graphical user
interface (GUI) facilitates others’ ability
to use the model quickly—and without
specialized knowledge or training—and
to comment accordingly.56 For today’s
proposal, DOT has significantly
expanded and refined this GUI,
providing the ability to observe the
model’s real-time progress much more
closely as it simulates year-by-year
compliance with either CAFE or CO2
standards.57 Although the model’s
ability to produce realistic results is
independent of the model’s GUI, it is
anticipated the CAFE model’s GUI will
facilitate stakeholders’ meaningful
review and comment during the
comment period.
Beyond these general considerations,
several specific related technical
comments and considerations underlie
the agencies’ decision in this area, as
discussed, where applicable, in the
remainder of this Section.
Other commenters expressed a
number of concerns with whether
DOT’s CAFE model could be used for
CAA analysis. Many of these concerns
focused on inputs used by the CAFE
model for prior rulemaking
analyses.58 59 60 Because inputs are
56 From Docket Number EPA–HQ–OAR–2015–
0827, see Comment by Global Automakers, Docket
ID EPA–HQ–OAR–2015–0827–9728, at 34.
57 The updated GUI provides a range of graphs
updated in real time as the model operates. These
graphs can be used to monitor fuel economy or CO2
ratings of vehicles in manufacturers’ fleets and to
monitor year-by-year CAFE (or average CO2 ratings),
costs, avoided fuel outlays, and avoided CO2-related
damages for specific manufacturers and/or specific
fleets (e.g., domestic passenger car, light truck).
Because these graphs update as the model
progresses, they should greatly increase users’
understanding of the model’s approach to
considerations such as multiyear planning,
payment of civil penalties, and credit use.
58 For example, EDF’s recent comments (EDF at
12, Docket ID. EPA–HQ–OAR–2015–0827–9203)
stated ‘‘the data that NHTSA needs to input into its
model is sensitive confidential business
information that is not transparent and cannot be
independently verified . . .’’ and claimed ‘‘the

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exogenous to any model, they do not
determine whether it would be
reasonable and appropriate for EPA to
use DOT’s model for analysis. Other
concerns focused on characteristics of
the CAFE model that were developed to
better align the model with EPCA and
EISA; the model has been revised to
accommodate both EPCA/EISA and
CAA analysis, as explained further
below. Some commenters also argued
that use of any models other than
ALPHA and OMEGA for CAA analysis
would constitute an arbitrary and
capricious delegation of EPA’s decisionmaking authority to DOT, if DOT
models are used for analysis instead.
These comments were made prior to the
development of the CAA analysis
function in the CAFE model, and,
moreover, appear to conflate the
analytical tool used to inform decisionmaking with the action of making the
decision. As explained elsewhere in this
document and as made repeatedly clear
over the past several rulemakings, the
CAFE model neither sets standards nor
dictates where and how to set standards;
it simply informs as to the effects of
setting different levels of standards. In
this rulemaking, EPA will be making its
own decisions regarding what CO2
standards would be appropriate under
the CAA. The CAA does not require
EPA to create a specific model or use a
specific model of its own creation in
setting GHG standards. The fact EPA’s
OMEGA model’s focus on direct technological
inputs and costs—as opposed to industry selfreported data—ensures the model more accurately
characterizes the true feasibility and cost
effectiveness of deploying greenhouse gas reducing
technologies.’’ Neither statement is correct, as
nothing about either the CAFE or OMEGA model
either obviates or necessitates the use of CBI to
develop inputs.
59 In recent comments (CARB at 28, Docket ID.
EPA–HQ–OAR–2015–0827–9197), CARB stated
‘‘another promising technology entering the market
was not even included in the NHTSA compliance
modeling’’ and that EPA assumes a five-year
redesign cycle, whereas NHTSA assumes a six to
seven-year cycle.’’ Though presented as criticisms
of the models, these comments—at least with
respect to the CAFE model—actually concern
model inputs. NHTSA did not agree with CARB
about the commercialization potential of the engine
technology in question (‘‘Atkinson 2’’) and applied
model inputs accordingly. Also, rather than
applying a one-size-fits-all assumption regarding
redesign cadence, NHTSA developed estimates
specific to each vehicle model and applied these as
model inputs.
60 NRDC’s recent comments (NRDC at 37, Docket
ID. EPA–HQ–OAR–2015–0827–9826) state EPA
should not use the CAFE model because it ‘‘allows
manufacturers to pay civil penalties in lieu of
meeting the standards, an alternative compliance
pathway currently allowed under EISA and EPCA.’’
While the CAFE model can simulate civil penalty
payment, NRDC’s comment appears to overlook the
fact that this result depends on model inputs; the
inputs can easily be specified such that the CAFE
model will set aside civil penalty payment as an
alternative to compliance.

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decision may be informed by non-EPAcreated models does not, in any way,
constitute a delegation of its statutory
power to set standards or decisionmaking authority.61 Arguing to the
contrary would suggest, for example,
that EPA’s decision would be invalid
because it relied on EIA’s Annual
Energy Outlook for fuel prices rather
than developing its own model for
estimating future trends in fuel prices.
Yet, all Federal agencies that have
occasion to use forecasts of future fuel
prices regularly (and appropriately)
defer to EIA’s expertise in this area and
rely on EIA’s NEMS-based analysis in
the AEO, even when those same
agencies are using EIA’s forecasts to
inform their own decision-making.
Moreover, DOT’s CAFE model with
inputs from DOE/Argonne’s Autonomie
model has produced analysis supporting
rulemaking under the CAA. In 2015,
EPA proposed new GHG standards for
MY 2021–2027 heavy-duty pickups and
vans, finalizing standards in 2016.
Supporting the NPRM and final rule,
EPA relied on analysis implemented by
DOT using DOT’s CAFE model, and
DOT used inputs developed by DOE/
Argonne using DOE/Argonne’s
Autonomie model.
The following sections provide a brief
technical overview of the CAFE model,
including changes NHTSA made to the
model since 2012, before discussing
inputs to the model and then diving
more deeply into how the model works.
For more information on the latter topic,
see the CAFE model documentation July
2018 draft, available in the docket for
this rulemaking and on NHTSA’s
website.
1. Brief Technical Overview of the
Model
The CAFE model is designed to
simulate compliance with a given set of
CAFE or CO2 standards for each
manufacturer selling vehicles in the
United States. The model begins with a
representation of the current (for today’s
analysis, model year 2016) vehicle
model offerings for each manufacturer
61 ‘‘[A] federal agency may turn to an outside
entity for advice and policy recommendations,
provided the agency makes the final decisions
itself.’’ U.S. Telecom. Ass’n v. FCC, 359 F.3d 554,
565–66 (D.C. Cir. 2004). To the extent commenters
meant to suggest outside parties have a reliance
interest in EPA using ALPHA and OMEGA to set
standards, EPA does not agree a reliance interest is
properly placed on an analytical methodology (as
opposed to on the standards themselves). Even if it
were, all parties that closely examined ALPHA and
OMEGA-based analyses in the past either also
simultaneously closely examined CAFE and
Autonomie-based analyses in the past, or were fully
capable of doing so, and thus, should face no
additional difficulty now they have only one set of
models and inputs/outputs to examine.

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that includes the specific engines and
transmissions on each model variant,
observed sales volumes, and all fuel
economy improvement technology that
is already present on those vehicles.
From there it adds technology, in
response to the standards being
considered, in a way that minimizes the
cost of compliance and reflects many
real-world constraints faced by
automobile manufacturers. After
simulating compliance, the model
calculates impacts of the simulated
standard: technology costs, fuel savings
(both in gallons and dollars), CO2
reductions, social costs and benefits,
and safety impacts.
Today’s analysis reflects several
changes made to the CAFE model since
2012, when NHTSA used the model to
estimate the effects, costs, and benefits
of final CAFE standards for light-duty
vehicles produced during MYs 2017–
2021 and augural standards for MYs
2022–2025. Key changes relevant to this
analysis include the following:
• Expansion of model inputs,
procedures, and outputs to
accommodate technologies not included
in prior analyses,
• Updated approach to estimating the
combined effect of fuel-saving
technologies using large scale
simulation modeling,
• Modules that dynamically estimate
new vehicle sales and existing vehicle
scrappage in response to changes to new
vehicle prices that result from
manufacturers’ compliance actions,
• A safety module that estimates the
changes in light-duty traffic fatalities
resulting from changes to vehicle
exposure, vehicle retirement rates, and
reductions in vehicle mass to improve
fuel economy,
• Disaggregation of each
manufacturer’s fleet into separate
‘‘domestic’’ passenger car and ‘‘import’’
passenger car fleets to better represent
the statutory requirements of the CAFE
program,
• Changes to the algorithm used to
apply technologies, enabling more
explicit accounting of shared vehicle
components (engines, transmissions,
platforms) and ‘‘inheritance’’ of major
technology within or across powertrains
and/or platforms over time,
• An industry labor quantity module,
which estimates net changes in the
amount of U.S. automobile labor for
dealerships, Tier 1 and 2 supplier
companies, and original equipment
manufacturers (OEMs),
• Cost estimation of batteries for
electrification technologies incorporates
an updated version of Argonne National
Laboratory’s BatPAC (battery) model for
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hybrid electric vehicles (PHEVs), and
battery electric vehicles (BEVs),
consistent with how we estimate
effectiveness for those values,
• Expanded accounting for CAFE
credits carried over from years prior to
those included in the analysis (a.k.a.
‘‘banked’’ credits) and application to
future CAFE deficits to better evaluate
anticipated manufacturer responses to
proposed standards,62
• The ability to represent a
manufacturer’s preference for fine
payment (rather than achieving full
compliance exclusively through fuel
economy improvements) on a year-byyear basis,
• Year-by-year simulation of how
manufacturers could comply with EPA’s
CO2 standards, including
Æ Calculation of vehicle models’ CO2
emission rates before and after
application of fuel-saving (and,
therefore, CO2-reducing) technologies,
Æ Calculation of manufacturers’ fleet
average CO2 emission rates,
Æ Calculation of manufacturers’ fleet
average CO2 emission rates under
attribute-based CO2 standards,
Æ Accounting for adjustments to
average CO2 emission rates reflecting
reduction of air conditioner refrigerant
leakage,
Æ Accounting for the treatment of
alternative fuel vehicles for CO2
compliance,
Æ Accounting for production
‘‘multipliers’’ for PHEVs, BEVs,
compressed natural gas (CNG) vehicles,
and fuel cell vehicles (FCVs),
Æ Accounting for transfer of CO2
credits between regulated fleets,
Æ Accounting for carried-forward
(a.k.a. ‘‘banked’’) CO2 credits, including
credits from model years earlier than
modeled explicitly.

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2. Sensitivity Cases and Why We
Examine Them
Today’s notice presents estimated
impacts of the proposed CAFE and CO2
standards defining the proposals,
relative to a baseline ‘‘no action’’
regulatory alternative under which the
standards announced in 2012 remain in
place through MY 2025 and continue
unchanged thereafter. Relative to this
same baseline, today’s notice also
presents analysis estimating impacts
under a range of other regulatory
62 While EPCA/EISA precludes NHTSA from
considering manufacturers’ potential use of credits
in model years for which the agency is establishing
new standards, NHTSA considers credit use in
earlier model years. Also, as allowed by NEPA,
NHTSA’s EISs present results of analysis that
considers manufacturers’ potential use of credits in
all model years, including those for which the
agency is establishing new standards.

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alternatives the agencies are
considering. All but one involve
different standards, and three involve a
gradual discontinuation of CAFE and
GHG adjustments reflecting the
application of technologies that improve
air conditioner efficiency or, in other
ways, improve fuel economy under
conditions not represented by longstanding fuel economy test procedures.
Like the baseline no action alternative,
all of these alternatives are more
stringent than the preferred alternative.
Section III and Section IV describe the
preferred and other regulatory
alternatives, respectively.
These alternatives were examined
because they will be considered as
options for the final rule. The agencies
seek comment on these alternatives,
seek any relevant data and information,
and will review responses. That review
could lead to the selection of one of the
other regulatory alternatives for the final
rule or some combination of the other
regulatory alternatives (e.g., combining
passenger cars standards from one
alternative with light truck standards
from a different alternative).
Because outputs depend on inputs
(e.g., the results of the analysis in terms
of quantities and kinds of technologies
required to meet different levels of
standards, and the societal and private
benefits associated with manufacturers
meeting different levels of standards
depend on input data, estimates, and
assumptions), the analysis also explores
the sensitivity of results to many of
these inputs. For example, the net
benefits of any regulatory alternative
will depend strongly on fuel prices well
beyond 2025. Fuel prices a decade and
more from now are not knowable with
certainty. The sensitivity analysis
involves repeating the ‘‘central’’ or
‘‘reference case’’ analysis under
alternative inputs (e.g., higher fuel
prices in one case, lower fuel prices in
another case), and exploring changes in
analytical results, which is discussed
further in the agencies’ Preliminary
Regulatory Impact Analysis (PRIA)
accompanying today’s notice.
B. Developing the Analysis Fleet for
Assessing Costs, Benefits, and Effects of
Alternative CAFE Standards
The following sections describe what
the analysis fleet is and why it is used,
how it was developed for this NPRM,
and the analysis-fleet-related topics on
which comment is sought.
1. Purpose of Developing and Using an
Analysis Fleet
The starting point for the evaluation
of the potential feasibility of different
stringency levels for future CAFE and

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CO2 standards is the analysis fleet,
which is a snapshot of the recent
vehicle market. The analysis fleet
provides a snapshot to project what
vehicles will exist in future model years
covered by the standards and what
technologies they will have, and then
evaluate what additional technologies
can feasibly be applied to those vehicles
in a cost-effective way to raise their fuel
economy and lower their CO2 emission
levels.63
Part of reflecting what vehicles will
exist in future model years is knowing
which vehicles are produced by which
manufacturers, how many of each are
sold, and whether they are passenger
cars or light trucks. This is important
because it improves our understanding
of the overall impacts of different levels
of CAFE and CO2 standards; overall
impacts result from industry’s response
to standards, and industry’s response is
made up of individual manufacturer
responses to the standards in light of the
overall market and their individual
assessment of consumer acceptance.
Having an accurate picture of
manufacturers’ existing fleets (and the
vehicle models in them) that will be
subject to future standards helps us
better understand individual
manufacturer responses to those future
standards in addition to potential
changes in those standards.
Another part of reflecting what
vehicles will exist in future model years
is knowing what technologies are
already on those vehicles. Accounting
for technologies already being on
vehicles helps avoid ‘‘double-counting’’
the value of those technologies, by
assuming they are still available to be
applied to improve fuel economy and
reduce CO2 emissions. It also promotes
more realistic determinations of what
additional technologies can feasibly be
applied to those vehicles: if a
manufacturer has already started down
a technological path to fuel economy or
performance improvements, we do not
assume it will completely abandon that
path because that would be unrealistic
and would not accurately represent
manufacturer responses to standards.
Each vehicle model (and configurations
of each model) in the analysis fleet,
therefore, has a comprehensive list of its
technologies, which is important
because different configurations may
have different technologies applied to
63 The CAFE model does not generate compliance
paths a manufacturer should, must, or will deploy.
It is intended as a tool to demonstrate a compliance
pathway a manufacturer could choose. It is almost
certain all manufacturers will make compliance
choices differing from those projected in the CAFE
model.

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them.64 Additionally, the analysis
accounts for platforms within
manufacturers’ fleets, recognizing
platforms will share technologies, and
the vehicles that make up that platform
should receive (or not receive)
additional technological improvements
together. The specific engineering
characteristics of each model/
configuration are available in the
aforementioned input file.65 For the
regulatory alternatives considered in
today’s proposal, estimates of rates at
which various technologies might be
expected to penetrate manufacturers’
fleets (and the overall market) are
summarized below in Sections VI and
VII, and in Chapter 6 of the
accompanying PRIA and in detailed
model output files available at NHTSA’s
website. A solid characterization of a
recent model year as an analytical
starting point helps to realistically
estimate ways manufacturers could
potentially respond to different levels of
standards, and the modeling strives to
realistically simulate how
manufacturers could progress from that
starting point. Nevertheless,
manufacturers can respond in many
ways beyond those represented in the
analysis (e.g., applying other
technologies, shifting production
volumes, changing vehicle footprint),
such that it is impossible to predict with
any certainty exactly how each
manufacturer will respond. Therefore,
recent trends in manufacturer
performance and technology
application, although of interest in
terms of understanding manufacturers’
current compliance positions, are not in
themselves innately indicative of future
potential.
Yet, another part of reflecting what
vehicles will exist in future model years
is having reasonable real-world
assumptions about when certain
technologies can be applied to vehicles.
Each vehicle model/configuration in the
analysis fleet also has information about
its redesign schedule, i.e., the last year
it was redesigned and when the
agencies expect it to be redesigned
again. Redesign schedules are a key part
of manufacturers’ business plans, as
each new product can cost more than
$1.0B and involve a significant portion
of a manufacturer’s scarce research,
64 Considering each vehicle model/configuration
also improves the ability to consider the differential
impacts of different levels of potential standards on
different manufacturers, since all vehicle model/
configurations ‘‘start’’ at different places, in terms
of the technologies they already have and how
those technologies are used.
65 Available with the model and other input files
supporting today’s announcement at https://
www.nhtsa.gov/corporate-average-fuel-economy/
compliance-and-effects-modeling-system.

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development, and manufacturing and
equipment budgets and resources.66
Manufacturers have repeatedly told the
agencies that sustainable business plans
require careful management of resources
and capital spending, and that the
length of time each product remains in
production is crucial to recouping the
upfront product development and plant/
equipment costs, as well as the capital
needed to fund the development and
manufacturing equipment needed for
future products. Because the production
volume of any given vehicle model
varies within a manufacturer’s product
line and also varies among different
manufacturers, redesign schedules
typically vary for each model and
manufacturer. Some (relatively few)
technological improvements are small
enough they can be applied in any
model year; others are major enough
they can only be cost-effectively applied
at a vehicle redesign, when many other
things about the vehicle are already
changing. Ensuring the CAFE model
makes technological improvements to
vehicles only when it is feasible to do
so also helps the analysis better
represent manufacturer responses to
different levels of standards.
A final important aspect of reflecting
what vehicles will exist in future model
years and potential manufacturer
responses to standards is estimating
how future sales might change in
response to different potential
standards. If potential future standards
appear likely to have major effects in
terms of shifting production from cars to
trucks (or vice versa), or in terms of
shifting sales between manufacturers or
groups of manufacturers, that is
important for the agencies to consider.
For previous analyses, the CAFE model
used a static forecast contained in the
analysis fleet input file, which specified
changes in production volumes over
time for each vehicle model/
configuration. This approach yielded
results that, in terms of production
volumes, did not change between
scenarios or with changes in important
model inputs. For example, very
stringent standards with very high
technology costs would result in the
same estimated production volumes as
less stringent standards with very low
technology costs.
New for today’s proposal, the CAFE
model begins with the first-year
production volumes (i.e., MY 2016 for
today’s analysis) and adjusts ensuing
sales mix year by year (between cars and
66 Shea, T. Why Does It Cost So Much For
Automakers To Develop New Models?, Autoblog
(Jul. 27, 2010), https://www.autoblog.com/2010/07/
27/why-does-it-cost-so-much-for-automakers-todevelop-new-models/.

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trucks, and between manufacturers)
endogenously as part of the analysis,
rather than using external forecasts of
future car/truck split and future
manufacturer sales volumes. This leads
the model to produce different estimates
of future production volumes under
different standards and in response to
different inputs, reflecting the
expectation that regulatory standards
and other external factors will, in fact,
impact the market.
The input file for the CAFE model
characterizing the analysis fleet 67
includes a large amount of data about
vehicle models/configurations, their
technological characteristics, the
manufacturers and fleets to which they
belong, and initial prices and
production volumes which provide the
starting points for projection (by the
sales model) to ensuing model years.
The following sections discuss aspects
of how the analysis fleet was built for
this proposal and seek comment on
those topics.
2. Source Data for Building the Analysis
Fleet
The source data for the vehicle
models/configurations in the analysis
fleet and their technologies is a central
input for the analysis. The sections
below discuss pros and cons of different
potential sources and what was used for
this proposal.
(a) Use of Confidential Business
Information Versus Publicly-Releasable
Sources
Since 2001, CAFE analysis has used
either confidential, forward-estimating
product plans from manufacturers, or
publicly available data on vehicles
already sold, as a starting point for
determining what technologies can be
applied to what vehicles in response to
potential different levels of standards.
These two sources present a tradeoff.
Confidential product plans
comprehensively represent what
vehicles a manufacturer expects to
produce in coming years, accounting for
plans to introduce new vehicles and
fuel-saving technologies and, for
example, plans to discontinue other
vehicles and even brands. This
information can be very thorough and
can improve the accuracy of the
analysis, but for competitive reasons,
most of this information must be
redacted prior to publication with
rulemaking documentation. This makes
it difficult for public commenters to
reproduce the analysis for themselves as
67 Available with the model and other input files
supporting today’s announcement at https://
www.nhtsa.gov/corporate-average-fuel-economy/
compliance-and-effects-modeling-system.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
they develop their comments. Some
non-industry commenters have also
expressed concern manufacturers would
have an incentive in the submitted
plans to (deliberately or not)
underestimate their future fuel economy
capabilities and overstate their
expectations about, for example, the
levels of performance of future vehicle
models in order to affect the analysis.
Since 2010, EPA and NHTSA have
based analysis fleets almost exclusively
on information from commercial and
public sources, starting with CAFE
compliance data and adding
information from other sources.
An analysis fleet based primarily on
public sources can be released to the
public, solving the issue of commenters
being unable to reproduce the overall
analysis when they want to. However,
industry commenters have argued such
an analysis fleet cannot accurately
reflect manufacturers actual plans to
apply fuel-saving technologies (e.g.,
manufacturers may apply turbocharging
to improve not just fuel economy, but
also to improve vehicle performance) or
manufacturers’ plans to change product
offerings by introducing some vehicles
and brands and discontinuing other
vehicles and brands, precisely because
that information is typically
confidential business information (CBI).
A fully-publicly-releasable analysis fleet
holds vehicle characteristics unchanged
over time and arguably lacks some level
of accuracy when projected into the
future. For example, over time,
manufacturers introduce new products
and even entire brands. On the other
hand, plans announced in press releases
do not always ultimately bear out, nor
do commercially-available third-party
forecasts. Assumptions could be made
about these issues to improve the
accuracy of a publicly-releasable
analysis fleet, but concerns include that
this information would either be largely
incorrect, or information would be
released that manufacturers would
consider CBI. We seek comment on how
to address this issue going forward,
recognizing the competing interests
involved and also recognizing typical
timeframes for CAFE and CO2 standards
rulemakings.
(b) Use of MY 2016 CAFE Compliance
Data Versus Other Starting Points
Based on the assumption that a
publicly-available analysis fleet
continues to be desirable, for this
NPRM, an analysis fleet was constructed
starting with CAFE compliance

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information from manufacturers.68
Information from MY 2016 was chosen
as the foundation for today’s analysis
fleet because, at the time the rulemaking
analysis was initiated, the 2016 fleet
represented the most up-to-date
information available in terms of
individual vehicle models and
configurations, production technology
levels, and production volumes. If MY
2017 data had been used while this
analysis was being developed, the
agencies would have needed to use
product planning information that could
not be made available to the public until
a later date.
The analysis fleet was initially
developed with 2016 mid-model year
compliance data because final
compliance data was not available at
that time, and the timing provided
manufacturers the opportunity to review
and comment on the characterization of
their vehicles in the fleet. With a view
toward developing an accurate
characterization of the 2016 fleet to
serve as an analytical starting point,
corrections and updates to mid-year
data (e.g., to production estimates) were
sought, in addition to corroboration or
correction of technical information
obtained from commercial and other
sources (to the extent that information
was not included in compliance data),
although future product planning
information from manufacturers (e.g.,
future product offerings, products to be
discontinued) was not requested, as
most manufacturers view such
information as CBI. Manufacturers
offered a range of corrections to indicate
engineering characteristics (e.g.,
footprint, curb weight, transmission
type) of specific vehicle model/
configurations, as well as updates to
fuel economy and production volume
estimates in mid-year reporting. After
following up on a case-by-case basis to
investigate significant differences, the
analysis fleet was updated.
Sales, footprint, and fuel economy
values with final compliance data were
also updated if that data was available.
In a few cases, final production and fuel
economy values may be slightly
different for specific model year 2016
vehicle models and configurations than
are indicated in today’s analysis;
however, other vehicle characteristics
(e.g., footprint, curb weight, technology
content) important to the analysis
should be accurate. While some
commenters have, in the past, raised
concerns that non-final CAFE
compliance data is subject to change,
68 CO emissions rates are directly related to fuel
2
economy levels, and the CAFE model uses the latter
to calculate the former.

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the potential for change is likely not
significant enough to merit using final
data from an earlier model year
reflecting a more outdated fleet.
Moreover, even ostensibly final CAFE
compliance data can sometimes be
subject to later revision (e.g., if errors in
fuel economy tests are discovered), and
the purpose of today’s analysis is not to
support enforcement actions but rather
to provide a realistic assessment of
manufacturers’ potential responses to
future standards.
Manufacturers integrated a significant
amount of new technology in the MY
2016 fleet, and this was especially true
for newly-designed vehicles launched in
MY 2016. While subsequent fleets will
involve even further application of
technology, using available data for MY
2016 provides the most realistic detailed
foundation for analysis that can be made
available publicly in full detail,
allowing stakeholders to independently
reproduce the analysis presented in this
proposal. Insofar as future product
offerings are likely to be more similar to
vehicles produced in 2016 than to
vehicles produced in earlier model
years, using available data regarding the
2016 model year provides the most
realistic, publicly releasable foundation
for constructing a forecast of the future
vehicle market for this proposal.
A number of comments to the Draft
TAR, EPA’s Proposed Determination,
and EPA’s 2017 Request for Comment 69
stated that the most up-to-date analysis
fleet possible should be used, because a
more up-to-date analysis fleet will better
capture how manufacturers apply
technology and will account better for
vehicle model/configuration
introductions and deletions.70 On the
other hand, some commenters suggested
that because manufacturers continue
improving vehicle performance and
utility over time, an older analysis fleet
should be used to estimate how the fleet
could have evolved had manufacturers
applied all technological potential to
69 82

FR 39551 (Aug. 21, 2017).
example, in 2016 comments to dockets
EPA–HQ–OAR–2015–0827 and NHTSA–2016–
0068, the Alliance of Automobile Manufacturers
commented that ‘‘the Alliance supports the use of
the most recent data available in establishing the
baseline fleet, and therefore believes that NHTSA’s
selection [of, at the time, model year 2015] was
more appropriate for the Draft TAR.’’ (Alliance at
82, Docket ID. EPA–HQ–OAR–2015–0827–4089)
Global Automakers commented that ‘‘a one-year
difference constitutes a technology change-over for
up to 20% of a manufacturer’s fleet. It was also
generally understood by industry and the agencies
that several new, and potentially significant,
technologies would be implemented in MY 2015.
The use of an older, outdated baseline can have
significant impacts on the modeling of subsequent
Reference Case and Control Case technologies.’’
(Global Automakers at A–10, Docket ID. EPA–HQ–
OAR–2015–0827–4009).
70 For

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fuel economy rather than continuing to
improve vehicle performance and
utility.71 Because manufacturers change
and improve product offerings over
time, conducting analysis with an older
analysis fleet (or with a fleet using fuel
economy levels and CO2 emissions rates
that have been adjusted to reflect an
assumed return to levels of performance
and utility typical of some past model
year) would miss this real-world trend.
While such an analysis could
demonstrate what industry could do if,
for example, manufacturers devoted all
technological improvements toward
raising fuel economy and reducing CO2
emissions (and if consumers decided to
purchase these vehicles), we do not
believe it would be consistent with a
transparent examination of what effects
different levels of standards would have
on individual manufacturers and the
fleet as a whole.
Generally, all else being equal, using
a newer analysis fleet will produce more
realistic estimates of impacts of
potential new standards than using an
outdated analysis fleet. However, among
relatively current options, a balance
must be struck between, on one hand,
inputs’ freshness, and on the other,
inputs’ completeness and accuracy.72
During assembly of the inputs for
today’s analysis, final compliance data
was available for the MY 2015 model
year but not in a few cases for MY 2016.
However, between mid-year compliance
information and manufacturers’ specific
updates discussed above, a robust and
detailed characterization of the MY
2016 fleet was developed. However,
while information continued to develop
regarding the MY 2017 and, to a lesser
extent MY 2018 and even MY 2019
fleets, this information was—even in
mid-2017—too incomplete and
inconsistent to be assembled with
71 For example, in 2016 comments to dockets
EPA–HQ–OAR–2015–0827 and NHTSA–2016–
0068, UCS stated ‘‘in modeling technology
effectiveness and use, the agencies should use 2010
levels of performance as the baseline.’’ (UCS at 4,
Docket ID. EPA–HQ–OAR–2015–0827–4016).
72 Comments provided through a recent peer
review of the CAFE model recognize the need for
this balance. For example, referring to NHTSA’s
2016 analysis documented in the draft TAR, one of
the peer reviewers commented as follows: ‘‘The
NHTSA decision to use MY 2015 data is wise. In
the TAR they point out that a MY 2016 foundation
would require the use of confidential data, which
is less desirable. Clearly they would also have a
qualitative vision of the MY 2016 landscape while
employing MY 2015 as a foundation. Although MY
2015 data may still be subject to minor revision,
this is unlikely to impact the predictive ability of
the model . . . A more complex alternative
approach might be to employ some 2016 changes
in technology, and attempt a blend of MY 2015 and
MY 2016, while relying of estimation gained from
only MY 2015 for sales. This approach may add
some relevancy in terms of technology, but might
introduce substantial error in terms of sales.’’

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confidence into an analysis fleet for
modeling supporting deliberations
regarding today’s proposal.
In short, the 2016 fleet was, in fact,
the most up-to-date fleet that could be
produced for this NPRM. Moreover,
during late 2016 and early 2017, nearly
all manufacturers provided comments
on the characterization of their vehicles
in the analysis fleet, and many provided
specific feedback about their vehicles,
including aerodynamic drag
coefficients, tire rolling resistance
values, transmission efficiencies, and
other information used in the analysis.
NHTSA worked with manufacturers to
clarify and correct some information
and integrated the information into a
single input file for use in the CAFE
model. Accordingly, the current
analysis fleet is reasonable to use for
purposes of the NPRM analysis.
As always, however, ways to improve
the analysis fleet used for subsequent
modeling to evaluate potential new
CAFE and CO2 standards will undergo
continuous consideration. As described
above, the compliance data is only the
starting point for developing the
analysis fleet; much additional
information comes directly from
manufacturers (such as details about
technologies, platforms, engines,
transmissions, and other vehicle
information, that may not be present in
compliance data), and other information
must come from commercial and public
sources (for example, fleet-wide
information like market share, because
individual manufacturers do not
provide this kind of information). If
newer compliance data (i.e., MY 2017)
becomes available and can be analyzed
during the pendency of this rulemaking,
and if all of the other necessary steps
can be performed, the analysis fleet will
be updated, as feasible, and made
publicly available. The agencies seek
comment on the option used today and
any other options, as well as on the
tradeoffs between, on one hand, fidelity
with manufacturers’ actual plans and,
on the other, the ability to make detailed
analysis inputs and outputs publicly
available.
(c) Observed Technology Content of
2016 Fleet
As explained above, the analysis fleet
is defined not only by the vehicle
models/configurations it contains but
also by the technologies on those
vehicles. Each vehicle model/
configuration in the analysis fleet has an
associated list of observed technologies
and equipment that can improve fuel

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economy and reduce CO2 emissions.73
With a portfolio of descriptive
technologies arranged by manufacturer
and model, the analysis fleet can be
summarized and project how vehicles in
that fleet may improve over time via the
application of additional technology.
In many cases, vehicle technology is
clearly observable from the 2016
compliance data (e.g., compliance data
indicates clearly which vehicles have
turbochargers and which have
continuously variable transmissions),
but in some cases technology levels are
less observable. For the latter, like levels
of mass reduction, the analysis
categorized levels of technology already
used in a given vehicle. Similarly,
engineering judgment was used to
determine if higher mass reduction
levels may be used practicably and
safely in a given vehicle.
Either in mid-year compliance data
for MY 2016 or, separately and at the
agencies’ invitation (as discussed
above), most manufacturers identified
most of the technology already present
in each of their MY 2016 vehicle model/
configurations. This information was
not as complete for all manufacturers’
products as needed for today’s analysis,
so in some cases, information was
supplemented with publicly available
data, typically from manufacturer media
sites. In limited cases, manufacturers
did not supply information, and
information from commercial and
publicly available sources was used.
(d) Mapping Technology Content of
2016 Fleet to Argonne Technology
Effectiveness Simulation Work
While each vehicle model/
configuration in the analysis fleet has its
list of observed technologies and
equipment, the ways in which
manufacturers apply technologies and
equipment do not always coincide
perfectly with how the analysis
characterizes the various technologies
that improve fuel economy and reduce
CO2 emissions. To improve how the
observed vehicle fleet ‘‘fits into’’ the
analysis, each vehicle model/
configuration is ‘‘mapped’’ to the full73 These technologies are generally grouped into
the following categories: Vehicle technologies
include mass reduction, aerodynamic drag
reduction, low rolling resistance tires, and others.
Engine technologies include engine attributes
describing fuel type, engine aspiration, valvetrain
configuration, compression ratio, number of
cylinders, size of displacement, and others.
Transmission technologies include different
transmission arrangements like manual, 6-speed
automatic, 8-speed automatic, continuously
variable transmission, and dual-clutch
transmissions. Hybrid and electric powertrains may
complement traditional engine and transmission
designs or replace them entirely.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
vehicle simulation modeling 74 by
Argonne National Laboratory that is
used to estimate the effectiveness of the
fuel economy-improving/CO2
emissions-reducing technologies
considered. Argonne produces fullvehicle simulation modeling for many
combinations of technologies, on many
types of vehicles, but it did not simulate
literally every single vehicle model/
configuration in the analysis fleet
because it would be impractical to
assemble the requisite detailed
information—much of which would
likely only be provided on a
confidential basis—specific to each
vehicle model/configuration and
because the scale of the simulation
effort would correspondingly increase
by at least two orders of magnitude.

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74 Full-vehicle simulation modeling uses software
and physics models to compute and estimate energy
use of a vehicle during explicit driving conditions.
Section II.D below contains more information on
the Argonne work for this analysis.

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Instead, Argonne simulated 10 different
vehicle types, corresponding to the
‘‘technology classes’’ generally used in
CAFE analysis over the past several
rulemakings (e.g., small car, small
performance car, pickup truck, etc.).
Each of those 10 different vehicle types
was assigned a set of ‘‘baseline
characteristics,’’ to which Argonne
added combinations of fuel-saving
technologies and then ran simulations
to determine the fuel economy achieved
when applying each combination of
technologies to that vehicle type given
its baseline characteristics. These
inputs, discussed at greater length in
Sections II.D and II.G, provide the basis
for the CAFE model’s estimation of fuel
economy levels and CO2 emission rates.
In the analysis fleet, inputs assign
each specific vehicle model/
configuration to a technology class, and
once there, map to the simulation
within that technology class most
closely matching the combination of

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observed technologies and equipment
on that vehicle.75 This mapping to a
specific simulation result most closely
representing a given vehicle model/
configuration’s initial technology
‘‘state’’ enables the CAFE model to
estimate the same vehicle model/
configuration’s fuel economy after
application of some other combination
of technologies, leading to an alternative
technology state.
BILLING CODE 4910–59–P
75 Mapping vehicle model/configurations in the
analysis fleet to Argonne simulations was generally
straightforward, but occasionally the mapping was
complicated by factors like a vehicle model/
configuration being a great match for simulations
within more than one technology class (in which
case, the model/configuration was assigned to the
technology class that it best matched), or when
technologies on the model/configuration were
difficult to observe directly (like friction reduction
or parasitic loss characteristics of a transmission, in
which case the agencies relied on manufacturerreported data or CBI to help map the vehicle to a
simulation).

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Table 11-1- List of Tech

•th Data S

for Tech

A.

t

Tech Group

Single Overhead Cam

Public Specifications

Basic Engines

Public Specifications

Basic Engines

Overhead Valve

SOHC
DOHC
OHV

Public Specifications

Basic Engines

Variable Valve Timing

VVT

Public Specifications

Basic Engines

Variable Valve Lift

VVL

Public Specifications

Basic Engines

Stoichiometric Gasoline Direct Injection

SGDI

Public Specifications

Basic Engines

Cylinder Deactivation

DEAC

Public Specifications

Basic Engines

Turbocharged Engine

TURBOl

Public Specifications

Advanced Engines

Advanced Turbocharged Engine

TURB02

Manufacturer CBI

Advanced Engines

Turbocharged Engine with Cooled Exhaust Gas Recirculation

CEGRl

Manufacturer CBI

Advanced Engines

Fmt 4701

High Compression Ratio Engine

HCRl

Public Specifications

Advanced Engines

EPA High Compression Ratio Engine, with Cylinder Deactivation

HCR2

Not commercialized in MY 2016

Advanced Engines

Variable Compression Ratio Engine

VCR

Not commercialized in MY 2016

Advanced Engines

Advanced Cylinder Deactivation (Skip Fire)

ADEAC

Not commercialized in MY 2016

Advanced Engines

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Advanced Diesel Engine

ADSL

Public Specifications

Advanced Engines

Advanced Diesel Engine Improvements

DSLI

Not commercialized in MY 2016

Advanced Engines

Compressed Natural Gas

CNG

Public Specifications

Advanced Engines

Manual Transmission - 5 Speed

MT5

Public Specifications

Transmissions

Manual Transmission - 6 Speed

MT6

Public Specifications

Transmissions

24AUP2

Manual Transmission - 7 Speed

MT7

Public Specifications

Transmissions

Automatic Transmission- 5 Speed

AT5

Public Specifications

Transmissions

Automatic Transmission- 6 Speed

AT6

Public Specifications

Transmissions

Automatic Transmission - 6 Speed with Efficiency Improvements

AT6L2

Manufacturer CBI

Transmissions

Automatic Transmission - 7 Speed

AT7

Public Specifications

Transmissions

Automatic Transmission- 8 Speed

AT8

Public Specifications

Transmissions

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Dual Overhead Cam

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Data Source for Mapping

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Abbreviation

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Technology Name

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Manufacturer CBT

Transmissions

Automatic Transmission - 8 Speed with Maximum Efficiency
Improvements
Automatic Transmission- 9 Speed

AT8L3

Not commercialized in MY 2016

Transmissions

AT9

Public Specifications

Transmissions

Automatic Transmission- 10 Speed

ATlO

Public Specifications

Transmissions

Automatic Transmission- 10 Speed with Maximum Efficiency
Improvements
Dual Clutch Transmission - 6 Speed

AT10L2

Not commercialized in MY 2016

Transmissions

DCT6

Public Specifications

Transmissions

Dual Clutch Transmission - 8 Speed

DCT8

Public Specifications

Transmissions

Continuously Variable Transmission

CVT

Public Specifications

Transmissions

Continuously Variable Transmission with Efficiency
Improvements
No Electrification Technologies (Baseline)

CVTL2A/
CVT2B
CONV

Manufacturer CBI

Transmissions

Public Specifications

Electrification

12V Start-Stop

SS12V

Public Specifications

Electrification

Belt Integrated Starter Generator

BISG

Public Specifications

Electrification

Sfmt 4725

Crank Integrated Starter Generator

CISG

Public Specifications

Electrification

Strong Hybrid Electric Vehicle, Parallel

SHEVP2

Public Specifications

Electrification

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Strong Hybrid Electric Vehicle, Power Split

SHEVPS

Public Specifications

Electrification

Plug-in Hybrid Vehicle with 30 miles of range

PHEV30

Public Specifications

Electrification

Plug-in Hybrid Vehicle with 50 miles of range

PHEV50

Public Specifications

Electrification

Battery Electric Vehicle with 200 miles of range

BEV200

Public Specifications

Electrification

Fuel Cell Vehicle

FCV

Public Specifications

Electrification

Baseline Tire Rolling Resistance

ROLLO

Manufacturer CBI

Rolling Resistance

Tire Rolling Resistance, 10% Improvement

ROLLlO

Manufacturer CBI

Rolling Resistance

Tire Rolling Resistance, 20% Improvement

ROLL20

Manufacturer CBI

Rolling Resistance

Baseline Mass Reduction Technology

MRO

Mass Reduction

Mass Reduction- 5% of Glider

MRl

Public Specifications &
Manufacturer CBI
Public Specifications &
Manufacturer CBI

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23:42 Aug 23, 2018

Automatic Transmission - 8 Speed with Efficiency Improvements

Mass Reduction

43009

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Manufacturer CBI

Aerodynamic Drag

Aerodynamic Drag, 10% Drag Coefficient Reduction

AER010

Manufacturer CBI

Aerodynamic Drag

Aerodynamic Drag, 15% Drag Coefficient Reduction

AER015

Manufacturer CBI

Aerodynamic Drag

Aerodynamic Drag, 20% Drag Coefficient Reduction

AER020

Manufacturer CBI

Aerodynamic Drag

Electric Power Steering

EPS

Public Specifications

Improved Accessories

IACC

Manufacturer CBI

Low Drag Brakes

LDB

Manufacturer CBI

24AUP2

Secondary Axle Disconnect

SAX

Manufacturer CBI

Additional
Technologies
Additional
Technologies
Additional
Technologies
Additional
Technologies

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AER05

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AEROO

Aerodynamic Drag, 5% Drag Coefficient Reduction

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MR2

Mass Reduction - 10% of Glider

MR3

Mass Reduction- 15% of Glider

MR4

Mass Reduction - 20% of Glider

MR5

Mass Reduction
Mass Reduction
Mass Reduction
Mass Reduction
Aerodynamic Drag

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Baseline Aerodynamic Drag Technology

Public Specifications &
Manufacturer CBI
Public Specifications &
Manufacturer CBI
Public Specifications &
Manufacturer CBI
Public Specifications &
Manufacturer CBI
Manufacturer CBI

Mass Reduction - 7.5% of Glider

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
BILLING CODE 4910–59–C

sradovich on DSK3GMQ082PROD with PROPOSALS2

(e) Shared Vehicle Platforms, Engines,
and Transmissions
Another aspect of characterizing
vehicle model/configurations in the
analysis fleet is based on whether they
share a ‘‘platform’’ with other vehicle
model/configurations. A ‘‘platform’’
refers to engineered underpinnings
shared on several differentiated
products. Manufacturers share and
standardize components, systems,
tooling, and assembly processes within
their products (and occasionally with
the products of another manufacturer) to
cost-effectively maintain vibrant
portfolios.76
Vehicle model/configurations derived
from the same platform are so identified
in the analysis fleet. Many
manufacturers’ use of vehicle platforms
is well documented in the public record
and widely recognized among the
vehicle engineering community.
Engineering knowledge, information
from trade publications, and feedback
from manufacturers and suppliers was
also used to assign vehicle platforms in
the analysis fleet.
When the CAFE model is deciding
where and how to add technology to
vehicles, if one vehicle on the platform
receives new technology, other vehicles
on the platform also receive the
technology as part of their next major
redesign or refresh.77 Similar to vehicle
platforms, manufacturers create engines
that share parts.78 One engine family
76 The concept of platform sharing has evolved
with time. Years ago, manufacturers rebadged
vehicles and offered luxury options only on
premium nameplates (and manufacturers shared
some vehicle platforms in limited cases). Today,
manufacturers share parts across highly
differentiated vehicles with different body styles,
sizes, and capabilities that may share the same
platform. For instance, the Honda Civic and Honda
CR–V share many parts and are built on the same
platform. Engineers design chassis platforms with
the ability to vary wheelbase, ride height, and even
driveline configuration. Assembly lines can
produce hatchbacks and sedans to cost-effectively
utilize manufacturing capacity and respond to shifts
in market demand. Engines made on the same line
may power small cars or mid-size sport utility
vehicles. Additionally, although the agencies’
analysis, like past CAFE analyses, considers
vehicles produced for sale in the U.S., the agency
notes these platforms are not constrained to vehicle
models built for sale in the United States; many
manufacturers have developed, and use, global
platforms, and the total number of platforms is
decreasing across the industry. Several automakers
(for example, General Motors and Ford) either plan
to, or already have, reduced their number of
platforms to less than 10 and account for the
overwhelming majority of their production volumes
on that small number of platforms.
77 The CAFE model assigns mass reduction
technology at a platform level, but many other
technologies may be assigned and shared at a
vehicle nameplate or vehicle model level.
78 For instance, manufacturers may use different
piston strokes on a common engine block or bore

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may appear on many vehicles on a
platform, and changes to that engine
may or may not carry through to all the
vehicles. Some engines are shared
across a range of different vehicle
platforms. Vehicle model/configurations
in the analysis fleet that share engines
belonging to the same platform are also
identified as such.
It is important to note that
manufacturers define common engines
differently. Some manufacturers
consider engines as ‘‘common’’ if the
engines shared an architecture,
components, or manufacturing
processes. Other manufacturers take a
narrower definition, and only assume
‘‘common’’ engines if the parts in the
engine assembly are the same. In some
cases, manufacturers designate each
engine in each application as a unique
powertrain.79 Engine families for each
manufacturer were tabulated and
assigned 80 based on data-driven
criteria. If engines shared a common
cylinder count and configuration,
displacement, valvetrain, and fuel type,
those engines may have been considered
together. Additionally, if the
compression ratio, horsepower, and
displacement of engines were only
slightly different, those engines were
considered to be the same for the
purposes of redesign and sharing.
Vehicles in the analysis fleet with the
same engine family will therefore adopt
engine technology in a coordinated
fashion.81 By grouping engines together,
the CAFE model controls future engine
out common engine block castings with different
diameters to create engines with an array of
displacements. Head assemblies for different
displacement engines may share many components
and manufacturing processes across the engine
family. Manufacturers may finish crankshafts with
the same tools, to similar tolerances. Engines on the
same architecture may share pistons, connecting
rods, and the same engine architecture may include
both six and eight cylinder engines.
79 For instance, a manufacturer may have listed
two engines for a pair that share designs for the
engine block, the crank shaft, and the head because
the accessory drive components, oil pans, and
engine calibrations differ between the two. In
practice, many engines share parts, tooling, and
assembly resources, and manufacturers often
coordinate design updates between two similar
engines.
80 Engine family is referred to in the analysis as
an ‘‘engine code.’’
81 Specifically, if such vehicles have different
design schedules (i.e., refresh and redesign
schedules), and a subset of vehicles using a given
engine add engine technologies in the course of a
redesign or refresh that occurs in an early model
year (e.g., 2018), other vehicles using the same
engine ‘‘inherit’’ these technologies at the soonest
ensuing refresh or redesign. This is consistent with
a view that, over time, most manufacturers are
likely to find it more practicable to shift production
to a new version of an engine than to indefinitely
continue production of both the new engine and a
‘‘legacy’’ engine.

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families to retain reasonable powertrain
complexity.82
Like with engines, manufacturers
often use transmissions that are the
same or similar on multiple vehicles.83
To reflect this reality, shared
transmissions were considered for
manufacturers as appropriate. To define
common transmissions, the agencies
considered transmission type (manual,
automatic, dual-clutch, continuously
variable), number of gears, and vehicle
architecture (front-wheel-drive, rearwheel-drive, all-wheel-drive based on a
front-wheel-drive platform, or all-wheeldrive based on a rear-wheel-drive
platform). If vehicles shared these
attributes, these transmissions were
grouped for the analysis. Vehicles in the
analysis fleet with the same
transmission configuration 84 will adopt
transmission technology together, as
described above.85
Having all vehicles that share a
platform (or engines that are part of a
family) adopt fuel economy-improving/
CO2 emissions-reducing technologies
together, subject to refresh/redesign
constraints, reflects the real-world
considerations described above but also
overlooks some decisions manufacturers
might make in the real world in
response to market pull, meaning that
even though the analysis fleet is
incredibly complex, it is also oversimplified in some respects compared to
the real world. For example, the CAFE
model does not currently attempt to
simulate the potential for a
manufacturer to shift the application of
technologies to improve performance
rather than fuel economy. Therefore, the
model’s representation of the
‘‘inheritance’’ of technology can lead to
estimates a manufacturer might
eventually exceed fuel economy
82 This does mean, however, that for
manufacturers that submitted highly atomized
engine and transmission portfolios, there is a
practical cap on powertrain complexity and the
ability of the manufacturer to optimize the
displacement of (a.k.a. ‘‘right size’’) engines
perfectly for each vehicle configuration.
83 Manufacturers may produce transmissions that
have nominally different machining to castings, or
manufacturers may produce transmissions that are
internally identical, except for final output gear
ratio. In some cases, manufacturers sub-contract
with suppliers that deliver whole transmissions. In
other cases, manufacturers form joint-ventures to
develop shared transmissions, and these
transmission platforms may be offered in many
vehicles across manufacturers. Manufacturers use
supplier and joint-venture transmissions to a greater
extent than engines.
84 Transmission configurations are referred to in
the analysis as ‘‘transmission codes.’’
85 Similar to the inheritance approach outlined
for engines, if one vehicle application of a shared
transmission family upgraded the transmission,
other vehicle applications also upgraded the
transmission at the next refresh or redesign year.

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standards as technology continues to
propagate across shared platforms and
engines. In the past, there were some
examples of extended periods during
which some manufacturers exceeded
one or both of the CAFE and/or GHG
standards, but in plenty of other
examples, manufacturers chose to
introduce (or even reintroduce)
technological complexity into their
vehicle lineups in response to buyer
preferences. Going forward, and
recognizing the recent trend for
consolidating platforms, it seems likely
manufacturers will be more likely to
choose efficiency over complexity in
this regard; therefore, the potential
should be lower that today’s analysis
turns out to be over-simplified
compared to the real world.
Options will be considered to further
refine the representation of sharing and
inheritance of technology, possibly
including model revisions to account for
tradeoffs between fuel economy and
performance when applying technology.
Please provide comments on the sharing
and inheritance-related aspects of the
analysis fleet and the CAFE model along
with information that would support
refinement of the current approach or
development and implementation of
alternative approaches.
(f) Estimated Product Design Cycles

sradovich on DSK3GMQ082PROD with PROPOSALS2

Another aspect of characterizing
vehicle model/configurations in the
analysis fleet is based on when they can
next be refreshed or redesigned.
Redesign schedules play an important
role in determining when new
technologies may be applied. Many
technologies that improve fuel economy
and reduce CO2 emissions may be
difficult to incorporate without a major
product redesign. Therefore, each
vehicle model in the analysis fleet has
an associated redesign schedule, and the
CAFE model uses that schedule to
restrict significant advances in some
technologies (like major mass reduction)
to redesign years, while allowing
manufacturers to include minor
advances (such as improved tire rolling
resistance) during a vehicle ‘‘refresh,’’ or
a smaller update made to a vehicle,
which can happen between redesigns.

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In addition to refresh and redesign
schedules associated with vehicle
model/configurations, vehicles that
share a platform subsequently have
platform-wide refresh and redesign
schedules for mass reduction
technologies.
To develop the refresh/redesign
cycles used for the MY 2016 vehicles in
the analysis fleet, information from
commercially available sources was
used to project redesign cycles through
MY 2022, as for NHTSA’s analysis for
the Draft TAR published in 2016.86
Commercially available sources’
estimates through MY 2022 are
generally supported by detailed
consideration of public announcements
plus related intelligence from suppliers
and other sources, and recognize that
uncertainty increases considerably as
the forecasting horizon is extended. For
MYs 2023–2035, in recognition of that
uncertainty, redesign schedules were
extended considering past pacing for
each product, estimated schedules
through MY 2022, and schedules for
other products in the same technology
classes. As mentioned above, potentially
confidential forward-looking
information was not requested from
manufacturers; nevertheless, all
manufacturers had an opportunity to
review the estimates of product-specific
redesign schedules, a few manufacturers
provided related forecasts and, for the
most part, that information corroborated
the estimates.
Some commenters suggested
supplanting these estimated redesign
schedules with estimates applying faster
86 In some cases, data from commercially
available sources was found to be incomplete or
inconsistent with other available information. For
instance, commercially available sources identified
some newly imported vehicles as new platforms,
but the international platform was midway through
the product lifecycle. While new to the U.S. market,
treating these vehicles as new entrants would have
resulted in artificially short redesign cycles if
carried forward, in some cases. Similarly,
commercially available sources labeled some
product refreshes as redesigns, and vice versa. In
these limited cases, the data was revised to be
consistent with other available information or
typical redesign and refresh schedules, for the
purpose of the CAFE modeling. In these limited
cases, the forecast time between redesigns and
refreshes was updated to match the observed past
product timing.

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cycles (e.g., four to five years), and this
approach was considered for the
analysis.87 Some manufacturers tend to
operate with faster redesign cycles and
may continue to do so, and
manufacturers tend to redesign some
products more frequently than others.
However, especially considering that
information presented by manufacturers
largely supports estimates discussed
above, applying a ‘‘one size fits all’’
acceleration of redesign cycles would
likely not improve the analysis; instead,
doing so would likely reduce
consistency with the real world,
especially for light trucks. Moreover, if
some manufacturers accelerate
redesigns in response to new standards,
doing so would likely involve costs
(greater levels of stranded capital,
reduced opportunity to benefit from
‘‘learning’’-related cost reductions)
greater than reflected in other inputs to
the analysis. However, a wider range of
technologies can practicably be applied
during mid-cycle ‘‘freshenings’’ than
has been represented by past analyses,
and this part of the analysis has been
expanded, as discussed below in
Section II.D.88 Also, in the sensitivity
analysis supporting today’s proposal
and presented in Chapter 13 of the
PRIA, one case involving faster redesign
schedules and one involving slower
redesign schedules has been analyzed.
Manufacturers use diverse strategies
with respect to when, and how often
they update vehicle designs. While most
vehicles have been redesigned sometime
in the last five years, many vehicles
have not. In particular, vehicles with
lower annual sales volumes tend to be
redesigned less frequently, perhaps
giving manufacturers more time to
amortize the investment needed to bring
the product to market. In some cases,
manufacturers continue to produce and
sell vehicles designed more than a
decade ago.
87 In response to the EPA’s August 21, 2017,
Request for Comments (docket numbers EPA–HQ–
OAR–2015–0827 and NHTSA–2016–0068), see, e.g.,
CARB at 28 (Docket ID. EPA–HQ–OAR–2015–0827–
9197), EDF at 12 (Docket ID. EPA–HQ–OAR–2015–
0827–9203), and NRDC, et. al. at 29–33 (Docket ID.
EPA–HQ–OAR–2015–0827–9826).
88 NRDC, et al., at 32.

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Each manufacturer may use different
strategies throughout their product
portfolio, and a component of each
strategy may include the timing of

89 Technology class, or tech class, refers to a
group of fuel-economy simulations of similar

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refresh and redesign cycles. Table II–3
below summarizes the average time
between redesigns, by manufacturer, by
vehicle technology class.89 Dashes mean
the manufacturer has no volume in that

vehicle technology class in the MY 2016
analysis fleet. Across the industry,
manufacturers average 6.5 years
between product redesigns.

vehicles. As explained, each vehicle is assigned to

a representative simulation to estimate technology
effectiveness for purposes of the analysis.

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There are a few notable observations
from this table. Pick-up trucks have
much longer redesign schedules (7.8
years on average) than small cars (5.5
years on average). Some manufacturers
redesign vehicles often (every 5.2 years
in the case of Hyundai), while other
manufacturers redesign vehicles less
often (FCA waits on average 8.6 years
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industry, light-duty vehicle designs last
for about 6.5 years.
Even if two manufacturers have
similar redesign cadence, the model
years in which the redesigns occur may
still be different and dependent on
where each of the manufacturer’s
products are in their life cycle.
Table II–4 summarizes the average age
of manufacturers’ offering by vehicle

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technology class. A value of ‘‘0.0’’
means that every vehicle for a
manufacturer in that vehicle technology
class, represented in the MY 2016
analysis fleet was new in MY 2016.
Across the industry, manufacturers
redesigned MY 2016 vehicles an average
of 3.2 years earlier.

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Based on historical observations and
refresh/redesign schedule forecasts,
careful consideration to redesign cycles
for each manufacturer and each vehicle
is important. Simply assuming every
vehicle is redesigned by 2021 and by
2025 is not appropriate, as this would
misrepresent both the likely timing of
redesigns and the likely time between
redesigns in most cases.

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C. Development of Footprint-Based
Curve Shapes
As in the past four CAFE rulemakings,
the most recent two of which included
related standards for CO2 emissions,
NHTSA and EPA are proposing to set
attribute-based CAFE standards that are
defined by a mathematical function of
vehicle footprint, which has observable
correlation with fuel economy and

90 49

U.S.C. 32902(a)(3)(A).

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vehicle emissions. EPCA, as amended
by EISA, expressly requires that CAFE
standards for passenger cars and light
trucks be based on one or more vehicle
attributes related to fuel economy and
be expressed in the form of a
mathematical function.90 While the
CAA includes no specific requirements
regarding GHG regulation, EPA has
chosen to adopt standards consistent
with the EPCA/EISA requirements in
the interest of simplifying compliance
for the industry since 2010.91 Section
II.C.1 describes the advantages of
attribute standards, generally. Section
II.C.2 explains the agencies’ specific
decision to use vehicle footprint as the
attribute over which to vary stringency
for past and current rules. Section II.C.3
discusses the policy considerations in
selecting the specific mathematical
function. Section II.C.4 discusses the

Under attribute-based standards,
every vehicle model has fuel economy
and CO2 targets, the levels of which
depend on the level of that vehicle’s
determining attribute (for this proposed
rule, footprint is the determining
attribute, as discussed below). The
manufacturer’s fleet average
performance is calculated by the
harmonic production-weighted average
of those targets, as defined below:

91 Such an approach is permissible under section
202(a) of the CAA, and EPA has used the attribute-

based approach in issuing standards under
analogous provisions of the CAA.

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methodologies used to develop current
attribute-based standards, and the
agencies’ current proposal to continue
to do so for MYs 2022–2026. Section
II.C.5 discusses the methodologies used
to reconsider the mathematical function
for the proposed standards.
1. Why attribute-based standards, and
what are the benefits?

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Here, i represents a given model 92 in
a manufacturer’s fleet, Productioni
represents the U.S. production of that
model, and Targeti represents the target
as defined by the attribute-based
standards. This means no vehicle is
required to meet its target; instead,
manufacturers are free to balance
improvements however they deem best
within (and, given credit transfers, at
least partially across) their fleets.
The idea is to select the shape of the
mathematical function relating the
standard to the fuel economy-related
attribute to reflect the trade-offs
manufacturers face in producing more
of that attribute over fuel efficiency (due
to technological limits of production
and relative demand of each attribute).
If the shape captures these trade-offs,
every manufacturer is more likely to
continue adding fuel efficient
technology across the distribution of the
attribute within their fleet, instead of
potentially changing the attribute—and
other correlated attributes, including
fuel economy—as a part of their
compliance strategy. Attribute-based
standards that achieve this have several
advantages.
First, assuming the attribute is a
measurement of vehicle size, attributebased standards reduce the incentive for
manufacturers to respond to CAFE
standards by reducing vehicle size in
ways harmful to safety.93 Larger
vehicles, in terms of mass and/or crush
space, generally consume more fuel, but
are also generally better able to protect
occupants in a crash.94 Because each
92 If a model has more than one footprint variant,
here each of those variants is treated as a unique
model, i, since each footprint variant will have a
unique target.
93 The 2002 NAS Report described at length and
quantified the potential safety problem with average
fuel economy standards that specify a single
numerical requirement for the entire industry. See
Transportation Research Board and National
Research Council. 2002. Effectiveness and Impact of
Corporate Average Fuel Economy (CAFE)
Standards, Washington, DC: The National
Academies Press (‘‘2002 NAS Report’’) at 5, finding
12, available at https://www.nap.edu/catalog/
10172/effectiveness-and-impact-of-corporateaverage-fuel-economy-cafe-standards (last accessed
June 15, 2018). Ensuing analyses, including by
NHTSA, support the fundamental conclusion that
standards structured to minimize incentives to
downsize all but the largest vehicles will tend to
produce better safety outcomes than flat standards.
94 Bento, A., Gillingham, K., & Roth, K. (2017).
The Effect of Fuel Economy Standards on Vehicle
Weight Dispersion and Accident Fatalities. NBER
Working Paper No. 23340. Available at http://
www.nber.org/papers/w23340 (last accessed June
15, 2018).

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vehicle model has its own target
(determined by a size-related attribute),
properly fitted attribute-based standards
provide little, if any, incentive to build
smaller vehicles simply to meet a fleetwide average, because smaller vehicles
are subject to more stringent compliance
targets.
Second, attribute-based standards, if
properly fitted, better respect
heterogeneous consumer preferences
than do single-valued standards. As
discussed above, a single-valued
standard encourages a fleet mix with a
larger share of smaller vehicles by
creating incentives for manufacturers to
use downsizing the average vehicle in
their fleet (possibly through fleet
mixing) as a compliance strategy, which
may result in manufacturers building
vehicles for compliance reasons that
consumers do not want. Under a sizerelated, attribute-based standard,
reducing the size of the vehicle is a less
viable compliance strategy because
smaller vehicles have more stringent
regulatory targets. As a result, the fleet
mix under such standards is more likely
to reflect aggregate consumer demand
for the size-related attribute used to
determine vehicle targets.
Third, attribute-based standards
provide a more equitable regulatory
framework across heterogeneous
manufacturers who may each produce
different shares of vehicles along
attributes correlated with fuel
economy.95 A single, industry-wide
CAFE standard imposes
disproportionate cost burden and
compliance challenges on
manufacturers who produce more
vehicles with attributes inherently
correlated with lower fuel economy—
i.e. manufacturers who produce, on
average, larger vehicles. As discussed
above, retaining the ability for
manufacturers to produce vehicles
which respect heterogeneous market
preferences is an important
consideration. Since manufacturers may
target different markets as a part of their
business strategy, ensuring that these
manufacturers do not incur a
disproportionate share of the regulatory
cost burden is an important part of
conserving consumer choices within the
market.
95 2002

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2. Why footprint as the attribute?
It is important that the CAFE and CO2
standards be set in a way that does not
encourage manufacturers to respond by
selling vehicles that are less safe.
Vehicle size is highly correlated with
vehicle safety—for this reason, it is
important to choose an attribute
correlated with vehicle size (mass or
some dimensional measure). Given this
consideration, there are several policy
and technical reasons why footprint is
considered to be the most appropriate
attribute upon which to base the
standards, even though other vehicle
size attributes (notably, curb weight) are
more strongly correlated with fuel
economy and emissions.
First, mass is strongly correlated with
fuel economy; it takes a certain amount
of energy to move a certain amount of
mass. Footprint has some positive
correlation with frontal surface area,
likely a negative correlation with
aerodynamics, and therefore fuel
economy, but the relationship is less
deterministic. Mass and crush space
(correlated with footprint) are both
important safety considerations. As
discussed below and in the
accompanying PRIA, NHTSA’s research
of historical crash data indicates that
holding footprint constant, and
decreasing the mass of the largest
vehicles, will have a net positive safety
impact to drivers overall, while holding
footprint constant and decreasing the
mass of the smallest vehicles will have
a net decrease in fleetwide safety.
Properly fitted footprint-based standards
provide little, if any, incentive to build
smaller vehicles to meet CAFE and CO2
standards, and therefore help minimize
the impact of standards on overall fleet
safety.
Second, it is important that the
attribute not be easily manipulated in a
manner that does not achieve the goals
of EPCA or other goals, such as safety.
Although weight is more strongly
correlated with fuel economy than
footprint, there is less risk of
manipulation (changing the attribute(s)
to achieve a more favorable target) by
increasing footprint under footprintbased standards than there would be by
increasing vehicle mass under weightbased standards. It is relatively easy for
a manufacturer to add enough weight to
a vehicle to decrease its applicable fuel
economy target a significant amount, as
compared to increasing vehicle

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• Given the same industry-wide
average required fuel economy or CO2
standard, dramatically steeper standards
tend to place greater compliance
burdens on limited-line manufacturers
(depending of course, on which vehicles
are being produced).
• If cutpoints are adopted, given the
same industry-wide average required
fuel economy, moving small-vehicle
cutpoints to the left (i.e., up in terms of
fuel economy, down in terms of CO2
emissions) discourages the introduction
of small vehicles, and reduces the
incentive to downsize small vehicles in
ways that could compromise overall
highway safety.
• If cutpoints are adopted, given the
same industry-wide average required
fuel economy, moving large-vehicle
cutpoints to the right (i.e., down in
terms of fuel economy, up in terms of
CO2 emissions) better accommodates the
design requirements of larger vehicles
— especially large pickups — and
extends the size range over which
downsizing is discouraged.

3. What mathematical function should
be used to specify footprint-based
standards?
In requiring NHTSA to ‘‘prescribe by
regulation separate average fuel
economy standards for passenger and
non-passenger automobiles based on 1
or more vehicle attributes related to fuel
economy and express each standard in
the form of a mathematical function’’,
EPCA/EISA provides ample discretion
regarding not only the selection of the
attribute(s), but also regarding the

nature of the function. The CAA
provides no specific direction regarding
CO2 regulation, and EPA has continued
to harmonize this aspect of its CO2
regulations with NHTSA’s CAFE
regulations. The relationship between
fuel economy (and GHG emissions) and
footprint, though directionally clear
(i.e., fuel economy tends to decrease and
CO2 emissions tend to increase with
increasing footprint), is theoretically
vague, and quantitatively uncertain; in
other words, not so precise as to a priori
yield only a single possible curve.
The decision of how to specify this
mathematical function therefore reflects
some amount of judgment. The function
can be specified with a view toward
achieving different environmental and
petroleum reduction goals, encouraging
different levels of application of fuelsaving technologies, avoiding any
adverse effects on overall highway
safety, reducing disparities of
manufacturers’ compliance burdens,
and preserving consumer choice, among
other aims. The following are among the
specific technical concerns and
resultant policy tradeoffs the agencies
have considered in selecting the details
of specific past and future curve shapes:
• Flatter standards (i.e., curves)
increase the risk that both the size of
vehicles will be reduced, potentially
compromising highway safety, and
reducing any utility consumers would
have gained from a larger vehicle.
• Steeper footprint-based standards
may create incentives to upsize
vehicles, potentially oversupplying
vehicles of certain footprints beyond
what consumers would naturally
demand, and thus increasing the
possibility that fuel savings and CO2
reduction benefits will be forfeited
artificially.
• Given the same industry-wide
average required fuel economy or CO2
standard, flatter standards tend to place
greater compliance burdens on full-line
manufacturers.

Here, Target is the fuel economy
target applicable to vehicles of a given
footprint in square feet (Footprint). The

upper asymptote, a, and the lower
asymptote, b, are specified in mpg; the
reciprocal of these values represent the

lower and upper asymptotes,
respectively, when the curve is instead
specified in gallons per mile (gpm). The

98 The right cutpoint for the light truck curve was
moved further to the right for MYs 2017–2021, so
that more possible footprints would fall on the
sloped part of the curve. In order to ensure that, for
all possible footprints, future standards would be at
least as high as MY 2016 levels, the final standards

for light trucks for MYs 2017–2021 is the maximum
of the MY 2016 target curves and the target curves
for the give MY standard. This is defined further in
the 2012 final rule. See 77 FR 62624, at 62699–700
(Oct. 15, 2012).

96 See

74 FR at 14359 (Mar. 30, 2009).
74 FR 14196, 14363–14370 (Mar. 30, 2009)
for NHTSA discussion of curve fitting in the MY
2011 CAFE final rule.
97 See

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4. What mathematical functions have
been used previously, and why?
Notwithstanding the aforementioned
discretion under EPCA/EISA, data
should inform consideration of potential
mathematical functions, but how
relevant data is defined and interpreted,
and the choice of methodology for
fitting a curve to that data, can and
should include some consideration of
specific policy goals. This section
summarizes the methodologies and
policy concerns that were considered in
developing previous target curves (for a
complete discussion see the 2012 FRIA).
As discussed below, the MY 2011
final curves followed a constrained
logistic function defined specifically in
the final rule.97 The MYs 2012–2021
final standards and the MYs 2022–2025
augural standards are defined by
constrained linear target functions of
footprint, as shown below: 98

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footprint, which is a much more
complicated change that typically takes
place only with a vehicle redesign.
Further, some commenters on the MY
2011 CAFE rulemaking were concerned
that there would be greater potential for
gaming under multi-attribute standards,
such as those that also depend on
weight, torque, power, towing
capability, and/or off-road capability. As
discussed in NHTSA’s MY 2011 CAFE
final rule,96 it is anticipated that the
possibility of gaming is lowest with
footprint-based standards, as opposed to
weight-based or multi-attribute-based
standards. Specifically, standards that
incorporate weight, torque, power,
towing capability, and/or off-road
capability in addition to footprint would
not only be more complex, but by
providing degrees of freedom with
respect to more easily-adjusted
attributes, they could make it less
certain that the future fleet would
actually achieve the projected average
fuel economy and CO2 levels. This is
not to say that a footprint-based system
will eliminate gaming, or that a
footprint-based system will eliminate
the possibility that manufacturers will
change vehicles in ways that
compromise occupant protection, but
footprint-based standards achieve the
best balance among affected
considerations. Please provide
comments on whether vehicular
footprint is the most suitable attribute
upon which to base standards.

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slope, c, and the intercept, d, of the
linear portion of the curve are specified
as gpm per change in square feet, and
gpm, respectively.
The min and max functions will take
the minimum and maximum values
within their associated parentheses.
Thus, the max function will first find
the maximum of the fitted line at a
given footprint value and the lower
asymptote from the perspective of gpm.
If the fitted line is below the lower
asymptote it is replaced with the floor,
which is also the minimum of the floor
and the ceiling by definition, so that the
target in mpg space will be the
reciprocal of the floor in mpg space, or
simply, a. If, however, the fitted line is
not below the lower asymptote, the
fitted value is returned from the max
function and the min function takes the
minimum value of the upper asymptote
(in gpm space) and the fitted line. If the
fitted value is below the upper
asymptote, it is between the two
asymptotes and the fitted value is
appropriately returned from the min
function, making the overall target in
mpg the reciprocal of the fitted line in
gpm. If the fitted value is above the
upper asymptote, the upper asymptote
is returned is returned from the min
function, and the overall target in mpg
is the reciprocal of the upper asymptote
in gpm space, or b.
In this way curves specified as
constrained linear functions are
specified by the following parameters:
a = upper limit (mpg)
b = lower limit (mpg)
c = slope (gpm per sq. ft.)
d = intercept (gpm)

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The slope and intercept are specified
as gpm per sq. ft. and gpm instead of
mpg per sq. ft. and mpg because fuel
consumption and emissions appear
roughly linearly related to gallons per
mile (the reciprocal of the miles per
gallon).
(a) NHTSA in MY 2008 and MY 2011
CAFE (Constrained Logistic)
For the MY 2011 CAFE rule, NHTSA
estimated fuel economy levels by
footprint from the MY 2008 fleet after
normalization for differences in
technology,99 but did not make
adjustments to reflect other vehicle
attributes (e.g., power-to-weight ratios).
Starting with the technology-adjusted
passenger car and light truck fleets,
NHTSA used minimum absolute
deviation (MAD) regression without
sales weighting to fit a logistic form as
a starting point to develop mathematical
99 See 74 FR 14196, 14363–14370 (Mar. 30, 2009)
for NHTSA discussion of curve fitting in the MY
2011 CAFE final rule.

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functions defining the standards.
NHTSA then identified footprints at
which to apply minimum and
maximum values (rather than letting the
standards extend without limit) and
transposed these functions vertically
(i.e., on a gallons-per-mile basis,
uniformly downward) to produce the
promulgated standards. In the preceding
rule, for MYs 2008–2011 light truck
standards, NHTSA examined a range of
potential functional forms, and
concluded that, compared to other
considered forms, the constrained
logistic form provided the expected and
appropriate trend (decreasing fuel
economy as footprint increases), but
avoided creating ‘‘kinks’’ the agency
was concerned would provide
distortionary incentives for vehicles
with neighboring footprints.100
(b) MYs 2012–2016 Standards
(Constrained Linear)
For the MYs 2012–2016 rule,
potential methods for specifying
mathematical functions to define fuel
economy and CO2 standards were
reevaluated. These methods were fit to
the same MY 2008 data as the MY 2011
standard. Considering these further
specifications, the constrained logistic
form, if applied to post-MY 2011
standards, would likely contain a steep
mid-section that would provide undue
incentive to increase the footprint of
midsize passenger cars.101 A range of
methods to fit the curves would have
been reasonable, and a minimum
absolute deviation (MAD) regression
without sales weighting on a
technology-adjusted car and light truck
fleet was used to fit a linear equation.
This equation was used as a starting
point to develop mathematical functions
defining the standards. Footprints were
then identified at which to apply
minimum and maximum values (rather
than letting the standards extend
without limit). Finally, these
constrained/piecewise linear functions
were transposed vertically (i.e., on a
gpm or CO2 basis, uniformly downward)
by multiplying the initial curve by a
single factor for each MY standard to
produce the final attribute-based targets
for passenger cars and light trucks
described in the final rule.102 These
transformations are typically presented
100 See 71 FR 17556, 17609–17613 (Apr. 6, 2006)
for NHTSA discussion of ‘‘kinks’’ in the MYs 2008–
2011 light truck CAFE final rule (there described as
‘‘edge effects’’). A ‘‘kink,’’ as used here, is a portion
of the curve where a small change in footprint
results in a disproportionally large change in
stringency.
101 75 FR at 25362.
102 See generally 74 FR at 49491–96; 75 FR at
25357–62.

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as percentage improvements over a
previous MY target curve.
(c) MYs 2017 and Beyond Standards
(Constrained Linear)
The mathematical functions finalized
in 2012 for MYs 2017 and beyond
changed somewhat from the functions
for the MYs 2012–2016 standards. These
changes were made to both address
comments from stakeholders, and to
further consider some of the technical
concerns and policy goals judged more
preeminent under the increased
uncertainty of the impacts of finalizing
and proposing standards for model
years further into the future.103
Recognizing the concerns raised by fullline OEMs, it was concluded that
continuing increases in the stringency of
the light truck standards would be more
feasible if the light truck curve for MYs
2017 and beyond was made steeper than
the MY 2016 truck curve and the right
(large footprint) cut-point was extended
only gradually to larger footprints. To
accommodate these considerations, the
2012 final rule finalized the slope fit to
the MY 2008 fleet using a salesweighted, ordinary least-squares
regression, using a fleet that had
technology applied to make the
technology application across the fleet
more uniform, and after adjusting the
data for the effects of weight-tofootprint. Information from an updated
MY 2010 fleet was also considered to
support this decision. As the curve was
vertically shifted (with fuel economy
specified as mpg instead of gpm or CO2
emissions) upwards, the right cutpoint
was progressively moved for the light
truck curves with successive model
years, reaching the final endpoint for
MY 2021; this is further discussed and
shown in Chapter 4.3 of the PRIA.
5. Reconsidering the Mathematical
Functions for This Proposal
(a) Why is it important to reconsider the
mathematical functions?
By shifting the developed curves by a
single factor, it is assumed that the
underlying relationship of fuel
consumption (in gallons per mile) to
vehicle footprint does not change
significantly from the model year data
used to fit the curves to the range of
model years for which the shifted curve
shape is applied to develop the
standards. However, it must be
recognized that the relationship
103 The MYs 2012–2016 final standards were
signed April 1st, 2010—putting 6.5 years between
its signing and the last affected model year, while
the MYs 2017–2021 final standards were signed
August 28th, 2012—giving just more than nine
years between signing and the last affected final
standards.

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conducted that do not require
underlying engineering models of how
fuel economy and footprint might be
expected to be related, and a separate
analysis that uses vehicle simulation
results as the basis to estimate the
relationship from a perspective more
explicitly informed by engineering
theory was conducted as well. Despite
changes in the new vehicle fleet both in
terms of technologies applied and in
market demand, the underlying
statistical relationship between footprint
and fuel economy has not changed
significantly since the MY 2008 fleet
used for the 2012 final rule; therefore,
it is proposed to continue to use the
curve shapes fit in 2012. The analysis
and reasoning supporting this decision
follows.
(b) What statistical analyses did NHTSA
consider?
In considering how to address the
various policy concerns discussed
above, data from the MY 2016 fleet was
considered, and a number of descriptive
statistical analyses (i.e., involving
observed fuel economy levels and
footprints) using various statistical
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adjustments to the data to make the
fleets less technologically heterogeneous
were performed. There were several
adjustments to the data that were
common to all of the statistical analyses
considered.
With a view toward isolating the
relationship between fuel economy and
footprint, the few diesels in the fleet
were excluded, as well as the limited
number of vehicles with partial or full
electric propulsion; when the fleet is
normalized so that technology is more
homogenous, application of these
technologies is not allowed. This is
consistent with the methodology used
in the 2012 final rule.
The above adjustments were applied
to all statistical analyses considered,
regardless of the specifics of each of the
methods, weights, and technology level
of the data, used to view the
relationship of vehicle footprint and
fuel economy. Table II–5, below,
summarizes the different assumptions
considered and the key attributes of
each. The analysis was performed
considering all possible combinations of
these assumptions, producing a total of
eight footprint curves.

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between vehicle footprint and fuel
economy is not necessarily constant
over time; newly developed
technologies, changes in consumer
demand, and even the curves
themselves could, if unduly susceptible
to gaming, influence the observed
relationships between the two vehicle
characteristics. For example, if certain
technologies are more effective or more
marketable for certain types of vehicles,
their application may not be uniform
over the range of vehicle footprints.
Further, if market demand has shifted
between vehicle types, so that certain
vehicles make up a larger share of the
fleet, any underlying technological or
market restrictions which inform the
average shape of the curves could
change. That is, changes in the
technology or market restrictions
themselves, or a mere re-weighting of
different vehicles types, could reshape
the fit curves.
For the above reasons, the curve
shapes were reconsidered using the
newest available data, from MY 2016.
With a view toward corroboration
through different techniques, a range of
descriptive statistical analyses were

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(1) Current Technology Level Curves
The ‘‘current technology’’ level curves
exclude diesels and vehicles with
electric propulsion, as discussed above,
but make no other changes to each
model year fleet. Comparing the MY
2016 curves to ones built under the
same methodology from previous model
year fleets shows whether the observed
curve shape has changed significantly
over time as standards have become
more stringent. Importantly, these
curves will include any market forces
which make technology application
variable over the distribution of
footprint. These market forces will not
be present in the ‘‘maximum
technology’’ level curves: By making
technology levels homogenous, this
variation is removed. The current
technology level curves built using both
regression types and both regression
weight methodologies from the MY
2008, MY 2010, and MY 2016 fleets,
shown in more detail in Chapter 4.4.2.1
of the PRIA, support the curve slopes
finalized in the 2012 final rule. The
curves built from most methodologies
using each fleet generally shift, but
remain very similar in slope. This
suggests that the relationship of
footprint to fuel economy, including
both technology and market limits, has
not significantly changed.
(2) Maximum Technology Level Curves

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As in prior rulemakings, technology
differences between vehicle models
were considered to be a significant
factor producing uncertainty regarding
the relationship between fuel
consumption and footprint. Noting that
attribute-based standards are intended
to encourage the application of
additional technology to improve fuel
efficiency and reduce CO2 emissions
across the distribution of footprint in
the fleet, approaches were considered in
which technology application is
simulated for purposes of the curve
fitting analysis in order to produce fleets
that are less varied in technology
content. This approach helps reduce
‘‘noise’’ (i.e., dispersion) in the plot of
vehicle footprints and fuel consumption
levels and identify a more technologyneutral relationship between footprint

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and fuel consumption. The results of
updated analysis for maximum
technology level curves are also shown
in Chapter 4.4.2.2 of the PRIA.
Especially if vehicles progress over time
toward more similar size-specific
efficiency, further removing variation in
technology application both better
isolates the relationship between fuel
consumption and footprint and further
supports the curve slopes finalized in
the 2012 final rule.
(c) What other methodologies were
considered?
The methods discussed above are
descriptive in nature, using statistical
analysis to relate observed fuel economy
levels to observed footprints for known
vehicles. As such, these methods are
clearly based on actual data, answering
the question ‘‘how does fuel economy
appear to be related to footprint?’’
However, being independent of explicit
engineering theory, they do not answer
the question ‘‘how might one expect
fuel economy to be related to footprint?’’
Therefore, as an alternative to the above
methods, an alternative methodology
was also developed and applied that,
using full-vehicle simulation, comes
closer to answer the second question,
providing a basis to either corroborate
answers to the first, or suggest that
further investigation could be
important.
As discussed in the 2012 final rule,
several manufacturers have
confidentially shared with the agencies
what they described as ‘‘physics-based’’
curves, with each OEM showing
significantly different shapes for the
footprint-fuel economy relationships.
This variation suggests that
manufacturers face different curves
given the other attributes of the vehicles
in their fleets (i.e., performance
characteristics) and/or that their curves
reflected different levels of technology
application. In reconsidering the shapes
of the proposed MYs 2021–2026
standards, a similar estimation of
physics-based curves leveraging thirdparty simulation work form Argonne
National Laboratories (ANL) was
developed. Estimating physics-based
curves better ensures that technology
and performance are held constant for

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all footprints; augmenting a largely
statistical analysis with an analysis that
more explicitly incorporates engineering
theory helps to corroborate that the
relationship between fuel economy and
footprint is in fact being characterized.
Tractive energy is the amount of
energy it will take to move a vehicle.104
Here, tractive energy effectiveness is
defined as the share of the energy
content of fuel consumed which is
converted into mechanical energy and
used to move a vehicle—for internal
combustion engine (ICE) vehicles, this
will vary with the relative efficiency of
specific engines. Data from ANL
simulations suggest that the limits of
tractive energy effectiveness are
approximately 25% for vehicles with
internal combustion engines which do
not possess ISG, other hybrid, plug-in,
pure electric, or fuel cell technology.
A tractive energy prediction model
was also developed to support today’s
proposal. Given a vehicle’s mass, frontal
area, aerodynamic drag coefficient, and
rolling resistance as inputs, the model
will predict the amount of tractive
energy required for the vehicle to
complete the Federal test cycle. This
model was used to predict the tractive
energy required for the average vehicle
of a given footprint 105 and ‘‘body
technology package’’ to complete the
cycle. The body technology packages
considered are defined in Table II–6,
below. Using the absolute tractive
energy predicted and tractive energy
effectiveness values spanning possible
ICE engines, fuel economy values were
then estimated for different body
technology packages and engine tractive
energy effectiveness values.
104 Thomas, J. ‘‘Drive Cycle Powertrain
Efficiencies and Trends Derived from EPA Vehicle
Dynamometer Results,’’ SAE Int. J. Passeng. Cars—
Mech. Syst. 7(4):2014, doi:10.4271/2014–01–2562.
Available at https://www.sae.org/publications/
technical-papers/content/2014-01-2562/ (last
accessed June 15, 2018).
105 The mass reduction curves used elsewhere in
this analysis were used to predict the mass of a
vehicle with a given footprint, body style box, and
mass reduction level. The ‘Body style Box’ is 1 for
hatchbacks and minivans, 2 for pickups, and 3 for
sedans, and is an important predictor of
aerodynamic drag. Mass is an essential input in the
tractive energy calculation.

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Chapter 6 of the PRIA shows the
resultant CAFE levels estimated for the
vehicle classes ANL simulated for this
analysis, at different footprint values
and by vehicle ‘‘box.’’ Pickups are
considered 1-box, hatchbacks and
minivans are 2-box, and sedans are 3box. These estimates are compared with
the MY 2021 standards finalized in
2012. The general trend of the simulated
data points follows the pattern of the
previous MY 2021 standards for all
technology packages and tractive energy
effectiveness values presented in the
PRIA. The tractive energy curves are
intended to validate the curve shapes
against a physics-based alternative, and
the analysis suggests that the curve
shapes track the physical relationship
between fuel economy and tractive
energy for different footprint values.
Physical limitations are not the only
forces manufacturers face; they must
also produce vehicles that consumers
will purchase. For this reason, in setting
future standards, the analysis will
continue to consider information from
statistical analyses that do not
homogenize technology applications in
addition to statistical analyses which
do, as well as a tractive energy analysis
similar to the one presented above.
The relationship between fuel
economy and footprint remains
directionally discernable but
quantitatively uncertain. Nevertheless,
each standard must commit to only one
function. Approaching the question
‘‘how is fuel economy related to
footprint’’ from different directions and
applying different approaches will
provide the greatest confidence that the
single function defining any given
standard appropriately and reasonably
reflects the relationship between fuel
economy and footprint. Please provide
comments on this tentative conclusion
and the above discussion.
D. Characterization of Current and
Anticipated Fuel-Saving Technologies
The analysis evaluates a wide array of
technologies manufacturers could use to

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improve the fuel economy of new
vehicles, in both the near future and the
timeframe of this proposed rulemaking,
to meet the fuel economy and CO2
standards proposed in this rulemaking.
The analysis evaluated costs for these
technologies, and looked at how these
costs may change over time. The
analysis also considered how fuelsaving technologies may be used on
many types of vehicles (ranging from
small cars to trucks) and how the
technologies may perform in improving
fuel economy and CO2 emissions in
combination with other technologies.
With cost and effectiveness estimates for
technologies, the analysis can forecast
how manufacturers may respond to
potential standards and can estimate the
associated costs and benefits related to
technology and equipment changes.
This assists the assessment of
technological feasibility and is a
building block for the consideration of
economic practicability of potential
standards.
NHTSA, EPA, and CARB issued the
Draft Technical Assessment Report
(Draft TAR) 106 as the first step in the
EPA MTE process. The Draft TAR
provided an opportunity for the
agencies to share with the public
updated technical analysis relevant to
development of future standards. For
this NPRM, the analysis relies on
portions of the analysis presented in the
Draft TAR, along with new information
that has been gathered and developed
since conducting that analysis, and the
significant, substantive input that was
received during the public comment
period.
The Draft TAR considered many
technologies previously assessed in the
2012 final rule.107 In some cases,
manufacturers have nearly universally
adopted a technology in today’s new
vehicle fleet (for example, electric
power steering). In other cases,
106 Available at https://www.nhtsa.gov/staticfiles/
rulemaking/pdf/cafe/Draft-TAR-Final.pdf (last
accessed June 15, 2018).
107 77 FR 62624 (Oct. 15, 2012).

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manufacturers occasionally use a
technology in today’s new vehicle fleet
(like turbocharged engines). For a few
technologies considered in the 2012
rulemaking, manufacturers began
implementing the technologies but have
since largely pivoted to other
technologies due to consumer
acceptance issues (for instance, in some
cases drivability and performance feel
issues associated with dual clutch
transmissions without a torque
converter) or limited commercial
success. The analysis utilizes new
information as manufacturers’ use of
technologies evolves.
Some of the emerging technologies
described in the Draft TAR were not
included in this analysis, but this
includes some additional technologies
not previously considered. As industry
invents and develops new fuel-savings
technologies, and as suppliers and
manufacturers produce and apply the
technologies, and as consumers react to
the new technologies, efforts are
continued to learn more about the
capabilities and limitations of new
technologies. While a technology is in
early development, theoretical
constructs, limited access to test data,
and CBI is relied on to assess the
technology. After manufacturers
commercialize the technology and bring
products to market, the technology may
be studied in more detail, which
generally leads to the most reliable
information about the technology. In
addition, once in production, the
technology is represented in the fuel
economy and CO2 status of the baseline
fleet. The technology analysis is kept as
current as possible in light of the
ongoing technology development and
implementation in the automotive
industry.
Some technology assumptions have
been updated since the MYs 2017–2025
final rule and, in many cases, since the
2016 Draft TAR. In some cases, EPA and
NHTSA presented different analytical
approaches in the Draft TAR; the
analysis is now presented using the

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same direct manufacturing costs, retail
costs, and learning rates. In addition,
the effectiveness of fuel-economy
technologies is now assessed based on
the same assumptions, and with the
same tools. Finally, manufacturers’
response to stringency alternatives is
forecast with the same simulation
model.
Since the 2017 and later final rule,
many cost assessments, including tear
down studies, were funded and
completed, and presented as part of the
Draft TAR analysis. These studies
evaluated transmissions, engines,
hybrid technologies, and mass
reduction.108 As a result, the analysis
uses updated cost estimates for many
technologies, some of which have been
updated since the Draft TAR. In
addition to those studies, the analysis
also leveraged research reports from
other organizations to assess costs.109
Today’s analysis also updates the costs
to 2016 dollars, as in many cases
technology costs were estimated several
years ago.
The analysis uses an updated, peerreviewed model developed by ANL for
the Department of Energy to provide a
more rigorous estimate for battery costs.
The new battery model provides an
estimate future for battery costs for
hybrids, plug-in hybrids, and electric
vehicles, taking into account the
different battery design characteristics
and taking into account the size of the
battery for different applications.110
In the Draft TAR, two possible
methodologies to estimate indirect costs
from direct manufacturing costs,
described as ‘‘indirect cost multipliers’’
and ‘‘retail price equivalent’’ were
presented. Both of these methodologies
attempted to relate the price of parts for
108 FEV prepared several cost analysis studies for
EPA on subjects ranging from advanced 8-speed
transmissions to belt alternator starter, or Start/Stop
systems. NHTSA also contracted with Electricore,
EDAG, and Southwest Research on teardown
studies evaluating mass reduction and
transmissions. The 2015 NAS report on fuel
economy technologies for light-duty vehicles also
evaluated the agencies’ technology costs developed
based on these teardown studies, and the
technology costs used in this proposal were
updated accordingly. These studies are discussed in
detail in Chapter 6 of the PRIA accompanying this
proposal.
109 For example, the agencies relied on reports
from the Department of Energy’s Office of Energy
Efficiency & Renewable Energy’s Vehicle
Technologies Office. More information on that
office is available at https://www.energy.gov/eere/
vehicles/vehicle-technologies-office. Other agency
reports that were relied on for technology or other
information are referenced throughout this proposal
and accompanying PRIA.
110 For instance, battery electric vehicles with
high levels of mass reduction may use a smaller
battery than a comparable vehicle with less mass
reduction technology and still deliver the same
range on a charge.

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fuel-saving technologies to a retail price.
Today’s analysis utilizes the direct
manufacturing costs (DMC) and the
retail price equivalent (RPE)
methodology published in the Draft
TAR.
Two tools to estimate effectiveness of
fuel-saving technologies were used in
the Draft TAR, and for today’s analysis,
only one tool was used (Autonomie).111
Previously, EPA developed ‘‘ALPHA’’,
an in-house model that estimated fuelsavings for technologies, which
provided a foundation for EPA’s
analysis. EPA’s ‘‘ALPHA’’ results were
used to calibrate a much simpler
‘‘Lumped Parameter Model’’ that was
developed by EPA to estimate
technology effectiveness for many
technologies. The Lumped Parameter
Model (LPM) approximated simulation
modeling results instead of directly
using the results and lead to less
accurate estimates of technology
effectiveness. Many stakeholders
questioned the efficacy of the Lumped
Parameter Model and ALPHA
assumptions and outputs in
combination,112 especially as the tool
was used to evaluate increasingly
heterogeneous combinations of
technologies in the baseline fleet.113 For
today’s analysis, EPA and NHTSA used
an updated version of the Autonomie
model—an improved version of what
NHTSA presented in the 2016 Draft
TAR—to assess technology effectiveness
of technologies and combinations of
technologies. The Department of
Energy’s ANL developed Autonomie
and the underpinning model
assumptions leveraged research from
the DOE’s Vehicle Technologies Office
and feedback from the public.
Autonomie is commercially available
and widely used; third parties such as
suppliers, automakers, and academic
researchers (who publish findings in
peer reviewed academic journals)
commonly use the Autonomie
simulation software.
Similarly for today’s analysis, only
one tool is used. Previously, EPA
developed ‘‘OMEGA,’’ a tool that looked
at costs of technologies and
effectiveness of technologies (as
estimated by EPA’s Lumped Parameter
111 ANL’s Full-Vehicle Simulation Autonomie
Model is discussed in Chapter 6 of the PRIA and
in the ANL Model Documentation available at
Docket No. NHTSA–2018–0067.
112 At NHTSA–2016–0068–0082, p. 49, FCA
provided the following comments, ‘‘FCA believes
EPA is overestimating the benefits of technology. As
the LPM is calibrated to those projections, so too
is the LPM too optimistic.’’ FCA also shared the
chart, ‘‘LPM vs. Actual for 8 Speed Transmissions.’’
113 See e.g., Automotive News ‘‘CAFE math gets
trickier as industry innovates’’ (Kulisch), March 26,
2018.

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Model or ALPHA), and applied cost
effective technologies to manufacturers’
fleets in response to potential standards.
Many stakeholders commented that the
OMEGA model oversimplified fleetwide analysis, resulting in significant
shortcomings.114 For instance, OMEGA
assumed manufacturers would redesign
all vehicles in the fleet by 2021, and
then again by 2025; stakeholders
purported that these assumptions did
not reflect practical constraints in many
manufacturers’ business models.115
Additionally, stakeholders commented
that OMEGA did not adequately take
into consideration common parts like
shared engines, shared transmissions,
and engineering platforms. The CAFE
model does consider refresh and
redesign cycles and parts sharing. The
CAFE model can evaluate responses to
any policy alternative on a year-by-year
basis, as required by EPCA/EISA 116 and
as allowed by the CAA, and can also
account for manufacturers’ year-by-year
plans that involve ‘‘carrying forward’’
technology from prior model years, and
some other vehicles possibly applying
‘‘extra’’ technology in anticipation of
standards in ensuing model years. For
today’s analysis, an updated version of
the CAFE model is used—an improved
version of what NHTSA presented in
the 2016 Draft TAR—to assess
manufacturers’ response to policy
alternatives. See Section II.A.1 above for
further discussion of the decision to use
the CAFE model for the NPRM analysis.
Each aforementioned change is
discussed briefly in the remainder of
this section and in much greater detail
in Chapter 6 of the PRIA. A brief
summary of the technologies considered
in this proposal is discussed below.
Please provide comments on all aspects
of the analysis as discussed here and as
detailed in the PRIA.

114 The Alliance of Automobile Manufacturers
commented that ‘‘the OMEGA model is overoptimized and unrealistic . . . many of these issues
either are not present or are accounted for in DOT’s
Volpe model. The Alliance therefore recommends
that EPA focus on ensuring needs specific to its
regulatory analysis are appropriately addressed in
the Volpe model.’’ Alliance at 48 (Docket ID. EPA–
HQ–OAR–2015–0827–9194).
115 For example, FCA provided the following
comments: ‘‘EPA’s expectation of 10–20% mass
reduction rates across 70% of FCA’s fleet, which
includes a 70% truck mix, is simply unreasonable
as the magnitude of change would require complete
product redesigns in less than eight years
shortening existing production needed to amortize
the large capital cost involved.’’ FCA at 19 (Docket
ID. EPA–HQ–OAR–2015–0827–6160).
116 49 U.S.C. 32902(b)(2)(B).

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1. Data Sources and Processes for
Developing Individual Technology
Assumptions
Technology assumptions were
developed that provide a foundation for
conducting a fleet-wide compliance
analysis. As part of this effort, the
analysis estimated technology costs,
projected technology effectiveness
values, and identified possible
limitations for some fuel-saving
technologies. There is a preference to
use values developed from careful
review of commercialized technologies;
however, in some cases for technologies
that are new, and are not yet for sale in
any vehicle, the analysis relied on
information from other sources,
including CBI and third-party research
reports and publications. Many
emerging technologies are still being
evaluated for the analysis supporting
the final rule, including those that are
currently emerging.
For today’s analysis, one set of cost
assumptions, one set of effectiveness
values (developed with one tool), and
one set of assumptions about the
limitations of some technologies are
presented. Many sources of data were
evaluated, in addition to many
stakeholder comments received on the
Draft TAR. Throughout the process of
developing the assumptions for today’s
analysis, the preferred approach was to
harmonize on sources and
methodologies that were data-driven
and reproducible in independent
verification, produced using tools
utilized by OEMs, suppliers, and
academic institutions, and using tools
that could support both CAFE and CO2
analysis. A single set of assumptions
also facilitates and focuses public
comment by reducing burden on
stakeholders who seek to review all of
the supporting documentation for this
proposal.

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(a) Technology Costs
The analysis estimated present and
future costs for fuel-saving technologies,
taking into consideration the type of
vehicle, or type of engine if technology
costs vary by application. Cost estimates
were developed based on three main
inputs. First, direct manufacturing costs
(DMC), or the component costs of the
physical parts and systems, were
considered, with estimated costs
assuming high volume production.
DMCs generally do not include the
indirect costs of tools, capital
equipment, and financing costs, nor do
they cover indirect costs like
engineering, sales, and administrative
support. Second, indirect costs via a
scalar markup of direct manufacturing

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costs (the retail price equivalent, or
RPE) was taken into account. Finally,
costs for technologies may change over
time as industry streamlines design and
manufacturing processes. Potential cost
improvements with learning effects (LE)
were also considered. The retail cost of
equipment in any future year is
estimated to be equal to the product of
the DMC, RPE, and LE. Considering the
retail cost of equipment, instead of
merely direct manufacturing costs, is
important to account for the real-world
price effects of a technology, as well as
market realities. Absent government
mandate, a manufacturer will not
undertake expensive development and
support costs to implement technologies
without realistic prospects of consumer
willingness to pay enough for such
technology to allow for the
manufacturer to recover its investment.
(1) Direct Manufacturing Costs
In many instances, the analysis used
agency-sponsored tear-down studies of
vehicles and parts to estimate the direct
manufacturing costs of individual
technologies. In the simplest cases, the
studies produced results that confirmed
third-party industry estimates, and
aligned with confidential information
provided by manufacturers and
suppliers. In cases with a large
difference between the tear-down study
results and credible independent
sources, study assumptions were
scrutinized, and sometimes the analysis
was revised or updated accordingly.117
Studies were conducted on vehicles and
technologies that would cover a breadth
of fuel-savings technologies, but because
tear-down studies can be time-intensive
and expensive, the agencies did not
sponsor teardown studies for every
technology. For some technologies,
independent tear-down studies were
also utilized, in addition to other
publications and confidential business
information.118 Due to the variety of
technologies and their applications, a
detailed tear-down study could not be
conducted for every technology,
including pre-production technologies.
Many fuel-saving technologies were
considered that are pre-production, or
sold in very small pilot volumes. For
emerging technologies that could be
applied in the rulemaking timeframe, a
tear-down study cannot be conducted to
117 For instance, in previous analysis, EPA
referenced an old study that purported the first 7–
10% of mass reduction to be ‘‘free’’ or at a
significant ‘‘cost savings’’ to for many vehicles and
many manufacturers.
118 The analysis referenced studies from private
businesses and business analysts for emerging
technologies and for off-the-shelf technologies that
were commercially mature.

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assess costs because the product is not
yet in the marketplace for evaluation. In
these cases, third-party estimates and
confidential information from suppliers
and manufacturers are relied upon;
however, there are some common
pitfalls with relying on confidential
business information to estimate costs.
The agencies and the source may have
had incongruent or incompatible
definitions of ‘‘baseline.’’ 119 The source
may have provided direct manufacturer
costs at a date many years in the future,
and assumed very high production
volumes, important caveats to consider
for agency analysis. In addition, a
source, under no contractual obligation
to the agencies, may provide incomplete
and/or misleading information. In other
cases, intellectual property
considerations and strategic business
partnerships may have contributed to a
manufacturer’s cost information and
could be difficult to account for in the
model as not all manufacturer’s may
have access to proprietary technologies
at stated costs. New information is
carefully evaluated in light of these
common pitfalls, especially regarding
emerging technologies. The analysis
used third-party, forward looking
information for advanced cylinder
deactivation and variable compression
ratio engines, and while these cost
estimates may be cursory (as is the case
with many emerging technologies prior
to commercialization), the agencies took
care to use early information provided
fairly and reasonably. While costs for
fuel-saving technologies reflect the best
estimates available today, technology
cost estimates will likely change in the
future as technologies are deployed and
as production is expanded. For
emerging technologies, the best
information available at the time of the
analysis was utilized, and cost
assumptions will continue to be
updated.
(2) Indirect Costs
As explained above, in addition to
direct manufacturing costs, the analysis
estimates and considers indirect
manufacturing costs. To estimate
indirect costs, direct manufacturing
costs are multiplied by a factor to
represent the average price for fuelsaving technologies at retail. This factor,
referred to as the retail price
equivalence (RPE), accounts for indirect
costs like engineering, sales, and
administrative support, as well as other
overhead costs, business expenses,
warranty costs, and return on capital
119 ‘‘Baseline’’ here refers to a reference part,
piece of equipment, or engineering system that
efficiency improvements and costs are relative to.

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considerations. This approach to the
RPE remains unchanged from the RPE
approach NHTSA presented in the Draft
TAR.
The RPE was chosen for this analysis
instead of indirect cost multipliers
(ICM) because it provides the best
estimate of indirect costs. For a more
detailed discussion of the approach to
indirect costs, see PRIA Chapter 9.
(3) Stranded Capital Costs
Past analyses accounted for costs
associated with stranded capital when
fuel economy standards caused a
technology to be replaced before its
costs were fully amortized. The idea
behind stranded capital is that
manufacturers amortize research,
development, and tooling expenses over
many years, especially for engines and
transmissions. The traditional
production life-cycles for transmissions
and engines have been a decade or
longer. If a manufacturer launches or
updates a product with fuel-saving
technology, and then later replaces that
technology with an unrelated or
different fuel-saving technology before
the equipment and research and
development investments have been
fully paid off, there will be unrecouped,
or stranded, capital costs. Quantifying
stranded capital costs attempted to
account for such lost investments. In the
Draft TAR analysis, there were only a
few technologies for a few
manufacturers that were projected to
have stranded capital costs.
As more technologies are included in
this analysis, and as the CAFE model
has been expanded to account for
platform and engine sharing and
updated with redesign and refresh
cycles, accounting for stranded capital
has become increasingly complex.
Separately, the fact that manufacturers
may be shifting their investment
strategies in ways that may affect
stranded capital calculations was
considered. For instance, Ford and
General Motors agreed to jointly
develop next generation transmission
technologies,120 and some suppliers sell
similar transmissions to multiple
manufacturers. These arrangements
allow manufacturers to share in capital
expenditures, or amortize expenses
more quickly. Manufacturers
increasingly share parts on vehicles
around the globe, achieving greater scale
and greatly affecting tooling strategies
and costs. Given these trends in the
120 See, e.g., Nick Bunkley, Ford to invest $1.4
billion to build 10-speed transmissions for 2017 F–
150, Automotive News (Apr. 26, 2016), http://
www.autonews.com/article/20160426/OEM01/
160429878/ford-to-invest-$1.4-billion-to-build-10speed-transmissions-for-2017.

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industry and their uncertain effect on
capital amortization, and given the
difficulty of handling this uncertainty in
the CAFE model, this analysis does not
account for stranded capital. However,
these trends will be monitored to assess
the role of stranded capital moving
forward.
The analysis continues to rely on
projected refresh and redesign cycles in
the CAFE model to moderate the
cadence for technology adoption and
limit the occurrence of stranded capital
and the need to account for it. Stranded
capital is an important consideration to
appropriately account for costs if there
is too rapid of a turnover for certain
technologies.
(4) Cost Learning
Manufacturers make improvements to
production processes over time, often
resulting in lower costs. Today’s
analysis estimates cost learning by
considering Wright’s learning theory,
which states that as every time
cumulative volume for a product
doubles, the cost lowers by a scalar
factor. The analysis accounts for
learning effects with model year-based
cost learning forecasts for each
technology that reduce direct
manufacturing costs over time.
Historical use of technologies were
evaluated, and industry forecasts were
reviewed to estimate future volumes for
the purpose of developing the model
year-based technology cost learning
curves. The CAFE model does not
dynamically update learning curves,
based on compliance pathways chosen
in simulation.
As discussed above, cost inputs to the
CAFE model incorporate estimates of
volume-based learning. As an
alternative approach, Volpe Center staff
have considered modifications such that
the CAFE model would calculate
degrees of volume-based learning
dynamically, responding to the model’s
application of affected technologies.
While it is intuitive that the degree of
cost reduction achieved through
experience producing a given
technology should depend on the actual
accumulated experience (i.e., volume)
producing that technology, staff have
thus far found such dynamic
implementation in the CAFE model
infeasible. Insufficient data has been
available regarding manufacturers’
historical application of specific
technology. Also, insofar as underlying
direct manufacturing costs already make
some assumptions about volume and
scale, insufficient information is
currently available to determine how to
dynamically adjust these underlying
costs. It should be noted that if learning

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responds dynamically to volume, and
volume responds dynamically to
learning, an internally consistent model
solution would likely require iteration
of the CAFE model to seek a stable
solution within the model’s
representation multiyear planning. Thus
far, these challenges suggest it would be
infeasible to calculate degrees of
volume-based learning in a manner that
responds dynamically to modeled
technology application. Nevertheless,
the agencies invite comment on the
issue, and seek data and methods that
would provide the basis for a
practicable approach to doing so.
Today’s analysis also updates the way
learning effects apply to costs. In the
Draft TAR analysis, NHTSA applied
learning curves only to the incremental
direct manufacturing costs or costs over
the previous technology on the tech
tree. In practice, two things were
observed: (1) If the incremental direct
manufacturing costs were positive,
technologies could not become less
expensive than their predecessors on
the tech tree, and (2) absolute costs over
baseline technology depended on the
learning curves of root technologies on
the tech tree. Today’s analysis applies
learning effects to the incremental cost
over the null technology state on the
tech tree. After this step, the analysis
calculates year-by-year incremental
costs over preceding technologies on the
tech tree to create the CAFE model
inputs.
Direct manufacturing costs and
learning effects for many technologies
were reviewed by evaluating historical
use of technologies and industry
forecasts to estimate future volumes.
This approach produced reasonable
estimates for technologies already in
production. For technologies not yet in
production in MY 2016, the cumulative
volume in MY 2016 is zero, because
manufacturers have not yet produced
the technologies. For pre-production
cost estimates, the analysis often relies
on confidential business information
sources to predict future costs. Many
sources for pre-production cost
estimates include significant learning
effects, often providing cost estimates
assuming high volume production, and
often for a timeframe late in the first
production generation or early in the
second generation of the technology.
Rapid doubling and re-doubling of a low
cumulative volume base with Wright’s
learning curves can provide unrealistic
cost estimates. In addition, direct
manufacturing cost projections can vary
depending on the initial production
volume assumed. Direct costs with
learning were carefully examined, and
adjustments were made to the starting

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point for those technologies on the
learning curve to better align with the
assumptions used for the initial direct
cost estimate. See PRIA Chapter 9 for
more detailed information on cost
learning.
(b) Technology Effectiveness

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(1) Technology Effectiveness Simulation
Modeling
Full-vehicle simulation modeling was
used to estimate the fuel economy
improvements manufacturers could
make to their fleet by adding new
technologies, taking into account MY
2016 vehicle specifications, as well as
how combinations of technologies
interact. Full-vehicle simulation
modeling uses computer software and
physics-based models to predict how
combinations of technologies perform
together.
The simulation and modeling requires
detailed specifications for each
technology that describes its efficiency
and performance-related characteristics.
Those specifications generally come
from design specifications, laboratory
measurements, simulation or modeling,
and may involve additional analysis.
For example, the analysis used engine
maps showing fuel use vs. engine torque
vs. engine speed, and transmission
maps taking into account gear efficiency
for a range of loads and speeds. With
physics-based technology specifications,
full-vehicle simulation modeling can be
used to estimate technology
effectiveness for various combinations
and permutations of technologies for
many vehicle classes. To develop the
specifications used for the simulation
and modeling, laboratory test data was
evaluated for production and preproduction technologies, technical
publications, manufacturer and supplier
CBI, and simulation modeling of
specific technologies. Evaluating
recently introduced production
products to inform the technology
effectiveness models of emerging
technologies is preferred because doing
so allows for a more reliable analysis of
incremental improvements over
previous technologies; however, some
technologies were considered that are
not yet in production. As technologies
evolve and new applications emerge,
this work will be continued and may
include additional technologies and/or
updated modeling for the final rule. The
details of new and emerging
technologies are discussed in PRIA
Chapter 6.
Using full-vehicle simulation
modeling has two primary advantages
over using single or limited point
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improvements of technologies. First,
technology effectiveness often differs
significantly depending on the type of
vehicle and the other technologies that
are on the vehicle, and this is shown in
full-vehicle simulations. Different
technologies may provide different fuel
economy improvements depending on
whether they are implemented alone or
in tandem with other technologies.
Single point estimates often
oversimplify these important, complex
relationships and lead to less accurate
effectiveness estimates. Also, because
manufacturers often implement a
number of fuel-saving technologies
simultaneously at vehicle redesigns, it is
generally difficult to isolate the effect of
individual technologies using laboratory
measurement of production vehicles
alone. Simulation modeling offers the
opportunity to isolate the effects of
individual technologies by using a
single or small number of baseline
configurations and incrementally
adding technologies to those baseline
configurations. This provides a
consistent reference point for the
incremental effectiveness estimates for
each technology and for combinations of
technologies for each vehicle type and
reduces potential double counting or
undercounting technology effectiveness.
Note: It is most important that the
incremental effectiveness of each
technology and combinations be
accurate and relative to a consistent
baseline, because it is the incremental
effectiveness that is applied to each
vehicle model/configuration in the MY
2016 baseline fleet (and to each vehicle
model/configuration’s absolute fuel
economy value) to determine the
absolute fuel economy of the model/
configuration with the additional
technology. The absolute fuel economy
values of the simulation modeling runs
by themselves are used only to
determine the incremental effectiveness
and are never used directly to assign an
absolute fuel economy value to any
vehicle model/configuration for the
rulemaking analysis. Therefore,
commenters on technology effectiveness
should be specific about the incremental
effectiveness of technologies relative to
other specifically defined technologies.
The fuel economy of a specific vehicle
or simulation modeling run in isolation
may be less useful.
Second, full-vehicle simulation
modeling requires explicit
specifications and assumptions for each
technology; therefore, these
assumptions can be presented for public
review and comment. For instance,
transmission gear efficiencies, shift
logic, and gear ratios are explicitly

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stated as model inputs and are available
for review and comment. For today’s
analysis, every effort was made to make
the input specifications and modeling
assumptions available for review and
comment. PRIA Chapter 6 and
referenced documents provide more
detailed information.
Technology development and
application will be monitored to acquire
more information for the final rule. The
agencies may update the analysis for the
final rule based on comments and/or
new information that becomes available.
Today’s analysis utilizes effectiveness
estimates for technologies developed
using Autonomie software,121 a physicsbased full-vehicle simulation tool
developed and maintained by the
Department of Energy’s ANL.
Autonomie has a long history of
development and widespread
application by users in industry,
academia, research institutions and
government.122 Real-world use has
contributed significantly to aspects of
Autonomie important to producing
realistic estimates of fuel economy and
CO2 emission rates, such as estimation
and consideration of performance,
utility, and driveability metrics (e.g.,
towing capability, shift business,
frequency of engine on/off transitions).
This steadily increasing realism has, in
turn, steadily increased confidence in
the appropriateness of using Autonomie
to make significant investment
decisions. Notably, DOE uses
Autonomie for analysis supporting
budget priorities and plans for programs
managed by its Vehicle Technologies
Office (VTO) and to decide among
competing vehicle technology R&D
projects.
In the 2015 National Academies of
Science (NAS) study of fuel economy
improving technologies, the Committee
recommended that the agencies use fullvehicle simulation to improve the
analysis method of estimating
technology effectiveness.123 The
committee acknowledged that
developing and executing tens or
hundreds of thousands of constantly
changing vehicle packages models in
121 More information about Autonomie is
available at https://www.anl.gov/technology/
project/autonomie-automotive-system-design (last
accessed June 21, 2018).
122 ANL Model Documentation, ‘‘A Detailed
Vehicle Simulation Process To Support CAFE
Standards’’ ANL/ESD–18/6.
123 National Research Council. 2015. Cost,
Effectiveness, and Deployment of Fuel Economy
Technologies for Light-Duty Vehicles. Washington,
DC: The National Academies Press [hereinafter
‘‘2015 NAS Report’’] at pg. 263, available at https://
www.nap.edu/catalog/21744/cost-effectivenessand-deployment-of-fuel-economy-technologies-forlight-duty-vehicles (last accessed June 21, 2018).

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real-time is extremely challenging.
While initially this approach was not
considered practical to implement, a
process developed by Argonne in
collaboration with NHTSA and the DOT
Volpe Center has succeeded in enabling
large scale simulation modeling. For
more details about the Autonomie
simulation model and its submodels
and inputs, see PRIA Chapter 6.2.
Today’s analysis modeled more than
50 fuel economy-improving
technologies, and combinations thereof,
on 10 vehicle types (an increase from
five vehicle types in NHTSA’s Draft
TAR analysis). While 10 vehicle types
may seem like a small number, a large
portion of the production volume in the
MY 2016 fleet have specifications that
are very similar, especially in highly
competitive segments (for instance,
many mid-sized sedans, many small
SUVs, and many large SUVs coalesce
around similar specifications,
respectively), and baseline simulations
have been aligned around these modal
specifications. The sequential addition
of these technologies generated more
than 100,000 unique technology
combinations per vehicle class. The
analysis included 10 technology classes,
so more than one million full-vehicle

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simulations were run. In addition,
simulation modeling was conducted to
determine the appropriate amount of
engine downsizing needed to maintain
baseline performance across all modeled
vehicle performance metrics when
advanced mass reduction technology or
advanced engine technology was
applied, so these simulations take into
account performance neutrality, given
logical engine down-sizing
opportunities associated with specific
technologies.
Some baseline vehicle assumptions
used in the simulation modeling were
updated based on public comment and
the assessment of the MY 2016
production fleet. The analysis included
updated assumptions about curb weight,
component inertia, as well as
technology properties like baseline
rolling resistance, aerodynamic drag
coefficients, and frontal areas. Many of
the assumptions are aligned with
published research from the Department
of Energy’s Vehicle Technologies Office
and other independent sources.124
124 Pannone, G. ‘‘Technical Analysis of Vehicle
Load Reduction Potential for Advanced Clear Cars,’’
April 29, 2015. Available at https://www.arb.ca.gov/
research/apr/past/13-313.pdf (last accessed June 21,
2018).

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Additional transmission technologies
and more levels of aerodynamic
technologies than NHTSA presented in
the Draft TAR analysis were also added
for today’s analysis. Having additional
technologies allowed the agencies to
assign baselines and estimate fuelsavings opportunities with more
precision.
The 10 vehicle types (referred to as
‘‘technology classes’’ in the modeling
documentation) are shown in Table II–
7. Each vehicle type (technology class)
represented a large segment of vehicles,
such as medium cars, small SUVs, and
medium performance SUVs.125 Baseline
parameters were defined with ANL for
each technology class, including
baseline curb weight, time required to
accelerate from stop to 60 miles per
hour, time required to accelerate from
50 miles per hour to 80 miles per hour,
ability of the vehicle to maintain
constant 65 miles per hour speed on a
six percent upgrade, and (for some
classes) assumptions about towing
capability.
125 Separate technology classes were created for
high performance and low performance vehicles to
better account for performance diversity across the
fleet.

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From these baseline specifications,
incremental combinations of fuel saving
technologies were applied. As the
combinations of technologies change, so
too may predicted performance.
The analysis attempts to maintain
performance by resizing engines at a few
specific incremental technology steps.
Steps from one technology to another
typically associated with a major
vehicle redesign, or engine redesign,
were identified, and engine resizing was
restricted only to these steps. The
analysis allowed engine resizing when
mass reduction of 10% or greater was
applied to the vehicle glider mass,126
and when one powertrain architecture
was replaced with another
architecture.127 The analysis resized
126 The

vehicle glider is defined here as the
vehicle without the engine, transmission, and
driveline. See PRIA Chapter 6.3 for further
information.
127 Some engine and accessory technologies may
be added to an engine without an engine
architecture change. For instance, manufacturers
may adapt, but not replace engine architectures to
include cylinder deactivation, variable valve lift,

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engines to the extent that performance
was maintained for the least capable
performance criteria to maintain vehicle
utility for that criteria; therefore,
sometimes other performance attributes
may improve. For instance, the amount
of engine resizing may be determined
based on its high speed acceleration
time if it is the least capable criteria, but
that resizing may also improve the low
speed acceleration time.128 The analysis
did not re-size the engine in response to
adding technologies that have small
effects on vehicle performance. For
instance, if a vehicle’s weight is reduced
by a small amount causing the 0–60
mile per hour time to improve slightly,
the analysis would not resize the
belt-integrated starter generators, and other basic
technologies. However, switching from a naturally
aspirated engine to a turbo-downsized engine is an
engine architecture change typically associated
with a major redesign and radical change in engine
displacement.
128 The simulation database, or summary of
simulation outputs, includes all of the estimated
performance metrics for each combination of
technology as modeled.

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engine. Manufacturers have repeatedly
told the agencies that the high costs for
redesign and the increased
manufacturing complexity that would
result from resizing engines for such
small changes in the vehicle preclude
doing so. The analysis should not, in
fact, include engine resizing with the
application of every technology or for
combinations of technologies that drive
small performance changes so that the
analysis better reflects what is feasible
for manufacturers to do.129
2. CAFE model
The CAFE model is designed to
simulate compliance with a given set of
CAFE or CO2 standards for each
manufacturer that sells vehicles in the
United States. The model begins with a
129 For instance, a vehicle would not get a
modestly bigger engine if the vehicle comes with
floor mats, nor would the vehicle get a modestly
smaller engine without floor mats. This example
demonstrates small levels of mass reduction. If
manufacturers resized engines for small changes,
manufacturers would have dramatically more part
complexity, potentially losing economies of scale.

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representation of the MY 2016 vehicle
model offerings for each manufacturer
that includes the specific engines and
transmissions on each model variant,
observed sales volumes, and all fuel
economy improving technology that is
already present on those vehicles. From
there the model adds technology, in
response to the standards being
considered, in a way that minimizes the
cost of compliance and reflects many
real-world constraints faced by
automobile manufacturers. The model
addresses fleet year-by-year compliance,
taking into consideration vehicle refresh
and redesign schedules and shared
platforms, engines, and transmissions
among vehicles.
As a result of simulating compliance,
the CAFE model provides the
technology pathways that manufacturers
could use to comply with regulations,
including how technologies could be
applied to each of their vehicle model/
configurations in response to a given set
of standards. The model calculates the
impacts of the simulated standard:
Technology costs, fuel savings (both in
gallons and dollars), CO2 reductions,
social costs and benefits, and safety
impacts.
The current analysis reflects several
changes made to the CAFE model since
2012, when NHTSA used the model to
estimate the effects, costs, and benefits
of final CAFE standards for light-duty
vehicles produced during MYs 2017–
2021 and augural standards for MYs
2022–2025. The changes are discussed
in Section II.A.1, above, and PRIA
Chapter 6.
3. Assumptions About Individual
Technology Cost and Effectiveness
Values
Cost and effectiveness values were
estimated for each technology included
in the analysis, with a summary list of
all technologies provided in Table II–1
(List of Technologies with Data Sources
for Technology Assignments) of
Preamble Chapter II.B, above. In all,
more than 50 technologies were
considered in today’s analysis, and the
analysis evaluated many combinations
of these technologies on many
applications. Potential issues in
assessing technology effectiveness and
cost were identified, including:
• Baseline (MY 2016) vehicle
technology level assessed as too low, or
too high. Compliance information was
extensively reviewed and supplemented
with available literature on many MY
2016 vehicle models. Manufacturers
could also review the baseline
technology assignments for their
vehicles, and the analysis incorporates
feedback received from manufacturers.

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• Technology costs too low or too
high. Tear down cost studies, CBI,
literature, and the 2015 NAS study
information were referenced to estimate
technology costs. In cases that one
technology appeared exemplary on cost
and effectiveness relative to all other
technologies, information was acquired
from additional sources to confirm or
reject assumptions. Cost assumptions
for emerging technologies are
continuously being evaluated.
• Technology effectiveness too high
or too low in combination with other
vehicle technologies. Technology
effectiveness was evaluated using the
Autonomie full-vehicle simulation
modeling, taking into account the
impact of other technologies on the
vehicle and the vehicle type. Inputs and
modeling for the analysis took into
account laboratory test data for
production and some pre-production
technologies, technical publications,
manufacturer and supplier CBI, and
simulation modeling of specific
technologies. Evaluating recently
introduced production products to
inform the technology effectiveness
models of emerging technologies was
preferred; however, some technologies
that are not yet in production were
considered, via CBI. Simulation
modeling used carefully chosen baseline
configurations to provide a consistent,
reasonable reference point for the
incremental effectiveness estimates.
• Vehicle performance not considered
or applied in an infeasible manner.
Performance criteria, including low
speed acceleration (0–60 mph time),
high speed acceleration (50–80 mph
time), towing, and gradeability (six
percent grade at 65 mph) were also
considered. In the simulation modeling,
resizing was applied to achieve the
same performance level as the baseline
for the least capable performance
criteria but only with significant design
changes. The analysis struck a balance
by employing a frequency of engine
downsizing that took product
complexity and economies of scale into
account.
• Availability of technologies for
production application too soon or too
late. A number of technologies were
evaluated that are not yet in production.
CBI was gathered on the maturity and
timing of these technologies and the
likely cadence at which manufacturers
might adopt these technologies.
• Product complexity and design
cadence constraints too low or too high.
Product platforms, refresh and redesign
cycles, shared engines, and shared
transmissions were also considered in
the analysis. Product complexity and
the cadence of product launches were

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matched to historical values for each
manufacturer.
• Customer acceptance under
estimated or over estimated. Resale
prices for hybrid vehicles, electric
vehicles, and internal combustion
engine vehicles were evaluated to assess
consumer willingness to pay for those
technologies. The analysis accounts for
the differential in the cost for those
technologies and the amount consumers
have actually paid for those
technologies. Separately, new dualclutch transmissions and manual
transmissions were applied to vehicles
already equipped with these
transmission architectures.
Please provide comments on all
assumptions for fuel economy and CO2
technology costs, effectiveness,
availability, and applicability to
vehicles in the fleet.
The technology effectiveness
modeling results show effectiveness of a
technology often varies with the type of
vehicle and the other technologies that
are on the vehicle. Figure II–1 and
Figure II–2 show the range of
effectiveness for each technology for the
range of vehicle types and technology
combinations included in this NPRM
analysis. The data reflect the change in
effectiveness for applying each
technology by itself while all other
technologies are held unchanged. The
data show the improvement in fuel
consumption (in gallons per mile) and
tailpipe CO2 over the combined 2-cycle
test procedures. For many technologies,
effectiveness values ranged widely; only
a few technologies for which
effectiveness may be reasonably
represented as a fixed offset were
identified.
For engine technologies, the
effectiveness improvement range is
relative to a comparably equipped
vehicle with only variable valve timing
(VVT) on the engine. For automatic
transmission technologies, the
effectiveness improvement range is over
a 5-speed automatic transmission. For
manual transmission technologies, the
effectiveness improvement range is over
a 5-speed manual transmission. For road
load technologies like aerodynamics,
rolling resistance, and mass reduction,
the effectiveness improvement ranges
are relative to the least advanced
technology state, respectively. For
hybrid and electric drive systems that
wholly replace an engine and
transmission, the effectiveness
improvement ranges are relative to a
comparably equipped vehicle with a
basic engine with VVT only and a 5speed automatic transmission. For
hybrid or electrification technologies
that complement other advanced engine

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instance, parallel strong hybrids and
belt integrated starter generators retain
engine technologies, such as a turbo
charged engine or an Atkinson cycle
engine). Many technologies have a wide

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range of estimated effectiveness values.
Figure II–3 below shows a hierarchy of
technologies discussed.
BILLING CODE 4910–59–P

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and transmission technologies, the
effectiveness improvement ranges are
relative to a comparably equipped
vehicle without the hybrid or
electrification technologies (for

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Figure 11-2- Example of Technology Effectiveness Variation by Application

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4. Engine Technologies
There are a number of engine
technologies that manufacturers can use
to improve fuel economy and CO2.
Some engine technologies can be
incorporated into existing engines with
minor or moderate changes to the
engines, but many engine technologies
require an entirely new engine
architecture.
In this section and for this analysis,
the terms ‘‘basic engine technologies’’

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and ‘‘advanced engine technologies’’ are
used only to define how the CAFE
model applies a specific engine
technology and handles incremental
costs and effectiveness improvements.
‘‘Basic engine technologies’’ refer to
technologies that, in many cases, can be
adapted to an existing engine with
minor or moderate changes to the
engine. ‘‘Advanced engine
technologies’’ refer to technologies that
generally require significant changes or

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an entirely new engine architecture. In
the CAFE model, basic engine
technologies may be applied in
combination with other basic engine
technologies; advanced engine
technologies (defined by an engine map)
stand alone as an exclusive engine
technology. The words ‘‘basic’’ and
‘‘advanced’’ are not meant to confer any
information about the level of
sophistication of the technology. Also,
many advanced engine technology

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definitions include some basic engine
technologies, but these basic
technologies are already accounted for
in the costs and effectiveness values of
the advance engine. The ‘‘basic engine
technologies’’ need not be (and are not)
applied in addition to the ‘‘advanced
engine technologies’’ in the CAFE
model.
Engines come in a wide variety of
shapes, sizes, and configurations, and

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the incremental engine costs and
effectiveness values often depend on
engine architecture. The agencies
modeled single overhead cam (SOHC),
dual overhead cam (DOHC), and
overhead valve (OHV) engines
separately to account for differences in
engine architecture. The agencies
adjusted costs for some engine
technologies based on the number of
cylinders and number of banks in the

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engine, and the agencies evaluated
many production engines to better
understand how costs and capabilities
may vary with engine configuration.
Table II–8, Table II–9, Table II–10 below
shows the summary of absolute costs 130
for different technologies.
130 ‘‘Absolute’’ being in reference to cost above
the lowest level of technology considered in
simulations. For instance, an engine of the same
architecture with no VVT, VVL, SGDI, or DEAC.

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VVL

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SGDI
DEAC
TURB01

Technology Pathway
Basic Engine
Basic Engine
Basic Engine
Basic Engine
Turbocharged Engine

C-2017
$
111.97
$
417.59
$
450.04

C-2021
$
10R.79
$
405.74
$
437.26

C-2025
$
106.24
$
396.22
$
427.00

$
153.95
$ U47.98

$
146.07
$ 1,044.43

TURB02
CEGR1
HCR1
HCR2
VCR
ADEAC
ADSL
DSLI
CNG

Turbocharged Engine
Turbocharged Engine
HCREngine
HCREngine
VCR Engine
Adv. DEAC Engine
Diesel Engine
Diesel Engine
Alt. Fuel Engine

$ 1,722.96
$ 2,138.49
$
735.65
$
980.78
not estimated
$ 1,370.86
$ 5,110.08

$
149.58
$ 1,078.90
$ 1,612.78
$ 2,001.73
$
692.23
$
980.78
not estimated
$ 1,237.93
$ 5,110.08
$ 5,661.68
$
156.22

$ 5,661.68
$
159.54

$ 1,490.01
$ 1,849.36
$
683.64
$
980.78
not estimated
$ 1,156.83
$ 5,110.08
$ 5,661.68
$
153.41

C-2029
$ 104.13
$ 388.34
$ 418.51
$ 143.17
$ 1,022.34
$ 1,403.80
$ 1,742.36
$ 681.67
$ 980.78
not estimated
$ 1,108.63
$ 5,110.08
$ 5,661.68
$ 150.72

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table 11-8 - Summary of Absolute Engine Technology Cost vs. 14 Basic Engine, including Learning Effects and
Retail Price Eauival

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Name
VVT
VVL
SGDI
DEAC
TURB01

Technology Pathway
Basic Engine
Basic Engine
Basic Engine
Basic Engine
Turbocharged Engine

C-2017
$ 223.94
$ 682.38
$ 731.05
$ 265.92
$ 1253.70

TURB02
CEGR1
HCR1
HCR2
VCR
ADEAC
ADSL
DSLI

Turbocharged Engine
Turbocharged Engine
HCREngine
HCREngine
VCR Engine
Adv. DEAC Engine
Diesel Engine
Diesel Engine
Alt. Fuel Engine

$ 1,849.68
$ 2,265.21
$ 1,133.23
$ 1,490.32
not estimated
$ 2,115.07
$ 6,122.76
$ 6,841.17
$ 159.54

CNG

C-2021
$ 217.5R
$ 663.00
$ 710.29

C-2025
$ 212.4R
$ 647.45
$ 693.63

C-2029
$ 20R.25
$ 634.57
$ 679.83

$ 258.37
$ 1,178.26
$ 1,731.39
$ 2,12035
$ 1,066.34
$ 1,490.32
not estimated
$ 1,909.98
$ 6,122.76
$ 6,841.17
$ 156.22

$ 252.31
$ 1,140.61

$ 247.29
$ 1,116.49

$ 1,599.60
$ 1,958.95
$ 1,053.11
$ 1,490.32
not estimated
$ 1,784.85
$ 6,122.76

$ 1,507.05
$ 1,845.60
$ 1,050.09
$ 1,490.32
not estimated
$ 1,710.48
$ 6,122.76
$ 6,841.17
$ 150.72

$ 6,841.17
$ 153.41

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.024

Table 11-9- Summary of Absolute Engine Technology Cost vs. V6 Basic Engine, including Learning Effects and Retail Price
Eauival

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24AUP2

Technology Pathway
Basic Engine
Basic Engine
Basic Engine
Basic Engine
Turbocharged Engine

C-2017
$ 223.94
$ 835.19
$ 900.08

C-2021
$ 217.5R
$ 811.47
$ 874.52

C-2025
$ 212.4R
$ 792.44
$ 854.01

C-2029
$ 20R.25
$ 776.68
$ 837.03

$ 265.92
$ L929.02

$ 252.31
$ 1,755.01

TURB02
CEGR1
HCR1
HCR2
VCR
ADEAC
ADSL
DSLI

Turbocharged Engine
Turbocharged Engine
HCREngine
HCREngine
VCR Engine
Adv. DEAC Engine
Diesel Engine
Diesel Engine
Alt. Fuel Engine

$ 2,897.03
$ 3,312.55
$ L480.31
$ 1,935.14
not estimated
$ 2,741.71
$ 6,502.61
$ 7,221.02
$ 159.54

$ 258.37
$ 1,812.94
$ 2,711.76
$ 3,100.71
$ 1,392.94
$ 1,935.14
not estimated
$ 2,475.87
$ 6,502.61
$ 7,221.02
$ 156.22

$ 247.29
$ 1,717.90
$ 2,360.38
$ 2,698.94
$ 1,371.71
$ 1,935.14
not estimated
$ 2,217.26
$ 6,502.61
$ 7,221.02
$ 150.72

CNG

$ 2,505.34
$ 2,864.69
$ 1,375.66
$ 1,935.14
not estimated
$ 2,313.66
$ 6,502.61
$ 7,221.02
$ 153.41

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analysis, and engine maps were
developed for each combination of

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Many types of production powertrains
were reviewed and tested for this

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VVL
SGDI
DEAC
TURBO!

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

BILLING CODE 4910–59–C

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Table 11-10- Summary of Absolute Engine Technology Cost vs. V8 Basic Engine, including Learning Effects and Retail Price
Eauival

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

engine technologies. For a given engine
configuration, some production engines
may be less efficient than the engine
maps presented in the analysis, and
some may be more efficient. Developing
engine maps that reasonably
represented most vehicles equipped
with the engine technology, and that are
in production today, was the preferred
approach for this analysis. Additionally,
some advanced engines were included
in the simulation that are not yet in
production. The engine maps for these
engines were either based on CBI or
were theoretical. The most recently
released production engines are still
being reviewed, and the analysis may
include updated engine maps in the
future or add entirely new engine maps
to the analysis if either action could
improve the quality of the fleet-wide
analysis.
Stakeholders provided many
comments on the engine maps that were
presented in the Draft TAR. These
comments were considered, and today’s
analysis utilizes several engine maps
that were updated since the Draft TAR.
Most notably, for turbocharged and
downsized engines, the engine maps
were adjusted in high torque, low speed
operating conditions to address engine
knock with regular octane fuel to align
with the fuel octane that manufacturers
recommend be used for the majority of
vehicles. In the Draft TAR, NHTSA
assumed high octane fuel to develop
engine maps. See the discussion below
and in PRIA Chapter 6.3 for more
details. Please provide comment on the
appropriateness of assuming the use of
lower octane fuels.
(a) ‘‘Basic’’ Engine Technologies
The four ‘‘basic’’ engine technologies
in today’s model are Variable Valve
Timing (VVT), Variable Valve Lift
(VVL), Stoichiometric Gasoline Direct
Injection (SGDI), and basic Cylinder
Deactivation (DEAC). Over the last
decade, manufacturers upgraded many
engines with these engine technologies.
Implementing these technologies
involves changes to the cylinder head of
the engine, but the engine block,
crankshaft, pistons, and connecting rods
require few, if any, changes. In today’s
analysis, manufacturers may apply the
four basic engine technologies in
various combinations, just as
manufacturers have done recently.
(1) Variable Valve Timing (VVT)
Variable Valve Timing (VVT) is a
family of valve-train designs that
dynamically adjusts the timing of the
intake valves, exhaust valves, or both, in
relation to piston position. This family
of technologies reduces pumping losses.

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VVT is nearly universally used in the
MY 2016 fleet.
(2) Variable Valve Lift (VVL)
Variable Valve Lift (VVL) dynamically
adjusts the travel of the valves to
optimize airflow over a broad range of
engine operating conditions. The
technology increases effectiveness by
reducing pumping losses and may
improve efficiency by affecting incylinder charge (fuel and air mixture),
motion, and combustion.
(3) Stoichiometric Gasoline Direct
Injection (SGDI)
Stoichiometric Gasoline Direct
Injection (SGDI) sprays fuel at high
pressure directly into the combustion
chamber, which provides cooling of the
in-cylinder charge via in-cylinder fuel
vaporization to improve spark knock
tolerance and enable an increase in
compression ratio and/or more optimal
spark timing for improved efficiency.
SGDI appears in about half of basic
engines produced in MY 2016, and the
technology is used in many advanced
engines as well.
(4) Basic Cylinder Deactivation (DEAC)
Basic Cylinder Deactivation (DEAC)
disables intake and exhaust valves and
prevents fuel injection into some
cylinders during light-load operation.
The engine runs temporarily as though
it were a smaller engine, which reduces
pumping losses and improves
efficiency. Manufacturers typically
disable one-cylinder bank with basic
cylinder deactivation. In the MY 2016
fleet, manufacturers used DEAC on V6,
V8, V10, and V12 engines on OHV,
SOHC, and DOHC engine
configurations. With some engine
configurations in some operating
conditions, DEAC creates noisevibration-and-harshness (NVH)
challenges. NVH challenges are
significant for V6 and I4 DEAC
configurations. For I4 engine
configurations, manufacturers can
operate the DEAC function of an engine
in very few operating conditions, with
limited potential to save fuel. No
manufacturers sold I4 DEAC engines in
the MY 2016 fleet. Typically, the
smaller the engine displacement, the
less opportunity DEAC provides to
improve fuel consumption.
Manufacturers and suppliers continue
to evaluate more improved versions of
cylinder deactivation, including
advanced cylinder deactivation and
pairing basic cylinder deactivation with
turbo charged engines. No
manufacturers produced such
technologies in the MY 2016 fleet.
Advanced cylinder deactivation and

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turbo technologies were modeled and
considered separately in today’s
analysis.
(b) ‘‘Advanced’’ Engine Technologies
The analysis included ‘‘advanced’’
engine technologies that can deliver
high levels of effectiveness but often
require a significant engine design
change or a new engine architecture. In
the CAFE model, ‘‘basic’’ engine
technologies may be considered in
combination and applied before
advanced engine technologies.
‘‘Advanced’’ engine technologies
generally include one or more basic
engine technologies in the simulation,
without the need to layer on ‘‘basic’’
engine technologies on top of
‘‘advanced’’ engines. Once an advanced
engine technology is applied, the model
does not reconsider the basic engine
technologies. The characterization of
each advanced engine technology takes
into account the prerequisite
technologies.
Many of the newest advanced engine
technologies improve effectiveness over
their predecessors, but the engines may
also include sophisticated materials or
manufacturing processes that contribute
to efficiency improvements. For
instance, one recently introduced turbo
charged engine uses sodium filled valve
stems.131 Another recently introduced
high compression ratio engine uses a
sophisticated laser cladding process to
manufacture valve seats and improve
airflow.132 To fully consider these
advancements (and their potential
benefits), the incremental costs of these
technologies, as well as the effectiveness
improvements, must be accounted for.
(1) Turbocharged Engines
Turbo engines recover energy from
hot exhaust gas and compress intake air,
thereby increasing available airflow and
increasing specific power level. Due to
specific power improvements on turbo
engines, engine displacement can be
downsized. The downsizing reduces
pumping losses and improves fuel
economy at lower loads. For the NPRM
analysis, a level of downsizing is
assumed to be applied that achieves
performance similar to the baseline
naturally-aspirated engine. This
assumes manufacturers would apply the
benefits toward improved fuel economy
131 See Honda, ‘‘2018 Honda Accord Press Kit—
Powertrain,’’ Oct. 2, 2017. Available at http://
news.honda.com/newsandviews/article.aspx?g=
honda-automobiles&id=9932-en. (last accessed June
21, 2018).
132 Hakariya et al., ‘‘The New Toyota Inline 4Cylinder 2.5L Gasoline Engine,’’ SAE Technical
Paper 2017–01–1021 (Mar. 28, 2017), available at
https://www.sae.org/publications/technical-papers/
content/2017-01-1021/.

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and not trade off fuel economy
improvements to increase overall
vehicle performance. In practice,
manufacturers have often also improved
some vehicle performance attributes at
the expense of not maximizing potential
fuel economy improvements.
Manufacturers may develop engines
to operate on varying levels of boost,133
with higher levels of boost achieving
higher engine specific power and
enabling greater levels of engine
downsizing and corresponding
reductions in pumping losses for
improved efficiency. However, engines
operating at higher boost levels are
generally more susceptible to engine
knock,134 especially at higher torques
and low engine speeds. Additionally,
engines with higher boost levels
typically require larger induction and
exhaust system components, dissipate
greater amounts of heat, and with
greater levels of engine downsizing have
increased challenges with turbo lag.135
For these reasons, three levels of turbo
downsizing technologies are separately
modeled in this analysis.
The analysis also modeled
turbocharged engines with parallel
hybrid technology. In simulations with
high stringencies, many manufacturers
produced turbo-hybrid electric vehicles.
In the MY 2016 fleet, of the vehicles that
use parallel hybrid technology, many
use turbocharged engines.
Since the Draft TAR, the turbo family
engine maps were updated to reflect
operation on 87 AKI regular octane
fuel.136 In the Draft TAR, turbo engine
maps were developed assuming
premium fuel. For this rulemaking
analyses, pathways to improving fuel
economy and CO2 are analyzed, while
also maintaining vehicle performance,
capability, and other attributes. This
includes assuming there is no change in
the fuel octane required to operate the
vehicle. Using 87 AKI regular octane
fuel is consistent with the fuel octane
that manufacturers specify for the
majority of vehicles, and enables the
modeling to account for important
design and calibration issues associated
133 Boost refers to the degree to which the
turbocharger compresses the intake air for the
engine, which may affect the specific power of the
engine.
134 Knock refers to rapid uncontrolled combustion
in the cylinder part way through the combustion
process, which can create an audible sound and can
damage the engine.
135 Turbo lag refers to the delay time between
power demanded and power delivered; it is
typically associated with rapid accelerations from a
stopped vehicle at idle.
136 Specifically, 87 Anti-Knock Index (AKI) Tier
3 certification fuel. 87 AKI is also known as 87
(R+M)/2 or 87 (Research Octane + Motor Octane)/
2.

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with regular octane fuel. Using the
updated criteria assures the NPRM
analysis reflects real-world constraints
faced by manufacturers to assure engine
durability, and acceptable drivability,
noise and harshness, and addresses the
over-estimation of potential fuel
economy improvements related to the
fuel octane assumptions, which did not
fully account for these constraints, in
the Draft TAR. Compared with the
NHTSA analysis in the Draft TAR, these
engine maps adjust the fuel use at high
torque and low speed operation and at
high speed operation to fully account
for knock limitations with regular
octane fuel.
The analysis assumes engine
downsizing with the addition of turbo
technology. For instance, in the
simulations, manufacturers may have
replaced a naturally-aspirated V8 engine
with a turbo V6 engine, and
manufacturers may have replaced a
naturally-aspirated V6 engine with a
turbo I4 engine. When manufacturers
reduced the number of banks or
cylinders of an engine, some cost
savings is projected due to fewer
cylinders and fewer valves. Such cost
savings is projected to help offset the
additional costs of turbo charger specific
hardware, making turbo downsizing a
very attractive technology progression
for some engines.137
(a) TURBO1
Level 1 Turbo Charging (TURBO1)
adds a turbo charger to a DOHC engine
with SGDI, VVT, and continuously VVL.
The engine operates at up to 18 bar
brake mean effective pressure (BMEP).
Manufacturers used Turbo1
technology in a little less than a quarter
of the MY 2016 fleet with particularly
high concentrations in premium
vehicles.
(b) TURBO2
Level 2 Turbo Charging (TURBO2)
operates at up to 24 bar BMEP. The step
from Turbo1 to Turbo2 is accompanied
with additional displacement
downsizing for reduced pumping losses.
Very few manufacturers have Turbo2
technology in the MY 2016 fleet.
(c) CEGR1
Turbo Charging with Cooled Exhaust
Gas Recirculation (CEGR1) improves the
knock resistance of Turbo2 engines by
mixing cooled inert exhaust gases into
the engine’s air intake. That allows
greater boost levels, more optimal spark
timing for improved fuel economy, and
137 In particular, the step from a naturallyaspirated V6 to a turbo I4 was particularly cost
effective in agency simulations.

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43037

performance and greater engine
downsizing for lower pumping losses.
CEGR1 technology is used in only a few
vehicles in the MY 2016 fleet, and many
of these vehicles include highperformance utility either for towing or
acceleration.
(a) Turbocharged Engine Technologies
Not Considered
Previous analyses considered turbo
charged engines with even higher BMEP
than today’s Turbo2 and CEGR1
technologies, but today’s analysis does
not present 27 bar BMEP turbo engines.
Turbo engines with very high BMEP
have demonstrated limited potential to
improve fuel economy due to practical
limitations on engine downsizing and
tradeoffs with launch performance and
drivability. Based on the analysis, and
based on CBI, CEGR2 turbo engine
technology was not included in this
NPRM analysis.
(2) High Compression Ratio Engines
(Atkinson Cycle Engines)
Atkinson cycle gasoline engines use
changes in valve timing (e.g., lateintake-valve-closing or LIVC) to reduce
the effective compression ratio while
maintaining the expansion ratio. This
approach allows a reduction in topdead-center (TDC) clearance ratio (e.g.,
increase in ‘‘mechanical’’ or ‘‘physical’’
compression ratio) to increase the
effective expansion ratio without
increasing the effective compression
ratio to a point that knock-limited
operation is encountered. Increasing the
expansion ratio in this manner improves
thermal efficiency but also lowers peak
BMEP, particularly at lower engine
speeds.
Often knock concerns for these
engines limit applications in high load,
low RPM conditions. Some
manufacturers have mitigated knock
concerns by lowering back pressure
with long, intricate exhaust systems, but
these systems must balance knock
performance with emissions tradeoffs,
and the increased size of the exhaust
manifold can pose packaging concerns,
particularly on V-engine
configurations.138
Only a few manufacturers produced
internal combustion engine vehicles
with Atkinson cycle engines in MY
138 Some HCR1 4-cylinder (I–4) engines use an
intricate 4–2–1 exhaust manifold to lower
backpressure and to improve engine efficiency.
Manufacturers sometimes fitted such an exhaust
system into a front-wheel-drive vehicle with an I–
4 engine by using a high underbody tunnel or
rearward dashpanel (trading off some interior
space), but packaging such systems on rear-wheeldrive vehicles may pose challenges, especially if the
engine has two banks and would therefore require
room for two such exhaust manifolds.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

2016; however, these engines are
commonly paired with hybrid electric
vehicle technologies due to the synergy
of peak efficiency of Atkinson cycle
engines and immediate torque from
electric motors in strong hybrids.
Atkinson cycle engines are very
common on power split hybrids and are
sometimes observed as part of a parallel
hybrid system or plug-in hybrid system.
Atkinson cycle engines played a
prominent role in EPA’s January 2017
final determination, which has since
been withdrawn. Today’s analysis
recognizes that the technology is not
suitable for many vehicles due to
performance, emissions and packaging
issues, and/or the extensive capital and
resources that would be required for
manufacturers to shift from other
powertrain technology pathways (such
as turbocharging and downsizing) to
standalone Atkinson cycle engine
technology.
(a) HCR1
A number of Asian manufacturers
have launched Atkinson cycle engines
in smaller vehicles that do not use
hybrid technologies. These production
engines have been benchmarked to
characterize HCR1 technology for
today’s analysis.
Today’s analysis restricted the
application of stand-alone Atkinson
cycle engines in the CAFE model in
some cases. The engines benchmarked
for today’s analysis were not suitable for
MY 2016 baseline vehicle models that
have 8-cylinder engines and in many
cases 6-cylinder engines.
(b) HCR2

sradovich on DSK3GMQ082PROD with PROPOSALS2

EPA conceptualized a ‘‘future’’
Atkinson cycle engine and published
the theoretical engine map in an SAE
paper.139 140 For this engine, EPA staff
began with a best-in-class 2.0L Atkinson
cycle engine and then increased the
efficiency of the engine map further,
through the theoretical application of
additional technologies in combination,
like cylinder deactivation, engine
friction reduction, and cooled exhaust
gas recirculation. This engine remains
entirely speculative, as no production
139 Ellies, B., Schenk, C., and Dekraker, P.,
‘‘Benchmarking and Hardware-in-the-Loop
Operation of a 2014 MAZDA SkyActiv 2.0L 13:1
Compression Ratio Engine,’’ SAE Technical Paper
2016–01–1007, 2016. Available at https://
www.sae.org/publications/technical-papers/
content/2016-01-1007/.
140 Lee, S., Schenk, C., and McDonald, J., ‘‘Air
Flow Optimization and Calibration in HighCompression-Ratio Naturally Aspirated SI Engines
with Cooled-EGR,’’ SAE Technical Paper 2016–01–
0565, 2016. Available at https://www.sae.org/
publications/technical-papers/content/2016-010565/.

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engine as outlined in the EPA SAE
paper has ever been commercially
produced or even produced as a
prototype in a lab setting. Furthermore,
the engine map has not been validated
with hardware and bench data, even on
a prototype level (as no such engine
exists to test to validate the engine
map).
Previously, EPA relied heavily on the
HCR2 (or sometimes referred to as ATK2
in previous EPA analysis) engine as a
cost effective pathway to compliance for
stringent alternatives, but many engine
experts questioned its technical
feasibility and near term commercial
practicability. Stakeholders asked for
the engine to be removed from
compliance simulations until the
performance could be validated with
engine hardware.141 142 While for the
Draft TAR, the agencies ran full-vehicle
simulations with the theoretical engine
map and made these available in the
CAFE model, HCR2 technology as
described in EPA’s SAE paper was not
included in today’s analysis because
there has been no observable physical
demonstration of the speculative
technology, and many questions remain
about its practicability as specified,
especially in high load, low engine
speed operating conditions. Simulations
with EPA’s HCR2 engine map produce
results that approach (and sometimes
exceed) diesel powertrain efficiency.143
Given the prominence of this unproven
technology in previous rule-makings,
the CAFE model may be configured to
consider the application of HCR2
technology for reference only.
As new engines emerge that achieve
high thermal efficiency, questions may
be raised as to whether the HCR2 engine
is a simulation proxy for the new engine
technology. It is important to conduct a
thorough evaluation of the actual new
production engines to measure the brake
specific fuel consumption and to
characterize the improvements
141 At NHTSA–2016–0068–0082, FCA
recommended, ‘‘Remove ATK2 from OMEGA
model until the performance is validated.’’, p. viii.
And FCA stated, ‘‘ATK2—High Compression
engines coupled with Cylinder Deactivation and
Cooled EGR are unlikely to deliver modeled results,
meet customer needs, or be ready for commercial
application.’’, p. 6–9.
142 At Docket ID No EPA–HQ–OAR–2015–0827–
6156, The Alliance of Automobile Manufacturers
commented, ‘‘[There] is no current example of
combined Atkinson, plus cooled EGR, plus cylinder
deactivation technology in the present fleet to verify
EPA’s modeled benefits and . . . EPA could not
provide physical test results replicating its modeled
benefits of these combined technologies,’’ p. 40.
143 Thomas, J. ‘‘Drive Cycle Powertrain
Efficiencies and Trends Derived from EPA Vehicle
Dynamometer Results,’’ SAE Int. J. Passeng. Cars—
Mech. Syst. 7(4):2014. Available at https://
www.sae.org/publications/technical-papers/
content/2014-01-2562/.

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attributable to friction and thermal
efficiency before drawing conclusions.
Using vehicle level data may
misrepresent or conflate complex
interactions between a high thermal
efficiency engine, engine friction
reduction, accessory load
improvements, transmission
technologies, mass reduction,
aerodynamics, rolling resistance, and
other vehicle technologies. For instance,
some of the newest high compression
ratio engines show improved thermal
efficiency, in large part due to improved
accessory loads or reduced parasitic
losses from accessory systems.144 The
CAFE model allows for incremental
improvement over existing HCR1
technologies with the addition of
improved accessory devices (IACC), a
technology that is available to be
applied on many baseline MY 2016
vehicles with HCR1 engines and may be
applied as part of a pathway of
compliance to further improve the
effectiveness of existing HCR1 engines.
(c) Emerging Gasoline Engine
Technologies
Manufacturers and suppliers continue
to invest in many emerging engine
technologies, and some of these
technologies are on the cusp of
commercialization. Often,
manufacturers submit information about
new engine technologies that they may
soon bring into production. When this
happens, a collaborative effort is
undertaken with suppliers and
manufacturers to learn as much as
possible and sometimes begin
simulation modeling efforts. Bench data,
or performance data for preproduction
vehicles and engines, is usually closely
held confidential business information.
To properly characterize the
technologies, it is often necessary to
wait until the engine technologies are in
production to study them.
(1) Advanced Cylinder Deactivation
(ADEAC)
Advanced cylinder deactivation
systems (or rolling or dynamic cylinder
deactivation systems) allows a further
degree of cylinder deactivation than
DEAC. The technology allows the
engine to vary the percentage of
cylinders deactivated and the sequence
in which cylinders are deactivated,
essentially providing ‘‘displacement on
demand’’ for low load operations, so
long as the calibration avoids certain
frequencies.
144 For instance, the MY 2018 2.5L Camry engine
that uses HCR technology also reduces parasitic
losses with a variable capacity oil pump.

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ADEAC systems may be integrated
into the valvetrains with moderate
modifications on OHV engines.
However, while the ADEAC operating
concept remains the same on DOHC
engines, the valvetrain hardware
configuration is very different, and
application on DOHC engines is
projected to be more costly per cylinder
due to the valvetrain differences.
Some preproduction 8-cylinder OHV
prototype vehicles were briefly
evaluated for this analysis, but no
production versions of the technology
have been studied.
Today’s analysis relied on CBI to
estimate costs and effectiveness values
of ADEAC. Since no engine map was
available at the time of the NPRM
analysis, ADEAC was estimated to
improve a basic engine with VVL, VVT,
SGDI, and DEAC by three percent (for 4
cylinder engines) six percent (for
engines with more than 4 cylinders).
ADEAC systems will continue to be
studied as production begins.
(2) Variable Compression Ratio Engines
(VCR)
Engines using variable compression
ratio (VCR) technology appear to be at
a production-intent stage of
development but also appear to be
targeted primarily towards limited
production, high performance and very
high BMEP (27–30 bar) applications.
Variable compression ratio engines
work by changing the length of the
piston stroke of the engine to operate at
a more optimal compression ratio and
improve thermal efficiency over the full
range of engine operating conditions.
A number of manufacturers and
suppliers provided information about
VCR technologies, and several design
concepts were reviewed that could
achieve a similar functional outcome. In
addition to design concept differences,
intellectual property ownership
complicates the ability of the agencies to
define a VCR hardware system that
could be widely adopted across the
industry.
For today’s analysis, VCR engines
have a spot on the technology
simulation tree, but VCR is not actively
used in the NPRM simulation.
Reasonable representations of costs and
technology characterizations remain
open questions for VCR engine
technology and the analysis.
NHTSA is sponsoring work to
develop engine maps for additional
combinations of technologies. Some of
these technologies being researched
presently, including VCR, may be used
in the analysis supporting the final rule.
Please provide comment on variable
compression ratio engine technology.

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Should VCR technology be employed in
the timeframe of this proposed
rulemaking? Why or why not? Do
commenters believe VCR technology
will see widespread adoption in the US
vehicle fleet? Why or why not? What
vehicle segments may it best be suited
for, and which segments would it not be
best suited for? Why or why not? What
cost and effectiveness values should be
used if VCR is modeled for analysis?
Please provide supporting data.
Additionally, please provide any
comments on the sponsored work
related to VCR, described further in
PRIA Chapter 6.3.

modeling for past rulemakings, and is
not included in this NPRM analysis,
primarily because effectiveness, cost,
and mass market implementation
readiness data are not available.
Please comment on the potential use
of HCCI technology in the timeframe
covered by this rule. More specifically,
should HCCI be included in the final
rulemaking analysis for this proposed
rulemaking? Why or why not? Please
provide supporting data, including
effectiveness values, costs in relation
varying engine types and applications,
and production timing that supports the
timeframe of this rulemaking.

(3) Compression Ignition Gasoline
Engines (SpCCI, HCCI)
For many years, engine developers,
researchers, manufacturers have
explored ways to achieve the inherent
efficiency of a diesel engine while
maintaining the operating
characteristics of a gasoline engine. A
potential pathway for striking this
balance is utilizing compression
ignition for gasoline fueled engines,
more commonly referred to as
Homogeneous Charge Compression
Ignition (HCCI).
Ongoing, periodic discussions with
manufacturers on future fuel saving
technologies and powertrain plans have,
generally, included HCCI as a long-term
strategy. The technology appears to
always be a strong consideration as, in
theory, it provides the ‘‘best of both
worlds,’’ meaning a way to provide
diesel engine efficiency with gasoline
engine performance and emissions
levels.
Developments in both the research
and the potential production
implementation of HCCI for the US
market is continually assessed. In 2017,
a significant, potentially production
breakthrough was announced by Mazda
regarding a gasoline-fueled engine
employing Spark Controlled
Compression Ignition (SpCCI), where
HCCI is employed for a portion of its
normal operation and spark ignition is
used at other times.145 Soon after,
Mazda publicly stated they plan to
introduce this engine as part of the
Skyactiv family of engines in 2019.146
However, HCCI was not included in
the simulation and vehicle fleet

(d) Diesel Engines

145 Mazda Next-Generation Technology—Press
Information, Mazda USA (Oct. 24, 2017), https://
insidemazda.mazdausa.com/press-release/mazdanext-generation-technology-press-information/ (last
visited Apr. 13, 2018).
146 Mazda introduces updated 2019 CX–3 at 2018
New York International Auto Show, Mazda USA
(Mar. 28, 2018), https://
insidemazda.mazdausa.com/press-release/mazdaintroduces-2019-cx-3-2018-new-york-auto-show/
(last visited Apr. 13, 2018).

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Diesel engines have several
characteristics that give superior fuel
efficiency, including reduced pumping
losses due to lack of (or greatly reduced)
throttling, high pressure direct injection
of fuel, a combustion cycle that operates
at a higher compression ratio, and a very
lean air/fuel mixture relative to an
equivalent-performance gasoline engine.
This technology requires additional
enablers, such as a NOX adsorption
catalyst system or a urea/ammonia
selective catalytic reduction system for
control of NOX emissions during lean
(excess air) operation.
(e) Alternative Fuel Engines
(1) Compressed Natural Gas (CNG)
Compressed Natural Gas (CNG)
engines use compressed natural gas as a
fuel source. The fuel storage and supply
systems for these engines differ
tremendously from gasoline, diesel, and
flex fuel vehicles.
(2) Flex Fuel Engines
Flex fuel engines can run on regular
gasoline and fuel blended with ethanol.
These vehicles may require additional
equipment in the fuel system to
effectively supply different blends of
fuel to the engine.
(f) Lubrication and Friction Reduction
Low-friction lubricants including low
viscosity and advanced low friction
lubricant oils are now available (and
widely used). If manufacturers choose to
make use of these lubricants, they may
need to make engine changes and
conduct durability testing to
accommodate the lubricants. The level
of low friction lubricants exceeded 85%
penetration in the MY 2016 fleet.
Reduction of engine friction can be
achieved through low-tension piston
rings, roller cam followers, improved
material coatings, more optimal thermal
management, piston surface treatments,
and other improvements in the design of

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engine components and subsystems that
improve efficient engine operation.
Manufacturers have already widely
adopted both lubrication and friction
reduction technologies. This analysis
includes advanced engine maps that
already assume application of lowfriction lubricants and engine friction
reduction technologies. Therefore,
additional friction reduction is not
considered in today’s analysis.
The use and commercial development
of improved lubricants and friction
reduction components will continue to
be monitored, including conical boring
and oblong cylinders, and future
analyses may be updated if new
information becomes available.

sradovich on DSK3GMQ082PROD with PROPOSALS2

5. Fuel Octane
(a) What is fuel octane level?
Gasoline octane levels are an integral
part of potential engine performance.
According the United States Energy
Information Administration (EIA),
octane ratings are measures of fuel
stability. These ratings are based on the
pressure at which a fuel will
spontaneously combust (auto-ignite) in
a testing engine.147 Spontaneous
combustion is an undesired condition
that will lead to serious engine damage
and costly repairs for consumers if not
properly managed. The higher an octane
number, the more stable the fuel,
mitigating the potential for spontaneous
combustion, also commonly known as
‘‘knock.’’ Modern engine control
systems are sophisticated and allow
manufacturers to detect when ‘‘knock’’
occurs during engine operation. These
control systems are designed to adjust
operating parameters to reduce or
eliminate ‘‘knock’’ once detected.
In the United States, consumers are
typically able to select from three
distinct grades of fuel, each of which
provides a different octane rating. The
octane levels can vary from region to
region, but on the majority, the octane
levels offered are regular (the lowest
octane fuel–generally 87 Anti-Knock
Index (AKI) also expressed as (the
average of Research Octane + Motor
Octane), midgrade (the middle range
octane fuel–generally 89–90 AKI), and
premium (the highest octane fuel–
generally 91–94 AKI).148 At higher
elevations, the lowest octane rating
available can drop to 85 AKI.149
147 U.S. Energy Information Administration, What
is Octane?, https://www.eia.gov/energyexplained/
index.cfm?page=gasoline_home#tab2 (last visited
Mar. 19, 2018).
148 Id.
149 See e.g., U.S. Department of Energy and U.S.
Environmental Protection Agency, What is 85
octane, and is it safe to use in my vehicle?, https://
www.fueleconomy.gov/feg/octane.shtml#85 (last

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Currently, throughout the United
States, pump fuel is a blend of 90%
gasoline and 10% ethanol. It is standard
practice for refiners to manufacture
gasoline and ship it, usually via
pipelines, to bulk fuel terminals across
the country. In many cases, refiners
supply lower octane fuels than the
minimum 87-octane required by law to
these terminals. The terminals then
perform blending operations to bring the
fuel octane level up to the minimum
required by law, and higher. In some
cases, typically to lowest fuel grade, the
‘‘base fuel’’ is blended with ethanol,
which has a typical octane rating of
approximately 113. For example, in
2013, the State of Nebraska Ethanol
Board defined requirements for refiners
to 84-octane gas for blending to achieve
87-octane prior to final dispensing to
consumers.150
(b) Fuel Octane Level and Engine
Performance
A typical, overarching goal of optimal
spark-ignited engine design and
operation is to maximize the greatest
amount of energy from the fuel
available, without manifesting
detrimental impacts to the engine over
its expected operating conditions.
Design factors, such as compression
ratio, intake and exhaust value control
specifications, combustion chamber and
piston characteristics, among others, are
all impacted by octane (stability) of the
fuel consumers are anticipated to use.151
Vehicle manufacturers typically
develop their engines and engine
control system calibrations based on the
fuel available to consumers. In many
cases, manufacturers may recommend a
fuel grade for best performance and to
prevent potential damage. In some
cases, manufacturers may require a
specific fuel grade for both best
performance and/or to prevent potential
engine damage.
Consumers, though, may or may not
choose to follow the recommendation or
requirement for a specific fuel grade.
Additionally, regional fuel availability
visited Mar. 19, 2018). 85 octane fuel is available
in high-elevation regions where the barometric
pressure is lower causing naturally-aspirated
engines to operate with less air and, therefore, at
lower torque and power. This creates less benefit
and need for higher octane fuels as compared to at
lower elevations where engine airflow, torque, and
power levels are higher.
150 Nebraska Ethanol Board, Oil Refiners Change
Nebraska Fuel Components, Nebraska.gov, http://
ethanol.nebraska.gov/wordpress/oil-refinerschange-nebraska-fuel-components/ (last visited
Mar. 19, 2018).
151 Additionally, PRIA Chapter 6 contains a brief
discussion of fuel properties, octane levels used for
engine simulation and in real-world testing, and
how octane levels can impact performance under
these test conditions.

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could also limit consumer choice, or, in
the case of higher elevation regions,
present an opportunity for consumers to
use a fuel grade that is below the
minimum recommended. As such,
vehicle manufacturers employ strategies
for scenarios where a lower than
recommended, or required, fuel grade is
used, mitigating engine damage over the
life of a vehicle.
When knock (also referred to as
detonation) is encountered during
engine operation, at the most basic
level, non-turbo charged engines can
reduce or eliminate knock by adjusting
the timing of the spark that ignites the
fuel, as well as the amounts of fuel
injected at each intake stroke
(‘‘fueling’’). In turbo-charged
applications, boost levels are typically
reduced along with spark timing and
fueling adjustments. Past rulemakings
have also discussed other techniques
that may be employed to allow higher
compression ratios, more optimal spark
timing to be used without knock, such
as the addition of cooled exhaust gas
recirculation (EGR). Regardless of the
type of spark-ignition engine or
technology employed, reducing or
preventing knock results in the loss of
potential power output, creating a
‘‘knock-limited’’ constraint on
performance and efficiency.
Despite limits imposed by available
fuel grades, manufacturers continue to
make progress in extracting more power
and efficiency from spark-ignited
engines. Production engines are safely
operating with regular 87 AKI fuel with
compression ratios and boost levels
once viewed as only possible with
premium fuel. According to the
Department of Energy, the average
gasoline octane level has remained
fundamentally flat starting in the early
1980’s and decreased slightly starting in
the early 2000s. During this time,
however, the average compression ratio
for the U.S. fleet has increased from 8.4
to 10.52, a more than 20% increase,
yielding the statement that, ‘‘There is
some concern that in the future, auto
manufacturers will reach the limit of
technological increases in compression
ratios without further increases in the
octane of the fuel.’’ 152
As such, manufacturers are still
limited by the available fuel grades to
consumers and the need to safeguard
the durability of their products for all of
the available fuels; thus, the potential
152 Fact of the Week, Fact #940: August 29, 2016
Diverging Trends of Engine Compression Ratio and
Gasoline Octane Rating, U.S. Department of Energy,
https://www.energy.gov/eere/vehicles/fact-940august-29-2016-diverging-trends-enginecompression-ratio-and-gasoline-octane (last visited
Mar. 21, 2018).

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improvement in the design of sparkignition engines continues to be
overshadowed by the fuel grades
available to consumers.
(c) Potential of Higher Octane Fuels
Automakers and advocacy groups
have expressed support for increases to
fuel octane levels for the U.S. market
and are actively participating in
Department of Energy research programs
on the potential of higher octane fuel
usage.153 154 Some positions for potential
future octane levels include advocacy
for today’s premium grade becoming the
base grade of fuel available, which
could enable low cost design changes
that would improve fuel economy and
CO2. Challenges associated with this
approach include the increased fuel cost
to consumers who drive vehicles
designed for current regular octane
grade fuel that would not benefit from
the use of the higher cost higher octane
fuel. The net costs for a shift to higher
octane fuel would persist well into the
future. Net benefits for the transition
would not be achieved until current
regular octane fuel is not available in
the North American market, causing
manufacturers to redesign all engines to
operate the higher octane fuel, and then
after those vehicles have been in
production a sufficient number of model
years to largely replace the current onroad vehicle fleet. The transition to net
positive benefits could take many years.
In anticipation of this proposed
rulemaking, organizations such as the
High Octane Low Carbon Alliance
(HOLC) and the Fuel Freedom
Foundation (FFA), have shared their
positions on the potential for making
higher octane fuels available for the U.S.
market. Other stakeholders also
commented to past NHTSA rulemakings

sradovich on DSK3GMQ082PROD with PROPOSALS2

153 Mark Phelan, High octane gas coming—but
you’ll pay more for it, Detroit Free Press (Apr. 25,
2017), https://www.freep.com/story/money/cars/
mark-phelan/2017/04/25/new-gasoline-promiseslower-emissions-higher-mpg-and-cost-octanesociety-of-automotive-engineers/100716174/.
154 The octane game: Auto industry lobbies for 95
as new regular, Automotive News (April 17, 2018),
http://www.autonews.com/article/20180417/
BLOG06/180419780/the-octane-game-autoindustry-lobbies-for-95-as-new-regular.

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and/or the Draft TAR regarding the
potential for increasing octane levels for
the U.S. market.
In the meetings with HOLC and the
FFA, the groups advocated for the
potential benefits high octane fuels
could provide via the blending of nonpetroleum feedstocks to increase octane
levels available at the pump. The
groups’ positions on benefits took both
a technical approach by suggesting an
octane level of 100 is desired for the
marketplace, as well as, the benefits
from potential increased national energy
security by reduced dependencies on
foreign petroleum.
(d) Fuel Octane—Request for Comments
Please comment on the potential
benefits, or dis-benefits, of considering
the impacts of increased fuel octane
levels available to consumers for
purposes of the model. More
specifically, please comment on how
increasing fuel octane levels would play
a role in product offerings and engine
technologies. Are there potential
improvements to fuel economy and CO2
reductions from higher octane fuels?
Why or why not? What is an ideal
octane level for mass-market
consumption balanced against cost and
potential benefits? What are the
negatives associated with increasing the
available octane levels and, potentially,
eliminating today’s lower octane fuel
blends? Please provide supporting data
for your position(s).
6. Transmission Technologies
Transmissions transmit torque from
the engine to the wheels. Transmissions
may improve fuel efficiency primarily
through two mechanisms: (1)
Transmissions with more gears allow
the engine to operate more regularly at
the most efficient speed-load points,
and (2) transmissions may have
improvements in friction (gears,
bearings, seals, and so on), or
improvements in shift efficiency that
help the transmission transfer torque
more efficiently, lowering parasitic
losses. These mechanisms are very
different, so full-vehicle simulation is
helpful to understand how a

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transmission may work with
complementary equipment to improve
fuel economy.
Today’s analysis significantly
increased the number of transmissions
modeled in full-vehicle simulations,
attempting to more closely align the
Department of Energy full-vehicle
simulations with existing vehicles.
Previously, EPA included just five
transmissions 155 by vehicle class in
their analysis, and often EPA
represented upgrades among manual,
automatic, continuously variable, and
dual clutch transmissions with the same
effectiveness 156 and cost values 157
within a vehicle class. Today’s analysis
simulated nearly 20 transmissions, with
explicit assumptions about gear ratios,
gear efficiencies, gear spans, shift logic,
and transmission architecture.158 159
This analysis improves transparency by
making clear the assumptions
underlying the transmissions in the fullvehicle simulations and by increasing
the number of transmissions simulated
since the Draft TAR. Methods will be
continuously evaluated to improve
transmission models in full-vehicle
simulations. For the box plots of
effectiveness values, as shown in the
PRIA Chapter 6, all automatic
transmissions are relative to a 5-speed
automatic, and all manual transmissions
are relative to a 5-speed manual. Table
II–11 below shows the absolute costs of
transmission used for this analysis
including learning and retail price
equivalent.
155 Null,

TRX11, TRX12, TRX21, TRX22.
TAR, p. 5–297 through 5–298
summarizes effectiveness values previously
assumed for stepping between transmission
technologies (Null, TRX11, TRX12, TRX21, TRX22).
157 Draft TAR, p. 5–299. ‘‘For future vehicles, it
was assumed that the costs for transitioning from
one technology level (TRX11–TRX22) to another
level is the same for each transmission type (AT,
AMT, DCT, and CVT).’’
158 See PRIA Chapter 6.3.
159 Ehsan, I.S., Moawad, A., Kim, N., & Rousseau,
A. ‘‘A Detailed Vehicle Simulation Process To
Support CAFE Standards.’’ ANL/ESD–18/6. Energy
Systems Division, Argonne National Laboratory.
2018.
156 Draft

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(a) Automatic Transmissions
Five-, six-, seven-, eight-, nine- and
ten-speed automatic transmissions are
optimized by changing the gear ratios to
enable the engine to operate in a more
efficient operating range over a broader
range of vehicle operating conditions.
While a six speed transmission
application was most prevalent for the
MYs 2012–2016 final rule, eight and

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higher speed transmissions were more
prevalent in the MY 2016 fleet.
‘‘L2’’ and ‘‘L3’’ transmissions
designate improved gear efficiency and
reduced parasitic losses. Few
transmissions in the MY 2016 fleet have
achieved ‘‘L2’’ efficiency, and the
highest level of transmission efficiencies
modeled are assumed to be available in
MY 2022.

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(1) Continuously Variable
Transmissions
Continuously variable transmission
(CVT) commonly uses V-shaped pulleys
connected by a metal belt rather than
gears to provide ratios for operation.
Unlike manual and automatic
transmissions with fixed transmission
ratios, continuously variable
transmissions can provide fully variable
and an infinite number of transmission

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
ratios that enable the engine to operate
in a more efficient operating range over
a broader range of vehicle operating
conditions. In this NPRM, two levels of
CVTs are considered for future vehicles.
The second level CVT would have a
wider transmission ratio, increased
torque capacity, improvements in oil
pump efficiency, lubrication
improvements, and friction reduction.
While CVTs work well with light loads,
the technology as modeled is not
suitable for larger vehicles such as
trucks and large SUVs.
(2) Dual Clutch Transmissions

sradovich on DSK3GMQ082PROD with PROPOSALS2

Dual clutch or automated shift
manual transmissions (DCT) are similar
to manual transmissions except for the
vehicle controls shifting and launch
functions. A dual-clutch automated shift
manual transmission uses separate
clutches for even-numbered and oddnumbered gears, so the next expected

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gear is pre-selected, which allows for
faster and smoother shifting. The 2012–
2016 final rule limited DCT applications
to a maximum of 6-speeds. Both 6-speed
and 8-speed DCT transmissions are
considered in today’s proposal.
Dual clutch transmissions are very
effective transmission technologies, and
previous rule-making projected rapid,
and wide adoption into the fleet.
However, early DCT product launches
in the U.S. market experienced shift
harshness and poor launch
performance, resulting in customer
satisfaction issues—some so extreme as
to prompt vehicle buyback
campaigns.160 Most manufacturers are
not using DCTs in the U.S. market due
to the customer satisfaction issues.
Manufacturers used DCTs in about three
percent of the MY 2016 fleet. Today’s
160 Ford Powershift Transmission Settlement,
http://fordtransmissionsettlement.com/ (last visited
June 21, 2018).

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analysis limits the application of
improved DCTs to vehicles that already
use DCTs. Many of these vehicles are
imported performance products.
(b) Manual Transmissions
Manual 6- and 7-speed transmissions
offer an additional gear ratio, sometimes
with a higher overdrive gear ratio, over
a 5-speed manual transmission. Similar
to automatic transmissions, more gears
often means the engine may operate in
the efficient zone more frequently.
7. Vehicle Technologies
As discussed earlier in Section
II.D.1.b)(1), several technologies were
considered for this analysis, and Table
II–12, Table II–13, and Table II–14
below shows the full list of vehicle
technologies analyzed and the
associated absolute cost.161
161 Mass

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Name

Technology Pathway

C-2017

C-2021

C-2025

C-2029

LDB

DLR

$

88.32

$

81.14

$

75.14

$

70.94

SAX

DLR

$

93.43

$

83.15

$

77.05

$

72.87

ROLLO

ROLL

$

-

$

-

$

-

$

-

ROLLlO

ROLL

$

7.47

$

6.69

$

6.25

$

5.96

ROLL20

ROLL

$

58.32

$

47.14

$

42.24

$

39.54

MRO

MR

$

-

$

-

$

-

$

-

MRl

MR

$

0.42

$

0.37

$

0.34

$

0.32

MR2

MR

$

0.51

$

0.45

$

0.42

$

0.39

MR3

MR

$

0.78

$

0.71

$

0.66

$

0.62

MR4

MR

$

1.44

$

1.17

$

1.04

$

0.95

MRS

MR

$

2.62

$

2.11

$

1.87

$

1.70

AEROO

AERO

$

-

$

-

$

-

$

-

AER05

AERO

$

56.65

$

50.44

$

46.71

$

44.33

AEROlO

AERO

$ 115.82

$ 103.13

$

95.49

$

90.62

AER015

AERO

$ 163.66

$ 145.72

$ 134.93

$ 128.05

AER020

AERO

$ 289.56

$ 257.82

$ 238.72

$ 226.56

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Table 11-12 - Summary of Absolute Vehicle Technology Cost vs. Baseline for Cars,
I ncI ud.mg L earnmg
. Enects an d R eta•·1 P nee
. E;qmva
. I ent

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Name

Technology Pathway

C-2017

C-2021

C-2025

C-2029

LDB

DLR

$

88.32

$

81.14

$

75.14

$

70.94

SAX

DLR

$

93.43

$

83.15

$

77.05

$

72.87

ROLLO

ROLL

$

-

$

-

$

-

$

-

ROLLlO

ROLL

$

7.47

$

6.69

$

6.25

$

5.96

ROLL20

ROLL

$

58.32

$

47.14

$

42.24

$

39.54

MRO

MR

$

-

$

-

$

-

$

-

MRl

MR

$

0.25

$

0.22

$

0.20

$

0.19

MR2

MR

$

0.34

$

0.30

$

0.28

$

0.27

MR3

MR

$

0.59

$

0.54

$

0.50

$

0.47

MR4

MR

$

1.37

$

1.11

$

0.99

$

0.90

MRS

MR

$

2.44

$

1.96

$

1.74

$

1.58

AEROO

AERO

$

-

$

-

$

-

$

-

AER05

AERO

$

56.65

$

50.44

$

46.71

$

44.33

AEROlO

AERO

$ 115.82

$ 103.13

$

95.49

$

90.62

AER015

AERO

$ 163.66

$ 145.72

$ 134.93

$ 128.05

AER020

AERO

$ 289.56

$ 257.82

$ 238.72

$ 226.56

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Table 11-13- Summary of Absolute Vehicle Technology Cost vs. Baseline for SUVs,
I ncI u d.mg L earnmg
. Enects an d R eta•·1 P nee
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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

(a) Reduced Rolling Resistance
Lower-rolling-resistance tires have
characteristics that reduce frictional
losses associated with the energy
dissipated mainly in the deformation of
the tires under load, thereby improving
fuel economy and reducing CO2
emissions. New for this proposal, and
also marking an advance over low
rolling resistance tires considered
during the heavy duty greenhouse gas
rulemaking,162 is a second level of lower
rolling resistance tires that reduce
frictional losses even further. The first
level of low rolling resistance tires will
have 10% rolling resistance reduction
while the second level would have 20%
162 See 76 FR 57106, at 57207, 57229 (Sep. 15,
2011).

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rolling resistance reduction. In this
NPRM, baseline vehicle reference
rolling resistance values were
determined based on the MY 2016
vehicles rather than the MY 2008
vehicles used in the 2012 final rule.
Rolling resistance values were assigned
based on CBI shared by manufacturers.
In some cases, low rolling resistance
tires can affect traction, which may be
untenable for some high performance
vehicles. For cars and SUVs with more
than 405 horsepower, the analysis
restricted the application of the highest
levels of rolling resistance. For cars and
SUVs with more than 500 horsepower,
the analysis restricted the application of
any additional rolling resistance
technology.

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(b) Reduced Aerodynamic Drag
Coefficient
Aerodynamic drag reduction can be
achieved via two approaches, either by
reducing the drag coefficients or
reducing vehicle frontal area. To reduce
the drag coefficient, skirts, air dams,
underbody covers, and more
aerodynamic side view mirrors can be
applied. In the MY 2017–2025 final rule
and the 2016 Draft TAR, the analysis
included two levels of aerodynamic
technologies. The second level included
active grille shutters, rear visors, and
larger under body panels. This NPRM
expanded the aerodynamic drag
improvements from two levels to four to
provide more discrete levels. The NPRM
levels are 5%, 10%, 15%, and 20%

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
improvement relative to baseline
reference vehicles. The agencies relied
on the wind tunnel testing performed by
National Research Council (NRC),
Canada, Transport Canada (TC), and
Environment and Climate Change,
Canada (ECCC) to quantify the
aerodynamic drag impacts of various
OEM aerodynamic technologies and to
explore the improvement potential of
these technologies by expanding the
capability and/or improving the design
of MY 2016 state-of-the-art aerodynamic
treatments. The agencies estimated the
level of aerodynamic drag in each
vehicle model in the MY 2016 baseline
fleet and gathered CBI on aerodynamic
drag coefficients, so each vehicle has an
appropriate initial value for further
improvements.
Notably, today’s analysis assumes
aerodynamic drag reduction can only
come from reduction in the
aerodynamic drag coefficient and not
from reduction of frontal area.163 For
some bodystyles, the agencies have no
evidence that manufacturers may be
able to achieve 15% or 20%
aerodynamic drag coefficient reduction
relative to baseline for some bodystyles
(for instance, with pickup trucks) due to
form drag limitions. Previously, EPA
analysis assumed some vehicles from all
bodystyles could (and would) reduce
aerodynamic forces by 20%, which in
some cases led to future pickup trucks
having aerodynamic drag coefficients
better than some of today’s typical cars,
if frontal area were held constant. While
ANL created full-vehicle simulations for
trucks with 20% drag reduction, those
simulations were not used in the CAFE
previously assumed that manufacturers
could reduce frontal area as well as aerodynamic
drag coefficient to achieve 20% aerodynamic force
reduction relative to ‘‘Null’’ or initial aerodynamic
technology level; however, reducing frontal area
would likely degrade other utility features like
interior volume, or ingress/egress.

sradovich on DSK3GMQ082PROD with PROPOSALS2

163 EPA

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modeling. That level of drag reduction
is likely not technologically feasible
with today’s technology, and the
analysis accordingly restricted the
application of advanced levels of
aerodynamics in some instances, such
as in this case, due to bodystyle form
drag limitations. Separate from form
drag limitations, some high performance
vehicles already use advanced
aerodynamics technologies to generate
down force, and sometimes these
applications must trade-off between
aerodynamic drag coefficient reduction
and down force. Today’s analysis does
not apply 15% or 20% aerodynamic
drag coefficient reduction to cars and
SUVs with more than 405 horsepower.
(c) Mass Reduction
Mass Reduction can be achieved in
many ways, such as material
substitution, design optimization, part
consolidation, improving manufacturing
process, etc. The analysis utilizes mass
reduction levels of 5, 10, 15, and 20%
relative to a reference glider vehicle for
each vehicle subsegment. For HEV,
PHEV, and BEV vehicles, net mass
reduction was considered, including the
mass reduction applied to the glider and
the added mass of electrification
components. An extensive discussion of
mass reduction technologies as well as
the cost of mass reduction is located in
Chapter 6.3 of the PRIA. The analysis
included an estimated level of mass
reduction technology in each vehicle
model in the MY 2016 baseline fleet so
that each vehicle model has an
appropriate initial value for further
improvements.
(d) Low Drag Brakes (LDB)
Low-drag brakes reduce the sliding
friction of disc brake pads on rotors
when the brakes are not engaged
because the brake pads are pulled away
from the rotors.

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(e) Secondary Axle Disconnect (SAX)
Front or secondary axle disconnect for
all-wheel drive systems provides a
torque distribution disconnect between
front and rear axles when torque is not
required for the non-driving axle. This
results in the reduction of associated
parasitic energy losses.
8. Electrification Technologies
For this NPRM, the analysis of
electrification technologies relies
primarily on research published by the
Department of Energy, ANL.164 ANL’s
assumptions regarding all hybrid
systems, including belt-integrated
starter generators, strong parallel and
series hybrids, plug-in hybrids,165 and
battery electric vehicles, and most
projected technology costs were adopted
for this analysis. In addition, the most
recent ANL BatPaC model is used to
estimate battery costs. Table II–15 and
Table II–16 below show the absolute
costs of all electrification technologies
estimated for this NPRM analysis
relative to a basic internal combustion
engine vehicle with a 5-speed automatic
transmission.166
164 Moawad et al., Assessment of vehicle sizing,
energy consumption, and cost through large-scale
simulation of advanced engine technologies,
Argonne National Laboratory (March 2016),
available at https://www.autonomie.net/pdfs/
Report%20ANL%20ESD-1528%20-%20Assessment
%20of%20Vehicle%20Sizing,%20Energy%20
Consumption%20and%20Cost%20through
%20Large%20Scale%20Simulation%20
of%20Advanced%20Vehicle%20Technologies%20%201603.pdf.
165 Notably all power split hybrids, and all plugin hybrid vehicles were assumed to be paired with
a high compression ratio internal combustion
engine for this analysis.
166 Note: These costs do not include value loss for
HEVs, PHEVs, and BEVs. Powertrain hardware
between cars and small SUV’s is often similar,
especially if technology is used vehicles on the
same platform; however, battery pack sizes may
vary meaningfully to deliver similar range in
different applications.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table 11-15 - Summary of Car and Small SUV Absolute Electrification Technology Cost
Without Batteries vs. Baseline Internal Combustion Engine, Including Learning Effects
. E.qmva
. Ien t
an d R et a•·1 P nee
Name

Technology Pathway

C-2017

CONV

Electrification

$

SS12V

Electrification

$

BISG

C-2021

-

C-2025

C-2029

$

-

$

-

$

-

657.92

$

568.03

$

508.83

$

473.05

Electrification

$ 1,137.19

$

829.75

$

714.98

$

655.86

CISG

Electrification

$

$

781.09

$

691.89

$

651.54

SHEVP2

Hybrid/Electric

$ 2,206.07

$ 1,942.13

$ 1,732.29

$ 1,637.38

SHEVPS

Hybrid/Electric

$ 6,477.91

$ 5,664.33

$ 5,017.49

$ 4,724.85

PHEV30

Advanced Hybrid/Electric

$ 8,180.35

$ 6,956.06

$ 6,008.25

$ 5,587.55

PHEV50

Advanced Hybrid/Electric

$ 8,338.69

$ 7,011.23

$ 5,994.55

$ 5,546.75

BEV200

Advanced Hybrid/Electric

$ 2,976.02

$ 2,324.66

$ 1,859.67

$ 1,664.95

FCV

Advanced Hybrid/Electric

$19,673.32

$17,607.59

$16,485.05

$15,702.81

893.28

Name

Technology Pathway

C-2017

CONV

Electrification

$

-

$

-

$

SS12V

Electrification

$

735.31

$

634.85

$

568.69

$

528.70

BISG

Electrification

$

524.86

$

382.96

$

329.99

$

302.70

CISG

Electrification

$ 1,786.54

$ 1,562.17

$ 1,383.78

$ 1,303.07

SHEVP2

Hybrid/Electric

$ 1,924.68

$ 1,696.08

$ 1,514.34

$ 1,432.14

SHEVPS

Hybrid/Electric

$ 8,038.86

$ 7,029.24

$ 6,226.53

$ 5,863.38

PHEV30

Advanced Hybrid/Electric

$10,395.42

$ 8,839.62

$ 7,635.17

$ 7,100.55

PHEV50

Advanced Hybrid/Electric

$10,683.13

$ 8,982.46

$ 7,679.93

$ 7,106.23

BEV200

Advanced Hybrid/Electric

$ 4,351.27

$ 3,398.92

$ 2,719.04

$ 2,434.34

FCV

Advanced Hybrid/Electric

$25,969.16

$23,242.36

$21,760.59

$20,728.01

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-

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Table 11-16- Summary of Truck and Medium SUV Absolute Electrification Technology
Cost Without Batteries vs. Baseline Internal Combustion Engine, Including Learning
Enect san d R et a•·1 P nee
. E.qmva
. Ient

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
(a) Hybrid Technologies
(1) 12-Volt Stop-Start
12-volt Stop-Start, sometimes referred
to as idle-stop or 12-volt micro hybrid,
is the most basic hybrid system that
facilitates idle-stop capability. These
systems typically incorporate an
enhanced performance battery and other
features such as electric transmission
pump and cooling pump to maintain
vehicle systems during idle-stop.
(2) Higher Voltage Stop-Start/Belt
Integrated Starter Generator
Higher Voltage Stop-Start/Belt
Integrated Starter Generator (BISG),
sometimes referred to as a mild hybrid
system, provides idle-stop capability
and uses a higher voltage battery with
increased energy capacity over typical
automotive batteries. The higher system
voltage allows the use of a smaller, more
powerful electric motor. This system
replaces a standard alternator with an
enhanced power, higher voltage, higher
efficiency starter-alternator, that is belt
driven and that can recover braking
energy while the vehicle slows down
(regenerative braking). Today’s analysis
assumes 48V systems on cars and small
SUVs and high voltage systems for large
SUVs and trucks. Future analysis may
reference the application and operation
of 48V systems on large SUVs and
trucks, if applicable.

sradovich on DSK3GMQ082PROD with PROPOSALS2

(3) Integrated Motor Assist (IMA)/Crank
Integrated Starter Generator
Integrated Motor Assist (IMA)/Crank
integrated starter generator (CISG)
provides idle-stop capability and uses a
high voltage battery with increased
energy capacity over typical automotive
batteries. The higher system voltage
allows the use of a smaller, more
powerful electric motor and reduces the
weight of the wiring harness. This
system replaces a standard alternator
with an enhanced power, higher
voltage, higher efficiency starter
alternator that is crankshaft-mounted
and can recover braking energy while
the vehicle slows down (regenerative
braking).
(4) P2 Hybrid
P2 Hybrid (SHEVP2) is a newly
emerging hybrid technology that uses a
transmission-integrated electric motor
placed between the engine and a
gearbox or CVT, much like the IMA
system described above except with a
wet or dry separation clutch that is used
to decouple the motor/transmission
from the engine. In addition, a P2
hybrid would typically be equipped
with a larger electric machine.
Disengaging the clutch allows all-

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electric operation and more efficient
brake-energy recovery. Engaging the
clutch allows efficient coupling of the
engine and electric motor and, when
combined with a DCT transmission,
reduces gear-train losses relative to
power-split or 2-mode hybrid systems.
Battery costs are now considered
separately from other HEV hardware.
P2 Hybrid systems typically rely on
the internal combustion engine to
deliver high, sustained power levels.
While many vehicles may use HCR1
engines as part of a hybrid powertrain,
HCR1 engines may not be suitable for all
vehicles, especially high performance
vehicles, or vehicles designed to carry
or tow large loads. Many manufacturers
may prefer turbo engines (with high
specific power output) for P2 Hybrid
systems.
(5) Power-Split Hybrid
Power-split Hybrid (SHEVPS) is a
hybrid electric drive system that
replaces the traditional transmission
with a single planetary gearset and a
motor/generator. This motor/generator
uses the engine to either charge the
battery or supply additional power to
the drive motor. A second, more
powerful motor/generator is
permanently connected to the vehicle’s
final drive and always turns with the
wheels. The planetary gear splits engine
power between the first motor/generator
and the drive motor to either charge the
battery or supply power to the wheels.
The power-split hybrid technology is
included in this analysis as an enabling
technology supporting this proposal,
(the agencies evaluate the P2 hybrid
technology discussed above where
power-split hybrids might otherwise
have been appropriate). As stated above,
battery costs are now considered
separately from other HEV hardware.
Power-split hybrid technology as
modeled in this analysis is not suitable
for large vehicles that must handle high
loads.
The ANL Autonomie simulations
assumed all power-split hybrids use a
high compression ratio engine.
Therefore, all vehicles equipped with
SHEVPS technology in the CAFE model
inputs and simulations are assumed to
have high compression ratio engines.
(6) Plug-in Hybrid Electric
Plug-in hybrid electric vehicles
(PHEV) are hybrid electric vehicles with
the means to charge their battery packs
from an outside source of electricity
(usually the electric grid). These
vehicles have larger battery packs with
more energy storage and a greater
capability to be discharged than other
hybrid electric vehicles. They also use

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a control system that allows the battery
pack to be substantially depleted under
electric-only or blended mechanical/
electric operation and batteries that can
be cycled in charge sustaining operation
at a lower state of charge than is typical
of other hybrid electric vehicles. These
vehicles are sometimes referred to as
Range Extended Electric Vehicles
(REEV). In this NPRM analysis, PHEVs
with two all-electric ranges—both a 30
mile and a 50 mile all-electric range—
have been included as potential
technologies. Again, battery costs are
now considered separately from other
PHEV hardware.
The ANL Autonomie simulations
assumed all PHEVs use a high
compression ratio engine. Therefore, all
vehicles equipped with PHEV
technology in the CAFE model inputs
and simulations are assumed to have
high compression ratio engines. In
practice, many PHEVs recently
introduced in the marketplace use
turbo-charged engines in the PHEV
system, and this is particularly true for
PHEVs produced by European
manufacturers and for other PHEV
performance vehicle applications.
Please provide comment on the
modeling of PHEV systems. Should
turbo PHEVs be considered instead, or
in addition to high compression ratio
PHEVs? Why or why not? What vehicle
segments may turbo PHEVs best be
suited for, and which segments would it
not be best suited for? What vehicle
segments may high compression ratio
PHEVs best be suited for, and which
segments would it not be best suited
for? Similarly, the analysis currently
considers PHEVs with 30-mile and 50mile all-electric range, and should other
ranges be considered? For instance, a
20-mile all-electic range may decrease
the battery pack size, and hence the
battery pack cost relative to a 30-mile
all-electric range system, while still
providing electric-vehicle functionality
in many applications. Do commenters
believe PHEV technology will see
widespread adoption in the US vehicle
fleet? Why or why not? Please provide
supporting data.
(b) Full Electrification and Fuel Cell
Vehicles
(1) Battery Electric
Electric vehicles (EV) are equipped
with all-electric drive and with systems
powered by energy-optimized batteries
charged primarily from grid electricity.
EVs with range of 200 miles have been
included as a potential technology in
this NPRM. Battery costs are now
considered separately from other EV
hardware.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

(2) Fuel Cell Electric
Fuel cell electric vehicles (FCEVs)
utilize a full electric drive platform but
consume electricity generated by an
onboard fuel cell and hydrogen fuel.
Fuel cells are electrochemical devices
that directly convert reactants (hydrogen
and oxygen via air) into electricity, with
the potential of achieving more than
twice the efficiency of conventional
internal combustion engines. High
pressure gaseous hydrogen storage tanks
are used by most automakers for FCEVs.
The high pressure tanks are similar to
those used for compressed gas storage in
more than 10 million CNG vehicles
worldwide, except that they are
designed to operate at a higher pressure
(350 bar or 700 bar vs. 250 bar for CNG).
FCEVs are currently produced in
limited numbers and are available in
limited geographic areas.

(a) Electric Power Steering (EPS)

because it replaces a continuously
operated hydraulic pump, thereby
reducing parasitic losses from the
accessory drive. Manufacturers have
informed the agencies that full EPS
systems are being developed for all

and charge convenience is not taken
into account in the CAFE model. Also,
today’s analysis assumes HEVs, PHEVs,
and BEVs have the same survival rates
and mileage accumulation schedules as
vehicles with conventional powertrains,
and that HEVs, PHEVs, and BEVs never
receive replacement batteries before
being scrapped. The agencies invite
comment on these assumptions and on
data and practicable methods to
implement any alternatives.
9. Accessory Technologies
Two accessory technologies, electric
power steering (EPS) and improved
accessories (IACC) (accessory
technologies categorized for the 2012
rule) were considered in this analysis,
and are described below.167 Table II–17
and Table II–18 below shows the
estimated absolute costs including
learning effects and retail price
equivalent utilized in today’s analysis.

types of light-duty vehicles, including
large trucks. However, this analysis
applies the EHPS technology to large
trucks and the EPS technology to all
other light-duty vehicles.

EP24AU18.033

167 For further discussion of accessory
technologies, see Chapter 6 of the PRIA
accompanying this NPRM.

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Electric power steering (EPS)/
Electrohydraulic power steering (EHPS)
is an electrically-assisted steering
system that has advantages over
traditional hydraulic power steering

(c) Electric Vehicle Infrastructure
BEVs and PHEVs may be charged at
home or elsewhere. Home chargers may
access electricity from a regular wall
outlet, or they may require special
equipment to be installed at the home.
Commercial chargers may sometimes be
found at businesses or other travel
locations. These chargers often may
supply power to the vehicle at a faster
rate of charge but often require
significant capital investment to install.
Time to charge, and availability and
convenience of charging are significant
factors for plug-in vehicle operators. For
many consumers, accessible charging
stations present inconveniences that
may deter the adoption of battery
electric and plug-in hybrid vehicles.
More detail about charging and
charging infrastructure, including a
discussion of potential electric vehicle
impacts on the electric grid, is available
in the PRIA, Chapter 6. For today’s
analysis, costs for installing chargers

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
(b) Improved Accessories (IACC)
Improved accessories (IACC) may
include high efficiency alternators,
electrically driven (i.e., on-demand)
water pumps, variable geometry oil
pumps, cooling fans, a mild
regeneration strategy, and high
efficiency alternators. It excludes other
electrical accessories such as electric oil
pumps and electrically driven air
conditioner compressors. In the MY
2017–2025 final rule, two levels of IACC
were offered as a technology path (a low
improvement level and a high
improvement level). Since much of the
market has incorporated some of these
technologies in the MY 2016 fleet, the
analysis assumes all vehicles have
incorporated what was previously the
low level, so only the high level remains
as an option for some vehicles.

sradovich on DSK3GMQ082PROD with PROPOSALS2

10. Other Technologies Considered but
Not Included in This Aanalysis
Manufacturers, suppliers, and
researchers continue to create a diverse
set of fuel economy technologies. Many
high potential technologies that are still
in the early stages of the development
and commercialization process are still
being evaluated for any final analysis.
Due to uncertainties in the cost and
capabilities of emerging technologies,
some new and pre-production
technologies are not yet a part of the
CAFE model simulation. Evaluating and
benchmarking promising fuel economy
technologies continues to be a priority
as commercial development matures.
(a) Engine Technologies
• Variable compression ratio (VCR)—
varies the compression ratio and swept
volume by changing the piston stroke on
all cylinders. Manufacturers accomplish
this by changing the effective length of
the piston connecting rod, with some
prototypes having a range of 8:1 to 14:1
compression ratio. In turbocharged
form, early publications suggest VCR
may be possible to deliver up to 35%
improved efficiency over the existing
equivalent-output naturally-aspirated
engine.168
• Opposed-piston engine—sometimes
known as opposed-piston opposedcylinder (OPOC), operates with two
pistons per cylinder working in
opposite reciprocal motion and running
on a two-stroke combustion cycle. It has
no cylinder head or valvetrain but
requires a turbocharger and
168 See e.g., VC—Turbo—The world’s first
production-ready variable compression ratio
engine, Nissan Motor Corporation (Dec. 13, 2017),
https://newsroom.nissan-global.com/releases/
release-917079cb4af478a2d26bf8e5ac00ae49-vcturbo-the-worlds-first-production-ready-variablecompression-ratio-engine.

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supercharger for engine breathing. The
efficiency may be significantly higher
than MY 2016 turbocharged gasoline
engines with competitive costs. This
engine architecture could run on many
fuels, including gasoline and diesel.
Packaging constraints, emissions
compliance, and performance across a
wide range of operating conditions
remain as open considerations. No
production vehicles have been publicly
announced, and multiple manufacturers
continue to evaluate the
technology.169 170
• Dual-fuel—engine concepts such as
reactivity controlled compression
ignition (RCCI) combine multiple fuels
(e.g. gasoline and diesel) in cylinder to
improve brake thermal efficiency while
reducing NOX and particulate
emissions. This technology is still in the
research phase.171
• Smart accessory technologies—can
improve fuel efficiency through smarter
controls of existing systems given
imminent or expected controls inputs in
real world driving conditions. For
instance, a vehicle could adjust the use
of accessory systems to conserve energy
and fuel as a vehicle approaches a red
light. Vehicle connectivity and sensors
can further refine the operation for more
benefit and smoother operation.172
• High Compression Miller Cycle
Engine with Variable Geometry
Turbocharger or Electric Supercharger—
Atkinson cycle gasoline engines with
sophisticated forced induction system
that requires advanced controls. The
benefits of these technologies provide
better control of EGR rates and boost
which is achieved with electronically
controlled turbocharger or supercharger.
The electric version of this technology
which incorporates 48V is called Eboost.173 174
169 Murphy,

T. Achates: Opposed-Piston Engine
makers tooling up, Wards Auto (Jan. 23, 2017),
http://wardsauto.com/engines/achates-opposedpiston-engine-makers-tooling.
170 Our Formula, Achates Power, http://
achatespower.com/our-formula/opposed-piston/
(last visited June 21, 2018).
171 Robert Wagner, Enabling the Next Generation
of High Efficiency Engines, Oak Ridge National
Laboratory, U.S. Department of Energy (2012),
available at https://www.energy.gov/sites/prod/
files/2014/03/f8/deer12_wagner_0.pdf.
172 EfficientDynamics—The intelligent route to
lower emissions, BMW Group (2007), https://
www.bmwgroup.com/content/dam/bmw-groupwebsites/bmwgroup_com/responsibility/downloads/
en/2007/Alex_ED__englische_Version.pdf.
173 Volkswagen at the 37th Vienna Motor
Symposium, Volkswagen (Apr. 28, 2016), https://
www.volkswagen-media-services.com/en/
detailpage/-/detail/Volkswagen-at-the-37th-ViennaMotor-Symposium/view/3451577/
5f5a4dcc90111ee56bcca439f2dcc518?p_p_
auth=M2yfP3Ze.
174 These engines may be considered in the
analysis supporting the final rule, but these engine

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(b) Electrified Vehicle Powertrain
• Advanced battery chemistries—
many emerging battery technologies
promise to eventually improve the cost,
safety, charging time, and durability in
comparison to the MY 2016 automotive
lithium-ion batteries. For instance,
many view solid state batteries as a
promising medium-term automotive
technology. Solid state batteries replace
the battery’s liquid electrolyte with a
solid electrolyte to potentially improve
safety, power and energy density,
durability, and cost. Some variations
use ceramic, polymer, or sulfide-based
solid electrolytes. Multiple automakers
and suppliers are exploring the
technology and possible
commercialization that may occur in the
early 2020s.175 176 177
• Supercapacitors/Ultracapacitors—
An electrical energy storage device with
higher power density but lower energy
density than batteries. Advanced
capacitors may reduce battery
degradation associated with charge and
discharge cycles, with some tradeoffs to
cost and engineering complexity.
Supercapacitors/Ultracapacitors are
currently not used in parallel or as a
standalone traction motor energy storage
device.178
• Motor/Drivetrain:
Æ Lower-cost magnets for Brushless
Direct Current (BLDC) motors—BLDC
motor technology, common in hybrid
and battery electric vehicles, uses rare
earth magnets. By substituting and
eliminating rare earths from the
magnets, motor cost can be significantly
reduced. This technology is announced,
but not yet in production. The
capability and material configuration of
these systems remains a closely guarded
trade secret.179
maps were not available in time for the NPRM
analysis. Please see Chapter 6.3 of the PRIA
accompanying this proposal for more information.
175 Schmitt, B. Ultrafast-Charging Solid-State EV
Batteries Around The Corner, Toyota Confirms,
Forbes (Jul. 25, 2017), https://www.forbes.com/
sites/bertelschmitt/2017/07/25/ultrafast-chargingsolid-state-ev-batteries-around-the-corner-toyotaconfirms/#5736630244bb.
176 Moving toward clean mobility, Robert Bosch
GmbH, https://www.bosch.com/explore-andexperience/moving-toward-clean-mobility/ (last
visited June 21, 2018).
177 Reuters Staff, Honda considers developing all
solid-state EV batteries, Reuters (Dec. 21, 2017),
https://www.reuters.com/article/us-honda-nissan/
honda-considers-developing-all-solid-state-evbatteries-idUSKBN1EF0FM.
178 Burke, A. & Zhao,H. Applications of
Supercapacitors in Electric and Hybrid Vehicles,
Institute of Transportation Studies University of
California, Davis (Apr. 2015), available at https://
steps.ucdavis.edu/wp-content/uploads/2017/05/
2015-UCD-ITS-RR-15-09-1.pdf.
179 Buckland, K. & Sano, N. Toyota Readies
Cheaper Electric Motor by Halving Rare Earth Use,

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Æ Integrated multi-phase integrated
electric vehicle drivetrains. Research
has been conducted on 6-phase and 9phase integrated systems to potentially
reduce cost and improve power density.
Manufacturers may improve system
power density through integration of the
motor, inverter, control, and gearing.
These systems are in the research
phase.180 181
(c) Transmission Technologies
• Beltless CVT—Most MY 2016,
commercially available CVTs rely on
belt technology. A new architecture of
CVT replaces belts or pulleys with a
continuously variable variator, which is
a special type of planetary set with balls
and rings instead of gears. The
technology promises to improve
efficiency, handle higher torques, and
change modes more quickly. This
technology may be commercially
available as early as 2020.182
• Multi-speed electric motor
transmission—MY 2016 battery electric
vehicle transmissions are single-speed.
Multiple gears can allow for more
torque multiplication at lower speeds or
a downsized electric machine, increased
efficiency, and higher top speed. Twospeed transmission designs are available
but not currently in production.183
(d) Energy-Harvesting Technology

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• Vehicle waste heat recovery
systems—Internal combustion engines
convert the majority of the fuel’s energy
to heat. Thermoelectric generators and
heat pipes can convert this heat to
electricity.184 Thermoelectric
generators, often made of
semiconductors, have been tested by
automakers but have traditionally not
been implemented due to low efficiency
Bloomberg (Feb, 20, 2018), https://
www.bloomberg.com/news/articles/2018-02-20/
toyota-readies-cheaper-electric-motor-by-halvingrare-earth-use.
180 Burkhardt, Y., Spagnolo, A., Lucas, P.,
Zavesky, M., & Brockerhoff, P. ‘‘Design and analysis
of a highly integrated 9-phase drivetrain for EV
applications ’’ 20 November 2014. DOI. 10.1109/
ICELMACH.2014.6960219. IEEE xplore.
181 Patel, V., Wang, J., Nugraha, D., Vuletic, R., &
Tousen, J. ‘‘Enhanced Availability of Drivetrain
Through Novel Multi-Phase Permanent Magnet
Machine Drive’’ 2016. IEEE Transactions on
Industrial Electronics Pages. 469–480.
182 Murphy, T. Planets Aligning for Dana’s
VariGlide Beltless CVT, Wards Auto (Aug. 22,
2017), http://wardsauto.com/technology/planetsaligning-dana-s-variglide-beltless-cvt.
183 Faid, S. A Highly Efficient Two Speed
Transmission for Electric Vehicles (May 2015),
available at http://www.evs28.org/event_file/event_
file/1/pfile/EVS28_Saphir_Faid.pdf.
184 Orr et al., A review of car waste heat recovery
systems utilising thermoelectric generators and heat
pipes, 101 Applied Thermal Engineering 490–495
(May 25, 2016).

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and high cost.185 These systems are not
yet in production.
• Suspension energy recovery—
Multiple electromechanical and
electrohydraulic suspension
technologies exist that can convert
motion from uneven roads into
electricity.186 187 These technologies are
limited to luxury vehicles with limited
production volumes. This technology is
not produced in 2016 but planned for
production as early as 2018.188
11. Air Conditioning Efficiency and OffCycle Technologies
(a) Air Conditioning Efficiency
Technologies
Air conditioning (A/C) is a virtually
standard automotive accessory, with
more than 95% of new cars and light
trucks sold in the United States
equipped with mobile air conditioning
(MAC) systems. Most of the additional
air conditioning related load on an
engine is due to the compressor, which
pumps the refrigerant around the system
loop. The less the compressor operates
or the more efficiently it operates, the
less load the compressor places on the
engine, and the better fuel consumption
will be. This high penetration means A/
C systems can significantly impact
energy consumed by the light duty
vehicle fleet.
Vehicle manufacturers can generate
credits for improved A/C systems under
EPA’s GHG program and receive a fuel
consumption improvement value (FCIV)
equal to the value of the benefit not
captured on the 2-cycle test under
NHTSA’s CAFE program.189 Table II–19
provides a ‘‘menu’’ of qualifying A/C
technologies, with the magnitude of
each improvement value or credit
estimated based on the expected
reduction in CO2 emissions from the
technology.190 NHTSA converts the
improvement in grams per mile to a
FCIV for each vehicle for purposes of
measuring CAFE compliance. As part of
a manufacturer’s compliance data,
manufacturers will provide information
185 Patel, P. Powering Your Car with Waste Heat,
MIT Technology Review (May 25, 2011), https://
www.technologyreview.com/s/424092/poweringyour-car-with-waste-heat/.
186 Baeuml, B. et al., The Chassis of the Future,
Schaeffler, https://www.schaeffler.com/
remotemedien/media/_shared_media/08_media_
library/01_publications/schaeffler_2/symposia_1/
downloads_11/Schaeffler_Kolloquium_2014_27_
en.pdf (last visited June 21, 2018).
187 Advanced Suspension, Tenneco, http://
www.tenneco.com/overview/rc_advanced_
suspension/ (last visited June 21, 2018).
188 Audi A8 Active Chassis, Audi, https://
www.audi.com/en/innovation/design/more_
personal_comfort_a8_active_chassis.html (last
visited June 21, 2018).
189 77 FR 62624, 62720 (Oct. 15, 2012).
190 40 CFR 86.1868–12 (2016).

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about which off-cycle technologies are
present on which vehicles (see Section
X for further discussion of reporting offcycle technology information).
The 2012 final rule for MYs 2017 and
later outlined two test procedures to
determine credit or FCIV eligibility for
A/C efficiency menu credits, the idle
test, and the AC17 test. The idle test,
performed while the vehicle is at idle,
determined the additional CO2
generated at idle when the A/C system
is operated.191 The AC17 test is a fourpart performance test that combines the
existing SC03 driving cycle, the fuel
economy highway test cycle, and a preconditioning cycle, and solar soak
period.192 Manufacturers could use the
idle test or AC17 test to determine
improvement values for MYs 2014–
2016, while for MYs 2017 and later, the
AC17 test is the exclusive test that
manufacturers can use to demonstrate
eligibility for menu A/C improvement
values.
In MYs 2020 and later, manufacturers
will use the AC17 test to demonstrate
eligibility for A/C credits and to
partially quantify the amount of the
credit earned. AC17 test results equal to
or greater than the menu value will
allow manufacturers to claim the full
menu value for the credit. A test result
less than the menu value will limit the
amount of credit to that demonstrated
on the AC17 test. In addition, for MYs
2017 and beyond, A/C fuel consumption
improvement values will be available
for CAFE calculations, whereas
efficiency credits were previously only
available for GHG compliance. The
agencies proposed these changes in the
2012 final rule for MYs 2017 and later
largely as a result of new data collected,
as well as the extensive technical
comments submitted on the proposal.193
The pre-defined technology menu and
associated car and light truck credit
value is shown in Table II–19 below.
The regulations include a definition of
each technology that must be met to be
eligible for the menu credit.194
Manufacturers are not required to
submit any other emissions data or
information beyond meeting the
definition and useful life
requirements 195 to use the pre-defined
191 75 FR 25324, 25431 (May 7, 2010). The A/C
CO2 Idle Test is run with and without the A/C
system cooling the interior cabin while the vehicle’s
engine is operating at idle and with the system
under complete control of the engine and climate
control system.
192 77 FR 62624, 62723 (Oct. 15, 2012).
193 Id.
194 Id. at 62725.
195 Lifetime vehicle miles travelled (VMT) for MY
2017–2025 are 195,264 miles and 225,865 miles for
passenger cars and light trucks, respectively. The
manufacturer must also demonstrate that the off-

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credit value. Manufacturers’ use of
menu-based credits for A/C efficiency is
subject to a regulatory cap: 5.7 g/mi for
cars and trucks through MY 2016 and
separate caps of 5.0 g/mi for cars and
7.2g/mi for trucks for later MYs.196
In the 2012 final rule for MYs 2017
and later, the agencies estimated that
manufacturers would employ significant
advanced A/C technologies throughout
their fleets to improve fuel economy,
and this was reflected in the stringency

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cycle technology is effective for the full useful life
of the vehicle. Unless the manufacturer
demonstrates that the technology is not subject to
in-use deterioration, the manufacturer must account
for the deterioration in their analysis.
196 40 CFR 86.1868–12(b)(2) (2016).

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of the standards.197 Many manufacturers
have since incorporated A/C technology
throughout their fleets, and the
utilization of advanced A/C
technologies has become a significant
contributor to industry compliance
plans. As summarized in the EPA
Manufacturer Performance Report for
the 2016 model year,198 15 auto
manufacturers included A/C efficiency
credits as part of their compliance
demonstration in the 2016 MY. These
197 See e.g., 77 FR 62623, 62803–62806 (Oct. 15,
2012).
198 See Greenhouse Gas Emission Standards for
Light-Duty Vehicles: Manufacturer Performance
Report for the 2016 Model Year (EPA Report 420–
R18–002), U.S. EPA (Jan. 2018), available at https://
nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=
P100TGIA.pdf.

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amounted to more than 12 million Mg
of fuel consumption improvement
values of the total net fuel consumption
improvement values reported. This is
equivalent to approximately four grams
per mile across the 2016 fleet.
Accordingly, a significant amount of
new information about A/C technology
and the efficacy of test procedures has
become available since the 2012 final
rule.
The sections below provide a brief
history of the AC17 test procedure for
evaluating A/C efficiency improving
technology and discuss stakeholder
comments on the AC17 test procedure
approach and discuss A/C efficiency
technology valuation through the offcycle program.

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(1) Evaluation of the AC17 Test
Procedure Since the Draft TAR
In developing the AC17 test
procedure, the agencies sought to
develop a test procedure that could
more reliably generate an appropriate
fuel consumption improvement value
based on an ‘‘A’’ to ‘‘B’’ comparison,
that is, a comparison of substantially
similar vehicles in which one has the

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technology and the other does not.199
The agencies believe that the AC17 test
procedure is more capable of detecting
the effect of more efficient A/C
components and controls strategies
during a transient drive cycle rather
199 For

an explanation of how the agencies, in
collaboration with stakeholders, developed the
AC17 test procedure, see the 2017 and later final
rule at 77 FR 62624, 62723 (Oct. 15, 2012).

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than during just idle (as measured in the
old idle test procedure). As described
above and in the 2012 final rule,200 the
AC17 test is a four-part performance test
that combines the existing SC03 driving
200 See 77 FR 62624, 62723 (Oct. 15, 2012); Joint
Technical Support Document: Final Rulemaking for
2017–2025 Light-Duty Vehicle Greenhouse Gas
Emission and Corporate Average Fuel Economy
Standards, U.S. EPA, National Highway Traffic
Safety Administration at 5–40 (August 2012) .

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cycle, the fuel economy highway cycle,
as well as a pre-conditioning cycle, and
a solar soak period.
The agencies received several
comments on the Draft TAR evaluation
of the AC17 test procedure. FCA
commented generally that A/C
efficiency technologies ‘‘are not
showing their full effect on this AC17
test as most technologies provide benefit
at different temperatures and humidity
conditions in comparison to a standard
test conditions. All of these technologies
are effective at different levels at
different conditions. So there is not one
size fits all in this very complex testing
approach. Selecting one test that
captures benefits of all of these
conditions has not been possible.’’ 201
The agencies acknowledge that any
single test procedure is unlikely to
equally capture the real-world effect of
every potential technology in every
potential use case. Both the agencies
and stakeholders understood this
difficulty when developing the AC17
test procedure. While no test is perfect,
the AC17 test procedure represents an
industry best effort at identifying a test
that would greatly improve upon the
idle test by capturing a greater range of
operating conditions. General industry
evaluation of the AC17 test procedure is
in agreement that the test achieves this
objective.
FCA also noted that ‘‘[i]t is a major
problem to find a baseline vehicle that
is identical to the new vehicle but
without the new A/C technology. This
alone makes the test unworkable.’’ 202

The agencies disagree this makes the
test unworkable. The regulation
describes the baseline vehicle as a
‘‘similar’’ vehicle, selected with good
engineering judgment (such that the test
comparison is not unduly affected by
other differences). Also, OEMs
expressed confidence in using A-to-B
testing to qualify for fuel consumption
improvement values for software-based
A/C efficiency technologies. While
hardware technologies may pose a
greater challenge in locating a
sufficiently similar ‘‘A’’ baseline
vehicle, the engineering analysis
provision under 40 CFR 86.1868–
12(g)(2) provides an alternative to
locating and performing an AC17 test on
such a vehicle. Further, as the USCAR
program in general and the GM Denso
SAS compressor application specifically
have shown, the test is able to resolve
small differences in CO2 effectiveness
(1.3 grams in the latter case) when
carefully conducted.
Commenters on the Draft TAR also
expressed a desire for improvements in
the process by which manufacturers
without an ‘‘A’’ vehicle (for the A-to-B
comparison) could apply under the
engineering analysis provision, such as
development of standardized
engineering analysis and bench testing
procedures that could support such
applications. For example, Toyota
requested that ‘‘EPA consider an
optional method for validation via an
engineering analysis, as is currently
being developed by industry.’’ 203

Similarly, the Alliance commented that,
‘‘[t]he future success of the MAC credit
program in generating emissions
reductions will depend to a large extent
on the manner in which it is
administered by EPA, especially with
respect to making the AC17 A-to-B
provisions function smoothly, without
becoming a prohibitive obstacle to fully
achieving the MAC indirect credits.’’ 204
As described in the Draft TAR, in
2016, USCAR members initiated a
Cooperative Research Program (CRP)
through the Society of Automotive
Engineers (SAE) to develop bench
testing standards for the four hardware
technologies in the fuel consumption
improvement value menu (blower motor
control, internal heat exchanger,
improved evaporators and condensers,
and oil separator). The intent of the
program is to streamline the process of
conducting bench testing and
engineering analysis in support of an
application for A/C credits under 40
CFR part 86.1868–12(g)(2), by creating
uniform standards for bench testing and
for establishing the expected GHG effect
of the technology in a vehicle
application.
An update to the list of SAE standards
under development originally presented
in the Draft TAR is listed in Table II–
20. Since completion of the Draft TAR,
work has continued on these standards,
which appear to be nearing completion.
The agencies seek comment with the
latest completion of these SAE
standards.

(2) A/C Efficiency Technology Valuation
Through the Off-Cycle Program

by utilizing technologies on the menu;
however, the agencies recognize that
manufacturers will develop additional
technologies that are not currently listed
on the menu. These additional A/C
efficiency-improving technologies are
eligible for fuel consumption
improvement values on a case-by-case
basis under the off-cycle program.
Approval under the off-cycle program

also requires ‘‘A-to-B’’ comparison
testing under the AC17 test, that is,
testing substantially similar vehicles in
which one has the technology and the
other does not.
To date, the agencies have received
one type off-cycle application for an A/
C efficiency technology. In December
2014, General Motors submitted an offcycle application for the Denso SAS A/

203 See Comment by Toyota (revised), Docket ID
NHTSA–2016–0068–0088, at 23.

204 See Comment by Alliance of Automobile
Manufacturers, Docket ID EPA–HQ–OAR–2015–
0827–4089 and NHTSA–2016–0068–0072, at 160.

The A/C technology menu, discussed
at length above, includes several A/C
efficiency-improving technologies that
were well defined and had been
quantified for effectiveness at the time
of the 2012 final rule for MYs 2017 and
beyond. Manufacturers claimed the vast
majority of A/C efficiency credits to date
201 See Comment by FCA US LLC, Docket ID
NHTSA 2016–0068–0082, at 123–124.
202 Id. at 124.

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C compressor with variable crankcase
suction valve technology, requesting an
off-cycle GHG credit of 1.1 grams CO2
per mile. In December 2017, BMW of
North America, Ford Motor Company,
Hyundai Motor Company, and Toyota
petitioned and received approval to
receive the off-cycle improvement value
for the same A/C efficiency
technology.205 206 EPA, in consultation
with NHTSA, evaluated the applications
and found methodologies described
therein were sound and appropriate.207
Accordingly, the agencies approved the
fuel economy improvement value
applications.
The agencies received additional
stakeholder comments on the off-cycle
approval process as an alternate route to
receiving A/C technology credit values.
The Alliance requested that EPA
‘‘simplify and standardize the
procedures for claiming off-cycle credits
for the new MAC technologies that have
been developed since the creation of the
MAC indirect credit menu.’’ 208 Other
commenters noted the importance of
continuing to incentivize further
innovation in A/C efficiency
technologies as new technologies
emerge that are not listed on the menu
or when manufacturers begin to reach
regulatory caps. The commenters
suggested that EPA should consider
adding new A/C efficiency technologies
to the menu and/or update the fuel
consumption improvement values for
technology already listed on the menu,
particularly in cases where
manufacturers can show through an offcycle application that the technology
actually deserves more credit than that
listed on the menu. For example, Toyota
commented that ‘‘the incentive values
for A/C efficiency should be updated
along with including new technologies
being deployed.’’ 209
The agencies note that some of these
comments are directed towards the offcycle technology approval process
generally, which is described in more
detail in Section X of this preamble.
Regarding the A/C technology menu
specifically, the agencies do anticipate
205 EPA Decision Document: Off-Cycle Credits for
BMW Group, Ford Motor Company, and Hyundai
Motor Company, U.S. EPA (Dec. 2017), available at
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=
P100TF06.pdf.
206 Alternative Method for Calculating Off-cycle
Credits under the Light-Duty Vehicle Greenhouse
Gas Emissions Program: Applications from General
Motors and Toyota Motor North America, 83 FR
8262 (Feb. 26, 2018).
207 Id.
208 Comment by Alliance of Automobile
Manufacturers, Docket ID EPA–HQ–OAR–2015–
0827–4089 and NHTSA–2016–0068–0072, at 152.
209 Comment by Toyota (revised), Docket ID
NHTSA–2016–0068–0088, at 23.

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that new A/C technologies not currently
on the menu will emerge over the time
frame of the MY 2021–2026 standards.
This proposal requests comment on
adding one additional A/C technology
to the menu—the A/C compressor with
variable crankcase suction valve
technology, discussed below (and also
one off-cycle technology, discussed
below). The agencies also request
comment on whether to change any fuel
economy improvement values currently
assigned to technologies on the menu.
Next, as mentioned above, the menubased improvement values for A/C
efficiency established in the 2012 final
rule for MYs 2017 and by end are
subject to a regulatory cap. The rule set
a cap of 5.7 g/mi for cars and trucks
through MY 2016 and separate caps of
5.0 g/mi for cars and 7.2g/mi for trucks
for later MYs.210 Several commenters
asked EPA to reconsider the
applicability of the cap to non-menu A/
C efficiency technologies claimed
through the off-cycle process and
questioned the applicability of this cap
on several different grounds. These
comments appear to be in response to a
Draft TAR passage that stated:
‘‘Applications for A/C efficiency credits
made under the off-cycle credit program
rather than the A/C credit program will
continue to be subject to the A/C
efficiency credit cap’’ (Draft TAR, p. 5–
210). The agencies considered these
comments and present clarification
below. As additional context, the 2012
TSD states:
‘‘. . . air conditioner efficiency is an offcycle technology. It is thus appropriate [. . .]
to employ the standard off-cycle credit
approval process [to pursue a larger credit
than the menu value]. Utilization of bench
tests in combination with dynamometer tests
and simulations [. . .] would be an
appropriate alternate method of
demonstrating and quantifying technology
credits (up to the maximum level of credits
allowed for A/C efficiency) [emphasis added].
A manufacturer can choose this method even
for technologies that are not currently
included in the menu.’’ 211

This suggests the concept of placing a
limit on total A/C fuel consumption
improvement values, even when some
are granted under the off-cycle program,
is not entirely new and that EPA
considered the menu cap as being
appropriate at the time.
A/C regulatory caps specified under
40 CFR 86.1868–12(b)(2) apply to A/C
efficiency menu-based improvement
210 40

C.F.R § 86.1868–12(b)(2) (2016).
Technical Support Document: Final
Rulemaking for 2017–2025 Light-Duty Vehicle
Greenhouse Gas Emission and Corporate Average
Fuel Economy Standards, U.S. EPA, National
Highway Traffic Safety Administration at 5–58
(August 2012).
211 Joint

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values and are not part of the off-cycle
regulation (40 CFR 86.1869–12).
However, it should be noted that offcycle applications submitted via the
public process pathway are decided
individually on merits through a
process involving public notice and
opportunity for comment. In deciding
whether to approve or deny a request,
the agencies may take into account any
factors deemed relevant, including such
issues as the realization of claimed fuel
consumption improvement value in
real-world use. Such considerations
could include synergies or interactions
among applied technologies, which
could potentially be addressed by
application of some form of cap or other
applicable limit, if warranted.
Therefore, applying for A/C efficiency
fuel consumption improvement values
through the off-cycle provisions in 40
CFR 86.1869–12 should not be seen as
a route to unlimited A/C fuel
consumption improvement values. The
agencies discuss air conditioning
efficiency improvement values further
in Section X of this NPRM.
(b) Off-Cycle Technologies
‘‘Off-cycle’’ emission reductions and
fuel consumption improvements can be
achieved by employing off-cycle
technologies resulting in real-world
benefits but where that benefit is not
adequately captured on the test
procedures used to demonstrate
compliance with fuel economy emission
standards. EPA initially included offcycle technology credits in the MY
2012–2016 rule and revised the program
in the MY 2017–2025 rule.212 NHTSA
adopted equivalent off-cycle fuel
consumption improvement values for
MYs 2017 and later in the MY 2017–
2025 rule.213
Manufacturers can demonstrate the
value of off-cycle technologies in three
ways: First, they may select fuel
economy improvement values and CO2
credit values from a pre-defined
‘‘menu’’ for off-cycle technologies that
meet certain regulatory specifications.
As part of a manufacturer’s compliance
data, manufacturers will provide
information about which off-cycle
technologies are present on which
vehicles.
The pre-defined list of technologies
and associated off-cycle light-duty
vehicle fuel economy improvement
values and GHG credits is shown in
Table II–21 and Table II–22 below.214 A
212 77

FR 62624, 62832 (Oct. 15, 2012).
at 62839.
214 For a description of each technology and the
derivation of the pre-defined credit levels, see
Chapter 5 of the Joint Technical Support Document:
213 Id.

E:\FR\FM\24AUP2.SGM

24AUP2

43057

definition of each technology equipment
must meet to be eligible for the menu
credit is included at 40 CFR 86.1869–
12(b)(4). Manufacturers are not required

to submit any other emissions data or
information beyond meeting the
definition and useful life requirements
to use the pre-defined credit value.

Credits based on the pre-defined list are
subject to an annual manufacturer fleetwide cap of 10 g/mile.

Manufacturers can also perform their
own 5-cycle testing and submit test
results to the agencies with a request
explaining the off-cycle technology. The
additional three test cycles have
different operating conditions including
high speeds, rapid accelerations, high
temperature with A/C operation and

cold temperature, enabling
improvements to be measured for
technologies that do not impact
operation on the 2-cycle tests. Credits
determined according to this
methodology do not undergo public
review.
The third pathway allows
manufacturers to seek EPA approval to

use an alternative methodology for
determining the value of an off-cycle
technology. This option is only
available if the benefit of the technology
cannot be adequately demonstrated
using the 5-cycle methodology.
Manufacturers may also use this option
to demonstrate reductions that exceed

Final Rulemaking for 2017–2025 Light-Duty Vehicle
Greenhouse Gas Emission and Corporate Average

Fuel Economy Standards, U.S. EPA, National

Highway Traffic Safety Administration (August
2012).

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

43058

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

those available via use of the
predetermined menu list. The
manufacturer must also demonstrate
that the off-cycle technology is effective
for the full useful life of the vehicle.
Unless the manufacturer demonstrates
that the technology is not subject to inuse deterioration, the manufacturer
must account for the deterioration in
their analysis.
Manufacturers must develop a
methodology for demonstrating the
benefit of the off-cycle technology, and
EPA makes the methodology available
for public comment prior to an EPA
determination, in consultation with
NHTSA, on whether to allow the use of
the methodology to measure
improvements. The data needed for this
demonstration may be extensive.
Several manufacturers have requested
and been granted use of alternative test
methodologies for measuring
improvements. In 2013, Mercedes
requested off-cycle credits for the
following off-cycle technologies in use
or planned for implementation in the
2012–2016 model years: Stop-start
systems, high-efficiency lighting,
infrared glass glazing, and active seat
ventilation. EPA approved
methodologies for Mercedes to

VerDate Sep<11>2014

23:42 Aug 23, 2018

Jkt 244001

determine these off-cycle credits in
September 2014.215 Subsequently, FCA,
Ford, and GM requested off-cycle
credits using this same methodology.
FCA and Ford submitted applications
for off-cycle credits from high efficiency
exterior lighting, solar reflective glass/
glazing, solar reflective paint, and active
seat ventilation. Ford’s application also
demonstrated off-cycle benefits from
active aerodynamic improvements
(grille shutters), active transmission
warm-up, active engine warm-up
technologies, and engine idle stop-start.
GM’s application described real-world
benefits of an air conditioning
compressor with variable crankcase
suction valve technology. EPA approved
the credits for FCA, Ford, and GM in
September 2015.216 Note, however, that
although EPA granted the use of
alternative methodologies to determine
215 EPA Decision Document: Mercedes-Benz Offcycle Credits for MYs 2012–2016, U.S. EPA (Sept.
2014), available at https://nepis.epa.gov/Exe/
ZyPDF.cgi/P100KB8U.PDF?Dockey=
P100KB8U.PDF.
216 EPA Decision Document: Off-cycle Credits for
Fiat Chrysler Automobiles, Ford Motor Company,
and General Motors Corporation, U.S. EPA (Sept.
2015), available at https://nepis.epa.gov/Exe/
ZyPDF.cgi/P100N19E.PDF?Dockey=P100N19E.PDF.

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credit values, manufacturers have yet to
report credits to EPA based on those
alternative methodologies.
As discussed below, all three methods
have been used by manufacturers to
generate off-cycle improvement values
and credits.
(1) Use of Off-Cycle Technologies to
Date
Manufacturers used a wide array of
off-cycle technologies in MY 2016 to
generate off-cycle GHG credits using the
pre-defined menu. Table II–23 below
shows the percent of each
manufacturer’s production volume
using each menu technology reported to
EPA for MY 2016 by manufacturer.
Table II–24 shows the g/mile benefit
each manufacturer reported across its
fleet from each off-cycle technology.
Like Table II–23, Table II–24 provides
the mix of technologies used in MY
2016 by manufacturer and the extent to
which each technology benefits each
manufacturer’s fleet. Fuel consumption
improvement values for off-cycle
technologies were not available in the
CAFE program until MY 2017; therefore,
only GHG off-cycle credits have been
generated by manufacturers thus far.

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sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

anufacturer

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Ford
GM
Honda
Hyundai
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38.4

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0.0
0.0
0.0
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0.8

0.0

0.0

0.0

10.6

99.1

0.0

0.0

37.1

2.8
11.0
15.0
3.4
3.0
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0
1.0

Mercedes
Nissan
Subaru
Toyota
FCA
Fleet Total

0.0
26.9
33.6
3.6

0.0
0.0
0.0
0.2

0.0
0.0
0.0
0.0

0.0
0.0
0.0
0.0

17.2
5.3
0.0
0.0

4.6
0.0
0.0
0.0

0.0
16.9
0.0
0.0

0.0
16.5
0.0
19.7

0.0
70.9
0.0
0.0

81.1
0.6
0.0
9.2

81.5
65.7
48.1
59.0

0.0
0.2
0.0
0.0

27.7

2.4

91.8

0.0

10.8

98.6

3.1

51.5

22.7

11.9

69.0

0.0

14.6

0.4

23.5

2.3

12.2

51.9

13.2

20.7

28.2

5.8

49.1

0.0

d

~

VJ

(.)

VJ

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table 11-23- Percent of2016 Model Year Vehicle Production Volume with Credits from the Menu, by Manufacturer &

43059

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sradovich on DSK3GMQ082PROD with PROPOSALS2

43060

-.-----

Manufacturer

Active
Aerodynamics

Fmt 4701
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24AUP2

Rover generated the most off-cycle
credits on a fleet-wide basis, reporting
credits equivalent to approximately 6 g/
mile and 5 g/mile, respectively. Several
other manufacturers report fleet-wide
credits in the range of approximately 1
to 4 g/mile. In MY 2016, the fleet total
across manufacturers equaled
approximately 2.5 g/mile. The agencies

E:\FR\FM\24AUP2.SGM

was the first year that manufacturers
could generate credits using pre-defined
menu values, manufacturers have acted
quickly to generate substantial off-cycle
improvements. FCA and Jaguar Land

Frm 00076

EP24AU18.038

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weather while the vehicle is stopped and the engine
is off, thus allowing the engine stop-start system to
be active more frequently in cold weather.

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BMW
Ford
GM
Honda
Hyundai
Jaguar Land
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Kia
Mercedes
Nissan
Subaru
Toyota
FCA
Fleet Total

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0.2
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6.4
3.2
3.9
2.3
2.0
15.7

0.0
2.2
0.0

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0.
0
Note: "0.0" indicates the manufacturer implemented that technology, but the overall penetration rate was not high
enough to round to 0.1 g/mi whereas a dash indicates no use of a given technology by a manufacturer.

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Jkt 244001

In 2016, manufacturers generated the
vast majority of credits using the predefined menu.217 Although MY 2014

23:42 Aug 23, 2018

217 Thus far, the agencies have only granted one
manufacturer (GM) off-cycle credits for technology
based on 5-cycle testing. These credits are for an
off-cycle technology used on certain GM gasolineelectric hybrid vehicles, an auxiliary electric pump,
which keeps engine coolant circulating in cold

VerDate Sep<11>2014

Table 11-24- Model Year 2016 Off-Cycle Technology Fuel Consumption Improvement Value from the Menu, by

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
expect that as manufacturers continue
expanding their use of off-cycle
technologies, the fleet-wide effects will
continue to grow with some
manufacturers potentially approaching
the 10 g/mile fleet-wide cap.
E. Development of Economic
Assumptions and Information Used as
Inputs to the Analysis
1. Purpose of Developing Economic
Assumptions for Use in Modeling
Analysis

sradovich on DSK3GMQ082PROD with PROPOSALS2

(a) Overall Framework of Costs and
Benefits
It is important to report the benefits
and costs of this proposed action in a
format that conveys useful information
about how those impacts are generated
and that also distinguishes the impacts
of those economic consequences for
private businesses and households from
the effects on the remainder of the U.S.
economy. A reporting format will
accomplish the first objective to the

VerDate Sep<11>2014

23:42 Aug 23, 2018

Jkt 244001

extent that it clarifies the benefits and
costs of the proposed action’s impacts
on car and light truck producers,
illustrates how these are transmitted to
buyers of new vehicles, shows the
action’s collateral economic effects on
owners of used cars and light trucks,
and identifies how these impacts create
costs and benefits for the remainder of
the U.S. economy. It will achieve the
second objective by showing clearly
how the economy-wide or ‘‘social’’
benefits and costs of the proposed
action are composed of its direct effects
on vehicle producers, buyers, and users,
plus the indirect or ‘‘external’’ benefits
and costs it creates for the general
public.
Table II–25 through Table II–28
present the economic benefits and costs
of the proposed action to reduce CAFE
and CO2 emissions standards for model
years 2021–26 at three percent and
seven percent discount rates in a format
that is intended to meet these objectives.

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43061

Note: They include costs which are
transfers between different economic
actors—these will appear as both a cost
and a benefit in equal amounts (to
separate affected parties). Societal cost
and benefit values shown elsewhere in
this document do not show costs which
are transfers for the sake of simplicity
but report the same net societal costs
and benefits. As it indicates, the
proposed action first reduces costs to
manufacturers for adding technology
necessary to enable new cars and light
trucks to comply with fuel economy and
emission regulations (line 1). It may also
reduce fine payments by manufacturers
who would have failed to comply with
the more demanding baseline standards.
Manufacturers are assumed to transfer
these cost savings on to buyers by
charging lower prices (line 5); although
this reduces their revenues (line 3), on
balance, the reduction in compliance
costs and lower sales revenue leaves
them financially unaffected (line 4).

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43062

VerDate Sep<11>2014

Table 11-25 - Benefits and Costs Resulting from the Proposed CAFE Standards

Jkt 244001
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Private Benefits and (Costs)

Amount

Savings in technology costs to increase fuel economy

$252.6

Reduced fine payments for non-compliance

$3.0

assumed = -(1 + 2)

Net loss in revenue from lower vehicle prices

($255.6)

4

net= 1+2+3

Net benefits to manufacturers

$0.0

5

assumed= 3

Lower purchase prices for new vehicles

$255.6

Reduced injuries and fatalities from higher vehicle weight

$2.4

Higher fuel costs from lower fuel economy (at retail
prices)*

($152.6)

Inconvenience from more frequent refueling

($8.5)

Lost mobility benefits from reduced driving

($61.0)

net= 5+6+7+8+9

Net benefits to new vehicle buyers

$35.9

1
2
3

Source
CAFE model

Vehicle
Manufacturers

6

Frm 00078

7

Fmt 4701

9

8

New Vehicle
Buyers

Sfmt 4725

10

CAFE model

E:\FR\FM\24AUP2.SGM

11

Used Vehicle
Owners

CAFE model

Reduced costs for injuries and property damage costs from
driving in used vehicles

$88.3

12

All Private
Parties

net = 4+ 10+ 11

Net private benefits

$124.2

Line

Affected Party

Source

External Benefits and (Costs)

Amount

24AUP2

13
14
15

EP24AU18.039

Affected Party

Rest of U.S.
Economy

CAFE Model

Increase in climate damages from added GHG
Emissions**
Increase in health damages from added emissions of air
pollutants**
Increase in economic externalities from added petroleum
use**

($4.3)
($1.2)
($10.9)

16

Reduction in civil penalty revenue

($3.0)

17

Reduction in external costs from lower vehicle use***

$51.9

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Line

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Affected Party

Frm 00079

18
19

Fmt 4701
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Line
20
21
22

Affected Party
Entire U.S.
Economy

Source

Private Benefits and (Costs)

Amount

net= 13+ 14+ 15+ 16+ 17+ 18

Increase in Fuel Tax Revenues
Net external benefits

$19.7
$52.1

Source
total= 1+2+5+6+ 11 + 17+ 18
total= 3+7+8+9+ 13+ 14+ 15+ 16
net=20+21 (also=l2+19)

Economy-Wide Benefits and (Costs)
Total benefits
Total costs
Net Benefits

Amount
$673.5
($497.2)
$176.3

E:\FR\FM\24AUP2.SGM

*Value represents lost fuel savings from lowered fuel economy of MY's 2017-2029 and gained fuel savings from more quickly replacing
MY's 1977 to 2029 with newer vehicles.
**Value represents lost external benefits from lowered fuel economy of MY's 2017-2029 and lowered external costs from
more quickly replacing MY's 1977 to 2029 with newer vehicles.
*** Value includes lower external costs from reducing rebound effect and any change in overall fleet usage from more
quickly replacing MY's 1977 to 2029 with newer vehicles.

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Line

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43064

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table 11-26 - Benefits and Costs Resulting from the Proposed CAFE Standards
(present values discounted at 7%)
Line

Affected Party

Source

Private Benefits and (Costs)

Amount
$192.2

CAFE model

Savings in technology costs to increase fuel
economy
Reduced fine payments for non-compliance

$2.1

assumed= -(1 +2)

Net loss in revenue from lower vehicle prices

($194.3)

4

net= 1+2+3

Net benefits to manufacturers

$0.0

5

assumed = 3

Lower purchase prices for new vehicles

$194.3
$1.3

CAFE model

Reduced injuries and fatalities from higher
vehicle weight
Higher fuel costs from lower fuel economy (at
retail prices)*

8

Inconvenience from more frequent refueling

($5.4)

9

Lost mobility benefits from reduced driving

($37.1)

Net benefits to new vehicle buyers

$56.2
$45.9

1
2
3

Vehicle
Manufacturers

6
7

New Vehicle
Buyers

net= 5+6+7+8+9

10

12

Used Vehicle
Owners
All Private Parties

net = 4+1 0+ 11

Reduced costs for injuries and property damage
costs from driving in used vehicles
Net private benefits

Line

Affected Party

Source

External Benefits and (Costs)

Amount
($2.7)

CAFE Model

h1crease in climate damages from added GHG
Emissions**
Increase in health damages from added
emissions of air pollutants**
Increase in economic externalities from added
petroleUlll use**

11

CAFE model

13
14
15

RestofU.S.
Economy

$102.1

($1.1)
($6.9)

Reduction in civil penalty revenue

($2.1)

17

Reduction in external costs from lower vehicle
use***

$29.6

18

Increase in Fuel Tax Revenues

$12.7

net= 13+14+15+16+17+18

Net external benefits

$29.4

Source
total= 1+2+5+6+11+17+18
total= 3+7+8+9+13+14+15+16
net= 20+21 (also =12+19)

Economy-Wide Benefits and (Costs)
Total benefits
Total costs
Net Benefits

Amount
$478.1
($346.6)
$131.5

16

19
Line
20
21
22

Affected Party
Entire U.S.
Economy

*Value represents lost fuel savings from lowered fuel economy of MY's 2017-2029 and gained fuel savings from more quickly
replacing MY's 1977 to 2029 with newer vehicles.
**Value represents lost external benefits from lowered fuel economy of MY's 2017-2029 and lowered external costs
from more quickly replacing MY's 1977 to 2029 with newer vehicles.
*** Value includes lower external costs from reducing rebound effect and any change in overall fleet usage from
more quickly replacing MY's 1977 to 2029 with newer vehicles.

VerDate Sep<11>2014

23:42 Aug 23, 2018

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($96.9)

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

43065

Table 11-27- Benefits and Costs Resulting from the Proposed GHG Standards
(present values discounted at 3%)
Line

Affected Party

1
2
3
4
5

Vehicle
Manufacturers

Private Benefits and (Costs)

Source

Savings in technology costs to increase fuel
economy
Reduced fine payments for non-compliance
Net loss in revenue from lower vehicle prices
Net benefits to manufacturers
Lower purchase prices for new vehicles
Reduced injuries and fatalities from higher
vehicle weight
Higher fuel costs from lower fuel economy (at
retail prices)*

CAFE model
assumed= -(1 +2)
net= 1+2+3
assumed = 3

6
7
8

New Vehicle
Buyers

CAFE model

9
net= 5+6+7+8+9

10

$259.8
$0.0
($259.8)
$0.0
$259.8
$7.5
($165.2)

h1cmwenience from more frequent refueling

($9.4)

Lost mobility benefits from reduced driving

($69.5)

Net benefits to new vehicle buyers

$23.2

12

Used Vehicle
Owners
All Private Parties

net = 4+1 0+ 11

Reduced costs for injuries and property damage
costs from driving in used vehicles
Net private benefits

Line

Affected Party

Source

External Benefits and (Costs)

($0.8)

CAFE Model

mcrease in climate damages from added GHG
Emissions**
mcrease in health damages from added
emissions of air pollutants**
mcrease in economic externalities from added
petrolemn use**
Reduction in civil penalty revenue

$0.0

17

Reduction in external costs from lower vehicle
use***

$62.4

18

mcrease in Fuel Tax Revenues

$21.5

net= 13+14+15+16+17+18

Net external benefits

$66.5

11

CAFE model

13
14
15
16

Rest of U.S.
Economy

19

$111.0
$134.2
Amount
($4.7)

($11.9)

Line

Affected Party

Source

Economy-Wide Benefits and (Costs)

Amount

20
21
22

Entire U.S.
Economy

total= 1+2+5+6+11+17+18
total= 3+7+8+9+13+14+15+16
net= 20+21 (also =12+19)

Total benefits
Total costs
Net Benefits

$722.0
($521.3)
$200.7

*Value represents lost fuel savings from lowered fuel economy of MY's 2017-2029 and gained fuel savings from more quickly
replacing MY's 1977 to 2029 with newer vehicles.
**Value represents lost external benefits from lowered fuel economy of MY's 2017-2029 and lowered external costs
from more quickly replacing MY's 1977 to 2029 with newer vehicles.
***Value includes lower external costs from reducing rebound effect and any change in overall fleet usage from
more quickly replacing MY's 1977 to 2029 with newer vehicles.

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Table 11-28- Benefits and Costs Resulting from the Proposed GHG Standards
(present values discounted at 7%)
Line

Affected Party

1
2

Vehicle
Manufacturers

Source

Private Benefits and (Costs)

CAFE model

Savings in technology costs to increase fuel
economy
Reduced fine payments for non-compliance

$195.6
$0.0

assumed= -(1 +2)

Net loss in revenue from lower vehicle prices

($195.6)

4

net= 1+2+3

Net benefits to manufacturers

$0.0

5

assumed= 3

Lower purchase prices for new vehicles

$195.6

CAFE model

Reduced injuries and fatalities from higher
vehicle weight
Higher fuel costs from lower fuel economy (at
retail prices)*

3

6
7

New Vehicle
Buyers

$4.4
($105.3)

8

Inconvenience from more frequent refueling

($6.0)

9

Lost mobility benefits from reduced driving

($42.0)

net= 5+6+7+8+9

Net benefits to new vehicle buyers

$46.7

10
11

Used Vehicle
Owners

CAFE model

Reduced costs for injuries and property damage
costs from driving in used vehicles

$56.7

12

All Private Parties

net = 4+1 0+ 11

Net private benefits

$103.4

Line

Affected Party

Source

External Benefits and (Costs)

Amount

($1.0)

CAFE Model

Increase in climate damages from added GHG
Emissions**
Increase in health damages from added
emissions of air pollutants**
Increase in economic externalities from added
petroleum use**
Reduction in civil penalty revenue

$0.0

Reduction in external costs from lower vehicle
use***

$35.0

13
14
15
16

RcstofU.S.
Economy

17
18

Line
20
21
22

($3.0)

($7.6)

Increase in Fuel Tax Revenues

$13.8

net= 13+14+15+16+17+18

Net external benefits

$37.2

Affected Party

Source

Economy-Wide Benefits and (Costs)

Amount

Entire U.S.
Economy

total= 1+2+5+6+11+17+18
total= 3+7+8+9+13+14+15+16
net= 20+21 (also= 12+ 19)

Total benefits
Total costs
Net Benefits

$501.1
($360.5)
$140.6

19

*Value represents lost fuel savings from lowered fuel economy of MY's 2017-2029 and gained fuel savings from more quickly
replacing MY's 1977 to 2029 with newer vehicles.
**Value represents lost external benefits from lowered fuel economy of MY's 2017-2029 and lowered external costs
from more quickly replacing MY's 1977 to 2029 with newer vehicles.
***Value includes lower external costs from reducing rebound effect and any change in overall fleet usage from
more quickly replacing MY's 1977 to 2029 with newer vehicles.
As the tables show, most impacts of
the proposed action will fall on the
businesses and individuals who design,
manufacture, and sell (at retail and
wholesale) cars and light trucks, the

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consumers who purchase, drive, and
subsequently sell or trade-in new
models (and ultimately bear the cost of
fuel economy technology), and owners
of used cars and light trucks produced

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during model years prior to those
covered by this action. Compared to the
baseline standards, if the preferred
alternative is finalized, buyers of new
cars and light trucks will benefit from

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their lower purchase prices and
financing costs (line 5). They will also
avoid the increased risks of being
injured in crashes that would have
resulted from manufacturers’ efforts to
reduce the weight of new models to
comply with the baseline standards,
which represents another benefit from
reducing stringency vis-a`-vis the
baseline (line 6).
At the same time, new cars and light
trucks will offer lower fuel economy
with more lenient standards in place,
and this imposes various costs on their
buyers and users. Drivers will
experience higher costs as a
consequence of new vehicles’ increased
fuel consumption (line 7), and from the
added inconvenience of more frequent
refueling stops required by their
reduced driving range (line 8). They will
also forego some mobility benefits as
they use newly-purchased cars and light
trucks less in response to their higher
fueling costs, although this loss will be
almost fully offset by the fuel and other
costs they save by driving less (line 9).
On balance, consumers of new cars and
light trucks produced during the model
years subject to this proposed action
will experience significant economic
benefits (line 10).
By lowering prices for new cars and
light trucks, this proposed action will
cause some owners of used vehicles to
retire them from service earlier than
they would otherwise have done, and
replace them with new models. In
effect, it will transfer some driving that
would have been done in used cars and
light trucks under the baseline scenario
to newer and safer models, thus
reducing costs for injuries (both fatal
and less severe) and property damages
sustained in motor vehicle crashes. This
improvement in safety results from the
fact that cars and light trucks have
become progressively more protective in
crashes over time (and also slightly less
prone to certain types of crashes, such
as rollovers). Thus, shifting some travel
from older to newer models reduces
injuries and damages sustained by
drivers and passengers because they are
traveling in inherently safer vehicles
and not because it changes the risk
profiles of drivers themselves. This
reduction in injury risks and other
damage costs produces benefits to
owners and drivers of older cars and
light trucks. This also results in benefits
in terms of improved fuel economy and
significant reductions of emissions from
newer vehicles (line 11).
Table II–27 through Table II–28 also
show that the changes in fuel
consumption and vehicle use resulting
from this proposed action will in turn
generate both benefits and costs to the

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remainder of the U.S. economy. These
impacts are ‘‘external,’’ in the sense that
they are by-products of decisions by
private firms and individuals that alter
vehicle use and fuel consumption but
are experienced broadly throughout the
U.S. economy rather than by the firms
and individuals who indirectly cause
them. Increased refining and
consumption of petroleum-based fuel
will increase emissions of carbon
dioxide and other greenhouse gases that
theoretically contribute to climate
change, and some of the resulting (albeit
uncertain) increase in economic
damages from future changes in the
global climate will be borne throughout
the U.S. economy (line 13). Similarly,
added fuel production and use will
increase emissions of more localized air
pollutants (or their chemical
precursors), and the resulting increase
in the U.S. population’s exposure to
harmful levels of these pollutants will
lead to somewhat higher costs from its
adverse effects on health (line 14). On
the other hand, it is expected that the
proposed standards, by reducing new
vehicle prices relative to the baseline,
will accelerate fleet turnover to cleaner,
safer, more efficient vehicles (as
compared to used vehicles that might
otherwise continue to be driven or
purchased).
As discussed in PRIA Section 9.8,
increased consumption and imports of
crude petroleum for refining higher
volumes of gasoline and diesel will also
impose some external costs throughout
the U.S. economy, in the form of
potential losses in production and costs
for businesses and households to adjust
rapidly to sudden changes in energy
prices (line 15 of the table), although
these costs should be tempered by
increasing U.S. oil production.218
Reductions in driving by buyers of new
cars and light trucks in response to their
higher operating costs will also reduce
the external costs associated with their
contributions to traffic delays and noise
levels in urban areas, and these

additional benefits will be experienced
throughout much of the U.S. economy
(line 17). Finally, some of the higher
fuel costs to buyers of new cars and
light trucks will consist of increased
fuel taxes; this increase in revenue will
enable Federal and State government
agencies to provide higher levels of road
capacity or maintenance, producing
benefits for all road and transit users
(line 18).
On balance, Table II–27 through Table
II–28 show that the U.S. economy as a
whole will experience large net
economic benefits from the proposed
action (line 22). While the proposal to
establish less stringent CAFE and GHG
emission standards will produce net
external economic costs, as the increase
in environmental and energy security
externalities outweighs external benefits
from reduced driving and higher fuel
tax revenue (line 19), the table also
shows that combined benefits to vehicle
manufacturers, buyers, and users of cars
and light trucks, and the general public
(line 20), including the value of the lives
saved and injuries avoided, will greatly
outweigh the combined economic costs
they experience as a consequence of this
proposed action (line 21).
The finding that this action to reduce
the stringency of previously-established
CAFE and GHG standards will create
significant net economic benefits—
when it was initially claimed that
establishing those standards would also
generate large economic benefits to
vehicle buyers and others throughout
the economy—is notable. This contrast
with the earlier finding is explained by
the availability of updated information
on the costs and effectiveness of
technologies that will remain available
to improve fuel economy in model years
2021 and beyond, the fleet-wide
consequences for vehicle use, fuel
consumption, and safety from requiring
higher fuel economy (that is,
considering these consequences for used
cars and light trucks as well as new
ones), and new estimates of some
external costs of fuel in petroleum use.

218 Note: This output was based upon the EIA
Annual Energy Outlook from 2017. The 2018
Annual Energy Outlook projects the U.S. will be a
net exporter by around 2029, with net exports
peaking at around 0.5 mbd circa 2040. See Annual
Energy Outlook 2018, U.S. Energy Information
Administration, at 53 (Feb, 6, 2018), https://
www.eia.gov/outlooks/aeo/pdf/AEO2018.pdf.
Furthermore, pursuant to Executive Order 13783
(Promoting Energy Independence and Economy
Growth), agencies are expected to review and revise
or rescind policies that unduly burden the
development of domestic energy resources beyond
what is necessary to protect the public interest or
otherwise comply with the law. Therefore, it is
reasonable to anticipate further increases in
domestic production of petroleum. The agencies
may update the analysis and table to account for
this revised information.

2. Macroeconomic Assumptions That
Affect the Benefit Cost Analysis
Unlike previous CAFE and GHG
rulemaking analyses, the economic
context in which the alternatives are
simulated is more explicit. While both
this analysis and previous analyses
contained fuel price projections from
the Annual Energy Outlook, which has
embedded assumptions about future
macroeconomic conditions, this
analysis requires explicit assumptions
about future GDP growth, labor force
participation, and interest rates in order
to evaluate the alternatives.

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.
Table-11-29 - M acroeconomic
Calendar
Real
Year
Interest
Rate
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
The analysis simulates compliance
through MY 2032 explicitly and must
consider the full useful lives of those
vehicles, approximately 40 years, in

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2.70
1.00
-0.30
-0.30
1.10
1.70
2.00
2.20
2.40
2.40
2.60
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70
2.70

p ro.tec
. f IOns th rougJh CY 2050
Real
Labor Force
GDP
Participation
Growth (thousands)
Rate
2.60
1.60
2.90
3.00
3.00
2.90
2.70
2.40
2.20
2.20
2.20
2.10
2.20
2.20
2.20
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.10
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20

122,700
124,248
125,739
127,625
129,284
130,577
131,752
132,674
133,471
134,271
135,077
135,887
136,703
137,386
138,073
138,764
139,457
140,155
140,855
141,560
142,268
142,979
143,694
144,556
145,423
146,296
147,174
148,057
148,945
149,839
150,738
151,642
152,552
153,467
154,388
155,314

order to estimate their lifetime mileage
accumulation and fuel consumption.
This means that any macroeconomic
forecast influencing those factors must

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cover a similar span of years. Due to the
long time horizon, a source that
regularly produces such lengthy
forecasts of these factors was selected:

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the 2017 OASDI Trustees Report from
the U.S. Social Security Administration.
While Table–II–29 only displays
assumptions through CY 2050, the
remaining years merely continue the
trends present in the table.
The analysis once again uses fuel
price projections from the 2017 Annual
Energy Outlook.219 The projections by
central analysis supporting today’s
proposal uses reference case estimates of fuel prices
reported in the Energy Information
Administration’s (EIA’s) Annual Energy Outlook
2017 (AEO 2017). Today’s proposal also examines
the sensitivity of this analysis to changes in key
inputs, including fuel prices, and includes cases
that apply fuel prices from the AEO 2017 low oil
price and high oil price cases. The reference case
prices are considerably lower than AEO 2011-based
reference cases prices applied in the 2012

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219 The

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rulemaking, and this is one of several important
changes in circumstances supporting revision of
previously-issued standards.
After significant portions of today’s analysis had
already been completed, EIA released AEO 2018,
which reports reference case fuel prices about 10%
higher than reported in AEO 2017, though still well
below the above-mentioned prices from AEO 2011.
The sensitivity analysis therefore includes a case
that applies fuel prices from the AEO 2018
reference case. The AEO 2018 low oil price case
reports fuel prices somewhat higher than the AEO
2017 low oil price case, and the AEO 2018 high oil
price case reports fuel prices very similar to the
AEO 2017 high oil price case. Adding the AEO 2018
low and high oil price cases to the sensitivity
analysis would thus have provided little, if any,
additional insight into the sensitivity of the analysis
to fuel prices. As shown in the summary of the
sensitivity analysis, results obtained applying AEO
2018-based fuel prices are similar to those obtained
applying AEO 2017-based fuel prices. For example,

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43069

fuel calendar year and fuel type are
presented in Table–II–30, in real 2016
dollars. Fuel prices in this analysis
affect not only the value of each gallon
of fuel consumed but relative valuation
of fuel-saving technologies demanded
by the market as a result of their
associated fuel savings.
net benefits between the two are about five percent
different, especially considering that decisions
regarding future standards are not single-factor
decisions, but rather reflect a balancing of factors,
applying AEO 2018-based fuel prices would not
materially change the extent to which today’s
analysis supports the selection of the preferred
alternative.
Like other inputs to the analysis, fuel prices will
be updated for the analysis supporting the final rule
after consideration of related new information and
public comment.

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Calendar
Year

Gasoline
($/gallon)

Diesel
($/gallon)

Electricity
($/kwh)

2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050

2.55
2.21
2.30
2.28
2.48
2.59
2.71
2.83
2.86
2.88
2.93
2.98
2.99
2.98
3.01
3.06
3.10
3.14
3.13
3.17
3.19
3.25
3.26
3.27
3.32
3.35
3.37
3.37
3.36
3.37
3.38
3.39
3.41
3.41
3.42
3.46

2.76
2.31
2.63
2.90
3.08
3.19
3.27
3.35
3.41
3.45
3.51
3.57
3.59
3.60
3.64
3.71
3.76
3.82
3.82
3.86
3.88
3.95
3.97
3.97
4.02
4.05
4.07
4.07
4.07
4.09
4.10
4.13
4.17
4.16
4.18
4.24

0.11
0.10
0.10
0.10
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.11
0.12
0.12

3. New Vehicle Sales and Employment
Assumptions
In all previous CAFE and GHG
rulemaking analyses, static fleet
forecasts that were based on a

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combination of manufacturer
compliance data, public data sources,
and proprietary forecasts were used.
When simulating compliance with
regulatory alternatives, the analysis
projected identical sales across the

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alternatives, for each manufacturer
down to the make/model level where
the exact same number of each model
variant was simulated to be sold in a
given model year under both the least
stringent alternative (typically the

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baseline) and the most stringent
alternative considered. To the extent
that an alternative matched the
assumptions made in the production of
the proprietary forecast, using a static
fleet based upon those assumptions may
have been warranted. However, it seems
intuitive that any sufficiently large span
of regulatory alternatives would contain
alternatives for which that static forecast
was unrepresentative. A number of
commenters have encouraged
consideration of the potential impact of
CAFE/GHG standards on new vehicle
prices and sales, and the changes to
compliance strategies that those shifts
could necessitate.220 In particular, the
continued growth of the utility vehicle
segment creates compliance challenges
within some manufacturers’ fleets as
sales volumes shift from one region of
the footprint curve to another.
Any model of sales response must
satisfy two requirements: It must be
appropriate for use in the CAFE model,
and it must be econometrically
reasonable. The first of these
requirements implies that any variable
used in the estimation of the
econometric model, must also be
available as a forecast throughout the
duration of the years covered by the
simulations (this analysis explicitly
simulates compliance through MY
2032). Some values the model calculates
endogenously, making them available in
future years for sales estimation, but
others must be known in advance of the
simulation. As the CAFE model
simulates compliance, it accumulates
technology costs across the industry and
over time. By starting with the last
known transaction price and adding the
accumulated technology cost to that
value, the model is able to represent the
average selling price in each future
model year assuming that manufacturers
are able to pass all of their compliance
costs on to buyers of new vehicles.
Other variables used in the estimation
must enter the model as inputs prior to
the start of the compliance simulation.
(a) How do car and light truck buyers
value improved fuel economy?
How potential buyers value
improvements in the fuel economy of
new cars and light trucks is an
important issue in assessing the benefits
and costs of government regulation. If
buyers fully value the savings in fuel
costs that result from higher fuel
economy, manufacturers will
presumably supply any improvements
that buyers demand, and vehicle prices
220 See e.g., Comment by Alliance of Automobile
Manufacturers, Docket ID EPA–HQ–OAR–2015–
0827–4089 and NHTSA–2016–0068–0072.

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will fully reflect future fuel cost savings
consumers would realize from owning—
and potentially re-selling—more fuelefficient models. In this case, more
stringent fuel economy standards will
impose net costs on vehicle owners and
can only result in social benefits by
correcting externalities, since
consumers would already fully
incorporate private savings into their
purchase decisions. If instead
consumers systematically undervalue
the cost savings generated by
improvements in fuel economy when
choosing among competing models,
more stringent fuel economy standards
will also lead manufacturers to adopt
improvements in fuel economy that
buyers might not choose despite the cost
savings they offer.
The potential for car buyers to forego
improvements in fuel economy that
offer savings exceeding their initial
costs is one example of what is often
termed the ‘‘energy-efficiency gap.’’
This appearance of such a gap, between
the level of energy efficiency that would
minimize consumers’ overall expenses
and what they actually purchase, is
typically based on engineering
calculations that compare the initial
cost for providing higher energy
efficiency to the discounted present
value of the resulting savings in future
energy costs.
There has long been an active debate
about why such a gap might arise and
whether it actually exists. Economic
theory predicts that individuals will
purchase more energy-efficient products
only if the savings in future energy costs
they offer promise to offset their higher
initial costs. However, the additional
cost of a more energy-efficient product
includes more than just the cost of the
technology necessary to improve its
efficiency; it also includes the
opportunity cost of any other desirable
features that consumers give up when
they choose the more efficient
alternative. In the context of vehicles,
whether the expected fuel savings
outweigh the opportunity cost of
purchasing a model offering higher fuel
economy will depend on how much its
buyer expects to drive, his or her
expectations about future fuel prices,
the discount rate he or she uses to value
future expenses, the expected effect on
resale value, and whether more efficient
models offer equivalent attributes such
as performance, carrying capacity,
reliability, quality, or other
characteristics.
Published literature has offered little
consensus about consumers’
willingness-to-pay for greater fuel
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fuel savings from purchasing a model
with higher fuel economy. Most studies
have relied on car buyers’ purchasing
behavior to estimate their willingnessto-pay for future fuel savings; a typical
approach has been to use ‘‘discrete
choice’’ models that relate individual
buyers’ choices among competing
vehicles to their purchase prices, fuel
economy, and other attributes (such as
performance, carrying capacity, and
reliability), and to infer buyers’
valuation of higher fuel economy from
the relative importance of purchase
prices and fuel economy.221 Empirical
estimates using this approach span a
wide range, extending from substantial
undervaluation of fuel savings to
significant overvaluation, thus making it
difficult to draw solid conclusions about
the influence of fuel economy on
vehicle buyers’ choices (see Helfand &
Wolverton, 2011; Green (2010) for
detailed reviews of these cross-sectional
studies). Because a vehicle’s price is
often correlated with its other attributes
(both measured and unobserved),
analysts have often used instrumental
variables or other approaches to address
endogeneity and other resulting
concerns (e.g., Barry, et al. 1995).
Despite these efforts, more recent
research has criticized these crosssectional studies; some have questioned
the effectiveness of the instruments they
use (Allcott & Greenstone, 2012), while
others have observed that coefficients
estimated using non-linear statistical
methods can be sensitive to the
optimization algorithm and starting
values (Knittel & Metaxoglou, 2014).
Collinearity (i.e., high correlations)
among vehicle attributes—most notably
among fuel economy, performance or
power, and vehicle size—and between
vehicles’ measured and unobserved
features also raises questions about the
reliability and interpretation of
coefficients that may conflate the value
of fuel economy with other attributes
(Sallee, et al., 2016; Busse, et al., 2013;
Allcott & Wozny, 2014; Allcott &
Greenstone, 2012; Helfand & Wolverton,
2011).
In an effort to overcome shortcomings
of past analyses, three recently
published studies rely on panel data
from sales of individual vehicle models
to improve their reliability in
identifying the association between
vehicles’ prices and their fuel economy
(Sallee, et al. 2016; Allcott & Wozny,
2014; Busse, et al., 2013). Although they
differ in certain details, each of these
221 In a typical vehicle choice model, the ratio of
estimated coefficients on fuel economy—or more
commonly, fuel cost per mile driven—and purchase
price is used to infer the dollar value buyers attach
to slightly higher fuel economy.

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analyses relates changes over time in
individual models’ selling prices to
fluctuations in fuel prices, differences in
their fuel economy, and increases in
their age and accumulated use, which
affects their expected remaining life,
and thus their market value. Because a
vehicle’s future fuel costs are a function
of both its fuel economy and expected
gasoline prices, changes in fuel prices
have different effects on the market
values of vehicles with different fuel
economy; comparing these effects over
time and among vehicle models reveals
the fraction of changes in fuel costs that
is reflected in changes in their selling
prices (Allcott & Wozny, 2014). Using
very large samples of sales enables these
studies to define vehicle models at an
extremely disaggregated level, which
enables their authors to isolate
differences in their fuel economy from
the many other attributes, including
those that are difficult to observe or
measure, that affect their sale prices.222
These studies point to a somewhat
narrower range of estimates than
suggested by previous cross-sectional
studies; more importantly, they
consistently suggest that buyers value a

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222 These studies rely on individual vehicle
transaction data from dealer sales and wholesale
auctions, which includes actual sale prices and
allows their authors to define vehicle models at a
highly disaggregated level. For instance, Allcott &
Wozny (2014) differentiate vehicles by
manufacturer, model or nameplate, trim level, body
type, fuel economy, engine displacement, number
of cylinders, and ‘‘generation’’ (a group of
successive model years during which a model’s
design remains largely unchanged). All three
studies include transactions only through mid-2008
to limit the effect of the recession on vehicle prices.
To ensure that the vehicle choice set consists of true
substitutes, Allcott & Wozny (2014) define the
choice set as all gasoline-fueled light-duty cars,
trucks, SUVs, and minivans that are less than 25
years old (i.e., they exclude vehicles where the
substitution elasticity is expected to be small).
Sallee et al. (2016) exclude diesels, hybrids, and
used vehicles with less than 10,000 or more than
100,000 miles.

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large proportion—and perhaps even
all—of the future savings that models
with higher fuel economy offer.223
Because they rely on estimates of fuel
costs over vehicles’ expected remaining
lifetimes, these studies’ estimates of
how buyers value fuel economy are
sensitive to the strategies they use to
isolate differences among individual
models’ fuel economy, as well as to
their assumptions about buyers’
discount rates and gasoline price
expectations, among others. Since
Anderson et al. (2013) find evidence
that consumers expect future gasoline
prices to resemble current prices, we
use this assumption to compare the
findings of the three studies and
examine how their findings vary with
the discount rates buyers apply to future
fuel savings.224
223 Killian & Sims (2006) and Sawhill (2008) rely
on similar longitudinal approaches to examine
consumer valuation of fuel economy except that
they use average values or list prices instead of
actual transaction prices. Since these studies
remain unpublished, their empirical results are
subject to change, and they are excluded from this
discussion.
224 Each of the studies makes slightly different
assumptions about appropriate discount rates.
Sallee et al. (2016) use five percent in their base
specification, while Allcott & Wozny (2014) rely on
six percent. As some authors note, a five to six
percent discount rate is consistent with current
interest rates on car loans, but they also
acknowledge that borrowing rates could be higher
in some cases, which could be justify higher
discount rates. Rather than assuming a specific
discount rate, Busse et al. (2013) directly estimate
implicit discount rates at which future fuel costs
would be fully internalized; they find discount rates
of six to 21% for used cars and one to 13% for new
cars at assumed demand elasticities ranging from
¥2 to ¥3. Their estimates can be translated into
the percent of fuel costs internalized by consumers,
assuming a particular discount rate. To make these
results more directly comparable to the other two
studies, we assume a range of discount rates and
uses the authors’ spreadsheet tool to translate their
results into the percent of fuel costs internalized
into the purchase price at each rate. Because Busse
et al. (2013) estimate the effects of future fuel costs
on vehicle prices separately by fuel economy

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As Table 1 indicates, Allcott & Wozny
(2014) find that consumers incorporate
55% of future fuel costs into vehicle
purchase decisions at a six percent
discount rate, when their expectations
for future gasoline prices are assumed to
reflect prevailing prices at the time of
their purchases. With the same
expectation about future fuel prices, the
authors report that consumers would
fully value fuel costs only if they apply
discount rates of 24% or higher.
However, these authors’ estimates are
closer to full valuation when using
gasoline price forecasts that mirror oil
futures markets because the petroleum
market expected prices to fall during
this period (this outlook reduces the
discounted value of a vehicle’s expected
remaining lifetime fuel costs). With this
expectation, Allcott & Wozny (2014)
find that buyers value 76% of future
cost savings (discounted at six percent)
from choosing a model that offers higher
fuel economy, and that a discount rate
of 15% would imply that they fully
value future cost savings. Sallee et al.
(2016) begin with the perspective that
buyers fully internalize future fuel costs
into vehicles’ purchase prices and
cannot reliably reject that hypothesis;
their base specification suggests that
changes in vehicle prices incorporate
slightly more than 100% of changes in
future fuel costs. For discount rates of
five to six percent, the Busse et al.
(2013) results imply that vehicle prices
reflect 60 to 100% of future fuel costs.
As Table II–31 suggests, higher private
discount rates move all of the estimates
closer to full valuation or to overvaluation, while lower discount rates
imply less complete valuation in all
three studies.
quartile, these results depend on which quartiles of
the fuel economy distribution are compared; our
summary shows results using the full range of
quartile comparisons.

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The studies also explore the
sensitivity of the results to other
parameters that could influence their
results. Busse et al. (2013) and Allcott
& Wozny (2014) find that relying on
data that suggest lower annual vehicle
use or survival probabilities, which
imply that vehicles will not last as long,
moves their estimates closer to full
valuation, an unsurprising result
because both reduce the changes in
expected future fuel costs caused by fuel
price fluctuations. Allcott & Wozny’s
(2014) base results rely on an
instrumental variables estimator that
groups miles-per-gallon (MPG) into two
quantiles to mitigate potential
attenuation bias due to measurement
error in fuel economy, but they find that
greater disaggregation of the MPG
groups implies greater undervaluation
(for example, it reduces the 55%
estimated reported in Table 1 to 49%).
Busse et al. (2013) allow gasoline prices
to vary across local markets in their
main specification; using national
average gasoline prices, an approach
more directly comparable to the other
studies, results in estimates that are
closer to or above full valuation. Sallee
et al. (2016) find modest undervaluation
by vehicle fleet operators or
manufacturers making large-scale
purchases, compared to retail dealer
sales (i.e., 70 to 86%).
Since they rely predominantly on
changes in vehicles’ prices between

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repeat sales, most of the valuation
estimates reported in these studies
apply most directly to buyers of used
vehicles. Only Busse et al. (2013)
examine new vehicle sales; they find
that consumers value between 75 to
133% of future fuel costs for new
vehicles, a higher range than they
estimate for used vehicles. Allcott &
Wozny (2014) examine how their
estimates vary by vehicle age and find
that fluctuations in purchase prices of
younger vehicles imply that buyers
whose fuel price expectations mirror the
petroleum futures market value a higher
fraction of future fuel costs: 93% for
one- to three-year-old vehicles,
compared to their estimate of 76% for
all used vehicles assuming the same
price expectation.225
Accounting for differences in their
data and estimation procedures, the
three studies described here suggest that
car buyers who use discount rates of
five to six percent value at least half—
and perhaps all—of the savings in future
fuel costs they expect from choosing
models that offer higher fuel economy.
225 Allcott & Wozny (2014) and Sallee, et al.
(2016) also find that future fuel costs for older
vehicles are substantially undervalued (26–30%).
The pattern of Allcott and Wozny’s results for
different vehicle ages is similar when they use retail
transaction prices (adjusted for customer cash
rebates and trade-in values) instead of wholesale
auction prices, although the degree of valuation
falls substantially in all age cohorts with the
smaller, retail price based sample.

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Perhaps more important in assessing the
case for regulating fuel economy, one
study suggests that buyers of new cars
and light trucks value three-quarters or
more of the savings in future fuel costs
they anticipate from purchasing highermpg models, although this result is
based on more limited information.
In contrast, previous regulatory
analyses of fuel economy standards
implicitly assumed that buyers
undervalue even more of the benefits
they would experience from purchasing
models with higher fuel economy so
that without increases in fuel economy
standards little improvement would
occur, and the entire value of fuel
savings from raising CAFE standards
represented private benefits to car and
light truck buyers themselves. For
instance, in the EPA analysis of the
2017–2025 model year greenhouse gas
emission standards, fuel savings alone
added up to $475 billion (at three
percent discount rate) over the lifetime
of the vehicles, far outweighing the
compliance costs: $150 billion). The
assertion that buyers were unwilling to
take voluntary advantage of this
opportunity implies that collectively,
they must have valued less than a third
($150 billion/$475 billion = 32%) of the
fuel savings that would have resulted
from those standards.226 The evidence
226 In fact, those earlier analyses assumed that
new car and light truck buyers attach relatively

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reviewed here makes that perspective
extremely difficult to justify and would
call into question any analysis that
claims to show large private net benefits
for vehicle buyers.
What analysts assume about
consumers’ vehicle purchasing
behavior, particularly about potential
buyers’ perspectives on the value of
increased fuel economy, clearly matters
a great deal in the context of benefit-cost
analysis for fuel economy regulation. In
light of recent evidence on this
question, a more nuanced approach
than assuming that buyers drastically
undervalue benefits from higher fuel
economy, and that as a consequence,
these benefits are unlikely to be realized
without stringent fuel economy
standards, seems warranted. One
possible approach would be to use a
baseline scenario where fuel economy
levels of new cars and light trucks
reflected full (or nearly so) valuation of
fuel savings by potential buyers in order
to reveal whether setting fuel economy
standards above market-determined
levels could produce net social benefits.
Another might be to assume that, unlike
in the agencies’ previous analyses,
where buyers were assumed to greatly
undervalue higher fuel economy under
the baseline but to value it fully under
the proposed standards, buyers value
improved fuel economy identically
under both the baseline scenario and
with stricter CAFE standards in place.
The agencies ask for comment on these
and any alternative approaches they
should consider for valuing fuel savings,
new peer-reviewed evidence on vehicle
buyers’ behavior that casts light on how
they value improved fuel economy, the
appropriate private discount rate to
apply to future fuel savings, and thus
the degree to which private fuel savings
should be considered as private benefits
of increasing fuel economy standards.

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(b) Sales Data and Relevant
Macroeconomic Factors
Developing a procedure to predict the
effects of changes in prices and
attributes of new vehicles is
complicated by the fact that their sales
are highly pro-cyclical—that is, they are
very sensitive to changes in
macroeconomic conditions—and also
statistically ‘‘noisy,’’ because they
reflect the transient effects of other
factors such as consumers’ confidence
in the future, which can be difficult to
observe and measure accurately. At the
same time, their average sales price
little value to higher fuel economy, since their
baseline scenarios assumed that fuel economy
levels would not increase in the absence of
progressively tighter standards.

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tends to move in parallel with changes
in economic growth; that is, average
new vehicle prices tend to be higher
when the total number of new vehicles
sold is increasing and lower when the
total number of new sales decreases
(typically during periods of low
economic growth or recessions). Finally,
counts of the total number of new cars
and light trucks that are sold do not
capture shifts in demand among vehicle
size classes or body styles (‘‘market
segments’’); nor do they measure
changes in the durability, safety, fuel
economy, carrying capacity, comfort, or
other aspects of vehicles’ quality.
The historical series of new light-duty
vehicle sales exhibits cyclic behavior
over time that is most responsive to
larger cycles in the macro economy—
but has not increased over time in the
same way the population, for example,
has. While U.S. population has grown
over 35 percent since 1980, the
registered vehicle population has grown
at an even faster pace—nearly doubling
between 1980 and 2015.227 But annual
vehicle sales did not grow at a similar
pace –even accounting for the cyclical
nature of the industry. Total new lightduty sales prior to the 2008 recession
climbed as high as 16 million, though
similarly high sales years occurred in
the 1980’s and 1990’s as well. In fact,
when considering a 10-year moving
average to smooth out the effect of
cycles, most 10-year averages between
1992 and 2015 are within a few percent
of the 10-year average in 1992. And
although average transaction prices for
new vehicles have been rising steadily
since the recession ended, prices are not
yet at historical highs when adjusted for
inflation. The period of highest
inflation-adjusted transaction prices
occurred from 1996–2006, when the
average transaction price for a new
light-duty vehicle was consistently
higher than the price in 2015.
In an attempt to overcome these
analytical challenges, various
approaches were experimented with to
predict the response of new vehicle
sales to the changes in prices, fuel
economy, and other features. These
included treating new vehicle demand
as a product of changes in total demand
for vehicle ownership and demand
necessary to replace used vehicles that
are retired, analyzing total expenditures
227 There are two measurements of the size of the
registered vehicle population that are considered to
be authoritative. One is produced by the Federal
Highway Adminstration, and the other by R.L. Polk
(now part of IHS). The Polk measurement shows
fleet growth between 1980 and 2015 of about 85%,
while the FHWA measurement shows a slower
growth rate over that period; only about 60%. Both
are still considerably larger than the growth in new
vehicle sales over the same period.

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to purchase new cars and light trucks in
conjunction with the total number sold,
and other approaches. However, none of
these methods offered a significant
improvement over estimating the total
number of vehicles sold directly from its
historical relationship to directly
measurable factors such as their average
sales price, macroeconomic variables
such as GDP or Personal Disposable
Income, U.S. labor force participation,
and regularly published surveys of
consumer sentiment or confidence.
Quarterly, rather than annual data on
total sales of new cars and light trucks,
their average selling price, and
macroeconomic variables was used to
develop an econometric model of sales,
in order to increase the number of
observations and more accurately
capture the causal effects of individual
explanatory variables. Applying
conventional data diagnostics for timeseries economic data revealed that most
variables were non-stationary (i.e., they
reflected strong underlying time trends)
and displayed unit roots, and statistical
tests revealed co-integration between
the total vehicle sales—the model’s
dependent variable—and most
candidate explanatory variables.
(c) Current Estimation of Sales Impacts
To address the complications of the
time series data, the analysis estimated
an autoregressive distributed-lag (ARDL)
model that employs a combination of
lagged values of its dependent
variable—in this case, last year’s and the
prior year’s vehicle sales—and the
change in average vehicle price,
quarterly changes in the U.S. GDP
growth rate, as well as current and
lagged values of quarterly estimates of
U.S. labor force participation. The
number of lagged values of each
explanatory variable to include was
determined empirically (using the
Bayesian information criterion), by
examining the effects of including
different combinations of their lagged
values on how well the model
‘‘explained’’ historical variation in car
and light truck sales.
The results of this approach were
encouraging: The model’s predictions fit
the historical data on sales well, each of
its explanatory variables displayed the
expected effect on sales, and analysis of
its unexplained residual terms revealed
little evidence of autocorrelation or
other indications of statistical problems.
The model coefficients suggest that
positive GDP growth rates and increases
in labor force participation are both
indicators of increases in new vehicle
sales, while positive changes in average
new vehicle price reduce new sales.
However, the magnitude of the

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coefficient on change in average price is
not as determinative of total sales as the
other variables.
Based on the model, a $1,000 increase
in the average new vehicle price causes
approximately 170,000 lost units in the
first year, followed by a reduction of
another 600,000 units over the next ten
years as the initial sales decrease
propagates over time through the lagged
variables and their coefficients. The
price elasticity of new car and light
truck sales implied by alternative
estimates of the model’s coefficients
ranged from ¥0.2 to ¥0.3—meaning
that changes in their prices have
moderate effects on total sales—which
contrasts with estimates of higher
sensitivity to prices implied by some
models.228 The analysis was unable to
incorporate any measure of new car and
light truck fuel economy in the model
that added to its ability to explain
historical variation in sales, even after
experimenting with alternative
measures of such as the unweighted and
sales-weighted averages fuel economy of
models sold in each quarter, the level of
fuel economy they were required to
achieve, and the change in their fuel
economy from previous periods.
Despite the evidence in the literature,
summarized above, that consumers
value most, if not all, of the fuel
economy improvements when
purchasing new vehicles, the model
described here operates at too high a
level of aggregation to capture these
preferences. By modeling the total
number of new vehicles sold in a given
year, it is necessary to quantify
important measures, like sales price or
fuel economy, by averages. Our model
operates at a high level of aggregation,
where the average fuel economy
represents an average across many
vehicle types, usage profiles, and fuel
economy levels. In this context, the
average fuel economy was not a
meaningful value with respect to its
influence on the total number of new
vehicles sold. A number of recent
studies have indeed shown that
consumers value fuel savings (almost)
fully. Those studies are frequently based
on large datasets that are able to control
for all other vehicle attributes through a
variety of econometric techniques. They
represent micro-level decisions, where a
228 Effects on the used car market are accounted
for separately.

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buyer is (at least theoretically) choosing
between a more or less efficient version
of a pickup truck (for example) that is
otherwise identical. In an aggregate
sense, the average is not comparable to
the decision an individual consumer
faces.
Estimating the sales response at the
level of total new vehicle sales likely
fails to address valid concerns about
changes to the quality or attributes of
new vehicles sold—both over time and
in response to price increases resulting
from CAFE standards. However,
attempts to address such concerns
would require significant additional
data, new statistical approaches, and
structural changes to the CAFE model
over several years. It is also the case that
using absolute changes in the average
price may be more limited than another
characterization of price that relies on
distributions of household income over
time or percentage change in the new
vehicle price. The former would require
forecasting a deeply uncertain quantity
many years into the future, and the
latter only become relevant once the
simulation moves beyond the
magnitude of observed price changes in
the historical series. Future versions of
this model may use a different
characterization of cost that accounts for
some of these factors if their inclusion
improves the model estimation and
corresponding forecast projections are
available.
The changes in selling prices, fuel
economy, and other features of cars and
light trucks produced during future
model years that result from
manufacturers’ responses to lower CAFE
and GHG emission standards are likely
to affect both sales of individual models
and the total number of new vehicles
sold. Because the values of changes in
fuel economy and other features to
potential buyers are not completely
understood; however, the magnitude,
and possibly even the direction, of their
effect on sales of new vehicles is
difficult to anticipate. On balance, it is
reasonable to assume that the changes in
prices, fuel economy, and other
attributes expected to result from their
proposed action to amend and establish
fuel economy and GHG emission
standards are likely to increase total
sales of new cars and light trucks during
future model years. Please provide
comment on the relationship between

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price increases, fuel economy, and new
vehicle sales, as well as methods to
appropriately account for these
relationships.
(d) Projecting New Vehicle Sales and
Comparisons to Other Forecasts
The purpose of the sales response
model is to allow the CAFE model to
simulate new vehicle sales in a given
future model year, accounting for the
impact of a regulatory alternative’s
stringency on new vehicle prices (in a
macro-economic context that is
identical across alternatives). In order to
accomplish this, it is important that the
model of sales response be dynamically
stable, meaning that it responds to
shocks not by ‘‘exploding,’’ increasing
or decreasing in a way that is
unbounded, but rather returns to a
stable path, allowing the shock to
dissipate. The CAFE model uses the
sales model described above to
dynamically project future sales; after
the first year of the simulation, lagged
values of new vehicle sales are those
that were produced by the model itself
rather than observed. The sales response
model constructed here uses two lagged
dependent variables and simple
econometric conditions determine if the
model is dynamically stable. The
coefficients of the one-year lag and the
two-year lag, b1 and b2, respectively
must satisfy three conditions. Their sum
must be less than one, b2 ¥ b1 <1, and
the absolute value of b2 must be less
than one. The coefficients of this model
satisfy all three conditions.
Using the Augural CAFE standards as
the baseline, it is possible to produce a
series of future total sales as shown in
Table–II–32. For comparison, the table
includes the calculated total light-duty
sales of a proprietary forecast purchased
to support the 2016 Draft TAR analysis,
the total new light-duty sales in EIA’s
2017 Annual Energy Outlook, and a
(short) forecast published in the Center
for Automotive Research’s Q4 2017
Automotive Outlook. All of the forecasts
in Table–II–32 assume the Augural
Standards are in place through MY
2025, though assumptions about the
costs required to comply with them
likely differ. As the table shows, despite
differences among them, the
dynamically produced sales projection
from the CAFE model is not
qualitatively different from the others.

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While this forecast projects a
relatively high, but flat, level of new
vehicle sales into the future, it is worth
noting that it continues another trend
observed in the historical data. The time
series of annual new vehicle sales is
volatile from year to year, but multi-year
averages are less so being sufficient to
wash out the variation associated with
them peaks and valleys of the series.
Despite the fact that the moving average
annual new vehicle sales has been
growing over the last four decades, it
has not kept pace with U.S. population
growth. Data from the Federal Reserve
Bank of St. Louis shows that the percapita sales of new vehicles peaked in
1986 and has declined more than 25%
from this peak to today’s level.231 While
the sales projection in Table–II–32
would represent a historically high
average of new vehicle sales over the
analysis period, it would not be
sufficient to reverse the trend of
declining per-capita sales of new
229 Out of necessity, the analysis in today’s rule
conflates production year (or ‘‘model year’’) and
calendar year. The volumes cited in the CAFE
model forecast represent forecasted production
volumes for those model years, while the other
represent calendar year sales (rather than
production)—during which two, or possibly three,
different model year vehicles are sold. In the long
run, the difference is not important. In the early
years, there are likely to be discrepancies.
230 U.S. Total Sales by Make, Automotive News,
http://www.autonews.com/section/datalist18 (last
visited June 22, 2018).
231 Mislinski, J. Light Vehicle Sales Per Capita:
Our Latest Look at the Long-Term Trend, Advisor
Perspectives (June 1, 2018), https://
www.advisorperspectives.com/dshort/updates/
2018/05/01/light-vehicle-sales-per-capita-our-latestlook-at-the-long-term-trend.

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vehicles during the analysis period,
though it would continue the trend at a
slower rate.
In addition to the statistical model
that estimates the response of total new
vehicle sales to changes in the average
new vehicle price, the CAFE model
incorporates a dynamic fleet share
model that modifies the light truck (and,
symmetrically, passenger car) share of
the new vehicle market. A version of
this model first appeared in the 2012
final rule, when this fleet share
component was introduced to ensure
greater internal consistency within
inputs in the uncertainty analysis. For
today’s analysis, this dynamic fleet
share is enabled throughout the analysis
of alternatives.
The dynamic fleet share model is a
series of difference equations that
determine the relative share of light
trucks and passenger cars based on the
average fuel economy of each, the fuel
price, and average vehicle attributes like
horsepower and vehicle mass (the latter
of which explicitly evolves as a result of
the compliance simulation). While this
model was taken from EIA’s National
Energy Modeling System (NEMS), it is
applied at a different level. Rather than
apply the shares based on the regulatory
class distinction, the CAFE model
applies the shares to body-style. This is
done to account for the large-scale shift
in recent years to crossover utility
vehicles that have model variants in
both the passenger car and light truck
regulatory fleets. The agencies have
always modified their static forecasts of
new vehicle sales to reflect the PC/LT
split present in the Annual Energy

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Outlook; this integration continues that
approach in a way that ensures greater
internal consistency when simulating
multiple regulatory alternatives (and
conducting sensitivity analysis on any
of the factors that influence fleet share).
(e) Vehicle Choice Models as an
Alternative Method To Estimate New
Vehicle Sales
Another potential option to estimate
future new vehicle sales would be to use
a full consumer choice model. The
agencies simulate compliance with
CAFE and CO2 standards for each
manufacturer using a disaggregated
representation of its regulated vehicle
fleets. This means that each
manufacturer may have hundreds of
vehicle model variants (e.g., the Honda
Civic with the 6-cylinder engine, and
the Honda Civic with the 4-cylinder
engine would each be treated as
different, in some ways, during the
compliance simulation).232 While the
analysis accounts for a wide variety of
attributes across these vehicles, only a
few of them change during the
compliance simulation. However, all of
those attributes are relevant in the
context of consumer choice models.
Aside from the computational
intensity of simulating new vehicle
sales at the level of individual models—
for all manufacturers, under each
regulatory alternative, over the next
decade or more—it would be necessary
to include additional relationships
232 For more detail about the compliance
simulation and manufacturer fleet representation,
see Section II.G.

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about how consumers trade off among
vehicle attributes, which types of
consumers prefer which types of
attributes (and how much), and how
manufacturers might strategically price
these modified vehicles. This requires a
strategic pricing model, which each
manufacturer has and would likely be
unwilling to share. Some of this
strategic pricing behavior occurs on
small time-scale through the use of
dealer incentives, rebates on specific
models, and creative financing offers.
When simulating compliance at the
annual scale, it is effectively impossible
to account for these types of strategic
decisions.
It is also true consumers have
heterogeneous preferences that change
over time and determine willingness-topay for a variety of vehicle attributes.
These preferences change in response to
marketing, distribution, pricing, and
product strategies that manufacturers
may change over time. With enough
data, a consumer choice model could
stratify new vehicle buyers into types
and attempt to measure the strength of
each type’s preference for fuel economy,
acceleration, safety rating, perceived

quality and reliability, interior volume,
or comfort. However, other factors also
influence customers’ purchase decision,
and some of these can be challenging to
model. Consumer proximity to
dealerships, quality of service and
customer experience at dealerships,
availability and terms of financing, and
basic product awareness may
significantly factor into sales success.
Manufacturers’ marketing choices
may significantly and unpredictably
affect sales. Ad campaigns may increase
awareness in the market, and campaigns
may reposition consumers’ perception
of the brands and products. For
example, in 2011 the Volkswagen Passat
featured an ad with a child in a Darth
Vader costume (and showcased remote
start technology on the Passat). In MY
2012, Kia established the Kia Soul with
party rocking, hip-hop hamster
commercials showcasing push-button
ignition, a roomy interior, and design
features in the brake lights. Both
commercials raised awareness and
highlighted basic product features. Each
commercial also impressed
demographic groups with pop culture
references, product placement, and co-

branding. While the marketing budget of
individual manufacturers may help a
consumer choice model estimate market
share for a given brand, estimating the
impact of a given campaign on new
sales is more challenging as consumers
make purchasing decisions based upon
their own needs and desires.
Modelers must understand how
consumers and commercial buyers
select vehicles in order to effectively
develop and implement a consumer
choice model in a compliance
simulation. Consumers purchase
vehicles for a variety of reasons such as
family need, need for more space, new
technology, changes to income and
affordability of a new vehicle, improved
fuel economy, operating costs of current
vehicles, and others. Once committed to
buying a vehicle, consumers use
different processes to narrow down their
shopping list. Consumer choice decision
attributes include factors both related
and not related to the vehicle design.
The vehicle’s utility for those attributes
is researched across many different
information sources as listed in the table
below.

An objective, attribute-based
consumer choice model could lead to
projected swings in manufacturer
market shares and individual model
volumes. The current approach
simulates compliance for each
manufacturer assuming that it produces
the same set of vehicles that it produced
in the initial year of the simulation (MY
2016 in today’s analysis). If a consumer
choice model were to drive projected
sales of a given vehicle model below
some threshold, as consumers have
done in the real market, the simulation
currently has no way to generate a new
vehicle model to take its place. As
demand changes across specific market
segments and models, manufacturers
adapt by supplying new vehicle
nameplates and models (e.g., the
proliferation of crossover utility
vehicles in recent years). Absent that
flexibility in the compliance simulation,
even the more accurate consumer choice
model may produce unrealistic

projections of future sales volumes at
the model, segment, or manufacturer
level.
Comment is sought on the
development and use of potential
consumer choice model in compliance
simulations. Comment is also sought on
the appropriate breadth, depth, and
complexity of considerations in a
consumer choice model.

not so stringent as to’’ lead to ‘‘adverse
economic consequences, such as a
significant loss of jobs or unreasonable
elimination of consumer choice.’’ 233
EPA similarly conducted an industry
employment analysis under the broad
authority granted to the agency under
the Clean Air Act.234 Both agencies
recognized the uncertainties inherent in
estimating industry employment
impacts; in fact, both agencies dedicated
a substantial amount of discussion to
uncertainty in industry employment
analyses in the 2012 final rule for MYs
2017 and beyond.235 Notwithstanding
these uncertainties, CAFE and CO2
standards do impact industry labor
hours, and providing the best analysis
practicable better informs stakeholders

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(f) Industry Employment Baseline
(Including Multiplier Effect) and Data
Description
In the first two joint CAFE/CO2
rulemakings, the agencies considered an
analysis of industry employment
impacts in some form in setting both
CAFE and emissions standards; NHTSA
conducted an industry employment
analysis in part to determine whether
the standards the agency set were
economically practicable, that is,
whether the standards were ‘‘within the
financial capability of the industry, but

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233 67

FR 77015, 77021 (Dec. 16, 2002).
George E. Warren Corp. v. EPA, 159 F.3d
616, 623–624 (D.C. Cir. 1998) (ordinarily
permissible for EPA to consider factors not
specifically enumerated in the Act).
235 See 77 FR 62624, 62952, 63102 (Oct. 15, 2012).
234 See

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and the public about the standards’
impact than would omitting any
estimates of potential labor impacts.
Today many of the effects that were
previously qualitatively identified, but
not considered, are quantified. For
instance, in the PRIA for the 2017–2025
rule EPA identified ‘‘demand effects,’’
‘‘cost effects,’’ and ‘‘factor shift effects’’
as important considerations for industry
labor, but the analysis did not attempt
to quantify either the demand effect or
the factor shift effect.236 Today’s
industry labor analysis quantifies direct
labor changes that were qualitatively
discussed previously.
Previous analyses and new
methodologies to consider direct labor
effects on the automotive sector in the
United States were improved upon and
developed. Potential changes that were
evaluated include (1) dealership labor
related to new light duty vehicle unit
sales; (2) changes in assembly labor for
vehicles, for engines and for
transmissions related to new vehicle
unit sales; and (3) changes in industry
labor related to additional fuel savings
technologies, accounting for new
vehicle unit sales. All automotive labor
effects were estimated and reported at a
national level,237 in job-years, assuming
2,000 hours of labor per job-year.
The analysis estimated labor effects
from the forecasted CAFE model
technology costs and from review of
automotive labor for the MY 2016 fleet.
For each vehicle in the CAFE model
analysis, the locations for vehicle
assembly, engine assembly, and
transmission assembly and estimated
labor in MY 2016 were recorded. The
percent U.S. content for each vehicle
was also recorded. Not all parts are
made in the United States, so the
analysis also took into account the
percent U.S. content for each vehicle as
manufacturers add fuel-savings
technologies. As manufacturers added
fuel-economy technologies in the CAFE
model simulations, the analysis
assumed percent U.S. content would
remain constant in the future, and that
the U.S. labor added would be
proportional to U.S. content. From this
foundation, the analysis forecasted
automotive labor effects as the CAFE
model added fuel economy technology
and adjusted future sales for each
vehicle.
236 Regulatory Impact Analysis: Final Rulemaking
for 2017–2025 Light-Duty Vehicle Greenhouse Gas
Emission Standards and Corporate Average Fuel
Economy Standards, U.S. EPA at 8–24 to 8–32
(Aug. 2012).
237 The agencies recognize a few local production
facilities may contribute meaningfully to local
economies, but the analysis reported only on
national effects.

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The analysis also accounts for sales
projections in response to the different
regulatory alternatives; the labor
analysis considers changes in new
vehicle prices and new vehicle sales (for
further discussion of the sales model,
see Section 2.E). As vehicle prices rise,
the analysis expected consumers to
purchase fewer vehicles than they
would have at lower prices. As
manufacturers sell fewer vehicles, the
manufacturers may need less labor to
produce the vehicles and less labor to
sell the vehicles. However, as
manufacturers add equipment to each
new vehicle, the manufacturers will
require human resources to develop,
sell, and produce additional fuel-saving
technologies. The analysis also accounts
for the potential that new standards
could shift the relative shares of
passenger cars and light trucks in the
overall fleet (see Section 2.E); insofar as
different vehicles involved different
amounts of labor, this shifting impacts
the quantity of estimated labor. The
CAFE model automotive labor analysis
takes into account reduction in vehicle
sales, shifts in the mix of passenger cars
and light trucks, and addition of fuelsavings technologies.
For today’s analysis, it was assumed
that some observations about the
production of MY 2016 vehicles would
carry forward, unchanged into the
future. For instance, assembly plants
would remain the same as MY 2016 for
all products now, and in the future. The
analysis assumed percent U.S. content
would remain constant, even as
manufacturers updated vehicles and
introduced new fuel-saving
technologies. It was assumed that
assembly labor hours per unit would
remain at estimated MY 2016 levels for
vehicles, engines, and transmissions,
and the factor between direct assembly
labor and parts production jobs would
remain the same. When considering
shifts from one technology to another,
the analysis assumed revenue per
employee at suppliers and original
equipment manufacturers would remain
in line with MY 2016 levels, even as
manufacturers added fuel-saving
technologies and realized cost
reductions from learning.
The analysis focused on automotive
labor because adjacent employment
factors and consumer spending factors
for other goods and services are
uncertain and difficult to predict. The
analysis did not consider how direct
labor changes may affect the macro
economy and possibly change
employment in adjacent industries. For
instance, the analysis did not consider
possible labor changes in vehicle
maintenance and repair, nor did it

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consider changes in labor at retail gas
stations. The analysis did not consider
possible labor changes due to raw
material production, such as production
of aluminum, steel, copper and lithium,
nor did the agencies consider possible
labor impacts due to changes in
production of oil and gas, ethanol, and
electricity. The analysis did not analyze
effects of how consumers could spend
money saved due to improved fuel
economy, nor did the analysis assess the
effects of how consumers would pay for
more expensive fuel savings
technologies at the time of purchase;
either could affect consumption of other
goods and services, and hence affect
labor in other industries. The effects of
increased usage of car-sharing, ridesharing, and automated vehicles were
not analyzed. The analysis did not
estimate how changes in labor from any
industry could affect gross domestic
product and possibly affect other
industries as a result.
Finally, no assumptions were made
about full-employment or not fullemployment and the availability of
human resources to fill positions. When
the economy is at full employment, a
fuel economy regulation is unlikely to
have much impact on net overall U.S.
employment; instead, labor would
primarily be shifted from one sector to
another. These shifts in employment
impose an opportunity cost on society,
approximated by the wages of the
employees, as regulation diverts
workers from other activities in the
economy. In this situation, any effects
on net employment are likely to be
transitory as workers change jobs (e.g.,
some workers may need to be retrained
or require time to search for new jobs,
while shortages in some sectors or
regions could bid up wages to attract
workers). On the other hand, if a
regulation comes into effect during a
period of high unemployment, a change
in labor demand due to regulation may
affect net overall U.S. employment
because the labor market is not in
equilibrium. Schmalansee and Stavins
point out that net positive employment
effects are possible in the near term
when the economy is at less than full
employment due to the potential hiring
of idle labor resources by the regulated
sector to meet new requirements (e.g., to
install new equipment) and new
economic activity in sectors related to
the regulated sector longer run, the net
effect on employment is more difficult
to predict and will depend on the way
in which the related industries respond
to the regulatory requirements. For that
reason, this analysis does not include
multiplier effects but instead focuses on

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labor impacts in the most directly
affected industries. Those sectors are
likely to face the most concentrated
labor impacts.
Comment is sought on these
assumptions and approaches in the
labor analysis.
4. Estimating Labor for Fuel Economy
Technologies, Vehicle Components,
Final Assembly, and Retailers
The following sections discuss the
approaches to estimating factors related
to dealership labor, final assembly labor
and parts production, and fuel economy
technology labor.

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(a) Dealership Labor
The analysis evaluated dealership
labor related to new light-duty vehicle
sales, and estimated the labor hours per
new vehicle sold at dealerships,
including labor from sales, finance,
insurance, and management. The effect
of new car sales on the maintenance,
repair, and parts department labor is
expected to be limited, as this need is
based on the vehicle miles traveled of
the total fleet. To estimate the labor
hours at dealerships per new vehicle
sold, the National Automobile Dealers
Association 2016 Annual Report, which
provides franchise dealer employment
by department and function, was
referenced.238 The analysis estimated
that slightly less than 20% of dealership
employees’ work relates to new car sales
(versus approximately 80% in service,
parts, and used car sales), and that on
average dealership employees working
on new vehicle sales labor for 27.8
hours per new vehicle sold.
(b) Final Assembly Labor and Parts
Production
How the quantity of assembly labor
and parts production labor for MY 2016
vehicles would increase or decrease in
the future as new vehicle unit sales
increased or decreased was estimated.
Specific assembly locations for final
vehicle assembly, engine assembly, and
transmission assembly for each MY
2016 vehicle were identified. In some
cases, manufacturers assembled
products in more than one location, and
the analysis identified such products
and considered parallel production in
the labor analysis.
The analysis estimated industry
average direct assembly labor per
vehicle (30 hours), per engine (four
hours), and per transmission (five
hours) based on a sample of U.S.
238 NADA Data 2016: Annaul Financial Profile of
America’s Franchised New-Car Dealerships,
National Automobile Dealers Association, https://
www.nada.org/2016NADAdata/ (last visited June
22, 2018).

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assembly plant employment and
production statistics and other publicly
available information. The analysis
recognizes that some plants may use
less labor than the analysis estimates to
produce the vehicle, the engine, or the
transmission, and other plants may have
used more labor. The analysis used the
assembly locations and industry
averages for labor per unit to estimate
U.S. assembly labor hours for each
vehicle. U.S. assembly labor hours per
vehicle ranged from as high as 39 hours
if the manufacturer assembled the
vehicle, engine, and transmission at
U.S. plants, to as low as zero hours if
the manufacturer imported the vehicle,
engine, and transmission.
The analysis also considered labor for
part production in addition to labor for
final assembly. Motor vehicle and
equipment manufacturing labor
statistics from the U.S. Census Bureau,
the Bureau of Labor Statistics,239 and
other publicly available sources were
surveyed. Based on these sources, the
analysis noted that the historical
average ratio of vehicle assembly
manufacturing employment to
employment for total motor vehicle and
equipment manufacturing for new
vehicles remained roughly constant over
the period from 2001 through 2013, at
a ratio of 5.26. Observations from 2001–
2013 spanned many years, many
combinations of technologies and
technology trends, and many economic
conditions, yet the ratio remained about
the same. Accordingly, the analysis
scaled up estimated U.S. assembly labor
hours by a factor of 5.26 to consider U.S.
parts production labor in addition to
assembly labor for each vehicle.
The industry estimates for vehicle
assembly labor and parts production
labor for each vehicle scaled up or down
as unit sales scaled up or down over
time in the CAFE model.
(c) Fuel Economy Technology Labor
As manufacturers spend additional
dollars on fuel-saving technologies,
parts suppliers and manufacturers
require human resources to bring those
technologies to market. Manufacturers
may add, shift, or replace employees in
ways that are difficult for the agencies
to predict in response to adding fuelsavings technologies; however, it is
expected that the revenue per labor hour
at original equipment manufacturers
(OEMs) and suppliers will remain about
the same as in MY 2016 even as
industry includes additional fuel-saving
technology.
To estimate the average revenue per
labor hour at OEMs and suppliers, the
239 NAICS

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43079

analysis looked at financial reports from
publicly traded automotive
businesses.240 Based on recent figures, it
was estimated that OEMs would add
one labor year per $633,066 revenue 241
and that suppliers would add one labor
year per $247,648 in revenue.242 These
global estimates are applied to all
revenues, and U.S. content is applied as
a later adjustment. In today’s analysis, it
was assumed these ratios would remain
constant for all technologies rather than
that the increased labor costs would be
shifted toward foreign countries.
Comment is sought on the realism of
this assumption.
(d) Labor Calculations
The analysis estimated the total labor
as the sum of three components:
Dealership hours, final assembly and
parts production, and labor for fueleconomy technologies (at OEM’s and
suppliers). The CAFE model calculated
additional labor hours for each vehicle,
based on current vehicle manufacturing
locations and simulation outputs for
additional technologies, and sales
changes. The analysis applied some
constants to all vehicles,243 but other
constants were vehicle specific,244 or
year specific for a vehicle.245
While a multiplier effect of all U.S.
automotive related jobs on non-auto
related U.S. jobs was not considered for
today’s analysis, the analysis did
program a ‘‘global multiplier’’ that can
be used to scale up or scale down the
total labor hours. This multiplier exists
in the parameters file, and for today’s
analysis the analysis set the value at
1.00.
5. Additional Costs and Benefits
Incurred by New Vehicle Buyers
Some costs of purchasing and owning
a new or used vehicle scale with the
240 The analysis considered suppliers that won
the Automotive News ‘‘PACE Award’’ from 2013–
2017, covering more than 40 suppliers, more than
30 of which are publicly traded companies.
Automotive News gives ‘‘PACE Awards’’ to
innovative manufacturers, with most recent
winners earning awards for new fuel-savings
technologies.
241 The analysis assumed incremental OEM
revenue as the retail price equivalent for
technologies, adjusting for changes in sales volume.
242 The analysis assumed incremental supplier
revenue as the technology cost for technologies
before retail price equivalent mark-up, adjusting for
changes in sales volume.
243 The analysis applied the same assumptions to
all manufacturers for annual labor hours per
employee, dealership hours per unit sold, OEM
revenue per employee, supplier revenue per
employee, and factor for the jobs multiplier.
244 The analysis made vehicle specific
assumptions about percent U.S. content and U.S.
assembly employment hours.
245 The analysis estimated technology cost for
each vehicle, for each year based on the technology
content applied in the CAFE model, year-by-year.

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value of the vehicle. Where fuel
economy standards increase the
transaction price of vehicles, they will
affect both the absolute amount paid in
sales tax and the average amount of
financing required to purchase the
vehicle. Further, where they increase
the MSRP, they increase the appraised
value upon which both value-related
registration fees and a portion of
insurance premiums are based. The
analysis assumes that the transaction
price is a set share of the MSRP, which
allows calculation of these factors as
shares of MSRP. Below the assumptions
made about how each of these
additional costs of vehicle purchase and
ownership scale with the MSRP and
how the analysis arrived at these
assumptions are discussed.

national weighted-average sales tax rate
almost identical to that resulting from
the use of Census population estimates
as weights, just slightly above 5.5%. The
analysis opted to utilize Census
population rather than the registrationbased proxy of new vehicle sales as the
basis for computing this weighted
average, as the end results were
negligibly different and the analytical
approach involving new vehicle
registrations had not been as thoroughly
reviewed. Note: Sales taxes and
registration fees are transfer payments
between consumers and the Federal
government and are therefore not
considered a cost in the societal
perspective. However, these costs are
considered as additional costs in the
private consumer perspective.

(a) Sales Taxes
The analysis took auto sales taxes by
state 246 and weighted them by
population by state to determine a
national weighted-average sales tax of
5.46%. The analysis sought to weight
sales taxes by new vehicle sales by state;
however, such data were unavailable. It
is recognized that for this purpose, new
vehicle sales by state is a superior
weighting mechanism to Census
population; in effort to approximate
new vehicle sales by state, a study of the
change in new vehicle registrations
(using R.L. Polk data) by state across
recent years was conducted, resulting in
a corresponding set of weights. Use of
the weights derived from the study of
vehicle registration data resulted in a

(b) Financing Costs
The analysis assumes 85% of
automobiles are financed based on
Experian’s quarter 4, 2016 ‘‘State of the
Automotive Finance Market,’’ which
notes that 85.2% of 2016 new vehicles
were financed, as were 85.9% of 2015
new vehicle purchases.247 The analysis
used data from Wards Automotive and
JD Power on the average transaction
price of new vehicle purchases, average
financed new auto beginning principal,
and the average incentive as a percent
of MSRP to compute the ratio of the
average financed new auto principal to
the average new vehicle MSRP for
calendar years 2011–2016. Table–II–34
shows that the average financed auto
principal is between 82 and 84% of the

average new vehicle MSRP. Using the
assumption that 85% of new vehicle
purchases involve some financing, the
average share of the MSRP financed for
all vehicles purchased, including nonfinanced transactions, rather than only
those that are financed, was computed.
Table–II–34 shows that this share ranges
between 70 and 72%. From this, the
analysis assumed that on an aggregate
level, including all new vehicle
purchases, 70% of the value of all
vehicles’ MSRP is financed. It is likely
that the share financed is correlated
with the MSRP of the new vehicle
purchased, but for simplification
purposes, it is assumed that 70% of all
vehicle costs are financed, regardless of
the MSRP of the vehicle. In
measurements of the impacts on the
average consumer, this assumption will
not affect the outcome of our
calculation, though this assumption will
matter for any discussions about how
many, or which, consumers bear the
brunt of the additional cost of owning
more expensive new vehicles. For sake
of simplicity, the model also assumes
that increasing the cost of new vehicles
will not change the share of new vehicle
MSRP that is financed; the relatively
constant share from 2011–2016 when
the average MSRP of a vehicle increased
10% supports this assumption. It is
recognized that this is not indicative of
average individual consumer
transactions but provides a useful tool
to analyze the aggregate marketplace.

From Wards Auto data, the average
48- and 60-month new auto interest
rates were 4.25% in 2016, and the

average finance term length for new
autos was 68 months. It is recognized
that longer financing terms generally

include higher interest rates. The share
financed, interest rate, and finance term
length are added as inputs in the

246 See Car Tax by State,
FactoryWarrantyList.com, http://www.factory
warrantylist.com/car-tax-by-state.html (last visited
June 22, 2018). Note: County, city, and other
municipality-specific taxes were excluded from
weighted averages, as the variation in locality taxes
within states, lack of accessible documentation of

locality rates, and lack of availability of weights to
apply to locality taxes complicate the ability to
reliably analyze the subject at this level of detail.
Localities with relatively high automobile sales
taxes may have relatively fewer auto dealerships, as
consumers would endeavor to purchase vehicles in
areas with lower locality taxes, therefore reducing

the effect of the exclusion of municipality-specific
taxes from this analysis.
247 Zabritski, M. State of the Automotive Finance
Market: A look at loans and leases in Q4 2016,
Experian, https://www.experian.com/assets/
automotive/quarterly-webinars/2016-Q4-SAFMrevised.pdf (last visited June 22, 2018).

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parameters file so that they are easier to
update in the future. Using these inputs
the model computes the stream of

financing payments paid for the average
financed purchases as the following:

Note: The above assumes the interest
is distributed evenly over the period,
when in reality more of the interest is
paid during the beginning of the term.
However, the incremental amount
calculated as attributable to the standard
will represent the difference in the
annual payments at the time that they
are paid, assuming that a consumer does
not repay early. This will represent the
expected change in the stream of
financing payments at the time of
financing.
The above stream does not equate to
the average amount paid to finance the
purchase of a new vehicle. In order to
compute this amount, the share of
financed transactions at each interest
rate and term combination would have
to be known. Without having
projections of the full distribution of the
auto finance market into the future, the
above methodology reasonably accounts
for the increased amount of financing
costs due to the purchase of a more
expensive vehicle, on an average basis
taking into account non-financed
transactions. Financing payments are
also assumed to be an intertemporal
transfer of wealth for a consumer; for

this reason, it is not included in the
societal cost and benefit analysis.
However, because it is an additional
cost paid by the consumer, it is
calculated as a part of the private
consumer welfare analysis.
It is recognized that increased finance
terms, combined with rising interest
rates, lead to a longer period of time
before a consumer will have positive
equity in the vehicle to trade in toward
the purchase of a newer vehicle. This
has impacts in terms of consumers
either trading vehicles with negative
equity (thereby increasing the amount
financed and potentially subjecting the
consumer to higher interest rates and/or
rendering the consumer unable to
obtaining financing) or delaying the
replacement of the vehicle until they
achieve suitably positive equity to allow
for a trade. Comment is sought on the
effect these developments will have on
the new vehicle market, both in general,
and in light of increased stringency of
fuel economy and GHG emission
standards. Comment is also sought on
whether and how the model should
account for consumer decisions to
purchase a used vehicle instead of a

new vehicle based upon increased new
vehicle prices in response to increased
CAFE standard stringency.

To utilize the above framework,
estimates of the share of MSRP paid on
collision and comprehensive insurance
and of annual vehicle depreciations are
needed to implement the above
equation. Wards has data on the average
annual amount paid by model year for
new light trucks and passenger cars on
collision, comprehensive and damage
and liability insurance for model years
1992–2003; for model years 2004–2016,
they only offer the total amount paid for
insurance premiums. The share of total
insurance premiums paid for collision

and comprehensive coverage was
computed for 1979–2003. For cars the
share ranges from 49 to 55%, with the
share tending to be largest towards the
end of the series. For trucks the share
ranges from 43 to 61%, again, with the
share increasing towards the end of the
series. It is assumed that for model years
2004–2016, 60% of insurance premiums
for trucks, and 55% for cars, is paid for
collision and comprehensive. Using
these shares the absolute amount paid
for collision and comprehensive
coverage for cars and trucks is

computed. Then each regulatory class in
the fleet is weighted by share to estimate
the overall average amount paid for
collision and comprehensive insurance
by model year as shown in Table–II–35.
The average share of the initial MSRP
paid in collision and comprehensive
insurance by model year is then
computed. The average share paid for
model years 2010–2016 is 1.83% of the
initial MSRP. This is used as the share
of the value of a new vehicle paid for
collision and comprehensive in the
future.

(c) Insurance Costs

EP24AU18.051

More expensive vehicles will require
more expensive collision and
comprehensive (e.g., fire and theft) car
insurance. Actuarially fair insurance
premiums for these components of
value-based insurance will be the
amount an insurance company will pay
out in the case of an incident type
weighted by the risk of that type of
incident occurring. It is expected that
the same driver in the same vehicle type
will have the same risk of occurrence for
the entirety of a vehicle’s life so that the
share of the value of a vehicle paid out
should be constant over the life of a
vehicle. However, the value of vehicles
will decline at some depreciation rate so
that the absolute amount paid in valuerelated insurance will decline as the
vehicle depreciates. This is represented
in the model as the following stream of
expected collision and comprehensive
insurance payments:

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2017 data from Fitch Black Book was
used as a source for vehicle depreciation
rates; two- to six-year-old vehicles in
2016 had an average annual
depreciation rate of 17.3%.248 It is
assumed that future depreciation rates
will be like recent depreciation, and the
analysis used the same assumed
depreciation. Table–II–36 shows the

cumulative share of the initial MSRP of
a vehicle assumed to be paid in
collision and comprehensive insurance
in five-year age increments under this
depreciation assumption, conditional on
a vehicle surviving to that age—that is,
the expected insurance payments at the
time of purchase will be weighted by
the probability of surviving to that age.

If a vehicle lives to 10 years, 9.9% of the
initial MSRP is expected to be paid in
collision and comprehensive payments;
by 20 years 11.9% of the initial MSRP;
finally, if a vehicle lives to age 40,
12.4% of the initial MSRP. As can be
seen, the majority of collision and
comprehensive payments are paid by
the time the vehicle is 10 years old.

The increase in insurance premiums
resulting from an increase in the average
value of a vehicle is a result of an
increase in the expected amount
insurance companies will have to pay
out in the case of damage occurring to
the driver’s vehicle. In this way, it is a
cost to the private consumer,
attributable to the CAFE standard that
caused the price increase.

In previous rulemaking analyses,
NHTSA imposed an economic cost of
lost welfare to buyers of advanced
electric vehicles. NHTSA chose to

model a 75-mile EV for early adopters,
who we assume would not be concerned
with the lower range, and a 150-mile EV
for the broader market. The initial five
percent of EV sales were assumed to go
to early adopters, with the remainder
being 150-mile EVs. The broader market
was assumed to have some lower utility
for the 150-mile EV, due to the lower
driving range between refueling events
relative to a conventional vehicle. Thus,
an additional social cost of about $3,500
per vehicle was assigned to the EV150
to capture the lost utility to
consumers.249 Additionally, NHTSA
imposed a ‘‘relative value loss’’ of
1.94% of the vehicle’s MSRP to reflect

the economic value of the difference
between the useful life of a conventional
ICE and the 150-mile EV when it
reaches a 55% battery capacity (as a
result of battery deteroriation).250 In
subsequent analyses (the 2016 Draft
TAR analysis and today’s analysis),
NHTSA removed the low-range EVs
from its technology set due to both weak
consumer demand for low-range EVs in
the marketplace and subsequent
technology advances that make 200-mile
EVs a more practical option for new EVs
produced in future model years. The
exclusion of low-range EVs in the
technology set reduced the need to
account for consumer welfare losses

248 Fitch Ratings Vehicle Depreciation Report
February 2017, Black Book, http://
www.blackbook.com/wp-content/uploads/2017/02/
Final-February-Fitch-Report.pdf (last visited June
22, 2018).

249 Based on Michael K. Hidrue, George R.
Parsons, Willett Kempton, Meryl P. Gardner,
Willingness to pay for electric vehicles and their
attributes, Resource and Energy Economics,Volume
33, Issue 3, 2011, Pages 686–705.

250 The vehicle was assumed to be retired once
the capacity reached 55 percent of its initial
capacity, and the residual lifetime miles from that
point forward were valued, discounted, and
expressed as a fraction of initial MSRP.

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(d) Consumer Acceptance of Specific
Technologies

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43083

attributable to reduced driving range.
While the sensitivity analysis explores
some potential for continuing consumer
value loss, even in the improved
electrified powertrain vehicles, the
central analysis assumes that no value
loss exists for electrified powertrains.
However, ongoing low sales volumes
and a growing body of literature suggest
that consumer welfare losses may still
exist if manufacturers are forced to
produce electric vehicles in place of
vehicles with internal combustion
engines (forcing sacrifices to cargo
capacity or driving range) in order to
comply with standards. This topic will
receive ongoing investigation and
revision before the publication of the
final rule. Please provide comments and
any relevant data that would help to
inform the estimation of
implementation of any value loss
related to sacrificed attributes in electric
vehicles.
One reason it was necessary to
account for welfare losses from reduced
driving range in this way is that, in
previous rulemakings, the agencies
implicitly assumed that every vehicle in
the forecast would be produced and
purchased and that manufacturers
would pass on the entire incremental
cost of fuel-saving technologies to new
car (and truck) buyers. However, many
stakeholders commented that
consumers are not willing to pay the full
incremental costs for hybrids, plug-in
hybrids, and battery electric vehicles.251
For this analysis, consumer willingness
to pay for HEVs, PHEVs, BEVs relative
to comparable ICE vehicles was
investigated. The analysis compared the
estimated price premium the electrified
vehicles command in the used car
market and estimated the willingness to
pay premium for new vehicles with
electrification technologies at age zero
relative to their internal combustion
engine counterparts. For the analysis,
the willingness to pay was compared
with the expected incremental cost to
produce electrification technologies.
Manufacturers also contributed

confidential business information about
the costs, revenues, and profitability of
their electrified vehicle lines. The CBI
provided a valuable check on the
empirical work described below. As a
result of this examination, we no longer
assume manufacturers can pass on the
entire incremental cost of hybrid, plugin hybrid, and battery electric vehicles
to buyers of those vehicles. The
difference between the buyer’s
willingness-to-pay for those
technologies, and the cost to produce
them, must be recovered from buyers of
other vehicles in a manufacturer’s
product portfolio or sacrificed from its
profits, or sacrificed from dealership
profits, or supplemented with State or
Federal incentives (or, some
combination of the four).
Using data from the used vehicle
market, statistical models were fit to
estimate consumer willingness to pay
for new vehicles with varying levels of
electrification relative to comparable
internal combustion engine vehicles
was evaluated in four steps. The
analysis (1) gathered used car fair
market value for select vehicles; (2)
developed regression models to estimate
the portion of vehicle depreciation rate
attributable to the vehicle nameplate
and the portion attributable to the
vehicle’s technology content at each age
(using fixed effects for nameplates and
specific electrification technologies); (3)
estimated the value of vehicles at age
zero (i.e., when the vehicles were new);
and (4) compared new vehicle values for
comparable vehicles across different
electrification levels (i.e., internal
combustion, HEV, PHEV, and BEV) to
estimate willingness-to-pay for the
electric technology relative to an ICE.
The dataset used for estimation
consisted of vehicle attribute data from
Edmunds and transaction data from
Kelley Blue Book published online in
June and July of 2017 for select vehicles
of interest.252 253 The dataset was
constructed to contain pairs of vehicles
that were nearly the same, except for
type of powertrain (internal combustion

versus some amount of electrification).
For instance, the dataset contained used
vehicle prices for the Honda Accord and
Honda Accord Hybrid, Toyota Camry
and Toyota Camry Hybrid, Ford Fusion
and Ford Fusion Hybrid, Kia Soul and
Kia Soul EV, and so on for several
model years. In some cases, the
manufacturer produced no identically
equivalent internal combustion engine
vehicle, so a similar internal
combustion vehicle produced by the
same manufacturer was used as the
point of comparison. For example, the
Nissan Leaf was paired with the Nissan
Versa, as well as the Toyota Prius and
Toyota Corolla. Only vehicles available
for private sale, and in good vehicle
condition were included in the
analysis.254 The dataset contains fewer
observations for PHEVs and BEVs
because manufacturers have produced
fewer examples of vehicles with these
technologies, compared to HEV and ICE
vehicles. In all of these cases, trim level
and options packages were matched
between ICE and electric powertrains to
minimize the degree of non-powertrain
difference between vehicle pairs. The
resale price data spanned many model
years, but most observations in the
dataset represent MY 2013 through MY
2016.
The regression models used to
estimate the transaction price (or
‘‘Value’’) as a function of age, control for
the type of powertrain (ICE, HEV, PHEV,
and BEV) and nameplate to account for
their impact on the value of the vehicle
as it ages.255 The regression takes the
following form, with ICE, HEV, PHEV,
and BEV binary variables (0, or 1), and
age defined as 2017 minus the model
year was used:
1n(Value = ,b1(ICE * Age) + b2(HEV *
Age) + b3(PHEV * Age) + b4(BEV *
Age) + b5(HEV) + b6(PHEV) +
b7(BEV) + FENameplate
For each observation in the dataset,
the ‘‘Value’’ at age zero is determined by
setting the age variable to zero and
solving.

251 See e.g., Comment by Alliance of Automobile
Manufacturers, Docket ID EPA–HQ–OAR–2015–
0827–4089 and NHTSA–2016–0068–0072.
252 See Edmunds, https://www.edmunds.com/
(last visited June 22, 2018). Edmunds publishes
automotive data, reviews, and advice.
253 See Kelley Blue Book, https://www.kbb.com/
(last visited June 22, 2018). Kelley Blue Book, part
of Cox Automotive’s Autotrader brand, provides
automotive research, reviews, and advice, including
estimated market values of new and used vehicles.

254 It is possible ‘‘good’’ vehicles for all ages may
have inadvertently introduced a small bias in the
sample, as a ‘‘good’’ conditioning rating on a
vehicle just a year or two old may not be in average
condition relative to other vehicles of the vintage,
but a ‘‘good’’ rating for a much older car may reflect
an impeccably maintained vehicle.
255 In the case of electrified vehicles with no
internal combustion engine equivalent, the analysis
grouped like vehicle pairs together under the same
nameplate fixed effects (or FENameplate). Tesla
vehicles have no internal combustion engine

equivalent, and the used vehicle market for Tesla
has not cleared in the same way because of a variety
of unique business factors (previously, Tesla
guaranteed resale value prices for their products,
which was a factory incentive program that only
recently ended, no longer applying to vehicles sold
after July 1, 2016). These two factors impaired the
quality of used Tesla data for the purposes of the
analysis, so the agencies excluded Tesla vehicles
from today’s analysis on customer willingness-topay for electrified vehicles.

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The estimated willingness-to-pay for
electrified powertrain packages over an
internal combustion engine in an
otherwise similar vehicle is computed
as the difference between their
estimated initial values, using the
functions above. These pair-wise
differences are averaged to estimate a

price premium for new vehicles with
HEV, PHEV, and BEV technologies. This
analysis suggests that consumers are
willing to pay more for new electrified
vehicles than their new internal engine
combustion counterparts, but only a
little more, and not necessarily enough
to cover the relatively large projected

incremental cost to produce these
vehicles. Specifically, the analysis
estimated consumers are willing to pay
between $2,000 and $3,000 more for the
electrified powertrains considered here
than their internal combustion engine
counterparts.

Table–II–37 illustrates the variation in
willingness-to-pay by electrification
level (although the statistical model did
not distinguish between PHEV30 and
PHEV50 due to the small number of
available operations for plug-in
hybrids). As the table demonstrates, the
difference between the median and
mean predicted price premium for
PHEVs is significant. The limited
number of PHEV observations were not
uniformly distributed among the
nameplates present, and some of the

luxury vehicles in the set retained value
in a way that skewed the average. The
CBI acquired from manufacturers was
more consistent with the mean than
median value (except for the PHEVs).
Additionally, the Kelley Blue Book
data suggest that the used electrified
vehicles were often worth less than their
used internal combustion engine
counterpart vehicles after a few years of
use.256 As Table–II–38 illustrates, the
value of the price premium shrinks as
the vehicles age and depreciate. Using

the statistical model, we estimate that
strong hybrids hold less than $100 of
the initial price premium by age eight
(on average). While the battery electric
vehicles appear to be worth less than
their ICE counterparts by age eight,
there is limited data about this emerging
segment of the new vehicle market.
These independently-produced results
using publicly available data were in
line with manufacturers’ reported
confidential business information.

The ‘‘technology cost burden’’
numbers used in today’s analysis
represent the amount of a given
technology’s incremental cost that
manufacturers are unable to pass along
to the buyer of a given vehicle at the
time of purchase. The burden is defined
as the difference between estimated
willingness-to-pay, itself a combination
of the estimated values and confidential

business information received from
manufacturers any tax credits that can
be passed through in the price, and the
cost of the technology. In general, the
incremental willingness-to-pay falls
well short of the costs currently
projected for HEVs, PHEVs, and BEVs;
for example, BEV technology can add
roughly $18,000 in equipment costs to
the vehicle after standard retail price

equivalent markups (with a large
portion of those costs being batteries),
but the estimated willingness-to-pay is
only about $3,000. While tax credits
offset some, if not most of that
difference for PHEVs and BEVs, there is
some residual amount that buyers of
new electrified vehicles are currently
unwilling to cover, and that must either
come from forgone profits or be passed

256 The analysis did not identify an underlying
reason for this observation, but the agencies posit
for discussion purposes there could be some

interaction between maintenance costs and batteries
or maintenance costs and low volume vehicles.
Alternatively, new electrified vehicles may be

superior to previous generation vehicles, and new
electrified vehicles may be offered at lower prices
still because of a variety of market conditions.

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along to buyers of other vehicles in a
manufacturer’s portfolio.
Manufacturers may be able to recover
some or all of these costs by charging
higher prices for their other models, in
which case it will represent a welfare
loss to buyers of other vehicles (even if
not to buyers of HEVs, PHEVs, or BEVs
themselves). To the extent that they are
unable to do so and must absorb part or
all of these costs, their profits will
decline, and in effect this cost will be
borne by their investors. In practice, the
analysis estimates benefits and costs to
car and light truck manufacturers and
buyers under the assumption that each
manufacturer recovers all technology
costs and civil penalties it incurs from
buyers via higher average prices for the
models it produces and sells, although
sufficient information to support
specific assumptions about price
increases for individual models is not
present. In effect, this means that any
part of a manufacturer’s costs to convert
specific models to electric drive
technologies that it cannot recover by
charging higher prices to their buyers
will be borne collectively by buyers of
the other models they produce. Each of
those buyers is in effect assumed to pay
a slight premium (or ‘‘markup’’) over the
manufacturer’s cost to produce the
models they purchase (including the
cost of any technology used to improve
its fuel economy), this premium on
average is modeled to recover the full
cost of technology applied to all
vehicles to improve the fuel economy of
the fleet. So, even though electrified
vehicles are modeled as if their buyers
are unwilling to pay the full cost of the
technology associated with their fuel
economy improvement, the price borne
by the average new vehicle buyer
represents the average incremental
technology cost for all applied
technology, the sum of all technology
costs divided by the number of units
sold, across all classes, for each
manufacturer.
The willingness-to-pay analysis
described above relies on used vehicle
data that is widely available to the
public. Market tracking services update
used vehicle price estimates regularly as
fuel prices and other market conditions
change, making the data easy to update
in the future as market conditions
change. The used vehicle data also
account for consumer willingness-topay absent State and Federal rebates at
the time of sale, which are reflected in

both the initial purchase price of the
vehicle and its later value in the used
vehicle market. As such, the analysis
would continue to be relevant even if
incentive programs for vehicle
electrification change or phase out in
the future. By considering a variety of
nameplates and body styles produced
by several manufacturers, this analysis
produces average willingness-to-pay
estimates that can be applied to the
whole industry. By evaluating matched
pairs of vehicles from the same
manufacturer, the analysis accounts for
many additional factors that may be tied
to the brand, rather than the technology,
and influence the fair market price of
vehicles. In particular, the data
inherently include customer valuations
for fuel-savings and vehicle
maintenance schedules, as well as other
factors like noise-vibration-andharshness, interior space,257 and fueling
convenience in the context of the
vehicles considered.
There are some limitations to this
approach. There are currently few
observations of PHEV and BEV
technologies in the data, and most of the
observations for BEVs are sedans and
small cars, the values for which are
extrapolated to other market segments.
Additionally, the used vehicle data
supporting these estimates inherently
includes both older and newer
generations of technology, so the
historical regression may be slow to
react to rapid changes in the new
vehicle marketplace. As new vehicle
nameplates emerge, and existing
nameplates improve their
implementation of electrification
technologies, this model will require reestimation to determine how these new
entrants impact the estimated industry
average willingness-to-pay.
Additionally, the willingness-to-pay
analysis does not consider electric
vehicles with no direct ICE counterpart.
For example, today’s evaluation does
not consider Tesla because the Tesla
brand has no ICE equivalent, and
because the free-market prices for used
Tesla vehicles have been difficult (if not
impossible) to obtain, primarily due to
factory guaranteed resale values (which
is a program that still affects the used
market for many Tesla vehicles). Still,
Tesla vehicles have a large share of the
BEV market by both unit sales and
dollar sales, it may be possible to
include Tesla data in a future update to
this analysis. Similarly, the analysis did

257 Often HEVs and PHEVs place batteries in
functional storage space, such as the trunk or floor
storage bins, thereby forcing consumers to trade-off
fuel-savings with other functional vehicle
attributes.

258 See https://www.transportation.gov/sites/
dot.gov/files/docs/ValueofTravelTime
Memorandum.pdf (last accessed July 3, 2018).

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not include ICE vehicles with no similar
HEV, PHEV, or BEV nameplate or
counterpart, so the analysis presented
here looks at a small portion of all
transactions and is more likely to
include fuel efficient models where
market demand for hybrid (or higher)
versions may exist. One possible
alternative is to rely on new vehicle
transaction prices to estimate consumer
willingness-to-pay for new vehicles
with certain attributes. However, new
vehicle transaction data is highly
proprietary and difficult to obtain in a
form that may be disclosed to the
public.
While estimating willingness-to-pay
for electrification technologies from
depreciation and MSRP data is
appealing, many manufacturers handle
MSRP and pricing strategies differently,
with some preferring to deviate only a
little from sticker price and others
preferring to offer high discounts. There
is evidence of large differences between
MSRP and effective market prices to
consumers for many vehicles, especially
BEVs.
Please provide comments on methods
and data used to evaluate consumer
willingness-to-pay for electrification
technologies.
(e) Refueling Surplus
Direct estimates of the value of
extended vehicle range are not available
in the literature, so the reduction in the
required annual number of refueling
cycles due to improved fuel economy
was calculated and the economic value
of the resulting benefits assessed. Chief
among these benefits is the time that
owners save by spending less time both
in search of fueling stations and in the
act of pumping and paying for fuel.
The economic value of refueling time
savings was calculated by applying
DOT-recommended valuations for travel
time savings to estimates of how much
time is saved.258 The value of travel
time depends on average hourly
valuations of personal and business
time, which are functions of total hourly
compensation costs to employers. The
total hourly compensation cost to
employers, inclusive of benefits, in
2010$ is $29.68.259 Table–II–39 below
demonstrates the approach to estimating
the value of travel time ($/hour) for both
urban and rural (intercity) driving. This
approach relies on the use of DOTrecommended weights that assign a
lesser valuation to personal travel time
than to business travel time, as well as
259 Total hourly employer compensation costs for
2010 (average of quarterly observations across all
occupations for all civilians). See https://
www.bls.gov/ncs/ect/tables.htm (last accessed July
3, 2018).

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weights that adjust for the distribution
between personal and business travel.

260 Time spent on personal travel during rural
(intercity) travel is valued at a greater rate than that
of urban travel. There are several reasons behind

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time—independent of urban or rural
status—may be produced.
Note: The calculations above assume only
one adult occupant per vehicle. To fully
estimate the average value of vehicle travel
time, the presence of additional adult
passengers during refueling trips must be
accounted for. The analysis applies such an
adjustment as shown in Table–II–40; this

the divergence in these values: (1) Time is scarcer
on a long trip; (2) a long trip involves
complementary expenditures on travel, lodging,

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adjustment is performed separately for
passenger cars and for light trucks, yielding
occupancy-adjusted valuations of vehicle
travel time during refueling trips for each
fleet.
Note: Children (persons under age 16) are
excluded from average vehicle occupancy
counts, as it is assumed that the opportunity
cost of children’s time is zero.

food, and entertainment because time at the
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The estimates of the hourly value of
urban and rural travel time ($15.67 and
$21.93, respectively) shown in Table–II–
39 above must be adjusted to account
for the nationwide ratio of urban to rural
driving. By applying this adjustment (as
shown in Table–II–40 below), an overall
estimate of the hourly value of travel

The analysis estimated the amount of
refueling time saved using (preliminary)
survey data gathered as part of our
2010–2011 National Automotive
Sampling System’s Tire Pressure
Monitoring System (TPMS) study.263
The study was conducted at fueling
stations nationwide, and researchers
made observations regarding a variety of
characteristics of thousands of
individual fueling station visits from
August 2010 through April 2011.264

Among these characteristics of fueling
station visits is the total amount of time
spent pumping and paying for fuel.
From a separate sample (also part of the
TPMS study), researchers conducted
interviews at the pump to gauge the
distances that drivers travel in transit to
and from fueling stations, how long that
transit takes, and how many gallons of
fuel are being purchased.
This analysis of refueling benefits
considers only those refueling trips

which interview respondents indicated
the primary reason was due to a low
reading on the gas gauge.265 This
restriction was imposed so as to exclude
drivers who refuel on a fixed (e.g.,
weekly) schedule and may be unlikely
to alter refueling patterns as a result of
increased driving range. The relevant
TPMS survey data on average refueling
trip characteristics are presented below
in Table–II–41.

As an illustration of how the value of
extended refueling range was estimated,
assume a small light truck model has an
average fuel tank size of approximately
20 gallons and a baseline actual on-road
fuel economy of 24 mpg (its assumed

level in the absence of a higher CAFE
standard for the given model year).
TPMS survey data indicate that drivers
who indicated the primary reason for
their refueling trips was a low reading
on the gas gauge typically refuel when

their tanks are 35% full (i.e. as shown
in Table–II–41, with 7.0 gallons in
reserve, and the consumer purchases 13
gallons). By this measure, a typical
driver would have an effective driving
range of 312 miles (= 13.0 gallons × 24

261 See Travel Monitoring, Traffic Volume Trends,
U.S. Department of Transportation Federal Highway
Administration, https://www.fhwa.dot.gov/policy
information/travellmonitoring/tvt.cfm (last visited
June 22, 2018). Weights used for urban versus rural
travel are computed using cumulative 2011
estimates of urban versus rural miles driven
provided by the Federal Highway Administration.
262 Source: National Automotive Sampling
System 2010–2011 Tire Pressure Monitoring System
(TPMS) study. See next page for further background

on the TPMS study. TPMS data are preliminary at
this time, and rates are subject to change pending
availability of finalized TPMS data. Average
occupancy rates shown here are specific to
refueling trips and do not include children under
16 years of age.
263 TPMS data are preliminary and not yet
published. Estimates derived from TPMS data are
therefore preliminary and subject to change.
Observational and interview data are from distinct
subsamples, each consisting of approximately 7,000

vehicles. For more information on the National
Automotive Sampling System and to access TPMS
data when they are made available, see http://
www.nhtsa.gov/NASS.
264 The data collection period for the TPMS study
ranged from October 10, 2010, through April 15,
2011.
265 Approximately 60% of respondents indicated
‘‘gas tank low’’ as the primary reason for the
refueling trip in question.

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mpg) before he or she is likely to refuel.
Increasing this model’s actual on-road
fuel economy from 24 to 25 mpg would
therefore extend its effective driving
range to 325 miles (= 13.0 gallons × 25
mpg). Assuming that the truck is driven
12,000 miles/year,266 this one mpg
improvement in actual on-road fuel
economy reduces the expected number
of refueling trips per year from 38.5 (=
12,000 miles per year/312 miles per
refueling) to 36.9 (= 12,000 miles per
year/325 miles per refueling), or by 1.6
refuelings per year. If a typical fueling
cycle for a light truck requires a total of
6.83 minutes, then the annual value of
time saved due to that one mpg
improvement would amount to $3.97 (=
(6.83/60) × $21.81 × 1.6).
In the central analysis, this
calculation was repeated for each future
calendar year that light-duty vehicles of
each model year affected by the
standards considered in this rule would
remain in service. The resulting
cumulative lifetime valuations of time
savings account for both the reduction
over time in the number of vehicles of
a given model year that remain in
service and the reduction in the number
of miles (VMT) driven by those that stay
in service. The analysis also adjusts the
value of time savings that will occur in
future years both to account for
expected annual growth in real
wages 267 and to apply a discount rate to
determine the net present value of time
saved.268 A further adjustment is made
to account for evidence from the
interview-based portion of the TPMS
study which suggests that 40% of
refueling trips are for reasons other than
a low reading on the gas gauge. It is
therefore assumed that only 60% of the
theoretical refueling time savings will
be realized, as it was assumed that
owners who refuel on a fixed schedule
266 2009 National Household Travel Survey
(NHTS), U.S Department of Transportation Federal
Highway Administration at 48 (June 2011),
available at http://nhts.ornl.gov/2009/pub/stt.pdf.
12,000 miles/year is an approximation of a light
duty vehicle’s annual mileage during its initial
decade of use (the period in which the bulk of
benefits are realized). The CAFE model estimates
VMT by model year and vehicle age, taking into
account the rebound effect, secular growth rates in
VMT, and fleet survivability; these complexities are
omitted in the above example for simplicity.
267 See The Economics Daily, The compensationproductivity gap, U.S. Department of Labor Bureau
of Labor Statistics (Feb. 24, 2011), http://
www.bls.gov/opub/ted/2011/ted_20110224.htm. A
1.1% annual rate of growth in real wages is used
to adjust the value of travel time per vehicle
($/hour) for future years for which a given model
is expected to remain in service. This rate is
supported by a BLS analysis of growth in real wages
from 2000–2009.
268 Note: Here, as elsewhere in the analysis,
discounting is applied on an annual basis from CY
2017.

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will continue to do. Based on peer
reviewer comments to NHTSA’s initial
implementation of refueling time
savings (subsequent to the CAFE NPRM
issued in 2011), the analysis of refueling
time savings was updated for the final
rule to reflect peer reviewer
suggestions.269 Beyond updating time
values to current dollars, that analysis
has been used, unchanged, in today’s
analysis as well.
Because a reduction in the expected
number of annual refueling trips leads
to a decrease in miles driven to and
from fueling stations, the value of
consumers’ fuel savings associated with
this decrease can also be calculated. As
shown in Table–II–41, the typical
incremental round-trip mileage per
refueling cycle is 1.08 miles for light
trucks and 0.97 miles for passenger cars.
Going back to the earlier example of a
light truck model, a decrease of 1.6 in
the number of refuelings per year leads
to a reduction of 1.73 miles driven per
year (= 1.6 refuelings × 1.08 miles
driven per refueling). Again, if this
model’s actual on-road fuel economy
was 24 mpg, the reduction in miles
driven yields an annual savings of
approximately 0.07 gallons of fuel (=
1.73 miles/24 mpg), which at $3.25/
gallon 270 results in a savings of $0.23
per year to the owner.
Note: This example is illustrative only of
the approach used to quantify this benefit. In
practice, the societal value of this benefit
excludes fuel taxes (as they are transfer
payments) from the calculation and is
modeled using fuel price forecasts specific to
each year the given fleet will remain in
service.

The annual savings to each consumer
shown in the above example may seem
like a small amount, but the reader
should recognize that the valuation of
the cumulative lifetime benefit of this
savings to owners is determined
separately for passenger car and light
truck fleets and then aggregated to show
the net benefit across all light-duty
vehicles, which is much more
significant at the macro level.
Calculations of benefits realized in
future years are adjusted for expected
real growth in the price of gasoline, for
the decline in the number of vehicles of
a given model year that remain in
service as they age, for the decrease in
269 Peer review materials, peer reviewer
backgrounds, comments, and NHTSA responses for
this prior assessment are available at Docket
NHTSA–2012–0001.
270 Estimate of $3.25/gallon is the forecasted cost
per gallon (including taxes, as individual
consumers consider reduced tax expenditures to be
savings) for motor gasoline in 2025. Source of price
projections: U.S. Energy Information
Administration, Annual Energy Outlook Early 2018.

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the number of miles (VMT) driven by
those that stay in service, and for the
percentage of refueling trips that occur
for reasons other than a low reading on
the gas gauge; a discount rate is also
applied in the valuation of future
benefits. Using this direct estimation
approach to quantify the value of this
benefit by model year was considered;
however, it was concluded that the
value of this benefit is implicitly
captured in the separate measure of
overall valuation of fuel savings.
Therefore, direct estimates of this
benefit are not added to net benefits
calculations. It is noted that there are
other benefits resulting from the
reduction in miles driven to and from
fueling stations, such as a reduction in
greenhouse gas emissions—CO2 in
particular—which, as per the case of
fuel savings discussed in the preceding
paragraph, are implicitly accounted for
elsewhere.
Special mention must be made with
regard to the value of refueling time
savings benefits to owners of electric
and plug-in electric (both referred to
here as EV) vehicles. EV owners who
routinely drive daily distances that do
not require recharging on-the-go may
eliminate the need for trips to fueling or
charging stations. It is likely that early
adopters of EVs will factor this benefit
into their purchasing decisions and
maintain driving patterns that require
once-daily at-home recharging (a
process which generally takes five to
eleven hours for a full charge) 271 for
those EV owners who have purchased
and installed a Level Two charging
station to a high-voltage outlet at their
home or parking place. However, EV
owners who regularly or periodically
need to drive distances further than the
fully-charged EV range may need to
recharge at fixed locations. A
distributed network of charging stations
(e.g., in parking lots, at parking meters)
may allow some EV owners to recharge
their vehicles while at work or while
shopping, yet the lengthy charging
cycles of current charging technology
may pose a cost to owners due to the
value of time spent waiting for EVs to
charge and potential EV shoppers who
do not have access to charging at home
(e.g., because they live in an apartment
without a vehicle charging station, only
271 See generally All-New Nissan Leaf Range &
Charging, Nissan USA, https://www.nissanusa.com/
vehicles/electric-cars/leaf/range-charging.html (last
visited June 22, 2018); Home Charging Calculator,
Tesla, https://www.tesla.com/support/homecharging-calculator (last visited June 22, 2018);
2018 Chevrolet Bolt EV, GM, https://media.gm.com/
content/media/us/en/chevrolet/vehicles/bolt-ev/
2018/_jcr_content/iconrow/textfile/file.res/2018Chevrolet-Bolt-EV-Product-Guide.pdf (last visited
June 22, 2018).

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have street parking, or have garages with
insufficient voltage). Moreover, EV
owners who primarily recharge their
vehicles at home will still experience
some level of inconvenience due to their
vehicle being either unavailable for
unplanned use or to its range being
limited during this time should they
interrupt the charging process.
Therefore, at present EVs hold potential
in offering significant time savings but
only to owners with driving patterns
optimally suited for EV characteristics.
If fast-charging technologies emerge and
a widespread network of fast-charging
stations is established, it is expected
that a larger segment of EV vehicle
owners will fully realize the potential
refueling time savings benefits that EVs
offer. This is an area of significant
uncertainty.

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6. Vehicle Use and Survival
To properly account for the average
value of consumer and societal costs
and benefits associated with vehicle
usage under various CAFE and GHG
alternatives, it is necessary to estimate
the portion of these costs and benefits
that will occur at each age (or calendar
year) for each model year cohort. Doing
so requires some estimate of how many
miles the average vehicle of a given
type 272 is expected to drive at each age
and what share of the initial model year
cohort is expected to remain at each age.
The first estimates are referred to as the
vehicle miles travelled (VMT) schedules
and the second as the survival rate
schedules. In this section the data
sources and general methodologies used
to develop these two essential inputs are
briefly discussed. More complete
discussions of the development of both
the VMT schedules and the survival rate
schedules are present in the PRIA
Chapter 8.
(a) Updates to Vehicle Miles Traveled
Schedules Since 2012 FR
The MY 2017–2021 FRM built
estimates of average lifetime mileage
accumulation by body style and age
using the 2009 National Household
Travel Survey (NHTS), which surveys
odometer readings of the vehicles
present from the approximately 113,000
households sampled. Approximately
210,000 vehicles were in the sample of
self-reported odometer readings
collected between April 2008 and April
2009. This represents a sample size of
less than one percent of the more than
250 million light-duty vehicles
registered in 2008 and 2009. The NHTS
sample is now 10 years old and taken
272 Type here refers to the following body styles:
Pickups, vans/SUVs, and other cars.

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during the Great Recession. The 2017
NHTS was not available at the time of
this rulemaking. Because of the age of
the last available NHTS and the unusual
economic conditions under which it
was collected, NHTSA built the new
schedule using a similar method from a
proprietary dataset collected in the fall
of 2015. This new data source has the
advantages of both being newer, a larger
sample, and collected by a third party.
(1) Data Sources and Estimation (Polk
Odometer Data)
To develop new mileage
accumulation schedules for vehicles
regulated under the CAFE program
(classes 1–3), NHTSA purchased a data
set of vehicle odometer readings from
IHS/Polk (Polk). Polk collects odometer
readings from registered vehicles when
they encounter maintenance facilities,
state inspection programs, or
interactions with dealerships and
OEMs—these readings are more likely to
be precise than the self-reported
odometer readings collected in the
NHTS. The average odometer readings
in the data set NHTSA purchased are
based on more than 74 million unique
odometer readings across 16 model
years (2000–2015) and vehicle classes
present in the data purchase (all
registered vehicles less than 14,000 lbs.
GVW). This sample represents
approximately 28% of the light-duty
vehicles registered in 2015, and thus has
the benefit of not only being a newer,
but also, a larger, sample.
Comparably to the NHTS, the Polk
data provide a measure of the
cumulative lifetime vehicle miles
traveled (VMT) for vehicles, at the time
of measurement, aggregated by the
following parameters: Make, model,
model year, fuel type, drive type, door
count, and ownership type (commercial
or personal). Within each of these
subcategories they provide the average
odometer reading, the number of
odometer readings in the sample from
which Polk calculated the averages, and
the total number of that subcategory of
vehicles in operation.
In estimating the VMT models, each
data point was weighted (make/model
classification) by the share of each
make/model in the total population of
the corresponding vehicle body style.
This weighting ensures that the
predicted odometer readings, by body
style and model year, represent each
vehicle classification among observed
vehicles (i.e., the vehicles for which
Polk has odometer readings), based on
each vehicles’ representation in the
registered vehicle population of its body
style. Implicit in this weighting scheme
is the assumption that the samples used

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to calculate each average odometer
reading by make, model, and model year
are representative of the total
population of vehicles of that type.
Several indicators suggest that this is a
reasonable assumption.
First, the majority of vehicle make/
models is well-represented in the
sample. For more than 85% of make/
model combinations, the average
odometer readings are collected for 20%
or more of the total population. Most
make/model observations have
sufficient sample sizes, relative to their
representation in the vehicle
population, to produce meaningful
average odometer totals at that level.
Second, we considered whether the
representativeness of the odometer
sample varies by vehicle age because
VMT schedules in the CAFE model are
specific to each age. It is possible that,
for some of those models, an insufficient
number of odometer readings is
recorded to create an average that is
likely to be representative of all of those
models in operation for a given year. For
all model years other than 2015,
approximately 95% or more of vehicles
types are represented by at least five
percent of their population. For this
reason, observations from all model
years, other than 2015, were included in
the estimation of the new VMT
schedules.
Because model years are sold in in the
Fall of the previous calendar year,
throughout the same calendar year, and
even into the following calendar year—
not all registered vehicles of a make/
model/model year will have been
registered for at least a year (or more)
until age three. The result is that some
MY 2014 vehicles may have been driven
for longer than one year, and some less,
at the time the odometer was observed.
In order to consider this in the
definition of age, an age of a vehicle is
assigned to be the difference between
the average reading date of a make/
model and the average first registration
date of that make/model. The result is
that the continuous age variable reflects
the amount of time that a car has been
registered at the time of odometer
reading and presumably the time span
that the car has accumulated the miles.
After creating the ‘‘age’’ variable, the
analysis fits the make/model lifetime
VMT data points to a weighted quartic
polynomial regression of the age of the
vehicle (stratified by vehicle body
styles). The predicted values of the
quartic regressions are used to calculate
the marginal annual VMT by age for
each body style by calculating
differences in estimated lifetime mileage
accumulation by age. However, the Polk
data acquired by NHTSA only contains

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observations for vehicles newer than 16
years of age. In order to estimate the
schedule for vehicles older than the age
15 vehicles in the Polk data, information
about that portion of the schedule from
the VMT schedules used in both the
2017–2021 Final Light Duty Rule and
2019–2025 Medium-Duty NPRM was
combined. The light-duty schedules
were derived from the survey data
contained in the 2009 National
Household Travel Survey (NHTS).
From the old schedules, the annual
VMT is expected to be decreasing for all
ages. Towards the end of the sample, the
predictions for annual VMT increase. In
order to force the expected
monotonicity, a triangular smoothing
algorithm is performed until the
schedule is monotonic. This performs a
weighted average which weights the
observations close to the observation
more than those farther from it. The
result is a monotonic function, that
predicts similar lifetime VMT for the
sample span as the original function.
Because the analysis does not have data
beyond 15 years of age, it is not able to
correctly capture that part of the annual
VMT curve using only the new dataset.
For this reason, trends in the old data
to extrapolate the new schedule for ages
beyond the sample range are used.
To use the VMT information from the
newer data source for ages outside of the
sample, final in-sample age (15 years)
are used as a seed and then applied to
the proportional trend from the old
schedules to extrapolate the new
schedules out to age 40. To do this, the
annual percentage difference in VMT of
the old schedule for ages 15–40 is
calculated. The same annual percentage
difference in VMT is applied to the new
schedule to extend beyond the final insample value. This assumes that the
overall proportional trend in the outer
years is correctly modeled in the old
VMT schedule and imposes this same
trend for the outer years of the new
schedule. The extrapolated schedules
are the final input for the VMT
schedules in the CAFE model. PRIA
Chapter 8 contains a lengthier
discussion of both the data source and
the methodology used to create the new
schedules.

273 Though not included in today’s analysis,
corresponding schedules for heavy-duty pickups

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(2) Using New Schedules in the CAFE
Model/Analysis
While the Polk registration data set
contains odometer readings for
individual vehicles, the CAFE model
tabulates ‘‘mileage accumulation’’
schedules, which relate average annual
miles driven to vehicle age, based on
vehicles’ body style. For the purposes of
VMT accounting, the CAFE model
classifies vehicles in the analysis fleet as
being one of the following: Passenger
car, SUV, pickup truck, passenger van,
or medium-duty pickup/van.273 In order
to use the Polk data to develop VMT
schedules for each of these vehicle
classes in the CAFE model, a mapping
between the classification of each model
in the Polk data and the classes in the
CAFE model was first constructed. This
mapping enabled separate tabulations of
average annual miles driven at each age
for each of the vehicle classes included
in the CAFE model.
The only revision made to the
mappings used to construct the new
VMT schedules was to merge the SUV
and passenger van body styles into a
single class. These body styles were
merged because there were very few
examples of vans—only 38 models were
in use during 2014, where every other
body style had at least three times as
many models. Further, as shown in the
PRIA Chapter 8, there was not a
significant difference between the 2009
NHTS van and SUV mileage schedules,
nor was there a significant difference
between the schedules built with the
two body styles merged or kept separate
using the 2015 Polk data. Merging these
body styles does not change the
workings of the CAFE model in any
way, and the merged schedule is simply
entered as an input for both vans and
SUVs.
Although there is a single VMT by age
schedule used as an input for each body
style, the assumptions about the
rebound effect require that this schedule
be scaled for future analysis years to
reflect changes in the cost of travel from
the time the Polk sample was originally
collected. These changes result from
both changes in fuel prices between the
time the sample was collected and any
future analysis year and differences in
fuel economy between the vehicles
included in the sample used to build the
mileage schedules and the future-year

vehicles analyzed within the CAFE
Model simulation.
As discussed in Section 0, recent
literature supports a 20% ‘‘rebound
effect’’ for light-duty vehicle use, which
represents an elasticity of annual use
with respect to fuel cost per mile of
¥0.2. Because fuel cost per mile is
calculated as fuel price per gallon
divided by fuel economy (in miles per
gallon), this same elasticity applies to
changes in fuel cost per mile that result
from variation in fuel prices or
differences in fuel economy. It suggests
that a five percent reduction in the cost
per mile of travel for vehicles of a
certain body style will result in a one
percent increase in the average number
of miles they are driven annually.
The average cost per mile (CPM) of a
vehicle of a given age and vehicle style
in CY 2016 (the first analysis year of the
simulation) was used as the reference
point to calculate the rebound effect
within the CAFE model. However, this
does not perfectly align with the time of
the collection of the Polk dataset. The
Polk data were collected in 2015 (so that
2014 fuel prices were the last to
influence sampled vehicles’ odometer
readings), and represents the average
odometer reading at a single point in
time for age (model year) included in
the cross-section. We use the difference
in the average odometer reading for each
vintage during 2014 to calculate the
number of miles vehicles are driven at
each age (see PRIA Chapter 8 for
specific details on the analysis). For
example, we interpret the difference in
the average odometer reading between
the five- and six-year-old vehicles of a
given body style as the average number
of miles they are driven during the year
when they were five years old.
However, vehicles produced during
different model years do not have the
same average fuel economy, so it is
important to consider the average fuel
economy of each vintage (or model year)
used to measure mileage accumulation
at a given age when scaling VMT for the
rebound calculation.
The first step in doing so is to adjust
for any change in average annual use
that would have been caused by
differences in fuel prices between CYs
2014 and 2016. This is done by scaling
the original schedules of annual VMT
by age tabulated from the Polk sample
using the following equation:

and vans were developed using the same
methodology.

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Here, the average fuel economy for
vehicles of a given body style and age
refers to a different MY in 2016 than it
did in 2014; for example, a MY 2014
vehicle had reached age two vehicle
during CY 2016, whereas a 2012 model
year vehicle was age two during CY
2014.
To estimate the average annual use of
vehicles of a specified body type and
age during future calendar years under
a specific regulatory alternative, the
CAFE model adjusts the resulting
estimates of vehicle use by age for that
body type during CY 2016 to reflect (1)
the projected change in fuel prices from
2016 to each future calendar year; and

(2) the difference between the average
fuel economy for vehicles of that body
type and age during a future calendar
year and the average fuel economy for
vehicles of that same body type and age
during 2016. These two factors combine
to determine the average fuel cost per
mile for vehicles of that body type and
age during each future calendar year
and the average fuel cost per mile for
vehicles of that same body type and age
during 2016.
The elasticity of annual vehicle use
with respect to fuel cost per mile is
applied to the difference between these
two values because vehicle use is
assumed to respond identically to

differences in fuel cost per mile that
result from changes in fuel prices or
from differences in fuel economy. The
model then repeats this calculation for
each calendar year during the lifetimes
of vehicles of other body types, and
subsequently repeats this entire set of
calculations for each regulatory
alternative under consideration. The
resulting differences in average annual
use of vehicles of each body type at each
age interact with the number estimated
to remain in use at that age to determine
total annual VMT by vehicles of each
body type.
This adjustment is defined by the
equation below:

This equation uses the observed cost
per mile of a vehicle of each age and
style in CY 2016 as the reference point
for all future calendar years. That is, the
reference fuel price is fixed at 2016
levels, and the reference fuel economy
of vehicles of each age is fixed to the
average fuel economy of the vintage that
had reached that age in 2016. For
example, the reference CPM for a oneyear-old SUV is always the CPM of the
average MY 2015 SUV in CY 2016, and
the CPM for a two-year-old SUV is
always the CPM of the average
MYv2014 SUV in CY 2016.
This referencing ensures that the
model’s estimates of annual mileage
accumulation for future calendar years
reflect differences in the CPM of

vehicles of each given type and age
relative to CPM resulting from the
average fuel economy of vehicles of that
type and age and observed fuel prices
during the year when the mileage
accumulation schedules were originally
measured. This is consistent with a
definition of the rebound effect as the
elasticity of annual vehicle use with
respect to changes in the fuel cost per
mile of travel, regardless of the source
of changes in fuel cost per mile.
Alternative forms of referencing are
possible, but none can guarantee that
projected future vehicle use will
respond to both projected changes in
fuel prices and differences in individual
models’ fuel economy among regulatory
alternatives.

The mileage estimates described
above are a crucial input in the CAFE
model’s calculation of fuel consumption
and savings, energy security benefits,
consumer surplus from cheaper travel,
recovered refueling time, tailpipe
emissions, and changes in crashes,
fatalities, noise and congestion.

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Across all body styles and ages, the
previous VMT schedules estimate
higher average annual VMT than the
updated schedules. Table–II—42
compares the lifetime VMT under the
2009 NHTS and the 2015 Polk dataset.
The 40-year lifetime VMT gives the

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(3) Comparison to other VMT
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expected lifetime VMT of a vehicle
conditional on surviving to age 40. The
new schedules predict between 24 and
31% fewer miles for a 40-year old
vehicle depending on the body style.
The new schedules predict that the
average 40-year old vehicle will drive
between approximately 260k and 280k
miles depending on the body style
versus between approximately 350k and
380k for the previous schedules.
The static survival-weighted lifetime
VMT represents the expected number of
miles the average vehicle of each body
style will drive, weighting by the
likelihood it survives to each age using
the previous static scrappage schedules.

The dynamic survival-weighted lifetime
VMT represents the expected number of
miles driven by each body style,
weighting by the dynamic survival
schedules under baseline
assumptions.274 There is a similar
proportional reduction in expected
lifetime VMT under both survival
assumptions, with the dynamic
scrappage model predicting lifetime
mileage accumulation within 10,000
miles of the previous static model under
both VMT schedules. The expected
lifetime mileage accumulation reduces
between 13 and 15% under the current
VMT schedules when compared to the
previous schedules—a smaller

proportional reduction than the
unweighted lifetime assumptions. Using
the updated schedules, the expected
lifetime mileage accumulation is
between approximately 150k and 170k
miles depending on the body style,
rather than the approximately 180k to
210k miles under the previous
schedules. For more detail on when the
mileage and survival rates occur,
chapter 8 of the PRIA gives the full VMT
schedules by age. The section below
gives further estimates of how lifetime
VMT estimates vary under different
assumptions within the dynamic
scrappage model.

We have several reasons for preferring
the new VMT schedules over the prior
iterations. Before discussing these
reasons, it is important to note that
NHTSA uses the same general
methodology in developing both
schedules. We consider data on average
odometer readings by age and body style
collected once during a given window
of time; we then estimate a weighted
polynomial function between vehicle
age and lifetime accumulation for a
given vehicle style. As with the
previous schedules, we use the interannual differences as the estimate of
annual miles traveled for a given age.
The primary advantage of the current
schedules is the data source. The
previous schedules are based on data
that is outdated and self-reported, while
the observations from Polk are between
five and seven years newer than those
in the NHTS and represent valid
odometer readings (rather than self-

reported information). Further, the 2009
NHTS represents approximately one
percent of the sample of vehicles
registered in 2008/2009, while the 2015
Polk dataset represents approximately
30% of all registered light-duty vehicles;
it is a much larger dataset, and less
likely to oversample certain vehicles.
Additionally, while the NHTS may be a
representative sample of households, it
is less likely to be a representative
sample of vehicles. However, by
properly accounting for vehicle
population weights in the new averages
and models, we corrected for this issue
in the derivation of the new schedules.
Importantly, this methodology treats
the cross-section of ages in a single
calendar year as a panel of the same
model year vehicle, when in reality each
age represents a single model year, and
not a true panel. We have some concern
that where the most heavily driven
vehicles drop out of the sample that the
lifetime odometer readings will be lower
than they would be if the scrapped
vehicles had been left in the dataset
without additional mileage
accumulation. This would bias our
estimates of inter-annual mileage
accumulation downward and may result
in an undervaluation of costs and
benefits associated with additional
travel for vehicles of older ages. For the

next VMT schedule iteration, NHTSA
intends to use panel data to test the
magnitude of any attrition effect that
may exist. While this caveat is
important, all previous iterations were
also built from a single calendar year
cross-section and contain the same
inherent bias.

274 In estimating the dynamic survival rate to
weight the annual VMT schedules, we make the
following input assumptions: The reference vehicle
is MY 2016, GDP growth rates and fuel prices are
our central estimates, and the future average new
vehicle fuel economies by body style and overall
average new vehicle prices are those simulated by
the CAFE model when CAFE standards are omitted
(by setting standards at 1 mpg), such that only
technologies that pay back within 30 months are
applied.

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(b) How does CAFE affect vehicle
retirement rates?
Lightly used vehicles are a close
substitute for new vehicles; thus, there
is relationship between the two markets.
As the price for new vehicles increases,
there is an upward shift in the demand
for used vehicles. As a result of the
upward shift in the demand curve, the
equilibrium price and quantity of used
vehicles both increase; the value of used
vehicles increases as a result. The
decision to scrap or maintain a used
vehicle is closely linked with the value
of the vehicle; when the value is lesser
than the cost to maintain the vehicle, it
will be scrapped. In general, as a result
of new vehicle price increases, the
scrappage rate, or the proportion of
vehicles remaining on the road
unregistered in a given year, of used
vehicles will decline. Because older
vehicles are on average less efficient and
less safe, this will have important
implications for the evaluations of costs

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
and benefits of fuel economy standards,
which increase the cost of new vehicles
and reduce the average cost per mile of
fuel costs.
Fuel economy standards result in the
application of more fuel saving
technologies for at least some models,
which result in a higher cost for
manufacturers to produce otherwise
identical vehicles. This increase in
production cost amounts to an upward
shift in the supply curve for new
vehicles. This increases the equilibrium
price and reduces the quantity of
vehicles demanded. While the cost of
new vehicles increases under increased
fuel economy standards, the fuel cost
per mile of travel declines. Consumers
will place some value on the fuel
savings associated with the additional
technology, to the extent that they value
reduced operating expenses against the
increased price of a new vehicle,
increased financing costs (and
impediments to obtaining financing),
and increased insurance costs.
There is a trade-off between fuel
economy and other attributes that
consumers value such as: Vehicle
performance, interior volume, etc.
Where the additional value of fuel
savings associated with a technology is
greater than any loss of value from
trade-offs with other attributes, the
demand for new vehicles will also shift
upwards. Where the additional
evaluation of fuel savings is lesser than
any loss of value from changes to other
attributes, the demand will shift
downwards. Thus, the direction of the
demand shift is unknown. However, if
we assume that manufacturers pass all
costs associated with a model off to the
consumer of that vehicle, then the per
vehicle profit remains constant. If we
also assume that manufacturers are good
predictors of the valuation and elasticity
of certain vehicle attributes, then we can
assume that even if there is some
positive demand shift, it is not enough
to increase demand above the original
equilibrium levels, or manufacturers
would apply those technologies even in
the absence of regulation.
As noted above, the increase in the
price of new vehicles will result in
increased demand for used vehicles as
substitutes, extending the expected age
and lifetime vehicle miles travelled of
less efficient, and generally, less safe
vehicles. The additional usage of older
vehicles will result in fewer gallons
saved and more total on-road fatalities
under more stringent CAFE alternatives.
For more on the topic of safety, the
relative safety of specific model year
vehicles is discussed in Section 0 of the
preamble and PRIA Chapter 11. Both the
erosion of fuel savings and the increase

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in incremental fatalities will decrease
the societal net benefits of increasing
new vehicle fuel economy standards.
Our previous estimates of vehicle
scrappage did not include a dynamic
response to new vehicle price, but
recent literature has continued to
illustrate that this an omission which
could rival the rebound effect in
magnitude (Jacobsen & van Bentham,
2015). For this reason, we worked to
develop an econometric survival model
which captures the effect of increasing
the price of new vehicles on the survival
rate of used vehicles discussed in the
following sections and in more detail in
the PRIA Chapter 8. We discuss the
literature on vehicle scrappage rate and
discuss in the succeeding section. A
brief explanation of why we develop our
own models and the data sources and
econometric estimations we use to do
so, follows. We conclude the discussion
of the updates to vehicle survival
estimates with a summary of the results,
a description of how we use them in the
CAFE model, and finally, how the
updated schedules compare with the
previous static scrappage schedules.
(1) What does the literature say about
the relationship?
(a) How Fuel Economy Standards
Impact Vehicle Scrappage
The effects of differentiated
regulation 275 in the context of fuel
economy (particularly, emission
standards only affecting new vehicles)
was discussed in detail in Gruenspecht
(1981) and (1982), and has since been
coined the ‘‘Gruenspecht effect.’’
Gruenspecht recognized that because
fuel economy standards affect only new
vehicles, any increase in price (net of
the portion of reduced fuel savings
valued by consumers) will increase the
expected life of used vehicles and
reduce the number of new vehicles
entering the fleet. In this way, increased
fuel economy standards slow the
turnover of the fleet and the entrance of
any regulated attributes tied only to new
vehicles. Although Gruenspecht
acknowledges that a structural model
which allows new vehicle prices to
affect used vehicle scrappage only
through their effect on used vehicle
prices would be preferable, the data
available on used vehicle prices was
(and still is) limited. Instead he tested
his hypothesis in his 1981 dissertation
using new vehicle price and other
determinants of used car prices as a
275 Differentiated regulations are regulations
affecting segments of the market differently; here,
it references the fact that emission and fuel
economy standards have largely only applied to
new and not used vehicles.

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reduced form to approximate used car
scrappage in response to increasing fuel
economy standards.
Greenspan & Cohen (1996) offer
additional foundations from which to
think about vehicle stock and scrappage.
Their work identifies two types of
scrappage: Engineering scrappage and
cyclical scrappage. Engineering
scrappage represents the physical wear
on vehicles, which results in their being
scrapped. Cyclical scrappage represents
the effects of macroeconomic conditions
on the relative value of new and used
vehicles; under economic growth the
demand for new vehicles increases and
the value of used vehicles declines,
resulting in increased scrappage. In
addition to allowing new vehicle prices
to affect cyclical vehicle scrappage a` la
the Gruenspecht effect, Greenspan and
Cohen also note that engineering
scrappage seems to increase where EPA
emission standards also increase; as
more costs goes towards compliance
technologies, it becomes more
expensive to maintain and repair more
complicated parts, and scrappage
increases. In this way, Greenspan and
Cohen identify two ways that fuel
economy standards could affect vehicle
scrappage: (1) Through increasing new
vehicle prices, thereby increasing used
vehicle prices, and finally, reducing onroad vehicle scrappage, and (2) by
shifting resources towards fuel-saving
technologies—potentially reducing the
durability of new vehicles by making
them more complex.
(b) Aggregate vs. Atomic Data Source in
the Literature
One important distinction between
the literatures on vehicles scrappage is
between those that use atomic vehicle
data, data following specific individual
vehicles, and those that use some level
of aggregated data, data that counts the
total number of vehicles of a given type.
The decision to scrap a vehicle is an
atomic one—that is, made on an
individual vehicle basis. The decision
relates to the cost of maintaining a
vehicle, and the value of the vehicle
both on the used car market, and as
scrap metal. Generally, a used car owner
will decide to scrap a vehicle where the
value of the vehicle is less than the
value of the vehicle as scrap metal plus
the cost to maintain or repair the
vehicle. In other words, the owner gets
more value from scrapping the vehicle
than continuing to drive it or from
selling it.
Recent work is able to model
scrappage as an atomic decision due to
the availability of a large database of
used vehicle transactions. Following
works by other authors including:

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Busse, Knittel, & Zettelmeyer (2013);
Sallee, West, & Fan (2010); Alcott &
Wozny (2013); and Li, Timmins, & von
Haefen (2009)—Jacobsen & van Benthem
(2015) considers the impact of changes
in gasoline prices on used vehicle
values and scrappage rates. In turn, they
consider the impact of an increase in
used vehicle values on the scrappage
rate of those vehicles. They find that
increases in gasoline price result in a
reduction in the scrappage rate of the
most fuel efficient vehicles and an
increase in the scrappage rate of the
least fuel efficient vehicles. This has
important implications for the validity
of the average fuel economy values
linked to model years and assumed to
be constant over the life of that model
year fleet within this study. Future
iterations of this study could further
investigate the relationship between fuel
economy, vehicle usage, and scrappage,
as noted in other places in this
discussion.
While the decision to scrap a vehicle
is made atomically, the data available to
NHTSA on scrappage rates and
variables that influence these scrappage
rates are aggregate measures. This
influences the best available methods to
measure the impacts of new vehicle
prices on existing vehicle scrappage.
The result is that this study models
aggregate trends in vehicle scrappage
and not the atomic decisions that make
up these trends. Many other works
within the literature use the same data
source and general scrappage construct,
such as: Walker (1968); Park (1977),
Greene & Chen (1981); Gruenspecht
(1981); Gruenspecht (1982); Feeney &
Cardebring (1988); Greenspan & Cohen
(1996); Jacobsen & van Bentham (2015);
and Bento, Roth, & Zhuo (2016) all use
the same aggregate vehicle registration
data as the source to compute vehicle
scrappage.
Walker (1968) and Bento, Roth, &
Zhuo (2016) use aggregate data to
directly compute the elasticity of
scrappage from measures of used
vehicle prices. Walker (1968) uses the
ratio of used vehicle Consumer Price
Index (CPI) to repair and maintenance
CPI. Bento, Roth, & Zhuo (2016) use
used vehicle prices directly. While the
direct measurement of the elasticity of
scrappage is preferable in a theoretical
sense, the CAFE model does not predict
future values of used vehicles, only
future prices of new vehicles. For this
reason, any model compatible with the
current CAFE model must estimate a
reduced form similar to Park (1977);
Gruenspecht (1981); Greenspan & Cohen
(1996), who use some form of new
vehicle prices or the ratio of new
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repair prices to impute some measure of
the effect of new vehicle prices on
vehicle scrappage.
(c) Historical Trends in Vehicle
Durability
Waker (1968); Park (1977); Feeney &
Cardebring (1988); Hamilton &
Macauley (1999); and Bento, Ruth, &
Zhuo (2016) all note that vehicles
change in durability over time. Walker
(1968) simply notes a significant
distinction in expected vehicle lifetimes
pre- and post-World War I. Park (1977)
discusses a ‘durability factor’ set by the
producer for each year so that different
vintages and makes will have varying
expected lifecycles. Feeney &
Cardebring (1988) show that durability
of vehicles appears to have generally
increased over time both in the U.S. and
Swedish fleets using registration data
from each country. They also note that
the changes in median lifetime between
the Swedish and U.S. fleet track well,
with a 1.5 year lag in the U.S. fleet. This
lag is likely due to variation in how the
data is collected—the Swedish vehicle
registry requires a title to unregister a
vehicle, and therefore gets immediate
responses, where the U.S. vehicle
registry requires re-registration, which
creates a lag in reporting.
Hamilton & Macauley (1999) argue for
a clear distinction between embodied
versus disembodied impacts on vehicle
longevity. They define embodied
impacts as inherent durability similar to
Park’s producer supplied ‘durability
factor’ and Greenspan’s ‘engineering
scrappage’ and disembodied effects
those which are environmental, not
unlike Greenspan and Cohen’s ‘cyclical
scrappage.’ They use calendar year and
vintage dummy variables to isolate the
effects—concluding that the
environmental factors are greater than
any pre-defined ‘durability factor.’ Some
of their results could be due to some
inflexibility of assuming model year
coefficients are constant over the life of
a vehicle, and there may be some
correlation between the observed life of
the later model years of their sample
and the ‘stagflation’ 276 of the 1970’s.
Bento, Ruth, & Zhuo (2016) find that the
average vehicle lifetime has increased
27% from 1969 to 2014 by sub-setting
their data into three model year cohorts.
To implement these findings in the
scrappage model incorporated into the
CAFE model, this study takes pains to
estimate the effect of durability changes
in such a way that the historical
durability trend can be projected into
the future; for this reason, a continuous
276 Continued high inflation combined with high
unemployment and slow economic growth.

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‘durability’ factor as a function of model
year vintage is included.
(d) Models of the Gruenspecht Effect
Used in Other Policy Analyses
This is not the first estimation of the
‘Gruenspecht Effect’ for policy
considerations. In their Technical
Support Document (TSD) for the 2004
proposal to reduce greenhouse gas
emissions from motor vehicles,
California Air Resources Board (CARB)
outlines how they utilized the CARBITS
vehicle transaction choice model in an
attempt to capture the effect of
increasing new vehicle prices on vehicle
replacement rates. They consider data
from the National Personal
Transportation Survey (NPTS) as a
source of revealed preferences and a
University of California (UC) study as a
source of stated preferences for the
purchase and sale of household fleets
under different prices and attributes
(including fuel economy) of new
vehicles.
The transaction choice model
represents the addition and deletion of
a vehicle from a household fleet within
a short period of time as a
‘‘replacement’’ of a vehicle, rather than
as two separate actions. Their final data
set consists of 790 vehicle replacements,
292 additions, and 213 deletions; they
do not include the deletions, but assume
any vehicle over 19 years old that is
sold is scrapped. This allows them to
capture a slowing of vehicle
replacement under higher new vehicle
prices, but because their model does not
include deletions, does not explicitly
model vehicle scrappage, but assumes
all vehicles aged 20 and older are
scrapped rather than resold. They
calibrate the model so that the overall
fleet size is benchmarked to Emissions
FACtors (EMFAC) fleet predictions for
the starting year; the simulation then
produces estimates that match the
EMFAC predictions without further
calibration.
The CARB study captures the effect
on new vehicle prices on the fleet
replacement rates and offers some
precedence for including some estimate
of the Gruenspecht Effect. One
important thing to note is that because
vehicles that exited the fleet without
replacement were excluded, the effect of
new vehicle prices on scrappage rates
where the scrapped vehicle is not
replaced is not captured. Because new
and used vehicles are substitutes, it is
expected that used vehicle prices will
increase with new vehicle prices.
Because higher used vehicle prices will
lower the number of vehicles whose
cost of maintenance is higher than their
value, it is expected that not only will

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replacements of used vehicles slow, but
also, that some vehicles that would have
been scrapped without replacement
under lower new vehicle prices will
now remain on the road because their
value will have increased. Aggregate
measures of the Gruenspecht effect will
include changes to scrappage rates both
from slower replacement rates, and
slower non-replacement scrappage rates.
(2) Description of Data Sources
NHTSA purchases proprietary data on
the registered vehicle population from
IHS/Polk for safety analyses. IHS/Polk
has annual snapshots of registered
vehicle counts beginning in calendar
year (CY) 1975 and continuing until
calendar year 2015. The data includes
the following regulatory classes as
defined by NHTSA: Passenger cars, light
trucks (classes 1 and 2a), and medium
and heavy-duty trucks (classes 2b and
3). Polk separates these vehicles into
another classification scheme: Cars and
trucks. Under their schema, pickups,
vans, and SUVs are treated as trucks,
and all other body styles are included as
cars. In order to build scrappage models
to support the model year (MY) 2021–
2026 light duty vehicle (LDV) standards,
it was important to separate these
vehicle types in a way compatible with
the existing CAFE model.
There were two compatible choices to
aggregate scrappage rates: (1) By
regulatory class or (2) by body style.
Because for NHTSA’s purposes vans/
SUVs are sometimes classified as
passenger cars and sometimes as light
trucks, and there was no quick way to
reclassify some SUVs as passenger cars
within the Polk dataset, NHTSA chose
to aggregate survival schedules by body
style. This approach is also preferable
because NHTSA uses body style specific
lifetime VMT schedules. Vehicles
experience increased wear with use;
many maintenance and repair events are
closely tied to the number of miles on
a vehicle. The current version of the
CAFE model considers separate lifetime
VMT schedules for cars, vans/SUVs,
pickups and classes 2b and 3 vehicles.
These vehicles are assumed to serve
different purposes and, as a result, are
modelled to have different average
lifetime VMT patterns. These different
uses likely also result in different
lifetime scrappage patterns.
Once stratified into body style level
buckets, the data can be aggregated into
population counts by vintage and age.
These counts represent the population
of vehicles of a given body style and
vintage in a given calendar year. The
difference between the counts of a given
vintage and vehicle type from one
calendar year to the next is assumed to

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represent the number of vehicles of that
vintage and type scrapped in a given
year. There were a couple other
important data considerations for the
calculations of the historical scrappage
rates not discussed here but discussed
in detail in the PRIA Chapter 8.277
For historical data on vehicle
transaction prices, the models use data
from the National Automobile Dealers
Association (NADA), which records the
average transaction price of all lightduty vehicles. These transaction prices
represent the prices consumers paid for
new vehicles but do not include any
value of vehicles that may have been
traded in to dealers. Importantly, these
transaction prices were not available by
vehicle body styles; thus, the models
will miss any unique trends that may
have occurred for a particular vehicle
body style. This may be particularly
relevant for pickup trucks, which
observed considerable average price
increases as luxury and high option
pickups entered the market. Future
models will further consider
incorporating price series that consider
the price trends for cars, SUVs and vans,
and pickups separately.278
The models use the NADA price
series rather than the Bureau of Labor
Statistics (BLS) New Vehicle Consumer
Price Index (CPI), used by Park (1977)
and Greenspan & Cohen (1997), because
the BLS New Vehicle CPI makes quality
adjustments to the new vehicle prices.
BLS assumes that additions of safety
and fuel economy equipment are a
quality adjustment to a vehicle model,
which changes the good and should not
be represented as an increase in its
price. While this is good for some
purposes, it presumes consumers fully
value technologies that improve fuel
economy. Because it is the purpose to
this study to measure whether this is
true, it is important that vehicle prices
adjusted to fully value fuel economy
improving technologies, which would
obscure the ability to measure the
277 The first is any discontinuity caused by a
change in how Polk collected their data beginning
in calendar year 2010, and the second is the use of
the adjustment described in Greenspan & Cohen
(1996).
278 Note: Using historical data aggregated by body
styles to capture differences in price trends by body
style does not require the assertion technology costs
are or are not borne by the body style to which they
are applied. If the body-style level average price
change is used, then the assumption is
manufacturers do not cross-subsidize across body
styles, whereas if the average price change is used
then the assumption is they would proportion costs
equally for each vehicle. These are implementation
questions to be worked out once NHTSA has a
historical data source separating price series by
body styles, but these do not matter in the current
model which only considers the average price of all
light-duty vehicles.

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preference for more fuel efficient and
expensive new vehicles, are not used.
As further justification for using the
NADA price series over the BLS New
Vehicle CPI, Park (1977) cites a
discontinuity found in the amount of
quality adjustments made to the series
so that more adjustments are made over
time. This could further limit the ability
for the BLS New Vehicle CPI to predict
changes in vehicle scrappage.
Vehicle scrappage rates are also
influenced by fuel economy and fuel
prices. Historical data on the fuel
economy by vehicle style from model
years 1979–2016 was obtained from the
2016 EPA Motor Trends Report.279 The
van/SUV fuel economy values represent
a sales-weighted harmonic average of
the individual body styles. Fuel prices
were obtained from Department of
Energy (DOE) historical values, and
future fuel prices within the CAFE
model use the Annual Energy Outlook
(AEO) future oil price projections.280
From these values the average cost per
100 miles of travel for the cohort of new
vehicles in a given calendar year and
the average cost per 100 miles of travel
for each used model year cohort in that
same calendar year are computed.281 It
is expected that as the new vehicle fleet
becomes more efficient (holding all
other attributes constant) that it will be
more desirable, and the demand for
used vehicles should decrease
(increasing their scrappage). As a given
model year cohort becomes more
expensive to operate due to increases in
fuel prices, it is expected the scrappage
of that model year will increase. It is
perhaps worth noting that more efficient
model year vintages will be less
susceptible to changes in fuel prices, as
279 Light-Duty Automotive Technology, Carbon
Dioxide Emissions, and Fuel Economy Trends: 1975
Through 2016, U.S. EPA (Nov. 2016), available at
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=
P100PKK8.pdf.
280 Note: The central analysis uses the AEO
reference fuel price case, but sensitivity analysis
also considers the possibility of AEO’s low and high
fuel price cases.
281 Work by Jacobsen and van Bentham suggests
that these initial average fuel economy values may
not represent the average fuel economy of a model
year cohort as it ages—mainly, they find that the
most fuel efficient vehicles scrap earlier than the
least fuel efficient models in a given cohort. This
may be an important consideration in future
endeavors that work to link fuel economy, vehicle
miles travelled (VMT), and scrappage. Studies on
‘‘the rebound effect’’ suggest that lowering the fuel
cost per driven mile increases the demand for VMT.
With more miles, a vehicle will be worth less as its
perceived remaining useful life will be shorter; this
will result in the vehicle being more likely to be
scrapped. A rebound effect is included in the CAFE
model, but because reliable data on how average
VMT by age has varied over calendar year and
model year vintage is not available, expected
lifetime VMT is not included within the current
dynamic scrappage model.

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(3) Summary of Model Estimation
The most predictive element of
vehicle scrappage is what Greenspan

and Cohen deem ‘engineering
scrappage.’ This source of scrappage is
largely determined by the age of a
vehicle and the durability of a specific
model year vintage. Vehicle scrappage
typically follows a roughly logistic
function with age—that is,
instantaneous scrappage increases to
some peak, and then declines, with age
as noted in Walker (1968); Park (1977);
Greene & Chen (1981); Gruenspecht
(1981); Feeney & Cardebring (1988);
Greenspan & Cohen (1996); Hamilton &
Macauley (1999); and Bento, Roth, &
Zhuo (2016). Thus, this analysis also
uses a logistic function to capture this
trend of vehicle scrappage with age but
allows non-linear terms to capture any
skew to the logistic relationship.
Specific details about the final and
considered forms of engineering
scrappage by body styles is presented in
the PRIA Chapter 8.
The final and considered independent
variables intended to capture cyclical
elements of vehicle scrappage and the
considered forms of each are discussed
in PRIA Chapter 8; here only inclusion
of the GDP growth rate is discussed. The
GDP growth rate is not a single-period
effect; both the current and previous
GDP growth rates will affect vehicle
scrappage rates. A single year increase
will affect scrappage differently than a
multi-period trend. For this reason, an
optimal number of lagged terms are
included: The within-period GDP
growth rate, the previous period GDP
growth rate, and the growth rate from
two prior years for the car model, while
for vans/SUVs, and pickups, the current
and previous period GDP growth rate
are sufficient.
Similarly, the considered model
allows that one-period changes in new
vehicle prices will affect the used
vehicle market differently than a

consistent trend in new vehicle prices.
The optimal number of lags is three so
that the price trend from the current
year and the three prior years influences
the demand for and scrappage of used
vehicles. Note: The average lease length
is three years 283 so that the price of an
average vehicle coming off lease is
estimated to affect the scrappage rate of
used vehicles—this is a major source of
the newest used vehicles that enter the
used car fleet. Further, because
increases in new vehicle prices due to
increased stringency of CAFE standards
is the primary mechanism through
which CAFE standards influence
vehicle scrappage and the CAFE Model
assumes that usage, efficiency, and
safety vary with the age of the vehicle,
particular attention is paid to the form
of this effect. It is important to know the
likelihood of scrappage by the age of the
vehicle to correctly account for the
additional costs of additional fatalities
and increased fuel consumption from
deferred scrappage. Thus, the influence
of increasing new vehicle prices is
allowed to influence the demand for
used vehicles (and reduce their
scrappage) differently for different ages
of vehicles in the scrappage model. We
discuss both how we determined the
correct form and number of lags for each
body style in PRIA Chapter 8.
The final cyclical factor affecting
vehicle scrappage in the preferred
model is the cost per 100 miles of travel
both of new vehicles and of the vehicle
which is the subject of the decision to
scrap or not to scrap. The new vehicle
cost per 100 miles is defined as the ratio
of the average fuel price faced by new
vehicles in a given calendar year and
the average new vehicle fuel economy
for 100 miles in the same calendar year,
and varies only with calendar year:

The cost per 100 miles of the
potentially scrapped vehicle is
described as the ratio of the average fuel

price faced by that model year vintage
in a given calendar year and the average
fuel economy for 100 miles of travel for

that model year when it was new, and
varies both with calendar year and
model year:

282 The 2017 Annual Report of the Board of
Trustees of the Federal Old-Age and Survivors
Insurance and Federal Disability Insurance Trust
Funds, Social Security Administration (2017),

available at https://www.ssa.gov/oact/tr/2017/
tr2017.pdf.
283 See e.g., Edmunds January 2017 Lease Market
Report, Edmunds (Jan. 2017), https://

dealers.edmunds.com/static/assets/articles/leasereport-jan-2017.pdf.

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absolute changes in their cost per mile
will be smaller. The functional forms of
the cost per mile measures are further
discussed in the model specification
subsection 3 below.
Aggregate measures that cyclically
affect the value of used vehicles include
macroeconomic factors like the real
interest rate, the GDP growth rate,
unemployment rates, cost of
maintenance and repairs, and the value
of a vehicle as scrap metal or as parts.
Here only the GDP growth rate is
discussed, as this is the only measure
included in the final model. Extended
reasoning as to why other variables are
not included in the final model in the
PRIA Chapter 8 is offered, but the
discussion was omitted here for brevity
in describing only the final model.
Generally economic growth will result
in a higher demand for new vehicles—
cars in aggregate are normal goods—and
a reduction in the value of used
vehicles. The result should be an
increase in the scrappage rate of existing
vehicles so that we expect the GDP
growth rate to be an important predictor
of vehicle scrappage rates.
NHTSA sourced the GDP growth rate
from the 2017 OASDI Trustees
Report.282 The Trustees Report offers
credible projections beyond 2032.
Because the purpose of building this
scrappage model is to project vehicle
survival rates under different fuel
economy alternatives and the current
fuel economy projections go as far
forward as calendar year 2032, using a
data set that encompasses projections at
least through 2032 is an essential
characteristic of any source used for this
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The average per-gallon fuel price
faced by a model year vintage in a given
calendar year is the annual average fuel
price of all fuel types present in that

model year fleet for the given calendar
year, weighted by the share of each fuel
type in that model year fleet. Or the
following, where FT represents the set

of fuel types present in a given model
year vintage:

For these variables, the best fit model
includes the cost per mile of both the
new and the used vehicle for the current
and prior year. This is congruent with
research that suggests consumers
respond to current fuel prices and fuel
price changes. The selection process of
this form for the cost per mile and the
implications is discussed in PRIA
Chapter 8.
There are a couple other controlling
factors considered in our final model.
The 2009 Car Allowance Rebate System
(CARS) is not outlined here but is
outlined in PRIA Chapter 8. This
program aimed to accelerate the
retirement of less fuel efficient vehicles
and replace them with more fuel
efficient vehicles. Further discussion of
how this is controlled for is located in
PRIA Chapter 8. Finally, evidence of
autocorrelation was found, and
including three lagged values of the
dependent variable addresses the
concern. Treatment of autocorrelation is
discussed in PRIA Chapter 8.
One additional issue encountered in
the estimations of scrappage rates is that
the models predict too many vehicles
remain on the road in the later years.
This issue occurs because the data
beyond age 15 are progressively more
sparsely populated; vehicles over 15
years were not captured in the Polk data
until 1994, when each successive
collection year added an additional age
of vehicles until 2005 when all ages
began to be collected. This means that
for vehicles over the age of 25 there are
only 10 years of data. In order to correct
for this issue the fact that the final fleet
share converges to roughly the same
share for most model years for a given
vehicle type is used. The predicted
versus historical relationships seem to
deviate beginning around age 20; thus,
for scrappage rates for vehicles beyond
age 20 an exponential decay function
which guarantees that by age 40 the
final fleet share reaches the convergence
level observed in the historical data is
applied. The application of the decay
function and mathematical definition is
further defended in PRIA Chapter 8.
A sensitivity case is also developed to
isolate the magnitude of the
Greunspecht effect. The impacts on
costs and benefits are presented in

section VII.H.1 of this document. In
order to isolate the effect, the price of
new vehicles is held constant at CY
2016 levels. The specific methodology
used to do so is described in detail in
PRIA Chapter 8, as is the leakage
implied by comparing the reference and
no Gruenspecht effect sensitivity cases.
It is important to note here that the
leakage calculated ranges between 12
and 18% across regulatory alternatives.
This is in line with Jacobsen & van
Bentham (2015) estimates which put
leakage for their central case between 13
and 16%. Their high gasoline price case
is more in line this analysis’ central
case—with fuel prices of $3/gallon—and
predicts leakage of 21%. This further
validates the scrappage model effects
against examples in the literature.
The models used for this analysis are
able to capture the relationship for
vehicle scrappage as it varies with age
and how this relationship changes with
increases to new vehicle price, the cost
per mile of travel of new and used
vehicles, and how the rate varies
cyclically with the GDP growth rate. It
also controls for the CARS program and
checks the influence of a change in
Polk’s data collection procedures. The
goodness of fit measures and the
plausibility of the predictions of the
model are discussed at some length in
PRIA Chapter 8. In the next section, the
impacts of updating the static scrappage
models to the dynamic models on
average vehicle age and usage, by body
styles, and across different regulatory
assumptions are discussed.

increase. However, given the
distribution of the mileage
accumulation schedule by age, this
amounts only to a two percent increase
in the expected lifetime mileage
accumulation of an individual vehicle.
This range is consistent with DOT
expectations in terms of direction and
magnitude.
The use of a static retirement
schedule, while deemed a reasonable
approach in the past, is a limited
representation of scrappage behavior. It
fails to account for increasing vehicle
durability—occurring for the last several
decades—and the resulting increase in
average vehicle age in the on-road fleet,
which has nearly doubled since 1980.284
Thus, turning off the dynamic scrappage
model described above would not
impose a perspective on the analysis
that is neutral with respect to observed
scrappage behavior but would instead
represent a strong assumption that
asserts important trends in the historical
record will abruptly cease or change
direction.
As discussed above, the dynamic
scrappage model implemented to
support this proposal affects total fleet
size through several mechanisms.
Although the model accounts for the
influence of changes to average new
vehicle price and U.S. GDP growth, the
most influential mechanism, by far, is
the observed trend of increasing vehicle
durability over successive model years.
This phenomenon is prominently
discussed in the academic literature
related to vehicle retirement, where
there is no disagreement about its
existence or direction.285 In fact, when
the CAFE model is exercised in a way
that keeps average new vehicle prices at
(approximately) MY 2016 levels, the onroad fleet grows from an initial level of
228 million in 2016 to 340 million in
2050, an increase of 49% over the 35year period from 2016 to 2050.
The historical data show the size of
the registered vehicle population (i.e.,
the on-road fleet) growing by about 60%
in the 35 years between 1980 and

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(c) What is the estimated effect on
vehicle retirement and how do results
compare to previously estimated fleets
and VMT?
The expected lifetime of a car
estimated using the static scrappage
schedule from the 2012 final rule, both
in years and miles, is between the
expected lifetime of the dynamic
scrappage model in the absence of CAFE
standards and under the baseline
standards. Estimated by the dynamic
scrappage model, the average vehicle is
expected to live 15.1 years under the
influence of only market demand for
new technology, and 15.6 years under
the baseline scenario, a four percent

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284 Based

on data from FHWA and IHS/Polk.
(1968); Park (1977); Feeney &
Cardebring (1988); Hamilton & Macauley (1999);
and Bento, Ruth, & Zhuo (2016) note that vehicles
change in durability over time.
285 Waker

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2015.286 In the 35 years between 2016
and 2050, our simulation shows the onroad fleet growing from about 230
million vehicles to about 345 million
vehicles when the market adopts only
the amount of fuel economy, which it
naturally demands. The simulated
growth over this period is about 50%
from today’s level, rather than the 60%
observed in the historical data over the
last 35 years. Under the baseline
regulatory scenario, the growth over the
next 35 years is simulated to be about
54%—still short of the observed growth
over a comparable period of time. In
fact, the simulated annual growth rate in
the size of the on-road fleet in this
analysis, about 1.3%, is lower than the
long-term average annual growth rate of
about two percent dating back to the
1970s.287
Additionally, there are inherent
precision limitations in measuring
something as vast and complex as the
registered vehicle population. For
decades, the two authoritative sources
for the size of the on-road fleet have
been R.L. Polk (now IHS/Polk) and
FHWA. For two decades these two
sources differed by more than 10% each
year, only lately converging to within a
few percent of each other. These
discrepancies over the correct
interpretation of the data by each source
have consistently represented
differences of more than 10 million
vehicles.
The total number of new vehicles
projected to enter the fleet is slightly
higher than the historical trend (though
the impact of the great recession makes
it hard to say by how much). More
generally, the projections used in the
analysis cover long periods of time
without exhibiting the kinds of
fluctuation that are present in the
historical record. For example, the
forecast of GDP growth in our analysis
posits a world in which the United
States sees uninterrupted positive
annual growth in real GDP for four
decades. The longest such period in the
historical record is 17 years and still
included several years of low (but
positive) growth during that interval.
Over such a long period of time, in
the absence of deep insight into the
future of the U.S. auto industry, it is
sensible to assume that the trends
286 There are two measurements of the size of the
registered vehicle population that are considered to
be authoritative. One is produced by the Federal
Highway Administration, and the other by R.L. Polk
(now part of IHS). The Polk measurement shows
fleet growth between 1980 and 2015 of about 85%,
while the FHWA measurement shows a slower
growth rate over that period, only about 60%.
287 Based on calculations using Polk’s National
Vehicle Population Profile (NVPP).

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observed over the course of decades are
likely to persist. Analyzing fuel
economy standards requires an
understanding of the mechanisms that
influence new vehicle sales, the size of
the on-road fleet, and vehicle miles
traveled. It is upon these mechanisms
that the policy acts: Increasing/
decreasing new vehicle prices changes
the rate at which new vehicles are sold,
changing the attributes and prices of
these vehicles influences the rates at
which all used vehicles are retired, the
overall size of the on-road fleet
determines the total amount of VMT,
which in turn affects total fuel
consumption, fatalities, and other
externalities. The fact that DOT’s
bottom-up approach produces results in
line with historical trends is both
expected and intended.
This is not to say that all details of
this new approach will be immediately
intuitive for reviewers accustomed to
results that do not include a dynamic
sales model or dynamic scrappage
model, much less results that combine
the two. For example, some reviewers
may observe that today’s analysis shows
that, compared to the baseline
standards, the proposed standards
produce a somewhat smaller on-road
fleet (i.e., fewer vehicles in service)
despite somewhat increased sales of
new vehicles (consistent with reduced
new vehicle prices) and decreased
prices for used vehicles. While it might
be natural to assume that reduced prices
of new vehicles and increased sales
should lead to a larger on-road fleet, in
our modelling, the increased sales are
more than offset by the somewhat
accelerated scrappage that accompanies
the estimated decrease in new vehicle
prices. This outcome represents an onroad fleet that is both smaller and a little
younger on average (relative to the
baseline) and ‘‘turns over’’ more
quickly.
To further test the validity of the
scrappage model, a dynamic forecast
was constructed for calendar years 2005
through 2015 to see how well it predicts
the fleet size for this period. The last
true population the scrappage model
‘‘sees’’ is the 2005 registered vehicle
population. It then takes in known
production volumes for the new model
year vehicles and dynamically estimates
instantaneous scrappage rates for all
registered vehicles at each age for CYs
2006–2015, based only on the observed
exogenous values that inform the model
(GDP growth rate, observed new vehicle
prices, and cost per mile of operation),
fleet attributes of the vehicles (body
style, age, cost per mile of operation),
and estimated scrappage rates at earlier
ages. Within this exercise, the scrappage

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model relies on its own estimated
values as the previous scrappage rates at
earlier ages, forcing any estimation
errors to propagate through to future
years. This exercise is discussed further
in PRIA Chapter VII. While the years of
the recession represent a significant
shock to the size of the fleet, briefly
reversing many years of annual growth,
the model recovers quickly and
produces results within one percent of
the actual fleet size, as it did prior to the
recession.
In order to compare the magnitudes of
the sales and scrappage effects across
different fuel economy standards
considered it is important to define
comparable measures. The sales effect
in a single calendar year is simply the
difference in new vehicle sales across
alternatives. However, the scrappage
effect in a single calendar year is not
simply the change in fleet size across
regulatory alternatives. The scrappage
model predicts the probability that a
vehicle will be scrapped in the next year
conditional on surviving to that age; the
absolute probability that a vehicle
survives to a given age is conditional on
the scrappage effect for all previous
analysis years. In other words, if
successive calendar years observe lower
average new vehicle prices, the effect of
increased scrappage on fleet size will
accumulate with each successive
calendar year—because fewer vehicles
survived to previous ages, the same
probability of scrappage would result in
a smaller fleet size for the following year
as well, though fewer vehicles will have
been scrapped than in the previous year.
To isolate the number of vehicles not
scrapped in a single calendar year
because of the change in standards, the
first step is to calculate the number of
vehicles scrapped in every calendar year
for both the proposed standards and the
baseline; this is calculated by the interannual change in the size of the used
vehicle fleet (vehicles ages 1–39) for
each alternative. The difference in this
measure across regulatory alternatives
represents the change in vehicle
scrappage because of a change in the
standards. The resulting scrappage
effect for a single calendar year can be
compared to the difference across
regulatory alternatives in new vehicle
sales for the same calendar year as a
comparison of the relative magnitudes
of the two effects. In most years, under
the proposed standards relative to the
baseline standards, the analysis shows
that for each additional new vehicles
sold, two to four used vehicles are
removed from the fleet. Over the time
period of the analysis these predicted
differences in the numbers of vehicles
accumulate, resulting in a maximum of

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
seven million fewer vehicles by CY
2033 for the proposed CAFE standards
relative to the augural standards, and
nine million fewer vehicles by CY 2035
for the proposed GHG standards relative
to the current GHG standards. Tables
11–29 and 11–30 in the PRIA show the
difference in the fleet size by calendar
year for the proposed standards relative
to the augural standards for the CAFE
and GHG programs, respectively.
To understand why the sales and
scrappage effects do not perfectly offset
each other to produce a constant fleet
size across regulatory alternatives it is
important to remember that the decision
to buy a new vehicle and the decision
to scrap a used vehicle are often not
made by the same household as a joint
decision. The average length of initial
ownership for new vehicles is
approximately 6.5 years (and increasing
over time). Cumulative scrappage up to
age seven is typically less than 10%of
the initial fleet. This suggests that most
vehicles belong to more than one
household over the course of their
lifetimes. The household that is
deciding whether or not to purchase a
new vehicle is rarely the same
household deciding whether or not to
scrap a vehicle. So a vehicle not
scrapped in a given year is seldom the
direct substitute for a new vehicle
purchased by that household.
Considering this, it is not expected that
for every additional vehicle scrapped,
there is also an additional new one sold,
under the proposed standards relative to
the baseline standards.
Further, while sales and scrappage
decisions are both influenced by
changes in new vehicle prices, the
mechanism through which these
decisions change are different for the
two effects. A decrease in average new
vehicle prices will directly increase the
demand for new vehicles along the same
demand curve. This decrease in new
vehicle prices will cause a substitution
towards new vehicles and away from
used vehicles, shifting the entire
demand curve for used vehicles
downwards. This will decrease both the
equilibrium prices of used vehicles, as
shown in Figure 8–16 of the PRIA. Since
the decision to scrap a vehicle in a given
year is closely related to the difference
between the vehicle’s value and the cost
to maintain it, if the value of a vehicle
is lower than the cost to maintain it, the
current owner will not choose to
maintain the vehicle for their own use
or for resale in the used car market, and
the vehicle will be scrapped. That is, a
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vehicle to the used car market if the
price of the vehicle is greater than the
cost of supplying it. Lowering the
equilibrium price of used vehicles will
lower the increase the number of
scrapped vehicles, lowering the supply
of used vehicles, and decreasing the
equilibrium quantity. The change in
new vehicle sales is related to demand
of new vehicles at a given price, but the
change in used vehicle scrappage is
related to the shift in the demand curve
for used vehicles, and the resulting
change in the quantity current owners
will supply; these effects are likely not
exactly offsetting.
Our models indicate that the ratio of
the magnitude of the scrappage effect to
the sales effect is greater than one so
that the fleet grows under more
stringent scenarios. However, it is
important to remember that not all
vehicles are driven equally; used
vehicles are estimated to deliver
considerably less annual travel than
new vehicles. Further, used vehicles
only have a portion of their original life
left so that it will take more than one
used vehicle to replace the full lifetime
of a new vehicle, at least in the longrun. The result of the lower annual VMT
and shorter remaining lifetimes of used
vehicles, is that although the fleet is
1.5% bigger in CY 2050 for the augural
baseline than it is for the proposed
standards, the total non-rebound VMT
for CY 2050 is 0.4% larger in the
augural baseline than in the proposed
standards. This small increase in VMT
is consistent with a larger fleet size; if
more used vehicles are supplied, there
likely is some small resulting increase
in VMT.
Our models face some limitations,
and work will continue toward
developing methods for estimating
vehicle sales, scrappage, and mileage
accumulation. For example, our
scrappage model assumes that the
average VMT for a vehicle of a
particular vintage is fixed—that is, aside
from rebound effects, vehicles of a
particular vintage drive the same
amount annually, regardless of changes
to the average expected lifetimes. The
agencies seek comment on ways to
further integrate the survival and
mileage accumulation schedules. Also,
our analysis uses sales and scrappage
models that do not dynamically interact
(though they are based on similar sets of
underlying factors); while both models
are informed by new vehicle prices, the
model of vehicle sales does not respond
to the size and age profile of the on-road
fleet, and the model of vehicle

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scrappage rates does not respond to the
quantity of new vehicles sold. As one
potential option for development, the
potential for an integrated model of
sales and scrappage, or for a dynamic
connection between the two models will
be considered. Comment is sought on
both the sales and scrappage models, on
potential alternatives, and on data and
methods that may enable practicable
integration of any alternative models
into the CAFE model.
7. Accounting for the Rebound Effect
Caused by Higher Fuel Economy
(a) What is the rebound effect and how
is it measured?
Amending and establishing fuel
economy and GHG standards at a lesser
stringency than the augural standards
for future model years will lead to
comparatively lower fuel economy for
new cars and light trucks, thus
increasing the amount of fuel they
consume in traveling each mile than
they would under the augural standard.
The resulting increase in their per-mile
fuel and total driving costs will lead to
a reduction in the number of miles they
are driven each year over their lifetimes,
and example of the rebound effect that
is usually associated with energy
efficiency improvements working in
reverse. The fuel economy rebound
effect—a specific example of the energy
efficiency rebound effect for the case of
motor vehicles—refers to the welldocumented tendency of vehicles’ use
to increase when their fuel economy is
improved and the cost of driving each
mile declines as a result.
(b) What does the literature say about
the magnitude of this effect?
Table–II–43 summarizes estimates of
the fuel economy rebound effect for
light-duty vehicles from studies
conducted through 2008, when the
agencies originally surveyed research on
this subject.288 After summarizing all of
the estimates reported in published and
other publicly-available research
available at that time, it distinguishes
among estimates based on the type of
data used to develop them. As the table
reports, estimates of the rebound effect
ranged from 6% to as high as 75%, and
the range spanned by published
estimates was nearly as wide (7–75%).
288 Complete references to the studies
summarized in Table 8–2 are included in the PRIA,
and many of the unpublished studies are available
in the docket for this rulemaking.

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Most studies reported more than one
empirical estimate, and the authors of
published studies typically identified

the single estimate in which they were
most confident; these preferred

estimates spanned only a slightly
narrower range (9–75%).

Despite their wide range, these
estimates displayed a strong central
tendency, as Table–II–43 also shows.
The average values of all estimates,
those that were published, and authors’
preferred estimates from published
studies were 22–23%, and the median
estimates in each category were close to
these values, indicating nearly
symmetric distributions. The estimates
in each category also clustered fairly
tightly around their respective average
values, as shown by their standard
deviations in the table’s last column.
When classified by the type of data they
relied on, U.S. aggregate time-series data
produced slightly smaller values
(averaging 18%) than did panel-type
data for individual states (23%) or
household survey data (25%). In each
category, the median estimate was again
quite close to the average reported
value, and comparing the standard
deviations of estimates based on each
type of data again suggests a fairly tight
scatter around their respective means.

Of these studies, a then recentlypublished analysis by Small & Van
Dender (2007), which reported that the
rebound effect appeared to be declining
over time in response to increasing
income of drivers, was singled out.
These authors theorized that rising
income increased the opportunity cost
of drivers’ time, leading them to be less
responsive over time to reductions in
the fuel cost of driving each mile. Small
and Van Dender reported that while the
rebound effect averaged 22% over the
entire time period they analyzed (1967–
2001), its value had declined by half—
or to 11%—during the last five years
they studied (1997–2001). Relying
primarily on forecasts of its continued
decline over time, the analysis reduced
the 20% rebound effect that NHTSA
used to analyze the effects of CAFE
standards for light trucks produced
during model years 2005–07 and 2008–
11 to 10% for their analysis of CAFE
and GHG standards for MY 2012–16
passenger cars and light trucks.

Table–II–44 summarizes estimates of
the rebound effect reported in research
that has become available since the
agencies’ original survey, which
extended through 2008, and the
following discussion briefly summarizes
the approaches used by these more
recent studies. Bento et al. (2009)
combined demographic characteristics
of more than 20,000 U.S. households,
the manufacturer and model of each
vehicle they owned, and their annual
usage of each vehicle from the 2001
National Household Travel Survey with
detailed data on fuel economy and other
attributes for each vehicle model
obtained from commercial publications.
The authors aggregated vehicle models
into 350 categories representing
combinations of manufacturer, vehicle
type, and age, and use the resulting data
to estimate the parameters of a complex
model of households’ joint choices of
the number and types of vehicles to
own, and their annual use of each
vehicle.

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Bento et al. estimate the effect of
vehicles’ operating costs per mile,
including fuel costs, which depend in
part on each vehicle’s fuel economy, as
well as maintenance and insurance
expenses, on households’ annual use of
each vehicle they own. Combining the
authors’ estimates of the elasticity of
vehicle use with respect to per-mile
operating costs with the reported
fraction of total operating costs
accounted for by fuel (slightly less than
one-half) yields estimates of the
rebound effect. The resulting values
vary by household composition, vehicle
size and type, and vehicle age, ranging
from 21 to 38%, with a composite
estimate of 34% for all households,
vehicle models, and ages. The smallest
values apply to new luxury cars, while
the largest estimates are for light trucks
and households with children, but the
implied rebound effects differ little by
vehicle age.
Barla et al. (2009) analyzed the
responses of car and light truck
ownership, vehicle travel, and average
fuel efficiency to variation in fuel prices

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and aggregate economic activity
(measured by gross product) using
panel-type data for the 10 Canadian
provinces over the period from 1990
through 2004. The authors estimated a
system of equations for these three
variables using statistical procedures
appropriate for models where the
variables of interest are simultaneously
determined (that is, where each variable
is one of the factors explaining variation
in the others). This procedure enabled
them to control for the potential
‘‘reverse influence’’ of households’
demand for vehicle travel on their
choices of how many vehicles to own
and their fuel efficiency levels when
estimating the effect of variation in fuel
efficiency on vehicle use.
Their analysis found that provinciallevel aggregate economic activity had
moderately strong effects on car and
light truck ownership and use but that
fuel prices had only modest effects on
driving and the average fuel efficiency
of the light-duty vehicle fleet. Each of
these effects became considerably
stronger over the long term than in the

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year when changes in economic activity
and fuel prices initially occurred, with
three to five years typically required for
behavioral adjustments to stabilize.
After controlling for the joint
relationship among vehicle ownership,
driving demand, and the fuel efficiency
of cars and light trucks, Barla et al.
estimated elasticities of average vehicle
use with respect to fuel efficiency that
corresponded to a rebound effect of
eight percent in the short run, rising to
nearly 20% within five years. A notable
feature of their analysis was that
variation in average fuel efficiency
among the individual Canadian
provinces and over the time period they
studied was adequate to identify its
effect on vehicle use, without the need
to combine it with variation in fuel
prices in order to identify its effect.
Wadud et al. (2009) combine data on
U.S. households’ demographic
characteristics and expenditures on
gasoline over the period 1984–2003
from the Consumer Expenditure Survey
with data on gasoline prices and an
estimate of the average fuel economy of

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vehicles owned by individual
households (constructed from a variety
of sources). They employ these data to
explore variation in the sensitivity of
individual households’ gasoline
consumption to differences in income,
gasoline prices, the number of vehicles
owned by each household, and their
average fuel economy. Using an
estimation procedure intended to
account for correlation among
unmeasured characteristics of
households and among estimation errors
for successive years, the authors explore
variation in the response of fuel
consumption to fuel economy and other
variables among households in different
income categories and between those
residing in urban and rural areas.
Dividing U.S. households into five
equally-sized income categories, Wadud
et al. estimate rebound effects ranging
from 1–25%, with the smallest estimates
(8% and 1%) for the two lowest income
categories, and significantly larger
estimates for the middle (18%) and two
highest income groups (18 and 25%). In
a separate analysis, the authors estimate
rebound effects of seven percent for
households of all income levels residing
in U.S. urban areas and 21% for rural
households.
West & Pickrell (2011) analyzed data
on more than 100,000 households and
300,000 vehicles from the 2009
Nationwide Household Transportation
Survey to explore how households
owning multiple vehicles chose which
of them to use and how much to drive
each one on the day the household was
surveyed. Their study focused on how
the type and fuel economy of each
vehicle a household owned, as well as
its demographic characteristics and
location, influenced household
members’ decisions about whether and
how much to drive each vehicle. They
also investigated whether fuel economy
and fuel prices exerted similar
influences on vehicle use, and whether
households owning more than one
vehicle tended to substitute use of one
for another—or vary their use of all of
them similarly—in response to
fluctuations in fuel prices and
differences in their vehicles’ fuel
economy.
Their estimates of the fuel economy
rebound effect ranged from as low as
nine percent to as high as 34%, with
their lowest estimates typically applying
to single-vehicle households and their
highest values to households owning
three or more vehicles. They generally
found that differences in fuel prices
faced by households who were surveyed
on different dates or who lived in
different regions of the U.S. explained
more of the observed variation in daily

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vehicle use than did differences in
vehicles’ fuel economy. West and
Pickrell also found that while the
rebound effect for households’ use of
passenger cars appeared to be quite
large—ranging from 17% to nearly twice
that value—it was difficult to detect a
consistent rebound effect for SUVs.
Anjovic & Haas (2012) examined
variation in vehicle use and fuel
efficiency among six European nations
over an extended period (1970–2006),
using an elaborate model and estimation
procedure intended to account for the
existence of common underlying trends
among the variables analyzed and thus
avoid identifying spurious or
misleading relationships among them.
The six nations included in their
analysis were Austria, Germany,
Denmark, France, Italy, and Sweden; the
authors also conducted similar analyses
for the six nations combined. The
authors focused on the effects of average
income levels, fuel prices, and the fuel
efficiency of each nation’s fleet of cars
on the total distance they were driven
each year and their total fuel energy
consumption. They also tested whether
the responses of energy consumption to
rising and falling fuel prices appeared to
be symmetric in the different nations.
Anjovic and Haas report a long-run
aggregate rebound effect of 44% for the
six nations their study included, with
corresponding values for individual
nations ranging from a low of 19% (for
Austria) to as high as 56% (Italy). These
estimates are based on the estimated
response of vehicle use to variation in
average fuel cost per kilometer driven in
each of the six nations and for their
combined total. Other information
reported in their study, however,
suggests lower rebound effects; their
estimates of the response of total fuel
energy consumption to fuel efficiency
appear to imply an aggregate rebound
effect of 24% for the six nations, with
values ranging from as low as 0–3% (for
Austria and Denmark) to as high as 70%
(Sweden), although the latter is very
uncertain. These results suggest that
vehicle use in European nations may be
somewhat less sensitive to variation in
driving costs caused by changes in fuel
efficiency than to changes in driving
costs arising from variation in fuel
prices, but they find no evidence of
asymmetric responses of total fuel
consumption to rising and falling prices.
Using data on household characteristics
and vehicle use from the 2009
Nationwide Household Transportation
Survey (NHTS), Su (2012) analyzes the
effects of locational and demographic
factors on household vehicle use and
investigates how the magnitude of the
rebound effect varies with vehicles’

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annual use. Using variation in the fuel
economy and per-mile cost of and
detailed controls for the demographic,
economic, and locational characteristics
of the households that owned them (e.g.,
road and population density) and each
vehicle’s main driver (as identified by
survey respondents), the author
employs specialized regression methods
to capture the variation in the rebound
effect across 10 different categories of
vehicle use.
Su estimated the overall rebound
effect for all vehicles in the sample
averaged 13%, and that its magnitude
varied from 11–19% among the 10
different categories of annual vehicle
use. The smallest rebound effects were
estimated for vehicles at the two
extremes of the distribution of annual
use—those driven comparatively little,
and those used most intensively—while
the largest estimated effects applied to
vehicles that were driven slightly more
than average. Controlling for the
possibility that high-mileage drivers
respond to the increased importance of
fuel costs by choosing vehicles that offer
higher fuel economy narrowed the range
of Su’s estimated rebound effects
slightly (to 11–17%), but did not alter
the finding that they are smallest for
lightly- and heavily-driven vehicles and
largest for those with slightly above
average use.
Linn (2013) also uses the 2009 NHTS
to develop a linear regression approach
to estimate the relationship between the
VMT of vehicles belonging to each
household and a variety of different
factors: Fuel costs, vehicle
characteristics other than fuel economy
(e.g., horsepower, the overall ‘‘quality’’
of the vehicle), and household
characteristics (e.g., age, income). Linn
reports a fuel economy rebound effect
with respect to VMT of between 20–
40%.
One interesting result of the study is
that when the fuel efficiency of all
vehicles increases, which would be the
long-run effect of rising fuel efficiency
standards, two factors have opposing
effects on the VMT of a particular
vehicle. First, VMT increases when that
vehicle’s fuel efficiency increases. But
the increase in the fuel efficiency of the
household’s other vehicles causes the
vehicle’s own VMT to decrease. Because
the effect of a vehicle’s own fuel
efficiency is larger than the other
vehicles’ fuel efficiency, VMT increases
if the fuel efficiency of all vehicles
increases proportionately. Linn also
finds that VMT responds much more
strongly to vehicle fuel economy than to
gasoline prices, which is at variance
with the Hymel et al. and Greene results
discussed above.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
Like Su and Linn, Liu et al. (2014)
employed the 2009 NHTS to develop an
elaborate model of an individual
household’s choices about how many
vehicles to own, what types and ages of
vehicles to purchase, and how much
combined driving to do using all of
them. Their analysis used a complex
mathematical formulation and statistical
methods to represent and measure the
interdependence among households’
choices of the number, types, and ages
of vehicles to purchase, as well as how
intensively to use them.
Liu et al. employed their model to
simulate variation in households’ total
vehicle use to changes in their income
levels, neighborhood characteristics,
and the per-mile fuel cost of driving
averaged over all vehicles each
household owns. The complexity of the
relationships among the number of
vehicles owned, their specific types and
ages, fuel economy levels, and use
incorporated in their model required
them to measure these effects by
introducing variation in income,
neighborhood attributes, and fuel costs,
and observing the response of
households’ annual driving. Their
results imply a rebound effect of
approximately 40% in response to
significant (25–50%) variation in fuel
costs, with almost exactly symmetrical
responses to increases and declines.
A study of the rebound effect by
Frondel et al. (2012) used data from
travel diaries recorded by more than
2,000 German households from 1997
through 2009 to estimate alternative
measures of the rebound effect, and to
explore variation in their magnitude
among households. Each household
participating in the survey recorded its
automobile travel and fuel purchases
over a period of one to three years and
supplied information on its composition
and the personal characteristics of each
of its members. The authors converted
households’ travel and fuel
consumption to a monthly basis, and
used specialized estimation procedures
(quantile and random-effects panel
regression) to analyze monthly variation
in their travel and fuel use in relation
to differences in fuel prices, the fuel
efficiency of each vehicle a household
owned, and the fuel cost per mile of
driving each vehicle.
Frondel et al. estimate four separate
measures of the rebound effect, three of
which capture the response of vehicle
use to variation in fuel efficiency, fuel
price, and fuel cost per mile traveled,
and a fourth capturing the response of
fuel consumption to changes in fuel
price. Their first three estimates range
from 42% to 57%, while their fourth
estimate corresponds to a rebound effect

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of 90%. Although their analysis finds no
significant variation of the rebound
effect with household income, vehicle
ownership, or urban versus rural
location, it concludes that the rebound
effect is substantially larger for
households that drive less (90%) than
for those who use their vehicles most
intensively (56%).
Gillingham (2014) analyzed variation
in the use of approximately five million
new vehicles sold in California from
2001 to 2003 during the first several
years after their purchase, focusing
particularly on how their use responded
to geographic and temporal variation in
fuel prices. His sample consisted
primarily of personal or household
vehicles (87%) but also included some
that were purchased by businesses,
rental car companies, and government
agencies. Using county-level data, he
analyzed the effect of differences in the
monthly average fuel price paid by their
drivers on variation in their monthly
use and explored how that effect varied
with drivers’ demographic
characteristics and household incomes.
Gillingham’s analysis did not include
a measure of vehicles’ fuel economy or
fuel cost per mile driven, so he could
not measure the rebound effect directly,
but his estimates of the effect of fuel
prices on vehicle use correspond to a
rebound effect of 22–23% (depending
on whether he controlled for the
potential effect of gasoline demand on
its retail price). His estimation
procedure and results imply that vehicle
use requires nearly two years to adjust
fully to changes in fuel prices. He found
little variation in the sensitivity of
vehicle use to fuel prices among car
buyers with different demographic
characteristics, although his results
suggested that it increases with their
income levels.
Weber & Farsi (2014) analyzed
variation in the use of more than 70,000
individual cars owned by Swiss
households who were included in a
2010 survey of travel behavior. Their
analysis focuses on the simultaneous
relationships among households’
choices of the fuel efficiency and size
(weight) of the vehicles they own, and
how much they drive each one,
although they recognize that fuel
efficiency cannot be chosen
independently of vehicle weight. The
authors employ a model specification
and statistical estimation procedures
that account for the likelihood that
households intending to drive more will
purchase more fuel-efficient cars but
may also choose more spacious and
comfortable—and thus heavier—
models, which affects their fuel
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vehicles are generally less fuel-efficient.
The survey data they rely on includes
both owners’ estimates of their annual
use of each car and the distance it was
actually driven on a specific day;
because they are not closely correlated,
the authors employ them as alternative
measures of vehicle use to estimate the
rebound effect, but this restricts their
sample to the roughly 8,100 cars for
which both measures are available.
Weber and Farsi’s estimates of the
rebound effect are extremely large: 75%
using estimated annual driving and 81%
when they measure vehicle use by
actual daily driving. Excluding vehicle
size (weight) and limiting the choices
that households are assumed to consider
simultaneously to just vehicles’ fuel
efficiency and how much to drive
approximately reverses these estimates,
but both are still very large. Using a
simpler procedure that does not account
for the potential effect of driving
demand on households’ choices among
vehicle models of different size and fuel
efficiency produces much smaller
values for the rebound effect: 37% using
annual driving and 19% using daily
travel. The authors interpret these latter
estimates as likely to be too low because
actual on-road fuel efficiency has not
improved as rapidly as suggested by the
manufacturer-reported measure they
employ. This introduces an error in
their measure that may be related to a
vehicle’s age, and their more complex
estimation procedure may reduce its
effect on their estimates. Nevertheless,
even their lower estimates exceed those
from many other studies of the rebound
effect, as Table 8–2 shows.
Hymel, Small, & Van Dender (2010)—
and more recently, Hymel & Small
(2015)—extended the simultaneous
equations analysis of time-series and
state-level variation in vehicle use
originally reported in Small & Van
Dender (2007) and to test the effect of
including more recent data. As in the
original 2007 study, both subsequent
extensions found that the fuel economy
rebound effect had declined over time
in response to increasing personal
income and urbanization but had risen
during periods when fuel prices
increased. Because they rely on the
response of vehicle use to fuel cost per
mile to estimate the rebound effect,
however, none of these three studies is
able to detect whether its apparent
decline in response to rising income
levels over time truly reflects its effect
on drivers’ responses to changing fuel
economy—the rebound effect itself—or
simply captures the effect of rising
income on their sensitivity to fuel

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prices.289 These updated studies each
revised Small and Van Dender’s original
estimate of an 11% rebound effect for
1997–2011 upward when they included
more recent experience: To 13% for the
period 2001–04, and subsequently to
18% for 2000–2009.
In their 2015 update, Hymel and
Small hypothesized that the recent
increase in the rebound effect could be
traced to a combination of expanded
media coverage of changing fuel prices,
increased price volatility, and an
asymmetric response by drivers to
variation in fuel costs. The authors
estimated that about half of the apparent
increase in the rebound effect for recent
years could be attributed to greater
volatility in fuel prices and more media
coverage of sudden price changes. Their
results also suggest that households
curtail their vehicle use within the first
year following an increase in fuel prices
and driving costs, while the increase in
driving that occurs in response to
declining fuel prices—and by
implication, to improvements in fuel
economy—occurs more slowly.
West et al. (2015) attempted to infer
the fuel economy rebound effect using
data from Texas households who
replaced their vehicles with more fuelefficient models under the 2009 ‘‘Cash
for Clunkers’’ program, which offered
sizeable financial incentives to do so.
Under the program, households that
retired older vehicles with fuel economy
levels of 18 miles per gallon (MPG) or
less were eligible for cash incentives
ranging from $3,500–4,000, while those
retiring vehicles with higher fuel
economy were ineligible for such
rebates. The authors examined the fuel
economy, other features, and
subsequent use of new vehicles
households in Texas purchased to
replace older models that narrowly
qualified for the program’s financial
incentives because their fuel economy
was only slightly below the 18 MPG
threshold. They then compared these to
the fuel economy, features, and use of
new vehicles that demographically
comparable households bought to
replace older models, but whose slightly
higher fuel economy—19 MPG or
289 DeBorger et al. (2016) analyze the separate
effects of variation in household income on the
sensitivity of their vehicle use to fuel prices and the
fuel economy of vehicles they own. Their results
imply the decline in the fuel economy rebound
effect with income reported in Small & Van Dender
(2007) and its subsequent extensions appears to
result entirely from a reduction in drivers’
sensitivity to fuel prices as their incomes rise,
rather than from any effect of rising income on the
sensitivity of vehicle use to improving fuel
economy; i.e., on the fuel economy rebound effect
itself.

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above—made them barely ineligible for
the program.
The authors reported that the higher
fuel economy of new models that
eligible households purchased in
response to the generous financial
incentives offered under the ‘‘Cash for
Clunkers’’ did not prompt their buyers
to use them more than the older, lowMPG vehicles they replaced. They
attributed this apparent absence of a
fuel economy rebound effect—which
they described as an ‘‘attributeadjusted’’ measure of its magnitude—to
the fact that eligible households chose
to buy less expensive, smaller, and
lower-performing models to replace
those they retired. Because these
replacements offered lower-quality
transportation service, their buyers did
not drive them more than the vehicles
they replaced.
The applicability of this result to the
proposal’s analysis is doubtful because
previous regulatory analyses assumed
that manufacturers could achieve
required improvements in fuel economy
without compromising the performance,
carrying and towing capacity, comfort,
or safety of cars and light trucks from
recent model years.290 While this may
be technically true, doing so would
come at a combined greater cost. If this
argument is correct, then amending
future standards at a reduced stringency
from their previously-adopted levels
would lead to less driving attributable to
rebound, and should therefore not lead
to artificial constraints in new vehicles’
other features that offset the reduction
in their use stemming from lower fuel
economy.
Most recently, De Borger et al. (2017)
analyze the response of vehicle use to
changes in fuel economy among a
sample of nearly 350,000 Danish
households owning the same model
vehicle, of which almost one-third
replaced it with a different model
sometime during the period from 2001
to 2011. By comparing the changes in
households’ driving from the early years
of this period to its later years among
those who replaced their vehicles
during the intervening period to the
changes in driving among households
who kept their original vehicles, the
authors attempted to isolate the effect of
changes in fuel economy on vehicle use
from those of other factors. They
measured the rebound effect as the
290 As discussed, this does not mean attributes of
future cars and light trucks will be anything close
to those manufacturers could have offered if lower
standards had remained in effect. Instead, the
agencies asserted features other than fuel economy
could be maintained at the levels offered in recent
model years—that features will not likely be
removed, but may not be improved.

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change in households’ vehicle use in
response to differences in the fuel
economy between vehicles they had
owned previously and the new models
they purchased to replace them, over
and above any change in vehicle use
among households who did not buy
new cars (and thus saw no change in
fuel economy).
These authors’ data enabled them to
control for the effects of changes over
time in household characteristics and
vehicle features other than fuel
economy that were likely to have
contributed to observed changes in
vehicle use. They also employed
complex statistical methods to account
for the fact that some households
replacing their vehicles may have done
so in anticipation of changes in their
driving demands (rather than the
reverse), as well as for the possibility
that some households who replaced
their cars may have done so because
their driving behavior was more
sensitive to fuel prices than other
households. Their estimates ranged
from 8–10%, varying only minimally
among alternative model specifications
and statistical estimation procedures or
in response to whether their sample was
restricted to households that replaced
their vehicles or also included
households that kept their original
vehicles throughout the period.291
Finally, De Borger et al. found no
evidence that the rebound effect is
smaller among lower-income
households than among their higherincome counterparts.
(c) What value have the agencies
assumed in this rule?
On the basis of all of the evidence
summarized here, a fuel economy
rebound effect of 20% has been chosen
to analyze the effects of the proposed
action. This is a departure from the 10%
value used in regulatory analyses for
MYs 2012–2016 and previous analyses
for MYs 2017–2025 CAFE and GHG
standards and represents a return to the
value employed in the analyses for MYs
2005–2011 CAFE standards. There are
several reasons the estimate of the fuel
economy rebound effect for this analysis
has been increased.
First, the 10% value is inconsistent
with nearly all research on the
magnitude of the rebound effect, as
Table–II–43 and Table–II–44 indicate.
Instead, it is based almost exclusively
291 This latter result suggests their estimates were
not biased by any tendency for households whose
demographic characteristics, economic
circumstances, or driving demands changed over
the period in ways that prompted them to replace
their vehicles with models offering different fuel
economy.

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on the finding of the 2007 study by
Small and Van Dender that the rebound
effect had been declining over time in
response to drivers’ rising incomes and
on extending that decline through future
years using an assumption of steady
income growth. As indicated above,
however, subsequent extensions of
Small and Van Dender’s original
research have produced larger estimates
of the rebound effect for recent years:
While their original study estimated the
rebound effect at 11% for 1997–2001,
the 2010 update by Hymel, Small, and
Van Dender reported a value of 13% for
2004, and Hymel and Small’s 2015
update estimated the rebound effect at
18% for 2003–09. Further, the issues
with state-level measures of vehicle use,
fuel consumption, and fuel economy
identified previously raise some doubt
about the reliability of these studies’
estimates of the rebound effect.
At the same time, the continued
increases in income that were
anticipated to produce a continued
decline in the rebound effect have not
materialized. The income measure (real
personal income per Capita) used in
these analyses has grown only
approximately one percent annually
over the past two decades and is
projected to grow at approximately
1.5% for the next 30 years, in contrast
to the two to three percent annual
growth assumed by the agencies when
developing earlier forecasts of the future
rebound effect. Further, another recent
study by DeBorger et al. (2016) analyzed
the separate effects of variation in
household income on the sensitivity of
their vehicle use to fuel prices and the
fuel economy of vehicles they own.
These authors’ results indicate that the
decline in the fuel economy rebound
effect with income reported in Small &
Van Dender (2007) and subsequent
research results entirely from a
reduction in drivers’ sensitivity to fuel
prices as their incomes rise rather than
from any effect of rising income on the
sensitivity of vehicle use to fuel
economy itself. This latter measure,
which DeBorger et al. find has not
changed significantly as incomes have
risen over time, is the correct measure
of the fuel economy rebound effect, so
their analysis calls into question its
assumed sensitivity to income.
Some studies of households’ use of
individual vehicles also find that the
fuel economy rebound effect increases
with the number of vehicles they own.
Because vehicle ownership is strongly
associated with household income, this
common finding suggests that the
overall value of the rebound effect is
unlikely to decline with rising incomes
as the agencies had previously assumed.

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In addition, buyers of new cars and light
trucks belong disproportionately to
higher-income households that already
own multiple vehicles, which further
suggests that the higher values of the
rebound effect estimated by many
studies for such households are more
relevant for analyzing use of newlypurchased cars and light trucks.
Finally, research on the rebound
effect conducted since the agencies’
original 2008 review of evidence almost
universally reports estimates in the 10–
40% (and larger) range, as Table–II–43
shows. Thus, the 20% rebound effect
used in this analysis more accurately
represents the findings from both the
studies considered in 2008 review and
the more recent analyses.
(1) What are the implications of the
rebound effect for VMT?
The assumed rebound effect not only
influences the use of new vehicles in
today’s analysis but also affects the
response of the initial registered vehicle
population to changes in fuel price
throughout their remaining useful lives.
The fuel prices used in this analysis are
lower than the projections used to
inform the 2012 Final Rule but generally
increase from today’s level over time. As
they do so, the rebound effect acts as a
price elasticity of demand for travel—as
the cost-per-mile of travel increases,
owners of all vehicles in the registered
population respond by driving less. In
particular, they drive 20% less than the
difference between the cost-per-mile of
travel when they were observed in
calendar year 2016 and the relevant
cost-per-mile at any future age. For the
new vehicles subject to this proposal
(and explicitly simulated by the CAFE
model), fuel economies increase relative
to MY 2016 levels, and generally
improve enough to offset the effect of
rising fuel prices—at least during the
years covered by the proposal. For those
vehicles, the difference between the
initial cost-per-mile of travel and future
travel costs is negative. As the vehicles
become less expensive to operate, they
are driven more (20% more than the
difference between initial and present
travel costs, precisely). Of course, each
of the regulatory alternatives considered
in the analysis would result in lower
fuel economy levels for vehicles
produced in model year 2020 and later
than if the baseline standards remained
in effect, so total VMT is lower under
these alternatives than under the
baseline.
(2) What is the mobility benefit that
accrues to vehicle owners?
The increase in travel associated with
the rebound effect produces additional

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benefits to vehicle owners, which reflect
the value to drivers and other vehicle
occupants of the added (or more
desirable) social and economic
opportunities that become accessible
with additional travel. As evidenced by
the fact that they elect to make more
frequent or longer trips when the cost of
driving declines, the benefits from this
added travel exceed drivers’ added
outlays for the fuel it consumes
(measured at the improved level of fuel
economy resulting from stricter CAFE
standards). The amount by which the
benefits from this increased driving
travel exceed its increased fuel costs
measures the net benefits they receive
from the additional travel, usually are
referred to as increased consumer
surplus.
NHTSA’s analysis estimates the
economic value of the decreased
consumer surplus provided by reduced
driving using the conventional
approximation, which is one half of the
product of the increase in vehicle
operating costs per vehicle-mile and the
resulting decrease in the annual number
of miles driven. Because it depends on
the extent of the change in fuel
economy, the value of economic
impacts from decreased vehicle use
changes by model year and varies
among alternative CAFE standards.
(d) Societal Externalities Associated
With CAFE Alternatives
(1) Energy Security Externalities
Higher U.S. fuel consumption will
produce a corresponding increase in the
nation’s demand for crude petroleum,
which is traded actively in a worldwide
market. The U.S. accounts for a large
enough share of global oil consumption
that the resulting boost in global
demand will raise its worldwide price.
The increase in global petroleum prices
that results from higher U.S. demand
causes a transfer of revenue to oil
producers worldwide from not only
buyers of new cars and light trucks, but
also other consumers of petroleum
products in the U.S. and throughout the
world, all of whom pay the higher price
that results.
Although these effects will be
tempered by growing U.S. oil
production, uncertainty in the long-term
import-export balance makes it difficult
to precisely project how these effects
might change in response to that
increased production. Growing U.S.
petroleum consumption will also
increase potential costs to all U.S.
petroleum users from possible
interruptions in the global supply of
petroleum or rapid increases in global
oil prices, not all of which are borne by

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the households or businesses who
increase their petroleum consumption
(that is, they are partly ‘‘external’’ to
petroleum users). If U.S. demand for
imported petroleum increases, it is also
possible that increased military
spending to secure larger oil supplies
from unstable regions of the globe will
be necessary.
These three effects are often referred
to collectively as ‘‘energy security
externalities’’ resulting from U.S.
petroleum consumption, and increases
in their magnitude are sometimes cited
as potential social costs of increased
U.S. demand for oil. To the extent that
they represent real economic costs that
would rise incrementally with increases
in U.S. petroleum consumption of the
magnitude likely to result from less
stringent CAFE and GHG standards,
these effects represent potential
additional costs of this proposed action.
Chapter 7 of the Regulatory Impact
Analysis for this proposed action
defines each of these energy security
externalities in detail, assesses whether
its magnitude is likely to change as a
consequence of this action, and
identifies whether that change
represents a real economic cost or
benefit of this action.

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(2) Environmental Externalities
The change in criteria pollutant
emissions that result from changes in
vehicle usage and fuel consumption is
estimated as part of this analysis.
Criteria air pollutants include carbon
monoxide (CO), hydrocarbon
compounds (usually referred to as
‘‘volatile organic compounds,’’ or VOC),
nitrogen oxides (NOX), fine particulate
matter (PM2.5), and sulfur oxides (SOX).
These pollutants are emitted during
vehicle storage and use, as well as
throughout the fuel production and
distribution system. While increases in
domestic fuel refining, storage, and
distribution that result from higher fuel
consumption will increase emissions of
these pollutants, reduced vehicle use
associated with the fuel economy
rebound effect will decrease their
emissions. The net effect of less
stringent CAFE standards on total
emissions of each criteria pollutant
depends on the relative magnitudes of
increases in its emissions during fuel
refining and distribution, and decreases
in its emissions resulting from
additional vehicle use. Because the
relationship between emissions in fuel
refining and vehicle use is different for
each criteria pollutant, the net effect of
increased fuel consumption from the
proposed standards on total emissions
of each pollutant is likely to differ.

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The social damage costs associated
with changes in the emissions of criteria
pollutants and CO2 was calculated,
attributing benefits and costs to the
regulatory alternatives considered based
on the sign of the change in each
pollutant. In previous rulemakings, the
agencies have considered the social cost
of CO2 emissions from a global
perspective, accumulating social costs
for CO2 emissions based on adverse
outcomes attributable to climate change
in any country. In this analysis,
however, the costs of CO2 emissions and
resulting climate damages from both
domestic and global perspectives were
considered. Chapter 9 of the Regulatory
Impact Analysis provides a detailed
discussion of how the agencies estimate
changes in emissions of criteria air
pollutants and CO2 and reports the
values the agencies use to estimate
benefits or costs associated with those
changes in emissions.
(3) Traffic Externalities (Congestion,
Noise)
Increased vehicle use associated with
the rebound effect also contributes to
increased traffic congestion and
highway noise. To estimate the
economic costs associated with these
consequences of added driving, the
estimates of per-mile congestion and
noise costs caused by increased use of
automobiles and light trucks developed
previously by the Federal Highway
Administration (FHWA) were applied.
These values are intended to measure
the increased costs resulting from added
congestion and the delays it causes to
other drivers and passengers and noise
levels contributed by automobiles and
light trucks. NHTSA previously
employed these estimates in its analysis
accompanying the MY 2011 final CAFE
rule as well as in its analysis of the
effects of higher CAFE standards for MY
2012–16 and MY 2017–2021. After
reviewing the procedures used by
FHWA to develop them and considering
other available estimates of these values
and recognizing that no commenters
have addressed these costs directly in
their comments on previous rules, the
values continue to be appropriate for
use in this proposal. For this analysis,
FHWA’s estimates of per-mile costs are
multiplied by the annual increases in
automobile and light truck use from the
rebound effect to yield the estimated
increases in total congestion and noise
externality costs during each year over
the lifetimes of the cars and light trucks
in the on-road fleet. Due to the fact that
this proposal represents a decrease in
stringency, the fuel economy rebound
effect results in fewer miles driven
under the action alternatives relative to

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the baseline, which generates savings in
congestion and road noise relative to the
baseline.
F. Impact of CAFE Standards on Vehicle
Safety
In past CAFE rulemakings, NHTSA
has examined the effect of CAFE
standards on vehicle mass and the
subsequent effect mass changes will
have on vehicle safety. While setting
standards based on vehicle footprint
helps reduce potential safety impacts
associated with CAFE standards as
compared to setting standards based on
some other vehicle attribute, footprintbased standards cannot entirely
eliminate those impacts. Although prior
analyses noted that there could also be
impacts because of other factors besides
mass changes, those impacts were not
estimated quantitatively.292 In this
current analysis, the safety analysis has
been expanded to include a broader and
more comprehensive measure of safety
impacts, as discussed below. A number
of factors can influence motor vehicle
fatalities directly by influencing vehicle
design or indirectly by influencing
consumer behavior. These factors
include:
(1) Changes, which affect the
crashworthiness of vehicles impact
other vehicles or roadside objects, in
vehicle mass made to reduce fuel
consumption. NHTSA’s statistical
analysis of historical crash data to
understand effects of vehicle mass and
size on safety indicates reducing mass
in light trucks generally improves
safety, while reducing mass in
passenger cars generally reduces safety.
NHTSA’s crash simulation modeling of
vehicle design concepts for reducing
mass revealed similar trends.293
(2) The delay in the pace of consumer
acquisition of newer safer vehicles that
results from higher vehicle prices
associated with technologies needed to
meet higher CAFE standards. Because of
a combination of safety regulations and
voluntary safety improvements,
passenger vehicles have become safer
over time. Compared to prior decades,
fatality rates have declined significantly
292 NHTSA included a quantification of reboundassociated safety impacts in its Draft TAR analysis,
but because the scrappage model is new for this
rulemaking, did not include safety impacts
associated with the effect of standards on new
vehicle prices and thus on fleet turnover. The fact
that the scrappage model did not exist previously
does not mean that the effects that it aims to show
were not important considerations, simply that the
agency was unable to account for them
quantitatively prior to the current analysis.
293 DOT HS 812051a—Methodology for
evaluating fleet protection of new vehicle designs
Application to lightweight vehicle designs, DOT HS
812051b Methodology for evaluating fleet
protection of new vehicle designs_Appendices.

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because of technological safety
improvements as well as behavioral
shifts such as increased seat belt use.
The results of this analysis project that
vehicle prices will be nearly $1,900
higher under the augural CAFE
standards compared to the preferred
alternative that would hold stringency
at MY 2020 levels in MYs 2021–2026.
This will induce some consumers to
delay or forgo the purchase of newer
safer vehicles and slow the transition of
the on-road fleet to one with the
improved safety available in newer
vehicles. This same factor can also shift
the mix of passenger cars and light
trucks.
(3) Increased driving because of better
fuel economy. The ‘‘rebound effect’’
predicts consumers will drive more
when the cost of driving declines. More
stringent CAFE standards reduce
vehicle operating costs, and in response,
some consumers may choose to drive
more. Driving more increases exposure
to risks associated with on-road
transportation, and this added exposure
translates into higher fatalities.
Although all three factors influence
predicted fatality levels that may occur,
only two of them, the changes in vehicle
mass and the changes in the acquisition
of safer vehicles—are actually imposed
on consumers by CAFE standards. The
safety of vehicles has improved over
time and is expected to continue
improving in the future commensurate
with the pace of safety technology
innovation and implementation and
motor vehicle safety regulation. Safety
improvements will likely continue
regardless of changes to CAFE
standards. However, its pace may be
modified if manufacturers choose to
delay or forgo investments in safety
technology because of the demand
CAFE standards impose on research,
development, and manufacturing
budgets. Increased driving associated
with rebound is a consumer choice.
Improved CAFE will reduce driving
costs, but nothing in the higher CAFE
standards compels consumers to drive
additional miles. If consumers choose to
do so, they are making a decision that
the utility of more driving exceeds the
marginal operating costs as well as the
added crash risk it entails. Thus, while
the predicted fatality impacts with all
three factors embedded into the model
are measured, the fatalities associated
with consumer choice decisions are
accounted for separately from those
resulting from technologies
implemented in response to CAFE
regulations or economic limitations
resulting from CAFE regulation. Only
those safety impacts associated with
mass reduction and those resulting from

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higher vehicle prices are directly
attributed to CAFE standards.294 This is
reflected monetarily by valuing extra
rebound miles at the full value of their
added driving cost plus the added safety
risk consumers experience, which
completely offsets the societal impact of
any added fatalities from this voluntary
consumer choice.
The safety component of CAFE
analysis has evolved over time. In the
2012 final rule, the analysis accounted
for the change in projected fatalities
attributable to mass reduction of new
vehicles. The model assumed that
manufacturers would choose mass
reduction as a compliance method
across vehicle classes such that the net
effect of mass reduction on fatalities was
zero. However, in the 2016 draft
Technical Assessment Report, DOT
made two consequential changes to the
analysis of fatalities associated with the
CAFE standards. In particular, first, the
modelling assumed that mass reduction
technology was available to all vehicles,
regardless of net safety impact, and
second, it accounted for the incremental
safety costs associated with additional
miles traveled due to the rebound effect.
The current analysis extends the
analysis to report incremental fatality
impacts associated with additional
miles traveled due to the rebound effect,
and identifies the increase in fatalities
associated with additional driving
separately from changes in fatalities
attributable other sources.295
The current analysis adds another
element: The effect that higher new
vehicle prices have on new vehicle sales
and on used vehicle scrappage, which
influences total expected fatalities
294 It could be argued fatalities resulting from
consumer’s decision to delay the purchase of newer
safer vehicles is also a market decision implying
consumers fully accept the added safety risk
associated with this delay and value the time value
of money saved by the delayed purchase more than
this risk. This scenario is likely accurate for some
purchasers. For others, the added cost may
represent a threshold price increase effectively
preventing them from being financially able to
purchase a new vehicle. Presently there is no way
to determine the proportion of lost sales reflected
by these two scenarios. The added driving from the
rebound effect results from a positive benefit of
CAFE, which reduces the cost of driving. By
contrast, the effect of retaining older vehicles longer
results from costs imposed on consumers, which
potentially limit their purchase options. Thus,
fatalities are attributed to retaining older vehicles
due to CAFE but not those resulting from decisions
to drive more. Comments are sought on this
assumption.
295 Drivers who travel additional miles are
assumed to experience benefits that at least offset
the costs they incur in doing so, including the
increased safety risks they face. Thus while the
number of additional fatalities resulting from
increased driving is reported, the associated costs
are not included among the social costs of the
proposal.

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because older vehicle vintages are
associated with higher rates of
involvement in fatal crashes than newer
vehicles. Finally, a dynamic fleet share
model also predicts the effects of
changes in the standards on the share of
light trucks and passenger cars in future
model year light-duty vehicle fleets.
Vehicles of different body styles have
different rates of involvement in fatal
crashes, so that changing the share of
each in the projected future fleet has
safety impacts; the implied safety effects
are captured in the current modelling.
The agencies seek comment on changes
to the safety analysis made in this
proposal, they seek particular comment
on the following changes:
(1) The sales scrappage models as
independent models: Two separate models
capture the effects of new vehicle prices on
new vehicle demand and used vehicle
retirement rates—the sales model and the
scrappage model, respectively. We seek
public comment on the methods used for
each of these models, in particular we seek
comment on:
• The assumptions and variables included in
the independent models
• The techniques and data used to estimate
the independent models
• The structure and implementation of the
independent models
(2) Integration of the sales and scrappage
models: The new sales and scrappage models
use many of the same predictors, but are not
directly integrated. We seek public comment
on, and data supporting whether integrating
the two models is appropriate.
(3) Integration of the scrappage rates and
mileage accumulation: The current model
assumes that annual mileage accumulation
and scrappage rates are independent of one
another. We seek public comment on the
appropriateness of this assumption, and data
that would support developing an interaction
between scrappage rates and mileage
accumulation, or testing whether such an
interaction is important to include.
(4) Increased risk of older vehicles: The
observed increase in crash and injury risk
associated with older vehicles is likely due
to a combination of vehicle factors and driver
factors. For example, older vehicles are less
crashworthy because in general they’re
equipped with fewer or less modern safety
features, and drivers of older cars are on
average younger and may be less skilled
drivers or less risk-averse than drivers of new
vehicles. We fit a model which includes both
an age and vintage affect, but assume that the
age effect is entirely a result of changes in
average driver demographics, and not
impacted by changes in CAFE or GHG
standards. We seek comment on this
approach for attributing increased older
vehicle risk. Is the analysis likely to
overestimate or underestimate the safety
benefits under the proposed alternative?
(5) Changes in the mix of light trucks and
passenger cars: The dynamic fleet share
model predicts changes in the future share of
light truck and passenger car vehicles.
Changes in the mix of vehicles may result in

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increased or decreased fatalities. Does the
dynamic fleet share model reasonably
capture consumers’ decisions about how they
substitute between different types and sizes
of vehicles depending on changes in fuel
economy, relative and absolute prices, and
other vehicle attributes? We seek comment
on whether our safety analysis provides a
reasonable estimate of the effects of changes
in fleet mix on future fatalities.

1. Impact of Weight Reduction on Safety

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The primary goals of CAFE and CO2
standards are reducing fuel
consumption and CO2 emissions from
the on-road light-duty vehicle fleet; in
addition to these intended effects, the
potential of the standards to affect
vehicle safety is also considered.296 As
a safety agency, NHTSA has long
considered the potential for adverse
safety consequences when establishing
CAFE standards, and under the CAA,
EPA considers factors related to public
health and human welfare, including
safety, in regulating emissions of air
pollutants from mobile sources.
Safety trade-offs associated with fuel
economy increases have occurred in the
past, particularly before NHTSA CAFE
standards were attribute-based; past
safety trade-offs may have occurred
because manufacturers chose at the
time, in response to CAFE standards, to
build smaller and lighter vehicles.
Although the agency now uses attributebased standards, in part to protect
against excessive vehicle downsizing,
the agency must be mindful of the
possibility of related safety trade-offs in
the future. In cases where fuel economy
improvements were achieved through
reductions in vehicle size and mass, the
smaller, lighter vehicles did not fare as
well in crashes as larger, heavier
vehicles, on average.
Historically, as shown in FARS data
analyzed by NHTSA, the safest cars
generally have been heavy and large,
while cars with the highest fatal-crash
rates have been light and small. The
question, then, is whether past is
necessarily a prologue when it comes to
potential changes in vehicle size (both
footprint and ‘‘overhang’’) and mass in
296 In this rulemaking document, ‘‘vehicle safety’’
is defined as societal fatality rates per vehicle mile
of travel (VMT), including fatalities to occupants of
all vehicles involved in collisions, plus any
pedestrians. Injuries and property damage are not
within the scope of the statistical models discussed
in this section because of data limitations (e.g.,
limited information on observed or potential
relationships between safety standards and injury
and property damage outcomes, consistency of
reported injury severity levels). Rather, injuries and
property damage are represented within the CAFE
model through adjustment factors based on
observed relationships between societal costs of
fatalities and societal injury and property damage
costs.

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response to the more stringent future
CAFE and GHG standards.
Manufacturers stated they will reduce
vehicle mass as one of the cost-effective
means of increasing fuel economy and
reducing CO2 to meet standards, and
this approach is incorporated this
expectation into the modeling analysis
supporting the standards. Because the
analysis discerns a historical
relationship between vehicle mass, size,
and safety, it is reasonable to assume
these relationships will continue in the
future.
(a) Historical Analyses of Vehicle Mass
and Safety
Researchers have been using
statistical analysis to examine the
relationship of vehicle mass and safety
in historical crash data for many years
and continue to refine their techniques.
In the MY 2012–2016 final rule, the
agencies stated we would conduct
further study and research into the
interaction of mass, size, and safety to
assist future rulemakings and start to
work collaboratively by developing an
interagency working group between
NHTSA, EPA, DOE, and CARB to
evaluate all aspects of mass, size, and
safety. The team would seek to
coordinate government-supported
studies and independent research to the
greatest extent possible to ensure the
work is complementary to previous and
ongoing research and to guide further
research in this area.
The agencies also identified three
specific areas to direct research in
preparation for future CAFE/CO2
rulemaking regarding statistical analysis
of historical data. First, NHTSA would
contract with an independent
institution to review statistical methods
NHTSA and DRI used to analyze
historical data related to mass, size, and
safety, and to provide recommendations
on whether existing or other methods
should be used for future statistical
analysis of historical data. This study
would include a consideration of
potential near multicollinearity in the
historical data and how best to address
it in a regression analysis. The 2010
NHTSA report (hereinafter 2010 Kahane
report) was also peer reviewed by two
other experts in the safety field—Farmer
(Insurance Institute for Highway Safety)
and Lie (Swedish Transport
Administration).297
Second, NHTSA and EPA, in
consultation with DOE, would update
the MY 1991–1999 database where
297 All three peer reviews are available in Docket
No. NHTSA–2010–0152, Relationships Between
Fatality Risk, Mass, and Footprint, https://
www.regulations.gov/docket?D=NHTSA-2010-0152.

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safety analyses in the NPRM and final
rule are based with newer vehicle data
and create a common database that
could be made publicly available to
address concerns that differences in
data were leading to different results in
statistical analyses by different
researchers.
And third, to assess if the design of
recent model year vehicles
incorporating various mass reduction
methods affect relationships among
vehicle mass, size, and safety, the
agencies sought to identify vehicles
using material substitution and smart
design and to assess if there is sufficient
crash data involving those vehicles for
statistical analysis. If sufficient data
exists, statistical analysis would be
conducted to compare the relationship
among mass, size, and safety of these
smart design vehicles to vehicles of
similar size and mass with more
traditional designs.
By the time of the MY 2017–2025
final rule, significant progress was made
on these tasks: The independent review
of recent and updated statistical
analyses of the relationship between
vehicle mass, size, and crash fatality
rates had been completed. NHTSA
contracted with the University of
Michigan Transportation Research
Institute (UMTRI) to conduct this
review, and the UMTRI team led by
Green evaluated more than 20 papers,
including studies done by NHTSA’s
Kahane, Wenzel of the U.S. Department
of Energy’s Lawrence Berkeley National
Laboratory, Dynamic Research, Inc., and
others. UMTRI’s basic findings are
discussed in Chapter 11 of the PRIA
accompanying this NPRM.
Some commenters in recent CAFE
rulemakings, including some vehicle
manufacturers, suggested designs and
materials of more recent model year
vehicles may have weakened the
historical statistical relationships
between mass, size, and safety. It was
agreed that the statistical analysis would
be improved by using an updated
database reflecting more recent safety
technologies, vehicle designs and
materials, and reflecting changes in the
vehicle fleet. An updated database was
created and employed for assessing
safety effects for that final rule. The
agencies also believed, as UMTRI found,
different statistical analyses may have
produced different results because they
used slightly different datasets for their
analyses.
To try to mitigate this issue and to
support the current rulemaking, NHTSA
created a common, updated database for
statistical analysis consisting of crash
data of model years 2000–2007 vehicles
in calendar years 2002–2008, as

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compared to the database used in prior
NHTSA analyses, which was based on
model years 1991–1999 vehicles in
calendar years 1995–2000. The new
database was the most up-to-date
possible, given the processing lead time
for crash data and the need for enough
crash cases to permit statistically
meaningful analyses. NHTSA made the
preliminary version of the new
database, which was the basis for
NHTSA’s 2011 preliminary report
(hereinafter 2011 Kahane report),
available to the public in May 2011, and
an updated version in April 2012 (used
in NHTSA’s 2012 final report,
hereinafter 2012 Kahane report),298
enabling other researchers to analyze
the same data and hopefully minimize
discrepancies in results because of
inconsistencies across databases.299
Since the publication of the MYs
2017–2025 final rule, NHTSA has
sponsored, and is sponsoring, new
studies and research to inform the
current CAFE and CO2 rulemaking. In
addition, the National Academy of
Sciences published a new report in this
area.300 Throughout the rulemaking
process, NHTSA’s goal is to publish as
much of our research as possible. In
establishing standards, all available
data, studies, and information
objectively without regard to whether
they were sponsored by the agencies,
will be considered.
Undertaking these tasks has helped
come closer to resolving ongoing
debates in statistical analysis research of
historical crash data. It is intended that
these conclusions will be applied going
forward in future rulemakings, and it is
believed the research will assist the
public discussion of the issues. Specific
historical analyses (in addition to
NHTSA’s own analysis) on vehicle mass
and safety used to support this
rulemaking include:
• The 2011 and 2013 NHTSA
Workshops on Vehicle Mass, Size, and
Safety;
• the University of Michigan
Transportation Research Institute
(UMTRI) independent review of a set of
statistical relationships between vehicle
curb weight, footprint variables (track
width, wheelbase), and fatality rates
from vehicle crashes;
• the 2012 Lawrence Berkeley
National Laboratory (LBNL) Phase 1 and
Phase 2 reports on the sensitivity of
298 Those databases are available at ftp://
ftp.nhtsa.dot.gov/CAFE/.
299 See 75 FR 25324, 25395–25396 (May 7, 2010)
(for a discussion of planned statistical analyses).
300 Cost, Effectiveness and Deployment of Fuel
Economy Technologies for Light-Duty Vehicles,
National Academy of Sciences (2015).

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NHTSA’s baseline results and casualty
risk per VMT;
• the 2012 DRI reports on, among
other things, the effects of mass
reduction on crash frequency and
fatality risk per crash;
• LBNL’s subsequent review of DRI’s
study;
• the 2015 National Academy of
Sciences Report; and
• the 2017 NBER working paper
analyzing the relationships among
traffic fatalities, CAFE standards, and
distributions of MY 1989–2005 lightduty vehicle curb weights.
A detailed discussion of each analysis
is discussed in Chapter 11 of the PRIA
accompanying this proposed rule.
(b) Recent NHTSA Analysis Supporting
CAFE Rulemaking
As mentioned previously, NHTSA
and EPA’s 2012 joint final rule for MYs
2017 and beyond set ‘‘footprint-based’’
standards, with footprint being defined
as roughly equal to the wheelbase
multiplied by the average of the front
and rear track widths. Basing standards
on vehicle footprint ideally helps to
discourage vehicle manufacturers from
downsizing their vehicles; the agencies
set higher (more stringent) mile per
gallon (mpg) targets for smaller-footprint
vehicles but would not similarly
discourage mass reduction that
maintains footprint while potentially
improving fuel economy. Several
technologies, such as substitution of
light, high-strength materials for
conventional materials during vehicle
redesigns, have the potential to reduce
weight and conserve fuel while
maintaining a vehicle’s footprint and
maintaining or possibly improving the
vehicle’s structural strength and
handling.
In considering what technologies are
available for improving fuel economy,
including mass reduction, an important
corollary issue for NHTSA to consider is
the potential effect those technologies
may have on safety. NHTSA has thus far
specifically considered the likely effect
of mass reduction that maintains
footprint on fatal crashes. The
relationship between a vehicle’s mass,
size, and fatality risk is complex, and it
varies in different types of crashes. As
mentioned above, NHTSA, along with
others, has been examining this
relationship for more than a decade.301
The safety chapter of NHTSA’s April
2012 final regulatory impact analysis
(FRIA) of CAFE standards for MY 2017–
301 A complete discussion of the historical
analysis of vehicle mass and safety is located in
Chapter 10 of the PRIA accompanying this
proposed rulemaking.

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2021 passenger cars and light trucks
included a statistical analysis of
relationships between fatality risk,
mass, and footprint in MY 2000–2007
passenger cars and LTVs (light trucks
and vans), based on calendar year (CY)
2002–2008 crash and vehicleregistration data; 302 this analysis was
also detailed in the 2012 Kahane report.
The principal findings and
conclusions of the 2012 Kahane report
were mass reduction in the lighter cars,
even while holding footprint constant,
would significantly increase fatality
risk, whereas mass reduction in the
heavier LTVs would reduce societal
fatality risk by reducing the fatality risk
of occupants of lighter vehicles
colliding with those heavier LTVs.
NHTSA concluded, as a result, any
reasonable combination of mass
reductions that held footprint constant
in MY 2017–2021 vehicles—
concentrated, at least to some extent, in
the heavier LTVs and limited in the
lighter cars—would likely be
approximately safety-neutral; it would
not significantly increase fatalities and
might well decrease them.
NHTSA released a preliminary report
(2016 Puckett and Kindelberger report)
on the relationship between fatality risk,
mass, and footprint in June 2016 in
advance of the Draft TAR. The
preliminary report covered the same
scope as the 2012 Kahane report,
offering a detailed description of the
databases, modeling approach, and
analytical results on relationships
among vehicle size, mass, and fatalities
that informed the Draft TAR. Results in
the Draft TAR and the 2016 Puckett and
Kindelberger report are consistent with
results in the 2012 Kahane report;
chiefly, societal effects of mass
reduction are small, and mass reduction
concentrated in larger vehicles is likely
to have a beneficial effect on fatalities,
while mass reduction concentrated in
smaller vehicles is likely to have a
detrimental effect on fatalities.
For the 2016 Puckett and
Kindelberger report and Draft TAR,
NHTSA, working closely with EPA and
the DOE, performed an updated
statistical analysis of relationships
between fatality rates, mass and
footprint, updating the crash and
exposure databases to the latest
available model years. The agencies
analyzed updated databases that
included MY 2003–2010 vehicles in CY
2005–2011 crashes. For this proposed
302 Kahane, C.J. Relationships Between Fatality
Risk, Mass, and Footprint in Model Year 2000–2007
Passenger Cars and LTVs—Final Report, National
Highway Traffic Safety Administration (Aug. 2012),
available at https://crashstats.nhtsa.dot.gov/Api/
Public/ViewPublication/811665.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

rule, databases are the most up-to-date
possible (MY 2004–2011 vehicles in CY
2006–2012), given the processing time
for crash data and the need for enough
crash cases to permit statistically
meaningful analyses. As in previous
analyses, NHTSA has made the new
databases available to the public on its
website, enabling other researchers to
analyze the same data and hopefully
minimizing discrepancies in results that
would have been because of
inconsistencies across databases.

sradovich on DSK3GMQ082PROD with PROPOSALS2

(c) Updated Analysis for This
Rulemaking
The basic analytical method used to
analyze the impacts of weight reduction
on safety in this proposed rule is the
same as in NHTSA’s 2012 Kahane
report, 2016 Puckett and Kindelberger
report, and the Draft TAR: The agency
analyzed cross sections of the societal
fatality rate per billion vehicle miles of
travel (VMT) by mass and footprint,
while controlling for driver age, gender,
and other factors, in separate logistic
regressions by vehicle class and crash
type. ‘‘Societal’’ fatality rates include
fatalities to occupants of all the vehicles
involved in the collisions, plus any
pedestrians.
The temporal range of the data is now
MY 2004–2011 vehicles in CY 2006–
2012, updated from previous databases
of MY 2000–2007 vehicles in CY 2002–
2008 (2012 Kahane Report) and MY
2003–2010 vehicles in CY 2005–2011
(2016 Puckett and Kindelberger report
and Draft TAR). NHTSA purchased a
file of odometer readings by make,
model, and model year from Polk that
helped inform the agency’s improved
VMT estimates. As in the 2012 Kahane
report, 2016 Puckett and Kindelberger
report, and the Draft TAR, the vehicles
are grouped into three classes: Passenger
cars (including both two-door and fourdoor cars); CUVs and minivans; and
truck-based LTVs.
There are nine types of crashes
specified in the analysis. Single-vehicle
crashes include first-event rollovers,
collisions with fixed objects, and
collisions with pedestrians, bicycles and
motorcycles. Two-vehicle crashes
include collisions with: heavy-duty
vehicles; car, CUV, or minivan < 3,187
pounds (the median curb weight of
other, non-case, cars, CUVs and
303 Kahane, C. J. Relationships Between Fatality
Risk, Mass, and Footprint in Model Year 1991–1999
and Other Passenger Cars and LTVs (Mar. 24,
2010), in Final Regulatory Impact Analysis:
Corporate Average Fuel Economy for MY 2012–MY
2016 Passenger Cars and Light Trucks, National
Highway Traffic Safety Administration (Mar. 2010)
at 464–542.

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minivans in fatal crashes in the
database); car, CUV, or minivan ≥ 3,187
pounds; truck-based LTV < 4,360
pounds (the median curb weight of
other truck-based LTVs in fatal crashes
in the database); and truck-based LTV ≥
4,360 pounds. An additional crash type
includes all other fatal crash types (e.g.,
collisions involving more than two
vehicles, animals, or trains). Splitting
the ‘‘other’’ vehicles into a lighter and
a heavier group permits more accurate
analyses of the mass effect in collisions
of two light vehicles. Grouping partnervehicle CUVs and minivans with cars
rather than LTVs is more appropriate
because their front-end profile and
rigidity more closely resembles a car
than a typical truck-based LTV.
The curb weight of passenger cars is
formulated, as in the 2012 Kahane
report, 2016 Puckett and Kindelberger
report, and Draft TAR, as a two-piece
linear variable to estimate one effect of
mass reduction in the lighter cars and
another effect in the heavier cars. The
boundary between ‘‘lighter’’ and
‘‘heavier’’ cars is 3,201 pounds (which
is the median mass of MY 2004–2011
cars in fatal crashes in CY 2006–2012,
up from 3,106 for MY 2000–2007 cars in
CY 2002–2008 in the 2012 NHTSA
safety database, and up from 3,197 for
MY 2003–2010 cars in CY 2005–2011 in
the 2016 NHTSA safety database).
Likewise, for truck-based LTVs, curb
weight is a two-piece linear variable
with the boundary at 5,014 pounds
(again, the MY 2004–2011 median,
higher than the median of 4,594 for MY
2000–2007 LTVs in CY 2002–2008 and
the median of 4,947 for MY 2003–2010
LTVs in CY 2005–2011). Curb weight is
formulated as a simple linear variable
for CUVs and minivans. Historically,
CUVs and minivans have accounted for
a relatively small share of new-vehicle
sales over the range of the data,
resulting in less crash data available
than for cars or truck-based LTVs.
For a given vehicle class and weight
range (if applicable), regression
coefficients for mass (while holding
footprint constant) in the nine types of
crashes are averaged, weighted by the
number of baseline fatalities that would
have occurred for the subgroup MY
2008–2011 vehicles in CY 2008–2012 if
these vehicles had all been equipped
with electronic stability control (ESC).

The adjustment for ESC, a feature of the
analysis added in 2012, takes into
account results will be used to analyze
effects of mass reduction in future
vehicles, which will all be ESCequipped, as required by NHTSA’s
regulations.
Techniques developed in the 2011
(preliminary) and 2012 (final) Kahane
reports have been retained to test
statistical significance and to estimate
95 percent confidence bounds (sampling
error) for mass effects and to estimate
the combined annual effect of removing
100 pounds of mass from every vehicle
(or of removing different amounts of
mass from the various classes of
vehicles), while holding footprint
constant.
NHTSA considered the near
multicollinearity of mass and footprint
to be a major issue in the 2010 Kahane
report 303 and voiced concern about
inaccurately estimated regression
coefficients.304 High correlations
between mass and footprint and
variance inflation factors (VIF) have not
changed from MY 1991–1999 to MY
2004–2011; large vehicles continued to
be, on the average, heavier than small
vehicles to the same extent as in the
previous decade.305
Nevertheless, multicollinearity
appears to have become less of a
problem in the 2012 Kahane, 2016
Puckett and Kindelberger/Draft TAR,
and current NHTSA analyses.
Ultimately, only three of the 27 core
models of fatality risk by vehicle type in
the current analysis indicate the
potential presence of effects of
multicollinearity, with estimated effects
of mass and footprint reduction greater
than two percent per 100-pound mass
reduction and one-square-foot footprint
reduction, respectively; these three
models include passenger cars and
CUVs in first-event rollovers, and CUVs
in collisions with LTVs greater than
4,360 pounds. This result is consistent
with the 2016 Puckett and Kindelberger
report, which also found only three
cases out of 27 models with estimated
effects of mass and footprint reduction
greater than two percent per 100-pound
mass reduction and one-square-foot
footprint reduction.
Table II–45 presents the estimated
percent increase in U.S. societal fatality
risk per 10 billion VMT for each 100-

304 Van Auken and Green also discussed the issue
in their presentations at the NHTSA Workshop on
Vehicle Mass-Size-Safety in Washington, DC
February 25, 2011. More information on the NHTSA
Workshop on Vehicle Mass-Size-Safety is available
at https://one.nhtsa.gov/Laws-&-Regulations/CAFE%E2%80%93-Fuel-Economy/NHTSA-Workshopon-Vehicle-Mass%E2%80%93Size%E2%80%93
Safety.

305 Greene, W. H. Econometric Analysis 266–68
(Macmillan Publishing Company 2d ed. 1993); Paul
D. Allison, Logistic Regression Using the SAS
System 48–51 (SAS Institute Inc. 2001). VIF scores
are in the 6–9 range for curb weight and footprint
in NHTSA’s new database—i.e., in the somewhat
unfavorable 2.5–10 range where near
multicollinearity begins to become a concern in
logistic regression analyses.

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pound reduction in vehicle mass, while

holding footprint constant, for each of
the five vehicle classes:

None of the estimated effects have 95percent confidence bounds that exclude
zero, and thus are not statistically
significant at the 95-percent confidence
level. Two estimated effects are
statistically significant at the 85-percent
level. Societal fatality risk is estimated
to: (1) Increase by 1.2 percent if mass is
reduced by 100 pounds in the lighter
cars; and (2) decrease by 0.61 percent if
mass is reduced by 100 pounds in the
heavier truck-based LTVs. The

estimated increases in societal fatality
risk for mass reduction in the heavier
cars and the lighter truck-based LTVs,
and the estimated decrease in societal
fatality risk for mass reduction in CUVs
and minivans are not significant, even at
the 85-percent confidence level.
Confidence bounds estimate only the
sampling error internal to the data used
in the specific analysis that generated
the point estimate. Point estimates are
also sensitive to the modification of

components of the analysis, as
discussed at the end of this section.
However, this degree of uncertainty is
methodological in nature rather than
statistical.
It is useful to compare the new results
in Table II–45 to results in the 2012
Kahane report (MY 2000–2007 vehicles
in CY 2002–2008) and the 2016 Puckett
and Kindelberger report and Draft TAR
(MY 2003–2010 vehicles in CY 2005–
2011), presented in Table II–46 below:

New results are directionally the same
as in 2012; in the 2016 analysis, the
estimate for lighter LTVs was of
opposite sign (but small magnitude).
Consistent with the 2012 Kahane and
2016 Puckett and Kindelberger reports,
mass reductions in lighter cars are
estimated to lead to increases in
fatalities, and mass reductions in
heavier LTVs are estimated to lead to
decreases in fatalities. However, NHTSA
does not consider this conclusion to be
definitive because of the relatively wide
confidence bounds of the estimates. The
estimated mass effects are similar
among analyses for both classes of

passenger cars; for all reports, the
estimate for lighter passenger cars is
statistically significant at the 85-percent
confidence level, while the estimate for
heavier passenger cars is insignificant.
The estimated mass effect for heavier
truck-based LTVs is stronger in this
analysis and in the 2016 Puckett and
Kindelberger report than in the 2012
Kahane report; both estimates are
statistically significant at the 85-percent
confidence level, unlike the
corresponding insignificant estimate in
the 2012 Kahane report. The estimated
mass effect for lighter truck-based LTVs
is insignificant and positive in this
analysis and the 2012 Kahane report,

while the corresponding estimate in the
2016 Puckett and Kindelberger report
was insignificant and negative.
Vehicle mass continued an historical
upward trend across the MYs in the
newest databases. The average (VMTweighted) masses of passenger cars and
CUVs both increased by approximately
three percent from MYs 2004 to 2011
(3,184 pounds to 3,289 pounds for
passenger cars, and 3,821 pounds to
3,924 pounds for CUVs). Over the same
period, the average mass of minivans
increased by six percent (from 4,204
pounds to 4,462 pounds), and the
average mass of LTVs increased by 10%
(from 4,819 pounds to 5,311 pounds).

306 Median curb weights in the 2012 Kahane
report: 3,106 pounds for cars, 4,594 pounds for

truck-based LTVs. Median curb weights in the 2016

Puckett and Kindelberger report: 3,197 pounds for
cars, 4,947 pounds for truck-based LTVs.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Historical reasons for mass increases
within vehicle classes include:
Manufacturers discontinuing lighter
models; manufacturers re-designing
models to be heavier and larger; and
shifting consumer preferences with
respect to cabin size and overall vehicle
size.

The principal difference between
heavier vehicles, especially truck-based
LTVs, and lighter vehicles, especially
passenger cars, is mass reduction has a
different effect in collisions with
another car or LTV. When two vehicles
of unequal mass collide, the change in
velocity (delta V) is greater in the lighter

vehicle. Through conservation of
momentum, the degree to which the
delta V in the lighter vehicle is greater
than in the heavier vehicle is
proportional to the ratio of mass in the
heavier vehicle to mass in the lighter
vehicle:

Because fatality risk is a positive
function of delta V, the fatality risk in
the lighter vehicle in two-vehicle
collisions is also higher. Removing some
mass from the heavy vehicle reduces
delta V in the lighter vehicle, where
fatality risk is higher, resulting in a large
benefit, offset by a small penalty
because delta V increases in the heavy
vehicle where fatality risk is low—
adding up to a net societal benefit.
Removing some mass from the lighter
vehicle results in a large penalty offset
by a small benefit—adding up to net
harm.
These considerations drive the overall
result: Mass reduction is associated with
an increase in fatality risk in lighter
cars, a decrease in fatality risk in
heavier LTVs, CUVs, and minivans, and
has smaller effects in the intermediate
groups. Mass reduction may also be
harmful in a crash with a movable
object such as a small tree, which may
break if hit by a high mass vehicle
resulting in a lower delta V than may
occur if hit by a lower mass vehicle
which does not break the tree and
therefore has a higher delta V. However,
in some types of crashes not involving
collisions between cars and LTVs,
especially first-event rollovers and
impacts with fixed objects, mass
reduction may not be harmful and may
be beneficial. To the extent lighter
vehicles may respond more quickly to
braking and steering, or may be more
stable because their center of gravity is

lower, they may more successfully
avoid crashes or reduce the severity of
crashes.
Farmer, Green, and Lie, who reviewed
the 2010 Kahane report, again peerreviewed the 2011 Kahane report.307 In
preparing his 2012 report (along with
the 2016 Puckett and Kindelberger
report and Draft TAR), Kahane also took
into account Wenzel’s 308 assessment of
the preliminary report and its peer
reviews, DRI’s analyses published early
in 2012, and public comments such as
the International Council on Clean
Transportation’s comments submitted
on NHTSA and EPA’s 2010 notice of
joint rulemaking.309 These comments
prompted supplementary analyses,
especially sensitivity tests, discussed at
the end of this section.

The regression results are best suited
to predict the effect of a small change in
mass, leaving all other factors, including
footprint, the same. With each
additional change from the current
environment (e.g., the scale of mass
change, presence and prevalence of
safety features, demographic
characteristics), the model may become
less accurate. It is recognized that the
light-duty vehicle fleet in the MY 2021–
2026 timeframe will be different from
the MY 20042011 fleet analyzed here.
Nevertheless, one consideration
provides some basis for confidence in
applying regression results to estimate
effects of relatively large mass
reductions or mass reductions over
longer periods. This is NHTSA’s sixth
evaluation of effects of mass reduction
and/or downsizing,310 comprising

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307 Items 0035 (Lie), 0036 (Farmer) and 0037
(Green) in Docket No. NHTSA–2010–0152.
308 Wenzel, T. An Analysis of the Relationship
Between Casualty Risk Per Crash and Vehicle Mass
and Footprint for Model Year 2000–2007 Light Duty
Vehicles, Lawrence Berkeley National Laboratory
(Dec. 2011), available at http://etapublications.lbl.gov/sites/default/files/lbnl5695e.pdf; Tom Wenzel, Lawrence Berkeley
National Laboratory -Assessment of NHTSA Report
Relationships Btw Fatality Risk Mass and Footprint
in MY 2000–2007 PC and LTV,’’ Docket NHTSA–
2010–0131–0315; and a peer review of Wenzel’s
reports—Peer Review of LBNL Statistical Analysis
of the Effect of Vehicle Mass & Footprint Reduction
on Safety (LBNL Phase 1 and 2 Reports), prepared
for U.S. EPA (Feb. 2012), available at Docket ID
NHTSA–2010–0131–0328.
309 Comment by International Council on Clean
Transportation, Docket ID NHTSA–2010–0131–
0258.

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310 As outlined throughout this section, NHTSA’s
six related studies include the new analysis
supporting this rulemaking, and: Kahane, C. J.
Vehicle Weight, Fatality Risk and Crash
Compatibility of Model Year 1991–99 Passenger
Cars and Light Trucks, National Highway Traffic
Safety Administration (Oct. 2003), available at
https://crashstats.nhtsa.dot.gov/Api/Public/View
Publication/809662; Kahane, C. J. Relationships
Between Fatality Risk, Mass, and Footprint in
Model Year 1991–1999 and Other Passenger Cars
and LTVs (Mar. 24, 2010), in Final Regulatory
Impact Analysis: Corporate Average Fuel Economy
for MY 2012–MY 2016 Passenger Cars and Light
Trucks, National Highway Traffic Safety
Administration (Mar. 2010) at 464–542; Kahane, C.
J. Relationships Between Fatality Risk, Mass, and
Footprint in Model Year 2000–2007 Passenger Cars
and LTVs—Preliminary Report, National Highway
Traffic Safety Administration (Nov. 2011), available
at Docket ID NHTSA–2010–0152- 0023); Kahane, C.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
databases ranging from MYs 1985 to
2011.
Results of the six studies are not
identical, but they have been consistent
to a point. During this time period,
many makes and models have increased
substantially in mass, sometimes as
much as 30–40%.311 If the statistical
analysis has, over the past years, been
able to accommodate mass increases of
this magnitude, perhaps it will also
succeed in modeling effects of mass
reductions of approximately 10–20%,
should they occur in the future.

sradovich on DSK3GMQ082PROD with PROPOSALS2

J. Relationships Between Fatality Risk, Mass, and
Footprint in Model Year 2000–2007 Passenger Cars
and LTVs: Final Report, NHTSA Technical Report.
Washington, DC: NHTSA, Report No. DOT–HS–
811–665; and Puckett, S. M., & Kindelberger, J. C.
Relationships between Fatality Risk, Mass, and
Footprint in Model Year 2003–2010 Passenger Cars
and LTVs—Preliminary Report, National Highway
Traffic Safety Administration (June 2016), available
at https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/
2016-prelim-relationship-fatalityrisk-massfootprint-2003-10.pdf.
311 For example, one of the most popular models
of small 4-door sedans increased in curb weight
from 1,939 pounds in MY 1985 to 2,766 pounds in
MY 2007, a 43% increase. A high-sales mid-size
sedan grew from 2,385 to 3,354 pounds (41%); a
best-selling pickup truck from 3,390 to 4,742
pounds (40%) in the basic model with two-door cab
and rear-wheel drive; and a popular minivan from
2,940 to 3,862 pounds (31%).

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(d) Calculation of MY 2021–2026 Safety
Impact
Neither CAFE standards nor this
analysis mandate mass reduction, or
mandate mass reduction occur in any
specific manner. However, mass
reduction is one of the technology
applications available to manufacturers,
and thus a degree of mass reduction is
allowed within the CAFE model to: (1)
Determine capabilities of manufacturers;
and (2) to predict cost and fuel
consumption effects of improved CAFE
standards.
The agency utilized the relationships
between weight and safety from the new
NHTSA analysis, expressed as
percentage increases in fatalities per
100-pound weight reduction, and
examined the weight impacts assumed
in this CAFE analysis. The effects of
mass reduction on safety were estimated
relative to estimated baseline levels of
safety across vehicle classes and model
years. To identify baseline levels of
safety, the agency examined effects of
identifiable safety trends over lifetimes
of vehicles produced in each model
year. The projected effectiveness of
existing and forthcoming safety
technologies and expected on-road fleet
penetration of safety technologies were
incorporated into observed trends in
fatality rates to estimate baseline fatality
rates in future years across vehicle
classes and model years.

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43113

The agency assumed safety trends
will result in a reduction in the target
population of fatalities from which the
vehicle mass impacts are derived. Table
II–47 through Table II–52 show results
of NHTSA’s vehicle mass-size-safety
analysis over the cumulative lifetime of
MY 1977–2029 vehicles, for both the
CAFE and GHG programs, based on the
MY 2016 baseline fleet, accounting for
the projected safety baselines. The
reported fatality impacts are
undiscounted, but the monetized safety
impacts are discounted at three-percent
and seven-percent discount rates. The
reported fatality impacts are estimated
increases or decreases in fatalities over
the lifetime of the model year fleet. A
positive number means that fatalities are
projected to increase; a negative number
(in parentheses) means that fatalities are
projected to decrease.
Results are driven extensively by the
degree to which mass is reduced in
relatively light passenger cars and in
relatively heavy vehicles because their
coefficients in the logistic regression
analysis have the most significant
values. We assume any impact on
fatalities will occur over the lifetime of
the vehicle, and the chance of a fatality
occurring in any particular year is
directly related to the weighted vehicle
miles traveled in that year.

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#1

#2

#3

#4

#5

#6

#7

#8

20212026
O.O%Near
PC
O.O%Near
LT
No
Change

20212026
0.5%Near
PC
0.5%Near
LT
No
Change

20212026
0.5%Near
PC
0.5%Near
LT
Phaseout
20222026

20212026
l.O%Near
PC
2.0%Near
LT
No
Change

20222026
l.O%Near
PC
2.0%Near
LT
No
Change

20212026
2.0%Near
PC
3.0%Near
LT
No
Change

20212026
2.0%Near
PC
3.0%Near
LT
Phaseout
20222026

20222026
2.0%Near
PC
3.0%Near
LT
No
Change

Fatalities

-160

-147

-143

-173

-152

-73

-12

-30

Fatality Costs($ Billion, 3%
Discount Rate)
Fatality Costs ($ Billion, 7%
Discount Rate)

-0.9

-0.9

-0.8

-1.1

-0.9

-0.4

-0.1

-0.2

-0.5

-0.5

-0.5

-0.6

-0.5

-0.2

0.0

-0.1

Non-Fatal Crash Costs($
Billion, 3% Discount Rate)
Non-Fatal Crash Costs($
Billion, 7% Discount Rate)

-1.5

-1.3

-1.3

-1.7

-1.5

-0.7

-0.1

-0.3

-0.8

-0.7

-0.7

-1.0

-0.8

-0.4

-0.1

-0.2

Total Crash Costs ($ Billion,
3% Discount Rate)
Total Crash Costs ($ Billion,
7% Discount Rate)

-2.4

-2.2

-2.1

-2.7

-2.4

-1.1

-0.2

-0.5

-1.3

-1.2

-1.2

-1.6

-1.4

-0.6

-0.1

-0.3

Model Years Affected by
Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.070

Table 11-47- Comparison of the Calculated Vehicle-Mass-Related Fatality Impacts over the Lifetime of MY 1977 through MY
2029 Light-Duty Vehicles, by CAFE Policy Alternative, Relative to Augural Standards, Fatalities Undiscounted, Dollars
Discounted at 3% and 7%

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20222026

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202220212026
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20222026

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20222026
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3.0%Near
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No
Change

Fatalities

-281

-262

-234

-197

-167

-87

-17

-42

Fatality Costs($ Billion, 3%
Discount Rate)
Fatality Costs ($ Billion, 7%
Discount Rate)

-1.7

-1.6

-1.4

-1.2

-1.0

-0.5

-0.1

-0.3

-1.0

-0.9

-0.8

-0.7

-0.6

-0.3

-0.1

-0.1

Non-Fatal Crash Costs($ Billion,
3% Discount Rate)
Non-Fatal Crash Costs($ Billion,
7% Discount Rate)

-2.7

-2.5

-2.3

-1.9

-1.6

-0.8

-0.2

-0.4

-1.6

-1.5

-1.3

-1.1

-0.9

-0.5

-0.1

-0.2

-4.4

-4.2

-3.7

-3.1

-2.6

-1.4

-0.3

-0.7

-2.5

-2.4

-2.1

-1.8

-1.5

-0.8

-0.1

-0.4

Model Years Affected by Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Total Crash Costs($ Billion, 3%
Discount Rate)
Total Crash Costs($ Billion, 7%
Discount Rate)

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table 11-48- Comparison of the Calculated Vehicle-Mass-Related Fatality Impacts over the Lifetime of MY 1977 through MY
2029 Passenger Cars, by CAFE Policy Alternative, Relative to Augural Standards, Fatalities Undiscounted, Dollars
Discounted at 3% and 7%

43115

EP24AU18.071

sradovich on DSK3GMQ082PROD with PROPOSALS2

43116

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20222026

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20222026
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3.0%Near
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No
Change

Fatalities

120

116

92

25

15

14

6

12

Fatality Costs($ Billion, 3%
Discount Rate)
Fatality Costs($ Billion, 7%
Discount Rate)

0.8

0.8

0.6

0.2

0.1

0.1

0.0

0.1

0.5

0.5

0.4

0.1

0.1

0.1

0.0

0.0

1.2

1.2

0.9

0.2

0.2

0.1

0.1

0.1

0.8

0.7

0.6

0.1

0.1

0.1

0.0

0.1

2.0

2.0

1.5

0.4

0.3

0.2

0.1

0.2

1.3

1.2

1.0

0.2

0.2

0.1

0.0

0.1

Model Years Affected by
Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Non-Fatal Crash Costs ($
Billion, 3% Discount Rate)
Non-Fatal Crash Costs ($
Billion, 7% Discount Rate)
Total Crash Costs($ Billion,
3% Discount Rate)
Total Crash Costs($ Billion,
7% Discount Rate)

EP24AU18.072

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table 11-49- Comparison of the Calculated Vehicle-Mass-Related Fatality Impacts over the Lifetime of MY 1977 through MY
2029 Light Trucks, by CAFE Policy Alternative, Relative to Augural Standards, Fatalities U ndiscounted, Dollars Discounted
at3% and 7%

sradovich on DSK3GMQ082PROD with PROPOSALS2

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#4

#5

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#7

#8

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20222026

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20222026
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20222026

20222026
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No
Change

Fatalities

-468

-461

-410

-297

-219

-186

-111

-85

Fatality Costs($ Billion, 3%
Discount Rate)
Fatality Costs ($ Billion, 7%
Discount Rate)

-2.9

-2.9

-2.6

-1.9

-1.4

-1.2

-0.7

-0.5

-1.7

-1.7

-1.5

-1.1

-0.8

-0.7

-0.5

-0.3

Non-Fatal Crash Costs($
Billion, 3% Discount Rate)
Non-Fatal Crash Costs($
Billion, 7% Discount Rate)

-4.6

-4.5

-4.0

-2.9

-2.2

-1.9

-1.1

-0.8

-2.7

-2.7

-2.4

-1.7

-1.3

-1.1

-0.7

-0.5

Total Crash Costs ($ Billion,
3% Discount Rate)
Total Crash Costs ($ Billion,
7% Discount Rate)

-7.5

-7.4

-6.6

-4.8

-3.5

-3.1

-1.9

-1.4

-4.4

-4.4

-3.9

-2.8

-2.1

-1.9

-1.2

-0.8

Model Years Affected by
Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table 11-50- Comparison of the Calculated Vehicle-Mass-Related Fatality Impacts over the Lifetime of MY 1977 through MY
2029 Light-Duty Vehicles, by GHG Policy Alternative, Relative to Augural Standards, Fatalities Undiscounted, Dollars
Discounted at 3% and 7%

43117

EP24AU18.073

sradovich on DSK3GMQ082PROD with PROPOSALS2

43118

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20222026
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No
Change

Fatalities

-567

-551

-502

-389

-242

-205

-139

-92

Fatality Costs($ Billion, 3%
Discount Rate)
Fatality Costs ($ Billion, 7%
Discount Rate)

-3.6

-3.5

-3.2

-2.5

-1.5

-1.3

-0.9

-0.6

-2.1

-2.1

-1.9

-1.5

-0.9

-0.8

-0.6

-0.3

Non-Fatal Crash Costs($ Billion,
3% Discount Rate)
Non-Fatal Crash Costs($ Billion,
7% Discount Rate)

-5.6

-5.5

-5.0

-3.9

-2.4

-2.1

-1.4

-0.9

-3.3

-3.3

-3.0

-2.3

-1.4

-1.3

-0.9

-0.5

Total Crash Costs($ Billion, 3%
Discount Rate)
Total Crash Costs($ Billion, 7%
Discount Rate)

-9.2

-9.0

-8.2

-6.4

-3.9

-3.4

-2.3

-1.5

-5.5

-5.3

-4.9

-3.8

-2.3

-2.0

-1.5

-0.9

Model Years Affected by Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.074

Table 11-51- Comparison of the Calculated Vehicle-Mass-Related Fatality Impacts over the Lifetime of MY 1977 through MY
2029 Passenger Cars, by GHG Policy Alternative, Relative to Augural Standards, Fatalities Undiscounted, Dollars Discounted
at3% and 7%

sradovich on DSK3GMQ082PROD with PROPOSALS2

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3.0%/Year
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Fatalities

98

90

91

92

23

19

28

6

Fatality Costs($ Billion, 3%
Discount Rate)
Fatality Costs($ Billion, 7%
Discount Rate)

0.7

0.6

0.6

0.6

0.2

0.1

0.2

0.0

0.4

0.4

0.4

0.4

0.1

0.1

0.1

0.0

Non-Fatal Crash Costs ($
Billion, 3% Discount Rate)
Non-Fatal Crash Costs ($
Billion, 7% Discount Rate)

1.0

1.0

1.0

1.0

0.2

0.2

0.3

0.1

0.7

0.6

0.6

0.6

0.1

0.1

0.2

0.0

1.7

1.6

1.6

1.6

0.4

0.3

0.5

0.1

1.1

1.0

1.0

1.0

0.2

0.2

0.3

0.0

Model Years Affected by
Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Total Crash Costs ($Billion,
3% Discount Rate)
Total Crash Costs ($Billion,
7% Discount Rate)

43119

vehicles in all alternatives evaluated.
The effects of mass changes on fatalities

E:\FR\FM\24AUP2.SGM

decrease in fatalities over the
cumulative lifetime of MY 1977–2029

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

For all light-duty vehicles, mass
changes are estimated to lead to a

VerDate Sep<11>2014

EP24AU18.075

Table 11-52- Comparison of the Calculated Vehicle-Mass-Related Fatality Impacts over the Lifetime of MY 1977 through MY
2029 Light Trucks, by GHG Policy Alternative, Relative to Augural Standards, Fatalities Undiscounted, Dollars Discounted at
3% and 7%

43120

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

range from a combined decrease
(relative to the augural standards, the
baseline) of 12 fatalities for Alternative
#7 to a combined decrease of 173
fatalities for Alternative #4. The
difference in results by alternative
depends upon how much weight
reduction is used in that alternative and
the types and sizes of vehicles to which
the weight reduction applies. The
decreases in fatalities are driven by
impacts within passenger cars
(decreases of between 17 and 281
fatalities) and are offset by impacts
within light trucks (increases of between
6 and 120 fatalities).
Additionally, social effects of
increasing fatalities can be monetized
using NHTSA’s estimated
comprehensive cost per life of
$9,900,000 in 2016 dollars. This
consists of a value of a statistical life of
$9.6 million in 2015 dollars plus
external economic costs associated with
fatalities such as medical care,
insurance administration costs and legal
costs, updated for inflation to 2016
dollars.
Typically, NHTSA would also
estimate the effect on injuries and add

VerDate Sep<11>2014

23:42 Aug 23, 2018

Jkt 244001

that to social costs of fatalities, but in
this case NHTSA does not have a model
estimating the effect of vehicle mass on
injuries. Blincoe et al. estimates that
fatalities account for 39.5% of total
comprehensive costs due to injury.312 If
vehicle mass impacts non-fatal injuries
proportionally to its impact on fatalities,
then total costs would be approximately
2.53 (1⁄0.395) times the value of fatalities
alone or around $25.07 million per
fatality. NHTSA has selected this value
as representative of the relationship
between fatality costs and injury costs
because this approach is internally
consistent among NHTSA studies.
Changes in vehicle mass are estimated
to decrease social safety costs over the
lifetime of the nine model years by
between $176 million (for Alternative
#7) and $2.7 billion (for Alternative #4)
312 Blincoe, L. et al., The Economic and Social
Impact of Motor Vehicle Crashes, 2010 (Revised),
National Highway Traffic Safety Administration
(May 2015), available at https://
crashstats.nhtsa.dot.gov/Api/Public/View
Publication/812013. The estimate of 39.5% (see
Table 1–8) is equal to the estimated value of MAIS6
(fatal) injuries in vehicle incidents divided by the
estimated value of MAIS0–MAIS6 (non-fatal and
fatal) injuries in vehicle incidents.

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relative to the augural standards at a
three-percent discount rate and by
between $97 million and $1.6 billion at
a seven-percent discount rate. The
estimated decreases in social safety
costs are driven by estimated decreases
in costs associated with passenger cars,
ranging from $264 million (for
Alternative #7) to $4.4 billion (for
Alternative #1) relative to the Augural
standards at a three-percent discount
rate and by between $146 million and
$2.5 billion at a seven-percent discount
rate. The estimated decreases in costs
associated with passenger cars are offset
by estimated increases in costs
associated with light trucks, ranging
from $88 million (for Alternative #7) to
$2.0 billion (for Alternative #1) relative
to the Augural standards at a threepercent discount rate and by between
$49 million and $1.3 billion at a sevenpercent discount rate.
Table II–53 through Table II–55
presents average annual estimated safety
effects of vehicle mass changes, for CYs
2035–2045:

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Change

Fatalities

-22

-19

-17

-17

-16

-6

0

-2

Fatality Costs($ Billion,
3% Discount Rate)
Fatality Costs($ Billion,
7% Discount Rate)

-0.11

-0.10

-0.08

-0.08

-0.08

-0.03

0.00

-0.01

-0.04

-0.04

-0.03

-0.03

-0.03

-0.01

0.00

0.0

Non-Fatal Crash Costs ($
Billion, 3% Discount Rate)
Non-Fatal Crash Costs ($
Billion, 7% Discount Rate)

-0.17

-0.15

-0.13

-0.13

-0.13

-0.05

0.00

-0.02

-0.07

-0.06

-0.05

-0.05

-0.05

-0.02

0.00

0.0

Total Crash Costs($
Billion, 3% Discount Rate)
Total Crash Costs($
Billion, 7% Discount Rate)

-0.27

-0.24

-0.22

-0.21

-0.21

-0.07

0.00

-0.03

-0.11

-0.10

-0.09

-0.09

-0.09

-0.03

0.00

-0.01

Model Years Affected by
Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table 11-53- Comparison of the Calculated Annual Average Vehicle-Mass-Related Fatality Impacts for CY 2035-2045 in
Light-Duty Vehicles, by CAFE Policy Alternative, Relative to Augural Standards, Fatalities Undiscounted, Dollars Discounted
at3% and 7%

43121

EP24AU18.076

sradovich on DSK3GMQ082PROD with PROPOSALS2

43122

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No
Change

Fatalities

-33

-31

-27

-20

-18

-8

-1

-3

Fatality Costs($ Billion,
3% Discount Rate)
Fatality Costs($ Billion,
7% Discount Rate)

-0.17

-0.15

-0.13

-0.10

-0.09

-0.04

0.00

-0.02

-0.07

-0.06

-0.05

-0.04

-0.04

-0.02

0.00

-0.01

-0.26

-0.24

-0.21

-0.16

-0.14

-0.06

-0.01

-0.02

-0.11

-0.10

-0.09

-0.06

-0.06

-0.02

0.00

-0.01

-0.42

-0.39

-0.34

-0.26

-0.23

-0.10

-0.01

-0.04

-0.18

-0.16

-0.14

-0.11

-0.09

-0.04

-0.01

-0.02

Model Years Affected by
Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Non-Fatal Crash Costs($
Billion, 3% Discount Rate)
Non-Fatal Crash Costs($
Billion, 7% Discount Rate)
Total Crash Costs($
Billion, 3% Discount Rate)
Total Crash Costs($
Billion, 7% Discount Rate)

EP24AU18.077

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table 11-54- Comparison of the Calculated Annual Average Vehicle-Mass-Related Fatality Impacts for CY 2035-2045 in
Passenger Cars, by CAFE Policy Alternative, Relative to Augural Standards, Fatalities U ndiscounted, Dollars Discounted at
3% and 7%

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Change

#2
20212026
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LT
No
Change

#3
20212026
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0.5%/Year
LT
Phaseout
20222026

#4
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PC
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#5
20222026
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PC
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LT
No
Change

#6
20212026
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PC
3.0%/Year
LT
Phaseout
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#8
20222026
2.0%/Year
PC
3.0%/Year
LT
No
Change

Fatalities

12

11

10

4

2

2

1

1

Fatality Costs($ Billion,
3% Discount Rate)
Fatality Costs($ Billion,
7% Discount Rate)

0.06

0.06

0.05

0.02

0.01

0.01

0.00

0.01

0.02

0.02

0.02

0.01

0.00

0.00

0.00

0.00

Non-Fatal Crash Costs ($
Billion, 3% Discount Rate)
Non-Fatal Crash Costs ($
Billion, 7% Discount Rate)

0.09

0.09

0.08

0.03

0.01

0.01

0.01

0.01

0.04

0.04

0.03

0.01

0.01

0.01

0.00

0.00

Total Crash Costs($
Billion, 3% Discount Rate)
Total Crash Costs($
Billion, 7% Discount Rate)

0.15

0.15

0.12

0.05

0.02

0.02

0.01

0.01

0.06

0.06

0.05

0.02

0.01

0.01

0.00

0.01

Model Years Affected by
Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table 11-55- Comparison of the Calculated Annual Average Vehicle-Mass-Related Fatality Impacts for CY 2035-2045 in Light
Trucks, by CAFE Policy Alternative, Relative to Augural Standards, Fatalities Undiscounted, Dollars Discounted at 3% and

43123

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sradovich on DSK3GMQ082PROD with PROPOSALS2

43124

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#1
20212026
O.O%Near
PC
O.O%Near
LT
No
Change

#2
20212026
0.5%Near
PC
0.5%Near
LT
No
Change

#3
20212026
0.5%Near
PC
0.5%Near
LT
Phaseout
20222026

#4
20212026
l.O%Near
PC
2.0%Near
LT
No
Change

#5
20222026
l.O%Near
PC
2.0%Near
LT
No
Change

#6
20212026
2.0%Near
PC
3.0%Near
LT
No
Change

#7
20212026
2.0%Near
PC
3.0%Near
LT
Phaseout
20222026

#8
20222026
2.0%Near
PC
3.0%Near
LT
No
Change

Fatalities

-56

-52

-42

-34

-15

-13

-8

-5

Fatality Costs($ Billion,
3% Discount Rate)
Fatality Costs($ Billion,
7% Discount Rate)

-0.27

-0.25

-0.21

-0.17

-0.08

-0.07

-0.04

-0.02

-0.11

-0.11

-0.09

-0.07

-0.03

-0.03

-0.02

-0.01

-0.43

-0.40

-0.32

-0.26

-0.12

-0.11

-0.06

-0.04

-0.18

-0.16

-0.13

-0.11

-0.05

-0.04

-0.03

-0.02

-0.70

-0.65

-0.53

-0.43

-0.19

-0.17

-0.10

-0.06

-0.29

-0.27

-0.22

-0.18

-0.08

-0.07

-0.04

-0.02

Model Years Affected by
Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Non-Fatal Crash Costs($
Billion, 3% Discount Rate)
Non-Fatal Crash Costs($
Billion, 7% Discount Rate)
Total Crash Costs($
Billion, 3% Discount Rate)
Total Crash Costs($
Billion, 7% Discount Rate)

EP24AU18.079

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table 11-56- Comparison of the Calculated Annual Average Vehicle-Mass-Related Fatality Impacts for CY 2035-2045 in
Light-Duty Vehicles, by GHG Policy Alternative, Relative to Augural Standards, Fatalities Undiscounted, Dollars Discounted
at3% and 7%

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Altemative
#4
#5
202120222026
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2.0%Ncar 2.0%Ncar
LT
LT
No
No
Change
Change

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#1
20212026
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O.O%Ncar
LT
No
Change

#2
20212026
0.5%Near
PC
0.5%Ncar
LT
No
Change

#3
20212026
O.So/o!Y ear
PC
0.5o/o/Ycar

Fatalities

-65

-61

-53

-39

-20

-16

-11

-8

Fatality Costs ($Billion, 3% Discount
Rate)
Fatality Costs ($Billion, 7% Discount
Rate)

-0.32

-0.30

-0.26

-0.19

-0.10

-0.08

-0.06

-0.04

-0.13

-0.12

-0.11

-0.08

-0.04

-0.03

-0.02

-0.02

-0.50

-0.47

-0.41

-0.30

-0.15

-0.12

-0.09

-0.06

-0.21

-0.19

-0.17

-0.12

-0.06

-0.05

-0.04

-0.02

Total Crash Costs ($ Billion, 3%
Discount Rate)

-0.82

-0.77

-0.67

-0.49

-0.25

-0.20

-0.14

-0.10

Total Crash Costs ($ Billion, 7%
Discount Rate)

-0.41

-0.37

-0.25

-0.38

-0.23

-0.49

-0.33

-0.44

Model Y cars Affected by Policy
Annual Rate of Stringency Increase

AC/Off-Cycle Procedures

Non-Fatal Crash Costs ($Billion, 3%
Discount Rate)
Non-Fatal Crash Costs ($Billion, 7%
Discount Rate)

LT

Phaseout
20222026

#6
20212026
2.0%Near
PC
3.0%Ncar
LT

No
Change

#7
20212026
2.0%Near
PC
3.0%Ncar
LT
Phaseout
20222026

#8
2022-2026
2.0o/o/Year PC
3.0o/o/Year LT

No Change

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table 11-57- Comparison of the Calculated Annual Average Vehicle-Mass-Related Fatality Impacts for CY 2035-2045 in
Passenger Cars, by GHG Policy Alternative, Relative to Augural Standards, Fatalities Undiscounted, Dollars Discounted at
3% and 7%

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7%
Alternative

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2045. The effects of mass changes on
fatalities range from a combined

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average annual decrease in fatalities in
all alternatives evaluated for CYs 2035–

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#3

#4

#5

#6

#7

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O.Oo/o!Y ear
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No
Change

20212026
0.5%/Year
PC
0.5%/Year
LT
No
Change

20212026
0.5%/Year
PC
0.5%/Year
LT
Phaseout
20222026

20212026
1.0%/Year
PC
2.0%/Year
LT
No
Change

20222026
1.0%/Year
PC
2.0%/Year
LT
No
Change

20212026
2.0%/Year
PC
3.0%/Year
LT
No
Change

20212026
2.0%/Year
PC
3.0%/Year
LT
Phaseout
20222026

20222026
2.0%/Year
PC
3.0%/Year
LT
No
Change

Fatalities

10

9

10

5

5

2

3

3

Fatality Costs($ Billion,
3% Discount Rate)
Fatality Costs($ Billion,
7% Discount Rate)

0.05

0.05

0.05

0.02

0.02

0.01

0.02

0.02

0.02

0.02

0.02

0.01

0.01

0.00

0.01

0.01

Non-Fatal Crash Costs ($
Billion, 3% Discount Rate)
Non-Fatal Crash Costs ($
Billion, 7% Discount Rate)

0.08

0.07

0.08

0.04

0.04

0.02

0.03

0.02

0.03

0.03

0.03

0.02

0.01

0.01

0.01

0.01

Total Crash Costs($
Billion, 3% Discount Rate)
Total Crash Costs($
Billion, 7% Discount Rate)

0.12

0.12

0.14

0.06

0.06

0.03

0.04

0.04

0.05

0.05

0.06

0.03

0.02

0.01

0.02

0.02

Model Years Affected by
Policy
Annual Rate of Stringency
Increase

AC/Off-Cycle Procedures

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

For all light-duty vehicles, mass
changes are estimated to lead to an

VerDate Sep<11>2014

EP24AU18.081

Table 11-58- Comparison of the Calculated Annual Average Vehicle-Mass-Related Fatality Impacts for CY 2035-2045 in Light
Trucks, by GHG Policy Alternative, Relative to Augural Standards, Fatalities Undiscounted, Dollars Discounted at 3% and

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

decrease (relative to the Augural
standards) of 1 fatality per year for
Alternative #7 to a combined increase of
22 fatalities per year for Alternative #1.
The difference in the results by
alternative depends upon how much
weight reduction is used in that
alternative and the types and sizes of
vehicles to which the weight reduction
applies. The decreases in fatalities are
generally driven by impacts within
passenger cars (decreases of between 1
and 33 fatalities per year relative to the
Augural standards) and are generally
offset by impacts within light trucks
(increases of between 1 and 12 fatalities
per year).
Changes in vehicle mass are estimated
to decrease average annual social safety

VerDate Sep<11>2014

23:42 Aug 23, 2018

Jkt 244001

costs in CY 2035–2045 by between $2
million (for Alternative #7) and $271
million (for Alternative #1) relative to
the Augural standards at a three-percent
discount rate and by between $1 million
and $111 million at a seven-percent
discount rate. The estimated decreases
in social safety costs are generally
driven by estimated decreases in costs
associated with passenger cars,
decreasing between $13 million (for
Alternative #7) and $424 million (for
Alternative #1) relative to the Augural
standards at a three-percent discount
rate and decreasing between $5 million
and $175 million at a seven-percent
discount rate. The estimated decreases
in costs associated with passenger cars
are generally offset by estimated

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43127

increases in costs associated with light
trucks, decreasing between $11 million
(for Alternative #7) and $153 million
(for Alternative #1) relative to the
Augural standards at a three-percent
discount rate and decreasing between $5
million and $64 million at a sevenpercent discount rate.
To help illuminate effects at the
model year level, Table II–59 presents
the lifetime fatality impacts associated
with vehicle mass changes for passenger
cars, light trucks, and all light-duty
vehicles by model year under
Alternative #1, relative to the Augural
standards for the CAFE Program. Table
II–59 presents an analogous table for the
GHG Program.

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in MYs 2017 and 2018 to an increase of
14 fatalities in MYs 2026 through 2029.
Altogether, light-duty vehicle fatality
reductions associated with mass
changes under Alternative #1 are

E:\FR\FM\24AUP2.SGM

fatalities), peaking in MY 2025 (37
fatalities). Corresponding estimates of
light truck fatalities associated with
mass changes are generally positive,
ranging from a decrease of one fatality

PO 00000

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

1977

2017

2018

2019

2020

2021

2022

202
3

2024

2025

2026

2027

2028

2029

2016
-2

-3

-2

-3

-5

-11

-16

-29

-30

-37

-35

-35

-36

-36

-280

-2

-1

-1

3

2

11

13

12

13

12

14

14

14

14

118

-3

-3

-3

0

-3

1

-3

-16

-17

-24

-23

-22

-22

-22

-160

Passenger
Cars
Light
Trucks
Total

TOTAL

Table 11-60- Comparison of Lifetime Vehicle-Mass-Related Fatality Impacts by Model Year for GHG Program under
Alternative #1. Relative to Au!!ural Standards. Fatalities Undiscounted
MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

1977

2017

2018

2019

2020

2021

2022

202
3

2024

2025

2026

2027

2028

2029

2016
-2

-4

-9

-10

-22

-29

-37

-49

-57

-60

-68

-74

-75

-72

-568

-2

-1

0

1

2

10

13

11

12

13

11

7

9

11

97

-5

-4

-10

-9

-20

-19

-24

-38

-45

-47

-57

-66

-65

-60

-469

Passenger
Cars
Light
Trucks
Total

TOTAL

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Under Alternative #1, passenger car
fatalities associated with mass changes
are estimated to decrease generally from
MY 2017 (decrease of three fatalities)
through MY 2029 (decrease of 36

VerDate Sep<11>2014

EP24AU18.082

Table 11-59- Comparison of Lifetime Vehicle-Mass-Related Fatality Impacts by Model Year for CAFE Program under
Alternative #1. Relative to Ammral Standards. Fatalities Undiscounted

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

estimated to be concentrated among MY
2023 through MY 2029 vehicles (146 out
of 165, or 91% of net fatalities
mitigated).

VerDate Sep<11>2014

23:42 Aug 23, 2018

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Table II–61 and Table II–62 present
estimates of monetized lifetime social
safety costs associated with mass
changes by model year at three-percent
and seven-percent discount rates,

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43129

respectively for the CAFE Program.
Table II–63 and Table II–64 show
comparable tables from the perspective
of the GHG Program.

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sradovich on DSK3GMQ082PROD with PROPOSALS2

43130

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Passenge
r Cars
Light
Trucks
Total

19772016
-0.01

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

-0.02

-0.02

-0.01

-0.03

-0.07

-0.11

-0.19

-0.20

-0.23

-0.22

-0.21

-0.21

-0.20

-1.73

-0.01

0.00

-0.01

0.02

0.02

0.08

0.10

0.08

0.09

0.08

0.08

0.09

0.09

0.08

0.79

-0.02

-0.02

-0.02

0.01

-0.01

0.01

-0.01

-0.10

-0.11

-0.15

-0.14

-0.13

-0.13

-0.12

-0.94

Sfmt 4725
E:\FR\FM\24AUP2.SGM

Table II-62 - Com paris on of Lifetime Social Safety Costs Associated with Mass Changes for CAFE Program by Model Year
Amwral
Dollars
Discounted at 7'X
der ------------.Alternative #1. --------·Relative to
------------------ Standards.
·--------------------------------MY
MY MY MY MY MY MY MY MY MY MY MY MY MY TOTAL
-;;

24AUP2

Passenger
Cars
Light
Trucks
Total

---;;-

19772016
-0.01

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

-0.01

-0.01

-0.01

-0.02

-0.04

-0.07

-0.12

-0.12

-0.14

-0.13

-0.12

-0.11

-0.10

-0.99

0.00

0.00

0.00

0.02

0.02

0.06

0.06

0.06

0.05

0.05

0.05

0.05

0.05

0.04

0.49

-0.01

-0.01

-0.01

0.01

0.00

0.01

0.00

-0.06

-0.07

-0.09

-0.08

-0.07

-0.06

-0.06

-0.50

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.083

Table 11-61- Comparison of Lifetime Social Safety Costs Associated with Mass Changes for CAFE Program by Model Year
der Alternative #1. Relative to Amwral Standards. Dollars Discounted at 3'X
MY
MY MY MY MY MY MY MY MY MY MY MY MY MY TOTAL

sradovich on DSK3GMQ082PROD with PROPOSALS2

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24AUP2

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

-0.03

-0.06

-0.07

-0.16

-0.20

-0.25

-0.33

-0.37

-0.38

-0.42

-0.44

-0.44

-0.41

-3.59

-0.01

0.00

0.00

0.01

0.02

0.07

0.09

0.08

0.08

0.08

0.07

0.05

0.06

0.07

0.67

-0.02

-0.03

-0.07

-0.06

-0.14

-0.13

-0.16

-0.25

-0.29

-0.30

-0.35

-0.40

-0.38

-0.34

-2.92

Table 11-64 - Comparison of Lifetime Social Safety Costs Associated with Mass Changes for GHG Program by Model Year
Amwral
Dollars
Discounted at 7'X
der ------------.Alternative #1. --------·Relative to
------------------ Standards.
·--------------------------------MY
MY MY MY MY MY MY MY MY MY MY MY MY MY TOTAL
-;;

Passenger
Cars
Light
Trucks
Total

---;;-

19772016
-0.01

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

-0.02

-0.05

-0.05

-0.11

-0.14

-0.17

-0.21

-0.23

-0.23

-0.24

-0.25

-0.23

-0.21

-2.13

0.00

0.00

0.00

0.01

0.02

0.05

0.06

0.05

0.05

0.05

0.04

0.03

0.03

0.03

0.43

-0.01

-0.02

-0.05

-0.04

-0.10

-0.08

-0.10

-0.16

-0.18

-0.18

-0.20

-0.22

-0.20

-0.17

-1.70

43131

partially by increases associated with
light trucks. At a three-percent discount

E:\FR\FM\24AUP2.SGM

model year, with decreases associated
with passenger cars generally offset

PO 00000

Passenge
r Cars
Light
Trucks
Total

19772016
-0.01

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Lifetime social safety costs are
estimated to decrease generally by

VerDate Sep<11>2014

EP24AU18.084

Table 11-63 - Comparison of Lifetime Social Safety Costs Associated with Mass Changes for GHG Program by Model Year
der Alternative #1. Relative to Amwral Standards. Dollars Discounted at 3'X
MY
MY MY MY MY MY MY MY MY MY MY MY MY MY TOTAL

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

rate, decreases in lifetime social safety
costs related to passenger cars are
estimated to range from $13 million for
existing (MY 1977 through MY 2016)
cars, to $230 million for MY 2025 cars.
The corresponding estimates at a sevenpercent discount rate range from $7
million to $136 million. At a threepercent discount rate, impacts on
lifetime social safety costs related to
light trucks are estimated to range from
a decrease of $5 million for MY 2017
light trucks to an increase of $96 million
for MY 2022 light trucks. The
corresponding estimates at a sevenpercent discount rate range from $3
million to $65 million.
Consistent with the analysis of fatality
impacts by model year in Table II–61,
decreases in lifetime social safety costs
associated with mass changes are
generally concentrated in MY 2023
through MY 2029 light-duty vehicles
under Alternative #1. At a three-percent
discount rate, 93% of the reduction in
total lifetime costs ($872 million out of
$937 million) is attributed to MY 2023
through MY 2029 light-duty vehicles; at

VerDate Sep<11>2014

23:42 Aug 23, 2018

Jkt 244001

a seven-percent discount rate, 97% of
the reduction in total lifetime costs
($486 million out of $501 million) is
attributed to MY 2023 through MY 2029
light-duty vehicles.
(e) Sensitivity Analyses
Table II–65 shows the principal
findings and includes sampling-error
confidence bounds for the five
parameters used in the CAFE model.
The confidence bounds represent the
statistical uncertainty that is a
consequence of having less than a
census of data. NHTSA’s 2011, 2012,
and 2016 reports acknowledged another
source of uncertainty: The baseline
statistical model can be varied by
choosing different control variables or
redefining the vehicle classes or crash
types, which for example, could
produce different point estimates.
Beginning with the 2012 Kahane
report, NHTSA has provided results of
11 plausible alternative models that
serve as sensitivity tests of the baseline
model. Each alternative model was
tested or proposed by: Farmer (IIHS) or

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Green (UMTRI) in their peer reviews;
Van Auken (DRI) in his public
comments; or Wenzel in his parallel
research for DOE. The 2012 Kahane and
2016 Puckett and Kindelberger reports
provide further discussion of the models
and the rationales behind them.
Alternative models use NHTSA’s
databases and regression-analysis
approach but differ from the baseline
model in one or more explanatory
variables, assumptions, or data
restrictions. NHTSA applied the 11
techniques to the latest databases to
generate alternative CAFE model
coefficients. The range of estimates
produced by the sensitivity tests offers
insight to the uncertainty inherent in
the formulation of the models, subject to
the caveat these 11 tests are, of course,
not an exhaustive list of conceivable
alternatives.
The baseline and alternative results
follow, ordered from the lowest to the
highest estimated increase in societal
risk per 100-pound reduction for cars
weighing less than 3,201 pounds:

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EP24AU18.085

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

The sensitivity tests illustrate both the
fragility and the robustness of baseline
estimates. On the one hand, the
variation among NHTSA’s coefficients is
quite large relative to the baseline
estimate: In the preceding example of
cars < 3,201 pounds, the estimated
coefficients range from almost zero to
almost double the baseline estimate.
This result underscores the key
relationship that the societal effect of
mass reduction is small and, as Wenzel
has said, it ‘‘is overwhelmed by other
known vehicle, driver, and crash
factors.’’ 313 In other words, varying how
to model some of these other vehicle,
driver, and crash factors, which is
exactly what sensitivity tests do, can
appreciably change the estimate of the
societal effect of mass reduction.
On the other hand, variations are not
particularly large in absolute terms. The
ranges of alternative estimates are
generally in line with the sampling-error
confidence bounds for the baseline
estimates. Generally, in alternative
models as in the baseline models, mass
reduction tends to be relatively more
harmful in the lighter vehicles and more
beneficial in the heavier vehicles, just as
they are in the central analysis. In all
models, the point estimate of NHTSA’s
coefficient is positive for the lightest
vehicle class, cars < 3,201 pounds. In
nine out of 11 models, the point
estimate is negative for CUVs and
minivans, and in eight out of 11 models
the point estimate is negative for LTVs
≥ 5,014 pounds.
(f) Fleet Simulation Model
NHTSA has traditionally used real
world crash data as the basis for
projecting the future safety implications
for regulatory changes. However,
because lightweight vehicle designs are
introducing fundamental changes to the
structure of the vehicle, there is some
concern that historical safety trends may
not apply. To address this concern,
NHTSA developed an approach to
utilize lightweight vehicle designs to
evaluate safety in a subset of real-world
representative crashes. The
methodology focused on frontal crashes
because of the availability of existing
vehicle and occupant restraint models.
Representative crashes were simulated
between baseline and lightweight
vehicles against a range of vehicles and
roadside objects using two different size
belted driver occupants (adult male and
small female) only. No passenger(s) or
313 Wenzel, T. Assessment of NHTSA’s Report
‘‘Relationships Between Fatality Risk, Mass, and
Footprint in Model Year 2000–2007 Passenger Cars
and LTVs,’’ Lawrence Berkeley National Laboratory
at iv (Nov. 2011), available at Docket ID NHTSA–
2010–0152–0026.

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unbelted driver occupants were
considered in this fleet simulation. The
occupant injury risk from each
simulation was calculated and summed
to obtain combined occupant injury
risk. The combined occupant injury risk
was weighted according to the
frequency of real world occurrences to
develop overall societal risk for baseline
and light-weighted vehicles. Note: The
generic restraint system developed and
used in the baseline occupant
simulations was also used in the lightweighted vehicle occupant simulations
as the purpose of this fleet simulation
was to understand changes in societal
injury risks because of mass reduction
for different classes of vehicles in
frontal crashes. No modifications to the
restraint systems were made for lightweighted vehicle occupant simulations.
Any modifications to restraint systems
to improve occupant injury risks or
societal injury risks in the lightweighted vehicle would have conflated
results without identifying effects of
mass reduction only. The following
sections provide an overview of the fleet
simulation study:
NHTSA contracted with George
Washington University to develop a
fleet simulation model 314 to study the
impact and relationship of lightweighted vehicle design with injuries
and fatalities. In this study, there were
eight vehicles as follows:
• 2001 model year Ford Taurus finite
element model baseline and two simple
design variants included a 25% lighter
vehicle while maintaining the same
vehicle front end stiffness and 25%
overall stiffer vehicle while maintaining
the same overall vehicle mass.315
• 2011 model year Honda Accord
finite element baseline vehicle and its
20% light-weight vehicle designed by
Electricore. (This mass reduction study
was sponsored by NHTSA).316
314 Samaha, R. R. et al., Methodology for
Evaluating Fleet Protection of New Vehicle Designs:
Application to Lightweight Vehicle Designs,
National Highway Traffic Safety Administration
(Aug. 2014), available at https://www.nhtsa.gov/
crashworthiness/vehicle-aggressivity-and-fleetcompatibility-research (accessed by clicking on the
.zip file for DOT HS 812 051).
315 Samaha, R. R. et al., Methodology for
Evaluating Fleet Protection of New Vehicle Designs:
Application to Lightweight Vehicle Designs,
appendices, National Highway Traffic Safety
Administration (Aug. 2014), available at https://
www.nhtsa.gov/crashworthiness/vehicleaggressivity-and-fleet-compatibility-research
(accessed by clicking on the .zip file for DOT HS
812 051 [appendices are Part 2]).
316 Singh, H. et al., Update to future midsize
lightweight vehicle findings in response to
manufacturer review and IIHS small-overlap
testing, National Highway Traffic Safety
Administration (Feb. 2016), available at https://
www.nhtsa.gov/sites/nhtsa.dot.gov/files/812237_
lightweightvehiclereport.pdf.

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• 2009/2010 model year Toyota
Venza finite element baseline vehicle
and two design variants included a 20%
light-weight vehicle model (2010 Venza)
(Low option mass reduction vehicle
funded by EPA and International
Council on Clean Transportation (ICCT))
and a 35% light-weight vehicle (2009
Venza) (High option mass reduction
vehicle funded by California Air
Resources Board).317
Light weight vehicles were designed
to have similar vehicle crash pulses as
baseline vehicles. More than 440 vehicle
crash simulations were conducted for
the range of crash speeds and crash
configurations to generate crash pulse
and intrusion data points. The crash
pulse data and intrusion data points
will be used as inputs in the occupant
simulation models.
For vehicle to vehicle impact
simulations, four finite element models
were chosen to represent the fleet. The
partner vehicle models were selected to
represent a range of vehicle types and
weights. It was assumed vehicle models
would reflect the crash response for all
vehicles of the same type, e.g. mid-size
car. Only the safety or injury risk for the
driver in the target vehicle and in the
partner vehicle were evaluated in this
study.
As noted, vehicle simulations
generated vehicle deformations and
acceleration responses utilized to drive
occupant restraint simulations and
predict the risk of injury to the head,
neck, chest, and lower extremities. In
all, more than 1,520 occupant restraint
simulations were conducted to evaluate
the risk of injury for mid-size male and
small female drivers.
The computed societal injury risk
(SIR) for a target vehicle v in frontal
crashes is an aggregate of individual
serious crash injury risks weighted by
real-world frequency of occurrence (v)
of a frontal crash incident. A crash
incident corresponds to a crash with
different partners (Npartner) at a given
impact speed (Pspeed), for a given
driver occupant size (Loccsize), in the
target or partner vehicle (T/P), in a given
crash configuration (Mconfig), and in a
single- or two-vehicle crash (Kevent).
CIR (v) represents the combined injury
risk (by body region) in a single crash
incident. (v) designates the weighting
factor, i.e., percent of occurrence,
derived from National Automotive
Sampling System Crashworthiness Data
System (NASS CDS) for the crash
incident. A driver age group of 16 to 50
317 Light-Duty Vehicle Mass Reduction and Cost
Analysis — Midsize Crossover Utility Vehicle, U.S.
EPA (Aug. 2012), https://cfpub.epa.gov/si/si_
public_record_report.cfm?dirEntryID=230748.

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years old was chosen to provide a
population with a similar, i.e., more
consistent, injury tolerance.
The fleet simulation was performed
using the best available engineering
models, with base vehicle restraint and
airbag settings, to estimate societal risks
of future lightweight vehicles. The range
of the predicted risks for the baseline
vehicles is from 1.25% to 1.56%, with
an average of 1.39%, for the NASS
frontal crashes that were simulated. The
change in driver injury risk between the
baseline and light-weighted vehicles

will provide insight into the estimate of
modification needed in the restraint and
airbag systems of lightweight vehicles. If
the difference extends beyond the
expected baseline vehicle restraint and
airbag capability, then adjustments to
the structural designs would be needed.
Results from the fleet simulation study
show the trend of increased societal
injury risk for light-weighted vehicle
designs, as compared to their baselines,
occurs for both single vehicle and twovehicle crashes. Results are listed in
Table II–66.

In general, the societal injury risk in
the frontal crash simulation associated
with the small size driver is elevated
when compared to that of the mid-size
driver. However, both occupant sizes
had reasonable injury risk in the
simulated impact configurations
representative of the regulatory and
consumer information testing. NHTSA
examined three methods for combining
injuries with different body regions.
One observation was the baseline midsize CUV model was more sensitive to
leg injuries.

This study only looked at lightweight
designs for a midsize sedan and a midsize CUV and did not examine safety
implications for heavier vehicles. The
study was also limited to only frontal
crash configurations and considered just
mid-size CUVs whereas the statistical
regression model considered all CUVs
and all crash modes.
The change in the safety risk from the
MY 2010 fleet simulation study was
directionally consistent with results for
passenger cars from NHTSA 2012
regression analysis study,318 which
covered data for MY 2000–MY 2007.
The NHTSA 2012 regression analysis
study was updated in 2016 to reflect
newer MY 2003 to MY 2010. Comparing

the fleet simulation societal risk to the
2016 update of the NHTSA 2012
regression analysis and the updated
analysis used in this NPRM, the risk
assessment from the fleet simulation is
similarly directionally consistent with
the passenger car risk assessment from
the regression analysis. As noted, fleet
simulations were performed only in
frontal crash mode and did not consider
other crash modes including rollover
crashes.319
This fleet simulation study does not
provide information that can be used to
modify coefficients derived for the
NPRM regression analysis because of
the restricted types of crashes 320 and

vehicle designs. As explained earlier,
the fleet simulation study assumed
restraint equipment to be as in the
baseline model, in which restraints/
airbags are not redesigned to be optimal
with light-weighting.

318 The 2012 Kahane study considered only
fatalities, whereas, the fleet simulation study
considered severe (AIS 3+) injuries and fatalities
(DOT HS 811 665).

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319 The risk assessment for CUV in the regression
model combined CUVs and minivans in all crash
modes and included belted and unbelted
occupants.
320 The fleet simulation considered only frontal
crashes.

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2. Impact of Vehicle Scrappage and
Sales Response on Fatalities
Previous versions of the CAFE model,
and the accompanying regulatory
analyses relying on it, did not carry a
representation of the full on-road
vehicle population, only those vehicles
from model years regulated under
proposed (or final) standards. The
omission of an on-road fleet implicitly
assumed the population of vehicles
registered at the time a set of CAFE
standards is promulgated is not affected
by those standards. However, there are
several mechanisms by which CAFE
standards can affect the existing vehicle

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population. The most significant of
these is deferred retirement of older
vehicles. CAFE standards force
manufacturers to apply fuel saving
technologies to offered vehicles and
then pass along the cost of those
technologies (to the extent possible) to
buyers of new vehicles. These price
increases affect the length of loan terms
and the desired length of ownership for
new vehicle buyers and can discourage
some buyers on the margin from buying
a new vehicle in a given year. To the
extent new vehicle purchases offset
pending vehicle retirements, delaying
new purchases in favor of continuing to
use an aging vehicle affects the overall
safety of the on-road fleet even if the
vehicle whose retirement was delayed
was not directly subject to a binding
CAFE standard in the model year during
its production.
The sales response in the CAFE model
acts to modify new vehicle sales in two
ways:
1. Changes in new vehicle prices
either increase or decrease total sales
(passenger cars and light trucks
combined) each year in the context of
forecasted macroeconomic conditions.
2. Changes in new vehicle attributes
and fuel prices influence the share of
new vehicles sold that are light trucks,
and therefore also passenger cars.
These two responses change the total
number of new vehicles sold in each
model year across regulatory
alternatives and the relative proportion
of new vehicles that are passenger cars
and light trucks. This response has two
effects on safety. The first response
slows the rate at which new vehicles,
and their associated safety
improvements, enter the on-road
population. The second response
influences the mix of vehicles on the
road—with more stringent CAFE
standards leading to a higher share of
light trucks sold in the new vehicle
market, assuming all else is equal. Light
trucks have higher rates of fatal crashes
when interacting with passenger cars
and, as earlier sections discussed,
different directional responses to mass
reduction technology based on the
existing mass and body style of the
vehicle.
The sales response and scrappage
response influence safety outcomes
through the same basic mechanism, fleet
turnover. In the case of the scrappage
response, delaying fleet turnover keeps
drivers in older vehicles likely to be less
safe than newer model year vehicles
that could replace them. Similarly,
delaying the sale of new vehicles can
force households to keep older vehicles
in use longer, reallocate VMT within
their household fleet, and generally

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meet travel demand through the use of
older, less safe vehicles. As an
illustration, if we simplify by ignoring
that the share of new vehicles that are
passenger cars changes with the
stringency of the alternatives, simply
changing the number of new vehicles
between scenarios affects the mileage
accumulation of the fleet and therefore
all fleet level effects. Reducing the
number of new vehicles sold, relative to
a baseline forecasted value, reduces the
size of the registered vehicle fleet that
is able to service the underlying demand
for travel.
Consider a simple example where we
show sales effects operating on a microscale for a single household whose
choices of whether to purchase a new
vehicle is affected by vehicle price. A
household starts with three vehicles,
aged three, five, and eight years old. In
a scenario with no CAFE standards and
therefore no related changes in vehicle
sales prices, the household buys a new
car and scraps the eight-year old car; the
other two cars in the fleet each get a year
older. In a scenario where CAFE
standards become more stringent
causing vehicle sales prices to increase,
this household chooses to delay buying
a new car and each of their three
existing cars gets a year older. In both
cases, all three vehicles (including the
new car in the first scenario, and the
year-year-old car in the second scenario)
have to serve the family’s travel
demand.
The scrappage effect is visible in the
household’s vehicle fleet as it moves
from the first scenario to the second
scenario with changes in CAFE
standards. In the second scenario, the
nine-year-old car remains in the
household’s fleet to service demand for
travel, when it would otherwise have
been retired. While the scrappage effect
can be symmetrical to the sales effect, it
need not be. The ‘‘new car’’ in the
scenario without CAFE standards could
be a new vehicle from the current model
year or a used car that is of a newer
vintage than the 8-year-old vehicle it
replaces. The latter instance is an effect
of scrappage decisions that do not
directly affect new vehicle sales.
Eventually, new vehicles transition to
the used car market, but that on average
take several years, and the shift is slow.
At the household level, the scrappage
decision occurs in a single year, each
year, for every vehicle in the fleet. To
the extent CAFE standards affect new
vehicle prices and fuel economies,
relative to vehicles already owned,
scrappage could accelerate or decelerate
depending upon the direction (and
magnitude) of the changes.

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3. Safety Model
The analysis supporting the CAFE
rule for MYs 2017 and beyond did not
account for differences in exposure or
inherent safety risk as vehicles aged
throughout their useful lives. However,
the relationship between vehicle age
and fatality risk is an important one. In
a 2013 Research Note,321 NHTSA’s
National Center for Statistics and
Analysis concluded a driver of a vehicle
that is four to seven years old is 10%
more likely to be killed in a crash than
the driver of a vehicle zero to three
years old, accounting for the other
factors related to the crash. This trend
continued for older vehicles more
generally, with a driver of a vehicle 18
years or older being 71% more likely to
be killed in a crash than a driver in a
new vehicle. While there are more
registered vehicles that are zero to three
years old than there are 20 years or
older (nearly three times as many)
because most of the vehicles in earlier
vintages are retired sooner, the average
age of vehicles in the United States is
11.6 years old and has risen
significantly in the past decade.322 This
relationship reflects a general trend
visible in the Fatality Analysis
Reporting System (FARS) when looking
at a series of calendar years: Newer
vintages are safer than older vintages,
over time, at each age. This is likely
because of advancements in safety
technology, like side-impact airbags,
electronic stability control, and (more
recently) sophisticated crash avoidance
systems starting to work their way into
the vehicle population. In fact, the 2013
Research Note indicated that the
percentage of occupants fatally injured
in fatal crashes increased with vehicle
age: From 27% for vehicles three or
fewer years old, to 41% for vehicles 12–
14 years old, to 50% for vehicles 18 or
more years old.
With an integrated fleet model now
part of the analytical framework for
CAFE analysis, any effects on fleet
turnover (either from delayed vehicle
retirement or deferred sales of new
vehicles) will affect the distribution of
both ages and model years present in
the on-road fleet. Because each of these
vintages carries with it inherent rates of
fatal crashes, and newer vintages are
generally safer than older ones,
changing that distribution will change
321 National Center for Statistics and Analysis,
How Vehicle Age and Model Year Relate to Driver
Injury Severity in Fatal Crashes, National Highway
Traffic Safety Administration (Aug. 2013), available
at https://crashstats.nhtsa.dot.gov/Api/Public/View
Publication/811825.
322 Based on data acquired from Ward’s
Automotive.

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the total number of on-road fatalities
under each regulatory alternative.
To estimate the empirical relationship
between vehicle age, model year
vintage, and fatalities, DOT conducted a
statistical analysis linking data from the
FARS database, a time series of Polk
registration data to represent the onroad vehicle population, and assumed
per-vehicle mileage accumulation rates
(the derivation of which is discussed in
detail in PRIA Chapter 11). These data
were used to construct per-mile fatality
rates that varied by vehicle vintage,
accounting for the influence of vehicle
age. However, unlike the NCSA study
referenced above, any attempt to
account for this relationship in the
CAFE analysis faces two challenges. The
first challenge is the CAFE model lacks
the internal structure to account for
other factors related to observed fatal
crashes—for example, vehicle speed,
seat belt use, drug use, or age of
involved drivers or passengers. Vehicle
interactions are simply not modeled at

this level; the safety analysis in the
CAFE model is statistical, using
aggregate values to represent the totality
of fleet interactions over time. The
second challenge is perhaps the more
significant of the two: The CAFE
analysis is inherently forward-looking.
To implement a statistical model
analogous to the one developed by
NCSA, the CAFE model would require
forecasts of all factors considered in the
NCSA model—about vehicle speeds in
crashes, driver behavior, driver and
passenger ages, vehicle vintages, and so
on. In particular, the model would
require distributions (joint distributions,
in most cases) of these factors over a
period of time spanning decades. Any
such forecasts would be highly
uncertain and would be likely to assume
a continuation of current conditions.
Instead of trying to replicate the
NCSA work at a similar level of detail,
DOT conducted a simpler statistical
analysis to separate the safety impact of
the two factors the CAFE model

explicitly accounts for: The distribution
of vehicle ages in the fleet and the
number of miles driven by those
vehicles at each age. To accomplish this,
DOT used data from the FARS database
at a lower level of resolution; rather
than looking at each crash and the
specific factors that contributed to its
occurrence, staff looked at the total
number of fatal crashes involving lightduty vehicles over time with a focus on
the influence of vehicle age and vehicle
vintage. When considering the number
of fatalities relative to the number of
registered vehicles for a given model
year (without regard to the passenger
car/light-truck distinction, which has
evolved over time and can create
inconsistent comparisons), a somewhat
noisy pattern develops. Using data from
calendar year 1996 through 2015, some
consistent stories develop. The points in
Figure II–4 represent the number of
fatalities per registered vehicle with
darker circles associated with
increasingly current calendar years.

As shown in Figure II–4, fatalities per
registered vehicle have generally
declined over time across all vehicle
ages (the darker points representing
newer vintages being closer to the xaxis) and, across most recent calendar
years, fatality rates (per registered

vehicle) start out at a low point, rise
through age 15 or so, then decline
through age 30 (at which point little of
the initial model year cohort is still
registered). While this pattern is evident
in the registration data, it is magnified
by imposing a mileage accumulation

schedule on the registered population
and examining fatalities per billion
miles of VMT.
The mileage accumulation schedule
used in this analysis was developed
using odometer readings of vehicles
aged 0–15 years in calendar year 2015.

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The years spanned by the FARS
database cover all model years from
calendar year 1996 through 2015. Given
that there is a significant number of
years between the older vehicles in the
1996 CY data and the most recent model
years in the odometer data the informed
the mileage accumulation schedules,
staff applied an elasticity of ¥0.20 to
the change in the average cost per mile
of vehicles over their lives. While the
older vehicles had lower fuel

economies, which would be associated
with higher per-mile driving costs, they
also (mostly) faced lower fuel prices.
This adjustment increased the mileage
accumulation for older vehicles, but not
by large amounts. Because the CAFE
model uses the mileage accumulation
schedule and applies it to all vehicles in
the fleet, it is necessary to use the same
schedule to estimate per-mile fatality
rates in the statistical analysis—even if
the schedule is based on vehicles that

look different than the oldest vehicles in
the FARS dataset.
When the per-vehicle fatality rates are
converted into per-mile fatality rates,
the pattern observed in the registration
comparison becomes clearer. As Figure
II–5 shows, the trend present in the
fatality data on a per-registration basis is
even clearer on a per-mile basis: Newer
vintages are safer than older vintages, at
each age, over time.

The shape of the curve in Figure II–
5 suggests a polynomial relationship
between fatality rate and vehicle age, so

DOT’s statistical model is based on that
structure.
The final model is a weighted quartic
polynomial regression (by number of

registered vehicles) on vehicle age with
fixed effects for the model years present
in the dataset: 323

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323 Note: The dataset included MY 1975, but that
fixed effect is excluded from the set. The constant

term acts as the fixed effect for 1975 and all others
are relative to that one.

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The coefficient estimates and model
summary are in Table II–67.

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T abl e II-67 - D escn. pf IOn ofstafISf1caI mo dl
e
Coefficients:
Estimate
Std.
Error
28.59***
(Intercept)
3.067
-3.63***
Vehicle Age
0.2298
0.76***
0.03016
Age 2
Agej
-0.04***
0.001453
4
0.0005*** 2.25E-05
Age
MY 1976
-0.72
3.621
MY 1977
-2.24
3.425
MY 1978
-1.53
3.324
MY 1979
-4.46
3.268
MY 1980
-3.78
3.437
MY 1981
-2.88
3.38
MY 1982
-4.42
3.329
MY 1983
-4.93
3.236
MY 1984
-4.71
3.142
MY 1985
-4.78
3.113
MY 1986
-5.54.
3.092
MY 1987
-5.86.
3.086
MY 1988
-4.37
3.079
MY 1989
-4.78
3.074
MY 1990
-5.17.
3.077
MY 1991
-5.84.
3.072
MY 1992
-7.26*
3.07
-7.92**
MY 1993
3.062
-9.69**
MY 1994
3.058
-10.61 *** 3.053
MY 1995
-12.07*** 3.06
MY 1996
-12.8***
MY 1997
3.056
-13.88*** 3.057
MY 1998
-14.91 *** 3.055
MY 1999
-15.68*** 3.054
MY2000
-16.33*** 3.059
MY 2001
-17.1***
3.06
MY2002
-17.7***
3.065
MY2003
-18.24*** 3.069
MY2004
-18.91 *** 3.074
MY2005

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This function is now embedded in the
CAFE model, so the combination of
VMT per vehicle and the distribution of
ages and model years present in the onroad fleet determine the number of
fatalities in a given calendar year. The
model reproduces the observed fatalities
of a given model year, at each age,
reasonably well with more recent model
years (to which the VMT schedule is a
better match) estimated with smaller
errors.
While the final specification was not
the only one considered, the fact this
model was intended to live inside the
CAFE model to dynamically estimate
fatalities for a dynamically changing onroad vehicle population was a
constraining factor.
(a) Predicting Future Safety Trends
The base model predicts a net
increase in fatalities due primarily to
slower adoption of safer vehicles and
added driving because of less costly
vehicle operating costs. In earlier
calendar years, the improvement in
safety of the on-road fleet produces a net
reduction in fatalities, but from the mid2020s forward, the baseline model
predicts no further increase in safety,
and the added risk from more VMT and
older vehicles produces a net increase
in fatalities. This model thus reflects a
conservative limitation; it implicitly
assumes the trend toward increasingly
safe vehicles that has been apparent for
the past 3 decades will flatten in mid2020s. The agency does not assert this
is the most likely case. In fact, the
development of advanced crash
avoidance technologies in recent years
indicates some level of safety

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improvement is almost certain to occur.
The difficulty is for most of these
technologies, their effectiveness against
fatalities and the pace of their adoption
are highly uncertain. Moreover,
autonomous vehicles offer the
possibility of significantly reducing or
eventually even eliminating the effect of
human error in crash causation, a
contributing factor in roughly 94% of all
crashes. This conservative assumption
may cause the NPRM to understate the
beneficial effect of proposed standards
on improving (reducing) the number of
fatalities.
Advanced technologies that are
currently deployed or in development
include:
Forward Collision Warning (FCW)
systems are intended to passively assist
the driver in avoiding or mitigating the
impact of rear-end collisions (i.e., a
vehicle striking the rear portion of a
vehicle traveling in the same direction
directly in front of it). FCW uses
forward-looking vehicle detection
capability, such as RADAR, LIDAR
(laser), camera, etc., to detect other
vehicles ahead and use the information
from these sensors to warn the driver
and to prevent crashes. FCW systems
provide an audible, visual, or haptic
warning, or any combination thereof, to
alert the driver of an FCW-equipped
vehicle of a potential collision with
another vehicle or vehicles in the
anticipated forward pathway of the
vehicle.
Crash Imminent Braking (CIB)
systems are intended to actively assist
the driver by mitigating the impact of
rear-end collisions. These safety systems
have forward-looking vehicle detection

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capability provided by sensing
technologies such as RADAR, LIDAR,
video camera, etc. CIB systems mitigate
crash severity by automatically applying
the vehicle’s brakes shortly before the
expected impact (i.e., without requiring
the driver to apply force to the brake
pedal).
Dynamic Brake Support (DBS) is a
technology that actively increases the
amount of braking provided to the
driver during a rear-end crash avoidance
maneuver. If the driver has applied
force to the brake pedal, DBS uses
forward-looking sensor data provided by
technologies such as RADAR, LIDAR,
video cameras, etc. to assess the
potential for a rear-end crash. Should
DBS ascertain a crash is likely (i.e., the
sensor data indicate the driver has not
applied enough braking to avoid the
crash), DBS automatically intervenes.
Although the manner in which DBS has
been implemented differs among
vehicle manufacturers, the objective of
the interventions is largely the same: To
supplement the driver’s commanded
brake input by increasing the output of
the foundation brake system. In some
situations, the increased braking
provided by DBS may allow the driver
to avoid a crash. In other cases, DBS
interventions mitigate crash severity.
Pedestrian AEB (PAEB) systems
provide automatic braking for vehicles
when pedestrians are in the forward
path of travel and the driver has taken
insufficient action to avoid an imminent
crash. Like CIB, PAEB safety systems
use information from forward-looking
sensors to automatically apply or
supplement the brakes in certain driving

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situations in which the system
determines a pedestrian is in imminent
danger of being hit by the vehicle. Many
PAEB systems use the same sensors and
technologies used by CIB and DBS.
Rear Automatic Braking feature
means installed vehicle equipment that
has the ability to sense the presence of
objects behind a reversing vehicle, alert
the driver of the presence of the
object(s) via auditory and visual alerts,
and automatically engage the available
braking system(s) to stop the vehicle.
Semi-automatic Headlamp Beam
Switching device provides either
automatic or manual control of
headlamp beam switching at the option
of the driver. When the control is
automatic, headlamps switch from the
upper beam to the lower beam when
illuminated by headlamps on an
approaching vehicle and switch back to
the upper beam when the road ahead is
dark. When the control is manual, the
driver may obtain either beam manually
regardless of the conditions ahead of the
vehicle.
Rear Turn Signal Lamp Color Turn
signal lamps are the signaling element
of a turn signal system, which indicates
the intention to turn or change direction
by giving a flashing light on the side
toward which the turn will be made.

FMVSS No. 108 permits a rear turn
signal lamp color of amber or red.
Lane Departure Warning (LDW)
system is a driver assistance system that
monitors lane markings on the road and
alerts the driver when their vehicle is
about to drift beyond a delineated edge
line of their current travel lane.
Blind Spot Detection (BSD) systems
uses digital camera imaging technology
or radar sensor technology to detect one
or more vehicles in either of the
adjacent lanes that may not be apparent
to the driver. The system warns the
driver of an approaching vehicle’s
presence to help facilitate safe lane
changes.
These technologies are either under
development or are currently being
offered, typically in luxury vehicles, as
either optional or standard equipment.
To estimate baseline fatality rates in
future years, NHTSA examined
predicted results from a previous NCSA
study 324 that measured the effect of
known safety regulations on fatality
rates. This study relied on statistical
evaluations of the effectiveness of motor
vehicle safety technologies based on real
world performance in the on-road
vehicle fleet to determine the
effectiveness of each safety technology.
These effectiveness rates were applied

to existing fatality target populations
and adjusted for current technology
penetration in the on-road fleet, taking
into account the retirement of existing
vehicles and the pace of future
penetration required to meet statutory
compliance requirements, as well as
adjustments for overlapping target
populations. Based on these factors, as
well as assumptions regarding future
VMT, the study predicted future fatality
levels and rates. Because the safety
impact in the CAFE model
independently predicts future VMT, we
removed the VMT growth rate from the
NCSA study and developed a prediction
of vehicle fatality trends based only on
the penetration pace of new safety
technologies into the on-road fleet.
These data were then normalized into
relative safety factors with CY 2015 as
the baseline (to match the baseline
fatality year used in this CAFE analysis).
These factors were then converted into
equivalent fatality rates/100 million
VMT by anchoring them to the 2015
fatality rate/100 million VMT published
by NHTSA. Figure II–6 below illustrates
the modelling output and projected
fatality trend from the analysis of the
NCSA study, prior to adjustment to
fatality rates/100 million VMT.

324 Blincoe, L. & Shankar, U. The Impact of Safety
Standards and Behavioral Trends on Motor Vehicle

Fatality Rates, National Highway Traffic Safety
Administration (Jan. 2007), available at https://

www.nhtsa.gov/sites/nhtsa.dot.gov/files/
documents/810777v3.pdf.

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This model was based on inputs
representing the impact of technology
improvement through CY 2020.
Projecting this trend beyond 2020 can
be justified based on the continued
transformation of the on-road fleet to
100% inclusion of the known safety
technologies. Based on projections in
the NCSA study, significant further
technology penetration can be expected
in the on-road fleet for side impact
improvements (FMVSSS 214),
electronic stability control (FMVSS
126), upper interior head impact
protection (FMVSS 301), tire pressure
monitoring systems (FMVSS 138),
ejection mitigation (FMVSS 226), and

heavy truck stopping distance
improvements (FMVSS 121). These
technologies were estimated to be
installed in only 40–70% of the on-road
fleet as of CY 2020, implying further
safety improvement well beyond the
2020 calendar year.
The NCSA study focused on
projections to reflect known technology
adaptation requirements, but it was
conducted prior to the 2008 recession,
which disrupted the economy and
changed travel patterns throughout the
country. Thus, while the relative trends
it predicts seem reasonable, they cannot
account for the real-world disruption
and recovery that occurred in the 2008–

2015 timeframe. In addition, the NCSA
study did not attempt to adjust for safety
impacts that may have resulted from
changes in the vehicle sales mix
(vehicle types and sizes creating
different interactions in crashes), in
commuting patterns, or in shopping or
socializing habits associated with
internet access and use. To address this,
NHTSA also examined the actual
change in the fatality rate as measured
by fatality counts and VMT estimates.
Figure II–7 below illustrates the actual
fatality rates measured from 2000
through 2016 and the modeled fatality
rate trend based on these historical data.

The effect of the recession and
subsequent recovery can be seen in
chaotic shift in the fatality rate trend
starting in 2008. The generally gradual
decline that had been occurring over the
previous decade was interrupted by a
slowdown in the rate of change
followed by subsequent upward and
downward shifts. More recently, the rate
has begun to increase. These shifts
reflect some combination of factors not
captured in the NCSA analysis
mentioned above. The significance of
this is that although there was a steady
increase in the penetration of safety
technologies into the on-road fleet
between 2008 and 2015, other unknown
factors offset their positive influence
and eventually reversed the trend in

vehicle safety rates. Because of the
upward shift over the 2014–2015
period, this model, which does not
reflect technology trend savings after
2015, will predict an upward shift of
fatality rates after 2020.
Predicting future safety trends has
significant uncertainty. Although
further safety improvements are
expected because of advanced safety
technologies such as automatic braking
and eventually, fully automated
vehicles, the pace of development and
extent of consumer acceptance of these
improvements is uncertain. Thus, two
imperfect models exist for predicting
future safety trends. The NCSA model
reflects the expected trend from
required technologies and indicates

continued improvement well beyond
the 2020 timeframe, which is when the
historical fatality rate based model
breaks down. By contrast, the historical
fatality rate model reflects shifts in
safety not captured by the NCSA model,
but gives arguably implausible results
after 2020. It essentially represents a
scenario in which economic, market, or
behavioral factors minimize or offset
much of the potential impact of future
safety technology.
For the NPRM, the analysis examines
a scenario projecting safety
improvements beyond 2015 using a
simple average of the NCSA and
historical fatality rate models, accepting
each as an illustration of different and
conflicting possible future scenarios. As

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both models eventually curve up
because of their quadratic form, each
models’ results are flattened at the point
where they begin to trend upward. This
occurs in 2045 for the NCSA model and
in 2021 for the historical model. The
results are shown in Figure II–8 below.

The results indicate roughly a 19%
reduction in fatality rates between 2015
and 2050. This is a slower pace than
what has historically occurred over the
past several decades, but the biggest
influence on historical rates was
significant improvement in safety belt

use, which was below 10% in 1960 and
had risen to roughly 70% by 2000, and
is now more than 90%. Because belt use
is now above 90%, further such
improvements are unlikely unless they
come from new technologies.

A difficulty with these trend models
is they are based on calendar year
predictions, which are derived from the
full on-road vehicle fleet rather than the
model year fleet, which is the basis for
calculations in the CAFE model. As
such they are useful primarily as
indicators that vehicle safety has
steadily improved over the past several
decades, and given the advanced safety
technologies under current
development, we would expect some
continuation of improvement in MY
vehicle safety over the near and midterm future. To account for this, NHTSA
approximated a model year safety trend
continuing through about 2035 (Figure
II–9). For this trend the agency used
actual data from FARS to calculate the
change in fatality rates through 2007.
The recession, which struck our
economy in 2008, distorted normal
behavioral patterns and affected both

VMT and the mix of drivers and type of
driving to an extent we do not believe
the recession era gives an accurate
picture of the safety trends inherent in
the vehicles themselves. Therefore,
beginning in 2008, NHTSA
approximated a trend for safety
improvement through about MY 2035 to
reflect the continued effect of improved
safety technologies such as advanced
automatic braking, which manufacturers
have announced will be in all new
vehicles by MY 2022. The agency
recognize this is only an estimate, and
actual MY trends could be above or
below this line. NHTSA examined
alternate trends in a sensitivity analysis
and request comments on the best way
to address future safety trends.
NHTSA also notes although we
project vehicles will continue to become
safer going forward to about 2035, we do
not have corresponding cost information

for technologies enabling this
improvement. In a standard elasticity
model, sales impacts are a function of
the percent change in vehicle price.
Hypothetically, increasing the base
price for added safety technologies
would decrease the impact of higher
prices due to impacts of CAFE standards
on vehicle sales. The percentage change
in baseline price would decrease, which
would mean a lower elasticity effect,
which would mean a lower impact on
sales. NHTSA will consider possible
ways to address this issue before the
final rule, and we request comments on
the need and/or practicability for such
an adjustment, as well as any data and
other relevant information that could
support such an analysis of these costs,
as well as the future pace of
technological adoption within the
vehicle fleet.

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(b) Adjusting for Behavioral Impacts
The influence of delayed purchases of
new vehicles is estimated to have the
most significant effect on safety
imposed by CAFE standards. Because of
a combination of safety regulations and
voluntary safety improvements,
passenger vehicles have become safer
over time. Compared to prior decades,
fatality rates have declined significantly
because of technological improvements,
as well as behavioral shifts, such as
increased seat belt use. As these safer
vehicles replace older less safe vehicles
in the fleet, the on-road fleet is replaced
with vehicles reflecting the improved
fatality rates of newer, safer vehicles.
However, fatality rates associated with
different model year vehicles are
influenced by the vehicle itself and by
driver behavior. Over time, used
vehicles are purchased by drivers in
different demographic circumstances
who also tend to have different
behavioral characteristics. Drivers of
older vehicles, on average, tend to have
lower belt use rates, are more likely to
drive inebriated, and are more likely to
drive over the speed limit. Additionally,
older vehicles are more likely to be
driven on rural roadways, which
typically have higher speeds and
produce more serious crashes. These
relationships are illustrated graphically
in Chapter 11 of the PRIA
accompanying this proposed rule.
The behavior being modelled and
ascribed to CAFE involves decisions by
drivers who are contemplating buying a
new vehicle, and the purchase of a

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newer vehicle will not in itself cause
those drivers to suddenly stop wearing
seat belts, speed, drive under the
influence, or shift driving to different
land use areas. The goal of this analysis
is to measure the effect of different
vehicle designs that change by model
year. The modelling process for
estimating safety essentially involves
substituting fatality rates of older MY
vehicles for improved rates that would
have been experienced with a newer
vehicle. Therefore, it is important to
control for behavioral aspects associated
with vehicle age so only vehicle design
differences are reflected in the estimate
of safety impacts. To address this, the
CAFE safety model was run to control
for vehicle age. That is, it does not
reflect a decision to replace an older
model year vehicle that is, for example,
10 years old with a new vehicle. Rather,
it reflects the difference in the average
fatality rate of each model year across its
entire lifespan. This will account for
most of the difference because of vehicle
age, but it may still reflect a bias caused
by the upward trend in societal seat belt
use over time. Because of this secular
trend, each subsequent model year’s
useful life will occur under increasingly
higher average seat belt use rates. This
could cause some level of behavioral
safety improvement to be ascribed to the
model year instead of the driver cohort.
However, it is difficult to separate this
effect from the belt use impacts of
changing driver cohorts as vehicles age.
Glassbrenner (2012) analyzed the
effect of improved safety in newer

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vehicles for model years 2001 through
2008. She developed several statistical
regression models that specifically
controlled for most behavioral factors to
isolate model year vehicle
characteristics. However, her study did
not specifically report the change in MY
fatality rates—rather, she reported total
fatalities that could have been saved in
a baseline year (2008) had all vehicles
in the on-road fleet had the same safety
features as the MY 2001 through MY
2008 vehicles. This study potentially
provides a basis for comparison with
results of the CAFE safety estimates. To
make this comparison, the CY 2008
passenger car and light truck fatalities
total from FARS were modified by
subtracting the values found in Figure
II–9 of her study. This gives a stream of
comparable hypothetical CY 2008
fatality totals under progressively less
safe model year designs. Results
indicated that had the 2008 on-road
fleet been equipped with MY 2008
safety equipment and vehicle
characteristics, total fatalities would
have been reduced by 25% compared to
vehicles that were actually on the road
in 2008. Similar results were calculated
for each model years’ vehicle
characteristics back to 2001.
For comparison, predicted MY fatality
rates were derived from the CAFE safety
model and applied to the CY 2008 VMT
calculated by that model. This gives an
estimate of CY 2008 fatalities under
each model years’ fatality rate, which,
when compared to the predicted CY
fatality total, gives a trendline

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comparable to the Glassbrenner
trendline illustrating the change in MY
fatality rates. Both models are sensitive
to the initial 2008 baseline fatality total,
and because the predicted CAFE total is
somewhat lower than the actual total,
the agency ran a third trendline to
examine the influence of this difference.
Results are shown in Figure II–10.
Using the corrected fatality count, but
retaining the predicted VMT changes
the initial 2018 CY fatality rate to 12.62

(instead of 12.15) and produces the
result shown in Figure II–10. The CAFE
model trendline shifts up, which
narrows the difference in early years but
expands it in later years. However, VMT
and fatalities are linked in the CAFE
model, so the actual level of the MY
safety predicted by the CAFE curve has
uncertainty. Perhaps the most
meaningful result from this comparison
is the difference in slopes; the CAFE
model predicts more rapid change

through 2006, but in the last few years
change decreases. This might reflect the
trend in societal belt use, which rose
steadily through 2005 and levelled off.
Later model years’ fatality rates would
benefit from this trend while earlier
model years would suffer. This seems
consistent with our using lifetime MY
fatality rates to reflect MY change rather
than first year MY fatality rates
(although even first year rates would
reflect this bias, but not as much).

To provide another perspective on
safety impacts, NHTSA accessed data
from a comprehensive study of the
effects of safety technologies on motor
vehicle fatalities. Kahane (2015) 325
examined all safety effects of vehicle
safety technologies from 1960 through
2012 and found these technologies
saved more than 600,000 lives during

that time span. Kahane is currently
working under contract for NHTSA to
update this study through 2016. At
NHTSA’s request, Kahane accessed his
database to provide a measure of
relative MY vehicle design safety by
controlling for seat belt use. The result
was a MY safety index illustrating the
progress in vehicle safety by model year

which isolates vehicle design from the
primary behavioral impact—seat belt
usage. We normalized Kahane’s index to
MY 1975 and did the same to the ‘‘fixed
effects’’ we are currently using from our
safety model to compare the trends in
MY safety from the two methods.
Results are shown in Figure II–11.

325 Kahane, C.J. Lives Saved by Safety Standards
and Associated Vehicle Safety Technologies, 1960–
2012—Passenger Cars and LTVs—with Reviews of

26 FMVSS and the Effectiveness of their Associated
Safety Technologies in Reducing Fatalities, Injuries,
and Crashes, National Highway Traffic Safety

Administration (Jan. 2015), available at https://
crashstats.nhtsa.dot.gov/Api/Public/View
Publication/812069.

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From Figure II–11 both approaches
show similar long-term downward
trends, but this model shows a steeper
slope than Kahane’s model. The two
models involve completely different
approaches, so some difference is to be
expected. However, it is also possible
this reflects different methods used to
isolate vehicle design safety from
behavioral impacts. As discussed
previously, NHTSA addressed this issue
by removing vehicle age impacts from
its model, whereas Kahane’s model does
it by controlling for belt use. As noted
previously, aside from the age impact on
belt use associated with the different

demographics driving older vehicles,
there is a secular trend toward more belt
use reflecting the increase in societal
awareness of belt use importance over
time. This trend is illustrated in Figure
II–12 below.326 NHTSA’s current
approach removes the age trend in belt
use, but it’s not clear whether it
accounts for the full impacts of the
secular trend as well. If not, some
portion of the gap between the two
trendlines could reflect behavioral
impacts rather than vehicle design.
These models (NHTSA, Glassbrenner,
and Kahane) involve differing
approaches and assumptions

contributing to uncertainty, and given
this, their differences are not surprising.
It is encouraging they show similar
directional trends, reinforcing the basic
concept we are measuring. NHTSA
recognizes predicting future fatality
impacts, as well as sales impacts that
cause them, is a difficult and imprecise
task. NHTSA will continue to
investigate this issue, and we seek
comment on these estimates as well as
alternate methods for predicting the
safety effects associated with delayed
new vehicle purchases.

326 Note: The drop occurring in 1994 reflects a
shift in the basis for determining belt use rates.
Effective in 1994, data were reported from the

National Occupant Protection Survey (NOPUS).
Prior to this, a conglomeration of state studies
provided the basis. It is likely the pre-NOPUS

surveys produced inflated results, especially in the
1991–1993 period.

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4. Impact of Rebound Effect on Fatalities
Based on historical data, it is possible
to calculate a baseline fatality rate for
vehicles of any model year vintage. By
simply taking the total number of
vehicles involved in fatal accidents over
all ages for a model year and dividing
by the cumulative VMT over the useful
life of every vehicle produced in that
model year, one arrives at a baseline
hazard rate denominated in fatalities per
billion miles. The fatalities associated
with vehicles produced in that model
year are then proportional to the
cumulative lifetime VMT, where total
fatalities equal the product of the
baseline hazard rate and VMT. A more
comprehensive discussion of the
rebound effect and the basis for
calculating its impact on mileage and
risk is in Chapter 8 of the PRIA
accompanying this proposed rule.
5. Adjustment for Non-Fatal Crashes
Fatalities estimated to be caused by
various alternative CAFE standards are
valued as a societal cost within the
CAFE models’ cost/benefit accounting.
Their value is based on the
comprehensive value of a fatality
derived from data in Blincoe et al.
(2015), adjusted to 2016 economics and
updated to reflect the official DOT
guidance on the value of a statistical life

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in 2016. This gives a societal value of
$9.9 million for each fatality. The CAFE
safety model estimates effects on traffic
fatalities but does not address
corresponding effects on non-fatal
injuries and property damage that
would result from the same factors
influencing fatalities. To address this,
we developed an adjustment factor that
would account for these crashes.
Development of this factor is based on
the assumption nonfatal crashes will be
affected by CAFE standards in
proportion to their nationwide
incidence and severity. That is, NHTSA
assumes the same injury profile, the
relative number of cases of each injury
severity level, that occur nationwide,
will be increased or decreased because
of CAFE. The agency recognizes this
may not be the case, but the agency does
not have data to support individual
estimates across injury severities. There
are reasons why this may not be true.
For example, because older model year
vehicles are generally less safe than
newer vehicles, fatalities may make up
a larger portion of the total injury
picture than they do for newer vehicles.
This would imply lower ratios across
the non-fatal injury and PDO profile and
would imply our adjustment may
overstate total societal impacts. NHTSA
requests comments on this assumption

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and alternative methods to estimate
injury impacts.
The adjustment factor is derived from
Tables 1–8 and I–3 in Blincoe et al.
(2015). Incidence in Table I–3 reflects
the Abbreviated Injury Scale (AIS),
which ranks nonfatal injury severity
based on an ascending 5 level scale with
the most severe injuries ranked as level
5. More information on the basis for
these classifications is available from
the Association for the Advancement of
Automotive Medicine at https://
www.aaam.org/abbreviated-injury-scaleais/.
Table 1–3 in Blincoe lists injured
persons with their highest (maximum)
injury determining the AIS level
(MAIS). This scale is represented in
terms of MAIS level, or maximum
abbreviated injury scale. MAIS0 refers
to uninjured occupants in injury
vehicles, MAIS1 are generally
considered minor injuries, MAIS2
moderate injuries, MAIS3 serious
injuries, MAIS4 severe injuries, and
MAIS5 critical injuries. PDO refers to
property damage only crashes, and
counts for PDOs refer to vehicles in
which no one was injured. From Table
II–68, ratios of injury incidence/fatality
are derived for each injury severity level
as follows:

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327 Press Release, Kelley Blue Book, New-Car
Transaction Prices Remain High, Up More Than 3
Percent Year-Over-Year in January 2017, According
to Kelley Blue Book (Feb. 1, 2017), https://

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F = change in fatalities estimated for CAFE
due to retaining used vehicles
r = ratio of nonfatal injuries or PDO vehicles
to fatalities (F)
p = value of property damage prevented by
retaining older vehicle
328 Edmunds Used Vehicle Market Report,
Edmunds (Feb. 2017), https://
dealers.edmunds.com/static/assets/articles/2017_
Feb_Used_Market_Report.pdf.

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Where:
S = total property damage savings from
retaining used vehicles longer

2016. There is a minor timing
discrepancy in these data because the
new vehicle data represent January
2017, and the used vehicle price is for
the average over 2016. NHTSA was
unable to locate exact matching data at
this time, but the agency believes the
difference will be minor.
Based on these data, new vehicles are
on average worth 82% more than used
vehicles. To estimate the effect of higher
property damage costs for newer
vehicles on crashes, the per unit
property damage costs from Table I–9 in
Blincoe et al. (2015) were multiplied by
this factor. Results are illustrated in
Table II–69.

EP24AU18.100

The total property damage cost
reduction was then calculated as a
function of the number of fatalities
reduced or increased by CAFE as
follows:

vehicles are worth less and will cost less
to repair, if they are repaired at all. The
consumer’s property damage loss is thus
reduced by longer retention of these
vehicles. To estimate this loss, average
new and used vehicle prices were
compared. New vehicle transaction
prices were estimated from a study
published by Kelley Blue Book.327
Based on these data, the average new
vehicle transaction price in January
2017 was $34,968. Used vehicle
transaction prices were obtained from
Edmonds Used Vehicle Market Report
published in February of 2017.328
Edmonds data indicate the average used
vehicle transaction price was $19,189 in

EP24AU18.099

sradovich on DSK3GMQ082PROD with PROPOSALS2

For each fatality that occurs
nationwide in traffic crashes, there are
561 vehicles involved in PDOs, 139
uninjured occupants in injury vehicles,
105 minor injuries, 10 moderate
injuries, 3 serious injuries, and
fractional numbers of the most serious
categories which include severe and
critical nonfatal injuries. For each
fatality ascribed to CAFE it is assumed
there will be nonfatal crashes in these
same ratios.
Property damage costs associated with
delayed new vehicle purchases must be
treated differently because crashes that
subsequently occur damage older used
vehicles instead of newer vehicles. Used

43147

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

The number of fatalities ascribed to
CAFE because of older vehicle retention
was multiplied by the unit cost per
fatality from Table I–9 in Blincoe et al.
(2015) to determine the societal impact
accounted for by these fatalities.329
From Table I–8 in Blincoe et al. (2015),
NHTSA subtracted property damage
costs from all injury severity levels and
recalculated the total comprehensive
value of societal losses from crashes.
The agency then divided the portion of
these crashes because of fatalities by the
resulting total to estimate the portion of
crashes excluding property damage that
are accounted for by fatalities. Results
indicate fatalities accounted for
approximately 40% of all societal costs
exclusive of property damage. NHTSA
then divided the total cost of the added
fatalities by 0.4 to estimate the total cost
of all crashes prevented exclusive of the
savings in property damage. After
subtracting the total savings in property
damage from this value, we divided the
fatality cost by it to estimate that
overall, fatalities account for 43% of the
total costs that would result from older
vehicle retention.
For the fatalities that occur because of
mass effects or to the rebound effect, the

calculation was more direct, a simple
application of the ratio of the portion of
costs produced by fatalities. In this case,
there is no need to adjust for property
damage because all impacts were
derived from the mix of vehicles in the
on-road fleet. Again, from Table I–8 in
Blincoe et al (2015), we derive this ratio
based on all cost factors including
property damage to be .36. These
calculations are summarized as follows:

Where:
SV = Value of societal Impacts of all crashes
F = change in fatalities estimated for CAFE
due to retaining used vehicles
v = Comprehensive societal value of
preventing 1 fatality
x = Percent of total societal loss from crashes
excluding property damage accounted
for by fatalities
S = total property damage savings from
retaining used vehicles longer
M = change in fatalities due to changes in
vehicle mass to meet CAFE standards
c = Percent of total societal loss from all cost
factors in all crashes accounted for by
fatalities

For purposes of application in the
CAFE model, these two factors were
combined based on the relative

contribution to total fatalities of
different factors. As noted, although a
safety impact from the rebound effect is
calculated, these impacts are considered
to be freely chosen rather than imposed
by CAFE and imply personal benefits at
least equal to the sum of their added
costs and safety consequences. The
impacts of this nonfatal crash
adjustment affect costs and benefits
equally. When considering safety
impacts actually imposed by CAFE
standards, only those from mass
changes and vehicle purchase delays are
considered. NHTSA has two different
factors depending on which metric is
considered. The agency created these
factors by weighting components by the
relative contribution to changes in
fatalities associated with each
component. This process and results are
shown in Table II–70. Note: For the
NPRM, NHTSA applied the average
weighted factor to all fatalities. This will
tend to slightly overstate costs because
of sales and scrappage and understate
costs associated with mass and rebound.
The agency will consider ways to adjust
this minor discrepancy for the final rule.

Table II–71, Table II–72, Table II–73,
and Table II–74 summarize the safety
effects of CAFE standards across the
various alternatives under the 3% and

7% discount rates. As noted in Section
II.F.5, societal impacts are valued using
a $9.9 million value per statistical life
(VSL). Fatalities in these tables are

undiscounted; only the monetized
societal impact is discounted.

329 Note: These calculations used the original
values in the Blincoe et all (2015) tables without

adjusting for economics. These calculations

produce ratios and are thus not sensitive to
adjustments for inflation.

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n = the 8 injury severity categories

EP24AU18.101

43148

43149

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table 11-71 - Change in Safety Parameters from CAFE Augural Standards Baseline
A verage A nnuaI F at ar1f1es, CY 2036 -2045 3%0 D.ISCOUn t R at e
'
Change in Safety Parameters from Augural Standards Baseline
Alt 1

Alt2

Alt 3

Alt4

Alt 5

Alt 6

Alt 7

Alt 8

-22
-180
-202

-19
-162
-181

-17
-151
-168

-17
-112
-129

-16
-76
-92

-6
-59
-65

0
-24
-24

-2
-33
-35

-692
-894

-650
-831

-605
-773

-511
-640

-392
-484

-317
-382

-174
-198

-219
-254

-0.11
-0.90
-1.01

-0.10
-0.81
-0.91

-0.08
-0.76
-0.84

-0.08
-0.56
-0.64

-0.08
-0.38
-0.46

-0.03
-0.30
-0.33

0.00
-0.12
-0.12

-0.01
-0.16
-0.17

-3.43
-4.44

-3.21
-4.12

-3.00
-3.84

-2.53
-3.18

-1.94
-2.40

-1.57
-1.90

-0.86
-0.98

-1.09
-1.26

-0.17
-1.41
-1.58

-0.15
-1.27
-1.42

-0.13
-1.18
-1.31

-0.13
-0.88
-1.01

-0.13
-0.59
-0.72

-0.05
-0.46
-0.51

0.00
-0.19
-0.19

-0.02
-0.26
-0.27

-5.36
-6.94

-5.03
-6.45

-4.69
-6.00

-3.96
-4.97

-3.04
-3.76

-2.46
-2.97

-1.35
-1.53

-1.70
-1.97

-0.27
-2.31
-2.59

-0.24
-2.08
-2.33

-0.22
-1.94
-2.15

-0.21
-1.44
-1.65

-0.21
-0.97
-1.18

-0.07
-0.76
-0.83

0.00
-0.30
-0.31

-0.03
-0.42
-0.45

-8.79
-11.4

-8.24
-10.6

-7.69
-9.84

-6.49
-8.15

-4.98
-6.16

-4.03
-4.87

-2.21
-2.51

-2.79
-3.23

Fatalities
Mass changes
Sales Impacts
Subtotal CAFE
Atrb.
Rebound effect
Total
Fatalities
Societal $B
Mass changes
Sales Impacts
Subtotal CAFE
Atrb.
Rebound effect
Total
Nonfatal
Societal $B
Mass changes
Sales Impacts
Subtotal CAFE
Atrb.
Rebound effect
Total

sradovich on DSK3GMQ082PROD with PROPOSALS2

Total Societal
$B
Mass changes
Sales Impacts
Subtotal CAFE
Atrb.
Rebound effect
Total

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EP24AU18.103

Average Annual Fatalities, CY 2036-2045. 3% Discount Rate

43150

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table 11-72 - Change in Safety Parameters from CAFE Augural Standards Baseline
A veraee A nnuaI F at ar1f1es, CY 2036 -2045 7%0 D.ISCOUn t R at e

'

Change in Safety Parameters from Augural Standards Baseline

Fatalities
Mass
changes
Sales
Impacts
Subtotal
CAFE
Atrb.
Rebound
effect
Total
Fatalities
Societal
$B
Mass
changes
Sales
Impacts
Subtotal
CAFE
Atrb.
Rebound
effect
Total

sradovich on DSK3GMQ082PROD with PROPOSALS2

Nonfatal
Societal
$B
Mass
changes
Sales
Impacts
Subtotal
CAFE
Atrb.
Rebound
effect

VerDate Sep<11>2014

Alt 1

Alt2

Alt3

Alt4

Alt5

Alt 6

Alt 7

Alt 8

-22

-19

-17

-17

-16

-6

0

-2

-180

-162

-151

-112

-76

-59

-24

-33

-202

-181

-168

-129

-92

-65

-24

-35

-692

-650

-605

-511

-392

-317

-174

-219

-894

-831

-773

-640

-484

-382

-198

-254

-0.04

-0.04

-0.03

-0.03

-0.03

-0.01

0.00

0.00

-0.38

-0.34

-0.32

-0.24

-0.16

-0.12

-0.05

-0.07

-0.42

-0.38

-0.35

-0.27

-0.19

-0.14

-0.05

-0.07

-1.42

-1.33

-1.24

-1.05

-0.80

-0.65

-0.36

-0.45

-1.84

-1.71

-1.59

-1.32

-1.00

-0.79

-0.41

-0.52

-0.07

-0.06

-0.05

-0.05

-0.05

-0.02

0.00

-0.01

-0.59

-0.53

-0.50

-0.37

-0.25

-0.19

-0.08

-0.11

-0.66

-0.60

-0.55

-0.42

-0.30

-0.21

-0.08

-0.11

-2.22

-2.08

-1.94

-1.64

-1.26

-1.02

-0.56

-0.70

23:42 Aug 23, 2018

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EP24AU18.104

Average Annual Fatalities, CY 2036-2045. 7% Discount Rate

Total

sradovich on DSK3GMQ082PROD with PROPOSALS2

Total
Societal
$B
Mass
changes
Sales
Impacts
Subtotal
CAFE
Atrb.
Rebound
effect
Total

VerDate Sep<11>2014

-2.88

-2.67

-2.49

-2.06

-1.56

-1.23

-0.64

-0.82

-0.11

-0.10

-0.09

-0.09

-0.09

-0.03

0.00

-0.01

-0.97

-0.88

-0.81

-0.61

-0.41

-0.32

-0.13

-0.18

-1.09

-0.98

-0.90

-0.69

-0.50

-0.35

-0.13

-0.19

-3.64

-3.41

-3.18

-2.69

-2.06

-1.67

-0.92

-1.15

-4.72

-4.38

-4.08

-3.38

-2.56

-2.02

-1.04

-1.34

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43151

EP24AU18.105

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

43152

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table 11-73 - Change in Safety Parameters from CAFE Augural Standards Baseline
Total Fatalities MY 1977-2029, 3% Discount Rate
Change in Safctv Parameters from Augural Standards Baseline
Total Fatalities MY 1977-2029, 3% Discmmt Rate
Alt l

Alt2

Alt3

Alt4

Alt5

Alt 6

Alt 7

Alt 8

Mass changes

-160

-147

-143

-173

-152

-73

-12

-30

Sales Impacts

-6,180

-5,680

-5,260

-4,280

-3,170

-2,550

-1,030

-1,480

Subtotal CAFE
Atrb.
Rebound effect

-6,340

-5,830

-5,400

-4,460

-3,330

-2,630

-1,050

-1,520

-6,340

-5,960

-5,620

-4,850

-3,610

-3,320

-2,200

-2,170

Total

-12,700

-11,800

-11,000

-9,300

-6,940

-5,950

-3,240

-3,690

Fatalities

Fatalities Societal
$B
Mass changes

-0.9

-0.9

-0.8

-1.1

-0.9

-0.4

-0.1

-0.2

Sales Impacts

-34.4

-31.6

-29.3

-23.9

-17.6

-14.4

-6.2

-8.3

Subtotal CAFE
Atrb.
Rebound effect

-35.4

-32.4

-30.1

-24.9

-18.5

-14.8

-6.3

-8.4

-41.7

-39.2

-37.0

-31.9

-23.7

-22.1

-14.8

-14.3

Total

-77.0

-71.6

-67.1

-56.9

-42.2

-36.9

-21.1

-22.8

Nonfatal Societal
$B
Mass changes

-1.5

-1.3

-1.3

-1.7

-1.5

-0.7

-0.1

-0.3

Sales Impacts

-53.8

-49.4

-45.8

-37.3

-27.5

-22.5

-9.7

-12.9

Subtotal CAFE
Atrb.
Rebound effect

-55.3

-50.7

-47.1

-39.0

-29.0

-23.2

-9.8

-13.2

-65.2
-120

-61.3
-112

-57.9
-105

-50.0

-37.0

-34.6

-23.2

-22.4

-89.0

-66.0

-57.8

-33.0

-35.6

Mass changes

-2.4

-2.2

-2.1

-2.7

-2.4

-1.1

-0.2

-0.5

Sales Impacts

-88.2

-81.0

-75.1

-61.2

-45.1

-36.9

-15.9

-21.2

Subtotal CAFE
Atrb.
Rebound effect

-90.7

-83.1

-77.2

-63.9

-47.5

-38.0

-16.0

-21.6

-107

-101

-94.9

-81.9

-60.7

-56.7

-38.0

-36.7

Total

-197

-184

-172

-146

-108

-94.7

-54.1

-58.4

Total

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sradovich on DSK3GMQ082PROD with PROPOSALS2

Total Societal $B

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

VerDate Sep<11>2014

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noted in Section II.F.5, societal impacts
are valued using a $9.9 million value
per statistical life (VSL). Fatalities in
these tables are undiscounted; only the

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monetized societal impact is
discounted.

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EP24AU18.107

sradovich on DSK3GMQ082PROD with PROPOSALS2

Table II–75 through Table II–78
summarize the safety effects of GHG
standards across the various alternatives
under the 3% and 7% discount rates. As

43153

43154

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table 11-75- Change in Safety Parameters from GHG Augural Standards Baseline
A vera~e A nnuaI F at ar1f1es, CY 2036 -2045 3%0 D.ISCOUn t R at e

'

Change in Safety Parameters from Augural Standards Baseline
Average Annual Fatalities, CY 2036-2045. 3% Discount Rate
Alt 1

Alt2

Alt 3

Alt4

Alt 5

Alt 6

Alt 7

Alt 8

Mass changes
Sales Impacts

-56
-221

-52
-213

-42
-177

-34
-131

-15
-93

-13
-66

-8
-34

-5
-36

Subtotal CAFE
Atrb.
Rebound effect
Total

-277

-265

-219

-165

-108

-79

-42

-41

-872

-838

-1,150

-1,100

-726
-945

-594
-759

-415
-523

-336
-415

-165
-207

-215
-256

-0.27

-0.25

-0.21

-0.17

-0.08

-0.07

-0.04

-0.02

sradovich on DSK3GMQ082PROD with PROPOSALS2

Fatalities
Societal $B
Mass changes
Sales Impacts
Subtotal CAFE
Atrb.
Rebound effect
Total

-1.11
-1.39

-1.07
-1.33

-0.89
-1.10

-0.66
-0.83

-0.47
-0.54

-0.33
-0.40

-0.17
-0.21

-0.18
-0.21

-4.31
-5.70

-4.15
-5.47

-3.60
-4.69

-2.94
-3.76

-2.05
-2.59

-1.66
-2.06

-0.82
-1.03

-1.06
-1.27

Nonfatal
Societal $B
Mass changes

-0.43

-0.40

-0.32

-0.26

-0.12

-0.11

-0.06

-0.04

Sales Impacts
Subtotal CAFE
Atrb.
Rebound effect

-1.74
-2.17

-1.68
-2.07

-1.39
-1.71

-1.03
-1.29

-0.73
-0.85

-0.52
-0.62

-0.27
-0.33

-0.29
-0.32

-6.75

-6.48

-5.62

-4.60

-3.21

-2.60

-1.28

-1.66

Total

-8.92

-8.56

-7.34

-5.89

-4.06

-3.22

-1.60

-1.99

Total Societal
$B
Mass changes

-0.70

-0.65

-0.53

-0.43

-0.19

-0.17

-0.10

-0.06

Sales Impacts

-2.85

-2.75

-2.28

-1.69

-1.20

-0.85

-0.44

-0.47

Subtotal CAFE
Atrb.
Rebound effect

-3.56

-3.40

-2.81

-2.12

-1.39

-1.02

-0.54

-0.53

-11.1

-10.6

-9.22

-7.54

-5.26

-4.26

-2.10

-2.72

Total

-14.6

-14.0

-12.0

-9.65

-6.65

-5.28

-2.63

-3.26

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Fatalities

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

43155

Table 11-76- Change in Safety Parameters from GHG Augural Standards Baseline
A veraee A nnuaI F at ar1f1es, CY 2036 -2045 7%0 D.ISCOUn t R at e

'

Change in Safety Parameters from Augural Standards Baseline

Fatalities
Mass
changes
Sales
Impacts
Subtotal
CAFE
Atrb.
Rebound
effect
Total
Fatalities
Societal
$B
Mass
changes
Sales
Impacts
Subtotal
CAFE
Atrb.
Rebound
effect
Total

sradovich on DSK3GMQ082PROD with PROPOSALS2

Nonfatal
Societal
$B
Mass
changes
Sales
Impacts
Subtotal
CAFE
Atrb.
Rebound
effect

VerDate Sep<11>2014

Alt 1

Alt2

Alt3

Alt4

Alt5

Alt 6

Alt 7

Alt 8

-56

-52

-42

-34

-15

-13

-8

-5

-221

-213

-177

-131

-93

-66

-34

-36

-277

-265

-219

-165

-108

-79

-42

-41

-872

-838

-726

-594

-415

-336

-165

-215

-1,150

-1,100

-945

-759

-523

-415

-207

-256

-0.11

-0.11

-0.09

-0.07

-0.03

-0.03

-0.02

-0.01

-0.47

-0.45

-0.37

-0.28

-0.20

-0.14

-0.07

-0.08

-0.58

-0.56

-0.46

-0.35

-0.23

-0.17

-0.09

-0.09

-1.78

-1.71

-1.49

-1.22

-0.85

-0.69

-0.34

-0.44

-2.36

-2.27

-1.95

-1.56

-1.08

-0.86

-0.43

-0.53

-0.18

-0.16

-0.13

-0.11

-0.05

-0.04

-0.03

-0.02

-0.73

-0.71

-0.59

-0.44

-0.31

-0.22

-0.11

-0.12

-0.91

-0.87

-0.72

-0.54

-0.36

-0.26

-0.14

-0.14

-2.79

-2.68

-2.32

-1.90

-1.33

-1.07

-0.53

-0.69

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EP24AU18.109

Average Annual Fatalities, CY 2036-2045. 7% Discount Rate

43156
Total

sradovich on DSK3GMQ082PROD with PROPOSALS2

Total
Societal
$B
Mass
changes
Sales
Impacts
Subtotal
CAFE
Atrb.
Rebound
effect
Total

VerDate Sep<11>2014

-3.70

-3.55

-3.04

-2.44

-1.68

-1.34

-0.67

-0.83

-0.29

-0.27

-0.22

-0.18

-0.08

-0.07

-0.04

-0.02

-1.20

-1.16

-0.96

-0.72

-0.51

-0.36

-0.19

-0.20

-1.49

-1.43

-1.18

-0.89

-0.59

-0.43

-0.23

-0.22

-4.57

-4.39

-3.81

-3.12

-2.18

-1.76

-0.87

-1.13

-6.06

-5.82

-4.99

-4.00

-2.76

-2.20

-1.09

-1.35

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Table 11-77- Change in Safety Parameters from GHG Augural Standards Baseline
Total Fatalities MY 1977-2029~ 3% Discount Rate
Change in Safety Parameters from Augural Standards Baseline

Fatalities
Mass changes
Sales Impacts
Subtotal
CAFE Atrb.
Rebound
effect
Total
Fatalities
Societal $B
Mass changes

Alt2

Alt3

Alt 4

Alt 5

Alt 6

Alt 7

Alt 8

-468

-461

-410

-297

-219

-186

-111

-85

-7,880
-8,350

-7,600
-8,060

-6,630
-7,040

-5,460
-5,760

-4,150
-4,370

-3,240
-3,430

-1,530
-1,640

-2,090
-2,170

-7,300

-6,930

-6,340

-5,250

-3,480

-3,260

-2,110

-2,010

-15,600

-15,000

-13,400

-11,000

-7,850

-6,690

-3,760

-4,190

-2.9

-2.9

-2.6

-1.9

-1.4

-1.2

-0.7

-0.5

Sales Impacts

-43.3

-41.7

-36.6

-30.1

-22.5

-18.0

-8.9

-11.6

Subtotal
CAFE Atrb.
Rebound
effect
Total

-46.2

-44.6

-39.2

-32.0

-23.9

-19.2

-9.7

-12.1

-47.8

-45.3

-41.6

-34.4

-22.7

-21.5

-14.2

-13.3

-94.0

-89.9

-80.8

-66.4

-46.6

-40.7

-23.8

-25.4

Nonfatal
Societal $B
Mass changes

-4.6

-4.5

-4.0

-2.9

-2.2

-1.9

-1.1

-0.8

Sales Impacts

-67.8

-65.2

-57.3

-47.1

-35.2

-28.2

-13.9

-18.1

Subtotal
CAFE Atrb.
Rebound
effect
Total

-72.3

-69.7

-61.3

-50.0

-37.3

-30.0

-15.1

-18.9

-74.7

-70.8

-65.0

-53.9

-35.6

-33.7

-22.1

-20.8

-147

-141

-126

-104

-72.9

-63.7

-37.2

-39.7

-7.5
-111

-7.4
-107

-6.6
-93.9

-4.8
-77.2

-3.5
-57.7

-3.1
-46.2

-1.9
-22.8

-1.4
-29.7

-119

-114

-101

-82.0

-61.2

-49.2

-24.8

-31.0

-123

-116

-107

-88.3

-58.3

-55.2

-36.3

-34.1

-241

-231

-207

-170

-120

-104

-61.0

-65.1

Total Societal
$B
Mass changes
Sales Impacts
Subtotal
CAFE Atrb.
Rebound
effect
Total
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Total Fatalities MY 1977-2029, 3% Discount Rate

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

While NHTSA notes the value of
rebound effect fatalities, as well as total
fatalities from all causes, the agency
does not add rebound effects to the
other CAFE-related impacts because
rebound-related fatalities and injuries
result from risk that is freely chosen and
offset by societal valuations that at a
minimum exceed the aggregate value of
safety consequences plus added vehicle
operating and maintenance costs.330

These costs implicitly involve a cost
and a benefit that are offsetting. The
relevant safety impacts attributable to
CAFE are highlighted in bold in the
above tables.

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1. Specification of No-Action and Other
Regulatory Alternatives
(a) Mathematical Functions Defining
Passenger Car and Light Trucks
Standards for Each Model Year During
2016–2032
In the U.S. market, the stringency of
CAFE and CO2 standards can influence
the design of new vehicles offered for
sale by requiring manufacturers to
produce increasingly fuel efficient
vehicles in order to meet program

330 It would also include some level of consumer
surplus, which we have estimated using the
standard triangular function. This is discussed in
Chapter 8.5.1 of the PRIA.

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G. How the Model Analyzes Different
Potential CAFE and CO2 Standards

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requirements. This is also true in the
CAFE model simulation, where the
standards can be defined with a great
deal of flexibility to examine the impact
of different program specifications on
the auto industry. Standards are defined
for each model year and can represent
different slopes that relate fuel economy
to footprint, different regions of flat
slopes, and different rates of increase for
each of three regulatory classes covered
by the CAFE program (domestic
passenger cars, imported passenger cars,
and light trucks).
The CAFE model takes, as inputs, the
coefficients of the mathematical
functions described in Sections III and
IV. It uses these coefficients and the
function to which they belong to define
the target for each vehicle in the fleet,
then computes the standard using the
harmonic average of the targets for each
manufacturer and fleet. The model also
allows the user to define the extent and
duration of various compliance
flexibilities (e.g., limits on the amount
of credit that a manufacturer may claim
related to air conditioning efficiency
improvements or off-cycle fuel economy
adjustments) as well as limits on the
number of years that CAFE credits may
be carried forward or the amount that
may be transferred between a
manufacturer’s fleets.

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(b) Off-Cycle and A/C Efficiency
Adjustments Anticipated for Each
Model Year
Another aspect of credit accounting is
partially implemented in the CAFE
model at this point—those related to the
application of off-cycle and A/C
efficiency adjustments, which
manufacturers earn by taking actions
such as special window glazing or using
reflective paints that provide fuel
economy improvements in real-world
operation but do not produce
measurable improvements in fuel
consumption on the 2-cycle test.
NHTSA’s inclusion of off-cycle and
A/C efficiency adjustments began in MY
2017, while EPA has collected several
years’ worth of submissions from
manufacturers about off-cycle and A/C
efficiency technology deployment.
Currently, the level of deployment can
vary considerably by manufacturer with
several claiming extensive Fuel
Consumption Improvement Values
(FCIV) for off-cycle and A/C efficiency
technologies and others almost none.
The analysis of alternatives presented
here does not attempt to project how
future off-cycle and A/C efficiency
technology use will evolve or speculate
about the potential proliferation of FCIV
proposals submitted to the agencies.
Rather, this analysis uses the off-cycle
credits submitted by each manufacturer
for MY 2017 compliance and carries

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43159

these forward to future years with a few
exceptions. Several of the technologies
described in Section II.D are associated
with A/C efficiency and off-cycle FCIVs.
In particular, stop-start systems,
integrated starter generators, and full
hybrids are assumed to generate offcycle adjustments when applied to
vehicles to improve their fuel economy.
Similarly, higher levels of aerodynamic
improvements are assumed to include
active grille shutters on the vehicle,
which also qualify for off-cycle FCIVs.
The analysis assumes that any offcycle FCIVs that are associated with
actions outside of the technologies
discussed in Section II.D (either chosen
from the pre-approved ‘‘pick list,’’ or
granted in response to individual
manufacturer petitions) remain at the
levels claimed by manufacturers in MY
2017. Any additional A/C efficiency and
off-cycle adjustments that accrue as the
result of explicit technology application
are calculated dynamically in each
model year for each alternative. The offcycle FCIVs for each manufacturer and
fleet, denominated in grams CO2 per
mile,331 are provided in Table II–79.
331 For the purpose of estimating their
contribution to CAFE compliance, the grams CO2/
mile values in Table II–79 are converted to gallons/
mile and applied to a manufacturer’s 2-cycle CAFE
performance. When calculating compliance with
EPA’s GHG program, there is no conversion
necessary (as standards are also denominated in
grams/mile).

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The model currently accounts for any
off-cycle adjustments associated with
technologies that are included in the set
of fuel-saving technologies explicitly
simulated as part of this proposal (for
example, start-stop systems that reduce
fuel consumption during idle or active
grille shutters that improve
aerodynamic drag at highway speeds)
and accumulates these adjustments up
to the 10 g/mi cap. As a practical matter,
most of the adjustments for which
manufacturers are claiming off-cycle
FCIV exist outside of the technology
tree, so the cap is rarely reached during
compliance simulation. If those FCIVs
become a more important compliance
mechanism, it may be necessary to
model their application explicitly.
However, doing so will require data on
which vehicle models already possess
these improvements as well as the cost
and expected value of applying them to
other models in the future. Comment is
sought on both the data requirements
and strategic decisions associated with
manufacturers’ use of A/C efficiency
and off-cycle technologies to improve
CAFE and CO2 compliance.
(c) Civil Penalty Rate and OEMs’
Anticipated Willingness To Treat Civil
Penalties as a Program Flexibility
Throughout the history of the CAFE
program, some manufacturers have

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consistently achieved fuel economy
levels below their standard. As in
previous versions of the CAFE model,
the current version allows the user to
specify inputs identifying such
manufacturers and to consider their
compliance decisions as if they are
willing to pay civil penalties for noncompliance with the CAFE program.
The assumed civil penalty rate in the
current analysis is $5.50 per 1/10 of a
mile per gallon, per vehicle sold.
It is worth noting that treating a
manufacturer as if they are willing to
pay civil penalties does not necessarily
mean that it is expected to pay penalties
in reality. It merely implies that the
manufacturer will only apply fuel
economy technology up to a point, and
then stop, regardless of whether or not
its corporate average fuel economy is
above its standard. In practice, we
expect that many of these manufacturers
will continue to be active in the credit
market, using trades with other
manufacturers to transfer credits into
specific fleets that are challenged in any
given year, rather than paying penalties
to resolve CAFE deficits. The CAFE
model calculates the amount of
penalties paid by each manufacturer,
but it does not simulate trades between
manufacturers. In practice, some
(possibly most) of the total estimated

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penalties may be a transfer from one
OEM to another.
While the Energy Policy and
Conservation Act (EPCA), as amended
in 2007 by the Energy Independence
and Security Act, prescribes these
specific civil penalty provisions for
CAFE standards, the Clean Air Act
(CAA) does not contain similar
provisions. Rather, the CAA’s
provisions regarding noncompliance
constitute a de facto prohibition against
selling vehicles failing to comply with
emissions standards. Therefore, inputs
regarding civil penalties—including
inputs regarding manufacturers’
potential willingness to treat civil
penalty payment as an economic
choice—apply only to simulation of
CAFE standards.
(d) Treatment of Credit Provisions for
‘‘Standard Setting’’ and
‘‘Unconstrained’’ Analyses
NHTSA may not consider the
application of CAFE credits toward
compliance with new standards when
establishing the standards
themselves.332 As such, this analysis
considers 2020 to be the last model year
in which carried-forward or transferred
credits can be applied for the CAFE
program. Beginning in model year 2021,
332 49

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today’s ‘‘standard setting’’ analysis is
conducted assuming each fleet must
comply with the CAFE standard
separately in every model year.
The ‘‘unconstrained’’ perspective
acknowledges that these flexibilities
exist as part of the program and, while
not considered in NHTSA’s decision of
the preferred alternative, are important
to consider when attempting to estimate
the real impact of any alternative. Under
the ‘‘unconstrained’’ perspective, credits
may be earned, transferred, and applied
to deficits in the CAFE program
throughout the full range of model years
in the analysis. The Draft Environmental
Impact Analysis (DEIS) accompanying
today’s NPRM presents results of
‘‘unconstrained’’ modeling. Also,
because the CAA provides no direction
regarding consideration of any CO2
credit provisions, today’s analysis
includes simulation of carried-forward
and transferred CO2 credits in all model
years.
(e) Treatment of AFVs for ‘‘Standard
Setting’’ and ‘‘Unconstrained’’ Analyses
NHTSA is also prohibited from
considering the possibility that a
manufacturer might produce
alternatively fueled vehicles as a
compliance mechanism,333 taking
advantage of credit provisions related to
AFVs that significantly increase their
fuel economy for CAFE compliance
purposes. Under the ‘‘standard setting’’
perspective, these technologies (pure
battery electric vehicles and fuel cell
vehicles 334) are not available in the
compliance simulation to improve fuel
economy. Under the ‘‘unconstrained’’
perspective, such as is documented in
the DEIS, the CAFE model considers
these technologies in the context of all
other available technologies and may
apply them if they represent costeffective compliance pathways.
However, under both perspectives, the
analysis continues to include dedicated
AFVs that already exist in the MY 2016
fleet (and their projected future
volumes) in CAFE calculations. Also,
because the CAA provides no direction
regarding consideration of alternative
fuels, today’s analysis includes
simulation of the potential that some
manufacturers might introduce new
AFVs in response to CO2 standards. To
fully represent the compliance benefit
from such a response, NHTSA modified
the CAFE model to include the specific
provisions related to AFVs under the
CO2 standards. In particular, the CAFE
333 Id.
334 Dedicated compressed natural gas (CNG)
vehicles should also be excluded in this perspective
but are not considered as a compliance strategy
under any perspective in this analysis.

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model now carries a full representation
of the production multipliers related to
electric vehicles, fuel cell vehicles,
plug-in hybrids, and CNG vehicles, all
of which vary by year through MY 2021.

(3) For manufacturers assumed to be
willing to pay civil penalties (in the CAFE
program), the manufacturer reaches the point
at which doing so would be more costeffective (from the manufacturer’s
perspective) than adding further technology.

2. Simulation of Manufacturers’ [and
Buyers’] Potential Responses to Each
Alternative
The CAFE model provides a way of
estimating how manufacturers could
attempt to comply with a given CAFE
standard by adding technology to fleets
that the agencies anticipate they will
produce in future model years. This
exercise constitutes a simulation of
manufacturers’ decisions regarding
compliance with CAFE or CO2
standards.
This compliance simulation begins
with the following inputs: (a) The
analysis fleet of vehicles from model
year 2016 discussed above in Section
II.B, (b) fuel economy improving
technology estimates discussed above in
Section II.D, (c) economic inputs
discussed above in Section II.E, and (d)
inputs defining baseline and potential
new CAFE standards. For each
manufacturer, the model applies
technologies in both a logical sequence
and a cost-minimizing strategy in order
to identify a set of technologies the
manufacturer could apply in response to
new CAFE or CO2 standards. The model
applies technologies to each of the
projected individual vehicles in a
manufacturer’s fleet, considering the
combined effect of regulatory and
market incentives while attempting to
account for manufacturers’ production
constraints. Depending on how the
model is exercised, it will apply
technology until one of the following
occurs:

The model accounts explicitly for
each model year, applying technologies
when vehicles are scheduled to be
redesigned or freshened and carrying
forward technologies between model
years once they are applied (until, if
applicable, they are superseded by other
technologies). The model then uses
these simulated manufacturer fleets to
generate both a representation of the
U.S. auto industry and to modify a
representation of the entire light-duty
registered vehicle population. From
these fleets, the model estimates
changes in physical quantities (gallons
of fuel, pollutant emissions, traffic
fatalities, etc.) and calculates the
relative costs and benefits of regulatory
alternatives under consideration.
The CAFE model accounts explicitly
for each model year, in turn, because
manufacturers actually ‘‘carry forward’’
most technologies between model years,
tending to concentrate the application of
new technology to vehicle redesigns or
mid-cycle ‘‘freshenings,’’ and design
cycles vary widely among
manufacturers and specific products.
Comments by manufacturers and model
peer reviewers strongly support explicit
year-by-year simulation. Year-by-year
accounting also enables accounting for
credit banking (i.e., carry-forward), as
discussed above, and at least four
environmental organizations recently
submitted comments urging the
agencies to consider such credits, citing
NHTSA’s 2016 results showing impacts
of carried-forward credits.337 Moreover,
EPCA/EISA requires that NHTSA make
a year-by-year determination of the
appropriate level of stringency and then
set the standard at that level, while
ensuring ratable increases in average
fuel economy through MY 2020. The
multi-year planning capability,
(optional) simulation of ‘‘market-driven
overcompliance,’’ and EPCA credit
mechanisms (again, for purposes of
modeling the CAFE program) increase
the model’s ability to simulate
manufacturers’ real-world behavior,
accounting for the fact that

(1) The manufacturer’s fleet achieves
compliance 335 with the applicable standard
and continuing to add technology in the
current model year would be attractive
neither in terms of stand-alone (i.e., absent
regulatory need) cost-effectiveness nor in
terms of facilitating compliance in future
model years;
(2) The manufacturer ‘‘exhausts’’ available
technologies; 336 or
335 When determining whether compliance has
been achieved in the CAFE program, existing CAFE
credits that may be carried over from prior model
years or transferred between fleets are also used to
determine compliance status. For purposes of
determining the effect of maximum feasible CAFE
standards, NHTSA cannot consider these
mechanisms for years being considered (though
does so for model years that are already final) and
exercises the CAFE model without enabling these
options.
336 In a given model year, it is possible that
production constraints cause a manufacturer to
‘‘run out’’ of available technology before achieving
compliance with standards. This can occur when:
(a) An insufficient volume of vehicles are expected

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to be redesigned, (b) vehicles have moved to the
ends of each (relevant) technology pathway, after
which no additional options exist, or (c)
engineering aspects of available vehicles make
available technology inapplicable (e.g., secondary
axle disconnect cannot be applied to two-wheel
drive vehicles).
337 Comment by Environmental Law & Policy
Center, Natural Resources Defense Council (NRDC),
Public Citizen, and Sierra Club, Docket ID EPA–
HQ–OAR–2015–0827–9826, at 28–29.

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manufacturers will seek out compliance
paths for several model years at a time,
while accommodating the year-by-year
requirement. This same multi-year
planning structure is used to simulate
responses to standards defined in grams
CO2/mile, and utilizing the set of
specific credit provisions defined under
EPA’s program.

sradovich on DSK3GMQ082PROD with PROPOSALS2

(a) Representation of Manufacturers’
Production Constraints
After the light-duty rulemaking
analysis accompanying the 2012 final
rule that finalized NHTSA’s standards
through MY 2021, NHTSA began work
on changes to the CAFE model with the
intention of better reflecting constraints
of product planning and cadence for
which previous analyses did not
account.
(b) Product Cadence
Past comments on the CAFE model
have stressed the importance of product
cadence—i.e., the development and
periodic redesign and freshening of
vehicles—in terms of involving
technical, financial, and other practical
constraints on applying new
technologies, and DOT has steadily
made changes to both the CAFE model
and its inputs with a view toward
accounting for these considerations. For
example, early versions of the model
added explicit ‘‘carrying forward’’ of
applied technologies between model
years, subsequent versions applied
assumptions that most technologies will
be applied when vehicles are freshened
or redesigned, and more recent versions
applied assumptions that manufacturers
would sometimes apply technology
earlier than ‘‘necessary’’ in order to
facilitate compliance with standards in
ensuing model years. Thus, for example,
if a manufacturer is expected to redesign
many of its products in model years
2018 and 2023, and the standard’s
stringency increases significantly in
model year 2021, the CAFE model will
estimate the potential that the
manufacturer will add more technology
than necessary for compliance in MY
2018, in order to carry those product
changes forward through the next
redesign and contribute to compliance
with the MY 2021 standard. This
explicit simulation of multiyear
planning plays an important role in
determining year-by-year analytical
results.
As in previous iterations of CAFE
rulemaking analysis, the simulation of
compliance actions that manufacturers
might take is constrained by the pace at
which new technologies can be applied
in the new vehicle market. Operating at
the Make/Model level (e.g., Toyota

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Camry) allows the CAFE model to
explicitly account for the fact that
individual vehicle models undergo
significant redesigns relatively
infrequently. Many popular vehicle
models are only redesigned every six
years or so, with some larger/legacy
platforms (the old Ford Econoline Vans,
for example) stretching more than a
decade between significant redesigns.
Engines, which are often shared among
many different models and platforms for
a single manufacturer, can last even
longer—eight to ten years in most cases.
While these characterizations of
product cadence are important to any
evaluation of the impacts of CAFE or
CO2 standards, they are not known with
certainty—even by the manufacturers
themselves over time horizons as long
as those covered by this analysis.
However, lack of certainty about
redesign schedules is not license to
ignore them. Indeed, when
manufacturers meet with the agencies to
discuss manufacturers’ plans vis-a`-vis
CAFE and CO2 requirements,
manufacturers typically present specific
and detailed year-by-year information
that explicitly accounts for anticipated
redesigns. Such year-by-year analysis is
also essential to manufacturers’ plans to
make use of provisions (for CAFE,
statutory and specific) allowing credits
to be carried forward to future model
years, carried back from future model
years, transferred between regulated
fleets, and traded with other
manufacturers. Manufacturers are never
certain about future plans, but they
spend considerable effort developing,
continually adjusting, and
implementing them.
For every model that appears in the
MY 2016 analysis fleet, the model years
have been estimated in which future
redesigns (and less significant
‘‘freshenings,’’ which offer
manufacturers the opportunity to make
less significant changes to models) will
occur. These appear in the market data
file for each model variant. Mid-cycle
freshenings provide additional
opportunities to add some technologies
in years where smaller shares of a
manufacturer’s portfolio is scheduled to
be redesigned. In addition, the analysis
accounts for multiyear planning—that
is, the potential that manufacturers may
apply ‘‘extra’’ technology in an early
model year with many planned
redesigns in order to carry technology
forward to facilitate compliance in a
later model year with fewer planned
redesigns. Further, the analysis accounts
for the potential that manufacturers
could earn CAFE and/or CO2 credits in
some model years and use those credits
in later model years, thereby providing

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another compliance option in years with
few planned redesigns. Finally, it
should be noted that today’s analysis
does not account for future new
products (or discontinued products)—
past trends suggest that some years in
which an OEM had few redesigns may
have been years when that OEM
introduced significant new products.
Such changes in product offerings can
obviously be important to
manufacturers’ compliance positions
but cannot be systematically and
transparently accounted for with a fleet
forecast extrapolated forward 10 or more
years from a largely-known fleet. While
manufacturers’ actual plans reflect
intentions to discontinue some products
and introduce others, those plans are
considered CBI. Further research would
be required in order to determine
whether and, if so, how it would be
practicable to simulate such decisions,
especially without relying on CBI.
Additionally, each technology
considered for application by the CAFE
model is assigned to either a ‘‘refresh’’
or ‘‘redesign’’ cadence that dictates
when it can be applied to a vehicle.
Technologies that are assigned to
‘‘refresh/redesign’’ can be applied at
either a refresh or redesign, while
technologies that are assigned to
‘‘redesign’’ can only be applied during
a significant vehicle redesign. Table II–
80 and Table II–81 show the
technologies available to manufacturers
in the compliance simulation, the level
at which they are applied (described in
greater detail in the CAFE model
documentation), whether they are
available outside of a vehicle redesign,
and a short description of each. A brief
examination of the tables shows that
most technologies are only assumed to
be available during a vehicle redesign—
and nearly all engine improvements are
assumed to be available only during
redesign. In a departure from past CAFE
analyses, all transmission improvements
are assumed to be available during
refresh as well as redesign. While there
are past and recent examples of midcycle product changes, it seems
reasonable to expect that manufacturers
will tend to attempt to keep engineering
and other costs down by applying most
major changes mainly during vehicle
redesigns and some mostly modest
changes during product freshenings. As
mentioned below, comment is sought on
the approach to account for product
cadence.
(c) Component Sharing and Inheritance
(Engines, Transmissions, and Platforms)
In practice, manufacturers are limited
in the number of engines and
transmissions that they produce.

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Typically, a manufacturer produces a
number of engines—perhaps six or eight
engines for a large manufacturer—and
tunes them for slight variants in output
for a variety of car and truck
applications. Manufacturers limit
complexity in their engine portfolio for
much the same reason as they limit
complexity in vehicle variants: They
face engineering manpower limitations,
and supplier, production, and service
costs that scale with the number of parts
produced.
In previous analyses that used the
CAFE model (with the exception of the
2016 Draft TAR), engines and
transmissions in individual vehicle
models were allowed relative freedom
in technology application, potentially
leading to solutions that would, if
followed, create many more unique
engines and transmissions than exist in
the analysis fleet (or in the market) for
a given model year. This multiplicity
likely failed to sufficiently account for
costs associated with such increased
complexity in the product portfolio and
may have represented an unrealistic
diffusion of products for manufacturers
that are consolidating global production
to increasingly smaller numbers of
shared engines and platforms.338 The
lack of a constraint in this area allowed
the model to apply different levels of
technology to the engine in each vehicle
in which it was present at the time that
vehicle was redesigned or refreshed,
independent of what was done to other
vehicles using a previously identical
engine.
One peer reviewer of the CAFE model
recently commented, ‘‘The integration
of inheritance and sharing of engines,
transmissions, and platforms across a
manufacturer’s light duty fleet and
separately across its light duty truck
fleet is standard practice within the
industry.’’ In the current version of the
CAFE model, engines and transmissions
that are shared between vehicles must
apply the same levels of technology, in
all technologies, dictated by engine or
transmission inheritance. This forced
adoption is referred to as ‘‘engine
inheritance’’ in the model
documentation. In practice, the model
first chooses an ‘‘engine leader’’ among
vehicles sharing the same engine—the
vehicle with the highest sales in MY
2016. If there is a tie, the vehicle with
the highest average MSRP is chosen,
representing the idea that manufacturers
will choose to pilot the newest
technology on premium vehicles if
possible. The model applies the same
logic with respect to the application of
transmission changes. After the model
338 2015

NAS Report, at pg. 258–259.

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modifies the engine on the ‘‘engine
leader’’ (or ‘‘transmission leader’’), the
changes to that engine propagate
through to the other vehicles that share
that engine (or transmission) in
subsequent years as those vehicles are
redesigned. The CAFE model has been
modified to provide additional
flexibility vis-a`-vis product cadence. In
a recent public comment, NRDC noted:
EPA and NHTSA currently constrain their
model to apply significant fuel-efficient
technologies mainly during a productredesign as opposed to product-refresh (or
mid-cycle). This was identified as one of the
most sensitive assumptions affecting overall
program costs by NHTSA in the TAR. By
constraining the model, the agencies have
likely under-estimated the ability of auto
manufacturers to incorporate some
technologies during their product refreshes.
This is particularly true regarding the critical
powertrain technologies which are
undergoing continuous improvement. The
agency should account for these trends and
incorporate greater flexibility for
automakers—within their models—to
incorporate more mid-cycle
enhancements.339

While engine redesigns are only
applied to the engine leader when it is
redesigned in the model, followers may
now inherit upgraded engines (that they
share with the leader) at either refresh
or redesign. All transmission changes,
whether upgrades to the ‘‘leader’’ or
inheritance to ‘‘followers’’ can occur at
refresh as well as redesign. This
provides additional opportunities for
technology diffusion within
manufacturers’ product portfolios.
While ‘‘follower’’ vehicles are
awaiting redesign (or, for transmissions,
refreshing as applicable), they carry a
legacy version of the shared engine or
transmission. As one peer reviewer
recently stated, ‘‘Most of the time a
manufacturer will convert only a single
plant within a model year. Thus both
the ‘old’ and ‘new’ variant of the engine
(or transmission) will produced for a
finite number of years.’’ 340 The CAFE
model currently carries no additional
cost associated with producing both
earlier revisions of an engine and the
updated version simultaneously.
Further research would be needed to
determine whether sufficient data is
likely to be available to explicitly
specify and apply additional costs
involved with continuing to produce an
existing engine or transmission for some
vehicles that have not yet progressed to
a newer version of that engine or
transmission. Comment is sought on
339 Comment by Environmental Law & Policy
Center, Natural Resources Defense Council (NRDC),
Public Citizen, and Sierra Club, Docket ID EPA–
HQ–OAR–2015–0827–9826, at 32.
340 CAFE Model Peer Review, p. 19.

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possible data sources and approaches
that could be used to represent any
additional costs associated with phased
introduction of new engines or
transmissions.
There are some logical consequences
of this approach, the first of which is
that forcing engine and transmission
changes to propagate through to other
vehicles in this way effectively dictates
the pace at which new technology can
be applied and limits the total number
of unique engines that the model
simulates. In the past, NHTSA used
‘‘phase-in caps’’ (see discussion below)
to limit the amount of technology that
can be applied to any vehicle in a given
year. However, by explicitly tying the
engine changes to a specific vehicle’s
product cadence, rather than letting the
timing of changes vary across all the
vehicles that share an engine, the model
ensures that an engine is only changed
when its leader is redesigned (at most).
Given that most vehicle redesign cycles
are five to eight years, this approach still
represents shorter average lives than
most engines in the market, which tend
to be in production for eight to ten years
or more. It is also the case that vehicles
which share an engine in the analysis
fleet (MY 2016, for this analysis) are
assumed to share that same engine
throughout the analysis—unless one or
both of them are converted to powersplit hybrids (or farther) on the
electrification path. In the market, this
is not true—since a manufacturer will
choose an engine from among the
engines it produces to fulfill the
efficiency and power demands of a
vehicle model upon redesign. That
engine need not be from the same family
of engines as the prior version of that
vehicle. This is a simplifying
assumption in the model. While the
model already accommodates detailed
inputs regarding redesign schedules for
specific vehicles and commercial
information sources are available to
inform these inputs, further research
would be needed to determine whether
design schedules for specific engines
and transmissions can practicably be
simulated.
The CAFE model has implemented a
similar structure to address shared
vehicle platforms. The term ‘‘platform’’
is used loosely in industry but generally
refers to a common structure shared by
a group of vehicle variants. The degree
of commonality varies with some
platform variants exhibiting traditional
‘‘badge engineering’’ where two
products are differentiated by little more
than insignias, while other platforms
may be used to produce a broad suite of
vehicles that bear little outer
resemblance to one another.

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Given the degree of commonality
between variants of a single platform,
manufacturers do not have complete
freedom to apply technology to a
vehicle: While some technologies (e.g.
low rolling resistance tires) are very
nearly ‘‘bolt-on’’ technologies, others
involve substantial changes to the
structure and design of the vehicle, and
therefore necessarily are constant among
vehicles that share a common platform.
NHTSA has, therefore, modified the
CAFE model such that all mass
reduction technologies are forced to be
constant among variants of a platform.
Within the analysis fleet, each vehicle
is associated with a specific platform.
Similar to the application of engine and
transmission technologies, the CAFE
model defines a platform ‘‘leader’’ as the
vehicle variant of a given platform that
has the highest level of observed mass
reduction present in the analysis fleet.
If there is a tie, the CAFE model begins
mass reduction technology on the
vehicle with the highest sales in model
year 2016. If there remains a tie, the
model begins by choosing the vehicle
with the highest MSRP in MY 2016. As
the model applies technologies, it
effectively levels up all variants on a
platform to the highest level of mass
reduction technology on the platform.
So, if the platform leader is already at
MR3 in MY 2016, and a ‘‘follower’’
starts at MR0 in MY 2016, the follower
will get MR3 at its next redesign (unless
the leader is redesigned again before
that time, and further increases the MR
level associated with that platform, then
the follower would receive the new MR
level).
In the 2015 NPRM proposing new fuel
consumption and GHG standards for
heavy-duty pickups and vans, NHTSA
specifically requested comment on the
general use of shared engines,
transmissions, and platforms within
CAFE rulemakings. While no
commenter responded to this specific
request, comments from some
environmental organizations cited
examples of technology sharing between
light- and heavy-duty products. NHTSA
has continued to refine its
implementation of an approach
accounting for shared engines,
transmissions, and platforms, and again
seeks comment on the approach,
recommendations regarding any other
approaches, and any information that
would facilitate implementation of the
agency’s current approach or any
alternative approaches.
(d) Phase-In Caps
The CAFE model retains the ability to
use phase-in caps (specified in model
inputs) as proxies for a variety of

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practical restrictions on technology
application, including the
improvements described above. Unlike
vehicle-specific restrictions related to
redesign, refreshes or platforms/engines,
phase-in caps constrain technology
application at the vehicle manufacturer
level for a given model year. Introduced
in the 2006 version of the CAFE model,
they were intended to reflect a
manufacturer’s overall resource capacity
available for implementing new
technologies (such as engineering
research and development personnel
and financial resources), thereby
ensuring that resource capacity is
accounted for in the modeling process.
Compared to prior analyses of lightduty standards, these model changes
result in some changes in the broad
characteristics of the model’s
application of technology to
manufacturers’ fleets. Since the use of
phase-in caps has been de-emphasized
and manufacturer technology
deployment remains tied strongly to
estimated product redesign and
freshening schedules, technology
penetration rates may jump more
quickly as manufacturers apply
technology to high-volume products in
their portfolio. As a result, the model
will ignore a phase-in cap to apply
inherited technology to vehicles on
shared engines, transmissions, and
platforms.
In previous CAFE rulemakings,
redesign/refresh schedules and phase-in
caps were the primary mechanisms to
reflect an OEM’s limited pool of
available resources during the
rulemaking time frame and the years
preceding it, especially in years where
many models may be scheduled for
refresh or redesign. The newlyintroduced representation of platform-,
engine-, and transmission-related
considerations discussed above augment
the model’s preexisting representation
of redesign cycles and eliminate the
need to rely on phase-in caps. By
design, restrictions that enforce
commonality of mass reduction on
variants of a platform, and those that
enforce engine and transmission
inheritance, will result in fewer vehicletechnology combinations in a
manufacturer’s future modeled fleet.
The integration of shared components
and product cadence as a mechanism to
control the pace of technology
application also more accurately
represents each manufacturer’s unique
position in the market and its existing
technology footprint, rather than a
technology-specific phase-in cap that is
uniformly applied to all manufacturers
in a given year. Comment is sought
regarding this shift away from relying

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on phase-in caps and, if greater reliance
on phase-in caps is recommended, what
approach and information can be used
to define and apply these caps.
(e) Interactions Between Regulatory
Classes
Like earlier versions, the current
CAFE model provides the capability for
integrated analysis spanning different
regulatory classes, accounting both for
standards that apply separately to
different classes and for interactions
between regulatory classes. Light
vehicle CAFE and CO2 standards are
specified separately for passenger cars
and light trucks. However, there is
considerable sharing between these two
regulatory classes—where a single
engine, transmission, or platform can
appear in both the passenger car and
light truck regulatory class. For
example, some sport-utility vehicles are
offered in 2WD versions classified as
passenger cars and 4WD versions
classified as light trucks. Integrated
analysis of manufacturers’ passenger car
and light truck fleets provides the
ability to account for such sharing and
reduces the likelihood of finding
solutions that could involve introducing
impractical levels of complexity in
manufacturers’ product lines.
Additionally, integrated fleet analysis
provides the ability to simulate the
potential that manufacturers could earn
CAFE and CO2 credits by over
complying with the standard in one
fleet and use those credits toward
compliance with the standard in
another fleet (i.e., to simulate credit
transfers between regulatory classes).
While previous versions of the CAFE
model have represented manufacturers’
fleets by drawing a distinction between
passenger cars and light trucks, the
current version of the CAFE model adds
a further distinction, capturing the
difference between passenger cars
classified as domestic passenger cars
and those classified as imports. The
CAFE program regulates those passenger
cars separately, and the current version
of the CAFE model simulates all three
CAFE regulatory classes separately:
Domestic Passenger Cars (DC), Imported
Passenger Cars (IC), and Light Trucks
(LT). CAFE regulations state that
standards, fuel economy levels, and
compliance are all calculated separately
for each class. These requirements are
specified explicitly by the Energy Policy
and Conservation Act (EPCA), with the
2007 Energy Independence and Security
Act (EISA) having added the
requirement to enforce minimum
standards for domestic passenger cars.
This update to the accounting imposes
two additional constraints on

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manufacturers that sell vehicles in the
U.S.: (1) The domestic minimum floor,
and (2) Limited transfers between cars
classified as ‘‘domestic’’ versus those
classified as ‘‘imported.’’ The domestic
minimum floor creates a threshold that
every manufacturer’s domestic car fleet
must exceed without the application of
CAFE credits. If a manufacturer’s
calculated standard is below the
domestic minimum floor, then the
domestic floor is the binding constraint
(even for manufacturers that are
assumed to be willing to pay fines for
non-compliance). The second constraint
poses challenges for manufacturers that
sell cars from both the domestic and
imported passenger car categories.
While previous versions of the CAFE
model considered those fleets as a single
fleet (i.e., passenger cars), the model
now forces them to comply separately
and limits the volume of credits that can
be shifted between them for compliance.
However, the CAA provides no
direction regarding compliance by
domestic and imported vehicles; EPA
has not adopted provisions similar to
the aforementioned EPCA/EISA
requirements and is not doing so today.
Therefore, consistent with current and
proposed CO2 regulations, the CAFE
model determines compliance for
manufacturers’ overall passenger car
fleets for EPA’s program.
During 2015–2016, a single version of
the CAFE model was applied to produce
analyses supporting both a rulemaking
regarding heavy-duty pickups and vans
(HD PUV) and the 2016 draft TAR
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reflected integrated analysis of the lightduty and HD PUV fleets, thereby
accounting for sharing between the
fleets. However, for most OEMs, that
analysis showed considerably less
sharing between light-duty and HD PUV
fleets than initially expected. Today’s
analysis includes only vehicles subject
to CAFE and light-duty CO2 standards,
and the agencies invite comment on
whether integrated analysis of the two
fleets should be pursued further.
3. Technology Application Algorithm
(a) Technology Representation and
Pathways
While some properties of the
technologies included in the analysis
are specified by the user (e.g., cost of the
technology), the set of included
technologies is part of the model itself,
which contains the information about
the relationships between
technologies.341 In particular, the CAFE
model contains the information about
the sequence of technologies, the paths
on which they reside, any prerequisites
associated with a technology’s
application, and any exclusions that
naturally follow once it is applied.
341 Unlike the 2012 Final Rule, where each
technology had a single effectiveness value for the
CAFE analysis, technology effectiveness in the
current version of the CAFE model is based on the
ANL simulation project and defined for each
combination of technologies, resulting in more than
100,000 technology effectiveness values for each of
ten technology classes. This large database is
extracted locally the first time the model is run and
can be modified by the user in that location to
reflect alternative assumptions about technology
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The ‘‘application level’’ describes the
system of the vehicle to which the
technology is applied, which in turn
determines the extent to which that
decision affects other vehicles in a
manufacturer’s fleet. For example, if a
technology is applied at the ‘‘engine’’
level, it naturally affects all other
vehicles that share that same engine
(though not until they themselves are
redesigned, if it happens to be in a
future model year). Technologies
applied at the ‘‘vehicle’’ level can be
applied to a vehicle model without
impacting the other models with which
it shares components. Platform-level
technologies affect all of the vehicles on
a given platform, which can easily span
technology classes, regulatory classes,
and redesign cycles.
The ‘‘application schedule’’ identifies
when manufacturers are assumed to be
able to apply a given technology—with
many available only during vehicle
redesigns. The application schedule also
accounts for which technologies the
CAFE model tracks but does not apply.
These enter as part of the analysis fleet
(‘‘Baseline Only’’), and while they are
necessary for accounting related to cost
and incremental fuel economy
improvement, they do not represent a
choice that manufacturers make in the
model. As discussed in Section II.B, the
analysis fleet contains the information
about each vehicle model, engine, and
transmission selected for simulation and
defines the initial technology state of
the fleet relative to the sets of
technologies in Table II–80 and Table
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Table 11-80- CAFE Model Technologies (1)

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SOHC
DOHC
OHV
VVT
VVL
SGDI
DEAC
HCR
HCR2
TURBOl
TURB02
CEGRl
ADEAC
CNG
ADSL
DSLI

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Level
Engine
Engine
Engine
Engine
Engine
Engine
Engine
Engine
Engine
Engine
Engine
Engine
Engine
Engine
Engine
Engine

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Application
Schedule
Baseline Only
Baseline Only
Baseline Only
Baseline Only
Redesign Only
Redesign Only
Redesign Only
Redesign Only
Redesign Only
Redesign Only
Redesign Only
Redesign Only
Redesign Only
Baseline Only
Redesign Only
Redesign Only

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Description
Single Overhead Camshaft Engine
Double Overhead Camshaft Engine
Overhead Valve Engine (maps to SOHC)
Variable Valve Timing
Variable Valve Lift
Stoichiometric Gasoline Direct Injection
Cylinder Deactivation
High Compression Ratio Engine
High Compression Ratio Engine with DEAC and CEGR
Turbocharging and Downsizing, Level 1 (18 bar)
Turbocharging and Downsizing, Level 2 (24 bar)
Cooled Exhaust Gas Recirculation, Level 1 (24 bar)
Advanced Cylinder Deactivation
Compressed Natural Gas Engine
Advanced Diesel Engine
Diesel engine improvements

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As Table II–80 and Table II–81 show,
all of the engine technologies may only
be applied (for the first time) during
redesign. New transmissions can be
applied during either refresh or
redesign, except for manual
transmissions, which can only be
upgraded during redesign. Unlike
previous versions of the model, which
only allowed significant changes to
vehicle powertrains at redesign, this
version allows vehicles to inherit

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updates to shared components during
refresh. For example, assume Vehicle A
and Vehicle B share Engine 1, and
engine 1 is redesigned as part of Vehicle
A’s redesign in MY 2020. Vehicle B is
not redesigned until 2025 but is
refreshed in MY 2022. In the current
version of the CAFE model, Vehicle B
would inherit the updated version of
Engine 1 when it is freshened in MY
2022. This change allows more rapid
diffusion of powertrain updates (for

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example) throughout a manufacturer’s
portfolio and reduces the number of
years during which a manufacturer
would build both new and legacy
versions of the same engine. Despite
increasing the rate of technology
diffusion, this change still restricts the
pace at which new engines (for
example) can be designed and built (i.e.,
no faster than the redesign schedule of
the ‘‘leader’’ vehicle to which they are
tied). The only technology for which

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this does not hold is mass reduction
improvements; these occur at the
platform level, and each model on that
platform must be redesigned (not merely
refreshed) in order to receive the newest
version of the platform that contains the
most current mass reduction
technology.
The CAFE model defines several
‘‘technology classes’’ and ‘‘technology
pathways’’ for logically grouping all
available technologies for application on
a vehicle. Technology classes provide
costs and improvement factors shared
by all vehicles with similar body styles,
curb weights, footprints, and engine
types, while technology pathways
establish a logical progression of
technologies on a vehicle within a

system or sub-system (e.g., engine
technologies).
Technology classes, shown in Table–
II–82, are a means for specifying
common technology input assumptions
for vehicles that share similar
characteristics. Predominantly, these
classes signify the degree of
applicability of each of the available
technologies to a specific class of
vehicles and represent a specific set of
Autonomie simulations (conducted as
part of the Argonne National Lab largescale simulation study) that determine
the effectiveness of each technology to
improve fuel economy. The vehicle
technology classes also define, for each
technology, the additional cost
associated with application.342 Like the

TAR analysis, the model uses separate
technology classes for compact cars,
midsize cars, small SUVs, large SUVs,
and pickup trucks. However, in this
analysis, each of those distinctions also
has a ‘‘performance’’ version, that
represents another class with similar
body style but higher levels of
performance attributes (for a total of 10
technology classes). As the model
simulates compliance, identifying
technologies that can be applied to a
given manufacturer’s product portfolio
to improve fleet fuel economy, it relies
on the vehicle class to provide relevant
cost and effectiveness information for
each vehicle model.

The model defines technology
pathways for grouping and establishing
a logical progression of technologies on
a vehicle. Each pathway (or path) is
evaluated independently and in
parallel, with technologies on these
paths being considered in sequential
order. As the model traverses each path,
the costs and fuel economy
improvements are accumulated on an
incremental basis with relation to the
preceding technology. The system stops
examining a given path once a
combination of one or more
technologies results in a ‘‘best’’
technology solution for that path. After

evaluating all paths, the model selects
the most cost-effective solution among
all pathways. This parallel path
approach allows the modeling system to
progress through technologies in any
given pathway without being
unnecessarily prevented from
considering technologies in other paths.
Rather than rely on a specific set of
technology combinations or packages,
the model considers the universe of
applicable technologies, dynamically
identifying the most cost-effective
combination of technologies for each
manufacturer’s vehicle fleet based on
each vehicle’s initial technology content
and the assumptions about each

technology’s effectiveness, cost, and
interaction with all other technologies
both present and available.
(b) Technology Paths
The modeling system incorporates 16
technology pathways for evaluation as
shown in Table–II—83. Similar to
individual technologies, each path
carries an intrinsic application level that
denotes the scope of applicability of all
technologies present within that path
and whether the pathway is evaluated
on one vehicle at a time, or on a
collection of vehicles that share the
same platform, engine, or transmission.

342 Inputs are specified to assign each vehicle in
the analysis fleet to one of these technology classes,
as discussed in Section II.B.

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The technologies that comprise the
five Engine-Level paths available within
the model are presented in Figure-II-13.
Note: The baseline-level technologies
(SOHC, DOHC, OHV, and CNG) appear
in gray boxes. These technologies are
used to inform the modeling system of
the initial engine’s configuration and are
not otherwise applicable during the
analysis. Additionally, the VCR path
(intended to house fuel economy
improvements from variable

compression ratio engines) was not used
in this analysis but is present within the
model. Unlike earlier versions of the
CAFE model, that enforced strictly
sequential application of technologies
like VVL and SGDI, this version of the
CAFE model allows basic engine
technologies to be applied in any order
once an engine has VVT (the base state
of all ANL simulations). Once the model
progresses past the basic engine path, it
considers all of the more advanced

engine paths (Turbo, HCR, Diesel, and
ADEAC) simultaneously. They are
assumed to be mutually exclusive. Once
one path is taken, it locks out the others
to avoid situations where the model
could be perceived to force
manufacturers to radically change
engine architecture with each redesign,
incurring stranded capital costs and lost
opportunities for learning.

For all pathways, the technologies are
evaluated and applied to a vehicle in
sequential order, as shown from top to
bottom. In some cases, however, if a

technology is deemed ineffective, the
system will bypass it and skip ahead to
the next technology. If the modeling
system applies a technology that resides

later in the pathway, it will ‘‘backfill’’
anything that was previously skipped in
order to fully account for costs and fuel
economy improvements of the full

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technology combination.343 For any
technology that is already present on a
vehicle (either from the MY 2016 fleet
or previously applied by the model), the
system skips over those technologies as
well and proceeds to the next. These
skipped technologies, however, will not
be applied again during backfill.
While costs are still purely
incremental, technology effectiveness is
no longer constructed that way. The
non-sequential nature of the basic
engine technologies have no obvious
preceding technology except for VVT,
the root of our engine path. It was a
natural extension to carry this approach
to the other branches as well. The
technology effectiveness estimates are
now an integrated part of the CAFE
model and represent a translation of the
Argonne simulation database that
compares the fuel consumption of any
combination of technologies (across all
paths) to the base vehicle (that has only
VVT, 5-speed automatic transmission,
no electrification, and no body-level
improvements).344

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343 More detail about how the Argonne simulation
database was integrated into the CAFE model can
be found in PRIA Chapter 6.
344 This is true for all combinations other than
those containing manual transmissions. Because the
model does not convert automatic transmissions to

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The Basic Engine path begins with
SOHC, DOHC, and OHV technologies
defining the initial configuration of the
vehicle’s engine. Since these
technologies are not available during
modeling, the system evaluates this
pathway starting with VVT. Whenever a
technology pathway forks into two or
more branch points, as the engine path
does at the end of the basic engine path,
all of the branches are treated as
mutually exclusive. The model
evaluates all technologies forming the
branch simultaneously and selects the
most cost-effective for the application,
while disabling the unchosen remaining
paths.
The technologies that make up the
four Transmission-Level paths defined
by the modeling system are shown in
Figure-II-14. The baseline-level
technologies (AT5, MT5 and CVT)
appear in gray boxes and are only used
to represent the initial configuration of
a vehicle’s transmission. For simplicity,
all manual transmissions with five
forward gears or fewer have been
assigned the MT5 technology in the
manual transmissions, nor the inverse, technology
combinations containing manual transmissions use
a reference point identical to the base vehicle
description, but containing a 5-speed manual rather
than automatic transmission.

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analysis fleet. Similarly, all automatic
transmissions with five forward gears or
fewer have been assigned the AT5
technology. The model preserves the
initial configuration for as long as
possible, and prohibits manual
transmissions from becoming automatic
transmissions at any point. Automatic
transmissions may become CVT level 2
after progressing though the 6-speed
automatic. While the structure of the
model still allows automatic
transmissions to consider the move to
DCT, in practice they are restricted from
doing so in the market data file. This
allows vehicles that enter with a DCT to
improve it (if opportunities to do so
exist) but does not allow automatic
transmissions to become DCTs, in
recognition of low consumer
enthusiasm for the earlier versions the
transmission that have been introduced
over the last decade. The model does
not attempt to simulate ‘‘reversion’’ to
less advanced transmission
technologies, such as replacing a 6speed AT with a DCT and then
replacing that DCT with a 10-speed AT.
The agencies invite comment on
whether or not the model should be
modified to simulate such ‘‘reversion’’
and, if so, how this possible behavior
might be practicably simulated.

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larger architectural change of the two. In
general, the electrification technologies
are applied as vehicle-level
technologies, meaning that the model
applies them without affecting
components that might be shared with
other vehicles. In the case of the more
advanced electrification technologies,
where engines and transmissions are
removed or replaced, the model will
choose a new vehicle to be the leader on
that component (if necessary) and will
not force other vehicles sharing that
engine or transmission to become
hybrids (or EVs). In addition to the

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electrification technologies, there are
two electrical system improvements,
electric power steering (EPS) and
accessory improvements (IACC), which
were not part of the ANL simulation
project and are applied by the model as
fixed percentage improvements to all
technology combinations in a particular
technology class. Their improvements
are superseded by technologies in the
other electrification paths, BISG or
CISG, in the case of EPS, and strong
hybrids (and above) in the case of IACC,
which are assumed to include those
improvements already.

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The root of the Electrification path,
shown in Figure-II-15, is a conventional
powertrain (CONV) with no
electrification. The two strong hybrid
technologies (SHEVP2 and SHEVPS) on
the Hybrid/Electric path, are defined as
stand-alone and mutually exclusive.
These technologies are not incremental
over each other for cost or effectiveness
and do not follow a traditional
progression logic present on other paths.
While the SHEVP2 represents a hybrid
system paired with the existing engine
on a given vehicle, the SHEVPS removes
and replaces that engine, making it the

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The technology paths related to load
reduction of the vehicle are shown in
Figure-II-16. Of these, only the Mass
Reduction (MR) path is applied at the
platform level, thus affecting all
vehicles (across classes and body styles)
on a given platform. The remaining
technology paths are all applied at the

vehicle level, and technologies within
each path are considered purely
sequential. For mass reduction,
aerodynamic improvements, and
reductions in rolling resistance, the base
level of each path is the ‘‘zero state,’’ in
which a vehicle has exhibited none of
the improvements associated with the

technology path. In addition to choosing
among possible engine, transmission,
and electrification improvements to
improve a vehicle’s fuel economy, the
CAFE model will consider technologies
each of the possible load improvement
paths simultaneously.

Even though the model evaluates each
technology path independently, some of

the pathways are interconnected to
allow for additional logical progression

and incremental accounting of
technologies. For example, the cost of

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SHEVPS (power-split strong hybrid/
electric) on the Hybrid/Electric path is
defined as incremental over the
complete basic engine path (an engine
that contains VVT, VVL, SGDI, and
DEAC), the AT5 (5-speed automatic)
technology on the Automatic
Transmission path, and the CISG (crank
mounted integrated starter/generator)
technology on the Electrification path.
For that reason, whenever the model
evaluates the SHEVPS technology for
application on a vehicle, it ensures that,
at a minimum, all the aforementioned
technologies (as well as their
predecessors) have already been applied

on that vehicle. However, if it becomes
necessary for a vehicle to progress to the
power-split hybrid, the model will
virtually apply the technologies
associated with the reference point in
order to evaluate the attractiveness of
transitioning to the strong hybrid.
Of the 17 technology pathways
present in the model, all Engine paths,
the Automatic Transmission path, the
Electrification path, and both Hybrid/
Electric paths are logically linked for
incremental technology progression.
Some of the technology pathways, as
defined in the model and shown in
Figure-II-17, may not be compatible
with a vehicle given its state at the time

of evaluation. For example, a vehicle
with a 6-speed automatic transmission
will not be able to get improvements
from a Manual Transmission path. For
this reason, the model implements logic
to explicitly disable certain paths
whenever a constraining technology
from another path is applied on a
vehicle. On occasion, not all of the
technologies present within a pathway
may produce compatibility constraints
with another path. In such a case, the
model will selectively disable a
conflicting pathway (or part of the
pathway) as required by the
incompatible technology.

For any interlinked technology
pathways shown in Figure-II-17, the
model also disables all preceding
technology paths whenever a vehicle
transitions to a succeeding pathway. For
example, if the model applies SHEVPS
technology on a vehicle, the model
disables the Turbo, HCR, ADEAC, and
Diesel Engine paths, as well as the Basic
Engine, the Automatic Transmission,
and the Electrification paths (all of
which precede the Hybrid/Electric
path).345 This implicitly forces vehicles

to always move in the direction of
increasing technological sophistication
each time they are reevaluated by the
model.

MY 2016 analysis fleet contains
information about each manufacturer’s:

345 The only notable exception to this rule occurs
whenever SHEVP2 technology is applied on a

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4. Simulating Manufacturer Compliance
With Standards
As a starting point, the model needs
enough information to represent each
manufacturer covered by the program.
As discussed above in Section II.B, the
vehicle. This technology may be present in
conjunction with any engine-level technology, and
as such, the Basic Engine path is not disabled upon
application of SHEVP2 technology, even though
this pathway precedes the Hybrid/Electric path.

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• Vehicle models offered for sale—their
current (i.e., MY 2016) production volumes,
manufacturer suggested retail prices
(MSRPs), fuel saving technology content
(relative to the set of technologies described
in Table II–80 and Table II–81), and other
attributes (curb weight, drive type,
assignment to technology class and
regulatory class),
• Production constraints—product
cadence of vehicle models (i.e., schedule of
model redesigns and ‘‘freshenings’’), vehicle
platform membership, degree of engine and/
or transmission sharing (for each model
variant) with other vehicles in the fleet,

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• Compliance constraints and
flexibilities—historical preference for full
compliance or penalty payment/credit
application, willingness to apply additional
cost-effective fuel saving technology in
excess of regulatory requirements, projected
applicable flexible fuel credits, and current
credit balance (by model year and regulatory
class) in first model year of simulation.

Each manufacturer’s regulatory
requirement represents the productionweighted harmonic mean of their
vehicle’s targets in each regulated fleet.
This means that no individual vehicle
has a ‘‘standard,’’ merely a target, and
each manufacturer is free to identify a
compliance strategy that makes the most
sense given its unique combination of
vehicle models, consumers, and
competitive position in the various
market segments. As the CAFE model
provides flexibility when defining a set
of regulatory standards, each
manufacturer’s requirement is
dynamically defined based on the
specification of the standards for any
simulation and the distribution of
footprints within each fleet.
Given this information, the model
attempts to apply technology to each
manufacturer’s fleet in a manner than
minimizes ‘‘effective costs.’’ The
effective cost captures more than the
incremental cost of a given technology;
it represents the difference between
their incremental cost and the value of
fuel savings to a potential buyer over the
first 30 months of ownership.346 In
addition to the technology cost and fuel
savings, the effective cost also includes
the change in fines from applying a
given technology and any estimated
welfare losses associated with the
technology (e.g., earlier versions of the
CAFE model simulated low-range
electric vehicles that produced a welfare
loss to buyers who valued standard
operating ranges between re-fueling
events). The effective cost metric
applied by the model does not attempt
to reflect all costs of vehicle ownership.
Further research would be required in
order to support simulation that
assumes buyers behave as if they
actually consider all ownership costs,
and that assumes manufacturers
respond accordingly. The agencies will
continue to consider the metric applied
to represent manufactuers’ approach to
making decisions regarding the
application of fuel-saving technologies
and invite comment regarding any
practicable changes that might make
346 The length of time over which to value fuel
savings in the effective cost calculation is a model
input that can be modified by the user. This
analysis uses 30 months’ worth of fuel savings in
the effective cost calculation, using the price of fuel
at the time of vehicle purchase.

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this aspect of the model even more
realistic.
This construction allows the model to
choose technologies that both improve a
manufacturer’s regulatory compliance
position and are most likely to be
attractive to its consumers. This also
means that different assumptions about
future fuel prices will produce different
rankings of technologies when the
model evaluates available technologies
for application. For example, in a high
fuel price regime, an expensive but very
efficient technology may look attractive
to manufacturers because the value of
the fuel savings is sufficiently high to
both counteract the higher cost of the
technology and, implicitly, satisfy
consumer demand to balance price
increases with reductions in operating
cost. Similarly, technologies for which
there exist consumer welfare losses
(discussed in Section II.E) will be seen
as less attractive to manufacturers who
may be concerned about their ability to
recover the full amount of the
technology cost during the sale of the
vehicle. The model continues to add
technology until a manufacturer either:
(a) Reaches compliance with regulatory
standards (possibly through the
accumulation and application of
overcompliance credits), (b) reaches a
point at which it is more cost effective
to pay penalties than to add more
technology (for CAFE), or (c) reaches a
point beyond compliance where the
manufacturer assumes its consumers
will be unwilling to pay for additional
fuel saving/emissions reducing
technologies.
In general, the model adds technology
for several reasons but checks these
sequentially. The model then applies
any ‘‘forced’’ technologies. Currently,
only VVT is forced to be applied to
vehicles at redesign since it is the root
of the engine path and the reference
point for all future engine technology
applications.347 The model next applies
any inherited technologies that were
applied to a leader vehicle and carried
forward into future model years where
follower vehicles (on the shared system)
are freshened or redesigned (and thus
eligible to receive the updated version
of the shared component). In practice,
very few vehicle models enter without
VVT, so inheritance is typically the first
step in the compliance loop. Then the
model evaluates the manufacturer’s
compliance status, applying all costeffective technologies regardless of
compliance status (essentially any
347 As a practical matter, this affects very few
vehicles. More than 95% of vehicles in the market
file either already have VVT present or have
surpassed the basic engine path through the
application of hybrids or electric vehicles.

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technology for which the effective cost
is negative). Then the model applies
expiring overcompliance credits (if
allowed to under the perspective of
either the ‘‘unconstrained’’ or ‘‘standard
setting’’ analysis, for CAFE purposes).
At this point, the model checks the
manufacturer’s compliance status again.
If the manufacturer is still not compliant
(and is unwilling to pay civil penalties,
again for CAFE), the model will add
technologies that are not cost-effective
until the manufacturer reaches
compliance. If the manufacturer
exhausts opportunities to comply with
the standard by improving fuel
economy/reducing emissions (typically
due to a limited percentage of its fleet
being redesigned in that year), the
model will apply banked CAFE or CO2
credits to offset the remaining deficit. If
no credits exist to offset the remaining
deficit, the model will reach back in
time to alter technology solutions in
earlier model years.
The CAFE model implements multiyear planning by looking back, rather
than forward. When a manufacturer is
unable to comply through cost-effective
(i.e., producing effective cost values less
than zero) technology improvements or
credit application in a given year, the
model will ‘‘reach back’’ to earlier years
and apply the most cost-effective
technologies that were not applied at
that time and then carry those
technologies forward into the future and
re-evaluate the manufacturer’s
compliance position. The model repeats
this process until compliance in the
current year is achieved, dynamically
rebuilding previous model year fleets
and carrying them forward into the
future, accumulating CAFE or CO2
credits from over-compliance with the
standard wherever appropriate.
In a given model year, the model
determines applicability of each
technology to each vehicle model,
platform, engine, and transmission. The
compliance simulation algorithm begins
the process of applying technologies
based on the CAFE or CO2 standards
specified during the current model year.
This involves repeatedly evaluating the
degree of noncompliance, identifying
the next ‘‘best’’ technology (ranked by
the effective cost discussed earlier)
available on each of the parallel
technology paths described above and
applying the best of these. The
algorithm combines some of the
pathways, evaluating them sequentially
instead of in parallel, in order to ensure
appropriate incremental progression of
technologies.
The algorithm first finds the best next
applicable technology in each of the
technology pathways then selects the

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best among these. For CAFE purposes,
the model applies the technology to the
affected vehicles if a manufacturer is
either unwilling to pay penalties or if
applying the technology is more costeffective than paying penalties.
Afterwards, the algorithm reevaluates
the manufacturer’s degree of
noncompliance and continues
application of technology. Once a
manufacturer reaches compliance (i.e.,
the manufacturer would no longer need
to pay penalties), the algorithm
proceeds to apply any additional
technology determined to be costeffective (as discussed above).
Conversely, if a manufacturer is
assumed to prefer to pay penalties, the
algorithm only applies technology up to
the point where doing so is less costly
than paying penalties. The algorithm
stops applying additional technology to
this manufacturer’s products once no
more cost-effective solutions are
encountered. This process is repeated
for each manufacturer present in the
input fleet. It is then repeated again for
each model year. Once all model years
have been processed, the compliance
simulation algorithm concludes. The
process for CO2 standard compliance
simulation is similar, but without the
option of penalty payment.

sradovich on DSK3GMQ082PROD with PROPOSALS2

(a) Compliance Example
The following example will illustrate
the features discussed above for the
CAFE program. While the example
describes the actions that General
Motors takes to modify the Chevrolet
Equinox in order to comply with the
augural standards (the baseline in this
analysis), and the logical consequences
of these actions, a similar example
would develop if instead simulating
compliance with the EPA standards for
those years. The structure of GM’s fleet
and the mechanisms at work in the
CAFE model are identical in both cases,
but different features of each program
(unlimited credit transfers between
fleets, for example) would likely cause
the model to choose different
technology solutions.
At the start of the simulation in MY
2016, GM has 30 unique engines shared
across over 33 unique nameplates, 260
model variants, and three regulatory
classes. As discussed earlier, the CAFE
model will attempt to preserve that level
of sharing across GM’s fleets to avoid
introducing additional production
complexity for which the agencies do
not estimate additional costs. An even
smaller number of transmissions (16)
and platforms (12) are shared across the
same set of nameplates, model variants,
and regulatory classes.

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The Chevrolet Equinox is represented
in the model inputs as a single
nameplate, with five model variants
distinguished by the presence of allwheel drive and four distinct
powertrain configurations (two engines
paired with two different
transmissions). Across all five model
variants, GM produced above 220,000
units of the Equinox nameplate. About
150,000 units of that production volume
is regulated as Domestic Passenger Car,
with the remainder regulated as Light
Trucks. The easiest way to describe the
actions taken by the CAFE model is to
focus on a single model variant of the
Equinox (one row in the market data
file). The model variant of the Equinox
with the highest production volume,
about 130,000 units in MY 2016, is
vehicle code 110111.348 This unique
model variant is the basis for the
example. However, because it is only
one of five variants on the Equinox
nameplate, the modifications made to
that model in the simulation will affect
the rest of the Equinox variants and
other vehicles across all fleets.
The example Equinox variant is
designated as an engine and platform
leader. As discussed earlier, this implies
that modifications to its engine (11031,
a 2.4L I–4) are tied to the redesign
cadence of this Equinox, as are
modifications to its platform (Theta/TE).
The engine is shared by the Buick
LaCrosse, Regal, and Verano, and by the
GMC Terrain (as well as appearing in
two other variants of the Equinox). So
those vehicles, if redesigned after this
Equinox, will inherit changes to engine
11031 when they are redesigned,
carrying the legacy version of the engine
until then. Similarly, this Equinox
shares its platform with the Cadillac
SRX and GMC Terrain, which will
inherit changes made to this platform
when they are redesigned (if later than
the Equinox, as is the case with the
SRX).
This specific Equinox is a
transmission ‘‘follower,’’ getting updates
made to its transmission leader (the
Chevrolet Malibu) when it is freshened
or redesigned. Additionally, two other
variants of the Equinox nameplate (the
more powerful versions, containing a
3.6L V–6 engine) are not ‘‘leaders’’ on
any of the primary components. Those
variants are built on the same platform
as the example Equinox variant but
share their engine with the Buick
Enclave and LaCrosse, the Cadillac SRX
348 This numeric designation is not important to
understand the example but will allow an
interested reader to identify the vehicle in model
outputs to either recreate the example or use it as
a template to create similar examples for other
manufacturers and vehicles.

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and XTS,349 the Chevrolet Colorado,
Impala and Traverse (which is the
designated ‘‘leader’’), and the GMC
Acadia, Canyon, and Terrain. This is an
example of how shared and inherited
components interact with product
cadence: when the Equinox nameplate
is redesigned, the CAFE model has more
leverage over some variants than others
and cannot make changes to the engines
of the variants of the Equinox with V–
6 unless that change is consistent with
all of the other nameplates just listed.
The transmissions on the other variants
of the Equinox are similarly widely
shared and represent the same kind of
production constraint just described
with respect to the engine. When
accounting for the full set of engines,
transmissions, and platforms
represented across the Equinox
nameplate’s five variants, components
are shared across all three regulatory
classes.
This example uses a ‘‘standard
setting’’ perspective to minimize the
amount of credit generation and
application, in order to focus on the
mechanics of technology application
and component sharing. The actions
taken by the CAFE model when
operating on the example Equinox
during GM’s compliance simulation are
shown in Table–II–84. In general, the
example Equinox begins the compliance
simulation with the technology
observed in its MY 2016 incarnation—
a 2.6L I–4 with VVT and SGDI, a 6speed automatic transmission, low
rolling resistance tires (ROLL20) and a
10% realized improvement in
aerodynamic drag (AERO10). In MY
2018, the Equinox is redesigned, at
which time the engine adds VVL and
level-1 turbocharging. The transmission
on the Malibu is upgraded to an 8-speed
automatic in 2018, which the Equinox
also gets. The platform, for which this
Equinox is the designated leader, gets
level-4 mass reduction. The CAFE
model also applies a few vehicle-level
technologies: low-drag brakes,
electronic accessory improvements, and
additional aerodynamic improvements
(AERO20). Upon refresh in MY 2021, it
acquires an upgraded 10-speed
transmission (AT10) from the Malibu.
349 The agencies recognized that GM last
produced the Cadillac SRX for MY 2016, and note
this as one example of the limitations of using an
analysis fleet defined in terms of even a recent
actual model year. Section II.B discusses these
tradeoffs, and the tentative judgment that, as a
foundation for analysis presented here, it was better
to develop the analysis fleet using the best
information available for MY 2016 than to have
used manufacturers’ CBI to construct an analysis
fleet that, though more current, would have limited
the agencies’ ability to make public all analytical
inputs and outputs.

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Then in MY 2025 it is redesigned again
and upgrades the engine to level-2
turbocharging, replaces the 10-speed
automatic transmission with a 8-speed
automatic transmission, adds a P2
strong hybrid, and further reduces the

mass of the platform (MR5). Using an
‘‘unconstrained’’ perspective would
possibly lead to additional actions taken
after MY 2025, where GM may have
been simulated to use credits earned in
earlier model years to offset small,

persistent CAFE deficits in one or more
fleets. In the ‘‘standard setting’’
perspective, that forces compliance
without the use of CAFE credits, this is
not an issue.

The technology applications
described in Table–II–84 have
consequences beyond the single variant
of the Equinox shown in the table. In
particular, two other variants of the
Equinox (both of which are regulated as
Light Trucks) get the upgraded engine,
which they share with the example, in
MY 2018. Thus, this application of
engine technology to a single variant of
the Equinox in the Domestic Car fleet,
‘‘spills over’’ into the Light Truck fleet,
generating improvements in fuel
economy and additional costs.
Furthermore, the Buick LaCrosse and
Regal, and the GMC Terrain also get the
same engine, which they share with the
example, in MY 2018. Those vehicles
also span the Domestic Car and Light
Truck fleets. However, the Buick
Verano, which is not redesigned until
MY 2019, continues with the legacy
(i.e., MY 2016) version of the shared
engine until it is redesigned. When it
inherits the new engine in MY 2019, it
does so without modification; the

engine it inherits is the same one that
was redesigned in MY 2018. This means
that the Verano will improve its fuel
economy in MY 2019 when the new
engine is inherited but only to the
extent that the new version of the
engine is an improvement over the
legacy version in the context of the
Verano’s other technology (which it is—
the Verano moves from 32 MPG to 44
MPG when accounting for the other
technologies added during the MY 2019
redesign).
This same story continues with the
diffusion of platform improvements
simulated by the CAFE model in MY
2018. The GMC Terrain is simulated to
be redesigned in MY 2018, in
conjunction with the Equinox. The
performance variants of the Equinox,
with a 3.5L V–6, also upgrade their
engines in MY 2018 (in conjunction
with the estimated Chevrolet Traverse
redesign). However, when the Equinox
is next redesigned in MY 2025, the
engine shared with the Traverse is not

upgraded again until MY 2026, so the
performance versions of the Equinox
continue with the 2018 version of the
engine throughout the remainder of the
simulation. While these inheritances
and sharing dynamics are not a perfect
representation of each manufacturer’s
specific constraints, nor the flexibilities
available to shift strategies in real-time
as a response to changing market or
regulatory conditions, they are a
reasonable way to consider the resource
constraints that prohibit fleet-wide
technology diffusion over shorter
windows than have been observed
historically and for which the agencies
have no way to impose additional costs.
Aside from the technology application
and its consequences throughout the
GM product portfolio, discussed above,
there are other important conclusions to
draw from the technology application
example. The first of these is that
product cadence matters, and only by
taking a year-by-year perspective can
this be seen. When the example Equinox

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is redesigned in MY 2018, the CAFE
model takes actions that cause the
redesigned Equinox to significantly
exceed its fuel economy target. While no
single vehicle has a ‘‘standard,’’ having
high volume vehicles significantly
below their individual targets can
present compliance challenges for
manufacturers who must compensate by
exceeding targets on other vehicles.
While the example Equinox exceeds its
MY 2018 target by almost 9 mpg, this
version of the Equinox is not eligible to
see significant technology changes again
before MY 2025 (except for the
transmission upgrade that occurs in MY
2021). Thus, the CAFE model is
redesigning the Equinox in MY 2018
with respect to future targets and
standards—this Equinox is nearly 2 mpg
below its target in MY 2024 before being
redesigned in MY 2025. This reflects a
real challenge that manufacturers face in
the context of continually increasing
CAFE standards, and represents a clear
example of why considering two model
year snapshots where all vehicles are
assumed to be redesigned is
unrealistically simplistic. The MY 2018
version of the example Equinox persists
(with little change) through six model

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years and the standards present in those
years. This is one reason why the CAFE
model, rather than OMEGA, was chosen
to examine the impacts of the proposed
standards in this analysis.
Another feature of note in Table–II–84
is the cost of applying these
technologies. The costs are all
denominated in dollars and represent
incremental cost increases relative to
the MY 2016 version of the Equinox.
Aside from the cost increase of over
$5,000 in MY 2025 when the vehicle is
converted to a strong hybrid, the
incremental technology costs display a
consistent trend between application
events—decreasing steadily over time as
the cost associated with each given
combination of technologies ‘‘learns
down.’’ By MY 2032, even the most
expensive version of the example
Equinox costs nearly $800 less to
produce than it did in MY 2025.
The technology application in the
example occurs in the context of GM’s
attempt to comply with the augural
standards. As some of the components
on the Equinox nameplate are shared
across all three regulated fleets, Table–
II–85 shows the compliance status of
each fleet in MYs 2016–2025. In MY
2017, the CAFE model applies expiring

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credits to offset deficits in the DC and
LT fleets. In MY 2028, when GM is
simulated to aggressively apply
technology to the example Equinox, the
DC fleet exceeds its standard while the
LT fleet still generates deficits. The
CAFE model offset that deficit with
expiring (and possibly transferred)
credits. However, by MY 2020 the
‘‘standard setting’’ perspective removes
the option of using CAFE credits to
offset deficits and GM exceeds the
standard in all three fleets, though by
almost 2 mpg in DC and LT. As the
Equinox example showed, many of the
vehicles redesigned in MY 2020 will
still be produced at the MY 2020
technology level in MY 2025 where GM
is simulated to comply exactly across all
three fleets. Under an ‘‘unconstrained’’
perspective, the CAFE model would use
the CAFE credits earned through overcompliance with the standards in MYs
2020–2023 to offset deficits created by
under-compliance as the standards
continued to increase, pushing some
technology application until later years
when the standards stabilized and those
credits expired. The CAFE model
simulates compliance through MY 2032
to account for this behavior.

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(b) Representation of OEMs’ Potential
Responsiveness to Buyers’ Willingness
To Pay for Fuel Economy Improvements
The CAFE model simulates
manufacturer responses to both
regulatory standards and technology
availability. In order to do so, it requires
assumptions about how the industry
views consumer demand for additional
fuel economy because manufacturer
responses to potential standards depend
not just on what they think they are best
off producing to satisfy regulatory
requirements (considering the

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consequences of not satisfying those
requirements), but also on what they
think they can sell, technology-wise, to
consumers. In the 2012 final rule, the
agencies analyzed alternatives under the
assumption that manufacturers would
not improve the fuel economy of new
vehicles at all unless compelled to do so
by the existence of increasingly
stringent CAFE and GHG standards.350
This ‘‘flat baseline’’ assumption led the
agencies to attribute all of the fuel
350 See,

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savings that occurred in the simulation
after MY 2016 to the proposed standards
because none of the fuel economy
improvements were considered likely to
occur in the absence of increasing
standards. However, this assumption
contradicted much of the literature on
this topic and the industry’s recent
experience with CAFE compliance, and
for CAFE standards, the analysis
published in 2016 applied a reference
case estimate that manufacturers will
treat all technologies that pay for
themselves within the first three years

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of ownership (through reduced
expenditures on fuel) as if the cost of
that technology were negative.351
The industry has exceeded the
required CAFE level for both passenger
cars and light trucks in the past;
notably, by almost 5 mpg during the fuel
price spikes of the 2000s when CAFE
standards for passenger cars were still
frozen at levels established for the 1990
model year.352 In fact, a number of
manufacturers that traditionally paid
CAFE civil penalties even reached
compliance during years with
sufficiently high fuel prices.353 The
model attempts to account for this
observed consumer preference for fuel
economy, above and beyond that
required by the regulatory standards, by
allowing fuel price to influence the
ranking of technologies that the model
considers when modifying a
manufacturer’s fleet in order to achieve
compliance. In particular, the model
ranks available technology not by cost,
but by ‘‘effective cost.’’
When the model chooses which
technology to apply next, it calculates
the effective cost of available
technologies and chooses the
technology with the lowest effective
cost. The ‘‘effective cost’’ itself is a
combination of the technology cost, the
fuel savings that would occur if that
technology were applied to a given
vehicle, the resulting change in CAFE
penalties (as appropriate), and the
affected volumes. User inputs determine
how much fuel savings manufacturers
believe new car buyers will pay for
(denominated in the number of years
before a technology ‘‘pays back’’ its
cost).
Because the civil penalty provisions
specified for CAFE in EPCA do not
apply to CO2 standards, the effective
cost calculation applied when
simulating compliance with CO2
standards uses an estimate of the
potential value of CO2 credits. Including
a valuation of CO2 credits in the
effective cost metric provides a potential
basis for future explicit modeling of
credit trading.354 Manufacturers,
351 Draft TAR, p. 13–10, available at https://
www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/
Draft-TAR-Final.pdf (last accessed June 15, 2018).
352 NHTSA, Summary of Fuel Economy
Performance, 2014, available at https://
www.nhtsa.gov/sites/nhtsa.dot.gov/files/
performance-summary-report-12152014-v2.pdf (last
accessed June 27, 2018).
353 Ibid. Additional data available at https://
one.nhtsa.gov/cafe_pic/CAFE_PIC_Mfr_LIVE.html
(last accessed June 27, 2018).
354 By treating all passenger cars and light trucks
as being manufactured by a single ‘‘OEM,’’ inputs
to the CAFE model can be structured to simulate
perfect trading. However, competitive and other
factors make perfect trading exceedingly unlikely,

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though, have thus far declined to
disclose the actual terms of CAFE or
CO2 credit trades, so this calculation
currently uses the CAFE civil penalty
rate as the basis to estimate this value.
It seems reasonable to assume that the
CAFE civil penalty rate likely sets an
effective ceiling on the price of any
traded CAFE credits, and considering
that each manufacturer can only
produce one fleet of vehicles for sale in
the U.S., prices of CO2 credits might
reasonably be expected to be equivalent
to prices of CAFE credits. However, the
current CAFE model does not explicitly
simulate credit trading; therefore, the
change in the value of CO2 credits
should only capture the change in
manufacturer’s own cost of compliance,
so the compliance simulation algorithm
applies a ceiling at 0 (zero) to each
calculated value of the CO2 credits.355
Just as manufacturers’ actual
approaches to vehicle pricing are
closely held, manufacturers’ actual
future approaches to making decisions
about technology are not perfectly
knowable. The CAFE model is intended
to illustrate ways manufacturers could
respond to standards, given a set of
production constraints, not to predict
how they will respond. Alternatives to
these ‘‘effective cost’’ metrics have been
considered and will continue to be
considered. For example, instead of
using a dollar value, the model could
use a ratio, such as the net cost
(technology cost minus fuel savings) of
an application of technology divided by
corresponding quantity of avoided fuel
consumption or CO2 emissions. Any
alternative metric has the potential to
shift simulated choices among
technology application options, and
some metrics would be less suited to the
CAFE model’s consideration of
multiyear product planning, or less
adaptable than others to any future
simulation of credit trading. Comment is
sought regarding the definition and
application of criteria to select among
technology options and determine when
to stop applying technology (consider
not only standards, but also factors such
as fuel prices, civil penalties for CAFE,
and the potential value of credits for
both programs), and this aspect of the
model may be further revised. Any
future revision to the effective cost
would be considered in light of
and future efforts will focus consideration on more
plausible imperfect trading.
355 Having the model continue to add technology
in order to build a surplus of credits as warranted
by the estimated (whether specified as a model
input or calculated dynamically as a clearing price)
market value of credits would provide part of the
basis for having the model build the supply side of
an explicitly-simulated credit trading market.

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manufacturers different compliance
positions relative to the standards, and
in light of the likelihood that some
OEMs will continue to use civil
penalties as a means to resolve CAFE
deficits (at least for some fleets).
While described in greater detail in
the CAFE model documentation, the
effective cost reflects an assumption not
about consumers’ actual willingness to
pay for additional fuel economy but
about what manufacturers believe
consumers are willing to pay. The
reference case estimate for today’s
analysis is that manufacturers will treat
all technologies that pay for themselves
within the first 21⁄2 years of ownership
(through reduced expenditures on fuel)
as if the cost of that technology were
negative. Manufacturers have repeatedly
indicated to the agencies that new
vehicle buyers are only willing to pay
for fuel economy-improving technology
if it pays back within the first two to
three years of vehicle ownership.356
NHTSA has therefore incorporated this
assumption (of willingness to pay for
technology that pays back within 30
months) into today’s analysis.
Alternatives to this 30-month estimate
are considered in the sensitivity
analysis included in today’s notice. In
the current version of the model, this
assumption holds whether or not a
manufacturer has already achieved
compliance. This means that the most
cost-effective technologies (those that
pay back within the first 21⁄2 years) are
applied to new vehicles even in the
absence of regulatory pressure.
However, because the value of fuel
savings depends upon the price of fuel,
the model will add more technology
even without regulatory pressure when
fuel prices are high compared to
simulations where fuel prices are
assumed to be low. This assumption is
consistent with observed historical
compliance behavior (and consumer
demand for fuel economy in the new
vehicle market), as discussed above.
One implication of this assumption is
that futures with higher, or lower, fuel
prices produce different sets of
attractive technologies (and at different
times). For example, if fuel prices were
above $7/gallon, many of the
technologies in this analysis could pay
for themselves within the first year or
two and would be applied at high rates
in all of the alternatives. Similarly, at
the other extreme (significantly reduced
fuel prices), almost no additional fuel
economy would be observed.
356 This is supported by the 2015 NAS study,
which found that consumers seek to recoup added
upfront purchasing costs within two or three years.
See 2015 NAS Report, at pg. 317.

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While these assumptions about
desired payback period and consumer
preferences for fuel economy may not
affect the eventual level of achieved
CAFE and CO2 emissions in the later
years of the program, they will affect the
amount of additional technology cost
and fuel savings that are attributable to
the standard. The approach currently
only addresses the inherent trade-off
between additional technology cost and
the value of fuel savings, but other costs
could be relevant as well. Further
research would be required to support
simulations that assume buyers behave
as if they consider all ownership costs
(e.g., additional excise taxes and
insurance costs) at the time of purchase
and that manufacturers respond
accordingly. Comment is sought on the
approach described above, the current
values ascribed to manufacturers’ belief
about consumer willingness-to-pay for
fuel economy, and practicable
suggestions for future improvements
and refinements, considering the
model’s purpose and structure.

sradovich on DSK3GMQ082PROD with PROPOSALS2

(c) Representation of Some OEMs’
Willingness To Treat Civil Penalties as
a Program Flexibility
When considering technology
applications to improve fleet fuel
economy, the model will add
technology up to the point at which the

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effective cost of the technology (which
includes technology cost, consumer fuel
savings, consumer welfare changes, and
the cost of penalties for non-compliance
with the standard) is less costly than
paying civil penalties or purchasing
credits. Unlike previous versions of the
model, the current implementation
further acknowledges that some
manufacturers experience transitions
between product lines where they rely
heavily on credits (either carried
forward from earlier model years or
acquired from other manufacturers) or
simply pay penalties in one or more
fleets for some number of years. The
model now allows the user to specify,
when appropriate for the regulatory
program being simulated, on a year-byyear basis, whether each manufacturer
should be considered as willing to pay
penalties for non-compliance. This
provides additional flexibility,
particularly in the early years of the
simulation. As discussed above, this
assumption is best considered as a
method to allow a manufacturer to
under-comply with its standard in some
model years—treating the civil penalty
rate and payment option as a proxy for
other actions it may take that are not
represented in the CAFE model (e.g.,
purchasing credits from another
manufacturer, carry-back from future

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model years, or negotiated settlements
with NHTSA to resolve deficits).
In the current analysis, NHTSA has
relied on past compliance behavior and
certified transactions in the credit
market to designate some manufacturers
as being willing to pay CAFE penalties
in some model years. The full set of
assumptions regarding manufacturer
behavior with respect to civil penalties
is presented in Table–II–86, which
shows all manufacturers are assumed to
be willing to pay civil penalties prior to
MY 2020. This is largely a reflection of
either existing credit balances (which
manufacturers will use to offset CAFE
deficits until the credits reach their
expiration dates) or assumed trades
between manufacturers that are likely to
happen in the near-future based on
previous behavior. The manufacturers
in the table whose names appear in bold
all had at least one regulated fleet (of
three) whose CAFE was below its
standard in MY 2016. Because the
analysis began with the MY 2016 fleet,
and no technology can be added to
vehicles that are already designed and
built, all manufacturers can generate
civil penalties in MY 2016. However,
once a manufacturer is designated as
unwilling to pay penalties, the CAFE
model will attempt to add technology to
the respective fleets to avoid shortfalls.

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(d) Representation of CAFE and CO2
Credit Provisions
The model’s approach to simulating
compliance decisions accounts for the
potential to earn and use CAFE credits
as provided by EPCA/EISA. The model
similarly accumulates and applies CO2
credits when simulating compliance
with EPA’s standards. Like past
versions, the current CAFE model can
be used to simulate credit carry-forward
(a.k.a. banking) between model years
and transfers between the passenger car
and light truck fleets but not credit
carry-back (a.k.a. borrowing) from future
model years or trading between
manufacturers. Some manufacturers
have made occasional use of credit
carry-back provisions, although the
analysis does not assume use of carryback as a compliance strategy because of
the risk in relying on future
improvements to offset earlier

compliance deficits. Thus far, NHTSA
has not attempted to include simulation
of credit carry-back or trading in the
CAFE model. Unlike past versions, the
current CAFE model provides a basis to
specify (in model inputs) CAFE credits
available from model years earlier than
those being simulated explicitly. For
example, with this analysis representing
model years 2016–2032 explicitly,
credits earned in model year 2012 are
made available for use through model
year 2017 (given the current five-year
limit on carry-forward of credits). The
banked credits are specific to both
model year and fleet in which they were
earned. Comment and supporting
information are invited regarding
whether and, if so, how the CAFE model
and inputs might practicably be
modified to account for trading of
credits between manufacturers and/or
carry-back of credits from later to earlier
model years.
As discussed in the CAFE model
documentation, the model’s default
logic attempts to maximize credit carryforward—that is, to ‘‘hold on’’ to credits
for as long as possible. If a manufacturer
needs to cover a shortfall that occurs
when insufficient opportunities exist to
add technology in order to achieve
compliance with a standard, the model
will apply credits. Otherwise it carries
forward credits until they are about to
expire, at which point it will use them
before adding technology that is not
considered cost-effective. The model
attempts to use credits that will expire
within the next three years as a means
to smooth out technology application
over time to avoid both compliance
shortfalls and high levels of overcompliance that can result in a surplus
of credits. As further discussed in the
CAFE model documentation, model
inputs can be used to adjust this logic
to shift the use of credits ahead by one
or more model years. In general, the
logic used to generate credits and apply
them to compensate for compliance
shortfalls, both in a given fleet and
across regulatory fleets, is an area that
requires more attention in the next
phase of model development. While the
current model correctly accounts for
credits earned when a manufacturer
exceeds its standard in a given year, the
strategic decision of whether to earn
additional credits to bank for future
years (in the current fleet or to transfer
into another regulatory fleet) and when
to optimally apply them to deficits is

challenging to simulate. This will be an
area of focus moving forward.
NHTSA introduced the CAFE Public
Information Center 357 to provide public
access to a range of information
regarding the CAFE program, including
manufacturers’ credit balances.
However, there is a data lag in the
information presented on the CAFE PIC
that may not capture credit actions
across the industry for as much as
several months. Additionally, CAFE
credits that are traded between
manufacturers are adjusted to preserve
the gallons saved that each credit
represents.358 The adjustment occurs at
the time of application rather than at the
time the credits are traded. This means
that a manufacturer who has acquired
credits through trade, but has not yet
applied them, may show a credit
balance that is either considerably
higher or lower than the real value of
the credits when they are applied. For
example, a manufacturer that buys 40
million credits from Tesla, may show a
credit balance in excess of 40 million.
However, when those credits are
applied, they may be worth only 1/10 as
much—making that manufacturer’s true
credit balance closer to 4 million than
40 million.
Having reviewed credit balances (as of
October 23, 2017) and estimated the
potential that some manufacturers could
trade credits, NHTSA developed inputs
that make carried-forward credits
available as summarized in Table–II–87,
Table–II–88, and Table–II–89, after
subtracting credits assumed to be traded
to other manufacturers, adding credits
assumed to be acquired from other
manufacturers through such trades, and
adjusting any traded credits (up or
down) to reflect their true value for the
fleet and model year into which they
were traded.359 While the CAFE model
will transfer expiring credits into
another fleet (e.g., moving expiring
credits from the domestic car credit
bank into the light truck fleet), some of
these credits were moved in the initial
banks to improve the efficiency of
application and to better reflect both the
projected shortfalls of each
manufacturer’s regulated fleets, and to
represent observed behavior. For
context, a manufacturer that produces
one million vehicles in a given fleet,
and experiences a shortfall of 2 mpg,
would need 20 million credits to
completely offset the shortfall.

357 CAFE Public Information Center, http://
www.nhtsa.gov/CAFE_PIC/CAFE_PIC_Home.htm
(last visited June 22, 2018).
358 GHG credits for EPA’s program are
denominated in metric tons of CO2 rather than
gram/mile compliance credits and require no

adjustment when traded between manufacturers or
fleets.
359 The adjustments, which are based upon the
standard, CAFE and year of both the party
originally earning the credits and the party applying
them, were implemented assuming the credits

would be applied to the model year in which they
were set to expire. For example, credits traded into
a domestic passenger car fleet for MY 2014 were
adjusted assuming they would be applied in the
domestic passenger car fleet for MY 2019.

Several of the manufacturers in
Table–II–86 that are assumed to be
willing to pay civil penalties in the early
years of the program have no history of
paying civil penalties. However, several
of those manufacturers have either
bought or sold credits—or transferred
credits from one fleet to another to offset
a shortfall in the underperforming fleet.
As the CAFE model does not simulate
credit trades between manufacturers,
providing this additional flexibility in
the modeling avoids the outcome where
the CAFE model applies more
technology than would be needed in the
context of the full set of compliance
flexibilities at the industry level. By
statute, NHTSA cannot consider credit
flexibilities when setting standards, so
most manufacturers (those without a
history of civil penalty payment) are
assumed to comply with their standard
through fuel economy improvements for
the model years being considered in this
analysis. The notable exception to this
is FCA, who we expect will still satisfy
the requirements of the program through
a combination of credit application and
civil penalties through MY 2025 before
eventually complying exclusively
through fuel economy improvements in
MY 2026.
As mentioned above, the CAA does
not provide civil penalty provisions
similar to those specified in EPCA/
EISA, and the above-mentioned
corresponding inputs apply only to
simulation of compliance with CAFE
standards.

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Table-11-87- Estimated Domestic Car CAFE Credit Banks, MY 2011 -2015
Manufacturer
BMW
Daimler
FCA
Ford
General
Motors
Honda
Hyundai Kia-H
Hyundai Kia-K

Model Year
2011

2012

2013

2014

2015

-

-

-

-

3,533,996
24,094,037
7,682,752

18,886,353
26,139,750
7,246,220

42,604,131
40,611,410
24,976,993

1,682,307
30,152,856
7,338,835

7,089,840

-

99

1,379,203

813,612

39,580,944

52,537,420

JLR

-

Mazda

15,526

-

-

-

-

Nissan
Mitsubishi
Subaru
Tesla
Toyota

-

1,564,100

26,451,158

52,774,443

62,285,009

-

-

-

164,504
29,691,134

491,723
17,474,425

589,594
363,905
12,181,000

2,880,250
25,369,142
4,828,440

Volvo
VWA

-

-

-

-

-

1,529,328

2,836,482

4,390,945

4,479,510

31,937,216

T a bl e-II- 88 - E st.1mat ed Impor ted C ar CAFE C re d·t
1 B an ks, MY 2011 -2015
2012

2013

BMW
Daimler
FCA
Ford
General
Motors
Honda
Hyundai Kia-H
Hyundai Kia-K

-

-

-

-

1,576,672

251,275

1,385,379
2,780,629

101
28,338,076
15,078,920

99
16,403,710
12,759,767

5,431,859
44,063,236
11,603,509

JLR

-

-

Mazda
Nissan
Mitsubishi
Subaru
Tesla
Toyota

5,617,262
1,953,364

322,320
1,606,363

-

-

23:42 Aug 23, 2018

2015
6,329,325

-

-

3,646,294

1,304,196

2,142,966
10,185,700

1,356,300
9,658,416

-

-

1,270,772

293,436

894,783

15,430,643
2,161,883

13,254,400
9,086,088

6,804,584

1,894,165

22,616,350

1,867,661

-

-

-

-

6,326,946

39,697,080

62,935,487

66,791,277

47,709,001

50,293,119

-

-

-

-

-

8,593,792

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4,163,432

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2011

Volvo
VWA

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Manufacturer

43183

In addition to the inclusion of these
existing credit banks, the CAFE model
also updated its treatment of credits in
the rulemaking analysis. Congress has
declared that NHTSA set CAFE
standards at maximum feasible levels
for each model year under consideration
without consideration of the program’s
credit mechanisms. However, as CAFE
rulemakings have evaluated longer time
periods in recent years, the early actions
taken by manufacturers required more
nuanced representation. Therefore, the
CAFE model now allows a ‘‘last year to
consider credits,’’ set at the last year for
which new standards are not being
considered (MY 2019 in this analysis).
This allows the model to replicate the
practical application of existing credits
toward CAFE compliance in early years

but to examine the impact of proposed
standards based solely on fuel economy
improvements in all years for which
new standards are being considered.
Comment is sought regarding the
model’s representation of the CAFE and
CO2 credit provisions, recommendations
regarding any other options, and any
information that could help to refine the
current approach or develop and
implement an alternative approach.
The CAFE model has also been
modified to include a similar
representation of existing credit banks
in EPA’s CO2 program. While the life of
a CO2 credit, denominated in metric
tons CO2, has a five-year life, matching
the lifespan of CAFE credits, credits
earned in the early years of the EPA
program, MY 2009–2011, may be used
through MY 2021.360 The CAFE model

was not modified to allow exceptions to
the life-span of compliance credits
treating them all as if they may be
carried forward for no more than five
years, so the initial credit banks were
modified to anticipate the years in
which those credits might be needed.
The fact that MY 2016 is simulated
explicitly prohibited the inclusion of
these banked credits in MY 2016 (which
could be carried forward from MY 2016
to MY 2021), and thus underestimates
the extent to which individual
manufacturers, and the industry as a
whole, may rely on these early credits
to comply with EPA standards between
MY 2016 and MY 2021. The credit
banks with which the simulations in
this analysis were conducted are
presented in the following tables:

360 In response to comments, EPA placed limits
on credits earned in MY 2009, causing them to

expire prior to this rule. However, credits generated
in MYs 2010–2011 may be carried forward, or

traded, and applied to deficits generated through
MY 2021.

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T a bl e- II- 90 - E sf 1mat ed P assenger C ar CO2 C re d·t
1 B an k s, MY 2011 -2015
Manufacturer

Model Year
2011
790,137
688,000
4,089,000
1,911,000
2,040,000

BMW
Daimler
FCA
Ford
General
Motors
Honda
Hyundai Kia-H
Hyundai Kia-K
JLR
Mazda
Nissan
Mitsubishi
Subaru
Tesla
Toyota
Volvo
VWA

114,000
278,000

2012
1,213,000
777,000
4,554,000
2,546,000
3,804,000

1,236,000
343,000

2013
1,558,000
899,000
5,142,000
3,485,000
3,487,000

548,000
355,000

2014
1,833,000
1,199,000
6,574,000
4,743,000
4,882,000

2015
2,089,000
1,443,000
7,318,000
4,216,000
4,588,000

600,000

2,000,000

973,000
392,000
765,000

1,161,000
379,000
600,000
1,863,000

511,000

611,000

1,000,000

1,200,000

1,400,000

32,000
1,215,000

102,000
1,343,000

169,000
1,700,000

89,000
2,065,000

450,000
143,000
2,444,000

T a bl e- II-91 - E stimate
.
d L.Iglh t T rue k CO2 C re d.It B an k s, MY 2011 -2015
Manufacturer

Volvo
VWA

140,000
556,000
1,715,000

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729,000

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2013

2014

2015

-

-

-

914,000
6,106,000
2,875,000
11,216,000

1,149,000
2,742,000
4,656,000
9,164,000

274,000
1,920,000
6,089,000
6,049,000

446,000
3,614,000
2,122,000
4,829,000

218,000
981,000
1,973,000

945,000
300,000
973,000
1,940,000

1,400,000
300,000
1,219,000
2,168,000

200,000

450,000

500,000

153,000
591,000
1,635,000

193,000

While the CAFE model does not
simulate the ability to trade credits
between manufacturers, it does simulate

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2012

-

8,710,000

8,545,000

9,045,000

8,000,000

384,000

37,000
134,000

50,000
370,000

50,000
547,000

the strategic accumulation and
application of compliance credits, as
well as the ability to transfer credits

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compliance position of a less efficient
fleet by leveraging credits earned by a

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Mazda
Nissan
Mitsubishi
Subaru
Tesla
Toyota

2011
112,314
870,000
7,756,000
6,366,000
11,318,000

EP24AU18.129

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BMW
Daimler
FCA
Ford
General
Motors
Honda
Hyundai Kia-H
Hyundai Kia-K
JLR

Model Year

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
more efficient fleet. The model prefers
to hold on to earned compliance credits
within a given fleet, carrying them
forward into the future to offset
potential future deficits. This
assumption is consistent with observed
strategic behavior dating back to 2009.
From 2009 to present, no
manufacturer has transferred CAFE
credits into a fleet to offset a deficit in
the same year in which they were
earned. This has occurred with credits
acquired from other manufacturers via
trade but not with a manufacturer’s own
credits. Therefore, the current
representation of credit transfers
between fleets—where the model
prefers to transfer expiring, or soon-tobe-expiring credits rather than newly
earned credits—is both appropriate and
consistent with observed industry
behavior.
This may not be the case for GHG
standards, though it is difficult to be
certain at this point. The GHG program
seeded the industry with a large
quantity of early compliance credits
(earned in MYs 2009–2011 361) prior to
the existence formal standards of the
EPA program. These early credits do not
expire until 2021. So, for manufacturers
looking to offset deficits, it is more
sensible to use current-year credits that
expire in the next five years, rather than
draw down the bank of credits that can
be used until MY 2021. The first model
year for which earned credits outlive the
initial bank is MY 2017, for which final
compliance actions and deficit
resolutions are still pending. Regardless,
in order to accurately represent some of
the observed behavior in the GHG credit
system, the CAFE model allows (and
encourages) within-year transfers
between regulated fleets for the purpose
of simulating compliance with the GHG
standards.
In addition to more rigorous
accounting of CAFE and CO2 credits, the
model now also accounts for air
conditioning efficiency and off-cycle
adjustments. NHTSA’s program
considers those adjustments in a
manufacturer’s compliance calculation
starting in MY 2017, and the current
model uses the adjustments claimed by
each manufacturer in MY 2016 as the
starting point for all future years.
Because the air conditioning and offcycle adjustments are not credits in
NHTSA’s program, but rather
adjustments to compliance fuel
economy (much like the Flexible Fuel
Vehicle adjustments that are due to
361 In response to public comment, EPA
eliminated the use of credits earned in MY 2009 for
future model years. However, credits earned in MY
2010 and MY 2011 remain.

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phase out in MY 2019), they may be
included under either a ‘‘standard
setting’’ or ‘‘unconstrained’’ analysis
perspective.
When the CAFE model simulates
EPA’s program, the treatment of A/C
efficiency and off-cycle credits is
similar, but the model also accounts for
A/C leakage (which is not part of
NHTSA’s program). When determining
the compliance status of a
manufacturer’s fleet (in the case of
EPA’s program, PC and LT are the only
fleet distinctions), the CAFE model
weighs future compliance actions
against the presence of existing (and
expiring) CO2 credits resulting from
over-compliance with earlier years’
standards, A/C efficiency credits, A/C
leakage credits, and off-cycle credits.
5. Impacts on Each OEM and Overall
Industry
(a) Technology Application and
Penetration Rates
The CAFE model tracks and reports
technology application and penetration
rates for each manufacturer, regulatory
class, and model year, calculated as the
volume of vehicles with a given
technology divided by the total volume.
The ‘‘application rate’’ accounts only for
those technologies applied by the model
during the compliance simulation,
while the ‘‘penetration rate’’ accounts
for the total percentage of a technology
present in a given fleet, whether applied
by the CAFE model or already present
at the start of the simulation.
In addition to the aggregate
representation of technology
penetration, the model also tracks each
individual vehicle model on which it
has operated. Each row in the market
data file (the representation of vehicles
offered for sale in MY 2016 in the U.S.,
discussed in detail in Section II.B.a and
PRIA Chapter 6) contains a record for
every model year and every alternative,
that identifies with which technologies
the vehicle started the simulation,
which technologies were applied, and
whether those technologies were
applied directly or through inheritance
(discussed above). Interested parties
may use these outputs to assess how the
compliance simulation modified any
vehicle that was offered for sale in MY
2016 in response to a given regulatory
alternative.
(b) Required and Achieved CAFE and
Average CO2 Levels
The model fully represents the
required CAFE (and now, CO2) levels for
every manufacturer and every fleet. The
standard for each manufacturer is based
on the harmonic average of footprint

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targets (by volume) within a fleet, just
as the standards prescribe. Unlike
earlier versions of the CAFE model, the
current version further disaggregates
passenger cars into domestic and
imported classes (which manufacturers
report to NHTSA and EPA as part of
their CAFE compliance submissions).
This allows the CAFE model to more
accurately estimate the requirement on
the two passenger car fleets, represent
the domestic passenger car floor (which
must be exceeded by every
manufacturer’s domestic fleet, without
the use of credits, but with the
possibility of civil penalty payment),
and allows it to enforce the transfer cap
limit that exists between domestic and
imported passenger cars, all for
purposes of the CAFE program.
In calculating the achieved CAFE
level, the model uses the prescribed
harmonic average of fuel economy
ratings within a vehicle fleet. Under an
‘‘unconstrained’’ analysis, or in a model
year for which standards are already
final, it is possible for a manufacturer’s
CAFE to fall below its required level
without generating penalties because
the model will apply expiring or
transferred credits to deficits if it is
strategically appropriate to do so.
Consistent with current EPA
regulations, the model applies simple
(not harmonic) production-weighted
averaging to calculate average CO2
levels.
(c) Costs
For each technology that the model
adds to a given vehicle, it accumulates
cost. The technology costs are defined
incrementally and vary both over time
and by technology class, where the same
technology may cost more to apply to
larger vehicles as it involves more raw
materials or requires different
specifications to preserve some
performance attributes. While learningby-doing can bring down cost, and
should reasonably be implemented in
the CAFE model as a rate of cost
reduction that is applied to the
cumulative volume of a given
technology produced by either a single
manufacturer or the industry as a whole,
in practice this notion is implemented
as a function of time, rather than
production volume. Thus, depending
upon where a given technology starts
along its learning curve, it may appear
to be cost-effective in later years where
it was not in earlier years. As the model
carries forward technologies that it has
already applied to future model years, it
similarly adjusts the costs of those
technologies based on their individual
learning rates.

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sradovich on DSK3GMQ082PROD with PROPOSALS2

The other costs that manufacturers
incur as a result of CAFE standards are
civil penalties resulting from noncompliance with CAFE standards. The
CAFE model accumulates costs of $5.50
per 1/10–MPG under the standard,
multiplied by the number of vehicles
produced in that fleet, in that model
year. The model reports as the full
‘‘regulatory cost,’’ the sum of total
technology cost and total fines by the
manufacturer, fleet, and model year. As
mentioned above, the relevant EPCA/
EISA provisions do not also appear in
the CAA, so this option and these costs
apply only to simulated compliance
with CAFE standards.
(d) Sales
In all previous versions of the CAFE
model, the total number of vehicles sold
in any model year, in fact the number
of each individual vehicle model sold in
each year, has been a static input that
did not vary in response to price
increases induced by CAFE standards,
nor changes in fuel prices, or any other
input to the model. The only way to
alter sales, was to update the entire
forecast in the market input file.
However, in the 2012 final rule, NHTSA
included a dynamic fleet share model
that was based on a module in the
Energy Information Administration’s
NEMS model. This fleet share model
did not change the size of the new
vehicle fleet in any year, but it did
change the share of new vehicles that
were classified as passenger cars (or
light trucks). That capability was not
included in the central analysis but was
included in the uncertainty analysis,
which looked at the baseline and
preferred alternative in the context of
thousands of possible future states of
the world. As some of those futures
contained extreme cases of fuel prices,
it was important to ensure consistent
modeling responses within that context.
For example, at a gasoline price of $7/
gallon, it would be unrealistic to expect
the new vehicle market’s light truck
share to be the same as the future where
gasoline cost $2/gallon. The current
model has slightly modified, and fully
integrated, the dynamic fleet share
model. Every regulatory alternative and
sensitivity case considered in this
analysis reflects a dynamically
responsive fleet mix in the new vehicle
market.
While the dynamic fleet share model
adjusts unit sales across body styles
(cars, SUVs, and trucks), it does not
modify the total number of new vehicles
sold in a given year. The CAFE model
now includes a separate function to
account for changes in the total number
of new vehicles sold in a given year

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(regardless of regulatory class or body
style), in response to certain
macroeconomic inputs and changes in
the average new vehicle price. The price
impact is modest relative to the
influence of the macroeconomic factors
in the model. The combination of these
two models modify the total number of
new vehicles, the share of passenger
cars and light trucks, and, as a
consequence, the number of each given
model sold by a given manufacturer.
However, these two factors are
insufficient to cause large changes to the
composition of any of a manufacturer’s
fleets. In order to significantly change
the mix of models produced within a
given fleet, the CAFE model would
require a way to trade off the production
of one vehicle versus another both
within a manufacturer’s fleet and across
the industry. While NHTSA has
experimented with fully-integrated
consumer choice models, their
performance has yet to satisfy the
requirements of a rulemaking analysis.
There are multiple levels of sales
impacts that could result from
increasing the prices of new vehicles
across the industry. Any estimate of
impacts at the manufacturer, or model,
level would be subject to an assumed
pricing strategy that spreads technology
cost increases across available models in
a way that may cross-subsidize specific
models or segments at the expense of
others. However, at the industry level, it
is reasonable to assume that all
incremental technology costs can be
captured by the average price of a new
vehicle. To the extent that this factor
influences the total number of new
vehicles sold in a given model year, it
can be included in an empirical model
of annual sales. However, there is
limited historical evidence that the
average price of a new vehicle is a
strong determining factor in the total
number of annual new vehicle sales.
6. National Impacts
(a) Vehicle Stock and Fleet Turnover
The CAFE model carries a complete
representation of the registered vehicle
population in each calendar year,
starting with an aggregated version of
the most recent available data about the
registered population for the first year of
the simulation. In this analysis, the first
model year considered is MY 2016, and
the registered vehicle population enters
the model as it appeared at the end of
calendar year 2015. The initial vehicle
population is stratified by age (or model
year cohort) and regulatory class—to
which the CAFE model assigns average
fuel economies based on the reported
regulatory class industry average

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compliance value in each model year
(and class). Once the simulation begins,
new vehicles are added to the
population from the market data file and
age throughout their useful lives during
the simulation, with some fraction of
them being retired (or scrapped) along
the way. For example, in calendar year
2017, the new vehicles (age zero) are
MY 2017 vehicles (added by the CAFE
model simulation and represented at the
same level of detail used to simulate
compliance), the age one vehicles are
MY 2016 vehicles (added by the CAFE
model simulation), and the age two
vehicles are MY 2015 vehicles
(inherited from the registered vehicle
population and carried through the
analysis with less granularity). This
national registered fleet is used to
calculate annual fuel consumption,
vehicle miles traveled (VMT), pollutant
emissions, and safety impacts under
each regulatory alternative.
In addition to dynamically modifying
the total number of new vehicles sold,
a dynamic model of vehicle retirement,
or scrappage, has also been
implemented. The model implements
the scrappage response by defining the
instantaneous scrappage rate at any age
using two functions. For ages less than
20, instantaneous scrappage is defined
as a function of vehicle age, new vehicle
price, cost per mile of driving (the ratio
of fuel price and fuel economy), and a
small number of macroeconomic factors.
For ages greater than 20, the
instantaneous scrappage rate is a simple
exponential function of age. While the
scrappage response does not affect
manufacturer compliance calculations,
it impacts the lifetime mileage
accumulation (and thus fuel savings) of
all vehicles. Previous CAFE analyses
have focused exclusively on new
vehicles, tracing the fuel consumption
and social costs of these vehicles
throughout their useful lives; the
scrappage effect also impacts the
registered vehicle fleet that exists when
a set of standards is implemented.
As new vehicles enter the registered
population their retirement rates are
governed by the scrappage model, so are
the vehicles already registered at the
start of model year 2016. To the extent
that a given set of CAFE or CO2
standards accelerates or decelerates the
retirement of those vehicles, additional
fuel consumption and social costs may
accrue to those vehicles under that
standard. The CAFE model accounts for
those costs and benefits, as well as
tracking all of the standard benefits and
costs associated with the lifetimes of
new vehicles produced under the rule.
For more detail about the derivation of
the scrappage functions, see Section

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II.E, and PRIA Chapter 8. Comment is
sought on the specification and
inclusion of these factors in the current
model.

sradovich on DSK3GMQ082PROD with PROPOSALS2

(b) Highway Travel
In support of prior CAFE rulemakings,
the CAFE model accounted for new
travel that results from fuel economy
improvements that reduce the cost of
driving. The magnitude of the increase
in travel demand is determined by the
rebound effect. In both previous
versions and the current version of the
CAFE model, the amount of travel
demanded by the existing fleet of
vehicles is also responsive to the
rebound effect (representing the price
elasticity of demand for travel)—
increasing when fuel prices decrease
relative to the fuel price when the VMT
on which our mileage accumulation
schedules were built was observed.
Since the fuel economy of those
vehicles is already fixed, only the fuel
price influences their travel demand
relative to the mileage accumulation
schedule and so is identical for all
regulatory alternatives.
While the average mileage
accumulation per vehicle by age is not
influenced by the rebound effect in a
way that differs by regulatory
alternative, three other factors influence
total VMT in the model in a way that
produces different total mileage
accumulation by regulatory alternative.
The first factor is the total industry sales
response: New vehicles are both driven
more than older vehicles and are more
fuel efficient (thus producing more
rebound miles). To the extent that more
(or fewer) of these new models enter the
vehicle fleet in each model year, total
VMT will increase (or decrease) as a
result. The second factor is the dynamic
fleet share model. The fleet share
influences not only the fuel economy
distribution of the fleet, as light trucks
are less efficient than passenger cars on
average, but the total miles are
influenced by fact that light trucks are
driven more than passenger cars as well.
Both of the first two factors can magnify
the influence of the rebound effect on
vehicles that go through the compliance
simulation (MY 2016–2032) in the
manner discussed above and in Section
II.E. The third factor influencing total
annual VMT is the scrappage model. By
modifying the retirement rates of onroad vehicles under each regulatory
alternative, the scrappage model either
increases or decreases the lifetime miles
that accrue to vehicles in a given model
year cohort.

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(c) Fuel Consumption and GHG
Emissions
For every vehicle model in the market
file, the model estimates the VMT per
vehicle (using the assumed VMT
schedule, the vehicle fuel economy, fuel
price, and the rebound assumption).
Those miles are multiplied by the
volume for each vehicle. Fuel
consumption is the product of miles
driven and fuel economy, which can be
tracked by model year cohort in the
model. Carbon dioxide emissions from
vehicle tailpipes are the simple product
of gallons consumed and the carbon
content of each gallon.
In order to calculate calendar year
fuel consumption, the model needs to
account for the inherited on-road fleet
in addition to the model year cohorts
affected by this proposed rule. Using the
VMT of the average passenger car and
light truck from each cohort, the model
computes the fuel consumption of each
model year class of vehicles for its age
in a given CY. The sum across all ages
(and thus, model year cohorts) in a
given CY provides estimated CY fuel
consumption.
Rather than rely on the compliance
values of fuel economy for either
historical vehicles or vehicles that go
through the full compliance simulation,
the model applies an ‘‘on-road gap’’ to
represent the expected difference
between fuel economy on the laboratory
test cycle and fuel economy under realworld operation. This was a topic of
interest in the recent peer review of the
CAFE model. While the model currently
allows the user to specify an on-road
gap that varies by fuel type (gasoline,
E85, diesel, electricity, hydrogen, and
CNG), it does not vary over time, by
vehicle age, or by technology
combination. It is possible that the
‘‘gap’’ between laboratory fuel economy
and real-world fuel economy has
changed over time, that fuel economy
degrades over time as a vehicle ages, or
that specific combinations of fuel-saving
technologies have a larger discrepancy
between laboratory and real-world fuel
economy than others. Further research
would be required to determine whether
the model should include a functional
representation of the on-road gap to
address these various factors, and
comment is sought on the data sources
and implementation strategies available
to do so.
Because the model produces an
estimate of the aggregate number of
gallons sold in each CY, it is possible to
calculate both the total expenditures on
motor fuel and the total contribution to
the Highway Trust Fund (HTF) that
result from that fuel consumption. The

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Federal fuel excise tax is levied on every
gallon of gasoline and diesel sold in the
U.S., with diesel facing a higher pergallon tax rate. The model uses a
national perspective, where the state
taxes present in the input files represent
an estimated average fuel tax across all
U.S. states. Accordingly, while the
CAFE model cannot reasonably estimate
potential losses to state fuel tax revenue
from increasingly the fuel economy of
new vehicles, it can do so for the HTF,
and the agencies invite comment on the
proposed standards’ implications for the
HTF.
In addition to the tailpipe emissions
of carbon dioxide, each gallon of
gasoline produced for consumption by
the on-road fleet has associated
‘‘upstream’’ emissions that occur in the
extraction, transportation, refining, and
distribution of the fuel. The model
accounts for these emissions as well (on
a per-gallon basis) and reports them
accordingly.
(d) Criteria Pollutant Emissions
The CAFE model uses the entire onroad fleet, calculated VMT (discussed
above), and emissions factors (which are
an input to the CAFE model, specified
by model year and age) to calculate
tailpipe emissions associated with a
given alternative. Just as it does for
additional GHG emissions associated
with upstream emissions from fuel
production, the model captures criteria
pollutants that occur during other parts
of the fuel life cycle. While this is
typically a function of the number of
gallons of gasoline consumed (and miles
driven, for tailpipe criteria pollutant
emissions), the CAFE model also
estimates electricity consumption and
the associated upstream emissions
(resource extraction and generation,
based on U.S. grid mix).
(e) Highway Fatalities
Earlier versions of the CAFE model
accounted for the safety impacts
associated with reducing vehicle mass
in order to improve fuel economy. In
particular, NHTSA’s safety analysis
estimated the additional fatalities that
would occur as a result of new vehicles
getting lighter, then interacting with the
on-road vehicle population. In general,
taking mass out of the heaviest new
vehicles improved safety outcomes,
while taking mass from the lightest new
vehicles resulted in a greater number of
expected highway fatalities. However,
the change in fatalities did not
adequately account for changes in
exposure that occur as a result of
increased demand for travel as vehicles
become cheaper to operate. The current
version of the model resolves that

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limitation and addresses additional
sources of fatalities that can result from
the implementation of CAFE or CO2
standards. These are discussed in
greater detail in Section 0 and PRIA
Chapter 11.
NHTSA has observed that older
vehicles in the population are
responsible for a disproportionate
number of fatalities, both by number of
registrations and by number of miles
driven. Accordingly, any factor that
causes the population of vehicles to turn
over more slowly will induce additional
fatalities—as those older vehicles
continue to be driven, rather than being
retired and replaced with newer (even if
not brand new) vehicle models. The
scrappage effect, which delays (or

accelerates) the retirement of registered
vehicles, impacts the number of
fatalities through this mechanism—
importantly affecting not just new
vehicles sold from model years 2016–
2032 but existing vehicles that are
already part of the on-road fleet.
Similarly, to the extent that a CAFE or
CO2 alternative reduces new vehicle
sales, it can slow the transition from
older vehicles to newer vehicles,
reducing the share of total vehicle miles
that are driven by newer, more
technologically advanced vehicles.
Accounting for the change in vehicle
miles traveled that occurs when
vehicles become cheaper to operate has
led to a number of fatalities that can be
attributed to the rebound effect,

independent of any changes to new
vehicle mass, price, or longevity.
The CAFE model now estimates
fatalities by combining the effects
discussed above. In particular, the
model estimates the fatality rate per
billion miles VMT for each model year
vehicle in the population (the newest of
which are the new vehicles produced
that model year). This estimate is
independent of regulatory class and
varies only by year (and not vehicle
age). The estimated fatality rate is then
multiplied by the estimated VMT for
each vehicle in the population and the
product of the change in curb weight
and the relevant safety coefficient, as in
the equation below.

For the vehicles in the historical fleet,
meaning all those vehicles that are
already part of the registered vehicle
population in CY 2016, only the model
year effect that determines the
‘‘FatalityEstimate’’ is relevant. However,
each vehicle that is simulated explicitly
by the CAFE model, and is eligible to
receive mass reduction technologies,
must also consider the change between
its curb weight and the threshold
weights that are used to define safety
classes. For vehicles above the
threshold, reducing vehicle mass can
have a smaller negative impact on
fatalities (or even reduce fatalities, in
the case of the heaviest light trucks).
The ‘‘ChangePer100Lbs’’ depends upon
this difference. The sum of all estimated
fatalities for each model year vehicle in
the on-road fleet determines the
reported fatalities, which can be
summarized by either model year or
calendar year.

generate social costs. The most obvious
cost associated with the program is the
cost of additional fuel economy
improving/CO2 emissions reducing
technology that is added to new
vehicles as a result of the rule. However,
the model does not inherently draw a
distinction between costs and benefits.
For example, the model tracks fuel
consumption and the dollar value of
fuel consumed. This is the cost of travel
under a given alternative (including the
baseline). The ‘‘cost’’ or ‘‘benefit’’
associated with the value of fuel
consumed is determined by the
reference point against which each
alternative is considered. The CAFE
model reports absolute values for the
amount of money spent on fuel in the
baseline, then reports the amount spent
on fuel in the alternatives relative to the
baseline. If the baseline standard were
fixed at the current level, and an
alternative achieves 100 mpg by 2025,
the total expenditures on fuel in the
alternative would be lower, creating a
fuel savings ‘‘benefit.’’ This analysis
uses a baseline that is more stringent
than each alternative considered, so the

incremental fuel expenditures are
greater for the alternatives than for the
baseline.
Other social costs and benefits emerge
as the result of physical phenomena,
like tailpipe emissions or highway
fatalities, which are the result of
changes in the composition and use of
the on-road fleet. The social costs
associated with those quantities
represent an economic estimate of the
social damages associated with the
changes in each quantity. The model
tracks and reports each of these
quantities by: Model year and vehicle
age (the combination of which can be
used to produce calendar year totals),
regulatory class, fuel type, and social
discount rate.
The full list of potential costs and
benefits is presented in Table–II–92 as
well as the population of vehicles that
determines the size of the factor (either
new vehicles or all registered vehicles)
and the mechanism that determines the
size of the effect (whether driven by the
number of miles driven, the number of
gallons consumed, or the number of
vehicles produced).

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(f) Costs and Benefits
As the CAFE model simulates
manufacturer compliance with
regulatory alternatives, it estimates and
tracks a number of consequences that

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NHTSA and EPA are proposing that
the form of the CAFE and CO2 standards
for MYs 2021–2026 would follow the
form of those standards in prior model
years. NHTSA has specific statutory
requirements for the form of CAFE
standards: Specifically, EPCA, as
amended by EISA, requires that CAFE
standards be issued separately for
passenger cars and light trucks, and that
each standard be specified as a
mathematical function expressed in
terms of one or more vehicle attributes
related to fuel economy. Although the
CAA does not have comparable specific
requirements for the form of CO2
standards for light-duty vehicles, EPA
has concluded that it is appropriate to
set CO2 standards according to vehicle
footprint, consistent with the EPCA/
EISA requirements, which simplifies
compliance for the industry.362

For MYs since 2011 for CAFE and
since 2012 for CO2, standards have
taken the form of fuel economy and CO2
targets expressed as functions of vehicle
footprint (the product of vehicle
wheelbase and average track width).
NHTSA and EPA continue to believe
that footprint is the most appropriate
attribute on which to base the proposed
standards, as discussed in Section II.C.
Under the footprint-based standards, the
function defines a CO2 or fuel economy
performance target for each unique
footprint combination within a car or
truck model type. Using the functions,
each manufacturer thus will have a
CAFE and CO2 average standard for
each year that is unique to each of its
fleets,363 depending on the footprints
and production volumes of the vehicle
models produced by that manufacturer.
A manufacturer will have separate
footprint-based standards for cars and
for trucks. The functions are mostly
sloped, so that generally, larger vehicles
(i.e., vehicles with larger footprints) will
be subject to lower CAFE mpg targets

and higher CO2 grams/mile targets than
smaller vehicles. This is because,
generally speaking, smaller vehicles are
more capable of achieving higher levels
of fuel economy/lower levels of CO2
emissions, mostly because they tend not
to have to work as hard to perform their
driving task. Although a manufacturer’s
fleet average standards could be
estimated throughout the model year
based on the projected production
volume of its vehicle fleet (and are
estimated as part of EPA’s certification
process), the standards to which the
manufacturer must comply will be
determined by its final model year
production figures. A manufacturer’s
calculation of its fleet average standards
as well as its fleets’ average performance
at the end of the model year will thus
be based on the production-weighted
average target and performance of each
model in its fleet.364
For passenger cars, consistent with
prior rulemakings, NHTSA is proposing
to define fuel economy targets as
follows:

362 Such an approach is permissible under section
202(a) of the CAA and EPA has used the attributebased approach in issuing standards under
analogous provisions of the CAA.
363 EPCA/EISA requires NHTSA to separate
passenger cars into domestic and import passenger

car fleets whereas EPA combines all passenger cars
into one fleet.
364 As in prior rulemakings, a manufacturer may
have some vehicle models that exceed their target
and some that are below their target. Compliance
with a fleet average standard is determined by

comparing the fleet average standard (based on the
production-weighted average of the target levels for
each model) with fleet average performance (based
on the production-weighted average of the
performance of each model).

III. Proposed CAFE and CO2 Standards
for MYs 2021–2026
A. Form of the Standards

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Where:
TARGETFE is the fuel economy target (in
mpg) applicable to a specific vehicle
model type with a unique footprint
combination,
a, b, c, and d are as for passenger cars, but
taking values specific to light trucks,
e is a second minimum fuel economy target
(in mpg),
f is a second maximum fuel economy target
(in mpg),
g is the slope (in gpm per square foot) of a
second line relating fuel consumption
(the inverse of fuel economy) to
footprint, and
h is an intercept (in gpm) of the same second
line.

functions that are similar, with
coefficients a–h corresponding to those
listed above.365 For passenger cars, EPA
is proposing to define CO2 targets as
follows:
TARGETCO2 = MIN[b,MAX[a,c ×
FOOTPRINT + d]]

sradovich on DSK3GMQ082PROD with PROPOSALS2

Although the general model of the
target function equation is the same for
each vehicle category (passenger cars
and light trucks) and each model year,
the parameters of the function equation
differ for cars and trucks. For MYs
2020–2026, the parameters are
unchanged, resulting in the same
stringency in each of those model years.
Mathematical functions defining the
proposed CO2 targets are expressed as

Here, MIN and MAX are functions
that take the minimum and maximum
values, respectively, of the set of

Where:
TARGETCO2 is the CO2 target (in grams per
mile, or g/mi) applicable to a specific
vehicle model configuration,
a is a minimum CO2 target (in g/mi),
b is a maximum CO2 target (in g/mi),
c is the slope (in g/mi, per square foot) of a
line relating CO2 emissions to footprint,
and
d is an intercept (in g/mi) of the same line.

For light trucks, CO2 targets are
defined as follows:
TARGETCO2 = MIN[MIN[b, MAX[a,c ×
FOOTPRINT + d]], MIN[f,MAX[e, g
× FOOTPRINT + H]]
Where:
TARGETCO2 is the CO2 target (in g/mi)
applicable to a specific vehicle model
configuration,

included values. For example,
MIN[40,35] = 35 and MAX(40, 25) = 40,
such that MIN[MAX(40, 25), 35] = 35.
For light trucks, also consistent with
prior rulemakings, NHTSA is proposing
to define fuel economy targets as
follows:

a, b, c, and d are as for passenger cars, but
taking values specific to light trucks,
e is a second minimum CO2 target (in g/mi),
f is a second maximum CO2 target (in g/mi),
g is the slope (in g/mi per square foot) of a
second line relating CO2 emissions to
footprint, and
h is an intercept (in g/mi) of the same second
line.

To be clear, as has been the case since
the agencies began establishing
attribute-based standards, no vehicle
need meet the specific applicable fuel
economy or CO2 targets, because
compliance with either CAFE or CO2
standards is determined based on
corporate average fuel economy or fleet
average CO2 emission rates. The
required CAFE level applicable to a
given fleet in a given model year is
determined by calculating the
production-weighted harmonic average
of fuel economy targets applicable to
specific vehicle model configurations in
the fleet, as follows:

Where:
CAFErequired is the CAFE level the fleet is
required to achieve,
i refers to specific vehicle model/
configurations in the fleet,

PRODUCTIONi is the number of model
configuration i produced for sale in the
U.S., and
TARGETFE,i the fuel economy target (as
defined above) for model configuration i.

Similarly, the required average CO2
level applicable to a given fleet in a
given model year is determined by
calculating the production-weighted

365 EPA regulations use a different but
mathematically equivalent approach to specify
targets. Rather than using a function with nested
minima and maxima functions, EPA regulations

specify requirements separately for different ranges
of vehicle footprint. Because these ranges reflect the
combined application of the listed minima,
maxima, and linear functions, it is mathematically

equivalent and more efficient to present the targets
as in this Section.

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EP24AU18.135

c is the slope (in gallons per mile per square
foot, or gpm, per square foot) of a line
relating fuel consumption (the inverse of
fuel economy) to footprint, and
d is an intercept (in gpm) of the same line.

EP24AU18.134

Where:
TARGETFE is the fuel economy target (in
mpg) applicable to a specific vehicle
model type with a unique footprint
combination,
a is a minimum fuel economy target (in mpg),
b is a maximum fuel economy target (in
mpg),

EP24AU18.133

43190

applicable to specific vehicle model
configurations in the fleet, as follows:

Where:
CO2required is the average CO2 level the fleet
is required to achieve,
i refers to specific vehicle model/
configurations in the fleet,
PRODUCTIONi is the number of model
configuration i produced for sale in the
U.S., and
TARGETCO2,i is the CO2 target (as defined
above) for model configuration i.

Today’s action would set standards
that only apply to fuel economy and
CO2. EPA seeks comment on this
approach.
Comment is sought on the proposed
standards and on the analysis presented
here; we seek any relevant data and
information and will review responses.
That review could lead to selection of

Section II.C above discusses in detail
how the coefficients in Table III–1 were
developed for this proposal. The

coefficients result in the footprintdependent targets shown graphically
below for MYs 2021–2026. The MYs

one of the other regulatory alternatives
for the final rule.
B. Passenger Car Standards
For passenger cars, NHTSA and EPA
are proposing CAFE and CO2 standards,
respectively, for MYs 2021–2026 that
are defined by the following
coefficients:

2017–2020 standards are also shown for
comparison.

EP24AU18.137

average (not harmonic) of CO2 targets

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

·------"'

\

\

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Figure 111-1 -Passenger Car Fuel Economy Targets

While we do not know yet with
certainty what CAFE and CO2 levels
will ultimately be required of individual
manufacturers, because those levels will
depend on the mix of vehicles that they
produce for sale in future model years,
based on the market forecast of future
sales that was used to examine today’s
proposed standards, we currently
estimate that the target functions shown
above would result in the following
average required fuel economy and CO2
emissions levels for individual
manufacturers during MYs 2021–2026.
Prior to MY 2021, average required CO2
levels reflect underlying target functions
(specified above) that reflect the use of
automotive refrigerants with reduced
global warming potential (GWP) and/or
the use of technologies that reduce the
refrigerant leaks. EPA is proposing to
exclude air conditioning refrigerants
and leakage, and nitrous oxide and
methane GHGs from average
performance calculations after model
year 2020; CO2 targets and resultant
fleet average requirements for model

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43193

years 2021 and beyond do not reflect
these adjustments.
EPA seeks comments on whether to
proceed with this proposal to
discontinue accounting for A/C leakage,
methane emissions, and nitrous oxide
emissions as part of the CO2 emissions
standards to provide for better harmony
with the CAFE program, or whether to
continue to consider these factors
toward compliance and retain that as a
feature that differs between the
programs. A/C leakage credits, which
are accounted for in the baseline model,
have been extensively generated by
manufacturers, and make up a portion
of their compliance with EPA’s CO2
standards. In the 2016 MY,
manufacturers averaged six grams per
mile equivalent in A/C leakage credits,

ranging from three grams per mile
equivalent for Hyundai and Kia, to 17
grams per mile equivalent for Jaguar
Land Rover.367 As related to methane
(CH4) and nitrous oxide (N2O)
emissions, manufacturers averaged 0.1
grams per mile equivalent in deficits for
the 2016 MY, with deficits ranging from
0.1 grams per mile equivalent for GM,
Mazda, and Toyota, to 0.6 grams per
mile equivalent for Nissan.368
EPA notes that since the 2010
rulemaking on this subject, the agencies
have accounted for the ability to apply
A/C leakage credits by increasing EPA’s
CO2 standard stringency by the average
anticipated amount of credits when
compared to the CAFE stringency
requirements.369 For model years 2021–
2025, the A/C leakage offset, or

366 Prior to MY 2021, CO targets include
2
adjustments reflecting the use of automotive
refrigerants with reduced global warming potential
(GWP) and/or the use of technologies that reduce
the refrigerant leaks and optionally nitrous oxide
and methane emissions. EPA is proposing to
exclude air conditioning refrigerants and leakage,
and nitrous oxide and methane GHGs from average
performance calculations after model year 2020;
CO2 targets (and resultant fleet average
requirements) for model years 2021 and beyond do
not reflect these adjustments.

367 Other manufacturers’ A/C leakage credit grams
per mile equivalent include: BMW, Honda,
Mistubishi, Nissan, Toyota, and Volkswagen at 5 g/
mi; Mercedes at 6 g/mi; Ford, GM, and Volvo at 7
g/mi; and FCA at 14 g/mi.
368 Other manufacturers’ methane and nitrous
oxide deficit grams per mile equivalent include
BMW at 0.2 g/mi, and Ford at 0.3 g/mi. FCA and
Volkswagen numbers are not reported due to an
ongoing investigation and/or corrective actions.
369 75 FR 25330, May 7, 2010.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

equivalent stringency increase
compared to the CAFE standard, is 13.8
g/mi equivalent for passenger cars and
17.2 g/mi equivalent for light trucks.370
For those model years, manufacturers
are currently allowed to apply A/C
leakage credits capped at 18.8 g/mi
equivalent for passenger cars and 24.4 g/
mi equivalent for light trucks.371
For methane and nitrous oxide
emissions, as part of the MY 2012–2016
rulemaking, EPA finalized standards to
cap emissions of N2O at 0.010 g/mile
and CH4 at 0.030 g/mile for MY 2012
and later vehicles.372 However, EPA
also provided an optional CO2equivalent approach to address industry
concerns about technological feasibility
and leadtime for the CH4 and N2O
standards for MY 2012–2016 vehicles.
The CO2 equivalent standard option
allowed manufacturers to fold all 2cycle weighted N2O and CH4 emissions,
on a CO2-equivalent basis, along with
CO2, into their CO2 emissions fleet
average compliance level.373 EPA
estimated that on a CO2 equivalent

basis, folding in all N2O and CH4
emissions could add up to 3–4 g/mile to
a manufacturer’s overall CO2 emissions
level because the equivalent standard
must be used for the entire fleet, not just
for ‘‘problem vehicles.’’ 374 To address
this added difficulty, EPA amended the
MY 2012–2016 standards to allow
manufacturers to use CO2 credits, on a
CO2-equivalent basis, to meet the lightduty N2O and CH4 standards in those
model years. EPA subsequently
extended that same credit provision to
MY 2017 and later vehicles. EPA seeks
comment on whether to change existing
methane and nitrous oxide standards
that were finalized in the 2012 rule.
Specifically, EPA seeks information
from the public on whether the existing
standards are appropriate, or whether
they should be revised to be less
stringent or more stringent based on any
updated data.
If the agency moves forward with its
proposal to eliminate these factors, EPA
would consider whether it is
appropriate to initiate a new rulemaking

to regulate these programs
independently, which could include an
effective date that would result in no
lapse in regulation of A/C leakage or
emissions of nitrous oxide and methane.
If the agency decides to retain the A/C
leakage and nitrous oxide and methane
emissions provisions for CO2
compliance, it would likely re-insert the
current A/C leakage offset and increase
the stringency levels for CO2
compliance by the offset amounts
described above (i.e., 13.8 g/mi
equivalent for passenger cars and 17.2 g/
mi equivalent for light trucks), and
retain the current caps (i.e., 18.8 g/mi
equivalent for passenger cars and 24.4 g/
mi equivalent for light trucks). The
agency will publish an analysis of this
alternative approach in a memo to the
docket for this rulemaking. The agency
seeks comment on whether the current
offsets and caps would continue to be
appropriate in such circumstances or
whether changes are warranted.

We emphasize again that the values in
these tables are estimates, and not
necessarily the ultimate levels with
which each of these manufacturers will
have to comply, for the reasons
described above.

CAFE standard with both their
domestically-manufactured and
imported passenger car fleets—that is,
domestic and imported passenger car
fleets must comply separately with the
passenger car CAFE standard in each
model year.375 In doing so, they may use
whatever flexibilities are available to
them under the CAFE program, such as
using credits ‘‘carried forward’’ from
prior model years, transferred from

another fleet, or acquired from another
manufacturer. On top of this
requirement, EISA expressly requires
each manufacturer to meet a minimum
flat fuel economy standard for
domestically manufactured passenger
cars.376 According to the statute, the
minimum standard shall be the greater
of (A) 27.5 miles per gallon; or (B) 92%
of the average fuel economy projected
by DOT for the combined domestic and

374 In the final rule for MYs 2012–2016, EPA
acknowledged that advanced diesel or lean-burn
gasoline vehicles of the future may face greater
challenges meeting the CH4 and N2O standards than
the rest of the fleet. [See 75 FR 25422, May 7, 2010].
375 49 U.S.C. 32904(b) (2007).

376 Transferred or traded credits may not be used,
pursuant to 49 U.S.C. 32903(g)(4) and (f)(2), to meet
the domestically manufactured passenger
automobile minimum standard specified in 49
U.S.C. 32902(b)(4) and in 49 CFR 531.5(d).

C. Minimum Domestic Passenger Car
Standards
EPCA has long required
manufacturers to meet the passenger car
370 77

FR 62805, Oct. 15, 2012.
FR 62649, Oct. 15, 2012.
372 75 FR 25421–24, May 7, 2010.
373 77 FR 62798, Oct. 15, 2012.
371 77

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
nondomestic passenger automobile
fleets manufactured for sale in the
United States by all manufacturers in
the model year, which projection shall
be published in the Federal Register
when the standard for that model year
is promulgated.377 NHTSA discusses

this requirement in more detail in
Section V.A.1 below.
The following table lists the proposed
minimum domestic passenger car
standards (which very likely will be
updated for the final rule as the agency
updates its overall analysis and
resultant projection), highlighted as

D. Light Truck Standards

respectively, for MYs 2021–2026 that
are defined by the following
coefficients:

EP24AU18.142

377 49

‘‘Preferred (Alternative 3)’’ and
calculates what those standards would
be under the no action alternative (as
issued in 2012, and as updated by
today’s analysis) and under the other
alternatives described and discussed
further in Section IV, below.

U.S.C. 32902(b)(4).

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For light trucks, NHTSA and EPA are
proposing CAFE and CO2 standards,

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Section II.C above discusses in detail
how the coefficients in Table III–4 were
developed for this proposal. The

coefficients result in the footprintdependent targets shown graphically
below for MYs 2021–2026. The MYs

2017–2020 standards are also shown for
comparison.

378 Prior to MY 2021, average achieved CO levels
2
include adjustments reflecting the use of
automotive refrigerants with reduced global
warming potential (GWP) and/or the use of

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technologies that reduce the refrigerant leaks.
Because EPA is today proposing to exclude air
conditioning refrigerants and leakage, and nitrous
oxide and methane GHGs from average performance

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calculations after MY 2020, CO2 targets and
resultant fleet average requirements for MYs 2021
and beyond do not reflect these adjustments.

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Figure 111-4 - Light Truck C02 Targets378

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Figure 111-3 - Light Truck Fuel Economy Targets

While we do not know yet with
certainty what CAFE and CO2 levels
will ultimately be required of individual
manufacturers, because those levels will
depend on the mix of vehicles that they
produce for sale in future model years,
based on the market forecast of future
sales that were used to examine today’s
proposed standards, we currently
estimate that the target functions shown

above would result in the following
average required fuel economy and CO2
emissions levels for individual
manufacturers during MYs 2021–2026.
Prior to MY 2021, average required CO2
levels reflect underlying target functions
(specified above) that reflect the use of
automotive refrigerants with reduced
global warming potential (GWP) and/or
the use of technologies that reduce the

refrigerant leaks. Because EPA is today
proposing to exclude air conditioning
refrigerants and leakage, and nitrous
oxide and methane GHGs from average
performance calculations after model
year 2020, CO2 targets and resultant
fleet average requirements for model
years 2021 and beyond do not reflect
these adjustments.

We emphasize again the values in
these tables are estimates and not
necessarily the ultimate levels with
which each of these manufacturers will
have to comply for reasons described
above.

As discussed above in Chapter II,
today’s notice also presents the results
of analysis estimating impacts under a
range of other regulatory alternatives the
agencies are considering. Aside from the
no-action alternative, NHTSA and EPA
defined the different regulatory
alternatives in terms of percentincreases in CAFE and GHG stringency
from year to year. Under some
alternatives, the rate of increase is the
same for both passenger cars and light
trucks; under others, the rate of increase
differs. Two alternatives also involve a
gradual discontinuation of CAFE and
average GHG adjustments reflecting the
application of technologies that improve
air conditioner efficiency or, in other
ways, improve fuel economy under
conditions not represented by longstanding fuel economy test procedures.
For increased harmonization with
NHTSA CAFE standards, which cannot
account for such issues, under
Alternatives 1–8, EPA would regulate
tailpipe CO2 independently of A/C
refrigerant leakage, nitrous oxide and
methane emissions. Under the no action
alternative, EPA would continue to
regulate A/C refrigerant leakage, nitrous
oxide and methane emissions under the
overall CO2 standard.380 Like the

baseline no-action alternative, all of the
alternatives are more stringent than the
preferred alternative.
EPA also seeks comment on retaining
the existing credit program for
regulation of A/C refrigerant leakage,
nitrous oxide, and methane emissions as
part of the CO2 standard.
The agencies have examined these
alternatives because the agencies intend
to continue considering them as options
for the final rule. The agencies seek
comment on these alternatives and on
the analysis presented here, seek any
relevant data and information, and will
review responses. That review could
lead the agencies to select one of the

IV. Alternative CAFE and GHG
Standards Considered for MYs 2021/
22–2026

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43197

Agencies typically consider regulatory
alternatives in proposals as a way of
evaluating the comparative effects of
different potential ways of
accomplishing their desired goal.379
Alternatives analysis begins with a ‘‘noaction’’ alternative, typically described
as what would occur in the absence of
any regulatory action. Today’s proposal
includes a no-action alternative,
described below, as well as seven
‘‘action alternatives’’ besides the
proposal. The proposal may, in places,
be referred to as the ‘‘preferred
alternative,’’ which is NEPA parlance,
but NHTSA and EPA intend ‘‘proposal,’’
‘‘proposed action,’’ and ‘‘preferred
alternative’’ to be used interchangeably
for purposes of this rulemaking.
379 As Section V.A.3 explains, NEPA requires
agencies to compare the potential environmental
impacts of their proposed actions to those of a
reasonable range of alternatives. Executive Orders
12866 and 13563 and OMB Circular A–4 also
encourage agencies to evaluate regulatory
alternatives in their rulemaking analyses.

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380 For the CAFE program, carbon-based tailpipe
emissions (including CO2, CH4 and CO) are
measured, and fuel economy is calculated using a
carbon balance equation. EPA uses carbon-based
emissions (CO2, CH4 and CO, the same as for CAFE)

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to calculate tailpipe CO2 for its standards. In
addition, under the no action alternative EPA adds
CO2 equivalent (using Global Warming Potential
(GWP) adjustment) for AC refrigerant leakage and
nitrous oxide and methane emissions. The CAFE
program does not include A/C refrigerant leakage,
nitrous oxide and methane emissions because they
do not impact fuel economy. Under Alternatives 1–
8, the standards are completely aligned for gasoline
because compliance is based on tailpipe CO2, CH4
and CO for both programs and not emissions
unrelated to fuel economy. Diesel and alternative
fuel vehicles would continue to be treated
differently between the CAFE and CO2 programs.
While harmonization would be significantly
improved, standards would not be fully aligned
because of the small fraction of the fleet that uses
diesel and alternative fuels (e.g., about four percent
of the MY 2016 fleet), as well as differences
involving EPCA/EISA provisions EPA, lacking any
specific direction under the CAA, has declined to
adopt, such as minimum standards for domestic
passenger cars and limits on credit transfers
between regulated fleets.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

other regulatory alternatives for the final
rule.

A. What alternatives did NHTSA and
EPA consider?
The table below shows the different
alternatives evaluated in this proposal.

Also, as mentioned previously in
Section III.B., EPA seeks comments on

whether to proceed with this proposal
to discontinue accounting for A/C
leakage, methane emissions, and nitrous
oxide emissions as part of the CO2
emissions standards to provide for

381 Carbon dioxide equivalent of air conditioning
refrigerant leakage, nitrous oxide and methane
emissions are included for compliance with the
EPA standards for all MYs under the baseline/no
action alternative. Carbon dioxide equivalent is
calculated using the Global Warming Potential
(GWP) of each of the emissions.
382 Beginning in MY 2021, air conditioning
refrigerant leakage, nitrous oxide, and methane

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emissions may be regulated independently by EPA.
The GWP equivalent of each of the emissions would
no longer be included with the tailpipe CO2 for
compliance with tailpipe CO2 standards. A
lengthier discussion of this issue can be found in
Section III.B.

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better harmony with the CAFE program
or whether to continue to consider these
factors toward compliance and retain
that as a feature that differs between the
programs. EPA seeks comment on
whether to change existing methane and
nitrous oxide standards that were
finalized in the 2012 rule. Specifically,
EPA seeks information from the public
on whether the existing standards are
appropriate, or whether they should be

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draws attention the discussion of
‘‘enhanced flexibilities’’ in Section X.C.

2. Alternative 1 (Proposed)

tailpipe CO2 standards. Section III,
above, defines this alternative in greater
detail.

Alternative 1 holds the stringency of
targets constant and MY 2020 levels
through MY 2026. Beginning in MY
2021, air conditioning refrigerant
leakage, nitrous oxide, and methane
emissions are no longer included with
the tailpipe CO2 for compliance with

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B. Definition of Alternatives
1. No-Action Alternative
The No-Action Alternative applies the
augural CAFE and final GHG targets
announced in 2012 for MYs 2021–2025.

3. Alternative 2
Alternative 2 increases the stringency
of targets annually during MYs 2021–
2026 (on a gallon per mile basis, starting
from MY 2020) by 0.5% for passenger

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For MY 2026, this alternative applies
the same targets as for MY 2025. Carbon
dioxide equivalent of air conditioning
refrigerant leakage, nitrous oxide, and
methane emissions are included for
compliance with the EPA standards for
all model years under the baseline/no
action alternative.

cars and 0.5% for light trucks. Section
III describes the proposed standards
included in the preferred alternative.
Beginning in MY 2021, air conditioning
refrigerant leakage, nitrous oxide, and
methane emissions are no longer
included with the tailpipe CO2 for
compliance with tailpipe CO2 standards.

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EP24AU18.149

revised to be less stringent or more
stringent based on any updated data.
Additionally, the agencies note that
this proposal also seeks comment on a
number of additional compliance
flexibilities for the programs. See
Section X below, and EPA specifically

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Alternative 3 phases out A/C and offcycle adjustments and increases the
stringency of targets annually during
MYs 2021–2026 (on a gallon per mile
basis, starting from MY 2020) by 0.5%
for passenger cars and 0.5% for light

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trucks. The cap on adjustments for AC
efficiency improvements declines from
6 grams per mile in MY 2021 to 5, 4, 3,
2, and 0 grams per mile in MYs 2022,
2023, 2024, 2025, and 2026,
respectively. The cap on adjustments for
off-cycle improvements declines from
10 grams per mile in MY 2021 to 8, 6,

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4, 2, and 0 grams per mile in MYs 2022,
2023, 2024, 2025, and 2026,
respectively. Beginning in MY 2021, air
conditioning refrigerant leakage, nitrous
oxide, and methane emissions are no
longer included with the tailpipe CO2
for compliance with tailpipe CO2
standards.

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4. Alternative 3

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Alternative 4 increases the stringency
of targets annually during MYs 2021–

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2026 (on a gallon per mile basis, starting
from MY 2020) by 1.0% for passenger
cars and 2.0% for light trucks.
Beginning in MY 2021, air conditioning

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refrigerant leakage, nitrous oxide, and
methane emissions are no longer
included with the tailpipe CO2 for
compliance with tailpipe CO2 standards.

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5. Alternative 4

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Alternative 5 increases the stringency
of targets annually during MYs 2022–
2026 (on a gallon per mile basis, starting

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from MY 2021) by 1.0% for passenger
cars and 2.0% for light trucks.
Beginning in MY 2021, air conditioning
refrigerant leakage, nitrous oxide, and
methane emissions are no longer

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included with the tailpipe CO2 for
compliance with tailpipe CO2 standards,
and MY 2021 CO2 targets are adjusted
accordingly.

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6. Alternative 5

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Alternative 6 increases the stringency
of targets annually during MYs 2021–

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2026 (on a gallon per mile basis, starting
from MY 2020) by 2.0% for passenger
cars and 3.0% for light trucks.
Beginning in MY 2021, air conditioning

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refrigerant leakage, nitrous oxide, and
methane emissions are no longer
included with the tailpipe CO2 for
compliance with tailpipe CO2 standards.

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7. Alternative 6

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Alternative 7 phases out A/C and offcycle adjustments and increases the
stringency of targets annually during
MYs 2021–2026 (on a gallon per mile
basis, starting from MY 2020) by 1.0%
for passenger cars and 2.0% for light

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trucks. The cap on adjustments for AC
efficiency improvements declines from
6 grams per mile in MY 2021 to 5, 4, 3,
2, and 0 grams per mile in MYs 2022,
2023, 2024, 2025, and 2026,
respectively. The cap on adjustments for
off-cycle improvements declines from
10 grams per mile in MY 2021 to 8, 6,

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4, 2, and 0 grams per mile in MYs 2022,
2023, 2024, 2025, and 2026,
respectively. Beginning in MY 2021, air
conditioning refrigerant leakage, nitrous
oxide, and methane emissions are no
longer included with the tailpipe CO2
for compliance with tailpipe CO2
standards.

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8. Alternative 7

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Alternative 8 increases the stringency
of targets annually during MYs 2022–
2026 (on a gallon per mile basis, starting

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from MY 2021) by 2.0% for passenger
cars and 3.0% for light trucks.
Beginning in MY 2021, air conditioning
refrigerant leakage, nitrous oxide, and
methane emissions are no longer

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included with the tailpipe CO2 for
compliance with tailpipe CO2 standards,
and MY 2021 CO2 targets are adjusted
accordingly.

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9. Alternative 8

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V. Proposed Standards, the Agencies’
Statutory Obligations, and Why the
Agencies Propose To Choose Them
Over the Alternatives
A. NHTSA’s Statutory Obligations and
Why the Proposed Standards Appear to
be Maximum Feasible

sradovich on DSK3GMQ082PROD with PROPOSALS2

1. EPCA, as Amended by EISA
EPCA, as amended by EISA, contains
a number of provisions regarding how
NHTSA must set CAFE standards.
NHTSA must establish separate CAFE
standards for passenger cars and light
trucks 383 for each model year,384 and
each standard must be the maximum
feasible that NHTSA believes the
manufacturers can achieve in that
383 49
384 49

U.S.C. 32902(b)(1) (2007).
U.S.C. 32902(a) (2007).

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model year.385 In determining the
maximum feasible level achievable by
the manufacturers, EPCA requires that
NHTSA consider the four statutory
factors of technological feasibility,
economic practicability, the effect of
other motor vehicle standards of the
Government on fuel economy, and the
need of the United States to conserve
energy.386 In addition, NHTSA has the
authority to (and traditionally does)
consider other relevant factors, such as
the effect of the CAFE standards on
motor vehicle safety and consumer
preferences.387 The ultimate

determination of what standards can be
considered maximum feasible involves
a weighing and balancing of these
factors, and the balance may shift
depending on the information before
NHTSA about the expected
circumstances in the model years
covered by the rulemaking. The
agency’s decision must also support the
overarching purpose of EPCA, energy
conservation, while balancing these
factors.388
Besides the requirement that the
standards be maximum feasible for the
fleet in question and the model year in
question, EPCA/EISA also contain

385 Id.
386 49

U.S.C. 32902(f) (2007).
of these additional considerations also
relate, to some extent, to economic practicability,
but NHTSA also has the authority to consider them
independently of that statutory factor.
387 Both

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388 Center for Biological Diversity v. NHTSA, 538
F. 3d 1172, 1197 (9th Cir. 2008) (‘‘Whatever method
it uses, NHTSA cannot set fuel economy standards
that are contrary to Congress’ purpose in enacting
the EPCA—energy conservation.’’)

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several other requirements as explained
below.

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(a) Lead Time
EPCA requires that NHTSA prescribe
new CAFE standards at least 18 months
before the beginning of each model
year.389 For light-duty vehicles, NHTSA
has consistently interpreted the
‘‘beginning of each model year’’ as
September 1 of the CY prior, such that
the beginning of MY 2019 would be
September 1, 2018. Thus, if the first year
for which NHTSA is proposing to set
new standards in this NPRM is MY
2022, NHTSA interprets this provision
as requiring us to issue a final rule
covering MY 2022 standards no later
than April 1, 2020.
For amendments to existing
standards, EPCA requires that if the
amendments make an average fuel
economy standard more stringent, at
least 18 months of lead time must be
provided.390 EPCA contains no lead
time requirement unless amendments
make an average fuel economy standard
less stringent. NHTSA therefore
interprets EPCA as allowing
amendments to reduce a standard’s
stringency up until the beginning of the
model year in question. In this
rulemaking, NHTSA is proposing to
amend the standards for model year
2021. Since the agency proposes to
reduce these standards, this action is
not subject to a lead time requirement.
(b) Separate Standards for Cars and
Trucks, and Minimum Standards for
Domestic Passenger Cars
As discussed above, EPCA requires
NHTSA to set separate CAFE standards
for passenger cars and light trucks for
each model year.391 NHTSA interprets
this requirement as preventing the
agency from setting a single combined
CAFE standard for cars and trucks
together, based on the plain language of
the statute. Congress originally intended
separate CAFE standards for cars and
trucks to reflect the different fuel
economy capabilities of those different
types of vehicles, and over the history
of the CAFE program, has never revised
this requirement. Even as many cars and
trucks have come to resemble each other
more closely over time—many crossover
and sport-utility models, for example,
come in versions today that may be
subject to either the car standards or the
truck standards depending on their
characteristics—it is still accurate to say
that vehicles with truck-like
characteristics such as 4 wheel drive,
389 49

U.S.C. 32902(a) (2007).
U.S.C. 32902(g)(2) (2007).
391 49 U.S.C. 32902(b)(1) (2007).
390 49

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cargo-carrying capability, etc., need to
use more fuel per mile to perform those
jobs than vehicles without these
characteristics. Thus, regardless of the
plain language of the statute, NHTSA
believes that the different fuel economy
capabilities of cars and trucks would
generally make separate standards
appropriate for these different types of
vehicles.
EPCA, as amended by EISA, also
requires another separate standard to be
set for domestically-manufactured 392
passenger cars. Unlike under the
standards for passenger cars and light
trucks described above, the compliance
burden of the minimum domestic
passenger car standard is the same for
all manufacturers; the statute clearly
states that any manufacturer’s
domestically-manufactured passenger
car fleet must meet the greater of either
27.5 mpg on average, or
. . . 92 percent of the average fuel economy
projected by the Secretary for the combined
domestic and non-domestic passenger
automobile fleets manufactured for sale in
the United States by all manufacturers in the
model year, which projection shall be
published in the Federal Register when the
standard for that model year is promulgated
in accordance with [49 U.S.C. 32902(b)].393

Since that requirement was
promulgated, the ‘‘92 percent’’ has
always been greater than 27.5 mpg.
NHTSA published the 92-percent
minimum domestic passenger car
standards for model years 2017–2025 at
49 CFR 531.5(d) as part of the 2012 final
rule. For MYs 2022–2025, 531.5(e) states
that these were to be applied if, when
actually proposing MY 2022 and
subsequent standards, the previously
identified standards for those years are
deemed maximum feasible, but if
NHTSA determines that the previously
identified standards are not maximum
feasible, the 92-percent minimum
domestic passenger car standards would
also change. This is consistent with the
statutory language that the 92-percent
standards must be determined at the
time an overall passenger car standard
is promulgated and published in the
Federal Register. Thus, any time
NHTSA establishes or changes a
passenger car standard for a model year,
the minimum domestic passenger car
392 In the CAFE program, ‘‘domesticallymanufactured’’ is defined by Congress in 49 U.S.C.
§ 32904(b). The specifics of the definition are too
many for a footnote, but roughly, a passenger car
is ‘‘domestically manufactured’’ as long as at least
75% of the cost to the manufacturer is attributable
to value added in the United States, Canada, or
Mexico, unless the assembly of the vehicle is
completed in Canada or Mexico and the vehicle is
imported into the United States more than 30 days
after the end of the model year.
393 49 U.S.C. § 32902(b)(4) (2007).

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standard for that model year will also be
evaluated or reevaluated and
established accordingly. NHTSA
explained this in the rulemaking to
establish standards for MYs 2017 and
beyond and received no comments.394
The 2016 Alliance/Global petition for
rulemaking asked NHTSA to
retroactively revise the 92-percent
minimum domestic passenger car
standards for MYs 2012–2016 ‘‘to reflect
92 percent of the required average
passenger car standard taking into
account the fleet mix as it actually
occurred, rather than what was
forecast.’’ The petitioners stated that
doing so would be ‘‘fully consistent
with the statute.’’ 395
NHTSA understands that determining
the 92 percent value ahead of the model
year to which it applies, based on the
information then available to the
agency, results in a different mpg
number than if NHTSA determined the
92 percent value based on the
information available at the end of the
model year in question. NHTSA further
understands that determining the 92
percent value ahead of time can make
the domestic minimum passenger car
standard more stringent than it could be
if it were determined at the end of the
model year, if manufacturers end up
producing more larger-footprint
passenger cars than NHTSA originally
anticipated.
Accordingly, NHTSA seeks comment
on this request by Alliance/Global.
Additionally, recognizing the
uncertainty inherent in projecting
specific mpg values far into the future,
it is possible that NHTSA could define
the mpg values associated with a CAFE
standard (i.e., the footprint curve) as a
range rather than as a single number.
For example, the sensitivity analysis
included in this proposal and in the
accompanying PRIA could provide a
basis for such an mpg range ‘‘defining’’
the passenger car standard in any given
model year. If NHTSA took that
approach, 92 percent of that ‘‘standard’’
would also, necessarily, be a range. We
also seek comment on this or other
similar approaches.
(c) Attribute-Based and Defined by
Mathematical Function
EISA requires NHTSA to set CAFE
standards that are ‘‘based on 1 or more
394 77

FR 62624, 63028 (Oct. 15, 2012).
Alliance and Global Automakers
Petition for Direct Final Rule with Regard to
Various Aspects of the Corporate Average Fuel
Economy Program and the Greenhouse Gas Program
(June 20, 2016) at 5, 17–18, available at https://
www.epa.gov/sites/production/files/2016-09/
documents/petition_to_epa_from_auto_alliance_
and_global_automakers.pdf [hereinafter Alliance/
Global Petition].
395 Automobile

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attributes related to fuel economy and
express[ed] . . . in the form of a
mathematical function.’’ 396 NHTSA has
thus far based standards on vehicle
footprint and proposes to continue to do
so for all the reasons described in
previous rulemakings. As in previous
rulemakings, NHTSA proposes to define
the standards in the form of a
constrained linear function that
generally sets higher (more stringent)
targets for smaller-footprint vehicles and
lower (less stringent) targets for largerfootprint vehicles. These footprint
curves are discussed in much greater
detail in Section II.C above. We seek
comment both on the choice of footprint
as the relevant attribute and on the
rationale for the constrained linear
functions chosen to represent the
standards.

32902(c) and 32902(g). We therefore
believe that it is reasonable to interpret
section 32902(b)(3)(B) as applying only
to the establishing of new standards
rather than to the combined action of
establishing new standards and
amending existing standards.
Moreover, we believe it would be an
absurd result not intended by Congress
if the five year maximum limitation
were interpreted to prevent NHTSA
from revising a previously-established
standard that we have determined to be
beyond maximum feasible, while
concurrently setting five years of
standards not so distant from today. The
concerns Congress sought to address are
much starker when NHTSA is trying to
determine what standards would be
maximum feasible 10 years from now as
compared to three years from now.

(d) Number of Model Years for Which
Standards May Be Set at a Time
EISA also states that NHTSA shall
‘‘issue regulations under this title
prescribing average fuel economy
standards for at least 1, but not more
than 5, model years.’’ 397 In the 2012
final rule, NHTSA interpreted this
provision as preventing the agency from
setting final standards for all of MYs
2017–2025 in a single rulemaking
action, so the MYs 2022–2025 standards
were termed ‘‘augural,’’ meaning ‘‘that
they represent[ed] the agency’s current
judgment, based on the information
available to the agency [then], of what
levels of stringency would be maximum
feasible in those model years.’’ 398 That
said, NHTSA also repeatedly clarified
that the augural standards were in no
way final standards and that a future de
novo rulemaking would be necessary in
order to both propose and promulgate
final standards for MYs 2022–2025.
Today, NHTSA proposes to establish
new standards for MYs 2022–2026 and
to revise the previously-established final
standards for MY 2021. Legislative
history suggests that Congress included
the five year maximum limitation so
NHTSA would issue standards for a
period of time where it would have
reasonably realistic estimates of market
conditions, technologies, and economic
practicability (i.e., not set standards too
far into the future).399 However, the
concerns Congress sought to address by
imposing those limitations are not
present for nearer model years where
NHTSA already has existing standards.
Revisiting existing standards is
contemplated by both 49 U.S.C.

(e) Maximum Feasible
As discussed above, EPCA requires
NHTSA to consider four factors in
determining what levels of CAFE
standards would be maximum feasible,
and NHTSA presents in the sections
below its understanding of what those
four factors mean. All factors should be
considered, in the manner appropriate,
and then the maximum feasible
standards should be determined.

396 49

U.S.C. 32902(b)(3)(A).
U.S.C. 32902(b)(3)(B).
398 77 FR 62623, 62630 (Oct. 15, 2012).
399 See 153 Cong. Rec. 2665 (Dec. 28, 2007).
397 49

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(1) Technological Feasibility
‘‘Technological feasibility’’ refers to
whether a particular method of
improving fuel economy is available for
deployment in commercial application
in the model year for which a standard
is being established. Thus, NHTSA is
not limited in determining the level of
new standards to technology that is
already being commercially applied at
the time of the rulemaking. For this
proposal, NHTSA is considering a wide
range of technologies that improve fuel
economy, subject to the constraints of
EPCA regarding how to treat alternative
fueled vehicles, and considering the
need to account for which technologies
have already been applied to which
vehicle model/configuration, and the
need to realistically estimate the cost
and fuel economy impacts of each
technology. NHTSA has not attempted
to account for every technology that
might conceivably be applied to
improve fuel economy and considers it
unnecessary to do so given that many
technologies address fuel economy in
similar ways.400 Technological
400 For example, NHTSA has not considered highspeed flywheels as potential energy storage devices
for hybrid vehicles; while such flywheels have been
demonstrated in the laboratory and even tested in
concept vehicles, commercially available hybrid
vehicles currently known to NHTSA use chemical
batteries as energy storage devices, and the agency

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feasibility and economic practicability
are often conflated, as will be covered
further in the following section. To be
clear, whether a fuel-economyimproving technology does or will exist
(technological feasibility) is a different
question from what economic
consequences could ensue if NHTSA
effectively requires that technology to
become widespread in the fleet and the
economic consequences of the absence
of consumer demand for technology that
are projected to be required (economic
practicability). It is therefore possible
for standards to be technologically
feasible but still beyond the level that
NHTSA determines to be maximum
feasible due to consideration of the
other relevant factors.
(2) Economic Practicability
‘‘Economic practicability’’ has
traditionally referred to whether a
standard is one ‘‘within the financial
capability of the industry, but not so
stringent as to’’ lead to ‘‘adverse
economic consequences, such as a
significant loss of jobs or unreasonable
elimination of consumer choice.’’ 401 In
evaluating economic practicability,
NHTSA considers the uncertainty
surrounding future market conditions
and consumer demand for fuel economy
alongside consumer demand for other
vehicle attributes. NHTSA has
explained in the past that this factor can
be especially important during
rulemakings in which the auto industry
is facing significantly adverse economic
conditions (with corresponding risks to
jobs). Consumer acceptability is also a
major component to economic
practicability,402 which can involve
consideration of anticipated consumer
responses not just to increased vehicle
cost, but also to the way manufacturers
may change vehicle models and vehicle
sales mix in response to CAFE
standards. In attempting to determine
the economic practicability of attributebased standards, NHTSA considers a
wide variety of elements, including the
annual rate at which manufacturers can
increase the percentage of their fleet that
employs a particular type of fuel-saving
technology,403 the specific fleet mixes of
has considered a range of hybrid vehicle
technologies that do so.
401 67 FR 77015, 77021 (Dec. 16, 2002).
402 See, e.g., Center for Auto Safety v. NHTSA
(CAS), 793 F.2d 1322 (D.C. Cir. 1986)
(Administrator’s consideration of market demand as
component of economic practicability found to be
reasonable); Public Citizen v. NHTSA, 848 F.2d 256
(Congress established broad guidelines in the fuel
economy statute; agency’s decision to set lower
standards was a reasonable accommodation of
conflicting policies).
403 For example, if standards effectively require
manufacturers to widely apply technologies that

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
different manufacturers, and
assumptions about the cost of standards
to consumers and consumers’ valuation
of fuel economy, among other things.
Prior to the MYs 2005–2007
rulemaking under the non-attributebased (fixed value) CAFE standards,
NHTSA generally sought to ensure the
economic practicability of standards in
part by setting them at or near the
capability of the ‘‘least capable
manufacturer’’ with a significant share
of the market, i.e., typically the
manufacturer whose fleet mix was, on
average, the largest and heaviest,
generally having the highest capacity
and capability so as to not limit the
availability of those types of vehicles to
consumers. In the first several
rulemakings establishing attribute-based
standards, NHTSA applied marginal
cost-benefit analysis, considering both
overall societal impacts and overall
consumer impacts. Whether the
standards maximize net benefits has
thus been a touchstone in the past for
NHTSA’s consideration of economic
practicability. Executive Order 12866, as
amended by Executive Order 13563,
states that agencies should ‘‘select, in
choosing among alternative regulatory
approaches, those approaches that
maximize net benefits . . .’’ In practice,
however, agencies, including NHTSA,
must consider situations in which the
modeling of net benefits does not
capture all of the relevant
considerations of feasibility. Therefore,
as in past rulemakings, NHTSA is
considering net societal impacts, net
consumer impacts, and other related
elements in the consideration of
economic practicability.
NHTSA’s consideration of economic
practicability depends on a number of
elements. Expected availability of
capital to make investments in new
technologies matters; manufacturers’
expected ability to sell vehicles with
certain technologies matters; likely
consumer choices matter and so forth.
NHTSA’s analysis of the impacts of this
proposal incorporates assumptions to
capture aspects of consumer
preferences, vehicle attributes, safety,
and other elements relevant to an
impacts estimate; however, it is difficult
to capture every such constraint.
Therefore, it is well within the agency’s
discretion to deviate from the level at
which modeled net benefits are
maximized if the agency concludes that
that level would not represent the
maximum feasible level for future CAFE
consumers do not want, or to widely apply
technologies before they are ready to be
widespread, NHTSA believes that these standards
could potentially be beyond economically
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standards. Economic practicability is
complex, and like the other factors must
also be considered in the context of the
overall balancing and EPCA’s
overarching purpose of energy
conservation. Depending on the
conditions of the industry and the
assumptions used in the agency’s
analysis of alternative standards,
NHTSA could well find that standards
that maximize net benefits, or that are
higher or lower, could be at the limits
of economic practicability, and thus
potentially the maximum feasible level,
depending on how the other factors are
balanced.
While we discuss safety as a separate
consideration, NHTSA also considers
safety as closely related to, and in some
circumstances a subcomponent of
economic practicability. On a broad
level, manufacturers have finite
resources to invest in research and
development. Investment into the
development and implementation of
fuel saving technology necessarily
comes at the expense of investing in
other areas such as safety technology.
On a more direct level, when making
decisions on how to equip vehicles,
manufacturers must balance cost
considerations to avoid pricing further
consumers out of the market. As
manufacturers add technology to
increase fuel efficiency, they may
decide against installing new safety
equipment to reduce cost increases. And
as the price of vehicles increase beyond
the reach of more consumers, such
consumers continue to drive or
purchase older, less safe vehicles. In
assessing practicability, NHTSA also
considers the harm to the nation’s
economy caused by highway fatalities
and injuries.
(3) The Effect of Other Motor Vehicle
Standards of the Government on Fuel
Economy
‘‘The effect of other motor vehicle
standards of the Government on fuel
economy’’ involves analysis of the
effects of compliance with emission,
safety, noise, or damageability standards
on fuel economy capability and thus on
average fuel economy. In many past
CAFE rulemakings, NHTSA has said
that it considers the adverse effects of
other motor vehicle standards on fuel
economy. It said so because, from the
CAFE program’s earliest years 404 until
recently, the effects of such compliance
on fuel economy capability over the
history of the CAFE program have been
negative ones. For example, safety
standards that have the effect of
404 42 FR 63184, 63188 (Dec. 15, 1977). See also
42 FR 33534, 33537 (June 30, 1977).

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increasing vehicle weight thereby lower
fuel economy capability, thus
decreasing the level of average fuel
economy that NHTSA can determine to
be feasible. NHTSA has considered the
additional weight that it estimates
would be added in response to new
safety standards during the rulemaking
timeframe.405 NHTSA has also
accounted for EPA’s ‘‘Tier 3’’ standards
for criteria pollutants in its estimates of
technology effectiveness.406
In the 2012 final rule establishing
CAFE standards for MYs 2017–2021,
NHTSA also discussed whether EPA
GHG standards and California GHG
standards should be considered and
accounted for as ‘‘other motor vehicle
standards of the Government.’’ NHTSA
recognized that ‘‘To the extent the GHG
standards result in increases in fuel
economy, they would do so almost
exclusively as a result of inducing
manufacturers to install the same types
of technologies used by manufacturers
in complying with the CAFE
standards.’’ 407 NHTSA concluded that
‘‘the agency had already considered
EPA’s [action] and the harmonization
benefits of the National Program in
developing its own [action],’’ and that
‘‘no further action was needed.’’ 408
Considering the issue afresh in this
proposal, and looking only at the words
in the statute, obviously EPA’s GHG
standards applicable to light-duty
vehicles are literally ‘‘other motor
vehicle standards of the Government,’’
in that they are standards set by a
Federal agency that apply to motor
vehicles. Basic chemistry makes fuel
economy and tailpipe CO2 emissions
two sides of the same coin, as discussed
at length above, and when two agencies
functionally regulate both (because by
regulating fuel economy, you regulate
CO2 emissions, and vice versa), it would
be absurd not to link their standards.409
The global warming potential of N2O,
CH4, and HFC emissions are not closely
linked with fuel economy, but neither
do they affect fuel economy capabilities.
How, then, should NHTSA consider
EPA’s various GHG standards?
NHTSA is aware that some
stakeholders believe that NHTSA’s
obligation to set maximum feasible
CAFE standards can best be executed by
letting EPA decide what GHG standards
405 PRIA,

Chapter 5.
Chapter 6.
407 77 FR 62624, 62669 (Oct. 15, 2012).
408 Id.
409 In fact, EPA includes tailpipe CH , CO, and
4
CO2 in the measurement of tailpipe CO2 for GHG
compliance using a carbon balance equation so that
the measurement of tailpipe CO2 exactly aligns with
the measurement of fuel economy for the CAFE
compliance.
406 PRIA,

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are appropriate and reasonable under
the CAA. NHTSA disagrees. While EPA
and NHTSA consider some similar
factors under the CAA and EPCA/EISA,
respectively, they are not identical.
Standards that are appropriate under the
CAA may not be ‘‘maximum feasible’’
under EPCA/EISA, and vice versa.
Moreover, considering EPCA’s language
in the context in which it was written,
it seems unreasonable to conclude that
Congress intended EPA to dictate CAFE
stringency. In fact, Congress clearly
separated NHTSA’s and EPA’s
responsibilities for CAFE under EPCA
by giving NHTSA authority to set
standards and EPA authority to measure
and calculate fuel economy. If Congress
had wanted EPA to set CAFE standards,
it could have given that authority to
EPA in EPCA or at any point since
Congress amended EPCA.410
NHTSA and EPA are obligated by
Congress to exercise their own
independent judgment in fulfilling their
statutory missions, even though both
agencies’ regulations affect both fuel
economy and CO2 emissions. Because of
this relationship, it is incumbent on
both agencies to coordinate and look to
one another’s actions to avoid
unreasonably burdening industry
through inconsistent regulations, but
both agencies must be able to defend
their programs on their own merits. As
with other recent CAFE and GHG
rulemakings, the agencies are
continuing do all of these things in this
proposal.
With regard to standards issued by the
State of California, State tailpipe
standards (whether for greenhouse gases
or for other pollutants) do not qualify as
‘‘other motor vehicle standards of the
Government’’ under 49 U.S.C. 32902(f);
therefore, NHTSA will not consider
them as such in proposing maximum
feasible average fuel economy
standards. States may not adopt or
enforce tailpipe greenhouse gas
emissions standards when such
standards relate to fuel economy
standards and are therefore preempted
under EPCA, regardless of whether EPA
granted any waivers under the Clean Air
Act (CAA).411
Preempted standards of a State or a
political subdivision of a State include,
for example:
(1) A fuel economy standard; and
(2) A law or regulation that has the
direct effect of a fuel economy standard,
410 We note, for instance, that EISA was passed
after the Massachusetts v. EPA decision by the
Supreme Court. If Congress had wanted to amend
EPCA in light of that decision, they would have
done so at the time. They did not.
411 This topic is discussed further in Section VI
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but is not labeled as one (i.e., a State
tailpipe CO2 standard or prohibition on
CO2 emissions).
NHTSA and EPA agree that state
tailpipe greenhouse gas emissions
standards do not become Federal
standards and qualify as ‘‘other motor
vehicle standards of the Government,’’
when subject to a CAA preemption
waiver. EPCA’s legislative history
supports this position.
EPCA, as initially passed in 1975,
mandated average fuel economy
standards for passenger cars beginning
with model year 1978. The law required
the Secretary of Transportation to
establish, through regulation, maximum
feasible fuel economy standards 412 for
model years 1981 through 1984 with the
intent to provide steady increases to
achieve the standard established for
1985 and thereafter authorized the
Secretary to adjust that standard.
For the statutorily-established
standards for model years 1978–1980,
EPCA provided each manufacturer with
the right to petition for changes in the
standards applicable to that
manufacturer. A petitioning
manufacturer had the burden of
demonstrating a ‘‘Federal fuel economy
standards reduction’’ was likely to exist
for that manufacturer in one or more of
those model years and that it had made
reasonable technology choices. ‘‘Federal
standards,’’ for that limited purpose,
included not only safety standards,
noise emission standards, property loss
reduction standards, and emission
standards issued under various Federal
statutes, but also ‘‘emissions standards
applicable by reason of section 209(b) of
[the CAA].’’ 413 (Emphasis added).
Critically, all definitions, processes, and
required findings regarding a Federal
fuel economy standards reduction were
located within a single self-contained
subsection of 15 U.S.C. 2002 that
applied only to model years 1978–
1980.414
In 1994, Congress recodified EPCA.
As part of this recodification, the CAFE
provisions were moved to Title 49 of the
United States Code. In doing so,
412 As is the case today, EPCA required the
Secretary to determine ‘‘maximum feasible average
fuel economy’’ after considering technological
feasibility, economic practicability, the effect of
other Federal motor vehicle standards on fuel
economy, and the need of the Nation to conserve
energy. 15 U.S.C. 2002(e) (recodified July 5, 1994).
413 Section 202 of the CAA (42 U.S.C. 7521)
requires EPA to prescribe air pollutant emission
standards for new vehicles; Section 209 of the CAA
(42 U.S.C. 7543) preempts state emissions standards
but allows California to apply for a waiver of such
preemption.
414 As originally enacted as part of Public Law
94–163, that subsection was designated as section
502(d) of the Motor Vehicle Information and Cost
Savings Act.

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unnecessary provisions were deleted.
Specifically, the recodification
eliminated subsection (d). The House
report on the recodification declared
that the subdivision was ‘‘executed,’’
and described its purpose as
‘‘[p]rovid[ing] for modification of
average fuel economy standards for
model years 1978, 1979, and 1980.’’ 415
It is generally presumed, when Congress
includes text in one section and not in
another, that Congress knew what it was
doing and made the decision
deliberately.
NHTSA has previously considered the
impact of California’s Low Emission
Vehicle standards in establishing fuel
economy standards and occasionally
has done so under the ‘‘other standards’’
sections.416 During the 2012
rulemaking, NHTSA sought comment
on the appropriateness of considering
California’s tailpipe GHG emission
standards in this section and concluded
that doing so was unnecessary.417 In
light of the legislative history discussed
above, however, NHTSA now
determines that this was not
appropriate. Notwithstanding the
improper categorization of such
discussions, NHTSA may consider
elements not specifically designated as
factors to be considered under EPCA,
given the breadth of such factors as
technological feasibility and economic
practicability, and such consideration
was appropriate.418
(4) The Need of the United States To
Conserve Energy
‘‘The need of the United States to
conserve energy’’ means ‘‘the consumer
cost, national balance of payments,
environmental, and foreign policy
implications of our need for large
quantities of petroleum, especially
imported petroleum.’’ 419
(i) Consumer Costs and Fuel Prices
Fuel for vehicles costs money for
vehicle owners and operators. All else
equal, consumers benefit from vehicles
that need less fuel to perform the same
amount of work. Future fuel prices are
a critical input into the economic
415 H.R.

Rep. No. 103–180, at 583–584, tbl. 2A.
e.g., 68 FR 16896, 71 FR 17643.
417 See 77 FR 62669.
418 See, e.g., discussion in Center for Automotive
Safety v. National Highway Traffic Safety
Administration, et al., 793 F.2d. 1322 (D.C. Cir.
1986) at 1338, et seq., providing that NHTSA may
consider consumer demand in establishing
standards, but not ‘‘to such an extent that it ignored
the overarching goal of fuel conservation. At the
other extreme, a standard with harsh economic
consequences for the auto industry also would
represent an unreasonable balancing of EPCA’s
policies.’’
419 42 FR 63184, 63188 (Dec. 15, 1977).
416 See,

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analysis of potential CAFE standards
because they determine the value of fuel
savings both to new vehicle buyers and
to society, the amount of fuel economy
that the new vehicle market is likely to
demand in the absence of new
standards, and they inform NHTSA
about the ‘‘consumer cost . . . of our
need for large quantities of petroleum.’’
In this proposal, NHTSA’s analysis
relies on fuel price projections from the
U.S. Energy Information
Administration’s (EIA) Annual Energy
Outlook (AEO) for 2017. Federal
government agencies generally use EIA’s
price projections in their assessment of
future energy-related policies.

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(ii) National Balance of Payments
Historically, the need of the United
States to conserve energy has included
consideration of the ‘‘national balance
of payments’’ because of concerns that
importing large amounts of oil created a
significant wealth transfer to oilexporting countries and left the U.S.
economically vulnerable.420 As recently
as 2009, nearly half the U.S. trade
deficit was driven by petroleum,421 yet
this concern has largely laid fallow in
more recent CAFE actions, arguably in
part because other factors besides
petroleum consumption have since
played a bigger role in the U.S. trade
deficit. Given significant recent
increases in U.S. oil production and
corresponding decreases in oil imports,
this concern seems likely to remain
fallow for the foreseeable future.422
Increasingly, changes in the price of fuel
have come to represent transfers
between domestic consumers of fuel
and domestic producers of petroleum
rather than gains or losses to foreign
entities. Some commenters have lately
420 See 42 FR 63184, 63192 (Dec. 15, 1977) ‘‘A
major reason for this need [to reduce petroleum
consumption] is that the importation of large
quantities of petroleum creates serious balance of
payments and foreign policy problems. The United
States currently spends approximately $45 billion
annually for imported petroleum. But for this large
expenditure, the current large U.S. trade deficit
would be a surplus.’’
421 See Today in Energy: Recent improvements in
petroleum trade balance mitigate U.S. trade deficit,
U.S. Energy Information Administration (July 21,
2014), https://www.eia.gov/todayinenergy/
detail.php?id=17191.
422 For an illustration of recent increases in U.S.
production, see, e.g., U.S. crude oil and liquid fuels
production, Short-Term Energy Outlook, U.S.
Energy Information Administration (June 2018),
https://www.eia.gov/outlooks/steo/images/
fig13.png. While it could be argued that reducing
oil consumption frees up more domesticallyproduced oil for exports, and thereby raises U.S.
GDP, that is neither the focus of the CAFE program
nor consistent with Congress’ original intent in
EPCA. EIA’s Annual Energy Outlook (AEO) series
provides midterm forecasts of production, exports,
and imports of petroleum products, and is available
at https://www.eia.gov/outlooks/aeo/.

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raised concerns about potential
economic consequences for automaker
and supplier operations in the U.S. due
to disparities between CAFE standards
at home and their counterpart fuel
economy/efficiency and GHG standards
abroad. NHTSA finds these concerns
more relevant to technological
feasibility and economic practicability
than to the national balance of
payments. Moreover, to the extent that
an automaker decides to globalize a
vehicle platform to meet more stringent
standards in other countries, that
automaker would comply with United
States’s standards and additionally
generate overcompensation credits that
it can save for future years if facing
compliance concerns,or sell to other
automakers. While CAFE standards are
set at maximum feasible rates, efforts of
manufacturers to exceed those standards
are rewarded not only with additional
credits but a market advantage in that
consumers who place a large weight on
fuel savings will find such vehicles that
much more attractive.
(iii) Environmental Implications
Higher fleet fuel economy can reduce
U.S. emissions of various pollutants by
reducing the amount of oil that is
produced and refined for the U.S.
vehicle fleet but can also increase
emissions by reducing the cost of
driving, which can result in increased
vehicle miles traveled (i.e., the rebound
effect). Thus, the net effect of more
stringent CAFE standards on emissions
of each pollutant depends on the
relative magnitudes of its reduced
emissions in fuel refining and
distribution and increases in its
emissions from vehicle use. Fuel
savings from CAFE standards also
necessarily results in lower emissions of
CO2, the main GHG emitted as a result
of refining, distribution, and use of
transportation fuels. Reducing fuel
consumption directly reduces CO2
emissions because the primary source of
transportation-related CO2 emissions is
fuel combustion in internal combustion
engines.
NHTSA has considered
environmental issues, both within the
context of EPCA and the context of the
National Environmental Policy Act
(NEPA), in making decisions about the
setting of standards since the earliest
days of the CAFE program. As courts of
appeal have noted in three decisions
stretching over the last 20 years,423
423 CAS, 793 F.2d 1322, 1325 n. 12 (D.C. Cir.
1986); Public Citizen, 848 F.2d 256, 262–63 n. 27
(D.C. Cir. 1988) (noting that ‘‘NHTSA itself has
interpreted the factors it must consider in setting
CAFE standards as including environmental
effects’’); CBD, 538 F.3d 1172 (9th Cir. 2007).

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NHTSA defined ‘‘the need of the United
States to conserve energy’’ in the late
1970s as including, among other things,
environmental implications. In 1988,
NHTSA included climate change
concepts in its CAFE notices and
prepared its first environmental
assessment addressing that subject.424 It
cited concerns about climate change as
one of its reasons for limiting the extent
of its reduction of the CAFE standard for
MY 1989 passenger cars.425 Since then,
NHTSA has considered the effects of
reducing tailpipe emissions of CO2 in its
fuel economy rulemakings pursuant to
the need of the United States to
conserve energy by reducing petroleum
consumption.
(iv) Foreign Policy Implications
U.S. consumption and imports of
petroleum products impose costs on the
domestic economy that are not reflected
in the market price for crude petroleum
or in the prices paid by consumers for
petroleum products such as gasoline.
These costs include (1) higher prices for
petroleum products resulting from the
effect of U.S. oil demand on world oil
prices, (2) the risk of disruptions to the
U.S. economy caused by sudden
increases in the global price of oil and
its resulting impact of fuel prices faced
by U.S. consumers, and (3) expenses for
maintaining the strategic petroleum
reserve (SPR) to provide a response
option should a disruption in
commercial oil supplies threaten the
U.S. economy, to allow the U.S. to meet
part of its International Energy Agency
obligation to maintain emergency oil
stocks, and to provide a national
defense fuel reserve.426 Higher U.S.
consumption of crude oil or refined
petroleum products increases the
magnitude of these external economic
costs, thus increasing the true economic
cost of supplying transportation fuels
above the resource costs of producing
them. Conversely, reducing U.S.
consumption of crude oil or refined
petroleum products (by reducing motor
fuel use) can reduce these external
costs.
While these costs are considerations,
the United States has significantly
increased oil production capabilities in
424 53

FR 33080, 33096 (Aug. 29, 1988).
FR 39275, 39302 (Oct. 6, 1988).
426 While the U.S. maintains a military presence
in certain parts of the world to help secure global
access to petroleum supplies, that is neither the
primary nor the sole mission of U.S. forces
overseas. Additionally, the scale of oil consumption
reductions associated with CAFE standards would
be insufficient to alter any existing military
missions focused on ensuring the safe and
expedient production and transportation of oil
around the globe. See Chapter 7 of the PRIA for
more information on this topic.
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recent years to the extent that the U.S.
is currently producing enough oil to
satisfy nearly all of its energy needs and
is projected to continue to do so or
become a net energy exporter. This has
added new stable supply to the global
oil market and reduced the urgency of
the U.S. to conserve energy. We discuss
this issue in more detail below.
(5) Factors That NHTSA Is Prohibited
From Considering
EPCA also provides that in
determining the level at which it should
set CAFE standards for a particular
model year, NHTSA may not consider
the ability of manufacturers to take
advantage of several EPCA provisions
that facilitate compliance with CAFE
standards and thereby reduce the costs
of compliance.427 As discussed further
in Section X.B.1.c) below, NHTSA
cannot consider compliance credits that
manufacturers earn by exceeding the
CAFE standards and then use to achieve
compliance in years in which their
measured average fuel economy falls
below the standards. NHTSA also
cannot consider the use of alternative
fuels by dual fuel vehicles nor the
availability of dedicated alternative fuel
vehicles in any model year. EPCA
encourages the production of alternative
fuel vehicles by specifying that their
fuel economy is to be determined using
a special calculation procedure that
results in those vehicles being assigned
a higher fuel economy level than they
actually achieve.
The effect of the prohibitions against
considering these statutory flexibilities
in setting the CAFE standards is that the
flexibilities remain voluntarilyemployed measures. If NHTSA were
instead to assume manufacturer use of
those flexibilities in setting new
standards, higher standards would
appear less costly and therefore more
feasible, which would thus tend to
require manufacturers to use those
flexibilities in order to meet higher
standards. By keeping NHTSA from
including them in our stringency
determination, the provision ensures
that these statutory credits remain true
compliance flexibilities.
Additionally, for non-statutory
incentives that NHTSA developed by
regulation, NHTSA does not consider
these subject to the EPCA prohibition on
considering flexibilities, either. EPCA is
very clear as to which flexibilities are
not to be considered. When the agency
has introduced additional flexibilities
such as A/C efficiency and ‘‘off-cycle’’
technology fuel economy improvement
values, NHTSA has considered those

technologies as available in the analysis.
Thus, today’s analysis includes
assumptions about manufacturers’ use
of those technologies, as detailed in
Section X.B.1.c)(4)
(f) EPCA/EISA Requirements That No
Longer Apply Post-2020
Congress amended EPCA through
EISA to add two requirements not yet
discussed in this section relevant to
determination of CAFE standards during
the years between MY 2011 and MY
2020 but not beyond. First, Congress
stated that, regardless of NHTSA’s
determination of what levels of
standards would be maximum feasible,
standards must be set at levels high
enough to ensure that the combined
U.S. passenger car and light truck fleet
achieves an average fuel economy level
of not less than 35 mpg no later than
MY 2020.428 And second, between MYs
2011 and 2020, the standards must
‘‘increase ratably’’ in each model
year.429 Neither of these requirements
apply after MY 2020, so given that this
rulemaking concerns the standards for
MY 2021 and after, they are not relevant
to this rulemaking.
(g) Other Considerations in Determining
Maximum Feasible Standards
NHTSA has historically considered
the potential for adverse safety
consequences in setting CAFE
standards. This practice has been
consistently approved in case law. As
courts have recognized, ‘‘NHTSA has
always examined the safety
consequences of the CAFE standards in
its overall consideration of relevant
factors since its earliest rulemaking
under the CAFE program.’’ Competitive
Enterprise Institute v. NHTSA, 901 F.2d
107, 120 n. 11 (D.C. Cir. 1990) (‘‘CEI–I’’)
(citing 42 FR 33534, 33551 (June 30,
1977)). The courts have consistently
upheld NHTSA’s implementation of
EPCA in this manner. See, e.g.,
Competitive Enterprise Institute v.
NHTSA, 956 F.2d 321, 322 (D.C. Cir.
1992) (‘‘CEI–II’’) (in determining the
maximum feasible fuel economy
standard, ‘‘NHTSA has always taken
passenger safety into account’’) (citing
CEI–I, 901 F.2d at 120 n. 11);
Competitive Enterprise Institute v.
NHTSA, 45 F.3d 481, 482–83 (D.C. Cir.
1995) (‘‘CEI–III’’) (same); Center for
Biological Diversity v. NHTSA, 538 F.3d
1172, 1203–04 (9th Cir. 2008)
(upholding NHTSA’s analysis of vehicle
safety issues associated with weight in
connection with the MYs 2008–2011
light truck CAFE rulemaking). Thus, in
428 49

427 49

U.S.C. 32902(h).

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U.S.C. 32902(b)(2)(C).

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evaluating what levels of stringency
would result in maximum feasible
standards, NHTSA assesses the
potential safety impacts and considers
them in balancing the statutory
considerations and to determine the
maximum feasible level of the
standards.
The attribute-based standards that
Congress requires NHTSA to set help to
mitigate the negative safety effects of the
historical ‘‘flat’’ standards originally
required in EPCA, in recent
rulemakings, NHTSA limited the
consideration of mass reduction in
lower weight vehicles in its analysis,
which impacted the resulting
assessment of potential adverse safety
effects. That analytical approach did not
reflect, however, the likelihood that
automakers may pursue the most cost
effective means of improving fuel
efficiency to comply with CAFE
requirements. For this rulemaking, the
modeling does not limit the amount of
mass reduction that is applied to any
segment but rather considers that
automakers may apply mass reduction
based upon cost-effectiveness, similar to
most other technologies. NHTSA does
not, of course, mandate the use of any
particular technology by manufacturers
in meeting the standards. The current
proposal, like the Draft TAR, also
considers the safety effect associated
with the additional vehicle miles
traveled due to the rebound effect.
In this rulemaking, NHTSA is
considering the effect of additional
expenses in fuel savings technology on
the affordability of vehicles—the
likelihood that increased standards will
result in consumers being priced out of
the new vehicle market and choosing to
keep their existing vehicle or purchase
a used vehicle. Since new vehicles are
significantly safer than used vehicles,
slowing fleet turnover to newer vehicles
results in older and less safe vehicles
remaining on the roads longer. This
significantly affects the safety of the
United States light duty fleet, as
described more fully in Section 0 above
and in Chapter 11 of the PRIA
accompanying this proposal.
Furthermore, as fuel economy standards
become more stringent, and more fuel
efficient vehicles are introduced into the
fleet, fueling costs are reduced. This
results in consumers driving more
miles, which results in more crashes
and increased highway fatalities.
2. Administrative Procedure Act
To be upheld under the ‘‘arbitrary and
capricious’’ standard of judicial review
in the APA, an agency rule must be
rational, based on consideration of the
relevant factors, and within the scope of

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the authority delegated to the agency by
the statute. The agency must examine
the relevant data and articulate a
satisfactory explanation for its action
including a ‘‘rational connection
between the facts found and the choice
made.’’ Burlington Truck Lines, Inc., v.
United States, 371 U.S. 156, 168 (1962).
Statutory interpretations included in
an agency’s rule are subject to the twostep analysis of Chevron, U.S.A. v.
Natural Resources Defense Council, 467
U.S. 837 (1984). Under step one, where
a statute ‘‘has directly spoken to the
precise question at issue,’’ id. at 842, the
court and the agency ‘‘must give effect
to the unambiguously expressed intent
of Congress,’’ id. at 843. If the statute is
silent or ambiguous regarding the
specific question, the court proceeds to
step two and asks ‘‘whether the agency’s
answer is based on a permissible
construction of the statute.’’ Id.
If an agency’s interpretation differs
from the one that it has previously
adopted, the agency need not
demonstrate that the prior position was
wrong or even less desirable. Rather, the
agency would need only to demonstrate
that its new position is consistent with
the statute and supported by the record
and acknowledge that this is a departure
from past positions. The Supreme Court
emphasized this in FCC v. Fox
Television, 556 U.S. 502 (2009). When
an agency changes course from earlier
regulations, ‘‘the requirement that an
agency provide a reasoned explanation
for its action would ordinarily demand
that it display awareness that it is
changing position,’’ but ‘‘need not
demonstrate to a court’s satisfaction that
the reasons for the new policy are better
than the reasons for the old one; it
suffices that the new policy is
permissible under the statute, that there
are good reasons for it, and that the
agency believes it to be better, which the
conscious change of course adequately
indicates.’’ 430 The APA also requires
that agencies provide notice and
comment to the public when proposing
regulations,431 as we are doing today.
3. National Environmental Policy Act
As discussed above, EPCA requires
NHTSA to determine the level at which
to set CAFE standards for each model
year by considering the four factors of
technological feasibility, economic
practicability, the effect of other motor
vehicle standards of the Government on
fuel economy, and the need of the
United States to conserve energy. The
National Environmental Policy Act
(NEPA) directs that environmental
1181.
U.S.C. 553.

considerations be integrated into that
process.432 To accomplish that purpose,
NEPA requires an agency to compare
the potential environmental impacts of
its proposed action to those of a
reasonable range of alternatives.
To explore the environmental
consequences of this proposed rule in
depth, NHTSA has prepared a Draft
Environmental Impact Statement
(‘‘DEIS’’). The purpose of an EIS is to
‘‘provide full and fair discussion of
significant environmental impacts and
[to] inform decisionmakers and the
public of the reasonable alternatives
which would avoid or minimize adverse
impacts or enhance the quality of the
human environment.’’ 433
NEPA is ‘‘a procedural statute that
mandates a process rather than a
particular result.’’ Stewart Park &
Reserve Coal., Inc. v. Slater, 352 F.3d
545, 557 (2d Cir. 2003). The agency’s
overall EIS-related obligation is to ‘‘take
a ‘hard look’ at the environmental
consequences before taking a major
action.’’ Baltimore Gas & Elec. Co. v.
Natural Resources Defense Council,
Inc., 462 U.S. 87, 97 (1983).
Significantly, ‘‘[i]f the adverse
environmental effects of the proposed
action are adequately identified and
evaluated, the agency is not constrained
by NEPA from deciding that other
values outweigh the environmental
costs.’’ Robertson v. Methow Valley
Citizens Council, 490 U.S. 332, 350
(1989).
The agency must identify the
‘‘environmentally preferable’’
alternative but need not adopt it.
‘‘Congress in enacting NEPA . . . did
not require agencies to elevate
environmental concerns over other
appropriate considerations.’’ Baltimore
Gas & Elec. Co. v. Natural Resources
Defense Council, Inc., 462 U.S. 87, 97
(1983). Instead, NEPA requires an
agency to develop alternatives to the
proposed action in preparing an EIS. 42
U.S.C. 4322(2)(C)(iii). The statute does
not command the agency to favor an
environmentally preferable course of
action, only that it make its decision to
proceed with the action after taking a
hard look at the environmental
consequences.
We seek comment on the DEIS
associated with this NPRM.
4. Evaluating the EPCA Factors and
Other Considerations To Arrive at the
Proposed Standards
NHTSA well recognizes that the
decision it proposes to make in today’s
NPRM is different from the one made in

430 Ibid.,

432 NEPA

431 5

433 40

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the 2012 final rule that established
standards for MY 2021 and identified
‘‘augural’’ standard levels for MYs
2022–2025. Not only do we believe that
the facts before us have changed, but we
believe that those facts have changed
sufficiently that the balancing of the
EPCA factors and other considerations
must also change. The standards we are
proposing today reflect that balancing.
The overarching purpose of EPCA is
energy conservation; that fact remains
the same. Examining that phrasing
afresh, Merriam-Webster states that to
‘‘conserve’’ means, in relevant part, ‘‘to
keep in a safe or sound state; especially,
to avoid wasteful or destructive use
of.’’ 434 This is consistent with our
understanding of Congress’ original
intent for the CAFE program: To raise
fleet-wide fuel economy levels in
response to the Arab oil embargo in the
1970s and protect the country from
further gasoline price shocks and supply
shortages. Those price shocks, while
they were occurring, were disruptive to
the U.S. economy and significantly
affected consumers’ daily lives.
Congress therefore sought to keep U.S.
energy consumption in a safe and sound
state for the sake of consumers and the
economy and avoid such shocks in the
future.
Today, the conditions that led both to
those price shocks and to U.S. energy
vulnerability overall have changed
significantly. In the late 1970s, the U.S.
was a major oil importer and changes
(intentional or not) in the global oil
supply had massive domestic
consequences, as Congress saw. While
oil consumption exceeded domestic
production for many years after that, net
energy imports peaked in 2005, and
since then, oil imports have declined
while exports have increased.
The relationship between the U.S. and
the global oil market has changed for
two principal reasons. The first reason
is that the U.S. now consumes a
significantly smaller share of global oil
production than it did in the 1970s. At
the time of the Arab oil embargo, the
U.S. consumed about 17 million barrels
per day of the globe’s approximately 55
million barrels per day.435 While OPEC
(particularly Saudi Arabia) still has the
ability to influence global oil prices by
imposing discretionary supply
restrictions, the greater diversity of both
suppliers and consumers since the
1970s has reduced the degree to which
434 ‘‘Conserve,’’ Merriam-Webster, available at
https://www.merriam-webster.com/dictionary/
conserve (last visited June 25, 2018).
435 Short-Term Energy Outlook, U.S. Energy
Information Administration (June 2018), available
at https://www.eia.gov/outlooks/steo/pdf/steo_
full.pdf.

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a single actor (or small collection of
actors) can impact the welfare of
individual consumers. Oil is a fungible
global commodity, though there are
limits to the substitutability of different
types of crude for a given application.
The global oil market can, to a large
extent, compensate for any producer
that chooses not to sell to a given buyer
by shifting other supply toward that
buyer. And while regional proximity,
comparability of crude oil, and foreign
policy considerations can make some
transactions more or less attractive, as
long as exporters have a vested interest
in preserving the stability (both in terms
of price and supply) of the global oil
market, coordinated, large-scale actions
(like the multi-nation sanctions against
Iran in recent years) would be required
to impose costs or welfare losses on one
specific player in the global market. As
a corollary to the small rise in U.S.
petroleum consumption over the last
few decades, the oil intensity of U.S.
GDP has continued to decline since the
Arab oil embargo, suggesting that U.S.
GDP is less susceptible to increases in
global petroleum prices (sudden or
otherwise) than it was at the time of
EPCA’s passage or when these policies
were last considered in 2012. While the
U.S. still has a higher energy intensity
of GDP than some other developed
nations, our energy intensity has been
declining since 1950 (shrinking by
about 60% since 1950 and almost 30%
between 1990 and 2015).436
The second factor that has changed
the United States’ relationship to the
global oil market is the changing U.S.
reliance on imported oil over the last
decade. U.S. domestic oil production
began rising in 2009 with more costeffective drilling and production
technologies.437 Domestic oil
production became more cost-effective
for two basic reasons. First, technology
improved: The use of horizontal drilling
in conjunction with hydraulic fracturing
has greatly expanded the ability of
producers to profitably recover natural
gas and oil from low-permeability
geologic plays—particularly, shale
plays—and consequently, oil
production from shale plays has grown
rapidly in recent years.438 And second,
436 Today in Energy: Global energy intensity
continues to decline, U.S. Energy Information
Administration (July 12, 106), https://www.eia.gov/
todayinenergy/detail.php?id=27032.
437 Energy Explained, U.S. Energy Information
Administration, https://www.eia.gov/energy
explained/index.cfm (last visited June 25, 2018).
438 Review of Emerging Resources: U.S. Shale Gas
and Shale Oil Plays, U.S. Energy Information
Administration (July 8, 2011), https://www.eia.gov/
analysis/studies/usshalegas/. Practical application
of horizontal drilling to oil production began in the
early 1980s, by which time the advent of improved

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rising global oil prices themselves made
using those technologies more feasible.
As a hypothetical example, if it costs
$79 per barrel to extract oil from a shale
play, when the market price for that oil
is $60 per barrel, it is not worth the
producer’s cost to extract the oil; when
the market price is $80 per barrel, it
becomes cost-effective.
Recent analysis further suggests that
the U.S. oil supply response to a rise in
global prices is much larger now due to
the shale revolution, as compared to
what it was when U.S. production
depended entirely on conventional
wells. Unconventional wells may be not
only capable of producing more oil over
time but also may be capable of
responding faster to price shocks. One
2017 study concluded that ‘‘The longrun price responsiveness of supply is
about 6 times larger for tight oil on a per
well basis, and about 9 times larger
when also accounting for the rise in
unconventional-directed drilling.’’ That
same study further found that ‘‘Given a
price rise to $80 per barrel, U.S. oil
production could rise by 0.5 million
barrels per day in 6 months, 1.2 million
in 1 year, 2 million in 2 years, and 3
million in 5 years.’’ 439 Some analysts
suggest that shale drillers can respond
more quickly to market conditions
because, unlike conventional drillers,
they do not need to spend years looking
for new deposits, because there are
simply so many shale oil wells being
drilled, and because they are more
productive (although their supply may
be exhausted more quickly than a
conventional well, the sheer numbers
appear likely to make up for that
concern).440 Some commenters disagree
and suggest that the best deposits are
already known and tapped.441 Other
downhole drilling motors and the invention of
other necessary supporting equipment, materials,
and technologies (particularly, downhole telemetry
equipment) had brought some applications within
the realm of commercial viability. EIA’s AEO 2018
also projects that by the early 2040s, tight oil
production will account for nearly 70% of total U.S.
production, up from 54% of the U.S. total in 2017.
See also, Tight oil remains the leading source of
future U.S. crude oil production, U.S. Energy
Information Administration (Feb. 22, 2018), https://
www.eia.gov/todayinenergy/detail.php?id=35052.
439 Newell, R. G. & Prest, B.C. The
Unconventional Oil Supply Boom: Aggregate Price
Response from Microdata, Working Paper 23973,
National Bureau of Economic Research (Oct. 2017),
available at http://www.nber.org/papers/w23973
(last visited June 25, 2018).
440 Ip, G. America’s Emerging Petro Economy
Flips the Impact of Oil, Wall Street Journal (Feb. 21,
2018), available at https://www.wsj.com/articles/
americas-emerging-petro-economy-flips-the-impactof-oil-1519209000 (last visited June 25, 2018).
441 Olson, B. Shale Trailblazer Turns Skeptic on
Soaring U.S. Oil Production, Wall Street Journal
(Mar. 5, 2018), available at https://www.wsj.com/
articles/shale-trailblazer-turns-skeptic-on-soaringu-s-oil-production-1520257595.

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commenters raise the possibility that
even if the most productive deposits are
already tapped, any rises in global oil
prices should spur technology
development that improves output of
less productive deposits.442 Moreover,
even if U.S. production increases more
slowly than, for example, EIA currently
estimates, all increases in U.S.
production help to temper global prices
and the risk of oil shocks because they
reduce the influence of other producing
countries who might experience supply
interruptions due to geopolitical
instability or deliberately reduce supply
in an effort to raise prices.443
These changes in U.S. oil intensity,
production, and capacity cannot
entirely insulate consumers from the
effects of price shocks at the gas pump,
because although domestic production
may be able to satisfy domestic energy
demand, we cannot predict whether
domestically produced oil will be
distributed domestically or more
broadly to the global market. But it
appears that domestic supply may
dampen the magnitude, frequency, and
duration of price shocks. As global perbarrel oil prices rise, U.S. production is
now much better able to (and does)
ramp up in response, pulling those
prices back down. Corresponding pergallon gas prices may not fall
overnight,444 but it is foreseeable that
they could moderate over time and
likely respond faster than prior to the
shale revolution. EIA’s Annual Energy
Outlook for 2018 acknowledges
uncertainty regarding these new oil
sources but projects that while retail
prices of gasoline and diesel will
increase between 2018 and 2050, annual
average gasoline prices would not
exceed $4/gallon (in real dollars) during
that timeframe under EIA’s ‘‘reference
442 LeBlanc, R. In the Sweet Spot: The Key to
Shale, Wall Street Journal (Mar. 6, 2018), available
at http://partners.wsj.com/ceraweek/connection/
sweet-spot-key-shale/.
443 Alessi, C. & Sider, A. U.S. Oil Output Expected
to Surpass Saudi Arabia, Rivaling Russia for Top
Spot, Wall Street Journal (Jan. 19, 2018), available
at https://www.wsj.com/articles/u-s-crudeproduction-expected-to-surpass-saudi-arabia-in2018-1516352405.
444 To be clear, the fact that the risk of gasoline
price shocks may now be lower than in the past is
different from arguing that gasoline prices will
never rise again at all. The Energy Information
Administration tracks and reports on pump prices
around the country, and we refer readers to their
website for the most up-to-date information. EIA
projects under its ‘‘reference case’’ assumptions that
the structural changes in the oil market will keep
prices below $4/gallon through 2050. Prices will
foreseeably continue to rise and fall with supply
and demand changes; the relevant question for the
need of the U.S. to conserve energy is not whether
there will be any movement in prices but whether
that movement is likely to be sudden and large.

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case’’ projection.445 The International
Energy Agency (IEA)’s Oil 2018 report
suggests some concern that excessive
focus on investing in U.S. shale oil
production may increase price volatility
after 2023 if investment is not applied
more broadly but also states that U.S.
shale oil is capable of and expected to
respond quickly to rising prices in the
future, and that American influence on
global oil markets is expected to
continue to rise.446 From the supply
side, it is possible that the oil market
conditions that created the price shocks
in the 1970s may no longer exist.
Regardless of changes in the oil
supply market, on the demand side,
conditions are also significantly
different from the 1970s. If gasoline
prices increase suddenly and
dramatically, in today’s market
American consumers have more options
for fuel-efficient new vehicles. Fuelefficient vehicles were available to
purchasers in the 1970s, but they were
generally small entry-level vehicles with
features that did not meet the needs and
preferences of many consumers. Today,
most U.S. households maintain a
household vehicle fleet that serves a
variety of purposes and represents a
variety of fuel efficiency levels.
Manufacturers have responded to fuel
economy standards and to consumer
demand over the last decade to offer a
wide array of fuel-efficient vehicles in
different segments and with a wide
range of features. A household may now
respond to short-term increases in fuel
price by shifting vehicle miles traveled
within their household fleet away from
less-efficient vehicles and toward
models with higher fuel economy. A
similar option existed in the 1970s,
though not as widely as today, and
vehicle owners in 2018 do not have to
sacrifice as much utility as owners did
445 Annual Energy Outlook 2018, U.S. Energy
Information Administration (Feb. 6, 2018) at 57, 58,
available at https://www.eia.gov/outlooks/aeo/pdf/
AEO2018.pdf. The U.S. Energy Information
Administration (EIA) is the statistical and analytical
agency within the U.S. Department of Energy
(DOE). EIA is the nation’s premier source of energy
information and every fuel economy rulemaking
since 2002 (and every joint CAFE and CO2
rulemaking since 2009) has applied fuel price
projections from EIA’s Annual Energy Outlook
(AEO). AEO projections, documentation, and
underlying data and estimates are available at
https://www.eia.gov/outlooks/aeo/.
446 See Oil 2018: Analysis and Forecasts to 2023
Executive Summary, International Energy Agency
(2018), available at http://www.iea.org/Textbase/
npsum/oil2018MRSsum.pdf (last visited June 25,
2018). See also Kent, S. & Puko, T. U.S. Will Be the
World’s Largest Oil Producer by 2023, Says IEA,
Wall Street Journal (Mar. 5, 2018), available at
https://www.wsj.com/articles/u-s-will-be-theworlds-largest-oil-producer-by-2023-says-iea1520236810 (reporting on remarks at the 2018
CERAWeek energy conference by IEA Executive
Director Fatih Birol).

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in the 1970s when making fuelefficiency trade-offs within their
household fleets (or when replacing
household vehicles at the time of
purchase). On a longer-term basis, if oil
prices rise, consumers have more
options to invest in additional fuel
economy when purchasing new vehicles
than at any other time in history.
Global oil demand conditions are also
different than in previous years.
Countries that had very small markets
for new light-duty vehicles in the 1970s
are now driving global production as
their economies improve and growing
numbers of middle-class consumers are
able to purchase vehicles for personal
use. The global increase in drivers
inevitably affects global oil demand,
which affects oil prices. However, these
changes generally occur gradually over
time, unlike a disruption that causes a
gasoline price shock. Market growth
happens relatively gradually and is
subject to many different factors. Oil
supply markets likely have time to
adjust to increases in demand from
higher vehicle sales in countries like
China and India, and in fact, those
increases in demand may temper global
prices by keeping production increasing
more steadily than if demand was less
certain; clear demand rewards increased
production and encourages additional
resource development over time. It
therefore seems unlikely that growth in
these vehicle markets could lead to
gasoline price shocks. Moreover, even as
these vehicle markets grow, it is
possible that these and other vehicle
markets may be moving away from
petroleum usage under the direction of
their governments.447 If this occurs,
global oil production will fall in
response to reduced global demand, but
latent production capacity would exist
to offset the impacts of unexpected
supply interruptions and maintain a
level of global production that is
accessible to petroleum consumers.
This, too, would seem likely to reduce
the risk of gasoline price shocks.
Considering all of the above factors, if
gasoline price shocks are no longer as
much of a threat as they were when
EPCA was originally passed, it seems
reasonable to consider what the need of
the United States to conserve oil is
today and going forward. Looking to the
discussion above on what factors are
relevant to the need of the United States
to conserve oil, one may conclude that
the U.S. is no longer as dependent upon
petroleum as the engine of economic
447 Lynes, M. Plug-in electric vehicles: future
market conditions and adoption rates, U.S. Energy
Information Administration (Oct. 23, 2017), https://
www.eia.gov/outlooks/ieo/pev.php.

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prosperity as it was when EPCA was
passed. The national balance of
payments considerations are likely
drastically less important than they
were in the 1970s, at least in terms of
oil imports and vehicle fuel economy.
Foreign policy considerations appear to
have shifted along with the supply
shifts also discussed above.
Whether and how environmental
considerations create a need for CAFE
standards is, perhaps, more
complicated. As discussed earlier in this
document, carbon dioxide is a direct
byproduct of the combustion of carbonbased fuels in vehicle engines.448 Many
argue that it is likely that human
activities, especially emissions of
greenhouse gases like carbon dioxide,
contribute to the observed climate
warming since the mid-20th century.449
Even taking that premise as given, it is
reasonable to ask whether rapid ongoing
increases in CAFE stringency (or even,
for that matter, electric vehicle
mandates) can sufficiently address
climate change to merit their costs. To
‘‘conserve,’’ again, means ‘‘to avoid
wasteful or destructive use of.’’
Some commenters have argued
essentially that any petroleum use is
destructive because it all adds
incrementally to climate change. They
argue that as CAFE standards increase,
petroleum use will decrease; therefore
CAFE standard stringency should
increase as rapidly as possible. Other
commenters, recognizing that economic
practicability is also relevant, have
argued essentially that because more
stringent CAFE standards produce less
CO2 emissions, NHTSA should simply
set CAFE standards to increase at the
most rapid of the alternative rates that
NHTSA cannot prove is economically
impracticable. The question here, again,
is whether the additional fuel saved
(and CO2 emissions avoided) by more
rapidly increasing CAFE standards
better satisfies the U.S.’s need to avoid
destructive or wasteful use of energy
than more moderate approaches that
more appropiately balance other
statutory considerations.
In the context of climate change,
NHTSA believes it is hard to say that
increasing CAFE standards is necessary
to avoid destructive or wasteful use of
energy as compared to somewhat-lessrapidly-increasing CAFE standards. The
most stringent of the regulatory
448 Depending on the energy source, it may also
be a byproduct of consumption of electricity by
vehicles.
449 Climate Science Special Report: Fourth
National Climate Assessment, Volume I (Wuebbles,
D.J. et al., eds. 2017), available at https://
science2017.globalchange.gov/ (last accessed Feb.
23, 2018).

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alternatives considered in the 2012 final
rule and FRIA (under much more
optimistic assumptions about
technology effectiveness), which would
have required a seven percent average
annual fleetwide increase in fuel
economy for MYs 2017–2025 compared
to MY 2016 standards, was forecast to
only decrease global temperatures in
2100 by 0.02 °C in 2100. Under
NHTSA’s current proposal, we
anticipate that global temperatures
would increase by 0.003 °C in 2100
compared to the augural standards. As
reported in NHTSA’s Draft EIS,
compared to the average global mean
surface temperature for 1986–2005,
global surface temperatures are still
forecast to increase by 3.484–3.487 °C,
depending on the alternative. Because
the impacts of any standards are small,
and in fact several-orders-of-magnitude
smaller, as compared to the overall
forecast increases, this makes it hard for
NHTSA to conclude that the climate
change effects potentially attributable to
the additional energy used, even over
the full lifetimes of the vehicles in
question, is ‘‘destructive or wasteful’’
enough that the ‘‘need of the U.S. to
conserve energy’’ requires NHTSA to
place an outsized emphasis on this
consideration as opposed to others.450
Consumer costs are the remaining
issue considered in the context of the
need of the U.S. to conserve energy.
NHTSA has argued in the past,
somewhat paternalistically, that CAFE
standards help to solve consumers’
‘‘myopia’’ about the value of fuel
savings they could receive, when buying
a new vehicle if they chose a more fuelefficient model. There has been
extensive debate over how much
consumers do (and/or should) value fuel
savings and fuel economy as an attribute
in new vehicles, and that debate is
addressed in Section II.E. For purposes
of considering the need of the U.S. to
conserve energy, the question of
consumer costs may be closer to
whether U.S. consumers so need to save
money on fuel that they must be
required to save substantially more fuel
(through purchasing a new vehicle
made more fuel-efficient by more
stringent CAFE standards) than they
would otherwise choose.
Again, when EPCA originally passed,
Congress was trying to protect U.S.
consumers from the negative effects of
another gasoline price shock. It appears
450 The question of whether or how rapidly to
increase CAFE stringency is different from the
question of whether to set CAFE standards at all.
Massachusetts v. EPA, 549 U.S. 497 (2007)
(‘‘Agencies, like legislatures, do not generally
resolve massive problems in one fell regulatory
swoop.’’)

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23:42 Aug 23, 2018

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much more likely today that oil prices
will rise only moderately in the future
and that price shocks are less likely.
Accordingly, it is reasonable to believe
that U.S. consumers value future fuel
savings accurately and choose new
vehicles based on that view. This is
particularly true, since Federal law
requires that new vehicles be posted
with a window sticker providing
estimated costs or savings over a five
year period compared to average new
vehicles.451 Even if consumers do not
explicitly think to themselves ‘‘this new
car will save me $5,000 in fuel costs
over its lifetime compared to that other
new car,’’ gradual and relatively
predictable fuel price increases in the
foreseeable future allow consumers to
roughly estimate the comparative value
of fuel savings among vehicles and
choose the amount of fuel savings that
they want, in light of the other vehicle
attributes they value. It seems, then, that
consumer cost as an element of the need
of the U.S. to conserve energy is also
less urgent in the context of the
structural changes in oil markets over
the last several years.
Given the discussion above, NHTSA
tentatively concludes that the need of
the U.S. to conserve energy may no
longer function as assumed in previous
considerations of what CAFE standards
would be maximum feasible. The
overall risks associated with the need of
the U.S. to conserve oil have entered a
new paradigm with the risks
substantially lower today and projected
into the future than when CAFE
standards were first issued and in the
recent past. The effectiveness of CAFE
standards in reducing the demand for
fuel combined with the increase in
domestic oil production have
contributed significantly to the current
situation and outlook for the near- and
mid-term future. The world has
changed, and the need of the U.S. to
conserve energy, at least in the context
of the CAFE program, has also changed.
Of the other factors under 32902(g),
the changes are perhaps less significant.
We continue to believe that
technological feasibility, per se, is not
limiting during this rulemaking time
frame. The technologies considered in
this analysis either are already in
commercial production or likely will be
by MY 2021—some at great expense.
Based on our analysis, all of the
alternatives appear as though they could
narrowly be considered technologically
feasible, in that they could be achieved
based on the existence or the projected
future existence of technologies that
could be incorporated on future
451 49

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CFR 575.401; 40 CFR 600.302–12.

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vehicles. Any of the alternatives could
thus be achieved on a technical basis
alone but only if the level of resources
that might be required to implement the
technologies is not considered.
However, as discussed above, we no
longer view the need of the U.S. to
conserve energy as nearly infinite,
which means that it no longer combines
with boundless technological feasibility
to quickly push stringency upward.
The effect of other motor vehicle
standards of the Government on fuel
economy is similarly not limiting during
this rulemaking time frame. As
discussed above, the analysis projects
that safety standards will add some
mass to new vehicles during this time
frame and accounts for Tier 3
compliance in estimates of technology
effectiveness, but neither of these things
appear likely to make it significantly
harder for industry to comply with more
stringent CAFE standards. In terms of
EPA’s GHG standards, as also discussed
above, NHTSA and EPA’s coordination
in this proposal should make the two
sets of standards similarly binding,
although differences in compliance
provisions remain such that which
standards are more binding will vary
somewhat between manufacturers and
over time.
The remaining factor to consider is
economic practicability. NHTSA has
typically defined economic
practicability, as discussed above, as
whether a given CAFE standard is
‘‘within the financial capability of the
industry but not so stringent as’’ to lead
to ‘‘adverse economic consequences,
such as a significant loss of jobs or
unreasonable elimination of consumer
choice.’’ As part of that definition,
NHTSA looks at a variety of elements
that can lead to adverse economic
consequences. All of the alternatives
considered today arguably raise
economic practicability issues. NHTSA
believes there could be potential for
unreasonable elimination of consumer
choice, loss of U.S. jobs, and a number
of adverse economic consequences
under nearly all if not all of the
regulatory alternatives considered
today.
If a potential CAFE standard requires
manufacturers to add technology to new
vehicles that consumers do not want, or
to skip adding technology to new
vehicles that consumers do want, it
would seem to present issues with
elimination of consumer choice.
Depending on the extent and expense of
required fuel saving technology, that
elimination of consumer choice could
be unreasonable.
When deciding on which new vehicle
to purchase, American consumers

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
generally tend not to be interested in
better fuel economy above other
attributes, particularly when gasoline
prices are low.452 Manufacturers have
repeatedly indicated to the agencies that
new vehicle buyers are only willing to
pay for fuel economy-improving
technology if it pays back within the
first two to three years of vehicle
ownership.453 NHTSA has therefore
incorporated this assumption (of
willingness to pay for technology that
pays back within 30 months) into
today’s analysis. As a result, NHTSA’s
analysis finds that the most costeffective technology is applied with or
without CAFE (or CO2) standards,
diminishing somewhat the incremental
cost-effectiveness of new CAFE
standards.
Consumers not being interested in
better fuel economy can take two forms:
First, it can dampen sales of vehicles
with the additional technology required
to meet the standards, and second, it

sradovich on DSK3GMQ082PROD with PROPOSALS2

452 See, e.g., Comment by Global Automakers,
Docket ID NHTSA–2016–0068–0062 (citing a 2014
study by Strategic Vision that found that ‘‘. . .
generally, customers as a whole place a higher
priority on handling and ride than fuel economy.’’).
453 This is supported by the 2015 NAS study,
which found that consumers seek to recoup added
upfront purchasing costs within two or three years.
See 2015 NAS Report, at pg. 317.

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can increase sales of vehicles that do not
help manufacturers meet the standards
(such as vehicles that fall significantly
short of their fuel economy targets, due
to higher levels of performance (e.g.,
larger, less efficient engines) or other
features). Over the last several years,
despite record sales overall, most
manufacturers have been managing their
CAFE compliance obligations through
use of credits,454 because many
consumers have chosen to buy vehicles
that do not improve manufacturers’
compliance positions.
Consumer decisions to purchase
relatively low-fuel economy vehicles
might seem irrational if gasoline prices
were expected to rebound in the future,
but current indicators suggest this is not
particularly likely. Although we know
of no clear ‘‘tipping point’’ for gasoline
prices at which American consumers
suddenly become more interested in
454 See CAFE Public Information Center, National
Highway Traffic Safety Administration, https://
one.nhtsa.gov/cafe_pic/CAFE_PIC_Mfr_LIVE.html
(last visited June 25, 2018). Readers can examine
achieved versus required fuel economy by model
year and by individual manufacturer or by entire
fleets. When a manufacturer’s achieved fuel
economy falls short of required fuel economy but
the manufacturer has not paid civil penalties, the
manufacturer is using credits somehow to make up
the shortfall.

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43217

fuel economy over other attributes, In
addition, EIA’s latest AEO 2018
suggests, based on current assumptions,
that per-gallon prices are likely to stay
under $4 through 2050.455 It therefore
seems unlikely that consumer
preferences are going to change
dramatically in the foreseeable future
and certainly not within the time frame
of the standards covered by this
proposal.
Thus, if manufacturers are not
currently able to sell higher-fuel
economy vehicles without heavy
subsidization, particularly HEVs,
PHEVs, and EVs, it seems unlikely that
their ability to do so will improve
unless consumer preferences change or
fuel prices rise significantly, either of
which seem unlikely. Today’s analysis
indicates, perhaps predictably, that
electrification rates must increase as
stringency increases among the options
the agencies are considering.
455 As noted elsewhere in this proposal, the
agencies based analysis on AEO 2017 projections of,
for instance, fuel prices, as it was the best available
information at the time the analysis was conducted.
As such, where possible, the agency incorporated
latest AEO 2018 projections into the discussion, in
effort to re-confirm no discernible impact to
analysis results or to provide the best possible
information for the discussion.

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Model Years

2017-2021

2021-2026

2021-2026

2021-2026

2021-2026

2022-2026

2021-2026

2021-2026

2022-2026

Annual Rate of Increase in
Stringency 1

Augural
Standards

O.Oo/o!Year
PC
O.Oo/o!Year

0.5%/Year
PC
0.5%/Year
LT
No Change

0.5%/Year
PC
0.5%/Year

1.0%/Year

PC

2.0%/Year

2.0%/Year
PC
3.0%/Year

2.0%/Year

PC

1.0%/Year
PC
2.0%/Year

3.0%/Year

2.0%/Year
PC
3.0%/Year

LT

LT

LT

LT

LT

LT

No Change

No Change

No Change

1%

7%

8%

Phaseout
2022-2026
10%

No Change
8%

LT
AC/Off-Cycle Procedures

E:\FR\FM\24AUP2.SGM

Mild Hybrid Electric Systems
(48v)
Strong Hybrid Electric Systems
Sum of Strong Hybrid and Mild
Hybrid
Plug-In Hybrid Electric Vehicles
(PHEVs)
Dedicated Electric Vehicles (EVs)
Sum of Plug-in Vehicles

24AUP2

Total of All F1ectrified Vehicles

No
Change
20%

No Change

1%

1%

Phaseout
2022-2026
3%

24%
44%

4%
4%

4%
4%

4%
6%

4%
4%

4%
10%

6%
14%

12%
22%

7%
15%

1%

1%

1%

1%

1%

1%

1%

1%

1%

1%
2%

1%
1%

1%
1%

1%
1%

1%

1%
1%

1%
1%

1%

1%

2%

1%
2%

46%

6%

6%

8%

6%

12%

15%

24%

17%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.157

Table V-1- Projected Levels of Electrification Technology Required on the Overall Passenger Car Fleet to Comply with
CAFE Alternatives

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Model Years

2017-2021

2021-2026

2021-2026

2021-2026

2021-2026

2022-2026

2021-2026

2021-2026

2022-2026

Annual Rate of Increase in
Stringency 1

Augural
Standards

O.Oo/o!Year
PC
O.Oo/o!Year

0.5%/Year
PC
0.5%/Year
LT
No Change

0.5%/Year
PC
0.5%/Year

1.0%/Year

PC

2.0%/Year

2.0%/Year
PC
3.0%/Year

2.0%/Year

PC

1.0%/Year
PC
2.0%/Year

3.0%/Year

2.0%/Year
PC
3.0%/Year

LT

LT

LT

LT

LT

LT

No Change

No Change

No Change

1%

6%

7%

Phaseout
2022-2026
16%

No Change
9%

LT
AC/Off-Cycle Procedures

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Mild Hybrid Electric Systems
(48v)
Strong Hybrid Electric Systems
Sum of Strong Hybrid and Mild
Hybrid
Plug-In Hybrid Electric Vehicles
(PHEVs)
Dedicated Electric Vehicles (EVs)
Sum of Plug-in Vehicles

24AUP2

Total of All F1ectrified Vehicles

No
Change
20%

No Change
0%

0%

Phaseout
2022-2026
0%

24%
44%

3%
3%

3%
3%

3%
3%

3%
4%

4%
10%

6%
13%

15%
30%

II%
20%

2%

0%

0%

0%

0%

0%

0%

0%

0%

1%
3%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

47%

4%

4%

4%

5%

II%

14%

31%

21%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table V-3- Projected Levels of Electrification Technology Required on the Overall Passenger Car Fleet to Comply with GHG
Alternatives

43219

EP24AU18.158

sradovich on DSK3GMQ082PROD with PROPOSALS2

43220

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Stringency l

No
Action
20172021
Augural
Standards

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l*

2*

3*

4*

5*

6*

7*

8*

20212026
O.O%Near
PC
O.O%Near

20212026
0.5%Near
PC
0.5%Near

2021-2026

20212026
1.0%Near
PC
2.0%Near
LT
No
Change

20222026
1.0%Near
PC
2.0%Near

2021-2026
2.0%Near
PC
3.0%Near

20212026
2.0%Near
PC
3.0%Near

20222026
2.0%Near
PC
3.0%Near

LT

LT

LT

LT

No
Change

No Change

20%

35%

Phaseout
20222026
55%

No
Change
55%

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LT

LT

No
Change

No
Change

No
Change

0.5%Near
PC
0.5%Near
LT
Phaseout
2022-2026

Mild Hybrid Electric Systems
(48v)
Strong Hybrid Electric Systems
Sum of Strong Hybrid and Mild
Hvbrid
Plug-In Hybrid Electric Vehicles
(PHEVs)
Dedicated Electric Vehicles
(EVs)
Smn of Plug-in Vehicles

46%

0%

0%

2%

5%

24%
69%

1%
1%

1%
1%

1%
3%

1%

1%

6%

21%

2%
37%

13%
68%

6%
62%

1%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

0%

1%

1%

1%

1%

1%

1%

70%

1%
1%

1%

Total ofAll Electrified Vehicles

1%
1%

3%

7%

21%

37%

69%

62%

AC/Off-Cycle Procedures

EP24AU18.159

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table V-2- Projected Levels of Electrification Technology Required on the Overall Light Truck Fleet to Comply with CAFE
Alternatives

sradovich on DSK3GMQ082PROD with PROPOSALS2

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24AUP2

Annual Rate of Increase in
Stringency l

l*

2*

3*

4*

5*

6*

7*

8*

20212026
O.O%Near
PC
O.O%Near

20212026
0.5%Near
PC
0.5%Near

2021-2026

20212026
l.O%Near
PC
2.0%Near

20222026
l.O%Near
PC
2.0%Near

2021-2026
2.0%Near
PC
3.0%Near

20212026
2.0%Near
PC
3.0%Near

20222026
2.0%Near
PC
3.0%Near

0.5%Near
PC
0.5%Near

LT

LT

LT

LT

LT

LT

LT

LT

No
Change

No
Change

No
Change

Phaseout
2022-2026

No
Change

No
Change

No Change

Mild Hybrid Electric Systems
(48v)
Strong Hybrid Electric Systems
Sum of Strong Hybrid and Mild
Hybrid
Plug-In Hybrid Electric Vehicles
(PHEVs)
Dedicated Electric Vehicles
(EVs)
Smn ofP1ug-in Vehicles

56%

3%

4%

8%

10%

22%

27%

Phaseout
20222026
47%

No
Change
45%

17%
73%

1%
4%

1%
4%

1%
9%

2%
12%

3%
26%

4%
31%

9%
56%

5%
51%

0%

0%

0%

0%

0%

0%

0%

0%

0%

1%

0%

0%

0%

0%

0%

0%

1%

0%

1%

0%

0%

0%

0%

0%

0%

1%

0%

Total ofAll Electrified Vehicles

74%

4%

5%

9%

13%

26%

32%

57%

51%

AC/Off-Cycle Procedures

43221

options, sales of these vehicles are not
growing,’’ noting that even for hybrid

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offering more of these models every
year, with improved technology and

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20172021
Augural
Standards

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Manufacturers have commented to the
agencies that ‘‘Although automakers are

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Table V-4- Projected Levels of Electrification Technology Required on the Overall Light Truck Fleet to Comply with GHG
Alternatives

43222

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

vehicles, which require no adaptation
by consumers (for example, to range
limits or refueling by charging), sales
‘‘have declined from a peak of a 3.1
percent share of the market (in 2013) to
. . . 1.8 percent [in 2016].’’ 456 The same
source further stated that this decline
was despite the technology being
available in the market for more than 15
years, and that in 2016, ‘‘close to 75
percent of the people who have traded
in a hybrid or electric car to a dealer
have replaced it with a conventional
(non-hybrid) gasoline-powered car.’’ 457
While some consumers continue to seek
out hybrid and electric vehicles, then,
many other consumers seem
uninterested in them, even given the
generous incentives and subsidies often
available for consumers in the form of
tax credits, government rebates, High
Occupancy Vehicle Lane access,
preferred and/or subsidized parking,
among others. Despite this broad
ongoing lack of consumer interest, a
number of manufacturers nonetheless
continue to increase their offerings of
these vehicles. At best, this trend seems
economically inefficient; more
concerningly for economic
practicability, it seems likely to impact
consumer choice (as discussed further
below) in ways that could weigh heavily
on sales, jobs, and consumers
themselves. We seek comment on this
issue.
If the evidence indicates that hybrid
sales are declining as gasoline prices
remain low, it seems reasonable to
conclude that consumers will not
choose to buy more of them going
forward as gasoline prices are forecast to
remain low. This is consistent with the
analysis discussed in Section II.E, that
even while some consumers may be
willing to pay between $2,000 and
$3,000 more for vehicles with electrified
technologies, that incremental
willingness-to-pay falls well short of the

sradovich on DSK3GMQ082PROD with PROPOSALS2

456 Comment by Global Automakers, Docket ID
NHTSA–2016–0068–0062, citing IHS Global New
Vehicle Registration Data for 2013, 2015, and
January–June 2016.
457 Id. at B–6 and B–7, citing Matt Richtel,
American Drivers Regain Appetite for Gas Guzzlers,
New York Times (June 24, 2016), https://
www.nytimes.com/2016/06/28/science/cars-gasglobal-warming.html.

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23:42 Aug 23, 2018

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additional costs projected for HEVs,
PHEVs, and EVs. This trend may well
extend beyond electrification
technologies to other technologies.
When costs for fuel economy-improving
technology exceed the fuel savings,
consumers may very well be unwilling
to pay the full cost for vehicles with
higher fuel economy that would be
increasingly needed as to comply as the
stringency of the alternatives increases.
If consumers are not willing to pay
the full cost for vehicles with higher
fuel economy, it seems reasonably
foreseeable that they will consider
vehicles made more expensive by higher
CAFE standards to be not ‘‘available’’ to
them to purchase—or put more simply,
that they will be turned off by more
expensive vehicles with technologies
they do not want, and seek instead to
purchase cheaper vehicles without that
technology (or with different
technologies, such as those that improve
performance or safety). Manufacturers
have long cross-subsidized vehicle
models in their lineups in order to
recoup costs in cases where they do not
believe they can pass the full costs of
development and production forward as
price increases for the vehicle model in
question. Given that this crosssubsidization is ongoing, however, and
possibly deepening as manufacturers
have had to meet increasingly stringent
CAFE standards over the past several
years, it is unclear how much additional
distribution of costs could be supported
by the market. Certainly, if CAFE
standards continue to increase in
stringency as gasoline prices stay
relatively low and consumer willingness
to pay for significant additional fuel
economy improvements remains
correspondingly low, then additional
cross-subsidization of products to try to
ease those products into consumer
acceptance seems likely to impair
consumer choice, insofar as the vehicles
they want to buy will cost more and
may have technology for which they are
unwilling to pay. Models that have
historically been able to bear higher
percentages of the cross-subsidization
burden may not be able to bear much
more—a pickup truck buyer, for
example, may eventually decide to
purchase a used vehicle, another type of

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vehicle, or a pickup made by a different
manufacturer rather than pay the extra
cost that the manufacturer is trying to
recoup from higher-fuel economy
vehicles that had to be artificially
discounted to be sold. We seek
comment on the effect of fuel economy
standards on cross-subsidization across
models.
Moreover, assuming that
manufacturers try to pass the costs of
those technologies on to consumers in
the form of higher new vehicle prices,
rather than absorbing them and hurting
profitability, this can affect consumers’
ability to afford new vehicles. The
analysis assumes that the increased cost
of meeting standards is passed on to
consumers through higher new vehicle
prices, and looks at those increases as a
one-time payment. In the context of, for
example, a $30,000 new vehicle,
another $2,000 may not seem significant
to some readers. Yet manufacturers and
dealers have repeatedly commented to
NHTSA that the overall price of the
vehicle is less relevant to the majority
of consumers than the monthly payment
amount, which is a significant factor in
consumers’ ability to purchase or lease
a new vehicle. Amortizing a $2,000
price increase over, for example, 48
months may also not seem like a large
amount to some readers, even
accounting for interest payments. Yet
the corresponding up-front and monthly
costs may pose a challenge to lowincome or credit-challenged purchasers.
As discussed previously, such increased
costs will price many consumers out of
the market—leaving them to continue
driving an older, less safe, less efficient,
and more polluting vehicle, or
purchasing another used vehicle that
would likewise be less safe, less
efficient, and more polluting than an
equivalent new vehicle.
For example, the average MY 2025
prices estimated here under the baseline
and proposed CAFE standards are about
$34,800 and $32,750, respectively (and
$34,500 and $32,550 under the baseline
and proposed GHG standards). The
buyer of a new MY 2025 vehicle might
thus avoid the following purchase and
first-year ownership costs under the
proposed standards:

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43223

While the buyer of the average vehicle
would also purchase somewhat more
fuel under the proposed standards, this
difference might average only five
gallons per month during the first year
of ownership.462 Some purchasers may
consider it more important to avoid
these very certain (e.g., being reflected
in signed contracts) cost savings than
the comparatively uncertain (because,
e.g., some owners drive considerably
less than others, and may purchase fuel
in small increments as needed) fuel
savings. For some low-income
purchasers or credit-challenged
purchasers, the cost savings may make
the difference between being able or not
to purchase the desired vehicle. As
vehicles get more expensive in response
to higher CAFE standards, it will get

more and more difficult for
manufacturers and dealers to continue
creating loan terms that both keep
monthly payments low and do not
result in consumers still owing
significant amounts of money on the
vehicle by the time they can be expected
to be ready for a new vehicle.
Over the last decade, as vehicle sales
have rebounded in the wake of the
recession, historically low interest rates
and increases in the average duration of
financing terms have helped
manufacturers and dealers keep
consumers’ monthly payments low.
These trends (low interest rates and
longer loan periods), along with pent-up
demand for new vehicles, have helped
keep vehicle sales high. As interest rates
have increased, and most predict will
continue to rise, monthly payments will

foreseeably increase, and the ability to
offset such increases by extending
finance terms to account for increased
finance charges and vehicle prices due
to CAFE standards is limited by the fact
that doing so increases the amount of
time before consumers will have
positive equity in their vehicles (and
able to trade in the vehicle for a newer
model). This reduces the mechanisms
that manufacturers, captive finance
companies, dealers, and independent
lenders have in order to maintain sales
at comparable levels. In other words, if
vehicle sales have not already hit the
breaking point, they may be close.463
The agencies seek comment on the
impact that increased prices, interest
rates, and financing terms are likely to
have on the new vehicle market.

458 Using down payment assumption of $4,056.
See Press Release, Edmunds, New Vehicle Prices
Climb to All-Time High in December (Jan. 3, 2018),
https://www.edmunds.com/about/press/newvehicle-prices-climb-to-all-time-high-indecember.html.
459 Using average rate of 5.46% (discussed above
in Section II.E).
460 Using average rate of 4.25% (discussed above
in Section II.E).

461 Using average rate of 1.83% (discussed above
in Section II.E).
462 Based on estimated sales volumes and average
fuel consumption discussed below in Section VI,
and on average vehicle survival and mileage
accumulation rates (discussed above in Section II.E)
indicating that the average vehicle delivers about
11% of it lifetime service (i.e., distance driven)
during the first year of operation.
463 See, e.g., Comment by Global Automakers,
Docket ID NHTSA–2016–0068–0062, at 10

(‘‘Current sales are a poor predictor of future sales.
Many of the macroeconomic factors that have
contributed to the current boom may not exist six
to nine years into the future [i.e., during the mid2020s]. The low interest loans and extended time
loans that are now readily available may not be
available then. The automotive industry is a
cyclical business, and it appears to be near the top
of a cycle now.’’)

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The increasing risk that
manufacturers and dealers will hit a
wall in their ability to keep monthly
payments low may fall
disproportionately on new and lowincome buyers. To build on the
discussion above, manufacturers often
purposely cross-subsidize the prices of
entry-level vehicles to keep monthly
payments low and attract new and
young consumers to their brand. Higher
CAFE standard stringency leads to
higher costs for technology across
manufacturers’ fleets, meaning that
more and more cross-subsidization
becomes necessary to maintain
affordability for entry-level vehicle
purchasers. While this is clearly an
economic issue for industry, it may also
slow fleet-wide improvement in vehicle
characteristics like safety—both in terms
of manufacturers having to divert
resources to adding technology to
vehicles that consumers do not want
and then figuring out how to get
consumers to buy them and in terms of
new vehicles potentially becoming
unaffordable for certain groups of
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turn to the used vehicle market, where
levels of safety may not be comparable.
We seek comment on these
considerations.
Alternatively, rather than or in
addition to continuing to crosssubsidize fuel economy improvements
that consumers are unwilling to pay for
directly, manufacturers may choose to
try to improve their compliance position
under higher CAFE standards by
restricting sales of certain vehicle
models or options. If consumers tend to
want the 6-cylinder engine version of a
vehicle rather than the 4-cylinder
version, for example, the manufacturer
may choose to make fewer 6-cylinders
available. This solution, if chosen,
would directly impact consumer choice.
It seems increasingly likely that this
solution could be chosen as CAFE
stringency increases.
In terms of risks to employment,
today’s analysis focuses on employment
as a function of estimated changes in
vehicle price in response to different
levels of standards and assumes that all
cost increases to vehicle models are
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model. As Section VII.C on today’s sales
and employment analysis indicates, the
sales function of the CAFE model
appears fairly accurate at predicting
sales trends but does not presume that
sales are particularly responsive to
changes in vehicle price. We are
concerned, however, that the sales
model as it currently functions may
miss two key points about potential
future sales and employment effects.
First, the analysis does not account
for the risk discussed above that
manufacturers and dealers may not be
able to continue keeping monthly new
vehicle payments low, for a variety of
reasons. Interest rates and inflation may
rise; further lengthening loan terms may
not be practical as they increase the
period of time that the purchaser has
negative equity (which has secondary
impacts described above). While these
may be not-entirely-negative things for
the economy as a whole, they would
create negative pressure on vehicle sales
or employment associated with those
sales.
Second, as the cost of compliance
increases with CAFE stringency, it is
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production overseas to locations where
labor is cheaper. The CAFE program
contains no mandates with regard to
where vehicles are manufactured and
arguably disincentivizes domestic
production of passenger cars through
the minimum domestic passenger car
standard. If it becomes substantially
more expensive for manufacturers to
meet their CAFE obligations, they may
seek to cut costs wherever they can,
which could include layoffs or changing
production locations.
There may be other adverse economic
consequences besides those discussed
above. If manufacturers seek to avoid

losing sales by absorbing the additional
costs of meeting higher CAFE standards,
it is foreseeable that absorbing those
costs would hurt company profits. If
manufacturers choose that approach
year after year to avoid losing market
share, continued falling profits would
lead to negative earnings reports and
risks to companies’ long-term viability.
Thus, even if sales levels are maintained
despite higher standards, it seems
possible that industry could face
adverse economic consequences.
More broadly, when gasoline prices
stay relatively low (as they are expected
to remain through the lifetime of nearly

all vehicles covered by the rulemaking
time frame), higher stringency standards
are increasingly less cost-beneficial. As
shown and discussed in Section VII.C,
the analysis of consumer impacts shows
that consumers recoup only a portion of
the costs associated with increasing
stringency under all of the alternatives.
The fuel savings resulting from each of
the alternatives is substantially less than
the costs associated with the alternative,
meaning that net savings for consumers
improves as stringency decreases.
Figure V–2 below illustrates this
trend.464

We recognize that this is a
significantly different analytical result
from the 2012 final rule, which showed
the opposite trend. Using the
projections available to the agencies for
the 2012 rulemaking, all of the
alternatives considered in that
rulemaking were projected to have net
savings to consumers and to society
overall, and those net savings improved
as stringency increased. Put simply, the
result is different today from what it
was in 2012 because the facts and the
analysis are also different. While the
differences in the facts and the analysis
are described extensively in Section II
above and in the PRIA accompanying
this proposal, a few noteworthy ones
include:

• In 2012, we assumed in the main
analysis that manufacturers would add no
more technology than needed for
compliance, while today’s analysis assumes
logically that manufacturers will add
technologies that pay for themselves within
2.5 years, consistent with manufacturer
information on payback period.
• In 2012, we measured impacts of the
post-2017 standards relative to compliance
with pre-2017 standards, which meant that a
lot of cost-effective technology attributable to
the 2017–2020 standards was ‘‘counted’’
toward the 2025 standards.
• In 2012, we used analysis fleets based on
2008 or 2010 technology. Today’s analysis
uses a 2016-based analysis fleet.

These three points above mean that,
overall, the current analysis fleet reflects
the application of much additional
technology than the 2012-final-rule

analysis fleet reflected. When
technology is used by the analysis fleet,
it is ‘‘unavailable’’ to be used again for
compliance with future standards
because the same technology cannot be
used twice (once by a manufacturer for
its own reasons and then again by the
model to simulate manufacturer
responses to higher standards). Some of
this would happen necessarily in an
updated rulemaking because a later-intime analysis fleet inevitably includes
more technology; in this particular case,
2016 happened to be a somewhat
technology-heavy year, and 2008 and
2010 (the fleets used in 2012) arguably
did not reflect the state of technology in
2012 well.
Furthermore, readers should note the
following changes:

unchanged. Phasing out these procedures increases
compliance costs and reduces net savings relative

to leaving the procedures unchanged, net savings to
consumer with seven percent discount rate.

464 For the reader’s reference, Alternatives 3 and
7 phase out A/C and off-cycle procedures, while the
other alternatives leave those procedures

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• Estimates of effectiveness and cost are
different for a number of technologies, as
discussed in Section II above and in Chapter
6 of the PRIA, and indirect costs are
determined using the RPE rather than the
ICM;
• Fuel prices forecasts are considerably
lower in AEO 2017 than they were in AEO
2012;
• The current analysis uses a rebound
effect value of 20% instead of 10%;
• The current analysis newly accounts for
price impacts on fleet turnover;
• The social cost of carbon is different and
accounts only for domestic (not
international) impacts;
• The current analysis does not attempt to
purposely limit the appearance of potential
safety effects, and the value of a statistical
life is higher than in 2012.

All of these changes, together, mean
that the standards under any of the
regulatory alternatives (compared to the
preferred alternative) are more
expensive and have lower benefits than
if they had been calculated using the
inputs and assumptions of the 2012
analysis. This, in turn, helps lead the
agency to a different conclusion about
what standards might be maximum
feasible in the model years covered by
the rulemaking. NHTSA has thus both
relied on new facts and circumstances
in developing today’s proposal and
reasonably rejected prior facts and
analyses relied on in the 2012 final
rule.465
By directing NHTSA to determine
maximum feasible standards by
considering the four factors, Congress
recognized that ‘‘maximum feasible’’
may change over time as the agency
assessed the relative importance of each
factor.466 If one factor appears to be
more important than the others in the
time frame to be covered by the
standards, it makes sense to give it more
weight in the agency’s determination of
maximum feasible standards for those
model years. If no factor appears to be
particularly paramount, it makes sense
to determine maximum feasible
standards by more generally weighing
each factor, as long as EPCA’s direction
to establish maximum feasible standards
continues to be fulfilled in a manner
that does not undermine energy
conservation.
NHTSA tentatively concludes that
proposing CAFE standards that hold the
MY 2020 curves for passenger cars and
light trucks constant through MY 2026
would be the maximum feasible
standards for those fleets and would
465 See Fox v. FCC, 556 U.S. at 514–515; see also
NAHB v. EPA, 682 F.3d 1032 (D.C. Cir. 2012).
466 If this were not accurate, it seems illogical that
Congress would have, at various times, set specific
mpg goals for the CAFE program (e.g., 35 mpg by
2020).

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fulfill EPCA’s overarching purpose of
energy conservation in light of the facts
before the agency today and as we
expect them to be in the rulemaking
time frame. In the 2012 final rule that
established CAFE standards for MYs
2017–2021, and presented augural
CAFE standards for MYs 2022–2025,
NHTSA stated that ‘‘maximum feasible
standards would be represented by the
mpg levels that we could require of the
industry before we reach a tipping point
that presents risk of significantly
adverse economic consequences.’’ 467
However, the context of that rulemaking
was meaningfully different from the
current context. At that time, NHTSA
understood the need of the U.S. to
conserve energy as necessarily pushing
the agency toward setting stricter and
stricter standards. Combining a thenparamount need of the U.S. to conserve
energy with the perception that
technological feasibility should no
longer be seen as an important limiting
factor, NHTSA then concluded that only
significant economic harm would be a
basis for controlling the pace at which
CAFE stringency increased over time.
Today, the relative importance of the
need of the U.S. to conserve energy has
changed when compared to the
beginning of the CAFE program and a
great deal even since the 2012
rulemaking. As discussed above, the
effectiveness of CAFE standards in
reducing the demand for fuel combined
with the increase in domestic oil
production have contributed
significantly to the current situation and
outlook for the near- and mid-term
future. The world has changed, and the
need of the U.S. to conserve energy may
no longer disproportionately outhweigh
other statutorily-mandated
considerations such as economic
practicability—even when considering
fuel savings from potentially morestringent standards.
Thus, while more stringent standards
may be possible, insofar as productionready technology exists that the
industry could physically employ to
reach higher standards, it is not clear
that higher standards are now
economically practicable in light of
current U.S. consumer needs to
conserve energy. While vehicles can be
built with advanced fuel economyimproving technology, this does not
mean that consumers will buy the new
vehicles that might be required to
include such technology; that industry
could continue to subsidize their
production and sale; or that adverse
economic consequences would not
result from doing so. The effect of other
467 77

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motor vehicle standards of the
Government is minimal when the two
agencies regulating the same aspects of
vehicle performance are working
together to develop those regulations.
Therefore, NHTSA views the
determination of maximum feasible
standards as a question of the
appropriateness of standards given that
their need—either from the societalbenefits perspective in terms of risk
associated with gasoline price shocks or
other related catastrophes, or from the
private-benefits perspective in terms of
consumer willingness to purchase new
vehicles with expensive technologies
that may allow them to save money on
future fuel purchases—seems likely to
remain low for the foreseeable future.
When determining the maximum
feasible standards, and in particular the
economic practicability of higher
standards, we also note that the
proposed standards have the most
positive effect on on-road safety as
compared to the alternatives considered.
The analysis indicates that, compared to
the baseline standards defining the NoAction alternative, any regulatory
alternatives under consideration would
improve overall highway safety. Some
of this estimated reduction is
attributable to vehicles, themselves,
being generally safer if they do not
apply as much mass reduction to
passenger cars as might be applied
under the baseline standards.
Additionally, the analysis estimates that
the alternatives to the baseline
standards would cause the fleet to turn
over to newer and safer vehicles, which
will also be more fuel efficient than the
vehicles being replaced, more quickly
than otherwise anticipated.
Furthermore, the analysis estimates that
the alternatives to the baseline standard
would involve reduced overall demand
for highway travel. As discussed above
in Section II.F, and in Chapter 11 of the
accompanying PRIA, most of the
estimated overall improvement in
highway safety from this proposal is
attributable to reduced travel demand
(attributable to the rebound effect) and
accelerated turnover to safer vehicles.
The trend in these results is clear, with
the less stringent alternatives producing
the greatest estimated improvement in
highway safety and the proposed
standards producing the most favorable
outcomes from a highway safety
perspective. These considerations
bolster our determination that the
proposed standards are maximum
feasible based upon current and
projected technology for the model
years in question.
Standards that retain the MY 2020
curves through MY 2026 will save fuel

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beyond what the market would achieve
on its own for vehicles manufactured
during the rulemaking time frame and
will result in the highest net benefits
both for consumers and for society.
Such standards would avoid the risks
identified in the discussion of economic
practicability for more stringent
standards and are consistent with the
relatively lower need of the United
States to conserve energy and the
impact that has on consumer choice.
Moreover, as the fuel economy of the
new vehicle fleet improves over time,
the marginal benefits of continued
improvements diminish, making the
consumer willingness to bear them and
the economic practicability of them
diminish. It is much more expensive,
and saves much less fuel, for a vehicle
to improve from 40 to 50 mpg, than for
a vehicle to improve from 15 to 20
mpg.468 If obtaining the marginal
benefits of new cars and their fuel
economy technologies becomes too
expensive for consumers, some
consumers will choose to drive less
efficient used vehicles longer.
NHTSA recognizes that the Ninth
Circuit has previously held that NHTSA
must consider whether a ‘‘backstop’’ is
necessary for the CAFE standards based
on the EPCA factors in 49 U.S.C.
32902(f), given that the overarching
purpose of EPCA is energy
conservation.469 NHTSA and EPA
discussed the concept of backstops in
the context of the modern CAFE
program (as opposed to the CAFE
program at issue in the Ninth Circuit
decision) in the 2010 final rule
establishing CAFE and GHG standards
for MYs 2012–2016. In that document,
the agencies explained that even if the
statute did not preclude a backstop
beyond what was already provided for
in the minimum domestic passenger car
468 As the base level of fuel economy improves,
there are fewer gallons to be saved from improving
further. A typical assumption is that vehicles are
driven 15,000 miles per year. A vehicle that
improves from 30 mpg to 40 mpg reduces its annual
fuel consumption from 500 gallons/year to 375
gallons/year at 15,000 miles/year or by 125 gallons.
A vehicle that improves from 15 mpg to 20 mpg,
on the other hand, reduces its annual fuel
consumption from 1,000 gallons/year to 750
gallons/year—twice as much as the first example,
even though the mpg improvement is only half as
large. Going from 40 to 50 mpg would save only 75
gallons/year at 15,000 miles/year. If fuel prices are
high, the value of those gallons may be sufficient
to offset the cost of improving further, but (1) EIA
does not currently anticipate particularly high fuel
prices in the foreseeable future, and (2) as the
baseline level of fuel economy continues to
increase, the marginal cost of the next gallon saved
similarly increases with the cost of the technologies
required to meet the savings.
469 CBD v. NHTSA, 508 F.3d 508, 537 (9th Cir.
2007), opinion vacated and superseded on denial of
reh’g, 538 F.3d 1172 (9th Cir. 2008).

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CAFE standard and in the ‘‘flat’’
portions of the footprint curves at the
larger-footprint end, designing an
appropriate backstop was likely to be
fairly complex and likely to undermine
Congress’ objective in requiring
attribute-based standards. See,
particularly, 75 FR at 25369–70 (May 7,
2010).
As in 2010, NHTSA believes that
additional backstop standards are not
necessary. The current proposal is based
on the agency’s best current
understanding of the need of the U.S. to
conserve energy now and going forward,
in light of changed circumstances and
balanced against the other EPCA factors.
We seek comment on how an additional
backstop standard might be constructed
that addresses the concerns raised in the
2010 final rule and that also does not
obviate the agency’s assessment of what
CAFE levels would be maximum
feasible.
We seek comment on all aspects of
the above discussion.
B. EPA’s Statutory Obligations and Why
the Proposed Standards Appear To Be
Appropriate and Reasonable
1. Basis for the CO2 Standards Under
Section 202(a) of the Clean Air Act
Title II of the Clean Air Act (CAA)
provides for comprehensive regulation
of mobile sources, authorizing EPA to
regulate emissions of air pollutants from
all mobile source categories. Under
Section 202(a) 470 and relevant case law,
as discussed below, EPA considers such
issues as technology effectiveness, its
cost (both per vehicle, per manufacturer,
and per consumer), the lead time
necessary to implement the technology,
and based on this the feasibility and
practicability of potential standards; the
impacts of potential standards on
emissions reductions of both GHGs and
non-GHGs; the impacts of standards on
oil conservation and energy security; the
impacts of standards on fuel savings by
consumers; the impacts of standards on
the auto industry; other energy impacts;
as well as other relevant factors such as
impacts on safety.
This proposed rule would implement
a specific provision from Title II, section
202(a).471 Section 202(a)(1) of the Clean
Air Act (CAA) states that ‘‘the
Administrator shall by regulation
prescribe (and from time to time revise)
. . . standards applicable to the
emission of any air pollutant from any
class or classes of new motor vehicles
. . . , which in his judgment cause, or
contribute to, air pollution which may
470 42
471 42

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U.S.C. 7521(a).

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reasonably be anticipated to endanger
public health or welfare.’’ If EPA makes
the appropriate endangerment and
cause or contribute findings, then
section 202(a) authorizes EPA to issue
standards applicable to emissions of
those pollutants. Indeed, EPA’s
obligation to do so is mandatory:
Coalition for Responsible Regulation,
684 F.3d at 114; Massachusetts v. EPA,
549 U.S. at 533. Moreover, EPA’s
mandatory legal duty to promulgate
these emission standards derives from
‘‘a statutory obligation wholly
independent of DOT’s mandate to
promote energy efficiency.’’
Massachusetts, 549 U.S. at 532.
Consequently, EPA has no discretion to
decline to issue greenhouse standards
under section 202(a) or to defer issuing
such standards due to NHTSA’s
regulatory authority to establish fuel
economy standards. Rather, ‘‘[j]ust as
EPA lacks authority to refuse to regulate
on the grounds of NHTSA’s regulatory
authority, EPA cannot defer regulation
on that basis.’’ Coalition for Responsible
Regulation, 684 F.3d at 127.
Any standards under CAA section
202(a)(1) ‘‘shall be applicable to such
vehicles . . . for their useful life.’’
Emission standards set by the EPA
under CAA section 202(a)(1) are
technology-based, as the levels chosen
must be premised on a finding of
technological feasibility. Thus,
standards promulgated under CAA
section 202(a) are to take effect only
after providing ‘‘such period as the
Administrator finds necessary to permit
the development and application of the
requisite technology, giving appropriate
consideration to the cost of compliance
within such period’’ (CAA section 202
(a)(2); see also NRDC v. EPA, 655 F. 2d
318, 322 (D.C. Cir. 1981)). EPA must
consider costs to those entities which
are directly subject to the standards.
Motor & Equipment Mfrs. Ass’n Inc. v.
EPA, 627 F. 2d 1095, 1118 (D.C. Cir.
1979). Thus, ‘‘the [s]ection 202(a)(2)
reference to compliance costs
encompasses only the cost to the motorvehicle industry to come into
compliance with the new emission
standards.’’ Coalition for Responsible
Regulation, 684 F.3d at 128; see also id.
at 126–27 (rejecting arguments that EPA
was required to consider or should have
considered costs to other entities, such
as stationary sources, which are not
directly subject to the emission
standards). EPA is afforded considerable
discretion under section 202(a) when
assessing issues of technical feasibility
and availability of lead time to
implement new technology. Such
determinations are ‘‘subject to the

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restraints of reasonableness,’’ which
‘‘does not open the door to ‘crystal ball’
inquiry.’’ NRDC, 655 F. 2d at 328
(quoting International Harvester Co. v.
Ruckelshaus, 478 F. 2d 615, 629 (D.C.
Cir. 1973)). In developing such
technology-based standards, EPA has
the discretion to consider different
standards for appropriate groupings of
vehicles (‘‘class or classes of new motor
vehicles’’), or a single standard for a
larger grouping of motor vehicles
(NRDC, 655 F. 2d at 338). Finally, with
respect to regulation of vehicular
greenhouse gas emissions, EPA is not
‘‘required to treat NHTSA’s . . .
regulations as establishing the baseline
for the [section 202(a) standards].’’
Coalition for Responsible Regulation,
684 F.3d at 127 (noting further that ‘‘the
[section 202 (a)standards] provid[e]
benefits above and beyond those
resulting from NHTSA’s fuel-economy
standards’’).
Although standards under CAA
section 202(a)(1) are technology-based,
they are not based exclusively on
technological capability. EPA has the
discretion to consider and weigh
various factors along with technological
feasibility, such as the cost of
compliance (see section 202(a)(2)), lead
time necessary for compliance (section
202(a)(2)), safety (see NRDC, 655 F.2d at
336 n. 31) and other impacts on
consumers,472 and energy impacts
associated with use of the technology
(see George E. Warren Corp. v. EPA, 159
F.3d 616, 623–624 (D.C. Cir. 1998)
(ordinarily permissible for EPA to
consider factors not specifically
enumerated in the Act)).
In addition, EPA has clear authority to
set standards under CAA section 202(a)
that are technology forcing when EPA
considers that to be appropriate but is
not required to do so (as compared to
standards set under provisions such as
section 202(a)(3) and section 213(a)(3)).
EPA has interpreted a similar statutory
provision, CAA section 231, as follows:
While the statutory language of section 231
is not identical to other provisions in title II
of the CAA that direct EPA to establish
technology-based standards for various types
of engines, EPA interprets its authority under
section 231 to be somewhat similar to those
provisions that require us to identify a
reasonable balance of specified emissions
reduction, cost, safety, noise, and other
factors. See, e.g., Husqvarna AB v. EPA, 254
472 Since its earliest Title II regulations, EPA has
considered the safety of pollution control
technologies. See 45 FR 14496, 14503 (March 5,
1980). (‘‘EPA would not require a particulate
control technology that was known to involve
serious safety problems. If during the development
of the trap-oxidizer safety problems are discovered,
EPA would reconsider the control requirements
implemented by this rulemaking.’’)

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F.3d 195 (D.C. Cir. 2001) (upholding EPA’s
promulgation of technology-based standards
for small non-road engines under section
213(a)(3) of the CAA). However, EPA is not
compelled under section 231 to obtain the
‘‘greatest degree of emission reduction
achievable’’ as per sections 213 and 202 of
the CAA, and so EPA does not interpret the
Act as requiring the agency to give
subordinate status to factors such as cost,
safety, and noise in determining what
standards are reasonable for aircraft engines.
Rather, EPA has greater flexibility under
section 231 in determining what standard is
most reasonable for aircraft engines, and is
not required to achieve a ‘‘technology
forcing’’ result.473

This interpretation was upheld as
reasonable in NACAA v. EPA (489 F.3d
1221, 1230 (D.C. Cir. 2007)). CAA
section 202(a) does not specify the
degree of weight to apply to each factor,
and EPA accordingly has discretion in
choosing an appropriate balance among
factors. See Sierra Club v. EPA, 325 F.3d
374, 378 (D.C. Cir. 2003) (even where a
provision is technology-forcing, the
provision ‘‘does not resolve how the
Administrator should weigh all [the
statutory] factors in the process of
finding the ‘greatest emission reduction
achievable’ ’’); see also Husqvarna AB v.
EPA, 254 F. 3d 195, 200 (D.C. Cir. 2001)
(great discretion to balance statutory
factors in considering level of
technology-based standard, and
statutory requirement ‘‘[to give]
appropriate consideration to the cost of
applying . . . technology’’ does not
mandate a specific method of cost
analysis); Hercules Inc. v. EPA, 598 F.
2d 91, 106–07 (D.C. Cir. 1978) (‘‘In
reviewing a numerical standard, we
must ask whether the agency’s numbers
are within a ‘zone of reasonableness,’
not whether its numbers are precisely
right’’); Permian Basin Area Rate Cases,
390 U.S. 747, 797 (1968) (same); Federal
Power Commission v. Conway Corp.,
426 U.S. 271, 278 (1976) (same); Exxon
Mobil Gas Marketing Co. v. FERC, 297
F. 3d 1071, 1084 (D.C. Cir. 2002) (same).
As noted above, EPA has found that
the elevated concentrations of
greenhouse gases in the atmosphere may
reasonably be anticipated to endanger
public health and welfare.474 EPA
defined the ‘‘air pollution’’ referred to in
CAA section 202(a) to be the combined
mix of six long-lived and directly
emitted GHGs: Carbon dioxide (CO2),
methane (CH4), nitrous oxide (N2O),
hydrofluorocarbons (HFCs),
perfluorocarbons (PFCs), and sulfur
hexafluoride (SF6). The EPA further
found under CAA section 202(a) that
emissions of the single air pollutant
473 70
474 74

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FR 66496 (Dec. 15, 2009).

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defined as the aggregate group of these
same six greenhouse gases from new
motor vehicles and new motor vehicle
engines contribute to air pollution. As a
result of these findings, section 202(a)
requires EPA to issue standards
applicable to emissions of that air
pollutant. New motor vehicles and
engines emit CO2, CH4, N2O, and HFC.
EPA has established standards and other
provisions that control emissions of
CO2, HFCs, N2O, and CH4. EPA has not
set any standards for PFCs or SF6 as
they are not emitted by motor vehicles.
2. EPA’s Tentative Conclusion That the
Proposed CO2 Standards Are
Appropriate and Reasonable
In this section, EPA discusses the
factors, data and analysis the
Administrator has considered in the
selection of the EPA’s proposed revised
GHG emission standards for MYs 2021
and later. EPA requests comment on all
aspects of the proposed revised
standards, including all Alternatives
discussed in this section and section IV
of this preamble.
As discussed in Sections I and V.B of
this preamble, the primary purpose of
Title II of the Clean Air Act is the
protection of public health and welfare.
EPA’s light-duty vehicle GHG standards
serve this purpose, as the GHG
emissions from light-duty vehicles have
been found by EPA to endanger public
health and welfare (see EPA’s 2009
Endangerment Finding for on-highway
motor vehicles), and the goal of these
standards is to reduce these emissions
that contribute to climate change.
CAA section 202(a)(2) states when
setting emission standards for new
motor vehicles, the standards ‘‘shall
take effect after such period as the
Administrator finds necessary to permit
the development and application of the
requisite technology, giving appropriate
consideration to the cost of compliance
within such period.’’ 42 U.S.C.
7521(a)(2). That is, when establishing
emissions standards, the Administrator
must consider both the lead time
necessary for the development of
technology which can be used to
achieve the emissions standards and the
resulting costs of compliance on those
entities that are directly subject to the
standards.
The Administrator is not limited to
consideration of the factors specified in
CAA section 202(a)(2) when
establishing standards for light-duty
vehicles. In addition to feasibility and
cost of compliance, the Administrator
may (and historically has) considered
such factors as safety, energy use and
security, degree of reduction of both
GHG and non-GHG pollutants,

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technology cost-effectiveness, and costs
and other impacts on consumers. As
discussed in prior rulemakings setting
GHG standards,475 EPA may establish
technology-forcing standards under
section 202(a), but when it does so it
must provide sufficient basis for its
belief that the industry can develop the
needed technology in the available time.
However, EPA is not required to set
technology-forcing standards under
section 202(a). Rather, because section
202(a), unlike the text of section
202(a)(3) and section 213(a)(3),476 does
not specify that standards shall obtain
‘‘the greatest degree of emission
reduction achievable,’’ EPA retains
considerable discretion under section
202(a) in deciding how to weigh the
various factors, consistent with the
language and purpose of the Clean Air
Act, to determine what standards are
appropriate.
The analysis of alternatives supports
the Administrator’s consideration of a
range of alternative standards, from the
existing standards to several alternatives
that are less stringent. Specifically, the
analysis supports the consideration of
this range of alternative standards due
to factors relevant under the EPA’s
authority pursuant to section 202(a),
such as GHG emissions reductions, the
necessary technology and associated
lead-time, the costs of compliance on
automakers, the impact on consumers
with respect to cost and vehicle choice,
and effects on safety. These factors, and
the Administrator’s proposed
conclusion, after consideration of these
factors, indicate that Alternative 1
represents the most appropriate
standards for model years 2021 and
beyond are discussed further below.
(a) Consideration of the Development
and Application of Technology To
Reduce CO2 Emissions
When EPA establishes emissions
standards under section 202, it
considers both what technologies are
currently available and what
technologies under development may
become available. For today’s proposal,
EPA takes note of the analysis of the
potential penetration into the future
475 See,

e.g., 77 FR 62624, 62673 (Oct. 15, 2012).
202(a)(3) provides that regulations
applicable to emissions of certain specified
pollutants from heavy-duty vehicles or engines
‘‘shall contain standards which reflect the greatest
degree of emission reduction achievable through
the application of technology which the
Administrator determines will be available . . .
giving appropriate consideration to cost, energy,
and safety factors associated with the application of
such technology.’’ 42 U.S.C. 7521(a)(3). Section
213(a)(3) contains a similar provision for new
nonroad engines and new nonroad vehicles (other
than locomotives or engines used in locomotives).
42 U.S.C. 7547(a)(3).

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vehicle fleet of a wide range of
technologies that both reduce CO2 and
improve fuel economy (see PRIA
Chapter 6). The majority of these
technologies have already been
developed, have been commercialized,
and are in-use on vehicles today. These
technologies include, but are not limited
to, engine and transmission
technologies, vehicle mass reduction
technologies, technologies to reduce the
vehicles’ aerodynamic drag, and a range
of electrification technologies. The
electrification technologies include 12Volt stop-start systems, 48-Volt mild
hybrids, strong hybrid systems, plug-in
hybrid electric vehicles, and dedicated
electric vehicles.
If the Administrator’s consideration of
the appropriateness of the standards
were based solely on an assessment of
technology availability and
development, the Administrator might
consider a wide range of standards to be
appropriate. As shown in Sections
VII.B.2 and VIII.B.1.b), and in PRIA
Chapter 6.3.2, the projected penetration
of technologies varies across the
Alternatives presented in today’s
proposal. In general, the existing EPA
standards are projected to result in the
highest penetration of advanced
technologies, in particular mild hybrid
and strong hybrid technologies. Lower
stringency Alternatives in general are
projected to result in lower penetration
of technologies, in particular for the
mild hybrid and strong hybrid
technologies, with the Preferred
Alternative projected to result in the
lowest level of electrification technology
penetration. For example, the existing
CO2 standards are projected to require a
combined passenger car and truck fleet
penetration of mild hybrids plus strong
hybrids of 58% of new vehicle sales in
MY 2030, while Alternative 8 projects a
34% penetration, Alternative 6 projects
a 22% penetration, Alternative 4
projects an 8% penetration, and the
Proposed Alternative (Alternative 1)
projects a 4% penetration. These
technologies are available and in
production today, and MY 2020 through
MY 2025 standards are still a number of
years away. In light of the wide range
of existing technologies that have
already been developed, have been
commercialized, and are in-use on
vehicles today, including those
developed since the 2012 rule,
technology availability, development
and application, if it were considered in
isolation, is not necessarily a limiting
factor in the Administrator’s selection of
which standards are appropriate within
the range of the Alternatives presented
in this proposal. However, as described

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below, the Administrator weighs
technology availability along with
several other factors, including costs,
emissions impacts, safety, and
consumer impacts in determining the
appropriate standards under the Clean
Air Act.
(b) Consideration of the Cost of
Compliance
EPA is required to consider costs in
compliance before setting standards
under section 202(a). Compared to the
proposed standards, the EPA MY 2020–
2025 standards announced in 2012
would cost the automotive industry an
estimated total of $260 billion for the
vehicles produced from MY 2016
through MY 2029, as shown in Table
VIII–9. The additional per-vehicle
technology costs for these previouslyissued standards would be an estimated
$2,260 in MY 2030, relative to the
proposed standards, as shown in Table
VIII–31 and Table VIII–32. Especially
considering the change in reference
point, these costs are considerably larger
than EPA projected in 2012. Less
stringent standards would be less
burdensome. For example, compared to
the proposed standards, Alternative 8 is
projected to increase the per-vehicle
cost by $1,510 (also in MY 2030),
Alternative 6 increases the per-vehicle
costs by $1,120, and Alternative 4
increases the per-vehicle costs by $490.
(c) Consideration of Costs to Consumers
In addition to the costs to the
automotive industry described above,
which could be passed on to consumers,
the analysis estimates increased costs
for the consumer for changes in
maintenance, financing, insurance,
taxes, and other fees, as shown in Table
VIII–31 and Table VIII–32. Considering
these additional costs, EPA’s
previously-issued standards for MYs
2020–2025 would increase the projected
per-vehicle costs in MY 2030 to an
estimated $2,810 relative to the
proposed standards, at a seven percent
discount rate. The lower the increased
stringency of the Alternative, the lower
the total per-vehicle costs increase for
the consumer. For example, Alternative
8 increases the total costs for the
consumer on a per-vehicle basis by
$2,270 (in MY 2030 compared to the
costs of the proposed standards),
Alternative 6 increases the costs to the
consumer by $1,400 per-vehicle, and
Alternative 4 increases the costs by $610
per-vehicle, all at a seven percent
discount rate.
The analysis also projects the fuel
savings for the vehicle owner over the
life of the vehicles that come with lower
levels of CO2 emissions. For example, as

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shown in Table VIII–32 (at a seven
percent discount rate), for the
previously-announced EPA standards
for MYs 2021–2025 (in MY 2030
compared to the costs of the proposed
standards), the analysis projects a pervehicle life-time fuel savings, including
retail taxes, of $1,510 per vehicle, as
well as an additional savings to the
consumer from rebound driving and
time saved refueling the vehicle of $610
per vehicle, for a total savings of $2,120.
However, these savings to the consumer
are not enough to offset the
accompanying projected $2,810 increase
in consumer costs. Compared to the
proposed standards, the previouslyissued EPA standards for MYs 2021–
2025 would increase net costs to
consumers by $690 over the lifetime of
the MY 2030 vehicles. This imbalance
between costs and fuel savings contrasts
sharply with what EPA projected in
2012 when setting those standards then,
and the fuel savings is considerably
smaller (this is due in large part to lower
current and projected fuel prices). Also,
relative to the proposed standards, and
over the lifetime of MY 2030 vehicles,
the projected net cost increase to
consumers from adopting Alternative 8
is $300, Alternative 6 projects a net cost
increase to consumers of $100,
Alternative 4 projects a net savings to
consumers of $60, and Alternative 2
projects a net savings to consumers of
$10.

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(d) Consideration of GHG Emissions
As discussed above, the purpose of
CO2 standards established under CAA
Section 202 is to reduce GHG emissions,
which contribute to climate change. As
shown in Table VIII–34, the analysis
projects that, compared to the baseline
standards, the proposed CO2 standards
for MYs 2021–2026 would increase
vehicle CO2 emissions by 713 million
metric tons (MMT) over the lifetime of
the vehicles produced from MY 1979
through MY 2029, with an additional
159 MMT in CO2 reduction from
upstream sources for a total increase of
872 MMT. The modeling of proposed
revised and alternative standards
projects that more stringent standards
will result in smaller increases in GHG
emissions (also compared to the
baseline standards. Compared to the
baseline standards, Alternative 8 is
projected to increase CO2 emissions by
264 MMT from combined vehicle
tailpipe and upstream reductions over
the lifetime of the vehicles produced
through MY 2029. Alternative 6 is
projected to increase CO2 emissions by
422 MMT, Alternative 4 by 649 MMT of
CO2, and Alternative 2 by 825 MMT of

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CO2.477 As noted above, the purpose of
Title II emissions standards is to protect
the public health and welfare, and in
establishing emissions standards the
Administrator is cognizant of the
importance of this goal. At the same
time, as discussed above, unlike other
provisions in Title II, Section 202(a)
does not require the Administrator to set
standards which result in the greatest
degree of emissions control achievable,
though the Administrator has the
discretion to do so. Thus, in setting
these standards, the Administrator takes
into consideration other factors
discussed above and below, including
not only technological feasibility, leadtime, and the cost of compliance but
also potential impacts of vehicle
emission standards on safety and other
impacts on consumers. Notwithstanding
the fact that GHG emissions reductions
would be lower under today’s proposal
than for the existing EPA standards, in
light of the new assessment indicating
higher vehicle costs and associated
impacts on consumers, and safety
impacts, the Administrator believes
from a cost/benefit perspective that the
foregone GHG emission reduction
benefits from the proposed standards
are warranted.
(e) Consideration of Consumer Choice
As discussed previously, the EPA CO2
standards are based on vehicle footprint,
and in general smaller footprint vehicles
have individual CO2 targets that are
lower (more stringent) than larger
footprint vehicles. The passenger car
fleet has footprint curves that are
distinct from the light-truck fleet. One of
the goals EPA had in designing the
program with footprint-based standards,
in considering the shape, slope, and
stringency of the footprint standard
curves, and in adopting many
compliance flexibilities (e.g., the
477 This preamble and the PRIA document
estimates annual GHG emissions from light-duty
vehicles under the baseline CO2 standards, the
proposed standards, and the standards defined by
each of the other regulatory alternatives under
consideration. For the final rule issued in 2012,
EPA estimated changes in atmospheric CO2, global
temperature, and sea level rise using GCAM and
MAGICC with outputs from its OMEGA model.
Because the agencies are now using the same model
and inputs, outputs from NHTSA’s DEIS (that also
used GCAM and MAGICC) were analyzed. Today’s
analysis estimates that annual GHG emissions from
light-duty vehicles under the CO2 standards defined
by each regulatory alternative would be within
about one percent of emissions under the
corresponding CAFE standards. Especially
considering the uncertainties involved in estimating
future climate impacts, the very similar estimates of
future GHG emissions under CO2 standards and
corresponding CAFE standards means that climate
impacts presented in NHTSA’s draft EIS represent
well the potential climate impacts of the proposed
and alternative CO2 standards.

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emissions averaging, banking, and
trading program; air-conditioning
program credits; flexibility in how to
comply with the N2O and methane
standard; off-cycle credit program, etc.)
was to maintain consumer choice. The
EPA standards are designed to require
reductions of CO2 emissions over time
from the vehicle fleet as a whole but
also to provide sufficient flexibility to
the automotive manufacturers so that
firms can produce vehicles which serve
the needs of their customers. EPA
believes the past several model years in
the market place show the benefits of
this approach. Automotive companies
have been able to reduce their fleet-wide
CO2 emissions while continuing to
produce and sell the many diverse
products that serve the needs of
consumers in the market, e.g., full-size
pick-up trucks with high towing
capabilities, minivans, cross-over
vehicles, SUVs, and passenger cars;
vehicles with off-road capabilities;
luxury/premium vehicles, supercars,
performance vehicles, entry level
vehicles, etc.
At the same, the Administrator
recognizes that automotive customers
are a diverse group, that automotive
companies do not all compete for the
same segments of the market, and that
increasing stringency in the standards
can be expected to have different effects
not just on certain vehicle segments but
on certain manufacturers who have
developed market strategies around
those vehicle segments. The
Administrator further recognizes that
the diversity of the automotive customer
base, combined with the analysis, raises
concerns that the existing standards, if
they are not adjusted, may not continue
to fulfill the agency’s goal of providing
sufficient manufacturer flexibility to
meet consumer needs and consumer
choice preferences. The analysis
projects that high penetrations of
hybridized vehicles would be required
to achieve the previously-issued EPA
MYs 2021–2025 standards, specifically
37% mild hybrid penetration and 21%
strong hybrids for the new vehicle fleet
in MY 2030 (See Table VIII–24). For the
passenger car fleet, the projection is
20% mild hybrid and 24% strong
hybrid, and for the light-truck fleet 56%
mild hybrid and 17% strong hybrid (See
Table VIII–26 and Table VIII–28).
The Administrator is concerned that
this projected level of hybridization,
and the associated vehicle costs, arising
from the existing standards may be too
high from a consumer-choice
perspective. While consumers have
benefited from improvements over
several decades in traditional vehicle
technologies, such as advancements in

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transmissions and internal combustion
engines, advanced electrification
technologies are a departure from what
consumers have traditionally
purchased. Strong hybrid and other
advanced electrification technologies
have been available for many years (20
years for strong hybrids and eight years
for plug-in and all electric vehicles), and
sales levels have been relatively low, on
the order of two to three precent per
year for strong hybrids.478 As discussed
above, the analysis projects that the
2012 EPA standards are projected to
require a significant increase in
hybridization over the next 7 to 12
model years. This large increase may
require automotive companies to change
the choice of vehicle types and the
utility of the vehicles available to
customers from what the companies
would otherwise offer in the absence of
the existing standards.
EPA notes that in the EPA’s annual
Manufacturer Performance Report on
the compliance status of the automotive
companies for the EPA GHG standards,
EPA has reported that emissions trading
has occurred a number of times in the
past several years.479 Through MY 2016,
these trades have included 12 firms,
with five firms trading CO2 credits to
seven firms, and thus far in the EPA
GHG program credits generated in MY
2010 through MY 2016 have been
traded. This represents about one-half of
the automotive companies selling
vehicles in the U.S. market, but since
several of these firms are small players,
it is less than half of the volume. In
total, approximately 30 million
Megagrams of CO2 have been traded
between firms, which is approximately
10% of the MY 2016 industry-wide
bank of credits. Credit trading between
firms can lower the costs of compliance
for firms, both for those selling and
those purchasing credits, and this
program compliance flexibility is
another tool by which auto firms can
provide the types of vehicle offerings
that customers want. However, longterm planning is an important
consideration for automakers, and an
OEM who may want to purchase credits
as part of a future compliance strategy
cannot be guaranteed they will be able
to find credits.
478 Light-Duty Automotive Technology, Carbon
Dioxide Emissions, and Fuel Economy Trends: 1975
Through 2017, U.S. EPA Table 5.1 (Jan. 2018),
available at https://nepis.epa.gov/Exe/ZyPDF.cgi?
Dockey=P100TGDW.pdf.
479 See Greenhouse Gas Emission Standards for
Light-Duty Vehicles: Manufacturer Performance
Report for the 2016 Model Year (EPA Report 420–
R18–002), U.S. EPA (Jan. 2018), available at https://
nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=
P100TGIA.pdf.

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The automotive industry is highly
competitive, and firms may be reluctant
to base their future product strategy on
an uncertain future credit availability.
As can be seen in Table VIII–24, the
analysis projects that lower levels of
stringency (Alternatives 1–8) will
require lower penetrations of mild
hybrids and strong hybrids as compared
to the 2012 EPA standards. For example,
Alternative 8 projects a 34% penetration
of mild and strong hybrid new vehicle
sales in MY 2030, Alternative 6 projects
a 22% penetration of these technologies,
Alternative 4 projects an eight percent
penetration, and Alternative 2 projects a
four percent penetration of mild and
strong hybrids in MY 2030. The EPA
proposal, Alternative 1, projects a two
percent penetration of mild hybrids and
a two percent penetration of strong
hybrids. These are levels similar to what
auto manufacturers are selling today,
suggesting that auto companies will be
able to produce vehicles in the future
that meet the full range of needs from
consumers, thus preserving consumer
choice.
(f) Consideration of Safety
EPA has long considered the effects
on safety of its emission standards. See
45 FR 14496, 14503 (1980) (‘‘EPA would
not require a particulate control
technology that was known to involve
serious safety problems.’’). More
recently, EPA has considered the
potential impacts of emissions
standards on safety in past rulemakings
on GHG standards, including the 2010
rule which established the 2012–2016
light-duty vehicle GHG standards, and
the 2012 rule which previously
established the 2017–2025 light-duty
vehicle GHG standards. Indeed, section
202(a)(4)(A) specifically prohibits the
use of an emission control device,
system or element of design that will
cause or contribute to an unreasonable
risk to public health, welfare, or safety.
42 U.S.C. 7521(a)(4)(A).
The proposal’s safety analysis projects
that the 2012 EPA GHG standards for
MYs 2021 and later would increase
vehicle fatalities due to several reasons,
namely increased vehicle prices
resulting in delayed turnover of the
vehicle fleet to newer, safer vehicles,
increased fatalities and accidents due
the rebound effect, and passenger car
mass reduction. The assessment is
discussed in Section 0 of this preamble
and is detailed in Chapter 11 of the
PRIA. The assessment projects that
Alternative 1, which includes no change
in the GHG emissions standards for MY
2021 and later, would yield the lowest
number of vehicle fatalities. The
analysis projects that, compared to the

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proposed standards, the previouslyissued EPA standards would increase
highway fatalities by 15,680 over the
lifetime of vehicles produced through
MY 2029 (See Table VII–89).
EPA views the potential impacts of
emission standards on safety as an
important consideration in determining
the appropriate standards under section
202. The analysis projects adverse
impacts on safety that are significantly
different from the analysis included and
considered in the 2012 rule which
established the MY 2021–25 GHG
standards and the 2016 Draft Technical
Assessment Report. As discussed
previously in this document, previous
analyses limited the amount of mass
reduction assumed for certain vehicles,
while acknowledging that
manufacturers would not necessarily
choose to avoid mass reductions in the
ways that the agencies assumed. The
current analysis eliminates this
constraint. The Administrator considers
this difference to be a significant factor
indicating that it is appropriate to
consider a range of alternative revised
standards, including Alternative 1, the
preferred alternative.
(g) Balancing of Factors and EPA’s
Proposed Revised Standards for MY
2021 and Later
As discussed in this section, the
Administrator is required to consider a
number of factors when establishing
emission standards under Section
202(a)(2) of the Clean Air Act: The
standards ‘‘shall take effect after such
period as the Administrator finds
necessary to permit the development
and application of the requisite
technology, giving appropriate
consideration to the cost of compliance
within such period.’’ 42 U.S.C.
7521(a)(2). For this proposal, the
Administrator has considered a wide
range of potential emission standards
(Alternatives 1 through 9), ranging from
the existing EPA MY 2021 to MY 2025
standards, through a number of less
stringent alternatives, including
Alternative 1, the preferred Alternative.
In addition to technological feasibility,
lead-time, and the costs of compliance,
the Administrator has also considered
the impact of various standards on
projected emissions reductions,
consumer choice, and vehicle safety.
The Administrator believes the existing
EPA standards for MY 2021 and later,
considered as a whole, are too stringent.
The Administrator gives particular
consideration to the high projected costs
of the standards and the impact of the
standards on vehicle safety. The
analysis projects that, compared to the
proposed standards, the previously-

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issued EPA standards for MYs 2021–
2025 would increase MY 2030
compliance costs by nearly $1,900 per
vehicle. Although EPA projected a
similar cost 480 increase in the 2012 rule
announcing standards through 2025,
this prior estimate was relative to an
indefinite continuation of standards for
MY 2016, and assuming that absent
regulation, manufacturers would not
increase fuel economy at all. In
addition, as mentioned above, the
analysis projects that, compared to the
proposed standards, the previouslyissued EPA standards would increase
highway fatalities by 12,903 over the
lifetime of vehicles produced through
MY 2029. In evaluating the other
Alternatives under consideration, the
Administrator notes that Alternative 1
has the lowest cost of compliance and
the lowest number of fatalities. He also
notes that Alternative 1 will preserve
consumer choice in the vehicle market
and will provide a relatively high net
savings to consumers, when assessing
the increased costs of vehicles against
fuel savings over the lifetime of the
vehicle.
The Administrator recognizes that
Alternative 1 is projected to result in
less CO2 reductions compared to the
existing EPA standards and is not
projected to achieve additional GHG
reductions beyond the MY 2020
standards. However, the Administrator
notes that, unlike other provisions in
Title II referenced above, section 202(a)
does not require the Administrator to set
standards which result in the ‘‘greatest
degree of emissions control achievable.’’
In light of this statutory discretion and
the range of factors that the statute
authorizes and permits the
Administrator to consider, and his
consideration of the factors discussed
above, the EPA proposes to conclude
that maintaining the MY 2020 standards
going forward is an appropriate
approach under section 202(a).
Therefore, based on the data and
analysis detailed in this proposal, the
Administrator is proposing that the
existing MY 2021 and later GHG
standards are too stringent and is
proposing to revise the MY 2021 and
later standards to maintain the MY 2020
levels in subsequent model years. EPA
requests comment on all aspects of this
proposal and supporting assessments,
including the Administrator’s
consideration of the relevant factors
under section 202(a) of the Clean Air
Act, the proposed Alternative 1, the
previously-established EPA GHG
standards, and all of the Alternatives
discussed in section IV of this preamble.
480 77

FR 62624, 62665 (Oct. 15, 2012).

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VI. Preemption of State and Local Laws
Accomplishing the goals of EPCA
requires a set of uniform national fuel
economy standards. Achieving this
national standard requires the agencies
to clearly discuss the extent to which
state and local standards are expressly
or impliedly preempted. As described
herein, doing so is fundamental to the
effectiveness of the new proposed set of
fuel economy standards and to the
critical importance of ensuring that the
proposed Federal standards will
constitute uniform national
requirements, as Congress intended.
This is also a fundamental reason that
EPA is proposing the withdrawal of
CAA preemption waivers granted to
California relating to its GHG standards
and Zero Emissions Vehicle (ZEV)
mandate.
A. Preemption Under the Energy Policy
and Conservation Act
1. History of EPCA Preemption
Discussions in Rulemakings
NHTSA has asserted the preemption
of certain State emissions standards
under EPCA a number of times in CAFE
rulemakings dating back to 2002.481 The
initial rulemaking discussion was
prompted by a court filing by the State
of California claiming that NHTSA did
not treat California’s Greenhouse Gas
Emissions regulation as preempted.482
This continuous dialogue involves a
variety of parties (i.e., the states, the
Federal government—especially EPA—
and the general public) and occurs
through a variety of means, including
several rulemaking proceedings. After
NHTSA first raised the issue of
preemption in 2002 when proposing
standards for MYs 2005–2007 light
trucks, the agency explored preemption
at great length in response to extensive
public comment in its August 2005
NPRM and its April 2006 final rule for
MYs 2008–2011 light trucks.
During the period between the NPRM
and the final rule for MYs 2008–2011
light trucks, California separately
requested that the EPA grant a waiver of
CAA preemption, pursuant to Section
209 of that act, for its Greenhouse Gas
Emissions regulation. If EPA granted the
waiver, the CAA would under certain
circumstances allow other states to
adopt the same regulation pursuant to
CAA Section 177, without being
preempted by the CAA.
In 2007, the Supreme Court ruled in
Massachusetts v. EPA that carbon
481 67

FR 77025 (December 16, 2002).
Appellants Opening Brief filed on behalf
Michael P. Kenny in Central Valley ChryslerPlymouth, Inc. et al. v. Michael P. Kenny, No. 02–
16395, at p. 33 (9th Cir. 2002).
482 See

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dioxide is an ‘‘air pollutant’’ within the
meaning of the CAA and thus
potentially subject to regulation under
that statute. The Supreme Court did not
consider the issue of preemption under
EPCA of state laws or regulations
regulating CO2 tailpipe emissions from
automobiles, but it did address the
relationship between EPA and NHTSA
rulemaking obligations.483 Later that
year, two Federal district courts in
Vermont and California ruled that the
GHG motor vehicle emission standards
adopted by those states were not
preempted under EPCA.484 Still later
that year, Congress enacted EISA,
amending EPCA by mandating annual
increases in passenger car and light
truck CAFE standards through MY 2020
and maximum feasible fuel economy
standards subsequently.485
In March 2008, EPA denied
California’s request for a waiver of CAA
preemption.486 In May 2008, NHTSA
issued a proposal for MYs 2011–2015
standards, which included a significant
discussion of EPCA preemption and a
proposed regulatory statement to
provide that state vehicle tailpipe CO2
standards are related to fuel economy
and therefore expressly preempted
under EPCA, and that they conflict with
the goals and objectives of EPCA and
therefore also impliedly preempted.487
The Bush Administration did not issue
a final rule for MYs 2011–2015.
A number of significant actions
happened in quick succession at the
beginning of the prior Administration.
The first day post-inauguration, CARB
petitioned for reconsideration of EPA’s
denial of a waiver of CAA preemption
for California’s GHG emissions
standards for 2009 and later model year
vehicles.488 Several days later, on
January 26, 2009, President Obama
issued a memorandum requesting,
among other things (including
483 The Court reasoned that the fact that NHTSA
‘‘sets mileage standards in no way licenses EPA to
shirk its environmental responsibilities. EPA has
been charged with protecting the public’s ‘health’
and ‘welfare,’ . . . a statutory obligation wholly
independent of DOT’s mandate to promote energy
efficiency. . . . The two obligations may overlap,
but there is no reason to think the two agencies
cannot both administer their obligations and yet
avoid inconsistency.’’ Massachusetts v. EPA, 549
U.S. 497, 532 (2007).
484 Green Mountain Chrysler v. Crombie, 508
F.Supp.2d 295 (D. Vt. 2007); Central Valley
Chrysler-Jeep, Inc. v. Goldstene, 529 F.Supp.2d
1151 (E.D. Cal. 2007), as corrected (Mar. 26, 2008).
485 Public Law 110–140 (2007).
486 73 FR 12156 (Mar. 6, 2008).
487 73 FR 24352 (May 2, 2008).
488 For background on CARB’s petition, see EPA’s
Notice of Decision Granting a Waiver of Clean Air
Act Preemption for California’s 2009 and
Subsequent Model Year Greenhouse Gas Emission
Standards for New Motor Vehicles, 74 FR 32744
(Jul. 8, 2009).

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
consideration of EPCA preemption in
light of Massachusetts v. EPA and other
laws), that NHTSA’s rulemaking be
divided into two parts—one regulation
establishing standards for model year
2011 only, and another for subsequent
years. Less than two months after that
memorandum, on March 6, 2009,
NHTSA issued its final rule for MY
2011 vehicles and announced that it
would consider EPCA preemption in
subsequent rulemakings.489 Then, on
May 19, 2009, the White House
announced a coordinated program
addressing motor vehicle fuel economy
and greenhouse gas emissions, to be
known as the ‘‘National Program,’’
whereby NHTSA and EPA would jointly
establish rules to harmonize compliance
requirements for manufacturers. As part
of the National Program, several
manufacturers and their trade
associations announced their
commitment to take several actions,
including agreeing not to contest
forthcoming CAFE and GHG standards
for MYs 2012–2016; not to challenge
any grant of a CAA preemption waiver
for California’s GHG standards for
certain model years; and to stay and
then dismiss all pending litigation
challenging California’s regulation of
GHG emissions, including litigation
concerning EPCA preemption of state
GHG standards.490
Less than two months later, in July
2009, EPA granted California’s January
2009 request for reconsideration of the
CAA preemption waiver denial,
allowing California to establish its own
GHG standards under the CAA.491 In
granting the preemption waiver, EPA
acknowledged that its analysis was
based solely on CAA considerations and
did not ‘‘attempt to interpret or apply
EPCA,’’ concluding that ‘‘EPA takes no
position regarding whether or not
California’s GHG standards are
preempted under EPCA.’’ 492
In the subsequent MYs 2012–2016
CAFE rulemaking, NHTSA elected to
defer consideration of EPCA preemption
concerns because of the ‘‘consistent and
coordinated Federal standards that
apply nationally under the National
Program.’’ 493 Later, in establishing MYs
2017–2021 CAFE standards, NHTSA
pointed out that after finalization of the
MYs 2012–2016 CAFE standards,
California amended its GHG regulations
to provide that manufacturers could
elect to comply with the EPA GHG
489 74

FR 14196 (Mar. 6, 2009).
FR 25324, 25328 (May 7, 2010).
491 74 FR 32744 (Jul. 8, 2009).
492 74 FR at 32783 (Jul. 8, 2009).
493 75 FR 25324, 25546 (May 7, 2010); see also 74
FR 49454, 49635 (Sep. 28, 2009).
490 75

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requirements and be deemed to comply
with California’s standards, and that
this amendment facilitated the National
Program by allowing a manufacturer to
‘‘meet all standards with a single
national fleet.’’ 494 NHTSA, at the time,
erroneously saw this as obviating
consideration of EPCA preemption. At
the same time, the agency did not
address whether California’s ZEV
program would be preempted since it
has never been part of the National
Program.
2. Preemption Analysis
Present circumstances require NHTSA
to address the issue of preemption.
Despite past attempts by NHTSA and
EPA to harmonize their respective and
related regulations, the automotive
industry and U.S. consumers now face
regulatory uncertainty and increased
costs, in no small part as a result of
California’s separate GHG emissions and
ZEV program. NHTSA and EPA now
seek to address these concerns with this
rulemaking proposal, in the interest of
regulatory certainty and the clear
prospect for disharmony with
conflicting state requirements.495
NHTSA is also guided by a desire to
obtain comments from state and local
officials and other members of the
public to inform fully the agency’s
position on this important issue.496
(a) EPCA Preemption
EPCA’s express preemption language
is broad and clear:
When an average fuel economy standard
prescribed under this chapter is in effect, a
State or a political subdivision of a State may
not adopt or enforce a law or regulation
related to fuel economy standards or average
fuel economy standards for automobiles
covered by an average fuel economy standard
under this chapter.497

Unlike the CAA, EPCA does not allow
for a waiver of preemption. Nor does
EPCA allow for states to establish or
enforce an identical or equivalent
494 76

FR 74854, 74863 (Dec. 1, 2011).
California’s ‘‘deem to comply’’
provision provided some temporary relief from
three different sets of standards, its regulations still
mandate that some manufacturers comply with
burdensome filing requirements and California may
act to revoke the provision. In fact, California is
already seeking comment on potentially changing
the regulation to provide that manufacturers would
only be deemed to comply with CARB requirements
if meeting the currently-final EPA standards. See
https://www.arb.ca.gov/msprog/levprog/leviii/
leviii_dtc_notice05072018.pdf (last accessed May
17, 2018). Moreover, the ‘‘deem to comply’’
provision applies only to tailpipe CO2 emissions
requirements—not to the ZEV program.
496 See also E.O. 13132 (Federalism); E.O. 12988
sec. 3(b)(1)(B) (Civil Justice Reform); 54 FR 11765
(Mar. 22, 1989); 58 FR 68274 (Dec. 23, 1993); and
70 FR 21844 (Apr. 27, 2005).
497 49 U.S.C. 32919.
495 While

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regulation. In a further indication of
Congress’ intent to ensure that state
regulatory schemes do not impinge
upon EPCA’s goals, the statute preempts
state laws merely related to fuel
economy standards or average fuel
economy standards. Here, NHTSA
intends to assert preemption only over
state requirements that directly affect
corporate average fuel economy.
The Supreme Court has interpreted
similar statutory preemption language
on several occasions, concluding that a
state law ‘‘relates to’’ a Federal law if it
‘‘has a connection with or refers to’’ the
subject of the Federal law.498 The Court,
citing similar Federal statutory
language, extended the application of
the ‘‘related to’’ standard to the Airline
Deregulation Act in Morales v. Trans
World Airlines, Inc.,499 concluding
that,’’ [f]or purposes of the present case,
the key phrase, obviously, is ‘relating
to.’ The ordinary meaning of these
words is a broad one—‘to stand in some
relation; to have bearing or concern; to
pertain; refer; to bring into association
with or connection with,’ . . .—and the
words thus express a broad pre-emptive
purpose.’’ 500 Courts look ‘‘both to the
objectives of the . . . statute as a guide
to the scope of the state law that
Congress understood would survive,
[and] to the nature of the effect of the
state law on [the Federal standards].’’ 501
One of Congress’ objectives in EPCA
was to create a national fuel economy
standard, as clearly expressed in 49
U.S.C. 32919(a). In addition to the
statute’s plain language, which controls,
the legislative history of that provision
further confirms that Congress intended
the provision to be broadly preemptive.
As Congress debated proposals that
would eventually become EPCA, the
Senate bill 502 sought to preempt State
laws only if they were ‘‘inconsistent’’
with Federal fuel economy standards,
labeling, or advertising, while the House
bill 503 sought to preempt State laws
only if they were not ‘‘identical to’’ a
Federal requirement. The express
preemption provision, as enacted,
preempts all State laws that relate to
fuel economy standards. No exception is
made for State laws on the ground that
498 Shaw v. Delta Airlines, Inc., 463 U.S. 85, 97
(1983) (ERISA case).
499 504 U.S. 374, 383–84 (1992).
500 Id. at 383.
501 California Div. of Labor Standards
Enforcement v. Dillingham Constr., N.A., Inc., 519
U.S. 316, 325 (1997), (quoting N.Y Conference of
Blue Cross & Blue Shield Plans v. Travelers Ins. Co.,
514 U.S. 645, 656 (1995)).
502 S. 1883, 94th Cong., 1st Sess., Section 509.
503 H.R. 7014, 94th Cong., 1st Sess., Section 507
as introduced, Section 509 as reported.

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they are consistent with or identical to
Federal requirements.504
In enacting EISA, Congress did not
repeal or amend EPCA’s express
preemption provision. Congress did,
however, adopt a savings provision
regarding the effect of EISA, and the
amendments made by it:
Nothing in this Act or an amendment made
by this Act supersedes, limits the authority
provided or responsibility conferred by, or
authorizes any violation of any provision of
law (including a regulation), including any
energy or environmental law or regulation.

sradovich on DSK3GMQ082PROD with PROPOSALS2

We understand this statutory language
to prevent EISA from limiting preexisting authority or responsibility
conferred by any law or from
authorizing violation of any law. By the
same token, the savings provision does
not purport to expand pre-existing
authority or responsibility. Thus, to the
extent that EPCA’s express preemption
provision limited State authority and
responsibility prior to the enactment of
EISA, it continues to limit such
authority and responsibility to the same
extent after the enactment of EISA. We
recognize that the Congressional Record
contains statements regarding the
savings provision indicating that certain
members of Congress may have
considered this language as allowing
California to set tailpipe GHG emissions
standards in contravention of EPCA’s
express preemption provision. Note,
however, that statements made on the
floor of the Senate or House before the
votes on EISA cannot expand the scope
of the savings provision or even be used
to ‘‘clarify’’ it, given the unambiguous
plain meaning of both the savings
provision and EPCA’s express
preemption provision. If Congress had
wanted to narrow the express
preemption provision, it could have
chosen to include such an amendment
in EISA. It did not.
(b) Tailpipe CO2 Emissions Regulations
or Prohibitions are Related to Fuel
Economy Standards
This broad statutory preemption
provision also necessarily governs state
regulations over greenhouse gas
emissions. GHG emissions, and
particularly CO2 emissions, are
mathematically linked to fuel economy;
therefore, regulations limiting tailpipe
CO2 emissions are directly related to
fuel economy.505 To summarize, most
light vehicles are powered by gasoline
internal combustion engines. The
combustion of gasoline produces CO2 in
amounts that can be readily calculated.
CO2 emissions are always and directly
71 FR 17566, 17657 (April 6, 2006).
505 71 FR at 17659, et seq.

linked to fuel consumption because CO2
is a necessary and inevitable byproduct
of burning gasoline. The more fuel a
vehicle burns or consumes, the more
CO2 it emits. To the extent that light
vehicles are not powered by internal
combustion engines, their use generally
involves some release of CO2 or other
GHG emissions, even if indirectly,
associated with the vehicle performing
its work of traveling down the road.
CNG and LPG vehicles release CO2
during combustion. Even for batteryelectric vehicles, fossil fuels are used in
at least some part of production of
electricity in virtually all parts of the
country, and that electricity is used to
move the vehicles. And with hydrogen
vehicles, methane remains a major part
of the generation of hydrogen fuel,
which is also used to move those
vehicles. Carbon dioxide is thus a
byproduct of moving virtually if not
literally all light-duty vehicles, and the
amount of CO2 released directly
correlates to the amount of fossil fuels
used to power the vehicle so it can
move.
EPCA has specified since its inception
that compliance with CAFE standards is
to be determined in accordance with
test and calculation procedures
established by EPA.506 More
specifically, the tests are to be
performed using ‘‘the same procedures
for passenger automobiles the
Administrator used for model year 1975
. . . procedures that give comparable
results.’’ Under these procedures,
compliance with the CAFE standards is
and has always been based on the rates
of emission of CO2, CO, and
hydrocarbons from covered vehicles,
but primarily on the emission rates of
CO2. In the measurement and
calculation of a given vehicle model’s
fuel economy for purposes of
determining a manufacturer’s
compliance with Federal fuel economy
standards, the role of CO2 is
approximately 100 times greater than
the combined role of the other two
relevant carbon exhaust gases. Given
that the amount of CO2, CO, and
hydrocarbons emitted from a vehicle’s
tailpipe relates directly to the amount of
fuel it consumes, EPA can reliably and
accurately convert the amount of those
gases emitted by that vehicle into the
miles per gallon achieved by that
vehicle. In recognizing that 1975 test
procedures were sufficient to measure
fuel economy performance, Congress
recognized the direct relationship
between CO2 emissions and fuel
economy standards, while in the same
piece of legislation expressly

504 See

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506 49

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preempting state standards that are
related to fuel economy standards, when
Federal fuel economy standards are in
place.
In mandating Federal fuel economy
standards under EPCA, Congress has
expressly preempted any state laws or
regulations relating to fuel economy
standards. A state requirement limiting
tailpipe CO2 emissions is such a law or
regulation because it has the direct
effect of regulating fuel consumption.
Given that substantially reducing CO2
tailpipe emissions from automobiles is
unavoidably and overwhelmingly
dependent upon substantially
increasing fuel economy through
installation of engine technologies,
transmission technologies, accessory
technologies, vehicle technologies, and
hybrid technologies, increases in fuel
economy inevitably produce
commensurate reductions in CO2
tailpipe emissions. Since there is but
one pool of technologies 507 for reducing
tailpipe CO2 emissions and increasing
fuel economy available now and for the
foreseeable future, regulation of CO2
emissions and fuel consumption are
inextricably linked. Such state
regulations are therefore unquestionably
‘‘related’’ and expressly preempted
under 49 U.S.C. 32919.
Moreover, state standards that have
the effect of regulating tailpipe CO2
emissions or fuel economy are likewise
related to fuel economy standards and
likewise preempted. For instance, if a
state were to regulate all tailpipe GHG
emissions from a vehicle, and not just
CO2, the state would nonetheless
regulate tailpipe CO2 emissions, since
CO2 emissions comprise the
overwhelming majority of tailpipe
carbon emissions. EPCA preempts such
a standard.
Likewise, a state law prohibiting all
tailpipe emissions, carbon or otherwise,
from some or all vehicles sold in the
state, would relate to fuel economy
standards and be preempted by EPCA,
since the majority of tailpipe emissions
consist of CO2. We recognize that this
preempts state programs, such as
California’s ZEV mandate, that establish
requirements that a portion of a
vehicle’s fleet sold or purchased consist
of vehicles that produce no tailpipe
emissions.
(c) Other GHG Emissions Requirements
May Not Be Preempted by EPCA
While EPCA expressly preempts state
tailpipe CO2 emission limits, some GHG
emissions from vehicles have no
507 With the minor exception of regulating the
carbon intensity of fuels—an activity not preempted
by EPCA.

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relation to fuel economy and are
therefore outside the scope of EPCA
preemption. For instance, vehicle air
conditioning units can cause GHG
emissions by leaking refrigerants when
the system is recharged or when it is
crushed at the end of the vehicle’s life.
Since such emissions have no bearing
on a vehicle’s fuel economy
performance or tailpipe CO2 emissions,
states can pass laws specifically
regulating or even prohibiting such
vehicular refrigerant leakage without
relating to fuel economy if doing so
would be otherwise consistent with
Federal law. Therefore, EPCA would not
preempt such laws, if narrowly drafted
so as not to include tailpipe CO2
emissions. If, however, a state law
sought to limit the combined GHG
emissions from a motor vehicle, in a
manner that would include tailpipe CO2
emissions, EPCA would preempt that
portion of the law limiting tailpipe CO2
emissions.
Similarly, state safety requirements
may have a merely incidental impact on
fuel economy and not relate to fuel
economy. For instance, a state may
mandate that children traveling in
motor vehicles sit in child safety seats.
Child safety seats add weight, and
added weight has an impact on fuel
economy. This impact is merely
incidental, however, and does not
directly relate to fuel economy
standards.
Likewise, EPA has recognized that
California may apply for a waiver of
CAA preemption for vehicle emissions,
which must be granted in certain
circumstances. That said, EPCA does
preempt any regulation limiting or
prohibiting CO2 emissions or all tailpipe
emissions, as such regulations have the
effect of regulating CO2 emissions and
relate to fuel economy standards.508
508 NHTSA notes that over the last decade CARB
has complicated its regulation of smog-forming
emissions (the original purpose of the Section 209
CAA waiver) by combining it with regulation of
GHG and, principally, CO2 emissions as well as the
ZEV mandate. Since EPCA prohibits state
regulation of CO2 emissions, a state program that
combines regulation of the two groups of pollutants
is preempted to the extent that the program relates
to fuel economy. A regulatory regime in which
smog-forming pollutants are addressed without also
directly or indirectly regulating fuel economy is not
preempted under EPCA.
Additionally, NHTSA notes that some suggest
that insofar as carbon dioxide emissions cause
global climate change, they indirectly worsen air
quality by (1) increasing formation of smog, because
the chemical process that forms ground-level ozone
occurs faster at higher temperatures, and (2)
increasing ragweed pollen, which can cause asthma
attacks in allergy sufferers. Comment is sought on
the extent to which the zero-tailpipe-emissions
vehicles compelled to be sold by California’s ZEV
program reduce temperatures in the parts of
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NHTSA invites comments on the
extent to which a state standard can
have some incidental impact on fuel
economy or CO2 emissions without
being ‘‘related to’’ fuel economy
standards.
(d) A Waiver of CAA Preemption Does
Not Affect, in Any Way, EPCA
Preemption
When a state establishes a standard
related to fuel economy, it does so in
violation of EPCA’s preemption statute
and the standard is therefore void ab
initio.
Federal preemption is rooted in the
Supremacy Clause of the U.S.
Constitution.509 Courts have long
recognized that the Supremacy Clause
of the Constitution gives Congress the
power to specifically preempt State
law.510 Broadly speaking, the United
States Supreme Court has long held that
‘‘an act done in violation of a statutory
prohibition is void,’’ 511 and has
specifically noted that such acts are not
merely ‘‘voidable at the instance of the
government’’ but void from the
outset.512 The Ninth Circuit stated it
more plainly: ‘‘Under Federal law, an
act occurring in violation of a statutory
mandate is void ab initio.’’ 513
Discussing the Supremacy Clause, the
Supreme Court explicitly explained
that, ‘‘[i]t is basic to this constitutional
command that all conflicting state
provisions be without effect.’’ 514 And at
least one Federal Court of Appeals
explicitly stated that the Supremacy
Clause means ‘‘state laws that ‘interfere
with, or are contrary to the laws of
Congress’ are void ab initio.’’ 515
While both the CAA and EPCA may
preempt state laws limiting GHG
emissions from motor vehicles, avoiding
preemption (by waiver or otherwise)
under one Federal law has no bearing
on the other Federal law’s preemptive
effect. Section 209 of the CAA, which
provides for the possible waiver of CAA
and which contain dense populations of allergy
sufferers.
509 U.S. Const. art VI, cl. 2.
510 See Gibbons v. Ogden, 22 U.S. 1 (1824).
511 Ewert v. Bluejacket, 259 U.S. 129, 138 (1922),
quoting Waskey v. Hammer, 223 U.S. 85, 94 (1912).
512 Waskey, 223 U.S. at 92.
513 Cabazon Band of Mission Indians v. City of
Indio, Cal., 694 F.2d 634, 637 (9th Cir. 1982).
514 Maryland v. Louisiana, 451 U.S. 725, 746
(1981) (citing McCulloch v. Maryland, 4 Wheat. 316,
427 (1819)). Other courts have used similar
language to describe the impact of preemption. See,
e.g., Nathan Kimmel, Inc. v. DowElanco, 275 F.3d
1199, 1203 (9th Cir. 2002) (explaining preempted
state laws are ‘‘without effect’’); Sweat v. Hull, 200
F.Supp.2d 1162, 1172 (D. Ariz. 2001) (explaining
preempted state laws are ‘‘ineffective.’’).
515 Antilles Cement Corp. v. Fortuno, 670 F.3d
310, 323 (1st Cir. 2012) (quoting Gibbons v. Ogden,
22 U.S. (9 Wheat.) 1 (1824)).

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preemption, makes clear that waiver of
preemption under that statute operates
only to relieve ‘‘application of this
section’’—the preemption provision of
the CAA—and not application of other
statutes.516 EPA and NHTSA tentatively
agree that a waiver under the CAA does
not also waive EPCA preemption.
The Vermont and California Federal
district court decisions mentioned
above involved challenges to a
California Air Resources Board
regulation establishing vehicle tailpipe
GHG emission standards. The courts
concluded that EPCA did not preempt
such standards. In both decisions, the
courts placed much weight upon the
fact that California had petitioned EPA
for a waiver of CAA preemption
pursuant to 42 U.S.C. 7543(b).
NHTSA and EPA do not agree with
the district courts’ express preemption
analyses. EPCA preempts state laws and
regulations ‘‘related to fuel economy
standards or average fuel economy
standards for automobiles covered by an
average fuel economy standard.’’ 517 The
courts in Green Mountain Chrysler and
Central Valley Chrysler-Jeep recognized
the relationship between CO2 emissions
and fuel economy. Nonetheless, they
erroneously concluded that the ‘‘related
to’’ language in EPCA’s preemption
clause should be construed ‘‘very
narrowly’’ and adopted a novel
interpretation of ‘‘related to.’’ 518 The
courts failed to recognize precedent
providing broad effect to other
preemption statutes using terms similar
to ‘‘related to,’’ as discussed above.
(e) A Clean Air Act Waiver Does Not
‘‘Federalize’’ EPCA-Preempted State
Standards
The district court in Green Mountain
Chrysler concluded that it could resolve
the challenge to Vermont’s regulations
without directly considering the
application of EPCA’s preemption
provision. The court said that the
dispute did not concern preemption but
concerned reconciling two different
Federal statutes (EPCA and the CAA). In
this regard, the district court stated that
if EPA approved California’s waiver
petition (which had not yet occurred),
then Vermont’s GHG regulations
become ‘‘other motor vehicle standards’’
that NHTSA must consider in setting
516 42 U.S.C. 7543(b)(1) (emphasis added); see
also 42 U.S.C. 7543(b)(3) (‘‘compliance with such
State standards shall be treated as compliance with
applicable Federal standards for purposes of this
subchapter’’) (emphasis added).
517 49 U.S.C. 32919(a) (emphasis added).
518 E.g., 529 F.Supp.2d at 1176.

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CAFE standards.519 In the court’s view,
once EPA grants a waiver, compliance
with California’s standards is deemed to
satisfy all Federal standards—not just
those of the CAA. In states that adopt
California’s standards, compliance with
that standard would be deemed to
satisfy all Federal standards as well.
With this Federal accommodation of
state standards, the court concluded,
Vermont’s regulations would stand.
The court’s premise that preemption
provisions and principles do not apply
is not based on precedent and is not
supported by applicable law. In fact, the
district court in Central Valley ChryslerJeep recognized that ‘‘[t]he Green
Mountain court never actually offers a
legal foundation for the conclusion that
a state regulation granted waiver under
[CAA] section 209 [42 U.S.C. 7543] is
essentially a federal regulation such that
any conflict between the state regulation
and EPCA is a conflict between federal
regulations.’’ 520 NHTSA and EPA
disagree with the conclusion of these
decisions and reaffirm the longstanding
position that state standards regulating
tailpipe GHG emissions, such as the
standards challenged in the California
and Vermont district court cases, are
preempted by EPCA because they
‘‘relate to’’ fuel economy standards. We
also note that those courts failed to
consider, much less give any weight to,
NHTSA’s views of preemption, as the
expert agency with authority over the
Federal fuel economy program.521 The
United States opposed, as amicus
curiae, the Green Mountain Chrysler
decision on appeal to the Second
Circuit, but the Second Circuit did not
issue a decision on appeal 522 due to the
519 Green

Mountain Chrysler, 508 F.Supp.2d at

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398.
520 Central Valley Chrysler-Jeep, 529 F.Supp.2d at
1165. Congress must state its intention clearly to
accord a state law the status of Federal law, which
it did not do in either in Section 209(b) of the CAA
or in EPCA. See, e.g., Indep. Cmty. Bankers Ass’n
v. Bd. of Governors, 820 F.2d 428, 436–37 (D.C. Cir.
1987) (recognizing that, although Congress ‘‘has the
power to assimilate state law,’’ ‘‘[s]uch decisions
require an unequivocal congressional expression’’
because ‘‘some [state] restrictions would in all
likelihood conflict with [other] existing Federal
laws’’).
521 See Geier v. American Honda Motor Co., 529
U.S. 861, 883 (2000) (‘‘Congress has delegated to
DOT authority to implement the statute; the subject
matter is technical; and the relevant history and
background are complex and extensive. The agency
is likely to have a thorough understanding of its
own regulation and its objectives and is ‘uniquely
qualified’ to comprehend the likely impact of state
requirements.’’); Medtronic, Inc. v. Lohr, 518 U.S.
470, 496 (1996) (‘‘agency is uniquely qualified to
determine whether a particular form of state law
stands as an obstacle to the accomplishment and
execution of the full purposes and objectives of
Congress’’) (internal quotation marks omitted).
522 See Proof Brief for the United States as
Amicus Curiae, 07–4342–cv (2d Cir. filed Apr. 16,
2008).

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automotive industry’s withdrawal of
appeals. As explained above, the
withdrawal of those appeals was a precondition to the 2010 issuance of the
final rule establishing the ‘‘National
Program’’ of fuel economy standards
and GHG emission standards for MYs
2012–2016.
In their appeals of the Green
Mountain Chrysler decision, the vehicle
manufacturer associations argued that
the operation of EPCA’s express
preemption provision does not require
that a conflict be shown between the
Federal and state standards, that the
Federal and state standards be identical,
or that the Federal and state standards
serve the same purpose. We agree. The
conflict principles of implied
preemption do not apply in fields where
Congress has enacted an express
preemption provision prohibiting even
the existence of state standards. The
statutory test, whether the state
standards are ‘‘related to’’ the Federal
standards, is met by showing that the
state GHG emission standards are not
simply related to, but actually the
functional equivalent of, the Federal
fuel economy standards. The district
court itself recognized that ‘‘there is a
near-perfect correlation between fuel
consumed and carbon dioxide
released.’’ Neither the inclusion in the
state standard of emissions for which
that relationship does not exist, nor the
assigning to the state standard of a
purpose other than energy conservation,
diminishes the statutory implications of
the state standard’s meeting the
relatedness test. Those unrelated types
of emissions constitute a very low
percentage of the overall tailpipe
emissions. Finally, while there are
means of compliance with the state
standard other than improving fuel
economy, their contributions to
compliance are minor. Improving fuel
economy is the only feasible method of
achieving full compliance. Again,
NHTSA and EPA agree.
The Central Valley Chrysler-Jeep court
went further, noting that while NHTSA
is required to give consideration to
‘‘other standards,’’ including those
‘‘promulgated by EPA,’’ ‘‘[t]here is no
corresponding duty by EPA to give
consideration to EPCA’s regulatory
scheme. This asymmetrical allocation
by Congress of the duty to consider
other governmental regulations
indicates that Congress intended that
DOT, through NHTSA, is to have the
burden to conform its CAFE program
under EPCA to EPA’s determination of

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what level of regulation is necessary to
secure public health and welfare.’’ 523
In support of its position, the Central
Valley Chrysler-Jeep found persuasive
the Green Mountain Chrysler court’s
view that California emissions
regulations under CAA Section 209
have always been considered ‘‘other
standards’’ on fuel economy. As
mentioned previously in the discussion
of the ‘‘other standards’’ to be
considered as factors in establishing
maximum feasible fuel economy
standards, EPCA, as originally enacted,
contained a specific self-contained
provision that provided that any
manufacturer could apply to DOT for
modification of an average fuel economy
standard for model years 1978 through
1980 if it could show the likely
existence of a ‘‘Federal standards fuel
economy reduction,’’ defined to include
EPA-approved California emissions
standards that reduce fuel economy.
The court reasoned that ‘‘in 1975 when
EPCA was passed, Congress
unequivocally stated that federal
standards included EPA-approved
California emissions standards.’’ 524
However, when EPCA was recodified in
1994, ‘‘all reference to the modification
process applicable for model years 1978
through 1980, including the categories
of federal standards, was omitted as
executed.’’ 525 The court noted that the
legislative intent of the 1994
recodification was not intended to make
a substantive change to the law.526
Thus, the court concluded that ‘‘[i]f the
recodification worked no substantive
change in the law, then the term ‘other
motor vehicle standards of the
Government’ continues to include both
emission standards issued by EPA and
emission standards for which EPA has
issued a waiver under Section 209(b) of
the CAA, as it did when enacted in
1975.’’ 527
NHTSA believes that the district court
misread EPCA to the point of turning it
on its head. As discussed previously in
this document, the ‘‘federal standards’’
definition discussed by the court existed
in a self-contained scheme allowing
manufacturers to petition NHTSA for
modification of the fuel economy
requirements only between 1978 and
523 Central Valley Chrysler-Jeep, 529 F.Supp.2d at
1168.
524 Central Valley Chrysler-Jeep, 529 F.Supp.2d at
1173 (quoting Green Mountain Chrysler, 508
F.Supp.2d at 345). EPCA Section 502(d)(3)(D)(i)
provided: ‘‘Each of the following is a category of
Federal standards: . . . Emissions standards under
Section 202 of the Clean Air Act, and emissions
standards applicable by reason of Section 209(b) of
such Act.’’
525 Id.
526 Id.
527 Id.

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1980, and thus has no application either
at the time of the decision or today. And
even if that definition of ‘‘federal
standards’’ were applied to EPCA
generally, NHTSA would balance that
against other factors enumerated in
EPCA that it ‘‘shall’’ consider in setting
maximum feasible fuel economy
standards. However, the district courts’
view is that this factor instead creates an
‘‘obligation’’ to ‘‘harmonize’’ CAFE
standards with state emissions
regulations under a CAA Section 209
waiver.528 In other words, under the
district courts’ opinions, a state
standard controls what NHTSA does,
and the agency therefore has no further
discretion to consider the other factors
Congress directed it to consider.
Consistent with the legislative history
and NHTSA’s long-standing
interpretations, NHTSA interprets
EPCA, a statute which it administers in
implementing the national fuel
economy program, as providing that the
requirement to ‘‘consider’’ the four
EPCA statutory factors set forth in 49
U.S.C. 32902(f) does not mean the
agency is obligated to harmonize CAFE
standards with state tailpipe CO2
emissions standards. EPA concurs that a
CAA waiver does not also waive the
effect of any other Federal law,
including EPCA.
As discussed above in the ‘‘other
standards’’ section of this rulemaking,
NHTSA further believes that the district
courts in Green Mountain Chrysler and
Central Valley Chrysler-Jeep
misconstrued the provision in EPCA as
enacted in 1975 that allowed
manufacturers to petition NHTSA to
reduce CAFE standards that Congress
had set for model years 1978, 1979, and
1980 if there was a ‘‘Federal standards
fuel economy reduction.’’ 529 This
provision did not involve a factor to be
balanced in determining fuel economy
standards. It provided for a reduction in
fuel economy standards for cars at a
time when only conventional pollutants
were regulated. The provision was
specifically designed to address
California’s then-existing smog
regulations, particularly with regard to
the additional weight (which other
things being equal reduces fuel
economy) associated with catalytic
converters. In so doing, Congress
recognized the potential interplay for
three model years between California’s
smog regulations and the possibility that
it could reduce Federal fuel economy
standards for those model years.530
528 Id.

at 1170.
Law 94–163 sec. 502(d), 89 Stat. 904–

529 Public

05.
530 See

H.R. No. 94–340, at 87.

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Thus, EPCA went on to include
‘‘Emissions standards under Section 202
of the CAA, and emissions standards
applicable by reason of Section 209(b) of
such Act’’ in its list of ‘‘categor[ies] of
Federal standards.’’ 531
Because California standards to
combat smog (not GHG regulations) ‘‘by
reason of section 209(b)’’ could be
considered to reduce federal fuel
economy standards for three years, the
district courts erroneously believed that
state CO2 regulations are somehow now
‘‘federal’’ standards under 49 U.S.C.
32902(f). On its face, this language
applied only to three long past model
years and only to reducing standards,
not setting them. ‘‘For purposes of this
subsection’’ referred to section 502(d) of
EPCA—not EPCA section 502(e) [now
49 U.S.C. 32902(f)] which sets forth the
EPCA factor of ‘‘the effect of other
Federal motor vehicle standards on fuel
economy.’’ After MY 1980, section
502(d) became obsolete. When EPCA
was recodified in 1994, section 502(d)
was dropped as executed and therefore
surplusage. As the listing of Federal
standards in 502(d) never had any
application outside that subsection and
ceased to have significance when that
subsection became obsolete, it had and
has no bearing on the recodified version
of EPCA. The recodification to rescind
this subsection, which had no
substantive significance for 14 years,
was entirely non-substantive.532
NHTSA believes that the district
courts in Green Mountain Chrysler and
Central Valley Chrysler-Jeep sought to
give a CAA waiver for the California
GHG regulation an effect far beyond the
terms of the CAA provision authorizing
such a waiver. As discussed previously,
the courts overlooked the fact that the
CAA itself makes clear that waiver of
preemption under that statute operates
only to relieve application of the CAA
preemption statute.533 State GHG
regulations, even if subject to an EPA
waiver, would remain regulations
‘‘adopt[ed] or enforc[ed]’’ by ‘‘a State or
political subdivision of a State’’ and
therefore would be subject to
preemption by EPCA.534
The courts’ view suggests an apparent
misunderstanding of the underlying
concerns and purposes of the
531 Id.

§ 502(d)(3)(D).
recodification was ‘‘[t]o revise, codify,
and enact without substantive change’’ laws related
to transportation. Public Law 103–272 (emphasis
added).
533 42 U.S.C. 7543(b)(1) (emphasis added); see
also 42 U.S.C. 7543(b)(3) (‘‘compliance with such
State standards shall be treated as compliance with
applicable Federal standards for purposes of this
subchapter’’) (emphasis added).
534 49 U.S.C. 32919(a).
532 The

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requirement to consider other standards.
There is no hint in the histories of either
EPCA or EISA of an intent to give other
standards special, much less superior,
status under EPCA. The limited
concerns and purpose were to ensure
that any adverse effects of other
standards on fuel economy considered
in connection with the fuel economy
standards. Those concerns are evident
in a 1974 report, entitled ‘‘Potential for
Motor Vehicle Fuel Economy
Improvement,’’ submitted to Congress
by the Department of Transportation
and EPA.535 That report noted that the
weight added by safety standards would
and one set of emissions standards
might temporarily reduce the level of
achievable fuel economy.536 These
concerns can also be found in the
congressional reports on EPCA.537
(f) State Tailpipe GHG Emissions
Standards Conflict With EPCA and are
Therefore Preempted Impliedly
Notwithstanding that state standards
limiting or prohibiting tailpipe CO2
emissions are expressly preempted by
EPCA, they also clearly conflict with the
objectives of EPCA and would therefore
also be impliedly preempted.
State regulation of CO2 emissions
would frustrate Congress’ objectives in
establishing the CAFE program and
conflict with NHTSA’s efforts to
implement the program in a manner
consistent with EPCA. While the
overarching purpose of EPCA may be
energy conservation, Congress directed
NHTSA to consider four factors in
establishing maximum feasible fuel
economy standards. NHTSA balances
these factors to determine, through the
CAFE program, the amount of energy
the light-duty vehicle fleet should
conserve. Allowing a state to make a
state-specific determination for how
much energy should be conserved (in
the same way that the CAFE program
conserves energy) necessarily frustrates
NHTSA’s efforts to make that
determination for the country as a
whole because it sends the industry in
different directions in order to try to
meet multiple standards at once rather
than allowing the industry to focus its
resources and efforts on the path laid
out at the Federal level. This is
particularly true when considering that
when California sets standards, other
states can choose to adopt those
535 This report was prepared in compliance with
Section 10 of the Energy Supply and Environmental
Coordination Act of 1974, Public Law 93–319.
536 See id. at 6–8 and 91–93.
537 See page 22 of Senate Report 94–179, pages 88
and 90 of House Report 94–340, and pages 155–7
of the Conference Report, Senate Report 94–516.

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standards and thereby further increase
the compliance complexity.
A critical objective of EPCA was to
establish a single national program to
regulate vehicle fuel economy.
Congress, in passing EPCA,
accomplished this objective by
providing broad preemptive power
established in the language codified at
49 U.S.C. 32919(a). Other congressional
objectives underlying EPCA include
avoiding serious adverse economic
effects on manufacturers and
maintaining a reasonable amount of
consumer choice among a broad variety
of vehicles. To guide the agency toward
the selection of standards meeting these
competing objectives, Congress
specified four factors that NHTSA must
consider in determining the maximum
feasible level of average fuel economy
and thus the level at which each
standard must be set. As discussed
above, since the only practical way to
reduce tailpipe CO2 emissions is to
improve fuel economy, it would be
impossible for a state tailpipe CO2
emissions standard to be adopted
without interfering with CAFE
standards. If a state were to establish
standards that have the effect of
requiring a lower level of fuel economy
than CAFE standards, those standards
would be meaningless since they would
not reduce CO2 emissions. Instead, a
State could only establish a standard
that has the effect of requiring a higher
level of average fuel economy. Setting
standards that are more stringent than
the fuel economy standards
promulgated under EPCA would upset
the efforts of NHTSA to balance and
achieve Congress’s competing goals.
Setting a standard above the level
judged by NHTSA to be consistent with
the statutory consideration after careful
consideration of these issues in a
rulemaking proceeding would negate
the agency’s careful analysis and
decision-making.
For the same reasons, a state
regulation having the effect of regulating
tailpipe carbon dioxide emissions or
fuel economy is likewise impliedly
preempted under 49 U.S.C. Chapter 329.
The Vermont and California district
court decisions discussed above
addressed conflict preemption. The
Green Mountain Chrysler court
concluded that the Vermont GHG
standards presented no conflict
preemption concerns and rejected the
contention that Vermont’s GHG
regulations would conflict with
Congress’ intent that there be a single,
nationwide fuel economy standard and
that those regulations upset NHTSA’s
careful balancing of the EPCA statutory
factors in its rulemaking proceedings. In

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rejecting the manufacturers’ arguments,
the court held that the Vermont
standards do not create an obstacle to
achieving EPCA’s goals because the
Vermont standards are, in the court’s
judgment, consistent with EPCA’s
standard setting criteria. In reaching that
conclusion, the court did not consider
the impact of the Vermont standards on
the balancing done by NHTSA in setting
CAFE standards. For its part, the court
in Central Valley Chrysler-Jeep
concluded that there was no conflict
preemption, since if California’s
standards were granted a waiver under
CAA section 209 by EPA, they would
satisfy CAA objectives and be consistent
with EPCA.538 The court simply
assumed consistency. If this assumption
proved incorrect, to the extent of any
incompatibility between the two
regimes, ‘‘NHTSA is empowered to
revise its standards’’ to take into
account California’s regulations,
according to that court.
NHTSA disagreed with the two
district court rulings at the time and
continues to do so now. We note that
the Vermont decision was appealed and
briefed (including an Amicus Brief filed
by the United States) prior to the stay
and withdrawal of the litigation
pursuant to the National Program
arrangement described previously.
NHTSA was not a party to those cases
and is not bound by these decisions.
Those erroneous decisions further
support the need for NHTSA, as the
agency with expert authority to interpret
EPCA, to reaffirm its longstanding view
of the preemption provision. Moreover,
EPA, as the agency charged with
administering the CAA, further
determines that CAA waivers do not
‘‘federalize’’ state standards; therefore,
state standards directly affecting fuel
economy are subject to EPCA
preemption even if there is a CAA
waiver in place.
(g) ZEV Mandates
Another form of EPCA-preempted
state regulation is a zero-emission
vehicle (ZEV) mandate. Such laws
require that a certain number or
percentage of vehicles sold or delivered
for sale within a state must be ZEVs,
vehicles that produce neither smogforming nor CO2 tailpipe emissions.
ZEV mandates may require either that
actual ZEVs be sold or delivered for sale
or provide for generation and
application of ZEV credits, which may
or may not be traded. While NHTSA has
not previously commented on the
relationship between the ZEV mandates
and the CAFE program because the only
538 529

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feasible means to eliminate tailpipe CO2
emissions is by eliminating the use of
petroleum fuel (i.e., electric or fuel cell
propulsion), and because the purpose of
the ZEV program is to affect fuel
economy,539 ZEV mandates directly
relate to fuel economy and are thereby
expressly preempted. ZEV mandates are
also intended to force the development
and commercial deployment of ZEVs—
regardless of the technological
feasibility or economic practicability of
doing so—putting the program entirely
at odds with critical factors that
Congress required NHTSA to consider
in establishing fuel economy standards.
Therefore, ZEV mandates also interfere
with achieving the goals of EPCA and
are therefore impliedly preempted.
California’s ZEV mandate represents
the most prominent example. California
initially launched its ZEV mandate in
1990 to force the development and
deployment of ZEVs to reduce smogforming emissions. As California’s Low
Emission Vehicle and EPA’s Tier 3
standards for criteria pollutant
emissions have become increasingly
stringent, the greater impact of
California’s ZEV mandate is the
reduction of tailpipe GHG emissions. In
its latest iteration the ZEV mandate no
longer focuses on tailpipe smog forming
emissions, a fact that CARB
acknowledged in 2012 when applying
for a waiver for its Advanced Clean Car
Program, in stating ‘‘[t]here is no criteria
emissions benefit from including the
ZEV proposal in terms of vehicle (tankto-wheel or TTW) emissions. The LEV
III criteria pollutant fleet standard is
responsible for those emission
reductions in the fleet; the fleet would
become cleaner regardless of the ZEV
regulation because manufacturers would
adjust their compliance response to the
standard by making less polluting
conventional vehicles.’’ 540
In its current configuration, the ZEV
mandate requires manufacturers to
generate credits based upon the number
of vehicles delivered for retail sale.
Vehicles earn varying amounts of ZEV
credits depending upon technology and
range, with some vehicles earning
several credits. Manufacturers
delivering for sale certain plug-in hybrid
539 See, e.g., Fact Sheet: 2003 Zero Emission
Vehicle Program, California Air Resources Board
(March 18, 2004), available at https://
www.arb.ca.gov/msprog/zevprog/factsheets/
2003zevchanges.pdf (stating that one of the
‘‘significant features of the April 2003 changes to
the ZEV regulation’’ included removal of ‘‘all
references to fuel economy or efficiency,’’ after a
2002 lawsuit asserting that AT PZEV provisions
pertaining to the fuel economy of hybrid electric
vehicles were preempted by EPCA).
540 Docket No. EPA–HQ–OAR–2012–0562, Pp.
15–16.

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vehicles earn some limited ZEV credits,
even though they are not truly ZEVs, but
such credits can only satisfy a portion
of a manufacturer’s ZEV credit
requirements. The credit requirements
increase annually, with the number of
required credits equaling 4.5% of a
manufacturer’s light duty vehicle sales
in 2018, rising to 22% in 2025.541 To hit
this 22% credit requirement, a
manufacturer would need to deliver for
sale ZEVs totaling somewhere between
less than eight percent and 15.4% of
their light duty sales in California, per
various projections.542 With advance
notice, manufacturers may elect to use
credits earned from over-complying
with vehicle tailpipe GHG emission
requirements toward partial satisfaction
of the ZEV mandate.
The EPA has granted a waiver of CAA
preemption under Section 209 of the
CAA for California’s Advanced Clean
Car program, which includes
California’s ZEV mandate in addition to
California’s GHG regulation and LEV
program. Nine other states have elected
to adopt the ZEV mandate pursuant to
Section 177 of the CAA 543—which,
combined with California, represent
approximately 30% of United States
light duty vehicle sales annually.544
Manufacturers must satisfy the ZEV
mandate for each state. While,
traditionally, manufacturers could apply
credits earned in one state to satisfy the
requirements of another state, this
‘‘travel’’ provision is limited only to fuel
cell electric vehicles beginning with MY
2018.
Accordingly, manufacturers must
endeavor to design, produce, and
deliver for sale significant numbers of
vehicles that produce zero tailpipe CO2
emissions within each state that has
adopted the California ZEV mandate.
541 Cal.

Code Regs. tit.13, sec. 1962.2(b).
Air Resources Board initially projected
that 15.4% of new vehicles delivered for sale would
consist of ZEVs. See., e.g., Staff Report: Initial
Statement of Reasons 2012 Proposed Amendments
to the California Zero Emission Vehicle Program
Regulations, California Air Resources Board at 48
(Dec. 7, 2011), available at https://www.arb.ca.gov/
regact/2012/zev2012/zevisor.pdf (stating ‘‘[b]y
model year 2025, staff expects 15.4 percent of new
sales will be ZEVs and [Plug-In Hybrids].’’)
However, an increased supply of credits and
projected increases in battery electric range has
resulted in others projecting reduced required ZEV
fleet penetration. See, e.g., What is ZEV?, Union of
Concerned Scientists (Oct. 31, 2016), https://
www.ucsusa.org/clean-vehicles/california-andwestern-states/what-is-zev (projecting ‘‘about 8
percent of sales to be ZEVs’’ in 2025).
543 These states are Connecticut, Maine,
Maryland, Massachusetts, New Jersey, New York,
Oregon, Rhode Island, and Vermont.
544 See Automotive Retailing: State by State,
National Automobile Dealers Association, https://
www.nada.org/statedata/ (last visited June 25,
2018) (estimating that these states represented
28.6% of new motor vehicle registrations in 2016).

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This involves implementation of some
of the most expensive and advanced
technologies in the automotive industry,
regardless of consumer demand (which
tends to be lower during periods of
sustained relatively-low gasoline
prices). The California Air Resources
Board’s own midterm review report for
their Advanced Clean Car program cites
estimates from the 2016 Draft Technical
Assessment Report relating to the
incremental vehicle costs of ZEVs over
2016 vehicles with internal combustion
engines.545 While stating marginal
increased costs have fallen when
compared to previous estimates, CARB
nevertheless still shows battery electric
subcompact vehicles with 75 miles of
range, for which consumer demand
remains very low, as costing $7,505
more than ones with an internal
combustion engine, with large cars
costing $11,355 more. Battery electric
subcompacts with a 200-mile range, for
which consumer demand is slightly
higher than a 75-mile range, were
estimated to cost $12,001 more than
comparable vehicles with internal
combustion engines, and large cars
$16,746 more. Even subcompact plug-in
hybrids with 40 miles of electric range
cost $9,260 more than internal
combustion engine equivalents, and
$13,991 more for large cars. And as
discussed above, consumers have not
been willing to pay the full cost of this
technology—meaning manufacturers are
likely to spread the costs of the ZEV
mandate to non-ZEV vehicles (and to
vehicles sold in other states). This
expensive and market-distorting
mandate for manufacturers to eliminate
vehicle tailpipe CO2 emissions (and
thus petroleum fuel use) for part of their
fleets has always interfered with
NHTSA’s balancing of statutory factors
in establishing maximum feasible fuel
economy standards, and increasing ZEV
credit requirements through 2025 make
it all-the-more of an obstacle to
accomplishing EPCA’s goal of
establishing a coherent national fuel
economy program. Unlike NHTSA’s
CAFE program, the ZEV mandate forces
investment in specific technology
(electric and fuel cell technology) rather
than allowing manufacturers to improve
fuel economy through more costeffective technologies that better reflect
consumer demand.546 This appears to
conflict directly with Congress’ intent
that CAFE standards be performance545 California Air Resources Board, California’s
Advanced Clean Cars Midterm Review, Appendix
C, Zero Emission Vehicle and Plug-in Hybrid
Electric Vehicle Technology Assessment, Table 8, at
C–64 (Jan. 18, 2017), available at https://
www.arb.ca.gov/msprog/acc/mtr/appendix_c.pdf.
546 13 Cal. Code of Regulations 1962.2.

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based rather than design mandates.
Moreover, by forcing manufacturers to
design, produce, and deliver for sale
vehicles that produce no tailpipe CO2
emissions, the ZEV mandate forces
further expensive investments in fuelsaving technology than NHTSA has
determined appropriate to require in
setting fuel economy standards.547 We
seek comment on the extent to which
compliance with the ZEV mandate
frustrates manufacturers’ efforts to
comply with CAFE standards.
For the reasons outlined above, the
California ZEV mandate is expressly
and impliedly preempted by EPCA.
While EPA had previously granted a
waiver of CAA preemption for
California’s Advanced Clean Car
Program, which includes the California
ZEV mandate, this waiver has no effect
on EPCA preemption of the ZEV
mandate, as described above.
3. Conclusion and Severability
Given the importance of an effective,
smooth functioning national program to
regulate fuel economy and in light of the
failure of two Federal district courts to
consider NHTSA’s analysis and
carefully crafted position on
preemption, NHTSA is considering
taking the further step of summarizing
that position in an appendix to be added
to the parts in the Code of Federal
Regulations setting forth the passenger
car and light truck CAFE standards.
That proposed regulatory text may be
found at the end of this preamble.
NHTSA considers its proposed
decision on the maximum feasible
CAFE standards for MY 2021–2026 to be
severable from its decision on EPCA
preemption. Our proposed
interpretation of 49 U.S.C. 32919 does
not depend on our decision to finalize
and a court’s decision to uphold, the
CAFE standards being proposed today
under 49 U.S.C. 32902. NHTSA solicits
comment on the severability of these
actions.

547 See, e.g., Alan, J., Hardman, S. & Carley, S.
Cost implications for automakers’ compliance with
emission standards from Zero Emissions Vehicle
mandate, TRB 2018 Annual Meeting paper
submittal, https://trid.trb.org/view/1495714 (last
accessed June 28, 2018) (finding based on
independent research that in 2025, costs reach
approximately $1,500 per vehicle on average to
comply with CAFE alone and increase to around
$2,100 per vehicle on average to comply with both
CAFE and ZEV).

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B. Preemption Under the Clean Air Act
1. Background
(a) Statutory Background: Clean Air Act
Section 209(a) Preemption, Section
209(b)(1) California Waiver, and Section
209(b)(1)(A)–(C) Prohibitions on Waiver
EPA’s regulation of new motor
vehicles under Title II generally
preempts state standards in the same
subject area. Section 209(a) of the Act
provides that:
‘‘No State or any political subdivision
thereof shall adopt or attempt to enforce any
standard relating to the control of emissions
from new motor vehicles or new motor
vehicle engines subject to this part. No State
shall require certification, inspection or any
other approval relating to the control of
emissions from any new motor vehicle or
new motor vehicle engine as condition
precedent to the initial retail sale, titling (if
any), or registration of such motor vehicle,
motor vehicle engine, or equipment.’’ 548

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However, Title II affords special
treatment to California: Subject to
certain conditions, it may obtain from
EPA a waiver of section 209(a)
preemption. Specifically, section
209(b)(1) of the Act requires the
Administrator, after an opportunity for
public hearing, to waive application of
the prohibitions of section 209(a) to
California, if California determines that
its State standards will be, in the
aggregate, at least as protective of public
health and welfare as applicable Federal
standards.549 A waiver under section
209(b)(1) allows California to ‘‘adopt
[and] enforce a[] standard relating to the
control of emissions from new motor
vehicles or new motor vehicle engines.’’
CAA section 209(a), 42 U.S.C. 7543(a).
But California’s ability to obtain a
waiver is not unlimited. The statute
provides that ‘‘no such waiver will be
granted’’ if the Administrator finds any
of the following: ‘‘(A) [California’s]
determination [that its standards in the
aggregate will be at least as protective]
is arbitrary and capricious, (B)
[California] does not need such State
standards to meet compelling and
extraordinary conditions, or (C) such
State standards and accompanying
enforcement procedures are not
consistent with section [202(a)].’’
548 Clean Air Act (CAA) section 209(a), 42 U.S.C.
7543(a).
549 CAA section 209(b), 42 U.S.C. 7543(b). The
provision does not identify California by name.
Rather, it applies on its face to ‘‘any State which
has adopted standards (other than crankcase
emission standards) for the control of emissions
from new motor vehicles or new motor vehicle
engines prior to March 30, 1966.’’ California is the
only State that meets this requirement. See S. Rep.
No. 90–403 at 632 (1967). This proposal refers
interchangeably to ‘‘California’’ and ‘‘CARB’’ (the
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Section 209(b)(1)(A)–(C), 42 U.S.C.
7543(b)(1)(A–(C) (Emphasis added).550
Any one of these three findings operates
to forbid a waiver.
(1) EPA’s Proposed Action
EPA is proposing to withdraw the
January 9, 2013 waiver of preemption
for California’s Advanced Clean Car
(ACC) program, Zero Emissions Vehicle
(ZEV) mandate, and Greenhouse Gas
(GHG) standards that are applicable to
new model year (MY) 2021 through
2025. 78 FR 2145 (January 9,
2013.) 551 552 EPA proposes to do so on
multiple grounds.
First, EPA notes that elsewhere in this
notice NHTSA has proposed to find that
California’s GHG and ZEV standards are
preempted under EPCA. Although EPA
has historically declined to consider as
part of the waiver process whether
California standards are constitutional
or otherwise legal under other Federal
statutes apart from the Clean Air Act,
EPA believes that this notice presents a
unique situation and that it is
appropriate to consider the implications
of NHTSA’s proposed conclusion as
part of EPA’s reconsideration of the
waiver. In this regard, EPA is proposing
to conclude that state standards
preempted under EPCA cannot be
afforded a valid waiver of preemption
under CAA 209(b). Accordingly, EPA is
proposing to conclude that if NHTSA
finalizes a determination that
California’s GHG and ZEV standards are
preempted, then it would be necessary
to withdraw the waiver separate and
apart from the analysis under section
209(b)(1)(B), (C) that follows.
Second, under section 209(b)(1)(B)
(compelling and extraordinary
550 As presented in the United States Code, the
cross-reference in prong (C) is to ‘‘section 7521(a)
of this title,’’ i.e., CAA section 201(a), 42 U.S.C.
7521(a), which governs EPA’s administration of
‘‘Emission standards for new motor vehicles or new
motor vehicle engines administration of ‘‘Emissions
standards for new motor vehicles or new motor
vehicle engines.’’
551 This proposed action does not address
whether the statutory interpretations and their
policy consequences laid out in the proposal may
have implications for past waivers granted to
California for other standards besides its GHG and
ZEV standards. EPA proposes to take this action in
the context of this joint rulemaking with NHTSA,
and the California standards identified herein are
the focus of EPA’s proposal. As circumstances
require and resources permit, EPA may in future
actions consider whether this proposal, if finalized,
makes it appropriate or necessary to revisit past
grants of other waivers beyond those granted with
respect to California’s GHG and ZEV program.
552 EPA proposes to withdraw the waiver for
these model years because these are the model years
at issue in NHTSA’s proposal. EPA solicits
comment on whether one or more of the grounds
supporting the proposed withdrawal of this waiver
would also support withdrawing other waivers that
it has previously granted.

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conditions), EPA proposes to find that
California does not need its GHG and
ZEV standards to meet compelling and
extraordinary conditions because those
standards address environmental
problems that are not particular or
unique to California, that are not caused
by emissions or other factors particular
or unique to California, and for which
the standards will not provide any
remedy particular or unique to
California.
Third, under section 209(b)(1)(C)
(consistency with section 202(a)), EPA
proposes to find that California’s GHG
and ZEV standards are inconsistent with
section 202(a) because they are
technologically infeasible in that they
provide sufficient lead time to permit
the development of necessary
technology, giving appropriate
consideration to compliance costs.553
EPA therefore proposes to make
findings under sections 209(b)(1)(B) and
(C), either of which, as discussed above,
independently triggers the statutory
prohibition that ‘‘no such waiver will be
granted.’’
In addition, EPA proposes to
conclude that States may not adopt
California’s GHG standards pursuant to
section 177 because the text, context,
and purpose of section 177 support the
conclusion that this provision is limited
to providing States the ability, under
certain circumstances and with certain
conditions, to adopt and enforce
standards designed to control criteria
pollutants to address NAAQS
nonattainment.
(2) History of Waiver for California GHG
and ZEV Standards, and Associated
Issues of Statutory Interpretation
In December 2005, California for the
first time applied to EPA for a
preemption waiver for GHG standards
for MY 2009 and following. EPA denied
this request in March 2008, relying on
the second prong under section
209(b)(1)(B) and finding that California
did not need those standards to meet
compelling and extraordinary
conditions. In doing so, it noted that
GHG standards, unlike prior standards
for which California had requested and
received waivers, are designed to
address global air pollution problems—
not air pollution problems specific to
California. 73 FR 12156, March 6, 2008.
553 Under section 209(b)(1)(C) of the CAA, EPA
must deny California’s waiver request if EPA finds
that California’s standards and accompanying
enforcement procedures are not consistent with
section 202(a). Section 202(a) provides that an
emission standard shall take effect after such period
of time as the Administrator finds necessary to
permit development and application of the requisite
technology, giving appropriate consideration to
compliance costs.

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Due to this new circumstance, EPA
reconsidered its historic interpretation
and application of section 209(b)(1)(B).
Although today’s proposal contains
proposed findings under each prong of
209(b)(1), prong (B) was the only one at
issue in the 2008 waiver denial (and
EPA’s subsequent reversal), and it
merits extended discussion at the outset
due to its central significance in the
policy and legal context and the history
underlying today’s proposal.
As a general matter, EPA had
historically interpreted section
209(b)(1)(B) to require EPA to consider
whether, to meet compelling and
extraordinary conditions in California,
the state needs to have its own separate
new motor vehicle program in the
aggregate.554 Under this historical
approach, EPA considered California’s
need for a separate program as a whole,
rather than California’s need for the
particular aspect of the program for
which California sought a waiver in any
particular instance. (Typically, prior to
its ACC program waiver request,
California would seek a waiver for only
particular aspects of its new motor
vehicle program.) In the 2008 GHG
waiver denial, EPA determined that this
interpretation was inappropriate under
the circumstances.
In its 2008 waiver denial, EPA
proceeded under two alternative
constructions of the statute. Under both
of these constructions, EPA determined
that it was a reasonable interpretation of
section 209(b)(1)(B) to require a separate
review of California’s need for standards
designed to address a global air
pollution problem and its effects, as
distinct from other portions of
California’s new motor vehicle program,
which up until then had been designed
to address local or regional air pollution
problems.555 Under the first
construction, EPA found it relevant that
elevated GHG concentrations in
California were similar to
concentrations found elsewhere in the
world, and that local conditions in
California, such as the local topography,
the local climate, and the significant
number of motor vehicles in California,
were not the determining factors
causing the elevated GHG
concentrations found in California and
elsewhere. In sum, EPA found that
California did not need its GHG
standards to meet ‘‘compelling and
extraordinary conditions’’—interpreting
‘‘compelling and extraordinary
554 See,

e.g., 49 FR 18887 (May 3, 1984).
pollutants generally present public
health and environmental concern in proportion to
their ambient local concentration and California has
long had unusually severe problems in this regard.
555 Criteria

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conditions’’ to mean environmental
problems with causes that were specific
to California—given that those
standards were designed to address
global air pollution problems as
compared to local or regional air
pollution problems caused specifically
by certain conditions in California.
EPA in the 2008 waiver denial also
applied a second, alternative
construction of section 209(b)(1)(B).
Under this alternative construction, EPA
considered whether the impacts of
climate change in California were
sufficiently different enough from the
impacts felt in the rest of the country
such that California could be considered
to need its GHG standards to meet
compelling and extraordinary
conditions—interpreting ‘‘compelling
and extraordinary conditions’’ to mean
environmental effects specific to
California.
The next year, following a
presidential election and change in
administration, EPA reconsidered the
2008 denial at California’s request. On
reconsideration, EPA reversed course
and granted a waiver for California’s
GHG standards. 74 FR 32744 (July 9,
2009). In granting the waiver, EPA
reverted to its historical interpretation
of section 209(b)(1)(B), under which it
had construed ‘‘compelling and
extraordinary conditions’’ to mean
environmental problems caused by
conditions specific to California and/or
effects experienced to a unique degree
or in a unique manner in California, and
under which it had evaluated
California’s need for its own, separate
new motor vehicle program as a whole,
rather than California’s need for the
specific aspects of its separate program
for which it was seeking a waiver. In
reverting to this determination, the EPA
necessarily determined that it makes no
difference whether California seeks a
waiver to implement separate standards
in response to its own specific, local air
pollution problems, or whether
California seeks a waiver to implement
separate standards designed to address
a global air pollution problem.
Since 2009, EPA has continued to
adhere to this interpretation and
application of section 209(b)(1)(B) when
reviewing CARB’s waiver requests,
regardless of whether the waiver was
requested with regard to standards
designed to address traditional, local
environmental problems, or global
climate issues. In this proposal, the EPA
proposes to determine that this
reversion to the pre-2008 interpretation
was not appropriate.
On January 9, 2013, EPA granted
CARB’s request for a waiver of
preemption to enforce its ACC program

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regulations pursuant to CAA section
209(b). 78 FR 2112. The ACC program
is a single coordinated package
comprising regulations for ZEV and
low-emission vehicles (LEV)
regulations,556 for new passenger cars,
light-duty trucks, medium-duty
passenger vehicles, and certain heavyduty vehicles, for MY 2015 through
2025. Thus, in terms of proportion, the
ACC program is comparable to the
combined Federal Tier 3 Motor Vehicle
Emissions Standards and the 2017 and
later MY Light-duty Vehicle GHG
Standards.557 According to CARB, the
ACC program was intended to address
California’s near and long-term smog
issues as well as certain specific GHG
emission reduction goals.558 78 FR
2114. See also 78 FR 2122, 2130–31.
The ACC program regulations impose
multiple and varying complex
compliance obligations that have
simultaneous, and sometimes
overlapping, deadlines with each
556 The LEV regulations in question include
standards for both GHG and criteria pollutants
(including ozone and PM). Amendments for the
LEV III program included replacement of separate
nonmethane organic gas (NMOG) and oxides of
nitrogen (NOX) standards with combined NMOG
plus NOX standards, which provides automobile
manufacturers with additional flexibility in meeting
the new stringent standards; an increase of full
useful life durability requirements from 120,000
miles to 150,000 miles, which guarantees vehicles
sustain these extremely low emission levels longer;
a backstop to assure continued production of superultra-low-emission vehicles after partial-zeroemission vehicles (PZEVs) as a category are moved
from the ZEV regulations to the LEV regulations in
2018; more stringent particulate matter (PM)
standards for light- and medium-duty vehicles,
which will reduce the health effects and premature
deaths associated with these emissions; zero fuel
evaporative emission standards for PCs and LDTs,
and more stringent standards for medium- and
heavy-duty vehicles (MDVs); and, more stringent
supplemental federal test procedure (SFTP)
standards for PC and LDTs, which reflect more
aggressive real world driving and, for the first time,
require MDVs to meet SFTP standards. 78 FR 2114.
557 78 FR 23641, April 22, 2016; 77 FR 62624,
October 15, 2012.
558 ‘‘The Advanced Clean Cars program . . . will
reduce criteria pollutants . . . and . . . help
achieve attainment of air quality standards; The
Advanced Clean Cars Program will also reduce
greenhouse gases emissions as follows: by 2025,
CO2 equivalent emissions will be reduced by 13
million metric tons (MMT) per year, which is 12
percent from base line levels; the reduction
increases in 2035 to 31 MMT/year, a 27 percent
reduction from baseline levels; by 2050, the
proposed regulation would reduce emissions by
more than 40 MMT/year, a reduction of 33 percent
from baseline levels; and viewed cumulatively over
the life of the regulation (2017–2050), the proposed
Advanced Clean Cars regulation will reduce by
more than 850 MMT CO2-equivalent, which will
help achieve the State’s climate change goals to
reduce the threat that climate change poses to
California’s public health, water resources,
agriculture industry, ecology and economy.’’ 78 FR
2114. CARB Resolution 12–11, at 19, (January 26,
2012), available in the docket for the January 2013
waiver action, Document No. EPA–HQ–OAR–2012–
0562, the docket for the ACC program waiver.

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standard. These deadlines began in 2015
and are scheduled to be phased in
through 2025. For example, compliance
with the GHG requirements began in
2017 and will be phased-in through
2025. The implementation schedule and
the interrelationship of regulatory
provisions with each of the three
standards together demonstrates that
CARB intended that at least the GHG
and ZEV standards, if not also the LEV
standards, would be implemented as a
cohesive program. For example, in its
ACC waiver request, CARB stated that
the ‘‘ZEV regulation must be considered
in conjunction with the proposed LEV
III amendments. Vehicles produced as a
result of the ZEV regulation are part of
a manufacturer’s light-duty fleet and are
therefore included when calculating
fleet averages for compliance with the
LEV III GHG amendments.’’ CARB’s
Initial Statement of Reasons at 62–63.559
CARB also noted ‘‘[b]ecause the ZEVs
have ultra-low GHG emission levels that
are far lower than non-ZEV technology,
they are a critical component of
automakers’ LEV III GHG standard
compliance strategies.’’ Id. CARB
further explained that ‘‘the ultra-low
GHG ZEV technology is a major
component of compliance with the LEV
III GHG fleet standards for the overall
light duty fleet.’’ Id. CARB’s request also
repeatedly touted the GHG emissions
benefits of the ACC program.
Up until the ACC program waiver
request, CARB had relied on the ZEV
requirements as a compliance option for
reducing criteria pollutants.
Specifically, California first included
the ZEV requirement as part of its first
LEV program, which was then known as
LEV I, that mandated a ZEV sales
requirement that phased-in starting with
the 1998 MY through 2003 MY. EPA
issued a waiver of preemption for these
regulations on January 13, 1993 (58 FR
4166 (January 13, 1993). Since this
initial waiver of preemption, California
has made multiple amendments to the
ZEV requirements and EPA has
subsequently granted waivers for those
amendments. In the ACC program
waiver request California also included
a waiver of preemption request for ZEV
amendments that related to 2012 MY
through 2017 MY and imposed new
requirements for 2018 MY through 2025
MY (78 FR 2118–9). Regarding the ACC
program ZEV requirements, CARB’s
waiver request noted that there was no
criteria emissions benefit in terms of
vehicle (tank-to-wheel—TTW)
emissions because its LEV III criteria
559 Available in the docket for the January 2013
waiver decision, Docket No. EPA–HQ–OAR–2012–
0562.

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pollutant fleet standard was responsible
for those emission reductions.560 CARB
further noted that its ZEV regulation
was intended to focus primarily on zero
emission drive—that is, battery electric
(BEVs), plug-in hybrid electric vehicles
(PHEVs), and hydrogen fuel cell
vehicles (FCVs)—in order to move
advanced, low GHG vehicles from
demonstration phase to
commercialization (78 FR 2122, 2130–
31). Specifically, for 2018 MY through
2025 MY, the ACC program ZEV
requirements mandate use of
technologies such as BEVs, PHEVs and
FCVs, in up to 15% of a manufacturer’s
California fleet and in each of the
section 177 States by MY 2025 561 (78
FR 2114). Additionally, the ACC
program regulations provide various
compliance flexibilities allowing for
substitution of compliance with one
program requirement for another. For
instance, manufacturers may opt to
over-comply with the GHG fleet
standard in order to offset a portion of
their ZEV compliance requirement for
MY 2018 through 2021. Further, until
MY 2018, sales of BEVs (since MY 2018,
limited to FCVs) in California count
toward a manufacturer’s credit
requirement in section 177 States. This
is known as the ‘‘travel provision’’ (78
FR 2120).562 For their part, the GHG
emission regulations include an
optional compliance provision that
allows manufacturers to demonstrate
compliance with CARB’s GHG
standards by complying with applicable
Federal GHG standards. This is known
as the ‘‘deemed to comply’’
provision.563 A complete description of
560 CARB ACC waiver request at EPA–HQ–OAR–
2012–0562–0004.
561 Under section 177, any State that has state
implementation plan provisions approved under
part D of Subchapter I of the Act may opt to adopt
and enforce standards that are identical to
standards for which EPA has granted a waiver of
preemption to California under CAA section 209(b).
EPA’s longstanding interpretation of section 209(b)
and its relationship with section 177 is that it is not
appropriate under section 209(b)(1)(C) to review
California regulations, submitted by CARB, through
the prism of adopted or potentially adopted
regulations by section 177 States.
562 On March 11, 2013, the Association of Global
Automakers and Alliance of Automobile
Manufacturers filed a petition for reconsideration of
the January 2013 waiver grant, requesting that EPA
reconsider the decision to grant a waiver for MYs
2018 through 2025 ZEV standards on technological
feasibility grounds. Petitioners also asked for
consideration of the impact of the travel provision,
which they argue raise technological feasibility
issues in section 177 States, as part of the agency’s
review under section 209(b)(1)(C). EPA continues to
evaluate the petition.
563 On May 7, 2018, California issued a notice
seeking comments on ‘‘potential alternatives to a
potential clarification’’ of this provision for MY
vehicles that would be affected by revisions to the
Federal GHG standards. The notice is available at

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the ACC program can be found in
CARB’s waiver request, located in the
docket for the January 2013 waiver
action, Docket No. EPA–HQ–OAR–
2012–0562.
2. Statutory Provisions Applicable to the
Proposed Action
Under section 209(b) of the Clean Air
Act, EPA may reconsider a grant of a
waiver of preemption and withdraw
same if the Administrator makes any
one of the three findings in section
209(b)(1)(A), (B) and (C). EPA’s
authority to reconsider and withdraw
the grant of a waiver for the ACC
program is implicit in section 209(b)
given that the authority to revoke a grant
of authority is implied in the authority
for such a grant. Further support for
EPA’s authority is based on the
legislative history for section 209(b),
and the judicial principle that agencies
possess inherent authority to reconsider
their decisions.564 The legislative
history from the 1967 CAA amendments
where Congress enacted the provisions
now codified in section 209(a) and (b)
provides support for this view. The
Administrator has ‘‘the right . . . to
withdraw the waiver at any time [if]
after notice and an opportunity for
public hearing he finds that the State of
California no longer complies with the
conditions of the waiver.’’ S. Rep. No.
50–403, at 34 (1967). Additionally,
subject to certain limitations,
administrative agencies possess
inherent authority to reconsider their
decisions in response to changed
circumstances. It is well settled that
EPA has inherent authority to
reconsider, revise, or repeal past
decisions to the extent permitted by law
so long as the Agency provides a
reasoned explanation. This authority
exists in part because EPA’s
interpretations of the statutes it
administers ‘‘are not carved in stone.’’
Chevron U.S.A. Inc. v. NRDC, Inc., 467
U.S. 837, 863 (1984). An agency ‘‘must
consider varying interpretations and the
wisdom of its policy on a continuing
basis.’’ Id. at 863–64. This is true when,
as is the case here, review is undertaken
‘‘in response to . . . a change in
administration.’’ National Cable &
Telecommunications Ass’n v. Brand X
internet Services, 545 U.S. 967, 981
(2005). The EPA must also be cognizant
https://www.arb.ca.gov/msprog/levprog/leviii/
leviii_dtc_notice05072018.pdf.
564 In 2009, EPA reconsidered the 2008 GHG
waiver denial at CARB’s request and granted it
upon reconsideration. 72 FR 32744. The EPA noted
the authority to ‘‘withdraw a waiver in the future
if circumstances make such action appropriate.’’
See 74 FR 32780 n.222; see also 32752–53 n.50
(citing 50 S. Rep. No. 403, at 33–34), 32755 n.74.

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where it is changing a prior position and
articulate a reasoned basis for the
change. FCC v. Fox Television Stations,
Inc., 556 U.S. 502, 515 (2009). This
proposal reflects changed circumstances
that have arisen since the initial grant of
the 2013 ACC program waiver of
preemption. They include the agency’s
reconsideration of California’s record
support for, and EPA’s decision and
underlying statutory interpretation on,
California’s need for GHG and ZEV
standards, as well as costs and
technological feasibility considerations
that differ from California’s assumptions
and which were bases for agency
conclusions that were made at that time.
When California submits a package of
standards for EPA review pursuant to
CAA section 209, EPA has long
interpreted the statute as authorizing
EPA to approve certain provisions and
defer action on others. EPA believes this
approach of partially approving
submissions is implicit in section 209,
particularly given the fact that EPA’s
evaluation of the technological
feasibility of standards is best
understood as in effect an evaluation of
each standard for each year (i.e.,
standards that are submitted together
may vary substantially in their effect
and some may require longer lead time
than others). Furthermore, since
California always retains the authority
as a matter of state law to determine
whether to implement state standards
for which a waiver of preemption has
been granted, we do not believe this
approach poses the risk that a partial
approval could force California to
implement a program they would not
have chosen had they anticipated EPA’s
decision. EPA believes that because its
authority to grant waivers of preemption
is best understood as applying on a
granular level—where the feasibility of
compliance for a particular year can be
assessed—rather than being limited to
approving or disapproving preemption
for an entire package of standards
submitted together, it follows that EPA’s
authority to withdraw the grant of
waiver of preemption should also apply
on a granular level, i.e., for any model
year for which EPA concludes the
conditions for waiver of preemption no
longer exist or for which it concludes
that it erred in its prior determination
that one of the conditions triggering a
denial a waiver was not met. Further,
because neither the Clean Air Act nor
the Administrative Procedure Act
specify deadlines for reconsideration of
agency action, EPA may, issue a new
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taking into account statutory mandates
and any applicable court orders.565
EPA is proposing to withdraw the
grant of a waiver of preemption for
California to enforce the GHG and ZEV
standards of the ACC program for MY
2021–2025. EPA proposes to withdraw
due to separate proposed findings under
section 209(b)(1)(B), and (C).566
Under section 209(b)(1)(B), EPA is
proposing to find that California does
not need its ZEV and GHG standards to
meet compelling and extraordinary
conditions in California. EPA is
proposing to find that CARB does not
need its own GHG and ZEV standards
to meet compelling and extraordinary
conditions in California given that
‘‘compelling and extraordinary
conditions’’ mean environmental
problems with causes and effects in
California whereas GHG emissions
present global air pollution problems.
Additionally, California does not need
the ZEV requirements to meet
‘‘compelling and extraordinary’’
conditions in California given that it
allows manufacturers to generate credits
in section 177 states as a means to
satisfy those manufacturers’ obligations
to comply with the mandate that a
certain percentage of their vehicles sold
in California be ZEV (or be credited as
such from sales in section 177 States).
Under section 209(b)(1)(C), EPA is
proposing to find that CARB’s GHG and
ZEV standards are not consistent with
section 202(a) based on changed
circumstances since the January 2013
waiver. Specifically, the agency is, in
this action, jointly proposing with
NHTSA revisions to the Federal GHG
and fuel economy standards based on
proposed conclusions that the current
(or augural) standards for MY 2021
through 2025 are not feasible. The
proposed findings in this notice call
into question CARB’s projections and
assumptions that underlay the
technological feasibility findings for its
waiver application for the GHG
standards and thus the technological
findings made by EPA in 2013 in
connection with the grant of the waiver
for the ACC program.
Similarly, with regard to ZEV
standards, this notice also raises
565 On March 11, 2013, EPA received a petition
for reconsideration from the Association of Global
Automakers and Alliance of Automobile
Manufacturers of the decision to grant a waiver for
MYs 2018 through 2025 ZEV standards.
566 Under this provision, a waiver is not
permitted if (A) the protectiveness determination of
the State is arbitrary and capricious; (B) the State
does not need such State standards to meet
compelling and extraordinary conditions; or (C)
such State standards and accompanying
enforcement procedures are not consistent with
section 202(a) of the Act.

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questions as to CARB’s technological
projections for ZEV-type technologies,
which are a compliance option for both
the ZEV mandate and GHG standards.
As also previously discussed, above,
CARB’s ZEV regulations include the
travel provision, which previously
allowed manufacturers to earn credit for
ZEVs sold in California (which, despite
very slow ZEV sales, far outpaces any
other State in these sales) to comply
with credit requirements in section 177
States. Starting with MY 2018, this
provision only applies to FCVs. When
the travel provision was adopted, it was
anticipated that by MY 2018, incentives
of this type for BEV sales would no
longer be necessary—i.e., that
consumers would adopt such vehicles
on their own. Unfortunately, there has
been a serious lack of market
penetration, consumer demand levels,
and lack of or slow development of
necessary infrastructure for any ZEVs—
BEV or otherwise—in such States. This
in turn means that manufacturers’ sales
of ZEVs in section 177 States are
unlikely, contrary to CARB’s projections
in its submissions to support its
application for the ACC waiver, to
generate sufficient credits to satisfy
those manufacturers’ obligations to
comply with the mandate that a certain
percentage of their vehicles sold in
California be ZEV (or be credited as
such from sales in section 177 States).
In short, EPA is now of the view that
CARB’s projections and assumptions at
the time of the waiver request were
overly ambitious and likely will not be
realized within the provided lead time.
Thus, EPA is also proposing to find that
CARB’s ZEV standards for MY 2021
through 2025, and the GHG standards
which rely on the ZEV requirement as
a compliance option, are technologically
infeasible and therefore, not consistent
with section 209(b)(1)(C).
As described above, EPA is proposing
to withdraw the waiver with respect to
California’s ZEV standards based on
findings made pursuant to sections
209(b)(1)(B) and 209(b)(1)(C). EPA is
proposing to withdraw the waiver with
respect to California’s GHG standards
based on findings made under these
three prongs as well as a separate
finding made under section 209(b)(1)(B).
Additionally, because the ZEV and GHG
standards are closely interrelated, as
demonstrated by the description above
of their complex, overlapping
compliance regimes, EPA is proposing
to withdraw the waiver of preemption
for ZEV standards under the second and
third prongs of section 209(b)(1).
EPA believes that a finding made
pursuant to any of the prongs of section
209(b)(1) is an independent and

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adequate ground to withdraw the
waiver. In this regard, EPA notes that
the statute provides that ‘‘No such
waiver shall be granted if the
Administrator finds that—(B) the State
does not need such State standards to
meet compelling and extraordinary
conditions; or (C) such State standards
and accompanying enforcement
procedures are not consistent with
section 202(a) of the Act.’’ (Emphasis
added.) Consequently, a final waiver
withdrawal decision that relies on more
than one of these provisions would
present independent and severable
bases for the decision to withdraw. And,
separate and apart from its analysis
under 209(b)(1)(A)–(C), EPA proposes to
determine that if NHTSA finalizes its
proposed determination that EPCA
preempts California’s standards, that
would provide an independent and
adequate ground to withdraw the waiver
for those standards. EPA proposes to
interpret section 209(b)(1) to only
authorize it to waive CAA preemption
for standards that are not independently
preempted by EPCA.
Additionally, under CAA section 177,
States that have designated
nonattainment areas may opt to adopt
and enforce standards that are identical
to standards for which EPA has granted
a waiver of preemption to California
under CAA section 209(b). For States
that have adopted the ZEV standards,
the consequence of any final withdrawal
action would be that they cannot
implement these standards. (A State
may not ‘‘make attempt[s] to enforce’’
California standards for which EPA has
not waived preemption. Motor Vehicle
Mfrs. Ass’n v. NYS Dep. of Envtl
Conservation, 17 F.3d 521, 534 (2d Cir.
1994)). Where states have adopted
CARB’s ZEV and GHG standards into
their SIPs, under section 177, the
provisions of the SIP would continue to
be enforceable until revised. If this
proposal is finalized, EPA may
subsequently consider whether to
employ the appropriate provisions of
the CAA to identify provisions in
section 177 states’ SIPs that may require
amendment and to require submission
of such amendments.
EPA is taking comments on all aspects
of this proposal.
(a) Burden and Standard of Proof in
Waiver Decisions
Here, the Administrator is proposing
the withdrawal of a previously granted
waiver of preemption. As discussed in
section III.A. below, EPA proposes to
find that there is clear and compelling
evidence that California’s protectiveness
determination for its ZEV and GHG
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Motor and Equip. Mfrs. Ass’n v. EPA,
627 F.2d 1095, 1112 (D.C. Cir. 1979)
(MEMA I). Additionally, as discussed in
section III.B, below, EPA proposes to
find that there is clear and compelling
evidence that California does not need
its ZEV and GHG standards to meet
compelling and extraordinary
conditions. Similarly, as discussed in
section III.C, below, there is clear and
compelling evidence that both the ZEV
and GHG standards are not
technologically feasible.567
In MEMA I, 627 F.2d 1095, the U.S.
Court of Appeals for D.C. Circuit found
that ‘‘the burden of proving [that
California’s regulations do not comply
with the CAA] is on whoever attacks
them. California must present its
regulations and findings at the hearing
and thereafter the parties opposing the
waiver request bear the burden of
persuading the Administrator that the
waiver request should be denied.’’ 568
MEMA I dealt with a challenge
brought by Motor and Equipment
Manufacturers Association against
EPA’s grant of a waiver of preemption
for California’s accompanying
enforcement procedures, which in this
instance were vehicle in-use
maintenance regulations. The specific
challenge to EPA’s action contested
EPA’s findings that section 209 allowed
for a waiver of preemption for CARB’s
in-use maintenance regulations. MEMA
I also specifically considered the
standards of proof for two findings that
EPA must make in order to grant a
waiver for an ‘‘accompanying
enforcement procedure’’ (as opposed to
standards): (1) Protectiveness in the
aggregate and (2) consistency with
section 202(a) findings. The court
instructed that ‘‘the standard of proof
must take account of the nature of the
risk of error involved in any given
decision, and it therefore varies with the
finding involved. We need not decide
how this standard operates in every
waiver decision.’’ 569
The court upheld the Agency’s
position that denying a waiver required
‘‘clear and compelling evidence’’ to
show that proposed enforcement
procedures undermine the
protectiveness of California’s
standards.570 The court noted that this
standard of proof ‘‘also accords with the
congressional intent to provide
567 EPA is assuming without agreeing that the
burden of proof requires clear and compelling
evidence but believes a preponderance of the
evidence is the proper burden of proof. Regardless,
EPA firmly believes that it has clear and compelling
evidence to support the agency’s statutory findings.
568 MEMA I, 627 F.2d at 1122.
569 Id.
570 Id.

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California with the broadest possible
discretion in setting regulations it finds
protective of the public health and
welfare.’’ 571
With respect to the consistency
finding, MEMA I did not articulate a
standard of proof applicable to all
proceedings but found that the
opponents of the waiver were unable to
meet their burden of proof even if the
standard were a mere preponderance of
the evidence.
As the agency has consistently
explained, although MEMA I did not
explicitly consider the standard of proof
for ‘‘standards,’’ as compared to
‘‘accompanying enforcement
procedures,’’ nothing in the opinion
suggests that the court’s analysis would
not apply with equal force to such
determinations.572 Moreover, the
normal standard of proof in civil matters
is a preponderance of the evidence.
International Harvester Co. v.
Ruckelshaus, 478 F.2d 615, 643 (D.C.
Cir. 1979).
The role of the Administrator in
considering California’s application for
a preemption waiver is to make a
reasonable evaluation of the information
in the record in coming to the waiver
decision. The Administrator is required
to ‘‘consider all evidence that passes the
threshold test of materiality and . . .
thereafter assess such material evidence
against a standard of proof to determine
whether the parties favoring a denial of
the waiver have shown that the factual
circumstances exist in which Congress
intended a denial of the waiver.’’ 573
As the court in MEMA I stated, if the
Administrator ignores evidence
demonstrating that the waiver should
not be granted, or if he seeks to
overcome that evidence with
unsupported assumptions of his own,
he runs the risk of having his waiver
decision set aside as ‘‘arbitrary and
capricious.’’ 574 Therefore, the
Administrator’s burden is to act
‘‘reasonably.’’ 575
The instant action involves a decision
whether to withdraw a previous grant of
a waiver of preemption as compared to
the initial evaluation of and decision
whether to grant a waiver request from
California. Specifically, as discussed in
Section III, below, EPA is proposing
findings for the withdrawal of
preemption for CARB’s ACC program
under multiple criteria set out in section
209(b)(1). For example, EPA is
proposing to withdraw the waiver based
571 Id.
572 74

FR 32748.
I, 627 F.2d at 1122.
574 Id. at 1126.
575 Id.
573 MEMA

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
on considerations such as the nature of
GHG concentrations as a global air
pollution problem, rather than a
regional or local air pollution problem,
whether or not CARB’s particular GHG
standards actually would reduce GHG
emissions in California, whether a
waiver for CARB’s GHG standards is
permissible if those regulations are
preempted by EPCA, and the effect of
technological infeasibility for the 2012
Federal GHG standards for MY 2021–
2025. Natural Resources Defense
Council v. EPA, 655 F.2d 318, 331 (D.C.
Cir. 1981) (‘‘[T]here is substantial room
for deference to the EPA’s expertise in
projecting the likely course of
[technological] development.’’)
(Emphasis added.) EPA believes that
these are kinds of issues that extend
well beyond the boundaries of
California’s authority under section
209(b). EPA posits, therefore, that the
decision to withdraw the waiver would
warrant exercise of the Administrator’s
judgment.
Furthermore, that decision entails
matters not only of policy judgment but
of statutory interpretation, chief among
which is the question of what is the
appropriate inquiry under section
209(b)(1) when the Administrator is
faced with a request for a preemption
waiver for standards designed to
address a global environmental
problem. EPA has previously expressed
the view that certain waiver requests
might call for the Administrator to
exercise judgment in determining
California’s need for particular
standards, under section 209(b)(1)(B).
Specifically, in the March 6, 2008 GHG
waiver denial, EPA posited that it was
neither required nor appropriate for the
Agency to defer to California on the
statutory interpretation of the Clean Air
Act, including the issue of the confines
or limits of state authority established
by section 209(b)(1)(B), especially given
that EPA’s evaluation of California’s
request for a waiver to enforce GHG
standards would relate to the limits of
California’s authority to regulate GHG
emissions from new motor vehicles,
instead of particular regulatory
provisions that California was seeking to
enforce. There, EPA construed section
209(b)(1)(B) as calling for either a
consideration of environmental
problems with causes that were specific
to California, or in the alternative,
environmental effects specific to
California in comparison to the rest of
the nation. EPA further explained that
this interpretation called for its own
judgment because it necessitated a
determination of whether elevated
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confines of state air pollution programs
as covered by section 209(b)(1)(B). It
would also be consistent with the GHG
waiver denial for EPA to exercise its
own judgment in making the requisite
findings called for under section
209(b)(1)(B) in the instant action.
EPA is, thus, soliciting comments on
the appropriate burden and standard of
proof for withdrawing a previously
issued waiver, taking into consideration
that different approaches may apply to
the various criteria of Section 209(b)
and that EPA is not merely responsible
for evaluating a request by California
and comments thereon but is proposing
withdrawal of a grant of preemption.
3. Discussion: Analysis Under Section
209(b)(1)(B), (C)
(a) Proposed Finding Under Section
209(b)(1)(B): California Does Not Need
its Standards To Meet Compelling and
Extraordinary Conditions
(1) Introduction
Section 209(b)(1)(B) provides that no
waiver of section 209(a) preemption will
be granted if the Administrator finds
that California does not need ‘‘such
standards to meet compelling and
extraordinary conditions.’’ EPA is
proposing to withdraw the grant of
waiver of preemption for CARB’s GHG
and ZEV standards for 2021 MY through
2025 MY based on a finding that
California does not need these standards
to meet compelling and extraordinary
conditions, as contemplated under
section 209(b)(1)(B). As shown below,
EPA is proposing to determine that the
ACC program GHG and ZEV standards
are standards that would not
meaningfully address global air
pollution problems posed by GHG
emissions in contrast to local or regional
air pollution problem with causal ties to
conditions in California. As also shown
below, EPA is proposing to find that
while potential conditions related to
global climate change in California
could be substantial, they are not
sufficiently different from the potential
conditions in the nation as a whole to
justify separate state standards under
CAA section 209(b)(1)(B). Moreover, the
GHG and ZEV standards would not have
a meaningful impact on the potential
conditions related to global climate
change. EPA is thus proposing to find
that California does not need GHG
standards to meet compelling and
extraordinary conditions, as
contemplated under section
209(b)(1)(B). Additionally, California
does not need the ZEV requirements to
meet ‘‘compelling and extraordinary’’
conditions in California given that it
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in section 177 states as a means to
satisfy those manufacturers’ obligations
to comply with the mandate that a
certain percentage of their vehicles sold
in California be ZEV (or be credited as
such from sales in section 177 States).
This finding is premised on agency
review of the interpretation and
application of section 209(b)(1)(B) in the
January 2013 ACC waiver request. Thus,
EPA is required to articulate a reasoned
basis for the change in its position. FCC
v. Fox Television Stations, Inc., 556 U.S.
502, 515 (2009).
(2) Historical Waiver Practices Under
Section 209(b)(1)(B)
Up until the 2008 GHG waiver denial,
EPA had interpreted section 209(b)(1)(B)
as requiring a consideration of
California’s need for a separate motor
vehicle program designed to address
local or regional air pollution problems
and not whether the specific standard
that is the subject of the waiver request
is necessary to meet such conditions (73
FR 12156; March 6, 2008). Additionally,
California typically would seek a waiver
of particular aspects of its new motor
vehicle program up until the ACC
program waiver request. In the 2008
GHG waiver denial, which was a waiver
request for only GHG emissions
standards, however, EPA determined
that its prior interpretation of section
209(b)(1)(B) was not appropriate for
GHG standards because such standards
are designed to address global air
pollution problems in contrast to local
or regional air pollution problems
specific to and caused by conditions
specific to California (73 FR 12156–60).
In the 2008 denial, EPA further
explained that its previous reviews of
California’s waiver request under
section 209(b)(1)(B) had usually been
cursory and undisputed, as the
fundamental factors leading to
California’s air pollution problems—
geography, local climate conditions (like
thermal inversions), significance of the
motor vehicle population—had not
changed over time and over different
local and regional air pollutants. These
fundamental factors applied similarly
for all of California’s air pollution
problems that are local or regional in
nature.
In the 2008 denial, EPA noted that
atmospheric concentrations of GHG are
substantially uniform across the globe,
based on their long atmospheric life and
the resulting mixing in the atmosphere.
Therefore, with regard to atmospheric
GHG concentrations and their
environmental effects, the Californiaspecific causal factors that EPA had
considered when reviewing previous
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209(b)(1)(B)—the geography and climate
of California, and the large motor
vehicle population in California, which
were considered the fundamental causes
of the air pollution in California—do not
have the same relevance to the question
at hand. The atmospheric concentration
of GHG in California is not affected by
the geography and climate of California.
The long duration of these gases in the
atmosphere means they are well-mixed
throughout the global atmosphere, such
that their concentrations over California
and the U.S. are substantially the same
as the global average. The number of
motor vehicles in California, while still
a notable percentage of the national total
and still a notable source of GHG
emissions in the State, is not a
significant percentage of the global
vehicle fleet and bears no closer relation
to the levels of GHG in the atmosphere
over California than any other
comparable source or group of sources
of GHG anywhere in the world.
Emissions of greenhouse gases from
California cars do not generally remain
confined within California’s local
environment but instead become one
part of the global pool of GHG
emissions, with this global pool of
emissions leading to a relatively
homogenous concentration of GHG over
the globe. Thus, the emissions of motor
vehicles in California do not affect
California’s air pollution problem in any
way different from emissions from
vehicles and other pollution sources all
around the world. Similarly, the
emissions from California’s cars do not
only affect the atmosphere in California
but in fact become one part of the global
pool of GHG emissions that affect the
atmosphere globally and are distributed
throughout the world, resulting in
basically a uniform global atmospheric
concentration.
EPA then applied the reasoning laid
out above to the GHG standards at issue
in the 2008 waiver denial. Having
limited the meaning of this provision to
situations where the air pollution
problem was local or regional in nature,
EPA found that California’s GHG
standards did not meet this criterion.
In the 2008 waiver denial, EPA also
applied an alternative interpretation
where EPA would consider effects of the
global air pollution problem in
California in comparison to the effects
on the rest of the country and again
addressed the GHG standards separately
from the rest of California’s motor
vehicle program. Under this alternative
interpretation, EPA considered whether
impacts of global climate change in
California were sufficiently different
from impacts on the rest of the country
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to need its GHG standards to meet
compelling and extraordinary
conditions. EPA determined that the
waiver should be denied under this
alternative interpretation as well.
(3) Interpretation of Section 209(b)(1)(B)
Under section 209(b)(1)(B), EPA
cannot grant a waiver request if EPA
finds that California ‘‘does not need
such State standards to meet compelling
and extraordinary conditions.’’ The
statute does not define the phrase
‘‘compelling and extraordinary
conditions,’’ and EPA considers the text
of section 209(b)(1)(B), and in particular
the meaning and scope of this phrase, to
be ambiguous.
First, the provision is ambiguous with
respect to the scope of EPA’s analysis.
It is unclear whether EPA is meant to
evaluate the particular standard or
standards at issue in the waiver request
or all of California’s standards in the
aggregate. Section 209(b)(1)(B)
references the need for ‘‘such State
standards.’’ Section 209(b)(1)(B) does
not specifically employ terms that could
only be construed as calling for a
standard-by-standard analysis or each
individual standard. For example, it
does not contain phrases such as ‘‘each
State standard’’ or ‘‘the State standard.’’
Nor does the use of the plural term
‘‘standards’’ definitively answer the
question of the proper scope of EPA’s
analysis, given that the variation in the
use of singular and plural form of a
word in the same law 576 is often
insignificant and a given waiver request
typically encompasses multiple
‘‘standards.’’ Thus, while it is clear that
‘‘such State standards’’ refers at least to
all of the standards that are the subject
of the particular waiver request before
the Administrator, that phrase can
reasonably be considered as referring
either to the standards in the entire
California program, the program for
similar vehicles, or the particular
standards for which California is
requesting a waiver under the pending
request.
There are reasons to doubt that the
phrase ‘‘such State standards’’ in section
209(b)(1)(B) is intended to refer to all
standards in California’s program,
including all the standards it has
historically adopted and obtained
waivers for previously. The waiver
under 209(b) is a waiver of, and is
logically dependent on and presupposes
the existence of, the prohibition under
209(a), which forbids (absent a waiver)
any state to ‘‘adopt or attempt to enforce
576 ‘‘Words [in Acts of Congress] importing the
singular include and apply to several persons.’’ 1
U.S.C. 1.

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any standard [singular] relating to the
control of emissions from new motor
vehicles or new motor vehicle engines
subject to this part.’’ (Emphasis added.)
States are forbidden from adopting a
standard, singular; California requests
waivers seriatim by submitting a
standard or package of standards to
EPA; follows that EPA considers those
submissions as it receives them,
individually, not in the aggregate with
all standards for which it has previously
granted waivers.
Furthermore, reading the phrase
‘‘such State standards’’ as requiring EPA
always and only to consider California’s
entire program in the aggregate limits
the application of the criterion. Once
EPA had determined that California
needed its very first set of submitted
standards to meet extraordinary and
compelling conditions, it is unclear that
EPA would ever have the discretion to
determine that California did not need
any subsequent standards for which it
sought a successive waiver—unless EPA
is authorized to consider a later
submission separate from its earlier
finding. Moreover, up until the ACC
program waiver request, California’s
waiver request involved individual
standards or particular aspects of
California’s new motor vehicle
program.577 As previously explained,
however, the ACC waiver program
could be considered as the entire new
motor vehicle program for California
given that it is a single coordinated
program comprising a suite of standards
that California intended to be a cohesive
program for addressing emissions from
a wide variety of vehicles, specifically,
new passenger cars, light duty trucks,
medium passenger vehicles, and certain
heavy duty vehicles.
The application of the phrase ‘‘such
State standards’’ to state standards in
the aggregate may have appeared more
reasonable in the context of, for
example, the 1984 PM waiver request,
as opposed to the present context, as it
relates to an application for a waiver
with regard to GHG and ZEV
standards.578 In the 1984 request, the
agency confronted the need for a
reading of ‘‘such State standards’’ in
section 209(b)(1)(B) that would be
consistent with the State’s ‘‘in the
aggregate, at least as protective’’ finding
under the root text of 209(b)(1),’’
577 The 2009 and Subsequent MY GHG standards
for New Motor Vehicles, 73 FR 12156 (March 6,
2008); The On-Board diagnostics system
requirements (OBD II) 81 FR 78144 (November 7,
2016), The ZEV program regulations 76 FR 61096
(October 3, 2011), 71 FR 78190 (December 26,
2006)) and the Heavy-duty Truck idling
requirements 77 FR 9239 (February 16, 2012).
578 49 FR 18887 (May 3, 1984).

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because Congress explicitly allows
California to adopt some standards that
are less stringent than Federal
standards. EPA explained that the
phrase ‘‘in the aggregate’’ was
specifically aimed at allowing California
to adopt less stringent CO standards at
the same time when California wanted
to adopt NOX standards that were
tighter than the Federal NOX standards,
to address ozone problems.579 California
reasoned that a relaxed CO standard
would facilitate the technological
feasibility of the desired more stringent
NOX standards. When evaluating that
waiver request, EPA noted that it would
be inconsistent for Congress to allow
EPA to look at each air pollutant
separately for purposes of determining
compelling and extraordinary
conditions for that air pollution
problem, while at the same time
allowing California to adopt standards
for a particular air pollutant that was
less stringent than the Federal standards
for that same pollutant. EPA proposes to
determine that the balance of textual,
contextual, purposive, and legislativehistory evidence at minimum supports
the conclusion that it is ambiguous
whether the Administrator may
consider whether California needs the
particular standard or standards under
review to meet compelling and
extraordinary conditions.
Second, the statute does not speak
with precision as to the substance of
EPA’s analysis. ‘‘Compelling and
extraordinary conditions,’’ as the history
of the 2008 waiver denial and 2009
reconsideration and grant narrated
above demonstrates, is a phrase
susceptible of multiple interpretations,
particularly in the context of GHG
emissions and associated, global
environmental problems. EPA believes
that the term ‘‘extraordinary’’ is most
reasonably read to refer to
circumstances that are specific to
California and the term is reasonably
interpreted to refer to circumstances
that are primarily responsible for
causing the air pollution problems that
the standards are designed to address,
such as thermal inversions resulting
from California’s local geography and
wind patterns. (Conditions that are
similar on a global scale are not
579 The intent of the 1977 amendment was to
accommodate California’s particular concern with
NOX, which the State regards as a more serious
threat to public health and welfare than carbon
monoxide. California was eager to establish oxides
of nitrogen standards considerably higher than
applicable Federal standards, but technological
developments posed the possibility that emission
control devices could not be constructed to meet
both the high California oxides of nitrogen standard
and the high Federal carbon monoxide standard.
MEMA I, 627 F.2d at 1110 n.32.

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‘‘extraordinary,’’ especially where
‘‘extraordinary’’ conditions are a
predicate for a local deviation from
national standards.) Support for this
interpretation can be found in pertinent
legislative history that refers to
California’s ‘‘peculiar local conditions’’
and ‘‘unique problems.’’ S. Rep. No.
403, 90th Cong. 1st Sess., at 32 (1967).
This legislative history also indicates
that California is to demonstrate
‘‘compelling and extraordinary
circumstances sufficiently different from
the nation as a whole to justify
standards on automobile emissions
which may, from time to time, need to
be more stringent than national
standards.’’ Id. (Emphasis added.) EPA
believes this is evidence of
Congressional intent that separate
standards in California are justified only
by a showing of particular
circumstances in California that are
different from circumstances in the
nation as a whole to justify separate
standards in California. EPA thus, reads
the term ‘‘extraordinary’’ in this
statutory context as referring primarily
to factors that tend to produce higher
levels of pollution: Geographical and
climatic conditions (like thermal
inversions) that in combination with
large numbers and high concentrations
of automobiles, create serious air
pollution problems in California (73 FR
12156, 12159–60).
Additional relevant legislative history
supports a decision to examine
California’s need for GHG standards ‘‘in
the context of global climate change.’’
See, e.g., 73 FR 12161. Specifically, this
legislative history demonstrates that
Congress did not justify this provision
based on the need for California to enact
separate standards to address pollution
problems of a more national or global
nature. Rather relevant legislative
history ‘‘indicates that Congress allowed
waivers of preemption for California
motor vehicle standards based on the
particular effects of local conditions in
California on the air pollution problems
in California.’’ Congress discussed ‘‘the
unique problems faced in California as
a result of its climate and topography.’’
H.R. Rep. No. 728, 90th Cong. 1st Sess.,
at 21 (1967). See also Statement of Cong.
Holifield (CA), 113 Cong. Rec. 30942–43
(1967). Congress also noted the large
effect of local vehicle pollution on such
local problems. See, e.g., Statement of
Cong. Bell (CA) 113 Cong. Rec. 30946.
In particular, Congress focused on
California’s smog problem, which is
especially affected by local conditions
and local pollution. See Statement of
Cong. Smith (CA) 113 Cong. Rec.
30940–41 (1967); Statement of Cong.

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Holifield (CA), id., at 30942. See also,
MEMA I, 627 F.2d at 1109 (noting the
discussion of California’s ‘‘peculiar
local conditions’’ in the legislative
history).
The EPA thus, believes that it is
appropriate, in evaluating California’s
need for a waiver under section
209(b)(1)(B), to examine California’s
program as a whole to the extent that
the problem is designed to address local
or regional air pollution problems,
particularly in light of the fact that the
State’s aggregate analysis under the root
text of 209(1)(b)(1) is designed in part to
permit California to adopt standards for
some criteria pollutants that are less
stringent than the Federal standards as
a trade-off for standards for other
criteria pollutants, where the levels of
criteria pollutants addressed by
California’s standards are caused by
conditions specific to California, and
contribute primarily to environmental
effects that are specific to California.
EPA could also review California’s GHG
standards themselves even where, as in
the instant ACC waiver package, the
waiver request is for a single
coordinated package of requirements
and amendments that include standards
designed to address global
environmental effects caused by a
globally distributed a globally
distributed pollutant, such as GHGs as
well as requirements for a compliance
mechanism that could likely address
both criteria pollutants and GHG
emissions, which in this instance are
the ZEV requirements. The EPA further
notes that in keeping with its pre-2008
interpretation, its review of California’s
ACC program request under section
209(b)(1)(B) was cursory and
undisputed, given that view that the
fundamental factors leading to
California’s air pollution problems—
geography, local climate conditions (like
thermal inversions), significance of the
motor vehicle population—had not
changed over time and over different
local and regional air pollutants.
Additionally, as previously explained,
up until the ACC program waiver,
California had relied on the ZEV
requirements as a compliance
mechanism for criteria pollutants as
compared to the ACC program, where
CARB for the first time relied on it for
GHG emissions reductions. Here, as
previously explained, CARB specifically
noted that that there was no criteria
emissions benefit for its ZEV standards
in terms of vehicle emissions because its
LEV III criteria pollutant fleet standard
was responsible for those emission

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reductions.580 The EPA therefore,
believes a review of the grant of the ACC
program waiver and the agency
reasoning underpinning the grant are
appropriate at this time. As previously
explained, an agency ‘‘must consider
. . . the wisdom of its policy on a
continuing basis.’’ Chevron, 467 U.S. at
863–64. This is true when, as is the case
here, review is undertaken ‘‘in response
to . . . a change in administration.’’
Brand X Internet Services, 545 U.S. at
981. In sum, EPA proposed to conclude
that the pre-2008 interpretation of
section 209(b)(1)(B) would allow for
review of California’s GHG standards in
themselves, given that the ACC program
is a single coordinated motor vehicle
emission control program that is
designed to address both traditional,
local environmental causes and effects
(including via criteria pollutants) and
global air pollution problems. Thus,
EPA is proposing that at this time its
review has led it to propose to
determine that California does not need
its own GHG and ZEV standards, to the
extent California intended the ZEV
requirements to serve as a compliance
option for GHG standards, because GHG
emissions do not present conditions
specific to California—in the terms of
the legislative history discussed above,
GHG emissions do not present ‘‘unique
problems’’ in California as compared to
the whole country. As shown below,
GHG emissions could be associated with
potential adverse effects in California,
but EPA does not believe that these
would be sufficiently different from
potential adverse effects in either
coastal States like Florida,
Massachusetts, and Louisiana or the
nation as a whole, to constitute a
‘‘need’’ for separate state standards
under section 209(b)(1)(B). EPA is of the
view, therefore, that GHG emissions
would not be associated with ‘‘peculiar
local conditions’’ in California that
Congress alluded to in promulgating
section 209(b)(1)(B). In the alternative,
EPA is proposing to determine that
California does not need the ACC
program GHG and ZEV standards to
address compelling and extraordinary
conditions, because they will not
meaningfully address global air
pollution problems like the kinds
associated with GHG emissions and
would not have any meaningful impact
on potential adverse effects related to
global climate change in California. As
shown below, based on this reading of
section 209(b)(1)(B), the agency is
proposing to find that GHG emissions
impacts cannot be considered
580 CARB ACC waiver request at EPA–HQ–OAR–
2012–0562–0004.

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‘‘compelling and extraordinary
conditions’’ such that California
‘‘need[s]’’ separate GHG and ZEV
standards for new motor vehicles for
MY 2021 through MY 2025.
(4) Proposed Determination That
California Does Not Need Its ACC
Program Regulations To Meet
Compelling and Extraordinary
Conditions
EPA is proposing to withdraw the
waiver of preemption of the GHG and
ZEV standards on two alternative
grounds: (1) California ‘‘does not need’’
the standards ‘‘to meet compelling and
extraordinary conditions,’’ under
section 209(b)(1)(B); (2) even if
California does have compelling and
extraordinary conditions in the context
of global climate change, California does
not ‘‘need’’ these standards under
section 209(b)(1)(B) because they will
not meaningfully address global air
pollution problems of the sort
associated with GHG emissions. EPA is
interpreting section 209(b)(1)(B) to
permit the Agency to specifically review
California’s need for GHG standards—
i.e., standards for a globally distributed
air pollutant which is of concern for its
connection to global environmental
effects—as opposed to reviewing
California’s need for its motor vehicle
program as a whole (including both its
GHG-targeting and non-GHG-targeting
components), in part because the rest of
California’s ACC program consists of
standards that are designed to address
local or regional air pollution problems.
Accordingly, EPA is proposing to find
that GHG emitted by California motor
vehicles become part of the global pool
of GHG emissions that affect
concentrations of GHGs on a uniform
basis throughout the world. The local
climate and topography in California
have no significant impact on the longterm atmospheric concentrations of
greenhouse gases in California. More
importantly, California’s standards for
GHG emissions (both the GHG and ZEV
standards) would not materially affect
global concentrations of GHG levels.
Accordingly, even if EPA were to
assume California had compelling and
extraordinary conditions that were
uniquely impacted by high levels of
GHGs, California’s GHG and ZEV
standards would not meaningfully
address those concerns and conditions.
In the alternative, EPA believes that
even if California has compelling and
extraordinary conditions, California
does not need these standards under
section 209(b)(1)(B) because they will
not meaningfully address global air
pollution problems like the kinds
associated with GHG emissions. EPA

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believes that the number of motor
vehicles in California bears no
significant relationship to the levels of
GHGs in California. This is because
GHGs emissions from cars located in
California are relatively small part of the
global pool of GHG emissions. Thus,
GHG emissions of motor vehicles in
California do not affect California’s
conditions related to global climate
change in any way different from
emissions from vehicles and other
pollution sources all around the world.
Similarly, the GHG emissions from cars
in California become one part of the
global pool of GHG emissions that affect
the atmosphere globally and are
distributed throughout the world,
resulting in basically a uniform global
atmospheric concentration. This is in
contrast to the kinds of motor vehicle
emissions normally associated with
ozone levels, such as VOCs and NOX,
and the local climate and topography
that in the past have led to the
conclusion that California has the need
for state standards to meet compelling
and extraordinary conditions. Therefore,
California does not need its GHG and
ZEV standards to ‘‘meet’’ the conditions:
a problem does not cause you to ‘‘need’’
something that would not meaningfully
address the problem.
In justifying the need for its GHG
standards, CARB extensively described
climatic conditions in California.
‘‘Record-setting fires, deadly heat
waves, destructive storm surges, loss of
winter snowpack—California has
experienced all of these in the past
decade and will experience more in the
coming decades. California’s climate—
much of what makes the state so unique
and prosperous—is already changing,
and those changes will only accelerate
and intensify in the future. Extreme
weather will be increasingly common as
a result of climate change. In California,
extreme events such as floods, heat
waves, droughts and severe storms will
increase in frequency and intensity.
Many of these extreme events have the
potential to dramatically affect human
health and well-being, critical
infrastructure and natural systems’’ (78
FR 2129). CARB also provided a
summary report on the third assessment
from the California Climate Change
Center (2012), which described dramatic
sea level rises and increases in
temperatures (78 FR 2129). These are
similar, if not identical to, the
justifications that EPA addressed and
rejected in the 2008 GHG waiver denial.
Notably, in the 2008 denial EPA
observed that some of these events—
increased temperatures, heat waves, sea
level rises, wildfires—were also

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occurring across the U.S. (73 FR 12163,
12165–68). CARB further noted that the
South Coast and San Joaquin Valley Air
Basins continue to experience some of
the worst air quality in the nation and
continue to be in non-attainment with
the PM and ozone national ambient air
quality standards (78 FR 2128–9). The
EPA has typically considered
nonattainment air quality in California
as falling within the purview of
‘‘compelling and extraordinary
conditions.’’ California however, did not
indicate how the GHG standards would
help California in the attainment efforts
for these areas. Moreover, as previously
noted, the ACC ZEV requirements are
intended in part as a GHG compliance
mechanism for MYs 2018 through 2025.
EPA believes that any effects of global
climate change would apply to the
nation, indeed the world, in ways
similar to the conditions noted in
California.581 For instance, California’s
claims that it is uniquely susceptible to
certain risks because it is a coastal State
does not differentiate California from
other coastal States such as
Massachusetts, Florida, and
Louisiana.582 Any effects of global
climate change (e.g. water supply issues,
increases in wildfires, effects on
agriculture) could certainly affect
California. But those effects would also
affect other parts of the United States.
Many parts of the United States,
especially western States, may have
issues related to drinking water (e.g.,
increased salinity) and wildfires, and
effects on agriculture; these occurrences
are by no means limited to California.
These are issues of national, indeed
international, concern. Further, these
are some of the effects that EPA
considered as bases for the section
202(a) GHG endangerment finding,
which was a prerequisite for the Federal
GHG standards for motor vehicles.583
EPA has also previously opined that
evaluation of whether California’s
standards are necessary to meet
compelling and extraordinary
conditions is not contingent on or
directly related to EPA’s cause or
contribution finding for the section
202(a) GHG endangerment finding,
which was a completely different
581 IPCC. 2015. Intergovernmental Panel on
Climate Change (IPCC) Observed Climate Change
Impacts Database, available at http://sedac.ipccdata.org/ddc/observed_ar5/index.html.
582 They are also similar to previous claims
marshalled by Massachusetts over a decade ago.
Massachusetts v. EPA, 549 U.S. 497, 522–24 (2007).
According to Massachusetts, at the time, global sea
levels rose between 10 and 20 centimeters over the
20th century as a result of global warming and had
begun to swallow its coastal areas.
583 74 FR 66496, 66517–19, 66533 (December 15,
2009).

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determination than whether California
needs its mobile source pollution
program to meet compelling and
extraordinary conditions in California
(79 FR 46256, 46262: August 7, 2014).
See also Utility Air Regulatory Group
v. EPA, 134 S. Ct. 2427 (2014) (partially
reversing the GHG ‘‘Tailoring’’ Rule on
grounds that the section 202(a)
endangerment finding for GHG
emissions from motor vehicles did not
compel regulation of all sources of GHG
emissions under the Prevention of
Significant Deterioration and Title V
permit programs).
As also previously indicated,
California is to demonstrate
‘‘compelling and extraordinary
circumstances sufficiently different from
the nation as a whole to justify
standards on automobile emissions
which may, from time to time, need to
be more stringent than national
standards.’’ S. Rep. No. 403, 90th Cong.
1st Sess., at 32 (1967). (Emphasis
added.) EPA does not believe that these
conditions, mentioned above, merit
separate GHG standards in California.
Rather, these effects, as previously
explained, are widely shared and do not
present ‘‘unique problems’’ with respect
to the nature or degree of the effect
California would experience. In sum,
EPA finds that any effects of global
climate change in California are not
‘‘extraordinary’’ as compared to the rest
of the country. EPA is thus, proposing
to find that CARB has not demonstrated
that these negative impacts it attributes
to global climate change are
‘‘extraordinary’’ to merit separate GHG
and ZEV standards.
The ACC program waiver contained
references to the potential GHG benefits
or attributes of CARB’s GHG and ZEV
standards program (78 FR 2114, 2130–
2131). CARB repeatedly touted the
benefits of both the ZEV and GHG
standards as it related to the GHG
emissions reductions in California. In
one instance, CARB stated that the ACC
program regulations for the 2017
through 2025 MYs were designed to
respond to California’s identified goals
of reducing GHG emissions to 80%
below 1990 levels by 2050 and in the
near term to reduce GHG levels to 1990
levels by 2020 (78 FR 2114, 2130–31).
CARB’s Resolution 12–11, (January 26,
2012).584 In another instance, CARB
noted that the ZEV regulation
amendments were intended to focus
primarily on zero emission drive—that
is BEVs, FCVs, and PHEVs in order to
move advanced, low GHG vehicles from
584 Available in the docket for the January 2013
waiver decision, Docket No. EPA–HQ–OAR–2012–
0562.

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demonstration phase to
commercialization (78 FR 2130). CARB
further noted that ‘‘ZEVs have ultra-low
GHG emission levels that are far lower
than non-ZEV technology’’ (78 FR
2139). In yet another instance, CARB
relied on conclusions from the
September 2010 Joint Technical
Assessment Report (TAR), which was
developed by EPA, NHTSA, and CARB,
on effects of the ZEV requirements on
GHG standards. This report concluded
that ‘‘electric drive vehicles including
hybrid(s) . . . battery electric vehicles
. . . plug-in hybrid(s) . . . and
hydrogen fuel cell vehicles . . . can
dramatically reduce petroleum
consumption and GHG emissions
compared to conventional technologies.
The future rate of penetration of these
technologies into the vehicle fleet is not
only related to future GHG and
corporate average fuel economy (CAFE)
standards, but also to future reductions
in HEV/PHEV/EV battery costs, [and]
the overall performance and consumer
demand for the advanced technologies’’
(78 FR 2142). But nowhere does CARB
either show or purport to show a causal
connection between its GHG standards
and reducing any adverse effects of
climate change in California. EPA does
not believe that identifying methods for
reducing GHG emissions and then
noting the potential dangers of climate
change are sufficient to demonstrate that
California needs its standards to meet
compelling and extraordinary
circumstances as contemplated under
section 209(b)(1)(B). California also does
not need the ZEV requirements to meet
‘‘compelling and extraordinary’’
conditions in California given that the
FCV ‘‘travel provision’’ allow
manufacturers to generate credits in
section 177 states as a means to satisfy
those manufacturers’ obligations to
comply with the mandate that a certain
percentage of their vehicles sold in
California be ZEV (or be credited as
such from sales in section 177 States).
In sum, California did not quantify and
demonstrate climate benefits in
California that may result from the GHG
standards. EPA therefore, proposes to
find that it is not appropriate to waive
preemption for California to enforce its
GHGs standards. EPA continues to
believe that any problems related to
atmospheric concentrations of GHG are
global in nature and any reductions
achieved as a result of California’s
separate GHG standards will not accrue
meaningful benefits to California. Thus,
GHG emissions raise issues that do not
bear the same causal link between local
emissions and local benefits to health
and welfare in California as do local or

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regional air pollution problems (such as
criteria pollutants). EPA further finds
that atmospheric concentrations of
GHGs are not the kind of local or
regional air pollution problem Congress
intended to identify in the second
criterion of section 209(b)(1)(B). These
findings also apply to the ZEV
provisions given that CARB, in a change
from prior practice, and as previously
explained, cited its ZEV standards as a
means to reduce GHG emissions instead
of criteria pollutants for MY 2021
through MY 2025. Thus, EPA is
proposing to withdraw the waiver of
preemption for the GHG and ZEV
requirements for MYs 2021 through
2025 because California does not need
these provisions to meet compelling and
extraordinary conditions.
(b) Proposed Finding Under Section
209(b)(1)(C): California’s Standards Are
Not Consistent With Section 202(a)

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(1) Introduction
Under section 209(b)(1)(C), EPA
cannot grant a waiver request if EPA
finds that California’s ‘‘standards and
accompanying enforcement procedures
are not consistent with section 202(a) of
the Act.’’ 585 The EPA is also proposing
to find that both ZEV and GHG
standards for new MY 2021 through
2025 are not consistent with Section
202(a) of the Clean Air Act, as
contemplated by section 209(b)(1)(C).
Specifically, EPA is proposing to
determine that there is inadequate lead
time to permit the development of
technology necessary to meet those
requirements, giving appropriate
consideration to cost of compliance
within the lead time provided in the
2013 waiver. This finding reflects the
assessments in today’s proposal on the
technological feasibility of the Federal
GHG standards for MY 2021 through
2025.586
As previously explained, the MY 2021
through 2025 Federal and CARB GHG
standards were the results of
collaboration between CARB and EPA.
The respective standards are equally
stringent and have the same lead time.
(78 FR 2135) CARB’s GHG standards
585 Section 202(a) provides that an emission
standard shall take effect after such period of time
as the Administrator finds necessary to permit
development and application of the requisite
technology, giving appropriate consideration to
compliance costs.
586 Although this section generally discusses the
technological feasibility of CARB’s GHG standards
for MY 2021–2025, we believe the current Federal
standards are sufficiently similar to (if not less
stringent than) the current California standards to
serve as an appropriate proxy for considering the
technological feasibility of the current California
standards. Compare Cal. Code Regs. Tit. 13,
§ 1961.3 with 40 CFR 89.1818–12.

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also rely on emerging technology that
are similar to the ones for the Federal
GHG standards, including ZEV-type
technologies (78 FR 2136–7). Most
importantly, CARB’s feasibility finding,
and EPA’s decision to grant the waiver,
noted a ‘‘deemed to comply’’ provision
that allowed manufacturers of advanced
technology vehicles to comply with
CARB GHG standards through
compliance with the Federal GHG
standards as well as utilize the EPA
accounting provisions for these vehicles
(78 FR 2136). Revisions to the Federal
GHG standards, in light of the
technology development and
availability assessment for those
standards, would therefore, implicate
the technological feasibility findings
that served as the underpinning for
EPA’s grant of CARB’s GHG standards
waiver.
Further, because EPA believes that the
ZEV and GHG standards are intertwined
as shown in some of the program
complexities discussed above, EPA
believes that this provides further
justification for withdrawing the waiver
of preemption for both standards, under
section 209(b)(1)(C). For example, in the
waiver request CARB stated that the
‘‘ZEV regulation must be considered in
conjunction with the proposed LEV III
amendments. Vehicles produced as a
result of the ZEV regulation are part of
a manufacturer’s light-duty fleet and are
therefore included when calculating
fleet averages for compliance with the
LEV III GHG amendments.’’ CARB’s
Initial Statement of Reasons at 62–63,
which is in the docket for the waiver
decision.587 CARB also noted ‘‘[b]ecause
the ZEVs have ultra-low GHG emission
levels that are far lower than non-ZEV
technology, they are a critical
component of automakers’ LEV III GHG
standard compliance strategies.’’ Id.
CARB further explained that ‘‘the ultralow GHG ZEV technology is a major
component of compliance with the LEV
III GHG fleet standards for the overall
light duty fleet.’’ Id.
Similarly, with regard to CARB’s ZEV
standards, EPA is now cognizant that
certain ZEV sales requirements
mandated by CARB are technologically
infeasible within the provided lead-time
for purposes of CAA 209(b)(1)(C).
Specifically, today’s proposal also raises
questions as to CARB’s technological
projections for ZEV-type technologies,
which are a compliance option for both
the ZEV mandate and GHG standards.
CARB’s ZEV regulations also include
the travel provision, which allowed
manufacturers of ZEVs sold in
California to count toward compliance
587 Docket

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in section 177 States, but which was
limited to FCVs starting with MY 2018.
The manufacturer credit system was
premised on ever increasing numbers of
ZEVs that would be sold in each of the
section 177 States. Challenges for ZEVs
in these States include lack of market
penetration, consumer demand levels
that are lower than projections at the
time of the grant of the ACC waiver in
2013, and lack of or slow development
of necessary infrastructure. This in turn
means that manufacturers in section 177
States are unlikely to meet CARB’s
projections that their sales in those
States will generate the necessary
credits as CARB projected to support the
ZEV sales requirement mandate in the
lead time provided.
Today’s proposal indicates challenges
for the adoption of all ZEV technologies
such as lack of required infrastructure
and a lower level of consumer demand
for FCVs in both California and
individual section 177 States, and EPA
believes it is now unlikely that
manufacturers will be able to generate
requisite credits in section 177 States in
the lead time provided. In short, EPA is
now of the view that CARB’s projections
and assumptions that underlay its ACC
program and its 2013 waiver application
were overly ambitious and likely will
not be realized within the provided lead
time. Thus, EPA is also proposing to
find that CARB’s ZEV standards for MY
2021 through 2025 are not
technologically feasible and therefore,
are not consistent with section
209(b)(1)(C).
(2) Historical Waiver Practices Under
Section 209(b)(1)(C)
In prior waivers of Federal
preemption, under section 209(b), EPA
has explained that California’s
standards are not consistent with
section 202(a) if there is inadequate lead
time to permit the development of
technology necessary to meet those
requirements, given appropriate
consideration to the cost of compliance
within that time. California’s
accompanying enforcement procedures
would also be inconsistent with section
202(a) if the Federal and California test
procedures were inconsistent.
EPA also relies on two key decisions
handed down by the U.S. Court of
Appeals for the D.C. Circuit for
guidance regarding the lead time
requirements of section 202(a): Natural
Resources Defense Council v. EPA
(NRDC), 655 F.2d 318 (D.C. Cir. 1981)
(upholding EPA’s lead time projections
for emerging technologies as
reasonable), and International Harvester
v. Ruckelshaus (International
Harvester), 478 F.2d 615 (D.C. Cir. 1979)

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(reversing EPA’s refusal to extend
compliance deadline where technology
was presently available on grounds that
hardship would likely result if it were
a wrongful denial of compliance
deadline extension.). EPA further notes
the court’s conclusion in NRDC.

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Given this time frame [a 1980 decision on
1985 model year standards], we feel that
there is substantial room for deference to the
EPA’s expertise in projecting the likely
course of development. The essential
question in this case is the pace of that
development, and absent a revolution in the
study of industry, defense of such a
projection can never possess the inescapable
logic of a mathematical deduction. We think
that the EPA will have demonstrated the
reasonableness of its basis for projection if it
answers any theoretical objections to the
[projected control technology], identifies the
major steps necessary in refinement of the
technology, and offers plausible reasons for
believing that each of those steps can be
completed in the time available.

NRDC, 655 F.2d at 331 (emphasis
added).
With regard to appropriate lead time
in the section 209(b) waiver context,
EPA considers whether adequate control
technology is presently available or
already in existence and in use at the
time CARB adopts standards for which
it seeks a waiver. If adequate control
technology is not presently available,
EPA determines whether CARB has
provided adequate lead time for the
development and application of
necessary technology prior to the
effective date of applicable standards.
As explained above, considerations
under this criterion include adequacy of
lead time, technological feasibility and
costs as well as test procedures
consistency. Notably, there are similar
considerations for Federal standards
setting under section 202(a). For
example, in adopting the MY 2017
through 2025 GHG standards, section
202(a) required and EPA found in
October 2012 that the MY 2017 through
2025 GHG standards are feasible in the
lead time provided and that technology
costs were reasonable (77 FR 62671–73;
October 15, 2012). Even where
technology is available, EPA can
consider hardships that could result to
manufacturers from either a short lead
time or not granting a compliance
extension. International Harvester, 478
F.2d at 626.
Where CARB relies on emerging
technology (i.e., where technology is
unavailable at time of grant of waiver),
EPA will review CARB’s prediction of
future technological developments and
determine whether CARB has provided
reasoned explanations for the time
period selected. Any projections by
CARB would have to be subject to

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‘‘restraints of reasonableness and does
not open the door to crystal ball
inquiry.’’ NRDC v. EPA, 655 F.2d at 329.
‘‘The Clean Air Act requires the EPA to
look to the future in setting standards,
but the agency must also provide a
reasoned explanation of its basis for
believing that its projection is reliable.’’
Id.
EPA will make a consistency finding
where CARB provides for longer lead
time in instances in of emerging or
unavailable technology at the time
CARB adopts its standards. In sum,
EPA’s review of CARB’s technological
feasibility involves both evaluations of
predictions for future technological
advances and presently available
technology. EPA also believes that a
longer lead time would allow CARB
‘‘modify its standards if the actual
future course of technology diverges
from expectation.’’ Id.
As previously mentioned above, costs
considerations are also tied to the
compliance timing for a particular
standard and are thus, relevant for
purposes of the consistency
determination called for by the third
waiver criterion under section
209(b)(1)(C). In evaluating compliance
costs for CARB standards, EPA
considers the actual cost of compliance
in the time provided by applicable
California regulations. Compliance costs
‘‘relates to the timing of standards and
procedures.’’ MEMA I, 627 F.2d at 1118
(emphasis in original). Where
technology is not presently available,
EPA also considers the period necessary
to permit development and application
of the requisite technology.
In terms of waiver practice, EPA has
previously taken the position that
technology control costs must be
excessive for EPA to find that
California’s standards are inconsistent
with section 202(a).588 (See MEMA I,
627 F.2d at 1118 ‘‘Congress wanted to
avoid undue economic disruption in the
automotive manufacturing industry and
also sought to avoid doubling or tripling
of the cost of motor vehicles to
purchasers.’’) Consistent with this
practice, in the ACC program waiver,
EPA contended that control costs for the
ZEV standards were ‘‘not excessive.’’
‘‘Under EPA’s traditional analysis of
cost in the waiver context, because [an
incremental cost of $12,900 in MY 2020]
does not represent a ‘doubling or
tripling’ of the vehicle cost, such cost is
not excessive nor does it represent an
infeasible standard’’ (78 FR 2142). EPA
now believes that its prior view that a
588 74 FR 32744, 32774 (July 8, 2009); 47 FR 7306,
7309 (February 18, 1982); 46 FR 26371, 26373 (May
12, 1981), 43 FR 25735 (June 14, 1978).

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doubling or tripling of vehicle cost
constitutes an excessive cost or
represents an infeasible standard was
incorrect. Such a bright line (and
extreme) test is inappropriate. Instead,
the agency should holistically consider
whether technology control costs are
infeasible by considering the availability
of the technology, the reasonableness of
costs associated with adopting it within
the required lead time, and consumer
acceptance.
(3) Interpretation of Section 209(b)(1)(C)
EPA cannot grant a waiver, under
section 209(b)(1)(C), if California’s
‘‘standards and accompanying
enforcement procedures are not
consistent with section [202(a)].’’
Relevant legislative history from the
1967 CAA amendments indicates that
EPA is to grant a waiver unless it finds
that there is ‘‘inadequate time to permit
the development of the necessary
technology given the cost of compliance
within that time period.’’ This is similar
to language found in section 202(a),
which is discussed below. Additional
relevant legislative history indicates that
EPA is not to grant a waiver where
‘‘California standards are not consistent
with the intent of section 202(a) of the
Act, including economic practicability
and technological feasibility.’’ The
cross-reference to section 202(a) is an
indication of the role EPA plays in
reviewing California’s waiver request
under section 209(b)(1)(C).
With regard to section 202(a),
standards promulgated under section
202(a)(1) ‘‘shall take effect after such
period as the Administrator finds
necessary to permit the development
and application of the requisite
technology, giving appropriate
consideration to the cost of compliance
within such period.’’ Section 202(a).
Pertinent legislative history from the
1970 and 1977 amendments indicate
that EPA ‘‘was expected to press for the
development and application of
improved technology rather than be
limited by that which exists today.’’ S.
Rep. No. 1196, 91st Cong., 2d Sess. 24
(1970); H.R. Rep. No. 294, 95th Cong.,
1st Sess. 273 (1977). In sum, EPA
believes that section 202(a) allows for a
projection of lead time as to future
technological developments.
(4) Proposed Finding That California’s
Standards Are Not Consistent With
Section 202(a)
As previously mentioned, today’s
proposal now cast significant doubts on
EPA’s predictions for future and timely
availability of emerging technologies for
compliance with Federal GHG standards
for MY 2021–2025. It highlights in

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particular challenges for ZEV-type
technologies, such as BEVs and PHEVs,
that California relied on as compliance
options for the ZEV mandate
requirements and GHG standards. As
also previously mentioned CARB’s GHG
standards were developed jointly by
EPA and CARB with the result that
CARB’s GHG standards share a similar
structure with EPA GHG standards in
terms of both lead time and stringency.
For instance, the methodology and
underlying data used by CARB to assess
technologies and costs were similar to
and, in many instances, the same as
those used by EPA to assess the Federal
GHG standards (78 FR 2136). Also, the
technological feasibility analyses
underlying CARB’s standards were
based on several emerging technologies
similar to control technologies
considered by EPA and NHTSA in
evaluating Federal GHG standards for
MYs 2021–2025. Id. Additionally,
CARB’s feasibility finding was premised
on a finding of reduced compliance
costs and flexibility because of the
deemed to comply provisions, which
allowed for compliance with Federal
GHG standards in lieu of California’s
standards.589 In sum, EPA’s findings of
technological feasibility for the GHG
and ZEV standards were premised on
the availability of both current and
emerging technologies in the lead-time
CARB provided for new MY 2021–2025
motor vehicles (78 FR 2138–2139,
2143). These kinds of control
technologies would include ZEV-type
technologies, which are used as a
compliance option for CARB’s GHG
standards because their GHG emissions
levels are significantly lower than nonZEV technology. As the NPRM
589 On May 7, 2018, California issued a notice
seeking comments on ‘‘potential alternatives to a
potential clarification’’ of this provision for MY
vehicles that would be affected by revisions to the
Federal GHG standards. The notice is available at:
https://www.arb.ca.gov/msprog/levprog/leviii/
leviii_dtc_notice05072018.pdf. EPA proposes to
determine that the ‘‘deemed to comply’’ provision
in California’s program does not prevent EPA from
finding that California’s ZEV and GHG standards
are inconsistent with section 202(a), for two
reasons. First, the ‘‘deemed to comply’’ provision is
in flux; the state process that may ‘‘clarify[]’’ it
renders it unclear whether California will continue
to deem a program that may be revised as proposed
in this joint rulemaking to comply with its own
program. Second, EPA proposes to determine that
a ‘‘deemed to comply’’ provision is logically
incompatible with a preemption waiver analysis.
The entire premise of 209(a) preemption and
209(b)(1) waivers is that California’s standards will
differ from the Federal standards. If ‘‘deemed to
comply’’ provisions in California’s program
prevented EPA from determining that California’s
standards is inconsistent with section 202(a), then
those provisions’ presence would prevent EPA’s
analysis under this prong (209(b)(1)(C) from
denying it a waiver no matter the content of those
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discusses, certain control technology
would likely not be fully developed in
time for deployment in MY 2021
through 2025 motor vehicles.
With regard to the ZEV standards,
CARB’s waiver request contained
projections and explanations for ZEVs
that included projected sales of FCVs in
both California and section 177 States.
Specifically, CARB’s projections, at the
time, were that nearly every vehicle
manufacturer would be introducing
BEVs and PHEVs within the next one to
three years, and five manufacturers
would be commercially introducing
FCVs by 2015.590 As explained above,
the ZEV regulations contains the travel
provision that allow manufacturers to
comply with the ZEV sales mandate by
generating credits for vehicle sales in
section 177 States and vice versa. At the
grant of the ACC program waiver, EPA
found CARB’s assumptions and
projections appeared reasonable within
the provided lead time for MYs 2021
through 2025 (78 FR 2141–42).
Technological challenges may serve
as basis for either a future compliance
deadline extension or modifications to
the federal GHG standards that would
be consistent with today’s proposal and
would then raise questions as to CARB’s
predictions and projections of
technological feasibility and costs. At
this time, however, CARB has shown no
indication that it intends to either
extend the compliance deadline for or
modify its standards by providing
additional compliance flexibilities. EPA
believes it is reasonable, therefore, to
consider any expected hardship that
would be posed to manufacturers if EPA
does not withdraw CARB’s waiver.
NRDC, 655 F.2d at 330. An early
withdrawal of the waiver would also
provide a measure of certainty to all
manufacturers. ‘‘ ‘[T]the base hour for
commencement of production is
relatively distant, and until that time the
probable effect of a relaxation of the
standard would be to mitigate the
consequences of any strictness in the
final rule, not to create new
hardships.’’ 591 Further, from past
experience with waivers for challenging
standards, EPA is aware that CARB has
subsequently either modified
compliance deadlines or provided
additional compliance flexibilities for
such standards.592 EPA also notes that
590 CARB waiver request at 27–28, which can be
found in Docket ID No. EPA–HQ–OAR–2012–0562.
591 Id. The ‘‘hardships’’ referred to are hardships
that would be created for manufacturers able to
comply with the more stringent standards being
relaxed late in the process.
592 For example, CARB has made multiple
revisions to its on-Board diagnostics (OBD) (81 FR

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even at the time of the waiver request,
CARB was already cognizant of
challenges presented by both ZEV and
GHG standards. CARB noted that
although several individual
technologies offered substantial CO2
reduction potential many of the
technologies had only limited
deployment in new vehicle models (78
FR 2136). CARB also extended the travel
provisions beyond 2017 for FCVs due to
insufficient refueling infrastructure in
section 177 States as compared to other
kinds of ZEV technologies (78 FR 2120;
CARB Resolution 12–11 at 15). EPA is,
therefore, acting in anticipation of the
challenges presented by its GHG and
ZEV standards. As previously
explained, a late modification or
extension of time carries attendant
hardships for technologically advanced
manufacturers who might have made
major investment commitments
(International Harvester, 478 F.2d 615).
EPA believes that today’s proposal,
when finalized, would be sufficiently
ahead of the compliance deadline for
MY 2021 through 2025 and thus,
manufacturers would not incur any
hardships. Indeed, the expectation is
that the proposed withdrawal would
provide notice to manufacturers of the
intended compliance deadline
modifications for MYs 2021 through
2025.
Finally, the agency is acting on the
likelihood of increased compliance
costs as shown in today’s proposal.
(These are costs that will likely be
passed on to consumers in most
instances.). As previously explained,
because compliance technologies that
California relied on for both ZEV and
GHG standards are similar to the
technologies considered by EPA in
evaluating the feasibility of standards
for MYs 2021 through 2025, economies
of scale were expected to drive down
both manufacturing and technology
costs. The EPA, however, now expects
that manufacturers may no longer be
willing to commit to investments for a
limited market as compared to the
broader national market, which was
contemplated by the federal and
California GHG standards.
Today’s proposal also confirms slower
pace of development of ZEV technology
and differences in projected
manufacturing costs in states that have
adopted these standards under section
177 as well as lower consumer demands
for FCVs. The EPA also now expects
that the pace of technological
developments as it relates to
infrastructure for FCVs will slow down.
78144 (November 7, 2016)) and the ZEV program
regulations (76 FR 61096 (October 3, 2011)).

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The EPA is thus, proposing to find that
CARB’s ZEV standards for MYs 2021
through 2025 are technologically
infeasible in the lead time provided and
therefore, that CARB’s ZEV standards
are not consistent with section 202(a).
As previously mentioned EPA is
proposing to withdraw the grant of
waiver for both standards on grounds
that they are not consistent with section
202(a). In light of all the foregoing, the
agency finds that is necessary and
reasonable to reconsider the grant of
waiver for CARB’s GHG and ZEV
standards. EPA requests comments on
all aspects of this proposal, especially
specific costs for the ZEV requirements
as it relates to MYs 2021 through 2025.
4. States Cannot Adopt California’s GHG
Standards for NAAQS Nonattainment
Purposes Under Section 177
As explained above, CAA section 177
provides that other States, under certain
circumstances and with certain
conditions, may ‘‘adopt and enforce’’
standards that are ‘‘identical to the
California standards for which a waiver
has been granted for [a given] model
year.’’ 42 U.S.C. 7507. The EPA
proposes to determine that this section
does not apply to CARB’s GHG
standards.
In this regard, the EPA notes that the
section is titled ‘‘New motor vehicle
emission standards in nonattainment
areas’’ and that its application is limited
to ‘‘any State which has [state
implementation] plan provisions
approved under this part’’—i.e., under
CAA title I part D, which governs ‘‘Plan
requirements for nonattainment areas.’’
Areas are only designated
nonattainment with respect to criteria
pollutants for which EPA has issued a
NAAQS, and nonattainment SIPs are
intended to assure that those areas
attain the NAAQS. It would be illogical
to require approved nonattainment SIP
provisions as a predicate for allowing
States to adopt California’s standards if
states could use this authority to adopt
California standards that addressed
environmental problems other than
nonattainment of criteria pollutant
standards. Furthermore, the placement
of section 177 in title I part D, rather
than title II (the location of the
California waiver provision) would
make no sense if it functioned as a
waiver applicable to all subjects, as does
the California-focused provision under
section 209(b), rather than as a
provision specifically targeting criteria
pollutants and nonattainment areas, as
does the rest of title I part D.
Therefore, the text, context, and
purpose of section 177 suggest, and the
EPA proposes to conclude, that it is

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limited to providing States the ability,
under certain circumstances and with
certain conditions, to adopt and enforce
standards identical to those for which
California has obtained a waiver—
provided that those standards are
designed to control criteria pollutants to
address NAAQS nonattainment. EPA
solicits comment on how and when this
new interpretation should be adopted
and implemented, if finalized (e.g.,
whether EPA should adopt it as of the
effective date of a final rule, or as of a
later date, such as model year 2021 or
calendar year 2020, in order to allow
additional time for planning and
transition).
5. Severability and Judicial Review
EPA considers its proposed decision
on the appropriate federal standards for
light duty greenhouse gas vehicles for
MY 2021–2025 to be severable from its
decision on withdrawing the ACC
waiver, particularly with respect to the
requirements of CAA 209(b)(1)(B). Our
proposed interpretation of CAA
209(b)(1)(B), and our evaluation of the
ACC waiver under that provision, does
not depend on our decision to finalize,
and a court’s decision to uphold, the
light duty vehicles standards being
proposed today under CAA 202(a). EPA
solicits comment on the severability of
these actions, particularly with respect
to the other criteria of CAA 209(b).
Section 307(b)(1) of the CAA provides
in which Federal courts of appeal
petitions of review of final actions by
EPA must be filed. This section
provides, in part, that petitions for
review must be filed in the Court of
Appeals for the District of Columbia
Circuit if (i) the Agency action consists
of ‘‘nationally applicable regulations
promulgated, or final action taken, by
the Administrator,’’ or (ii) such action is
locally or regionally applicable, but
‘‘such action is based on a
determination of nationwide scope or
effect and if in taking such action the
Administrator finds and publishes that
such action is based on such a
determination.’’ Separate and apart from
whether a court finds this action to be
locally or regionally applicable, the
Administrator is proposing to find that
any final action resulting from this
rulemaking is based on a determination
of ‘‘nationwide scope or effect’’ within
the meaning of section 307(b)(1).
This decision, when finalized, will
affect persons in California and those
manufacturers and/or owners/operators
of new motor vehicles nationwide who
must comply with California’s new
motor vehicle requirements. For
instance, manufacturers may generate
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to satisfy those manufacturers’
obligations to comply with the mandate
that a certain percentage of their
vehicles sold in California be ZEV (or be
credited as such from sales in section
177 States). In addition, because other
states have adopted aspects of
California’s ACC program this decision
would also affect those states and those
persons in such states, which are
located in multiple EPA regions and
federal circuits. For these reasons, EPA
determines and finds for purposes of
section 307(b)(1) that any final
withdrawal action would be of national
applicability, and also that such action
would be based on a determination of
nationwide scope or effect for purposes
of section 307(b)(1). Pursuant to section
307(b)(1), judicial review of this final
action may be sought only in the United
States Court of Appeals for the District
of Columbia Circuit. Judicial review of
any final action may not be obtained in
subsequent enforcement proceedings,
pursuant to section 307(b)(2).
VII. Impacts of the Proposed CAFE and
CO2 Standards
A. Overview
New CAFE and CO2 standards will
have a range of impacts. EPCA/EISA
and NEPA require DOT to consider such
impacts when making decisions about
new CAFE standards, and the CAA
requires EPA to do so when making
decisions about new emissions
standards. Like past rulemakings,
today’s announcement is supported by
the analysis of many potential impacts
of new standards. Today’s
announcement proposes new standards
through model year 2026, explicitly
estimates manufacturers’ responses to
standards through model year 2029, and
considers impacts, throughout those
vehicles’ useful lives. The agencies do
not know today what would actually
come to pass decades from now under
the proposed standards or under any of
alternatives under consideration. The
analysis is thus properly interpreted not
as a forecast, but rather as an
assessment—reflecting the best
judgments regarding many different
factors—of impacts that could occur.593
As discussed below, the analysis was
conducted to explore the sensitivity of
this assessment to a variety of potential
changes in key analytical inputs (e.g.,
fuel prices).
This section summarizes various
impacts of the preferred alternative (i.e.,
the proposed standards) defined above
in Section III. The no-action alternative
593 ‘‘Prediction is very difficult, especially if it’s
about the future.’’ Attributed to Niels Bohr, Nobel
laureate in Physics.

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defined in Section IV provides the
baseline relative to which all impacts
are shown. Because the proposed
standards (and other standards
considered below), being of a
‘‘deregulatory’’ nature, are less stringent
than the no-action alternative, all
impacts are directionally opposite
impacts reported in recent CAFE and
CO2 rulemakings. For example, while
past rulemakings reported positive
values for fuel consumption avoided
under new standards, today’s proposal
reports negative values, as fuel
consumption will be somewhat greater
under today’s proposed standards than
under standards defining the baseline
no-action alternative. Reported negative
values for avoided fuel consumption
could also be properly interpreted as
simply ‘‘additional fuel consumption.’’
Similarly, reported negative values for
costs could be properly interpreted as
‘‘avoided costs’’ or ‘‘benefits,’’ and
reported negative values for benefits
could be properly interpreted as
‘‘foregone benefits’’ or ‘‘costs.’’
However, today’s notice retains
reporting conventions consistent with
past rulemakings, anticipating that,
compared to other options, doing so will
facilitate review by most stakeholders.
Today’s analysis presents results for
individual model years in two different
ways. The first way is similar to past
rulemakings and shows how
manufacturers could respond in each
model year under the proposed
standards and each alternative covering
MYs 2021/2–2026. The second,
expanding on the information provided
in past rulemakings, evaluates
incremental impacts of new standards
proposed for each model year, in turn.
In past rulemaking analyses, NHTSA
modeled year-by-year impacts under the
aggregation of standards applied in all
model years, and EPA modeled
manufacturers’ hypothetical compliance
with a single model years’ standards in
that model year. Especially considering
multiyear planning effects, neither
approach provides a clear basis to
attribute impacts to specific standards
first introduced in each of a series of
model years. For example, of the
technology manufacturers applied in
MY 2016, some would have been
applied even under the MY 2014
standards, and some were likely applied
to position manufacturers toward
compliance with (including credit
banking to be used toward) MY 2018
standards. Therefore, of the impacts
attributable to the model year 2016 fleet,
only a portion can be properly
attributed to the MY 2016 standards,
and the impacts of the MY 2016

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standards involve fleets leading up and
extending well beyond MY 2016.
Considering this, the proposed
standards were examined on an
incremental basis, modeling each new
model year’s standards over the entire
span of included model years, using
those results as a baseline relative to
which to measure impacts attributable
to the next model year’s standards. For
example, incremental costs attributable
to the standards proposed today for MY
2023 are calculated as follows:
COSTProposed,MY 2023 = (COSTProposed_
through_MY 2023¥COSTNo-Action_through_
MY 2023—(COSTProposed_through_MY
2022¥COSTNo-Action_through_MY 2022)
Where:
COSTProposed,MY 2023: Incremental technology
cost during MYs 2017–2030 and
attributable to the standards proposed for
MY 2023.
COSTProposed_through_MY 2022: Technology cost
for MYs 2017–2030 under standards
proposed through MY 2022.
COSTProposed_through_MY 2023: Technology cost
for MYs 2017–2030 under standards
proposed through MY 2023.
COSTNo-Action_through_MY 2022: Technology cost
for MYs 2017–2030 under no-action
alternative standards through MY 2022.
COSTNo-Action_through_MY 2023: Technology cost
for MYs 2017–2030 under no-action
alternative standards through MY 2023.

Additionally, today’s analysis
includes impacts on new vehicle sales
volumes and the use (i.e., survival) of
vehicles of all model years, such that
standards introduced in a model year
produce impacts attributable to vehicles
having been in operation for some time.
For example, as modeled here,
standards for MY 2021 will impact the
prices of new vehicles starting in MY
2017, and those price impacts will affect
the survival of all vehicles still in
operation in calendar years 2017 and
beyond (e.g., MY 2021 standards impact
the operation of MY 2007 vehicles in
calendar year 2027). Therefore, while
past rulemaking analyses focused
largely on impacts over the useful lives
of the explicitly modeled fleets, much of
today’s analysis considers all model
years through 2029, as operated,
throughout those vehicles’ useful lives.
For some impacts, such as on
technology penetration rates, average
vehicle prices, and average vehicle
ownership costs, the focus was on the
useful life of the MY 2030 fleet, as the
simulation of manufacturers’ technology
application and credit use (when
included in the analysis) continues to
evolve after model year 2026, stabilizing
by model year 2030.
Effects were evaluated from four
perspectives: The social perspective, the
manufacturer perspective, the private
perspective, and the physical

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perspective. The social perspective
focuses on economic benefits and costs,
setting aside economic transfers such as
fuel taxes but including economic
externalities such as the social cost of
CO2 emissions. The manufacturer
perspective focuses on average
requirements and levels of performance
(i.e., average fuel economy level and
CO2 emission rates), compliance costs,
and degrees of technology application.
The private perspective focuses on costs
of vehicle purchase and ownership,
including outlays for fuel (and fuel
taxes). The physical perspective focuses
on national-scale highway travel, fuel
consumption, highway fatalities, and
greenhouse gas and criteria pollutant
emissions.
This analysis does not explicitly
identify ‘‘co-benefits’’ from its proposed
action to change fuel economy
standards, as such a concept would
include all benefits other than cost
savings to vehicle buyers. Instead, it
distinguishes between private benefits—
which include economic impacts on
vehicle manufacturers, buyers of new
cars and light trucks, and owners (or
users) of used cars and light trucks—and
external benefits, which represent
indirect benefits (or costs) to the
remainder of the U.S. economy that
stem from the proposal’s effects on the
behavior of vehicle manufacturers,
buyers, and users. In this accounting
framework, changes in fuel use and
safety impacts resulting from the
proposal’s effects on the number of used
vehicles in use represent an important
component of its private benefits and
costs, despite the fact that previous
analyses have failed to recognize these
effects. The agency’s presentation of
private costs and benefits from its
proposed action clearly distinguishes
between those that would be
experienced by owners and users of cars
and light trucks produced during
previous model years and those that
would be experienced by buyers and
users of cars and light trucks produced
during the model years it would affect.
Moreover, it clearly separates these into
benefits related to fuel consumption and
those related to safety consequences of
vehicle use. This is more meaningful
and informative than simply identifying
all impacts other than changes in fuel
savings to buyers of new vehicles as
‘‘co-benefits.’’
For the social perspective, the
following effects for model years
through 2029 as operated throughout
those vehicles’ useful lives are
summarized:
• Technology Costs: Incremental cost, as
expected to be paid by vehicle purchasers, of

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
fuel-saving technology beyond that added
under the no-action alternative.
• Welfare Loss: Loss of value to vehicle
owners resulting from incremental increases
in the numbers of strong and plug-in hybrid
electric vehicles (strong HEVs or SHEVs, and
PHEVs) and/or battery electric vehicles
(BEVs), beyond increases occurring under the
no-action alternative. The loss of value is a
function of the factors that lead to different
valuations for conventional and electric
versions of similar-size vehicles (e.g.,
differences in: travel range, recharging time
versus refueling time, performance, and
comfort).
• Pre-tax Fuel Savings: Incremental
savings, beyond those achieved under the noaction alternative, in outlays for fuel
purchases, setting aside fuel taxes.
• Mobility Benefit: Value of incremental
travel, beyond that occurring under the noaction alternative.
• Refueling Benefit: Value of incremental
reduction, compared to the no-action
alternative, of time spent refueling vehicles.
• Non-Rebound Fatality Costs: Social
value of additional fatalities, beyond those
occurring under the no-action alternative,
setting aside any additional travel
attributable to the rebound effect.
• Rebound Fatality Costs: Social value of
additional fatalities attributable to the
rebound effect, beyond those occurring under
the no-action alternative.
• Benefits Offsetting Rebound Fatality
Costs: Assumed further value, offsetting
rebound fatality costs, of additional travel
attributed to the rebound effect.
• Non-Rebound Non-Fatal Crash Costs:
Social value of additional crash-related losses
(other than fatalities), beyond those occurring
under the no-action alternative, setting aside
any additional travel attributable to the
rebound effect.
• Rebound Non-Fatal Crash Costs: Social
value of additional crash-related losses (other
than fatalities) attributable to the rebound
effect, beyond those occurring under the noaction alternative.
• Benefits Offsetting Rebound Non-Fatal
Crash Costs: Assumed further value,
offsetting rebound non-fatal crash costs, of
additional travel attributed to the rebound
effect.
• Additional Congestion and Noise (Costs):
Value of additional congestion and noise
resulting from incremental travel, beyond
that occurring under the no-action
alternative.
• Energy Security Benefit: Value of
avoided economic exposure to petroleum
price ‘‘shocks,’’ the avoided exposure
resulting from incremental reduction of fuel
consumption beyond that occurring under
the no-action alternative.
• Avoided CO2 Damages (Benefits): Social
value of incremental reduction of CO2
emissions, compared to emissions occurring
under the no-action alternative.
• Other Avoided Pollutant Damages
(Benefits): Social value of incremental
reduction of criteria pollutant emissions,
compared to emissions occurring under the
no-action alternative.
• Total Costs: Sum of incremental
technology costs, welfare loss, fatality costs,

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non-fatal crash costs, and additional
congestion and noise costs.
• Total Benefits: Sum of pretax fuel
savings, mobility benefits, refueling benefits,
Benefits Offsetting Rebound Fatality Costs,
Benefits Offsetting Rebound Non-Fatal Crash
Costs, energy security benefits, and benefits
from reducing emissions of CO2, other GHGs,
and criteria pollutants.
• Net Benefits: Total benefits minus total
costs.
• Retrievable Electrificaiton Costs: The
portion of HEV, PHEV, and BEV technology
costs which can be passed onto consumers,
using the willingness to pay analysis
described above.
• Electrification Tax Credits: Estimates of
the portion of HEV, PHEV, and BEV
technology costs which are covered by
federal or state tax incentives.
• Irretreivable Electrification Costs: The
portion of HEV, PHEV, and BEV technology
costs OEM’s must either absorb as a profit
loss, or cross-subsidize with the prices of
internal combustion engine (ICE) vehicles.
• Total Electrification Costs: Total
incremental technology costs attributable to
HEV, PHEV, or BEV vehicles.

For the manufacturer perspective, the
following effects for the aggregation of
model years 2017–2029 are
summarized:
• Average Required Fuel Economy:
Average of manufacturers’ CAFE
requirements for indicated fleet(s) and model
year(s).
• Percent Change in Stringency from
Baseline: Percentage difference between
averages of fuel economy requirements under
no-action and indicated alternatives.
• Average Required Fuel Economy:
Industry-wide average of fuel economy levels
achieved by indicated fleet(s) in indicated
model year(s).
• Percent Change in Stringency from
Baseline: Percentage difference between
averages of fuel economy levels achieved
under no-action and indicated alternatives.
• Total Technology Costs ($b): Cost of fuelsaving technology beyond that applied under
no-action alternative.
• Total Civil Penalties ($b): Cost of civil
penalties (for the CAFE program) beyond
those levied under no-action alternative.
• Total Regulatory Costs ($b): Sum of
technology costs and civil penalties.
• Sales Change (millions): Change in
number of vehicles produced for sale in U.S.,
relative to the number estimated to be
produced under the no-action alternative.
• Revenue Change ($b): Change in total
revenues from vehicle sales, relative to total
revenues occurring under the no-action
alternative.
• Curb Weight Reduction: Reduction of
average curb weight, relative to MY 2016.
• Technology Penetration Rates: MY 2030
average technology penetration rate for
indicated ten technologies (three engine
technologies, advanced transmissions, and
six degrees of electrification).
• Average Required CO2: Average of
manufacturers’ CO2 requirements for
indicated fleet(s) and model year(s).
• Percent Change in Stringency from
Baseline: Percentage difference between

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43255

averages of CO2 requirements under noaction and indicated alternatives.
• Average Achieved CO2: Average of
manufacturers’ CO2 emission rates for
indicated fleet(s) and model year(s).

For the private perspective, the
following effects for the MY 2030 fleet
are summarized:
• Average Price Increase: Average increase
in vehicle price, relative to the average
occurring under the no-action alternative.
• Welfare Loss (Costs): Average loss of
value to vehicle owners resulting from
incremental increases in the numbers of
strong HEVs, PHEVs) and/or BEVs, beyond
increases occurring under the no-action
alternative. The loss of value is a function of
the factors that lead to different valuations
for conventional and electric versions of
similar-size vehicles (e.g., differences in:
Travel range, recharging time versus
refueling time, performance, and comfort).
• Ownership Costs: Average increase in
some other costs of vehicle ownership (taxes,
fees, financing), beyond increase occurring
under no-action alternative.
• Fuel Savings: Average of fuel outlays
(including taxes) avoided over a vehicles’
expected useful lives, compared to outlays
occurring under no-action alternative.
• Mobility Benefit: Average incremental
value of additional travel over average
vehicles’ useful lives, compared to travel
occurring under no-action alternative.
• Refueling Benefit: Average incremental
value of avoided time spent refueling over
average vehicles’ useful lives, compared to
time spent refueling under no-action
alternative.
• Total Costs: Sum of average price
increase, welfare loss, and ownership costs.
• Total Benefits: Sum of fuel savings,
mobility benefit, and refueling benefit.
• Net Benefits: Total benefits minus total
costs.

For the physical perspective, the
following effects for model years
through 2029 as operated throughout
those vehicles’ useful lives are
summarized:
• Greenhouse gases include carbon
dioxide (CO2), methane (CH4), and nitrous
oxide (N2O), and values are reported
separately for vehicles (tailpipe) and
upstream processes (combining fuel
production, distribution, and delivery) and
shown as reductions relative to the no-action
alternative.
• Criteria pollutants include carbon
monoxide (CO), volatile organic compounds
(VOC), nitrogen oxides (NOX), sulfur dioxide
(SO2) and particulate matter (PM), and values
are shown as reductions relative to the noaction alternative.
• Fuel consumption aggregates all fuels,
with electricity, hydrogen, and compressed
natural gas (CNG) included on a gasolineequivalent-gallon (GEG) basis, and values are
shown as reductions relative to the no-action
alternative.
• VMT, with rebound (billion miles):
Increase in highway travel (as vehicle miles
traveled), relative to the no-action alternative,
and including the rebound effect.

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• VMT, without rebound (billion miles):
Increase in highway travel (as vehicle miles
traveled), relative to the no-action alternative,
and excluding the rebound effect.
• Fatalities, with rebound: Increase in
highway fatalities, relative to the no-action
alternative, and including the rebound effect.
• Fatalities, without rebound: Increase in
highway fatalities, relative to the no-action
alternative, and excluding the rebound effect.
• Fuel Consumption, with rebound (billion
gallons): Reduction of fuel consumption,
relative to the no-action alternative, and
including the rebound effect.
• Fuel Consumption, without rebound
(billion gallons): Reduction of fuel
consumption, relative to the no-action
alternative, and excluding the rebound effect.

Below, this section tabulates results
for each of these four perspectives and
does so separately for the proposed
CAFE and CO2 standards. More detailed
results are presented in the Preliminary
Regulatory Impact Analysis (PRIA)
accompanying today’s notice, and
additional and more detailed analysis of
environmental impacts for CAFE
regulatory alternatives is provided in
the corresponding Draft Environmental
Impact Statement (DEIS). Underlying
CAFE model output files are available
(along with input files, model, source
code, and documentation) on NHTSA’s
website.594 Summarizing and tabulating
results for presentation here involved
considerable ‘‘off model’’ calculations
(e.g., to combine results for selected
model years and calendar years, and to
combine various components of social
and private costs and benefits); tools
Volpe Center staff used to perform these
calculations are also available on
NHTSA’s website.595
While the National Environmental
Policy Act (NEPA) requires NHTSA to
prepare an EIS documenting estimating
environmental impacts of the regulatory
alternatives under consideration in

sradovich on DSK3GMQ082PROD with PROPOSALS2

594 Compliance and Effects Modeling System,
National Highway Traffic Safety Administration,
https://www.nhtsa.gov/corporate-average-fueleconomy/compliance-and-effects-modeling-system
(last visited June 25, 2018).
595 These tools, available at the same location, are
scripts executed using R, a free software
environment for statistical computing. R is available
through https://www.r-project.org/.

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CAFE rulemakings, NEPA does not
require EPA to do so for EPA
rulemakings. CO2 standards for each
regulatory alternative being harmonized
as practical with corresponding CAFE
standards, environmental impacts of
GHG standards should be directionally
identical and similar in magnitude to
those of CAFE standards. Nevertheless,
in this section, following the series of
tables below, today’s announcement
provides a more detailed analysis of
estimated impacts of the proposed
CAFE and CO2 standards. Results
presented herein for the CAFE standards
differ slightly from those presented in
the DEIS; while, as discussed above,
EPCA/EISA requires that the Secretary
determine the maximum feasible levels
of CAFE standards in manner that, as
presented here, sets aside the potential
use of CAFE credits or application of
alternative fuels toward compliance
with new standards, NEPA does not
impose such constraints on analysis
presented in corresponding DEISs, and
the DEIS presents results of an
‘‘unconstrained’’ analysis that considers
manufacturers’ potential application of
alternative fuels and use of CAFE
credits.
In terms of all estimated impacts,
including estimated costs and benefits,
results of today’s analysis are different
for CAFE and CO2 standards.
Differences arise because, even when
the mathematical functions defining
fuel economy and CO2 targets are
‘‘harmonized,’’ surrounding regulatory
provisions may not be. For example,
while both CAFE and CO2 standards
allow credits to be transferred between
fleets and traded between
manufacturers, EPCA/EISA places
explicit and specific limits on the use of
such credits, such as by requiring that
each domestic passenger car fleet meet
a minimum CAFE standard (as
discussed above). The CAA provides no
specific direction regarding CO2
standards, and while EPA has adopted
many regulatory provisions harmonized
with specific EPCA/EISA provisions
(e.g., separate standards for passenger
cars and light trucks), EPA has not

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adopted all such provisions. For
example, EPA has not adopted the
EPCA/EISA provisions limiting transfers
between regulated fleet or requiring
separate compliance by domestic and
imported passenger car fleets. Such
differences introduce differences
between impacts estimated under CAFE
standards and under CO2 standards.
Also, as mentioned above, Congress has
required that new CAFE standards be
considered in a manner that sets aside
the potential use of CAFE credits and
the potential additional application of
alternative fuel vehicles (such as electric
vehicles) during the model years under
consideration. Congress has provided no
corresponding direction regarding the
analysis of potential CO2 standards, and
today’s analysis does consider these
potential responses to CO2 standards.
As mentioned above, analysis was
conducted to examine the sensitivity of
results to changes in key inputs.
Following the detailed consideration of
potential environmental impacts, this
section concludes with a tabular
summary of results of this sensitivity
analysis.
B. Impacts of Proposed Standards on
Requirements, Performance, and Costs
to Manufacturers in Specific Model
Years
As mentioned above, impacts are
presented from two different
perspectives for today’s proposal. From
either perspective, overall impacts are
the same. The first perspective,
following the approach taken by
NHTSA in past CAFE rulemakings,
examines impacts of the overall
proposal—i.e., the entire series of yearby-year standards—on each model year.
This perspective is especially relevant
to understanding how the overall
proposal may impact manufacturers in
terms of year-by-year compliance,
technology pathways, and costs. The
second, presented below in Section
VII.C, provides a clearer
characterization of the incremental
impacts attributable to standards
introduced in each successive model
year.

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Manufacturer
BMW
BMW
Daimler
Daimler
Fiat Chrysler
Fiat Chrysler
Ford
Ford
General Motors
General Motors
Honda
Honda
Hyundai
Hyundai
Kia
Kia
Jaguar/Land
Rover
Jaguar/Land
Rover
Mazda
Mazda
Nissan
Mitsubishi
Nissan
Mitsubishi
Subaru
Subaru

I

I

Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved

2o16
34.3
324
33.4
31.2
30.9
27.9
30.9
29.7
30.8
28.9
34.3
36.7
36.7
39.0
35.3
35.1
30.2

I

2011
36.0
34.3
34.8
32.9
31.9
30.0
31.9
31.3
31.7
30.2
35.8
39.0
38.7
41.8
37.1
36.8
30.9

I

2o18
37.2
35.3
35.8
32.9
32.7
33.5
32.5
31.6
32.3
32.4
36.8
40.8
40.1
43.0
38.3
38.9
31.6

I

2o19
38.3
36.5
36.9
35.3
33.3
35.5
33.2
32.0
33.1
34.5
38.0
41.5
41.6
44.9
39.6
40.1
32.3

I

2020
39.7
37.0
38.2
35.4
34.3
35.9
34.0
36.9
34.0
36.3
39.2
41.7
43.2
45.8
41.0
41.7
33.2

I

2021
41.7
37.0
40.2
35.9
36.4
38.1
35.9
40.5
35.8
39.9
41.3
44.0
45.1
49.5
43.0
47.2
35.4

I

2022
43.6
37.5
42.1
36.4
38.1
38.9
37.6
42.2
37.5
40.6
43.3
47.2
47.2
52.4
45.0
48.5
37.0

I

2o23
45.7
37.8
44.0
36.7
39.9
39.8
39.4
42.3
39.2
41.1
45.3
49.2
49.4
53.0
47.1
50.0
38.8

I

2o24
47.8
37.9
46.1
36.8
41.7
39.8
41.2
43.0
41.1
41.4
47.4
49.5
51.7
54.0
49.3
52.3
40.6

I

2o25
50.1
37.9
48.2
36.8
43.7
40.6
43.1
43.1
43.0
42.9
49.6
49.6
54.2
54.2
51.7
52.4
42.5

I

2o26
50.0
38.1
48.2
36.9
43.7
43.7
43.0
43.1
43.0
43.1
49.6
49.7
54.2
54.4
51.6
52.5
42.5

I

50.0
38.1
48.2
36.9
43.6
43.7
43.0
43.3
42.9
43.1
49.6
49.9
54.2
54.4
51.6
52.6
42.5

2o2s
50.0
38.1
48.1
36.9
43.6
44.0
42.9
43.2
42.9
43.1
49.6
50.1
54.2
54.3
51.6
52.5
42.5

2021

I

I

2o29
50.0
38.1
48.1
36.9
43.6
44.1
42.9
43.2
42.9
43.0
49.6
50.1
54.2
54.3
51.6
52.5
42.5

26.0

27.3

27.9

28.8

29.3

30.7

30.9

31.3

31.3

31.6

31.6

31.6

31.6

31.7

35.1
38.8
34.9

36.8
39.4
36.5

37.9
42.9
37.6

39.1
43.4
38.9

40.4
44.6
40.2

42.6
44.8
42.3

44.6
45.7
44.3

46.7
52.2
46.3

48.9
52.4
48.5

51.1
52.5
50.8

51.1
52.5
50.8

51.1
52.5
50.7

51.1
52.5
50.6

51.1
52.5
50.6

37.0

38.2

38.7

41.2

43.7

47.6

49.1

49.9

51.1

52.3

52.4

52.4

52.4

52.4

33.9
36.5

35.3
40.0

36.3
40.0

37.3
40.3

38.4
41.7

40.7
47.5

42.7
48.8

44.6
49.1

46.8
49.1

49.0
49.1

49.0
49.3

49.0
49.5

48.9
49.5

48.9
49.5

I

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-1- Required and Achieved CAFE Levels in MYs 2016-2029 under Baseline CAFE Standards (No-Action

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31.5

32.6

33.4

34.4

35.4

37.1

38.8

40.6

42.5

44.5

44.5

44.5

44.4

44.4

228.5
33.4
33.0
31.6
31.4
36.0
34.7
32.8
32.2

260.2
34.7
33.9
32.6
32.3
37.7
38.8
34.0
33.9

259.6
35.6
36.7
33.4
32.3
39.0
42.3
34.9
35.8

259.8
36.6
38.4
34.3
34.9
40.3
43.5
35.8
37.3

260.6
37.7
42.0
35.4
34.9
41.7
45.7
36.9
39.4

260.5
39.8
46.0
37.5
34.9
43.8
46.4
39.0
42.4

260.4
41.6
46.5
39.2
35.0
45.8
48.5
40.8
43.7

260.3
43.6
46.6
41.0
35.9
47.9
49.8
42.7
44.5

260.2
45.6
47.6
43.0
36.1
50.2
53.3
44.7
45.1

260.1
47.7
47.9
45.0
36.1
52.5
54.8
46.8
45.7

260.1
47.7
48.4
45.0
36.1
52.5
55.0
46.7
46.3

259.8
47.7
48.4
45.0
36.4
52.5
55.1
46.7
46.3

259.6
47.6
48.4
44.9
36.4
52.5
55.2
46.7
46.4

259.6
47.6
48.5
44.9
36.4
52.5
55.2
46.6
46.4

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

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Manufacturer
BMW
BMW
Daimler
Daimler
Fiat Chrysler
Fiat Chrysler
Ford
Ford
General Motors
General Motors
Honda
Honda
Hyundai
Hyundai
Kia
Kia
Jaguar/Land
Rover
Jaguar/Land
Rover
Mazda
Mazda
Nissan
Mitsubishi
Nissan
Mitsubishi
Subarn
Subarn
Tesla

I

I

Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required

2o16
34.3
32.4
33.4
31.2
30.9
27.9
30.9
29.7
30.8
28.9
34.3
36.7
36.7
39.0
35.3
35.1
30.2

I

2011
36.0
34.3
34.8
32.9
31.9
29.8
31.9
31.3
31.7
30.1
35.8
37.9
38.7
41.8
37.1
36.8
30.9

I

2o18
37.2
35.2
35.8
32.9
32.7
32.0
32.5
31.4
32.3
31.5
36.8
38.8
40.1
43.0
38.3
38.8
31.6

I

2o19
38.3
36.4
36.9
35.3
33.3
32.5
33.2
31.6
33.1
32.7
38.0
39.3
41.6
44.6
39.6
40.0
32.3

I

2020
39.7
36.9
38.2
35.4
34.3
32.8
34.0
34.2
34.0
34.0
39.2
39.4
43.2
45.4
41.0
41.0
33.2

I

2021
39.7
36.9
38.2
35.9
34.3
33.8
33.9
34.8
33.9
35.5
39.2
39.6
43.2
47.8
41.0
44.4
33.2

I

2022
39.7
37.3
38.2
36.3
34.3
34.1
34.0
35.0
34.0
35.6
39.2
41.3
43.2
48.3
41.0
44.5
33.2

I

2o23
39.7
37.6
38.2
36.6
34.3
34.4
34.0
35.1
34.0
35.6
39.2
42.1
43.2
48.4
41.0
45.3
33.2

I

2o24
39.7
37.8
38.2
36.7
34.3
34.4
34.0
35.2
34.0
35.7
39.2
42.1
43.2
48.5
41.0
46.2
33.2

I

2o2s
39.7
37.8
38.2
36.7
34.3
34.6
34.0
35.2
34.0
36.1
39.2
42.2
43.2
48.5
41.0
46.2
33.2

I

2o26
39.8
38.0
38.2
36.9
34.3
35.6
34.0
35.3
34.0
36.3
39.3
42.2
43.2
48.8
41.0
46.3
33.2

I

2021
39.8
38.0
38.2
36.9
34.3
35.6
34.0
35.4
34.0
36.3
39.3
42.6
43.2
48.8
41.0
46.5
33.2

I

2o28
39.7
38.1
38.2
36.9
34.3
35.7
34.0
35.4
34.0
36.3
39.2
42.6
43.2
48.8
41.0
46.5
33.2

I

2o29
39.8
38.1
38.2
36.9
34.3
35.8
34.0
35.4
34.0
36.3
39.3
42.6
43.2
48.8
41.0
46.5
33.2

26.0

27.3

27.9

28.8

29.3

30.7

30.9

31.3

31.3

31.6

31.6

31.6

31.6

31.7

35.1
38.8
34.9

36.8
39.4
36.5

37.9
42.1
37.6

39.1
42.6
38.9

40.4
43.0
40.2

40.4
43.1
40.2

40.4
43.2
40.2

40.4
43.6
40.2

40.5
43.6
40.2

40.5
43.7
40.2

40.5
43.7
40.3

40.5
43.7
40.3

40.5
44.0
40.3

40.5
44.0
40.3

37.0

38.2

38.7

40.1

42.1

43.1

43.8

44.0

44.1

44.2

44.3

44.3

44.3

44.3

33.9
36.5
31.5

35.3
39.9
32.6

36.3
39.9
33.4

37.3
40.2
34.4

38.4
40.6
35.4

38.4
42.4
35.1

38.4
42.6
35.1

38.4
42.7
35.1

38.4
42.7
35.1

38.4
42.7
35.2

38.4
43.2
35.2

38.4
43.3
35.2

38.4
43.3
35.2

38.4
43.3
35.2

I

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-2- Required and Achieved CAFE Levels in MYs 2016-2029 under Proposed CAFE Standards (Preferred

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Achieved

Toyota
Toyota
Volvo
Volvo
VWA
VWA
Ave./Total
Ave./Total

Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved

228.5
33.4
33.0
31.6
31.4
36.0
34.7
32.8
32.2

260.2
34.7
33.9
32.6
32.3
37.7
37.9
34.0
33.7

259.6
35.6
36.2
33.4
32.3
39.0
40.1
34.9
35.0

259.8
36.6
37.6
34.3
34.9
40.3
40.9
35.8
36.0

260.6
37.7
39.5
35.4
34.9
41.7
42.2
36.9
37.2

260.5
37.7
41.0
35.3
34.9
41.7
42.3
36.9
38.3

260.6
37.7
41.4
35.4
34.9
41.7
42.9
36.9
38.7

260.6
37.7
41.4
35.4
35.8
41.7
43.0
36.9
39.0

260.6
37.7
41.6
35.4
35.9
41.7
43.0
37.0
39.1

260.8
37.8
41.7
35.4
35.9
41.7
43.1
37.0
39.2

261.0
37.8
42.2
35.4
35.9
41.8
43.2
37.0
39.5

260.9
37.8
42.2
35.4
36.3
41.8
43.2
37.0
39.6

260.9
37.8
42.2
35.4
36.3
41.8
43.2
37.0
39.6

260.9
37.8
42.2
35.4
36.3
41.8
43.3
37.0
39.7

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
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VerDate Sep<11>2014

Table VII-3- Undiscounted Regulatory Costs ($b) in MYs 2017-2029 under Baseline and Proposed CAFE Standards
Manufacturer
BMW
BMW

Jkt 244001

Daimler

PO 00000

Daimler
Fiat Chrysler

Frm 00277

Fiat Chrysler

Fmt 4701

Ford
Ford

Sfmt 4725

General Motors

E:\FR\FM\24AUP2.SGM

General Motors
Honda
Honda
Hyundai

24AUP2

Hyundai
Kia
Kia

JLR

I

I 2017 I 2018 I 2019 I 2020 I 2021 I 2022 I 2023 I 2024 I 2025 I 2026 I 2027 I 2028 I 2029 I
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline

0.0

0.1

0.1

0.2

0.2

0.3

0.3

0.3

0.4

0.4

0.4

0.4

0.4

Sum
3.4

0.0

0.0

0.0

0.0

0.0

-0.1

-0.1

-0.2

-0.2

-0.2

-0.2

-0.2

-0.2

-1.5

0.1

0.1

0.2

0.2

0.2

0.3

0.3

0.3

0.4

0.4

0.4

0.4

0.4

3.4

0.0

0.0

0.0

0.0

0.0

-0.1

-0.1

-0.1

-0.2

-0.2

-0.2

-0.2

-0.2

-1.2

l.l

3.3

5.1

5.1

6.2

6.6

7.2

7.2

7.7

9.5

9.4

9.4

9.3

87.0

-0.6

-2.3

-3.7

-3.6

-4.5

-4.7

-5.2

-5.2

-5.7

-7.0

-6.8

-6.8

-6.7

-62.7

0.2

0.5

1.2

5.3

7.8

8.6

8.4

8.6

8.3

8.1

8.0

7.8

7.7

80.7

0.0

-0.2

-0.7

-3.6

-6.1

-6.8

-6.6

-6.8

-6.6

-6.4

-6.3

-6.1

-6.0

-62.3

0.7

2.7

4.2

5.0

7.5

8.1

8.4

8.5

9.8

9.7

9.6

9.5

9.3

92.9

-0.3

-1.5

-2.7

-3.1

-5.2

-5.9

-6.3

-6.3

-7.6

-7.4

-7.3

-7.2

-7.0

-67.7

0.3

0.6

0.7

0.8

1.7

2.8

3.8

3.9

3.9

3.8

3.9

3.9

3.8

33.9

-0.2

-0.4

-0.4

-0.4

-1.4

-2.3

-3.2

-3.3

-3.2

-3.2

-3.2

-3.2

-3.2

-27.6

0.1

0.1

0.2

0.3

0.5

0.7

0.8

0.9

0.9

1.0

1.0

0.9

0.9

8.2

0.0

0.0

0.0

-0.1

-0.2

-0.4

-0.5

-0.7

-0.7

-0.7

-0.7

-0.7

-0.7

-5.2

0.3

0.4

0.4

0.6

1.2

1.5

1.7

1.9

1.9

1.8

1.8

1.8

1.8

17.0

0.0

0.0

0.0

-0.1

-0.6

-1.0

-1.1

-1.3

-1.3

-1.3

-1.3

-1.2

-1.2

-10.5

0.1

0.1

0.1

0.1

0.2

0.2

0.2

0.3

0.3

0.3

0.3

0.3

0.3

2.8

I
Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

I

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Mazda

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Mazda
Nissan/Mitsubishi

PO 00000

Nissan/Mitsubishi

Frm 00278

Subam
Sub am

Fmt 4701

Tesla
Tesla

Sfmt 4725

Toyota

E:\FR\FM\24AUP2.SGM

Toyota
Volvo
Volvo

24AUP2

VWA
VWA
Ave.!Total
Ave.!Total

EP24AU18.169

Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal

0.0

0.0

0.0

0.0

0.0

-0.1

-0.1

-0.1

-0.1

-0.1

-0.1

-0.1

-0.1

-1.0

0.0

0.1

0.1

0.3

OJ

0.5

1.3

1.3

1.2

1.2

1.2

1.2

1.1

9.9

0.0

-0.1

-0.1

-0.2

-0.2

-0.4

-1.2

-1.2

-1.1

-1.1

-1.1

-1.0

-1.0

-8.7

0.2

0.2

0.5

1.0

1.5

1.6

1.7

1.9

2.1

2.1

2.0

2.0

2.0

18.9

0.0

0.0

-0.2

-0.3

-0.7

-0.8

-0.8

-1.0

-1.2

-1.2

-1.2

-1.2

-1.2

-9.9

0.3

0.3

0.3

0.6

1.0

1.1

1.1

1.1

1.0

1.0

1.0

1.0

1.0

11.0

0.0

0.0

0.0

-0.2

-0.5

-0.7

-0.7

-0.7

-0.6

-0.6

-0.6

-0.6

-0.6

-5.9

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

1.4

2.0

3.7

5.4

5.4

5.3

5.8

5.9

5.9

5.8

5.8

5.9

58.4

0.0

-0.4

-0.7

-1.8

-3.2

-3.2

-3.2

-3.6

-3.7

-3.7

-3.6

-3.6

-3.6

-34.2

0.0

0.0

0.0

0.0

0.0

0.0

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.6

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

-0.3

0.9

1.5

1.6

2.0

2.0

2.3

2.5

2.9

3.0

2.9

2.8

2.8

2.7

30.0

-0.5

-0.9

-1.0

-1.1

-1.2

-1.4

-1.7

-2.1

-2.2

-2.1

-2.1

-2.0

-2.0

-20.2

4.3

11.4

16.8

25.0

35.7

40.0

43.1

45.0

46.9

48.2

47.7

47.3

46.7

458.2

-1.6

-5.8

-9.5

-14.5

-24.0

-27.9

-30.8

-32.6

-34.6

-35.2

-34.7

-34.3

-33.8

-319.1

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

JLR

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Manufacturer
BMW

Jkt 244001

BMW

PO 00000

Daimler
Daimler

Frm 00279

Fiat Chrysler
Fiat Chrysler

Fmt 4701

Ford

Sfmt 4725

Ford

E:\FR\FM\24AUP2.SGM

General
Motors
General
Motors
Honda
Honda

24AUP2

Hyundai
Hyundai
Kia

Price T
2017

Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline

2018

($)
in MYs 2017-2029 under Baser
dP
d CAFE Standard
' '
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028

2029

50

200

350

400

500

600

700

850

950

950

900

900

900

0

0

0

0

-100

-200

-300

-400

-550

-500

-500

-500

-500

200

250

450

500

600

750

850

950

1,050

1,050

1,000

1,000

1,000

0

0

0

0

-I 00

-200

-300

-400

-5 00

-500

-500

-500

-5 00

550

1,550

2,300

2,300

2,800

2,950

3,200

3,200

3,450

4,250

4,150

4,150

4)00

-300

-1,050

-1,700

-1,600

-2,000

-2,100

-2,300

-2,350

-2,550

-3,100

-3,050

-3,000

-2,950

100

250

550

2,300

3,400

3,750

3,650

3,750

3,650

3,550

3,500

3,400

3,300

0

-100

-300

-1,600

-2,650

-2,950

-2,900

-3,000

-2,900

-2,800

-2,750

-2,650

-2,600

250

1,000

1,550

1,850

2,700

2,950

3,050

3,100

3,600

3,550

3,500

3,450

3,350

-I 00

-550

-I ,000

-I, 150

-I ,900

-2,150

-2,300

-2,300

-2,750

-2,700

-2,650

-2,600

-2,500

150

350

400

400

900

1,450

1,950

2,000

2,000

1,950

2,000

2,000

1,950

-150

-200

-200

-200

-700

-1,200

-1,650

-1,700

-1,650

-1,650

-1,650

-1,650

-1,600

100

150

250

350

650

900

1,000

1,200

1,250

1,300

1,250

1,250

1,250

0

0

-50

-100

-300

-550

-650

-850

-900

-900

-900

-900

-900

350

450

500

700

1,500

1,950

2,100

2,400

2,400

2,350

2,350

2,300

2,250

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VTT-4 - A

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JLR

Jkt 244001

JLR
Mazda

PO 00000

Mazda

Frm 00280

Nissan/Mitsubishi
Nissan!Milsubishi

Fmt 4701

Subaru
Subaru

Sfmt 4725

Tcsla

E:\FR\FM\24AUP2.SGM

Toyota

24AUP2

VWA

Testa

Toyota
Volvo
Volvo

VWA
Ave.frotal
Ave.frotal

EP24AU18.171

Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. tmdcr
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs tmdcr
Baseline
Chg. under
Proposal

0

0

0

-200

-850

-L250

-1,400

-1,700

-1,650

-1,650

-1,650

-1,600

-1,550

200

250

350

350

600

700

800

900

1,000

1,000

1,000

950

950

0

0

0

0

-100

-200

-300

-400

-450

-450

-450

-450

-450

50

250

300

650

600

950

2,600

2,600

2,500

2,450

2,400

2,350

2,300

0

-100

-100

-400

-400

-750

-2,400

-2,350

-2,300

-2,250

-2,200

-2,100

-2,050

100

150

350

700

1,000

1,100

I, 150

1,300

1,400

1,400

1,400

1,400

1,350

0

0

-100

-200

-450

-500

-600

-700

-850

-850

-850

-850

-850

600

600

600

1,000

1,600

L750

1,800

1,750

1,700

1,700

1,700

1,650

1,600

-50

-50

-50

-400

-900

-1,050

-1,100

-1,100

-1,050

-1,000

-1,000

-950

-950

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

550

750

1,450

2,100

2,100

2,100

2,250

2,300

2,300

2,300

2,250

2,300

0

-150

-250

-700

-1,250

-I ,250

-1,250

-1,400

-1,450

-1,450

-1,400

-1,400

-1,450

50

50

200

250

350

400

550

650

750

750

750

750

750

0

0

0

0

-100

-200

-250

-350

-450

-450

-450

-450

-450

1,550

2,600

2,750

3,300

3,350

3,800

4,200

4,850

4,950

4,850

4,750

4,650

4,550

-800

-1,550

-1,600

-1,900

-2,000

-2,400

-2,800

-3,500

-3,650

-3,550

-3,500

-3,450

-3,350

250

650

950

1,400

2,000

2,250

2,450

2,550

2,650

2,700

2,700

2,650

2,600

-100

-350

-550

-800

-1,350

-1,550

-1,750

-1,850

-1,950

-2,000

-1,950

-1,950

-1,900

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Kia

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

Table VII-5 - Technol

Costs. A
-

-

-

-- /

Standards

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2018

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2020

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2021
2022

E:\FR\FM\24AUP2.SGM

2023
2024
2025

24AUP2

2026
2027
2028
2029

.s
C)

<1.)

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.173

*The change in MSRP may not match the change in technology costs reported in other tables. The change in MSRP noted here will include shifts in the average
value of a vehicle, before technology application, due to the dynamic fleet share model (more light trucks arc projected under the augural standards than the
proposed standards, and light trucks are on average more expensive than passenger cars), in addition to the price changes from differential technology application
and civil penalties, reported elsewhere.

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

I

Technology

der Basel'

dP

- t: -

d CAFE Standards - Industrv
- - - - a!. A . -- -e:-

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

I

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3820

3790

3760

3720

3690

3670

3660

3650

3640

3620

3620

3610

3610

3820
6%
6%
27%
25%
0%
0%
48%
48%
12%
13%
2%
0%
3%
2%
0%
0%
1%
1%
0%
0%

3800
10%
10%
38%
31%
0%
0%
65%
66%
12%
13%
9%
0%
3%
2%
0%
0%
1%
1%
0%
0%

3770
14%
12%
41%
32%
3%
0%
73%
75%
13%
13%
14%
0%
4%
2%
0%
0%
1%
1%
0%
0%

3740
18%
14%
46%
36%
4%
0%
82%
86%
14%
13%
21%
0%
7%
2%
1%
0%
1%
1%
0%
0%

3720
23%
16%
54%
39%
5%
0%
83%
92%
13%
13%
29%
0%
11%
2%
1%
0%
1%
1%
0%
0%

3710
25%
17%
57%
44%
5%
0%
81%
92%
15%
13%
32%
0%
13%
2%
1%
0%
1%
1%
0%
0%

3700
25%
17%
59%
46%
5%
0%
79%
93%
16%
13%
34%
0%
16%
2%
1%
0%
1%
1%
0%
0%

3690
26%
17%
59%
47%
6%
0%
77%
93%
16%
13%
34%
0%
18%
2%
1%
0%
1%
1%
0%
0%

3690
26%
17%
59%
48%
6%
0%
73%
93%
15%
14%
32%
0%
22%
2%
1%
0%
1%
1%
0%
0%

3670
26%
17%
63%
51%
6%
0%
71%
93%
14%
14%
32%
0%
24%
2%
1%
0%
1%
1%
0%
0%

3670
26%
17%
63%
51%
6%
0%
71%
93%
14%
14%
32%
0%
24%
2%
1%
0%
1%
1%
0%
0%

3670
26%
17%
64%
51%
6%
0%
72%
93%
14%
14%
32%
0%
24%
2%
1%
0%
1%
1%
0%
0%

3670
26%
17%
64%
51%
6%
0%
72%
93%
14%
14%
32%
0%
24%
2%
1%
0%
1%
1%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-6- Technol - C!ll Penetraf

43267

EP24AU18.174

sradovich on DSK3GMQ082PROD with PROPOSALS2

43268

VerDate Sep<11>2014

I

Technology

-~.::

Penetraf

der Basel'

dP

-.--

d CAFE Standards -BMW

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

I

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3830

3820

3750

3730

3730

3710

3690

3690

3690

3690

3690

3680

3680

3830
0%
0%
96%
96%
0%
0%
80%
80%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3820
0%
0%
96%
96%
0%
0%
82%
82%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3750
0%
0%
96%
96%
0%
0%
82%
82%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3730
0%
0%
96%
96%
0%
0%
90%
90%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3730
0%
0%
96%
96%
0%
0%
90%
90%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3710
0%
0%
96%
96%
0%
0%
90%
90%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3680
0%
0%
96%
96%
0%
0%
90%
90%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3680
0%
0%
96%
96%
0%
0%
91%
91%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3680
0%
0%
96%
96%
0%
0%
91%
91%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3680
0%
0%
96%
96%
0%
0%
91%
91%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3680
0%
0%
96%
96%
0%
0%
91%
91%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3670
0%
0%
96%
96%
0%
0%
91%
91%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3670
0%
0%
96%
96%
0%
0%
91%
91%
91%
91%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.175

Table VII-7 - Technol

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

I

Technology

-~.::

Penetraf

der Baser

dP

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022

I

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

-.-- d CAFE Standards - Daiml
I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

4130

4130

4060

4060

4040

3990

3980

3980

3980

3970

3980

3980

3980

4130
0%
0%
85%
85%
0%
0%
13%
13%
83%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

4130
0%
0%
85%
85%
0%
0%
13%
13%
82%
82%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

4060
0%
0%
98%
98%
0%
0%
59%
59%
83%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

4060
0%
0%
98%
98%
0%
0%
74%
74%
83%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

4040
0%
0%
98%
98%
0%
0%
83%
83%
83%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3990
0%
0%
98%
98%
0%
0%
84%
84%
83%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3970
0%
0%
98%
98%
0%
0%
85%
85%
82%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3970
0%
0%
98%
98%
0%
0%
85%
85%
82%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3970
0%
0%
98%
98%
0%
0%
85%
85%
82%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3960
0%
0%
98%
98%
0%
0%
85%
85%
82%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3960
0%
0%
98%
98%
0%
0%
85%
85%
82%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3960
0%
0%
98%
98%
0%
0%
85%
85%
82%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3960
0%
0%
98%
98%
0%
0%
85%
85%
82%
83%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-8 - Technol

43269

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

4170 I 4120 I 4030 I 4010 I 3990 I 3980 I 3960 I 3960 I 3950 I 3910 I 3910 I 3870 I 3860
4170
0%
0%
16%
16%
0%
0%
62%
64%
12%
12%
3%
0%
4%
0%
0%
0%
0%
0%
0%
0%

I 4140 I 4070 I 4050 I 4030 I 4020 I 4010 I 4010 I 4010 I 3980 I 3980 I 3960 I 3960
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
40% 43% I 42% 48% 48% I 52% 52% I 52% 80% I 81% 82% 82%
32% 32% I 32% 36% 36% I 40% 40% I 40% 59% I 60% 61% 61%
0% 13% I 13% 15% 15% I 15% 15% I 15% 15% I 15% 15% 15%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
82% 78% I 84% 79% 73% I 66% 66% I 59% 43% I 43% 44% 44%
85% 85% I 91% 95% 94% I 95% 95% I 95% 95% I 95% 95% 95%
13% 11%1 11% 11% 11% I 10% 10% I 5%
0% I 0%
0%
0%
13% 12% I 12% 12% 12% I 12% 12% I 14% 14% I 14% 14% 14%
23% 37% I 37% 37% 37% I 37% 37% I 39% 43% I 43% 44% 44%
0%
0% I 0%
1%
1% I 1%
1% I 1%
1% I 1%
1%
1%
4%
8% I 8% 17% 23% I 30% 29% I 37% 53% I 53% 52% 52%
0%
0% I 0%
1%
2% I 2%
2% I 2%
2% I 2%
2%
2%
0%
0% I 0%
1%
1% I 1%
1% I 1%
1% I 1%
1%
1%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%

I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.177

Table VII-9 - Technology Penetration under Baseline and Proposed CAFE Standards - Fiat Chrysler
Technology
I
I 2017 I 2018 I 2019 I 2020 I 2021 I 2022 I 2023 I 2024 I 2025 I 2026 I 2027 I 2028 I 2029

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

I

Technology

-~.::

Penetraf

der Basel'

dP

-.--

d CAFE Standards -Ford

I 2o17 I 2o1s I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2o27 I 2o2s I 2o29 I

I

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

4040

4040

4040

3920

3910

3890

3890

3890

3880

3880

3870

3870

3870

4040
3%
3%
46%
46%
0%
0%
41%
41%
8%
8%
0%
0%
2%
2%
1%
1%
0%
0%
0%
0%

4040
3%
3%
48%
46%
0%
0%
47%
47%
10%
8%
3%
0%
2%
2%
1%
1%
0%
0%
0%
0%

4040
3%
3%
55%
54%
0%
0%
47%
47%
10%
8%
11%
0%
2%
2%
1%
1%
0%
0%
0%
0%

3940
3%
3%
76%
67%
0%
0%
70%
81%
9%
8%
41%
0%
13%
2%
1%
1%
0%
0%
0%
0%

3930
3%
3%
89%
68%
0%
0%
63%
85%
2%
8%
59%
0%
24%
2%
1%
1%
0%
0%
0%
0%

3910
3%
3%
94%
68%
0%
0%
58%
85%
2%
8%
63%
0%
29%
2%
1%
1%
0%
0%
0%
0%

3910
3%
3%
95%
68%
0%
0%
59%
86%
2%
8%
63%
0%
29%
2%
1%
1%
0%
0%
0%
0%

3900
3%
3%
96%
68%
0%
0%
56%
86%
0%
8%
64%
0%
32%
2%
1%
1%
0%
0%
0%
0%

3900
3%
3%
97%
68%
0%
0%
56%
86%
0%
8%
64%
0%
32%
2%
1%
1%
0%
0%
0%
0%

3890
3%
3%
97%
68%
0%
0%
56%
86%
0%
8%
64%
0%
32%
2%
1%
1%
0%
0%
0%
0%

3870
3%
3%
97%
68%
0%
0%
57%
86%
0%
8%
64%
0%
32%
2%
1%
1%
0%
0%
0%
0%

3870
3%
3%
97%
68%
0%
0%
57%
86%
0%
8%
64%
0%
32%
2%
1%
1%
0%
0%
0%
0%

3870
3%
3%
97%
68%
0%
0%
57%
86%
0%
8%
64%
0%
32%
2%
1%
1%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-10- Technol

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

der Basel'

dP

d CAFE Standards - G

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

I Mot
2027 2028

4290

4240

4170

4150

4070

4060

4060

4040

4020

4000

4000

4000

4000

4290
0%
0%
27%
27%
0%
0%
14%
14%
16%
15%
6%
0%
0%
0%
0%
0%
0%
0%
0%
0%

4250
0%
0%
47%
36%
0%
0%
45%
45%
17%
15%
25%
0%
0%
0%
0%
0%
0%
0%
0%
0%

4200
0%
0%
52%
36%
12%
0%
66%
66%
18%
15%
38%
0%
0%
0%
0%
0%
0%
0%
0%
0%

4190
0%
0%
58%
41%
13%
0%
82%
83%
16%
15%
45%
0%
2%
1%
0%
0%
0%
0%
0%
0%

4130
0%
0%
67%
49%
22%
0%
91%
95%
10%
15%
72%
0%
5%
1%
0%
0%
0%
0%
0%
0%

4130
0%
0%
69%
49%
22%
0%
87%
95%
6%
15%
77%
0%
10%
1%
0%
0%
0%
0%
0%
0%

4120
0%
0%
69%
50%
22%
0%
84%
95%
5%
15%
81%
0%
13%
1%
0%
0%
0%
0%
0%
0%

4110
0%
0%
69%
50%
24%
0%
82%
95%
2%
15%
81%
0%
14%
1%
0%
0%
0%
0%
0%
0%

4100
0%
0%
70%
61%
27%
0%
64%
95%
0%
15%
66%
0%
32%
1%
0%
0%
0%
0%
0%
0%

4070
0%
0%
70%
62%
28%
0%
64%
95%
0%
15%
66%
0%
32%
1%
0%
0%
0%
0%
0%
0%

4070
0%
0%
70%
62%
28%
0%
65%
95%
0%
15%
66%
0%
32%
1%
0%
0%
0%
0%
0%
0%

4070
0%
0%
70%
62%
28%
0%
66%
97%
0%
15%
66%
0%
32%
1%
0%
0%
0%
0%
0%
0%

4070
0%
0%
70%
62%
28%
0%
66%
97%
0%
15%
66%
0%
32%
1%
0%
0%
0%
0%
0%
0%

-

-

.

2029

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.179

Table VII-11 - Technol
Penetraf
.10~

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

der Basel'

dP

2017

2018

2019

2020

2021

2022

3450

3420

3410

3410

3400

3360

3450
0%
0%
29%
6%
0%
0%
75%
75%
6%
6%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3430
0%
0%
55%
18%
0%
0%
87%
87%
6%
6%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3420
0%
0%
58%
21%
0%
0%
97%
97%
6%
6%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3420
0%
0%
62%
21%
0%
0%
97%
97%
6%
6%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3420
0%
0%
83%
21%
0%
0%
97%
97%
12%
6%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3420
0%
0%
99%
60%
0%
0%
97%
97%
32%
6%
3%
0%
0%
0%
0%
0%
0%
0%
0%
0%

d CAFE Standards - Bond
-

2023
3310

2024
3310

2025
3310

2026
3300

2027
3280

2028
3270

2029
3270

3400

3400

3400

3400

3400

3400

3400

0%
0%
100%
76%
0%
0%
97%
97%
49%
6%
13%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
100%
76%
0%
0%
97%
97%
55%
6%
13%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
100%
76%
0%
0%
97%
97%
55%
6%
13%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
100%
76%
0%
0%
97%
97%
55%
6%
13%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
100%
76%
0%
0%
97%
97%
55%
6%
13%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
100%
76%
0%
0%
97%
97%
55%
6%
13%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
100%
76%
0%
0%
97%
97%
55%
6%
13%
0%
0%
0%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-12 - Technol
. 0~:y Penetraf

43273

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sradovich on DSK3GMQ082PROD with PROPOSALS2

43274

VerDate Sep<11>2014

I

Technology

-~.::

der Basel'

Penetraf

dP

-.--

d CAFE Standards - Hvund
·
"-

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

I

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3160

3160

3150

3140

3100

3100

3090

3080

3080

3080

3080

3080

3080

3160
7%
7%
12%
12%
0%
0%
53%
53%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
18%
18%
12%
12%
0%
0%
68%
68%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
25%
25%
15%
15%
0%
0%
79%
79%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
25%
25%
18%
18%
0%
0%
79%
79%
1%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
55%
55%
18%
18%
0%
0%
82%
82%
4%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
82%
58%
18%
18%
0%
0%
82%
82%
13%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
82%
58%
18%
18%
0%
0%
82%
82%
15%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
82%
58%
18%
18%
0%
0%
82%
82%
22%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
82%
58%
18%
18%
0%
0%
82%
82%
23%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3140
82%
59%
18%
18%
0%
0%
82%
82%
23%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3140
81%
59%
19%
18%
0%
0%
82%
82%
23%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3140
81%
59%
19%
18%
0%
0%
82%
82%
23%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3140
81%
59%
19%
18%
0%
0%
82%
82%
23%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.181

Table VII-13 - Technol

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

I

1

Jkt 244001

Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

PO 00000
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Fmt 4701
Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

2011

1

2o18

1

2o19

1

2020

1

2021

d CAFE Standards - K.

dP
1

2022

1

2o23

1

2o24

1

2o25

1

2o26

1

2021

1

2o28

1

2o29

3290

3300

3290

3290

3240

3240

3230

3220

3220

3220

3220

3220

3220

3290
0%
0%
5%
5%
0%
0%
0%
0%
0%
0%
0%
0%
2%
2%

3300
31%
31%
5%
5%
0%
0%
29%
29%
0%
0%
0%
0%
2%
2%

3290
31%
31%
5%
5%
0%
0%
53%
53%
0%
0%
0%
0%
2%
2%

3290
45%
37%
5%
5%
0%
0%
67%
67%
14%
0%
0%
0%
2%
2%

3280
76%
67%
5%
5%
0%
0%
93%
93%
21%
0%
23%
0%
2%
2%

3280
76%
67%
5%
5%
0%
0%
93%
93%
21%
0%
47%
0%
2%
2%

3280
76%
67%
13%
13%
0%
0%
93%
93%
21%
0%
53%
0%
2%
2%

3280
76%
67%
23%
22%
0%
0%
89%
93%
21%
0%
53%
0%
6%
2%

3280
76%
67%
23%
22%
0%
0%
89%
93%
21%
0%
53%
0%
6%
2%

3270
76%
67%
23%
22%
0%
0%
89%
93%
21%
0%
53%
0%
6%
2%

3270
75%
67%
23%
22%
0%
0%
89%
93%
20%
0%
53%
0%
6%
2%

3270
75%
67%
23%
22%
0%
0%
89%
93%
20%
0%
53%
0%
6%
2%

3270
75%
67%
23%
22%
0%
0%
89%
93%
20%
0%
53%
0%
6%
2%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

1%
1%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

0%
0%
0%
0%

1

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

I Technology

der Basel·

Table VII-14- Technol ogy Penetraf

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24AUP2

Technology
Curb Weight (lb.)
Curb Weight (lb.)
High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
Mild HEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell V chicles
Fuel Cell Vehicles

I

I

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

2011
4830
4830
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I

2o18
4830
4830
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

der Basel·
I

2o19
4800
4800
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I

2o2o
4790
4790
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

dP
I

2021
4660
4660
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

~
I

2022
4650
4650
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

d CAFE Standards - J g
I

2o23
4610
4610
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I

2o24
4620
4610
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I

2o25
4590
4590
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I

2o26
4590
4590
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

/Land R,
I

2021
4590
4590
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I

2o28
4590
4590
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I

2o29
4590
4590
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.183

Table VII-15 - Technol og~ Penetrat·

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3300 I 3310 I 3310 I 3290 I 3290 I 3270 I 3220 I 3220 I 3220 I 3220 I 3220 I 3230 I 3230
3300 I 3310 I 3310 I 3310 I 3310 I 3310 I 3310 I 3310 I 3310 I 3300 I 3300 I 3300 I 3300
94% 94% I 94% 94% 94% I 94% 94% I 94% 94% I 94% 94% 94% I 94%
94% 94% I 94% 94% 94% I 94% 94% I 94% 94% I 95% 95% 95% I 95%
6%
6% I 6%
6%
6% I 6%
6% I 6%
6% I 6%
6%
6% I 6%
6%
6% I 6%
6%
6% I 6%
6% I 6%
6% I 5%
5%
5% I 5%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
22% 82% I 93% 93% 94% I 94% 60% I 58% 58% I 58% 58% 58% I 58%
22% 82% I 93% 93% 94% I 94% 94% I 94% 94% I 94% 94% 94% I 94%
0%
0% I 0% 14% 14% I 14% 14% I 14% 14% I 14% 14% 14% I 14%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
7%
7% I 25% 44% I 44% 44% I 44% 44% 44% I 44%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0%
0% I 0% 34% I 36% 36% I 36% 36% 36% I 36%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0% I 0%
0%
0% I 0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-16- Technology Penetration under Baseline and Proposed CAFE Standards- Mazda
Technology
I
I 2017 I 2018 I 2019 I 2020 I 2021 I 2022 I 2023 I 2024 I 2025 I 2026 I 2027 I 2028 I 2029

43277

EP24AU18.184

sradovich on DSK3GMQ082PROD with PROPOSALS2

43278

VerDate Sep<11>2014

I

Technology

-~.::

Penetraf

der Basel'

dP

-.--

d CAFE Standards - N'

I Mitsubish'

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

I

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3400

3410

3390

3340

3310

3290

3290

3270

3250

3250

3250

3240

3240

3400
0%
0%
4%
4%
0%
0%
86%
86%
2%
2%
0%
0%
0%
0%
1%
1%
1%
1%
0%
0%

3410
0%
0%
4%
4%
0%
0%
92%
92%
1%
1%
0%
0%
0%
0%
1%
1%
1%
1%
0%
0%

3390
19%
1%
5%
5%
0%
0%
92%
92%
2%
2%
0%
0%
0%
0%
1%
1%
1%
1%
0%
0%

3350
35%
16%
5%
5%
0%
0%
91%
91%
2%
2%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3350
63%
16%
5%
5%
0%
0%
91%
91%
2%
2%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3330
70%
18%
5%
5%
0%
0%
94%
94%
1%
2%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3330
76%
18%
6%
6%
0%
0%
95%
95%
1%
2%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3330
81%
18%
6%
6%
0%
0%
95%
95%
1%
2%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3320
86%
18%
6%
6%
0%
0%
96%
96%
1%
2%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3320
86%
18%
6%
6%
0%
0%
96%
96%
1%
2%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3320
86%
18%
6%
6%
0%
0%
96%
96%
1%
2%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3320
86%
18%
6%
6%
0%
0%
96%
96%
1%
2%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

3320
86%
18%
6%
6%
0%
0%
96%
96%
1%
2%
0%
0%
0%
0%
2%
2%
1%
1%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.185

Table VII-17 - Technol

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

I

Technology

-~.::

Penetraf

der Basel'

dP

-.--

d CAFE Standards - Sub

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

I

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3440

3440

3440

3440

3280

3210

3210

3210

3210

3190

3190

3190

3190

3440
0%
0%
35%
35%
0%
0%
91%
91%
10%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3440
0%
0%
35%
35%
0%
0%
92%
92%
9%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3440
0%
0%
35%
35%
0%
0%
91%
91%
9%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3440
0%
0%
35%
35%
0%
0%
81%
92%
9%
10%
0%
0%
11%
1%
0%
0%
0%
0%
0%
0%

3390
0%
0%
59%
35%
0%
0%
81%
92%
9%
10%
0%
0%
12%
1%
0%
0%
0%
0%
0%
0%

3370
0%
0%
59%
35%
0%
0%
81%
92%
0%
10%
9%
0%
12%
1%
0%
0%
0%
0%
0%
0%

3370
0%
0%
59%
35%
0%
0%
81%
92%
0%
10%
9%
0%
12%
1%
0%
0%
0%
0%
0%
0%

3370
0%
0%
59%
35%
0%
0%
81%
92%
0%
10%
9%
0%
11%
1%
0%
0%
0%
0%
0%
0%

3370
0%
0%
59%
35%
0%
0%
81%
92%
0%
11%
9%
0%
11%
1%
0%
0%
0%
0%
0%
0%

3360
0%
0%
69%
35%
0%
0%
81%
92%
0%
11%
9%
0%
11%
0%
0%
0%
0%
0%
0%
0%

3330
0%
0%
69%
35%
0%
0%
81%
92%
0%
11%
9%
0%
11%
1%
0%
0%
0%
0%
0%
0%

3330
0%
0%
68%
35%
0%
0%
81%
92%
0%
11%
9%
0%
11%
1%
0%
0%
0%
0%
0%
0%

3330
0%
0%
68%
35%
0%
0%
81%
92%
0%
11%
9%
0%
11%
0%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-18 - Technol

43279

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
StrongHEVs
StrongHEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EVs
Dedicated EVs
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3740

3700

3690

der Basel'
I 2020 I 2021
3630

3590

dP -.-- d CAFE Standards - Tovot
-.::I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28
3590

3590

3570

3550

3520

3530

3530

I 2o29 I
3520

3740 3700 3690 3640 3600 3590 3590 3570 3560 3530 3530 3530 3530
21% 34% 45% 62% 62% 63% 64% 63% 64% 65% 65% 65% 65%
21% 34% 45% 46% 46% 47% 47% 47% 47% 48% 48% 48% 48%
3% 10% 11% 19% 29% 31% 31% 31% 32% 32% 32% 33% 33%
3% 10% 10% 18% 23% 24% 24% 24% 24% 24% 24% 24% 24%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
38% 61% 74% 84% 85% 86% 87% 80% 80% 80% 79% 79% 79%
38% 62% 75% 87% 97% 98% 99% 99% 99% 99% 99% 99% 99%
0%
0%
6%
6%
6%
6%
6%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
7%
8% 14% 16% 17% 17% 17% 16% 16% 16% 16% 16%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
9% 10% 10% 13% 21% 21% 21% 28% 28% 28% 29% 29% 29%
9%
9%
9%
9%
9%
9%
9%
9%
9%
9%
9%
9%
9%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.187

Table VII-19- Technol
Penetraf
Technology
I 2011 I 2o18 I 2o19
I

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Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

2017
4170
4170
0%
0%
100%
100%
0%
0%
70%
70%
70%
70%
0%
0%
0%
0%
2%
2%
0%
0%
0%
0%

Penetraf
2018
4170
4170
0%
0%
100%
100%
0%
0%
71%
71%
71%
71%
0%
0%
0%
0%
2%
2%
0%
0%
0%
0%

2019
4070
4070
0%
0%
100%
100%
0%
0%
91%
91%
70%
70%
0%
0%
0%
0%
2%
2%
0%
0%
0%
0%

der Basel·
2020
4070
4070
0%
0%
100%
100%
0%
0%
98%
98%
70%
70%
0%
0%
0%
0%
2%
2%
0%
0%
0%
0%

2021
4070
4070
0%
0%
100%
100%
0%
0%
98%
98%
70%
70%
0%
0%
0%
0%
2%
2%
0%
0%
0%
0%

dP
2022
4070
4070
0%
0%
100%
100%
0%
0%
98%
98%
70%
70%
0%
0%
0%
0%
2%
2%
0%
0%
0%
0%

d CAFE Standards
2023
4070
4070
0%
0%
100%
100%
0%
0%
98%
98%
70%
70%
0%
0%
0%
0%
2%
2%
0%
0%
0%
0%

2024
4060
4050
0%
0%
100%
100%
0%
0%
97%
98%
71%
70%
0%
0%
0%
0%
3%
2%
0%
0%
0%
0%

2025
4060
4050
0%
0%
100%
100%
0%
0%
97%
98%
71%
70%
0%
0%
0%
0%
3%
2%
0%
0%
0%
0%

2026
4060
4050
0%
0%
100%
100%
0%
0%
97%
98%
71%
70%
0%
0%
0%
0%
3%
2%
0%
0%
0%
0%

Vol
2027
4060
4050
0%
0%
100%
100%
0%
0%
97%
98%
71%
70%
0%
0%
0%
0%
3%
2%
0%
0%
0%
0%

2028
4060
4050
0%
0%
100%
100%
0%
0%
97%
98%
71%
70%
0%
0%
0%
0%
3%
2%
0%
0%
0%
0%

2029
4060
4050
0%
0%
100%
100%
0%
0%
97%
98%
71%
70%
0%
0%
0%
0%
3%
2%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-20 - Tech
Technology
Curb Weight (lb.)
Curb Weight (lb.)
High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

43281

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

der Basel'

dP

d CAFE Standards - VW
-

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

3480

3420

3400

3360

3360

3330

3300

3290

3280

3270

3260

3240

3240

3480
0%
0%
91%
91%
0%
0%
36%
45%
15%
41%
24%
0%
10%
0%
1%
1%
1%
1%
0%
0%

3420
0%
0%
94%
95%
0%
0%
32%
54%
3%
47%
34%
0%
24%
0%
1%
1%
1%
1%
0%
0%

3410
0%
0%
94%
95%
0%
0%
38%
64%
3%
51%
34%
0%
27%
0%
1%
1%
1%
1%
0%
0%

3370
0%
0%
94%
95%
0%
0%
48%
74%
3%
51%
46%
6%
31%
0%
1%
1%
1%
1%
0%
0%

3370
0%
0%
94%
95%
0%
0%
48%
74%
2%
51%
46%
6%
32%
0%
1%
1%
1%
1%
0%
0%

3330
0%
0%
94%
96%
0%
0%
40%
74%
0%
51%
44%
6%
43%
0%
2%
1%
1%
1%
0%
0%

3320
0%
0%
94%
96%
0%
0%
20%
74%
0%
51%
27%
6%
63%
0%
2%
1%
1%
1%
0%
0%

3320
0%
0%
85%
96%
0%
0%
14%
74%
0%
51%
22%
6%
59%
0%
11%
1%
1%
1%
0%
0%

3320
0%
0%
82%
97%
0%
0%
14%
74%
0%
51%
22%
6%
56%
0%
15%
1%
1%
1%
0%
0%

3320
0%
0%
82%
97%
0%
0%
14%
73%
0%
51%
22%
6%
56%
0%
15%
1%
1%
1%
0%
0%

3320
0%
0%
82%
97%
0%
0%
14%
74%
0%
51%
21%
6%
56%
0%
15%
1%
1%
1%
0%
0%

3320
0%
0%
82%
97%
0%
0%
14%
74%
0%
51%
21%
6%
56%
0%
15%
1%
1%
1%
0%
0%

3320
0%
0%
82%
97%
0%
0%
14%
73%
0%
51%
21%
6%
56%
0%
15%
1%
1%
1%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.189

Table VII-21- Technol
. 0~~ Penetraf

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VerDate Sep<11>2014
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24AUP2

Manufacturer
BMW
BMW
Daimler
Daimler
Fiat Chrysler
Fiat Chrysler
Ford
Ford
General Motors
General Motors
Honda
Honda
Hyundai
Hyundai
Kia
Kia
Jaguar/Land
Rover
Jaguar/Land
Rover
Mazda
Mazda
Nissan
Mitsubishi
Nissan
Mitsubishi
Subarn
Subarn
Tesla

I

I

Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required

2o16
248
250
256
269
277
302
277
286
278
293
248
222
232
209
241
234
283

240
236
248
253
272
284
272
273
273
286
241
220
222
198
232
231
282

2o18
229
225
238
246
262
250
263
269
265
264
231
216
213
192
222
218
270

316

313

304

280

262

221

216

183

183

181

242
214
244

234
210
236

224
196
226

216
194
217

206
189
208

194
189
195

185
186
186

176
167
177

167
167
168

220

216

213

205

199

189

185

182

251
224
282

245
217
275

234
217
265

225
215
256

217
214
246

202
185
230

192
179
219

183
178
209

I

2011

I

I

2o19
220
203
229
210
254
232
256
264
257
246
222
214
203
185
213
211
262

I

2020
211
198
219
210
245
225
248
231
247
234
213
213
194
181
203
202
254

I

2021
198
196
206
199
228
209
232
212
232
212
200
201
183
169
193
176
234

I

2022
189
186
196
183
217
205
221
205
221
210
190
180
174
165
183
173
223

I

2o23
180
177
187
176
207
202
211
204
210
208
181
170
166
164
175
167
213

I

2o24
172
171
178
173
197
202
201
201
201
206
172
167
158
162
166
160
202

I

2o25
163
164
169
173
188
201
191
201
191
203
164
166
150
162
158
160
192

I

2o26
163
163
169
171
188
193
191
197
191
201
164
166
150
158
158
160
192

I

2021

I

2o2s

I

2o29

163
163
169
171
188
192
191
193
192
199
164
165
150
151
158
158
192

163
163
169
169
188
188
191
191
192
194
164
165
150
151
158
159
192

163
163
170
169
188
187
191
192
192
192
165
165
150
150
158
159
192

194

194

194

188

159
164
161

159
154
161

159
154
161

159
152
161

159
153
161

166

158

159

159

159

159

174
174
199

165
174
190

165
168
190

166
167
190

166
167
190

166
167
190

I

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-22- Required and Achieved Ave. C02 Levels in MYs 2016-2029 under Baseline C02 Standards (No-Action

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Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved

(19)
256
254
270
260
236
244
260
259

(19)
249
252
266
255
228
221
254
251

(19)
239
232
256
255
218
202
244
236

(19)
231
220
246
207
209
197
236
225

(19)
222
202
237
208
200
186
227
213

(19)
208
188
221
208
188
182
212
198

(19)
198
186
210
209
180
175
202
192

(19)
189
186
201
183
170
160
193
187

(19)
179
184
191
178
163
155
183
183

(19)
171
181
181
178
154
154
175
182

129
171
171
181
179
154
157
175
178

129
171
171
182
180
155
152
175
176

129
172
170
182
179
155
151
175
175

129
172
169
182
180
155
151
175
174

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.191

Tcsla
Toyota
Toyota
Volvo
Volvo
VWA
VWA
Ave./Total
Ave./Total

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24AUP2

Manufacturer
BMW
BMW
Daimler
Daimler
Fiat Chrysler
Fiat Chrysler
Ford
Ford
General Motors
General Motors
Honda
Honda
Hyw1dai
Hytmdai
Kia
Kia
Jaguar/Land
Rover
Jaguar/Land
Rover
Mazda
Mazda
Nissan
Mitsubishi
Nissan
Mitsubishi
Subaru
Subaru
Tesla
Tesla

I

I

Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved

2o16
248
250
256
269
277
302
277
286
278
293
248
222
232
209
241
234
283

I

2011
240
238
248
256
272
286
272
273
273
288
241
221
222
198
232
232
282

I

2o18
229
229
239
254
262
265
263
270
265
274
231
218
213
192
222
219
270

I

2o19
221
214
229
226
254
255
256
269
257
262
222
216
203
185
213
212
262

I

2020
211
212
219
224
245
250
248
251
247
253
213
215
194
182
203
207
253

I

2021
224
225
232
233
259
259
261
262
261
256
227
227
206
186
217
203
268

I

2022
224
222
232
231
259
259
261
260
261
256
227
216
206
184
217
204
268

I

2o23
224
220
232
229
259
258
261
260
261
255
227
211
206
184
217
200
268

I

2o24
224
220
232
229
259
258
261
259
261
254
227
211
206
183
217
196
268

I

2o25
224
220
232
229
259
258
261
259
261
253
227
211
206
183
217
196
268

I

2o26
223
222
232
229
259
256
261
259
261
253
226
211
206
182
217
196
268

I

223
222
232
229
259
252
261
258
261
253
226
209
206
182
217
195
268

2o28
223
221
232
228
259
250
261
258
261
253
226
209
206
182
217
195
268

2021

I

I

2o29
223
221
232
228
259
249
261
258
261
252
226
208
206
182
217
195
268

316

313

304

288

282

267

265

261

261

260

260

260

260

260

242
214
244

234
210
236

224
196
226

216
194
217

206
192
208

219
206
221

219
206
221

219
203
221

219
203
221

219
203
221

219
203
221

219
203
221

219
202
221

219
202
221

220

216

213

206

202

213

211

210

210

209

210

209

210

209

251
224
282
(19)

245
217
275
(19)

234
217
265
(19)

225
215
256
(19)

217
215
246
(19)

231
221
260
(4)

231
220
260
(4)

231
219
260
(4)

231
219
260
(4)

231
219
259
(4)

231
218
259
125

231
218
259
125

231
218
259
125

231
218
259
125

I

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-23- Required and Achieved Ave. C02 Levels in MYs 2016-2029 under Proposed C02 Standards (Preferred

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Required
Achieved
Required
Achieved
Required
Achieved
Required
Achieved

256
254
270
260
236
244
260
259

249
252
266
256
228
224
254
252

239
240
256
256
218
211
244
243

231
234
246
237
209
206
236
235

222
226
237
238
200
200
227
228

236
235
252
254
213
213
241
236

236
234
252
255
213
212
241
234

236
233
252
249
213
211
241
233

236
232
252
248
213
211
241
232

235
232
252
248
213
210
240
232

235
230
251
249
213
212
240
232

235
230
251
247
213
212
240
230

235
230
251
247
213
211
240
230

235
230
251
247
213
211
240
230

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.193

Toyota
Toyota
Volvo
Volvo
VWA
VWA
Ave./Total
Ave./Total

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

Table VII-24- Undiscounted Regulatory Costs ($b) in MYs 2017-2029 under Baseline and Proposed C02 Standards
Manufacturer
BMW

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Ford

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General Motors
Honda
Honda
Hyw1dai

24AUP2

Hyw1dai
Kia
Kia
JLR

I 2011 I 2o18

I
Costs tmdcr
Baseline
Chg. tmder
Proposal
Costs tmdcr
Baseline
Chg. tmdcr
Proposal
Costs tmdcr
Baseline
Chg. tmdcr
Proposal
Costs under
Baseline
Chg. tmder
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline

0.1

0.4

l2o19
0.8

-0.1

-0.2

0.2

I 2o2o I 2021 I 2022 I 2o23 I 2o24 I 2o2s I 2o26 I 2021 I 2o28 I 2o29 I

Sum

0.9

0.9

1.2

1.4

l.5

l.7

1.8

1.8

1.8

l.7

15.9

-0.5

-0.6

-0.6

-0.8

-1.0

-1.2

-1.3

-1.5

-1.4

-1.4

-1.4

-12.0

OJ

0.8

0.7

0.9

l.3

1.4

l.5

l.5

1.6

l.5

1.6

l.5

14.8

-0.1

-0.2

-0.4

-0.4

-0.6

-0.9

-1.0

-1.1

-1.1

-1.2

-1.2

-1.2

-1.2

-10.7

l.3

3.4

5.1

5.5

6.7

7.1

7.6

7.4

7.5

8.6

8.6

9.3

9.2

87.1

-0.6

-2.1

-3.3

-3.6

-4.4

-4.9

-5.4

-5.2

-5.4

-6.4

-6.4

-7.0

-6.9

-61.5

0.2

05

0.9

3.8

5.6

6.1

6.1

6.3

6.1

7.0

7.8

7.9

7.8

66.1

00

-0.2

-0.6

-3.0

-4.6

-5.2

-5.1

-53

-5.2

-6.1

-6.9

-7.0

-6.9

-56.0

0.4

2.2

3.4

3.8

5.6

5.9

6.1

6.3

7.2

7.6

7.9

8.7

9.4

74.6

-OJ

-1.6

-2.5

-2.7

-4.2

-4.5

-4.7

-4.8

-5.6

-5.9

-6.3

-7.1

-7.8

-57.9

0.1

0.2

0.3

0.3

1.1

2.3

3.4

3.6

3.6

3.5

3.6

3.6

3.6

29.2

00

-0.1

-0.1

-0.1

-0.8

-1.8

-2.8

-3.0

-3.0

-2.9

-2.9

-3.0

-2.9

-233

0.1

0.1

0.2

0.2

0.3

0.4

0.4

0.4

0.4

0.7

0.9

0.9

0.9

5.8

00

00

00

0.0

0.0

-0.1

-0.1

-0.1

-0.2

-0.4

-0.6

-0.6

-0.7

-2.8

00

0.1

0.2

0.4

0.9

1.0

1.2

1.4

1.4

1.4

1.4

1.4

1.4

12.0

00

00

00

-0.1

-0.5

-0.7

-0.8

-1.0

-1.0

-1.0

-1.0

-1.0

-1.0

-8.1

00

00

0.4

0.5

1.2

1.2

1.8

1.7

1.7

1.7

1.6

1.6

1.9

15.3

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

I

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Mazda

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Nissan/Mitsubishi

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Tesla
Tesla

Sfmt 4725

Toyota

E:\FR\FM\24AUP2.SGM

Toyota
Volvo
Volvo

24AUP2

VWA
VWA
Ave./Total
Ave./Total

EP24AU18.195

Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. m1der
Proposal
Costs under
Baseline
Chg. tmder
Proposal
Costs tmdcr
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. m1der
Proposal
Costs m1der
Baseline
Chg. tmder
Proposal
Costs tmdcr
Baseline
Chg. under
Proposal

0.0

0.0

-0.2

-0.4

-0.6

-0.7

-1.3

-1.2

-1.2

-1.2

-1.2

-1.1

-1.5

-10.7

0.0

0.1

0.1

0.2

0.2

0.2

0.9

0.9

0.9

1.3

1.3

1.3

1.3

8.6

0.0

0.0

0.0

-0.1

-0.1

-0.1

-0.8

-0.8

-0.8

-1.2

-1.2

-1.2

-1.2

-7.3

0.0

0.0

0.2

0.4

0.7

0.8

0.9

1.4

1.7

1.7

1.7

1.7

1.7

12.7

0.0

0.0

0.0

-0.1

-0.4

-0.5

-0.6

-1.0

-1.3

-1.3

-1.3

-1.3

-1.3

-9.3

0.0

0.0

0.0

0.1

0.5

0.6

0.6

0.6

0.6

0.7

0.8

0.8

0.8

6.1

0.0

0.0

0.0

0.0

-0.4

-0.5

-0.5

-0.5

-0.5

-0.6

-0.7

-0.7

-0.7

-4.9

0.0

0.0

0.0

0.0

0.0

0.0

0.0

00

0.0

00

0.0

0.0

0.0

0.0

0.0

0.0

0.0

00

0.0

00

0.0

00

0.0

00

0.0

0.0

0.0

0.0

0.0

1.0

1.5

2.5

3.3

3.4

3.4

3.8

4.3

5.6

5.6

5.7

5.9

46.1

0.0

-0.7

-1.1

-1.9

-2.5

-2.6

-2.6

-3.0

-3.5

-4.7

-4.8

-4.9

-5.0

-37.5

0.0

0.0

0.3

0.2

0.2

0.2

0.4

0.4

0.4

0.4

0.4

0.4

0.3

3.6

0.0

0.0

-0.2

-0.2

-0.2

-0.2

-0.4

-0.4

-0.4

-0.3

-0.3

-0.3

-0.3

-3.3

0.4

0.9

1.0

1.4

1.5

1.8

2.6

2.8

2.8

2.8

3.0

3.0

2.9

26.9

-0.3

-0.6

-0.6

-1.0

-1.1

-1.3

-2.2

-2.3

-2.3

-2.3

-2.6

-2.5

-2.5

-21.7

3.0

9.2

14.9

20.9

29.5

33.6

38.0

40.0

41.7

46.2

47.9

49.6

50.2

424.8

-1.4

-5.7

-9.5

-14.2

-21.0

-24.9

-29.1

-31.1

-32.8

-37.2

-38.8

-40.4

-41.0

-327.0

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

JLR

sradovich on DSK3GMQ082PROD with PROPOSALS2

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BMW

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General Motors
Honda
Honda
Hyundai

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Kia
Kia

JLR

I

I 2017 I 2018 I
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline

I

2020
2,050

I

2021
2,100

I

2022
2,850

I

2023
3,250

I

2024
3,650

I

2025
4,100

I

2026
4,450

I

2027
4,300

I

2028
4,250

I

2029 II
4,150

350

850

2019
1,850

-250

-550

-1,200

-1,350

-1,300

-1,950

-2,400

-2,800

-3,250

-3,650

-3,550

-3,500

-3,400

550

750

2,200

2,100

2,600

3,650

4,000

4,250

4,100

4,400

4,350

4,500

4,350

-300

-450

-1,250

-1,200

-1,550

-2,600

-2,950

-3,250

-3,100

-3,450

-3,400

-3,550

-3,450

600

1,600

2,350

2,500

3,000

3,200

3,400

3,300

3,350

3,850

3,850

4,100

4,050

-300

-950

-1,500

-1,600

-2,000

-2,200

-2,400

-2,350

-2,400

-2,850

-2,800

-3,050

-3,000

100

200

400

1,650

2,450

2,700

2,650

2,750

2,650

3,050

3,400

3,450

3,350

0

-100

-250

-L300

-2,000

-2,250

-2,250

-2,300

-2,250

-2,650

-3,000

-3,050

-2,950

150

850

1,250

1,400

2,050

2,150

2,250

2,300

2,600

2,750

2,850

3,150

3,400

-100

-600

-900

-1,000

-1,550

-1,650

-1,700

-1,750

-2,050

-2,150

-2,300

-2,550

-2,800

50

100

150

150

550

1,200

1,700

1,850

1,850

1,800

1,850

1,850

1,800

0

-50

-50

-50

-400

-950

-1,400

-1,550

-1,550

-1,500

-1,500

-1,500

-1,500

100

150

200

250

400

500

500

550

550

900

1,150

1,200

1,250

0

0

0

0

-50

-150

-150

-200

-200

-500

-800

-850

-900

50

150

250

450

1,100

1,250

1,500

1,750

1,750

1,750

1,800

1,750

1,750

0

0

0

-200

-650

-850

-1,050

-1,250

-1,250

-1,250

-1,250

-L250

-1,250

0

50

1,200

1,800

3,800

4,050

5,800

5,600

5,500

5,300

5,150

5,000

5,950

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-25- Average Price Increases($) in MYs 2017-2029 under Baseline and Proposed C0 2 Standards

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Toyota

E:\FR\FM\24AUP2.SGM

Toyota
Volvo
Volvo

24AUP2

VWA
VWA
Ave./Total
Ave./Total

EP24AU18.197

Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal
Costs under
Baseline
Chg. under
Proposal

0

0

-700

-1,200

-2,100

-2,350

-4,150

-4,000

-3,950

-3,800

-3,700

-3,600

-4,600

50

150

200

300

300

500

1,750

1,750

1,800

2,650

2,550

2,700

2,650

0

0

0

-100

-100

-300

-1,550

-1,500

-1,550

-2,400

-2,350

-2,450

-2,400

0

0

100

250

450

550

600

950

1,150

1,150

1,150

1,150

Ll50

0

0

0

-100

-250

-350

-400

-700

-900

-900

-900

-900

-900

50

50

50

100

800

950

950

1,050

1,050

1,200

1,250

1,250

L250

0

0

0

-50

-600

-750

-750

-850

-850

-1,000

-1,050

-1,050

-1,050

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

400

600

1,000

1,300

1,300

1,300

1,500

1,650

2,200

2,200

2,250

2,300

0

-300

-450

-750

-1,000

-1,000

-1,000

-1,200

-1,350

-1,850

-1,900

-1,900

-1,950

50

50

2,650

2,550

2,450

2,350

3,850

4,050

3,900

3,750

3,650

3,550

3,450

-50

-50

-2,450

-2,350

-2,250

-2,200

-3,600

-3,800

-3,650

-3,500

-3,350

-3,250

-3,150

750

1,500

1,650

2,400

2,500

2,950

4,400

4,650

4,650

4,650

5,050

5,000

4,850

-450

-

-1,050

-1,750

-1,750

-2,200

-3,650

-3,900

-3,900

-3,950

-4,350

-4,300

-4,200

200

1,000
550

850

1,200

1,650

1,900

2,150

2,250

2,350

2,600

2,700

2,800

2,800

-100

-350

-550

-800

-1,200

-1,400

-1,650

-1,750

-1,850

-2,100

-2,200

-2,250

-2,300

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

JLR

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

Table VTT-26 - Techno)

c

A

'"0

<1.)

Jkt 244001

MY

PO 00000

2017

.s
C)

rn
C\l

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2019

Fmt 4701

2020

Sfmt 4725

2021
2022

E:\FR\FM\24AUP2.SGM

2024

24AUP2

2026

2023

2025

2027
2028
2029

~

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Fmt 4701
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*The change in MSRP may not match the change in technology costs reported in other tables. The change in MSRP noted here will include shifts in the average
value of a vehicle, before technology application, due to the dynamic fleet share model (more light trucks arc projected under the augural standards than the
proposed standards, and light trucks are on average more expensive than passenger cars), in addition to the price changes from differential technology application
and civil penalties, reported elsewhere.

E:\FR\FM\24AUP2.SGM
24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.199

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VerDate Sep<11>2014

Technology

Jkt 244001
PO 00000
Frm 00309
Fmt 4701
Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

der Baser

dP

d CO,.., Standards- Industrv A
-

-

.

-

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

3810

3790

3760

3720

3680

3660

3650

3640

3630

3600

3590

3570

3570

3820
6%
6%
24%
24%
0%
0%
48%
48%
13%
12%
3%
0%
2%
2%
0%
0%
1%
1%
0%
0%

3800
10%
8%
33%
28%
0%
0%
64%
64%
13%
12%
8%
0%
2%
2%
0%
0%
1%
1%
0%
0%

3770
12%
9%
36%
28%
2%
0%
71%
73%
12%
12%
14%
1%
4%
2%
0%
0%
1%
1%
0%
0%

3750
15%
9%
42%
29%
2%
0%
83%
85%
17%
12%
17%
1%
4%
2%
0%
0%
1%
1%
0%
0%

3720
18%
12%
51%
31%
5%
0%
88%
91%
17%
11%
25%
1%
6%
2%
0%
0%
1%
1%
0%
0%

3710
19%
12%
57%
35%
2%
0%
87%
93%
20%
11%
26%
1%
8%
2%
0%
0%
1%
1%
0%
0%

3710
20%
12%
59%
37%
2%
0%
85%
93%
21%
11%
28%
1%
10%
2%
1%
0%
1%
1%
0%
0%

3700
24%
12%
60%
38%
2%
0%
84%
93%
23%
11%
28%
1%
12%
2%
1%
0%
1%
1%
0%
0%

3700
25%
12%
61%
38%
2%
0%
83%
93%
24%
11%
29%
1%
13%
2%
1%
0%
1%
1%
0%
0%

3690
25%
12%
62%
39%
2%
0%
79%
93%
18%
11%
37%
1%
16%
2%
1%
0%
1%
1%
0%
0%

3680
26%
12%
62%
40%
2%
0%
79%
93%
16%
11%
40%
1%
17%
2%
1%
0%
1%
1%
0%
0%

3680
26%
12%
62%
40%
4%
0%
76%
94%
15%
11%
38%
1%
20%
2%
1%
0%
1%
1%
0%
0%

3680
26%
12%
62%
41%
7%
0%
75%
94%
15%
11%
37%
1%
21%
2%
1%
0%
1%
1%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-27 - Technol
. o~v Penetraf

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E:\FR\FM\24AUP2.SGM
24AUP2

Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

der Basel'

d CO,.., Standards - BMW

dP

-

-

.

-

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

3820

3800

3690

3670

3670

3630

3580

3560

3540

3520

3520

3500

3500

3820
0%
0%
96%
96%
0%
0%
80%
80%
79%
91%
13%
0%
0%
0%
2%
2%

3800
0%
0%
97%
97%
0%
0%
80%
82%
66%
91%
26%
0%
3%
0%
2%
2%

3670
0%
0%
96%
97%
0%
0%
83%
90%
29%
91%
58%

3670
0%
0%
96%
97%
0%
0%
83%
90%
29%
91%
58%

3630
0%
0%
96%
97%
0%
0%
67%
91%
9%
91%
58%

3600
0%
0%
95%
97%
0%
0%
55%
91%
0%
91%
56%

3600
0%
0%
95%
97%
0%
0%
34%
92%
0%
91%
34%

3600
0%
0%
94%
97%
0%
0%
19%
92%
0%
91%
19%

3600
0%
0%
92%
97%
0%
0%
2%
92%
0%
91%
2%

3600
0%
0%
92%
97%
0%
0%
2%
92%
0%
91%
2%

3590
0%
0%
92%
97%
0%
0%
2%
92%
0%
91%
2%

3590
0%
0%
92%
97%
0%
0%
2%
92%
0%
91%
2%

1%

1%

1%

1%

1%

1%

1%

1%

1%

1%

1%
1%

1%
1%

3690
0%
0%
97%
97%
0%
0%
78%
83%
35%
91%
55%
0%
6%
0%
2%
2%
2%

8%
0%
2%
2%
2%

8%
0%
2%
2%
2%

27%
0%
2%
2%
3%

39%
0%
2%
2%
3%

61%
0%
2%
2%
3%

76%
0%
0%
2%
5%

92%
0%
0%
2%
6%

92%
0%
0%
2%
6%

92%
0%
0%
2%
6%

92%
0%
0%
2%
6%

1%

1%

1%

1%

1%

1%

1%

1%

1%

1%

1%

0%
0%

0%
0%

0%
0%

0%
0%

0%
0%

0%
0%

0%
0%

0%
0%

0%
0%

0%
0%

0%
0%

0%
0%

0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.201

Table VII-28 - Technol
. 0~~ Penetraf

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

I

Technology

-~.::

Penetraf

der Basel'

dP

-.--

d CO,.., Standards - Daiml
-

-

.

-

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

I

Jkt 244001
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E:\FR\FM\24AUP2.SGM
24AUP2

Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

4110

4120

4000

4000

3960

3880

3840

3820

3820

3780

3770

3770

3780

4110
0%
0%
85%
85%
0%
0%
13%
13%
67%
82%
12%
1%
3%
0%
0%
0%
0%
0%
0%
0%

4120
0%
0%
84%
85%
0%
0%
13%
13%
66%
81%
12%
2%
4%
0%
2%
0%
0%
0%
0%
0%

4000
0%
0%
97%
98%
0%
0%
41%
59%
50%
73%
18%
19%
28%
0%
2%
0%
0%
0%
0%
0%

4000
0%
0%
97%
98%
0%
0%
53%
74%
50%
73%
18%
19%
28%
0%
2%
0%
0%
0%
0%
0%

3960
0%
0%
97%
98%
0%
0%
55%
83%
37%
75%
20%
19%
40%
0%
2%
0%
0%
0%
0%
0%

3920
0%
0%
97%
98%
0%
0%
31%
84%
13%
75%
20%
19%
64%
0%
2%
0%
0%
0%
0%
0%

3900
0%
0%
97%
98%
0%
0%
21%
85%
3%
75%
20%
19%
75%
0%
2%
0%
0%
0%
0%
0%

3900
0%
0%
97%
98%
0%
0%
9%
85%
3%
75%
9%
19%
86%
0%
2%
0%
0%
0%
0%
0%

3900
0%
0%
97%
98%
0%
0%
9%
85%
3%
75%
9%
18%
86%
0%
2%
0%
0%
0%
0%
0%

3890
0%
0%
97%
98%
0%
0%
1%
85%
0%
75%
3%
18%
94%
0%
2%
0%
0%
0%
0%
0%

3890
0%
0%
97%
98%
0%
0%
1%
85%
0%
75%
3%
18%
94%
0%
1%
0%
1%
0%
0%
0%

3890
0%
0%
94%
98%
0%
0%
1%
85%
0%
75%
1%
18%
94%
0%
1%
0%
3%
0%
0%
0%

3890
0%
0%
94%
98%
0%
0%
1%
85%
0%
75%
1%
18%
94%
0%
1%
0%
3%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-29 - Technol

43295

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

4160 I 4100 I 4010 I 3980 I 3950 I 3930 I 3930 I 3930 I 3920 I 3890 I 3880 I 3840 I 3840
4160
0%
0%
20%
20%
0%
0%
64%
64%
15%
12%
11%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I 4100 I 4010 I 3980 I 3950 I 3950 I 3950 I 3950 I 3950 I 3930 I 3930 I 3920 I 3920
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
44% 47% I 57% 68% 74% I 77% 77%177% 82% I 82% 82% 82%
22% 22% I 22% 22% 23% I 23% 23% I 23% 28% I 40% 44% 46%
0% 13% I 13% 15% 15% I 15% 15% I 16% 16% I 16% 16% 16%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
85% 83% I 89% 89% 84% I 78% 78% I 75% 63% I 61% 45% 45%
85% 85% I 91% 96% 96% I 97% 97% I 97% 97% I 97% 97% 97%
15%
8% I 11% 10% 10% I 8%
8% I 8%
0% I 0%
0%
0%
13% 13% I 13% 13% 13% I 13% 13% I 13% 13% I 13% 13% 13%
32% 54% I 54% 58% 59% I 60% 60% I 59% 63% I 61% 45% 45%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
2% I 2%
7% 12% I 19% 19% I 22% 34% I 36% 52% 52%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0% I 0%
0% I 0%
0%
0%

I
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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.203

Table VII-30 - Technology Penetration under Baseline and Proposed C02 Standards- Fiat Chrysler
Technology
I
I 2017 I 2018 I 2019 I 2020 I 2021 I 2022 I 2023 I 2024 I 2025 I 2026 I 2027 I 2028 I 2029

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

der Basel'

dP

d CO,.., Standards -Ford
-

-

.

-

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

4040

4040

4030

3920

3910

3890

3890

3890

3890

3850

3780

3780

3780

4040
3%
3%
46%
46%
0%
0%
41%
41%
8%
8%
0%
0%
2%
2%
1%
1%
0%
0%
0%
0%

4040
3%
3%
48%
46%
0%
0%
47%
47%
10%
8%
3%
0%
2%
2%
1%
1%
0%
0%
0%
0%

4030
3%
3%
55%
46%
0%
0%
47%
47%
17%
8%
3%
0%
2%
2%
1%
1%
0%
0%
0%
0%

3960
3%
3%
76%
46%
0%
0%
81%
81%
45%
8%
16%
0%
2%
2%
1%
1%
0%
0%
0%
0%

3950
3%
3%
89%
46%
0%
0%
85%
84%
40%
8%
43%
0%
2%
2%
1%
1%
0%
0%
0%
0%

3950
3%
3%
94%
46%
0%
0%
85%
85%
43%
8%
49%
0%
2%
2%
1%
1%
0%
0%
0%
0%

3950
3%
3%
95%
46%
0%
0%
86%
86%
43%
8%
49%
0%
2%
2%
1%
1%
0%
0%
0%
0%

3940
3%
3%
96%
46%
0%
0%
86%
86%
41%
8%
53%
0%
2%
2%
1%
1%
0%
0%
0%
0%

3940
3%
3%
97%
46%
0%
0%
86%
85%
41%
8%
53%
0%
2%
2%
1%
1%
0%
0%
0%
0%

3930
3%
3%
97%
46%
0%
0%
83%
85%
14%
8%
77%
0%
5%
2%
1%
1%
0%
0%
0%
0%

3920
3%
3%
97%
46%
0%
0%
81%
85%
5%
8%
85%
0%
7%
2%
1%
1%
0%
0%
0%
0%

3920
3%
3%
97%
46%
0%
0%
78%
85%
0%
8%
86%
0%
10%
2%
1%
1%
0%
0%
0%
0%

3920
3%
3%
97%
46%
0%
0%
78%
85%
0%
8%
86%
0%
10%
2%
1%
1%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-31- Technol
. o~y Penetraf

43297

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

4300 I 4250 I 4180 I 4160 I 4070 I 4060 I 4060 I 4050 I 4020 I 3990 I 3990 I 3970 I 3960
4300
0%
0%
27%
27%
0%
0%
14%
14%
15%
15%
4%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I 4280 I 4230 I 4210 I 4160 I 4150 I 4140 I 4130 I 4120 I 4090 I 4090 I 4090 I 4090
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
47% 52% I 58% 67% 69% 69% 69% I 70% 70% 70% 70% 69%
36% 36% I 41% 50% 50% 50% 50% I 50% 50% 50% 50% 50%
0%
0% I 0% 22%
0%
0%
0% I 0%
0%
0% 14% 29%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
45% 66% I 84% 96% 97% 97% 97% I 96% 95% 92% 89% 83%
45% 66% I 84% 96% 96% 96% 96% I 96% 96% 96% 98% 98%
21% 21% I 24% 30% 35% 37% 38% I 28% 15%
6%
3%
0%
15% 15% I 15% 15% 15% 15% 15% I 15% 15% 15% 15% 15%
18% 30% I 33% 51% 51% 56% 60% I 69% 81% 88% 86% 83%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0% I 1%
2%
4% 10% 16%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%

I
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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.205

Table VII-32 - Technology Penetration under Baseline and Proposed C02 Standards- General Motors
Technology
I
I 2017 I 2018 I 2019 I 2020 I 2021 I 2022 I 2023 I 2024 I 2025 I 2026 I 2027 I 2028 I 2029

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3310

3310

3310

3280

3270

3280

3430
3470 I 3470 I 3460 I 3460 I 3460 I 3440 I 3430
0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0%
0% I 0%
6% 18% 21% 21% 41% 80% 96% I 100%
6% 18% 21% 21% 21% 60% 76% I 76%
0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0%
0% I 0%
75% 75% 75% 85% 87% 97% 97% I 97%
75% 75% 75% 85% 87% 97% 97% I 97%
6%
6%
6%
6%
6% 22% 39% I 45%
6%
6%
6%
6%
6%
6%
6% I 6%
0%
0%
0%
0%
0%
3%
3% I 3%
0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0%
0% I 0%
0%
0%
0%
0%
0%
0%
0% I 0%

3430

3430

3420

3420

3410

0%
0%
100%
76%
0%
0%
97%
97%
45%
6%
3%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
100%
76%
0%
0%
97%
97%
45%
6%
3%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
100%
76%
0%
0%
97%
97%
45%
6%
3%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
100%
76%
0%
0%
97%
97%
45%
6%
3%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
100%
76%
0%
0%
97%
97%
45%
6%
3%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3450 I 3430 I 3430 I 3420 I 3420 I 3380 I 3310

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-33 - Technology Penetration under Baseline and Proposed C02 Standards - Honda
Technology
I
I 2017 I 2018 I 2019 I 2020 I 2021 I 2022 I 2023 I 2024 I 2025 I 2026 I 2027 I 2028 I 2029

43299

EP24AU18.206

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43300

VerDate Sep<11>2014

I

Technology

-~.::

Penetraf

der Baser

dP

-.--

d CO,.., Standards - Hvund
·
"-

-

.

-

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

I

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3160

3160

3160

3150

3150

3150

3150

3140

3140

3060

3050

3060

3040

3160
7%
7%
12%
12%
0%
0%
53%
53%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
18%
18%
12%
12%
0%
0%
68%
68%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
25%
25%
15%
15%
0%
0%
79%
79%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
25%
25%
18%
18%
0%
0%
79%
79%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
55%
55%
18%
18%
0%
0%
82%
82%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
58%
58%
18%
18%
0%
0%
82%
82%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
58%
58%
18%
18%
0%
0%
82%
82%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
58%
58%
18%
18%
0%
0%
82%
82%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3160
58%
59%
18%
18%
0%
0%
82%
82%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3140
58%
59%
19%
18%
0%
0%
82%
82%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3140
81%
59%
19%
18%
0%
0%
82%
82%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3140
81%
59%
19%
18%
0%
0%
81%
82%
0%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

3140
81%
59%
19%
18%
0%
0%
81%
82%
2%
0%
0%
0%
3%
3%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.207

Table VII-34 - Technol

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

der Basel'

dP

d CO,.., Standards - K'
-

-

.

-

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

3300

3300

3300

3300

3250

3250

3230

3220

3220

3220

3220

3220

3220

3300
0%
0%
5%
5%
0%
0%
0%
0%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3300
31%
31%
5%
5%
0%
0%
29%
29%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3300
31%
31%
5%
5%
0%
0%
53%
53%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3300
45%
34%
5%
5%
0%
0%
67%
67%
14%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3290
77%
66%
5%
5%
0%
0%
96%
96%
45%
0%
5%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3290
77%
66%
5%
5%
0%
0%
96%
96%
69%
0%
5%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3280
77%
66%
13%
13%
0%
0%
96%
96%
74%
0%
12%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3280
77%
66%
23%
22%
0%
0%
96%
96%
81%
0%
12%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3280
77%
66%
23%
22%
0%
0%
96%
96%
80%
0%
12%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3280
77%
66%
23%
22%
0%
0%
96%
96%
80%
0%
12%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3280
77%
66%
23%
22%
0%
0%
96%
96%
80%
0%
12%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3280
77%
66%
23%
22%
0%
0%
96%
96%
80%
0%
12%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3280
77%
66%
23%
22%
0%
0%
96%
96%
80%
0%
12%
0%
1%
1%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-35 - Technol
. 0~~y Penetraf

43301

EP24AU18.208

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43302

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I

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E:\FR\FM\24AUP2.SGM
24AUP2

Technology
Curb Weight (lb.)
Curb Weight (lb.)
High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
Mild HEVs
Mild HEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

I

I

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

p
2011
4830
4830
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

der Baser
I

2o18
4830
4830
0%
0%
89%
89%
0%
0%
100%
100%
87%
87%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

I

2o19
4780
4780
0%
0%
89%
89%
0%
0%
76%
100%
76%
89%
0%
11%
24%
0%
0%
0%
0%
0%
0%
0%

I

2020
4770
4770
0%
0%
86%
89%
0%
0%
73%
100%
73%
89%
0%
11%
24%
0%
0%
0%
3%
0%
0%
0%

I

2021
4580
4580
0%
0%
86%
89%
0%
0%
34%
100%
23%
39%
11%
61%
62%
0%
0%
0%
3%
0%
0%
0%

I

2022
4550
4570
0%
0%
85%
89%
0%
0%
29%
100%
17%
39%
11%
61%
67%
0%
0%
0%
4%
0%
0%
0%

/Land R,

d CO,- Standards - J

dP
I

2o23
4480
4530
0%
0%
67%
89%
0%
0%
11%
100%
0%
39%
11%
61%
67%
0%
18%
0%
4%
0%
0%
0%

I

2o24
4480
4530
0%
0%
67%
89%
0%
0%
11%
100%
0%
39%
11%
61%
67%
0%
18%
0%
4%
0%
0%
0%

I

2o25
4430
4500
0%
0%
67%
89%
0%
0%
11%
100%
0%
39%
11%
61%
67%
0%
18%
0%
4%
0%
0%
0%

I

2o26
4430
4500
0%
0%
67%
89%
0%
0%
11%
100%
0%
39%
11%
61%
67%
0%
18%
0%
4%
0%
0%
0%

I

2021
4440
4500
0%
0%
67%
89%
0%
0%
11%
100%
0%
39%
11%
61%
67%
0%
18%
0%
4%
0%
0%
0%

I

2o28
4430
4500
0%
0%
68%
89%
0%
0%
11%
100%
0%
39%
11%
61%
68%
0%
18%
0%
4%
0%
0%
0%

I

2o29
4440
4500
0%
0%
68%
89%
0%
0%
11%
100%
0%
39%
11%
61%
68%
0%
0%
0%
21%
0%
0%
0%

I

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.209

Table VII-36 - Technol

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

I

Technology

-~.::

Penetraf

der Basel'

dP

-.--

d CO,.., Standards - Mazd
-

-

.

-

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

I

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Fmt 4701
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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3300

3310

3310

3290

3290

3270

3220

3220

3220

3190

3200

3150

3150

3300
94%
94%
6%
6%
0%
0%
22%
22%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3310
94%
94%
6%
6%
0%
0%
82%
82%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3310
94%
94%
6%
6%
0%
0%
93%
93%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3310
94%
94%
6%
6%
0%
0%
93%
93%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3310
94%
94%
6%
6%
0%
0%
94%
94%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3310
94%
94%
6%
6%
0%
0%
94%
94%
0%
0%
5%
0%
0%
0%
0%
0%
0%
0%
0%
0%

3310
94%
94%
6%
6%
0%
0%
79%
94%
0%
0%
44%
0%
15%
0%
0%
0%
0%
0%
0%
0%

3310
94%
94%
6%
6%
0%
0%
79%
94%
0%
0%
46%
0%
15%
0%
0%
0%
0%
0%
0%
0%

3300
94%
95%
6%
5%
0%
0%
79%
94%
0%
0%
46%
0%
15%
0%
0%
0%
0%
0%
0%
0%

3300
94%
95%
6%
5%
0%
0%
60%
94%
0%
0%
60%
0%
35%
0%
0%
0%
0%
0%
0%
0%

3300
94%
95%
6%
5%
0%
0%
60%
94%
0%
0%
60%
0%
34%
0%
0%
0%
0%
0%
0%
0%

3300
94%
95%
6%
5%
0%
0%
60%
94%
0%
0%
60%
0%
34%
0%
0%
0%
0%
0%
0%
0%

3300
94%
95%
6%
5%
0%
0%
60%
94%
0%
0%
60%
0%
34%
0%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-37 - Technol

43303

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43304

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24AUP2

Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3400

3410

der Baser
dP
2019 2020 2021 2022

d CO,.., Standards - N'
2023 2024 2025 2026

I Mitsubish ·
2027 2028 2029

3390

3300

3270

3360

3320

3300

-

-

.

-

3290

3270

3270

3270

3270

3400 3410 3390 3370 3370 3360 3360 3360 3340 3340 3340 3340 3340
0%
0%
1%
3%
7% 14% 20% 67% 85% 86% 87% 87% 87%
0%
0%
1%
3%
3%
4%
4%
4%
4%
4%
4%
4%
4%
4%
5%
5%
5%
5%
5%
7%
7%
7%
7%
7%
7%
7%
4%
5%
5%
5%
5%
5%
6%
6%
7%
7%
7%
7%
7%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
86% 92% 92% 92% 92% 95% 96% 96% 97% 97% 97% 97% 97%
86% 92% 92% 92% 92% 95% 96% 96% 97% 97% 97% 97% 97%
2%
1%
2%
2%
2%
2%
1%
1%
1%
1%
1%
1%
1%
2%
1%
2%
2%
2%
2%
2%
2%
2%
2%
2%
2%
2%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.211

Table VII-38 - Technol
. 0~~ Penetraf
Technology
2017 2018

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

I

Technology

-~.::

Penetraf

der Baser

dP

-.--

d CO,.., Standards - Sub
-

-

.

-

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

I

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Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3440

3440

3440

3440

3280

3210

3210

3210

3210

3190

3190

3190

3190

3440
0%
0%
7%
7%
0%
0%
91%
91%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3440
0%
0%
7%
7%
0%
0%
92%
92%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3440
0%
0%
7%
7%
0%
0%
91%
91%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3440
0%
0%
7%
7%
0%
0%
92%
92%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3360
0%
0%
34%
7%
0%
0%
92%
92%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3330
0%
0%
34%
7%
0%
0%
92%
92%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3330
0%
0%
35%
8%
0%
0%
92%
92%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3330
0%
0%
35%
8%
0%
0%
92%
92%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3330
0%
0%
35%
8%
0%
0%
92%
92%
0%
0%
0%
0%
1%
1%
0%
0%
0%
0%
0%
0%

3310
0%
0%
35%
8%
0%
0%
92%
91%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%

3310
0%
0%
35%
8%
0%
0%
92%
91%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%

3310
0%
0%
35%
8%
0%
0%
92%
91%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%

3310
0%
0%
35%
8%
0%
0%
92%
91%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-39 - Technol

43305

EP24AU18.212

sradovich on DSK3GMQ082PROD with PROPOSALS2

43306

VerDate Sep<11>2014

I

Technology

-~.::

Penetraf

der Baser

dP

-.--

d CO,.., Standards - Tovot
-.::-

-

.

-

I 2011 I 2o18 I 2o19 I 2020 I 2021 I 2022 I 2o23 I 2o24 I 2o25 I 2o26 I 2021 I 2o28 I 2o29 I

I

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24AUP2

Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3740

3700

3690

3630

3580

3580

3590

3550

3540

3480

3480

3480

3470

3740
21%
21%
3%
3%
0%
0%
38%
38%
0%
0%
0%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3720
34%
21%
10%
3%
0%
0%
62%
62%
0%
0%
1%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3710
45%
21%
10%
3%
0%
0%
75%
75%
0%
0%
2%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3690
62%
21%
18%
3%
0%
0%
87%
87%
0%
0%
2%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3650
63%
21%
27%
4%
0%
0%
97%
97%
1%
0%
7%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3650
63%
22%
27%
4%
0%
0%
98%
98%
3%
0%
7%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3650
63%
22%
27%
4%
0%
0%
99%
99%
3%
0%
7%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3640
63%
22%
28%
4%
0%
0%
99%
99%
14%
0%
7%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3630
63%
22%
29%
4%
0%
0%
99%
99%
33%
0%
7%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3610
64%
23%
32%
4%
0%
0%
99%
99%
38%
0%
27%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3610
64%
23%
32%
4%
0%
0%
99%
99%
41%
0%
30%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3610
64%
23%
33%
4%
0%
0%
99%
99%
41%
0%
30%
0%
9%
9%
0%
0%
0%
0%
0%
0%

3610
64%
23%
33%
4%
0%
0%
99%
99%
41%
0%
30%
0%
9%
9%
0%
0%
0%
0%
0%
0%

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.213

Table VII-40 - Technol

sradovich on DSK3GMQ082PROD with PROPOSALS2

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24AUP2

Technology
Curb Weight (lb.)
Curb Weight (lb.)
High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

I

I

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

2011
4170
4170
0%
0%
100%
100%
0%
0%
70%
70%
69%
70%
2%
0%
0%
0%
2%
2%
0%
0%
0%
0%

I

s;;;za::

der Basel·

Penetraf

2o18
4170
4170
0%
0%
100%
100%
0%
0%
71%
71%
69%
71%
2%
0%
0%
0%
2%
2%
0%
0%
0%
0%

I

2o19
4020
4070
0%
0%
100%
100%
0%
0%
48%
91%
38%
70%
10%
0%
43%
0%
2%
2%
0%
0%
0%
0%

I

2020
4020
4070
0%
0%
100%
100%
0%
0%
54%
98%
38%
70%
10%
0%
43%
0%
2%
2%
0%
0%
0%
0%

I

2021
4020
4070
0%
0%
100%
100%
0%
0%
54%
98%
38%
70%
10%
0%
43%
0%
2%
2%
0%
0%
0%
0%

I

dP
2022
4020
4070
0%
0%
100%
100%
0%
0%
54%
98%
38%
70%
10%
0%
43%
0%
2%
2%
0%
0%
0%
0%

I

d C07- Standards - Vol

I

2o23
3970
4070
0%
0%
100%
100%
0%
0%
20%
98%
1%
70%
12%
0%
78%
0%
2%
2%
0%
0%
0%
0%

I

2o24
3950
4050
0%
0%
100%
100%
0%
0%
12%
98%
0%
70%
12%
0%
85%
0%
3%
2%
0%
0%
0%
0%

I

2o25
3950
4050
0%
0%
100%
100%
0%
0%
12%
98%
0%
70%
12%
0%
85%
0%
3%
2%
0%
0%
0%
0%

I

2o26
3950
4050
0%
0%
100%
100%
0%
0%
12%
98%
0%
70%
12%
0%
85%
0%
3%
2%
0%
0%
0%
0%

I

2021
3950
4050
0%
0%
100%
100%
0%
0%
12%
98%
0%
70%
12%
0%
85%
0%
3%
2%
0%
0%
0%
0%

I

2o28
3950
4040
0%
0%
100%
100%
0%
0%
12%
98%
0%
70%
12%
0%
85%
0%
3%
2%
0%
0%
0%
0%

I

2o29
3960
4040
0%
0%
100%
100%
0%
0%
12%
98%
0%
70%
12%
0%
85%
0%
3%
2%
0%
0%
0%
0%

I

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VII-41 - Technol

43307

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sradovich on DSK3GMQ082PROD with PROPOSALS2

43308

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24AUP2

characterization of the incremental
impacts attributable to standards
introduced in each successive model
year. For example, the standards
proposed for MY 2023 are likely to
impact manufacturers’ application of

E:\FR\FM\24AUP2.SGM

the same. The first perspective, taken
above in VI.A, examines impacts of the
overall proposal — i.e., the entire series
of year-by-year standards — on each
model year. The second perspective,
presented here, provides a clearer

PO 00000

Curb Weight (lb.)

Baseline

Curb Weight (lb.)

Proposal

High CR NA Engines
High CR NA Engines
Turbo SI Engines
Turbo SI Engines
Dynamic Deac
Dynamic Deac
Adv. Transmission
Adv. Transmission
12V SS Systems
12V SS Systems
MildHEVs
MildHEVs
Strong HEVs
Strong HEVs
Plug-In HEVs
Plug-In HEVs
Dedicated EV s
Dedicated EV s
Fuel Cell Vehicles
Fuel Cell Vehicles

Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal
Baseline
Proposal

3480 I 3420 I 3400 I 3360 I 3360 I 3320 I 3300 I 3290 I 3280 I 3260 I 3250 I 3240 I 3240
3480
0%
0%
91%
91%
0%
0%
45%
45%
44%
17%
4%
0%
0%
0%
1%
1%
1%
1%
0%
0%

I 3420 I 3400 I 3360 I 3360 I 3320 I 3320 I 3320 I 3310 I 3310 I 3310 I 3310 I 3310
0%
0% I 0%
0%
0% I 0%
0%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0%
0%
0% I 0%
0%
0%
95% 95% I 95% 95% 96% I 85% 85% 85% 85% I 76% 76% 76%
95% 95% I 95% 95% 96% I 96% 96% 97% 97% I 97% 97% 97%
0%
0% I 0%
0%
0% I 0%
0%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0%
0%
0% I 0%
0%
0%
45% 55% I 55% 55% 47% I 24% 10%
5%
2% I 2%
2%
2%
54% 64% I 74% 74% 74% I 74% 74% 74% 73% I 73% 73% 73%
41% 41% I 41% 40% 32% I 11%
0%
0%
0% I 0%
0%
0%
17% 17% I 17% 17% 17% I 17% 17% 17% 17% I 17% 17% 17%
11% 14% I 14% 14% 15% I 16% 13%
7%
3% I 3%
3%
3%
0%
0% I 0%
0%
0% I 0%
0%
0%
0% I 0%
0%
0%
9%
9% I 25% 26% 40% I 52% 66% 73% 77% I 67% 68% 68%
0%
0% I 0%
0%
0% I 0%
0%
0%
0% I 0%
0%
0%
1%
1% I 1%
1%
1% I 13% 13% 13% 13% I 22% 22% 22%
1%
1% I 1%
1%
1% I 1%
1%
1%
1% I 1%
1%
1%
1%
1% I 1%
1%
1% I 1%
1%
1%
1% I 1%
1%
1%
1%
1% I 1%
1%
1% I 1%
1%
1%
1% I 1%
1%
1%
0%
0% I 0%
0%
0% I 0%
0%
0%
0% I 0%
0%
0%
0%
0% I 0%
0%
0% I 0%
0%
0%
0% I 0%
0%
0%

I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

C. Incremental Impacts of Standards
Proposed for Each Model Year
As mentioned above, impacts are
presented from two different
perspectives for today’s proposal. From
either perspective, overall impacts are

VerDate Sep<11>2014

EP24AU18.215

Table VII-42 - Technology Penetration under Baseline and Proposed C02 Standards- VW
Technology
I
I 2017 I 2018 I 2019 I 2020 I 2021 I 2022 I 2023 I 2024 I 2025 I 2026 I 2027 I 2028 I 2029

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
useful lives. Tables appearing below
summarize results as aggregated across
these model and calendar years.
Underlying model output files 596 report
physical impacts and specific
monetized costs and benefits
attributable to each model year in each
calendar (thus providing information
needed to, for example, differentiate
between impacts attributable to the MY

1977–2016 and MY 2017–2029 cohorts).
The PRIA presents costs and benefits for
individual model years (with MY’s
1977–2016 in a single bucket) for the
preferred alternative.
1. What are the Social Costs and
Benefits of the Proposed Standards?
(a) CAFE Standards

596 Available at https://www.nhtsa.gov/corporateaverage-fuel-economy/compliance-and-effectsmodeling-system.

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EP24AU18.216

sradovich on DSK3GMQ082PROD with PROPOSALS2

technology in model years prior to MY
2023, as well as model years after MY
2023. By conducting analysis that
successively introduces standards for
each MY, in turn, isolates the
incremental impacts attributable to new
standards introduced in each MY,
considering the entire span of MYs
(1977–2029) included in the underlying
modeling, throughout those vehicles’

43309

43310

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table VII-44- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, CAFE
P rogram, U n d.1scounted , M·lr
I lOllS 0 f$2016
Model Year Standards
MY
MY
MY
MY
MY
MY
TOTAL
Through
2021
2022
2023
2024
2025
2026
-691
-781
-1500
Retrievable Electrification
-24.2
-2.09
0.164
0.00
Costs
Electrification Tax Credits
-0.112
-35.3
0.197
0.112
0.000
0.00
-35.1
-132
-184
-379
Irretrievable Electrification
-3.41
-37.1
-22.3
0.00
Costs
-823
-965
-1910
Total Electrification costs
-27.7
-74.5
-21.9
0.00

sradovich on DSK3GMQ082PROD with PROPOSALS2

Model Year Standards
MY
Through
2021
Societal Costs and Benefits Through MY
Technology Costs
-30.5
Pre-tax Fuel Savings
-30.8
Mobility Benefit
-13.7
Refueling Benefit
-2.0
Non-Rebound Fatality Costs
-6.8
Rebound Fatality Costs
-9.4
Benefits Offsetting Rebound -9.4
Fatality Costs
Non-Rebound Non-Fatal
-10.7
Crash Costs
Rebound Non-Fatal Crash
-14.8
Costs
Benefits Offsetting Rebound -14.8
Non-Fatal Crash Costs
Congestion and Noise
-10.8
Energy Security Benefit
-2.5
-1.0
C02 Damages
Other Pollutant Damages
-0.5
Total Costs
-83.0
Total Benefits
-74.7
Net Benefits
8.4

VerDate Sep<11>2014

23:42 Aug 23, 2018

Jkt 244001

PO 00000

MY
2022
2029 ($b)
-40.4
-19.8
-10.4
-1.2
-4.7
-6.3
-6.3

MY
2023

MY
2024

MY
2025

MY
2026

TOTAL

-51.4
-25.5
-12.2
-1.6
-7.2
-8.3
-8.3

-73.9
-33.2
-14.1
-2.1
-8.6
-10.0
-10.0

-56.4
-23.6
-10.7
-1.6
-8.2
-7.6
-7.6

0.0
0.0
0.0
0.0
0.0
0.0
0.0

-252.6
-132.9
-61.1
-8.5
-35.4
-41.7
-41.7

-7.3

-11.2

-13.4

-12.7

0.0

-55.3

-9.9

-12.9

-15.6

-11.9

0.0

-65.1

-9.9

-12.9

-15.6

-11.9

0.0

-65.1

-7.6
-1.6
-0.6
-0.2
-76.3
-50.1
26.2

-10.2
-2.1
-0.8
-0.3
-101.0
-63.7
37.4

-12.6
-2.7
-1.1
-0.3
-134.0
-79.1
55.0

-10.7
-2.0
-0.8
0.1
-108.0
-58.2
49.4

0.0
0.0
0.0
0.0
0.0
0.0
0.0

-51.9
-10.9
-4.3
-1.2
-502.1
-325.8
176.4

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24AUP2

EP24AU18.217

Table VII-45- Combined LDV Societal Net Benefits for MYs 1977-2029, CAFE Program, 3%
Discount Rate

43311

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table VII-46- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, CAFE
Pro~ram, 3% Discount Rate, Millions of $2016
Model Year Standards Through
Retrievable Electrification
Costs
Electrification Tax Credits
Irretrievable Electrification
Costs
Total Electrification costs

MY

MY

MY

MY

MY

MY

2021
-18.6

2022
-1.61

2023
0.124

2024
-572

2025
-606

2026
0.00

TOTAL
-1200

-0.0919
-2.70

-28.8
-27.0

0.158
-17.2

0.0885
-119

0.00
-148

0.00
0.00

-28.6
-314

-21.3

-57.4

-16.9

-692

-755

0.00

-1540

Table VII-47- Combined LDV Societal Net Benefits for MYs 1977-2029, CAFE Program, 7%
Discount Rate
Model Year Standards
MY
MY
MY
MY
MY
MY
TOTAL
2021
2022
2023
2024
2025
2026
Through
Societal Costs and Benefits Through MY 2029 ($b)
Technology Costs
-23.9
-39.0
-56.5
0.0
-192.3
-31.0
-41.9
Pre-tax Fuel Savings
-20.0
-12.6
-16.0
-20.8
-14.8
0.0
-84.2
Mobility Benefit
0.0
-37.1
-8.6
-6.3
-7.3
-8.5
-6.3
Refueling Benefit
0.0
-1.3
-0.8
-1.0
-1.4
-1.0
-5.4
Non-Rebound Fatality
0.0
-3.8
-2.4
-3.7
-4.5
-4.0
-18.4
Costs
Rebound Fatality Costs
Benefits Offsetting
Rebound Fatality Costs
Non-Rebound Non-Fatal
Crash Costs
Rebound Non-Fatal Crash
Costs
Benefits Offsetting
Rebound Non-Fatal Crash
Costs
Congestion and Noise
Energy Security Benefit
C0 2 Damages
Other Pollutant Damages
Total Costs
Total Benefits
Net Benefits

-6.1
-6.1

-3.9
-3.9

-5.1
-5.1

-6.2
-6.2

-4.6
-4.6

0.0
0.0

-25.8
-25.8

-6.0

-3.8

-5.8

-7.0

-6.2

0.0

-28.8

-9.5

-6.2

-7.9

-9.6

-7.2

0.0

-40.4

-9.5

-6.2

-7.9

-9.6

-7.2

0.0

-40.4

-6.6
-1.6
-0.6
-0.4
-55.8
-48.1
7.7

-4.4
-1.0
-0.4
-0.2
-51.7
-31.4
20.3

-5.8
-1.3
-0.5
-0.2
-67.2
-39.4
27.8

-7.2
-1.7
-0.7
-0.3
-90.9
-49.2
41.7

-5.8
-1.3
-0.5
0.0
-69.6
-35.8
33.9

0.0
0.0
0.0
0.0
0.0
0.0
0.0

-6.9
-2.7
-1.1
-335.2
-203.9
131.4

Table VII-48- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, CAFE
Pro~ram, 7% Discount Rate, Millions of $2016

VerDate Sep<11>2014

23:42 Aug 23, 2018

Jkt 244001

MY

MY

MY

MY

MY

2022
-1.15

2023
0.0875

2024
-456

2025
-441

2026
0.00

-911

-0.0716
-2.01

-22.1
-17.9

0.119
-12.4

0.0652
-105

0.00
-113

0.00
0.00

-22.0
-250

-15.3

-41.2

-12.2

-561

-554

0.00

-1180

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24AUP2

TOTAL

EP24AU18.219

Retrievable Electrification
Costs
Electrification Tax Credits
Irretrievable Electrification
Costs
Total Electrification costs

MY
2021
-13.3

EP24AU18.218

sradovich on DSK3GMQ082PROD with PROPOSALS2

Model Year Standards Through

43312

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

(b) CO2 Standards

Table VII-49- Combined LDV Societal Net Benefits for MYs 1977-2029, GHG Program,
U ndiscounted
MY

MY

2022
Societal Costs and Benefits Through MY 2029 ($b)

sradovich on DSK3GMQ082PROD with PROPOSALS2

Technology Costs
Pre-tax Fuel Savings
Mobility Benefit
Refueling Benefit
Non-Rebound Fatality Costs
Rebound Fatality Costs
Benefits Offsetting Rebound
Fatality Costs
Non-Rebound Non-Fatal
Crash Costs
Rebound Non-Fatal Crash
Costs
Benefits Offsetting Rebound
Non-Fatal Crash Costs
Congestion and Noise
Energy Security Benefit
C02 Damages
Other Pollutant Damages
Total Costs
Total Benefits
Net Benefits

VerDate Sep<11>2014

23:42 Aug 23, 2018

Jkt 244001

2021

MY

MY

MY

MY

2023

2024

2025

2026

TOTAL

-51.4
-54.0
-25.9
-3.3
-11.9
-16.8
-16.8

-57.0
-55.0
-26.6
-3.5
-15.6
-18.2
-18.2

-59.4
-31.7
-16.7
-2.1
-14.6
-11.4
-11.4

-82.0
-36.1
-20.2
-2.5
-20.4
-13.5
-13.5

-77.2
-31.6
-17.5
-2.3
-20.2
-12.3
-12.3

0.0
0.0
0.0
0.0
0.0
0.0
0.0

-327.0
-208.4
-106.9
-13.6
-82.7
-72.2
-72.2

-18.6

-24.3

-22.8

-31.8

-31.6

0.0

-129.1

-26.3

-28.4

-17.9

-21.1

-19.3

0.0

-113.0

-26.3

-28.4

-17.9

-21.1

-19.3

0.0

-113.0

-19.3
-4.4
-1.8

-22.0
-4.5
-1.8

-17.2
-2.6
-1.0

-22.9
-3.1
-1.2

-22.3
-2.8
-1.0

0.0
0.0
0.0

-103.7
-17.3
-6.8

-0.9
-144.0
-133.0
10.9

-0.7
-166.0
-139.0
27.0

0.0
-143.0
-83.4
59.9

0.7
-192.0
-96.9
94.8

0.9
-183.0
-85.9
97.0

0.0
0.0
0.0
0.0

0.1
-828.0
-538.2
289.6

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Model Year Standards
Through

43313

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table VII-50- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, GHG
P rogram, U nd.1scounted, M·lr
I lOllS 0 f$2016
iHVU'-'i

~

l;;dl

Ql

llllUUbh

Retrievable Electrification
Costs
Electrification Tax Credits
Irretrievable Electrification
Costs
Total Electrification costs

TOTAL

MY
2021
-61.1

MY
2022
0.905

MY
2023
-933

MY
2024
-60.6

MY
2025
-843

MY
2026
0.00

0.00
-12.3

0.00
0.102

0.00
-77.2

-133
-236

-16.0
-206

0.00
0.00

-149
-531

-73.4

1.01

-1010

-430

-1060

0.00

-2570

-1900

Table VII-51- Combined LDV Societal Net Benefits for MYs 1977-2029, GHG Program, 3% Discount
Rate
Model Year Standards
Through

MY

MY

2022
Societal Costs and Benefits Through MY 2029 ($b)
Technology Costs
Pre-tax Fuel Savings
Mobility Benefit
Refueling Benefit
Non-Rebound Fatality Costs
Rebound Fatality Costs
Benefits Offsetting Rebound
Fatality Costs
Non-Rebound Non-Fatal
Crash Costs
Rebound Non-Fatal Crash
Costs
Benefits Offsetting Rebound
Non-Fatal Crash Costs
Congestion and Noise
Energy Security Benefit
C02 Damages
Other Pollutant Damages
Total Costs
Total Benefits
Net Benefits

2021

MY

MY

MY

MY

2023

2024

2025

2026

TOTAL

-42.0
-36.9
-17.3
-2.3
-7.2
-11.4
-11.4

-45.8
-37.2
-17.4
-2.4
-9.0
-12.1
-12.1

-46.9
-22.1
-10.8
-1.4
-8.0
-7.5
-7.5

-65.0
-25.3
-13.0
-1.7
-11.2
-8.8
-8.8

-60.1
-22.3
-11.1
-1.6
-10.9
-8.0
-8.0

0.0
0.0
0.0
0.0
0.0
0.0
0.0

-259.8
-143.8
-69.6
-9.4
-46.3
-47.8
-47.8

-11.2

-14.1

-12.5

-17.5

-17.0

0.0

-72.3

-17.9

-18.9

-11.7

-13.8

-12.4

0.0

-74.7

-17.9

-18.9

-11.7

-13.8

-12.4

0.0

-74.7

-12.4
-3.0
-1.2

-13.7
-3.0
-1.2

-10.2
-1.8
-0.7

-13.4
-2.2
-0.8

-12.8
-2.0
-0.7

0.0
0.0
0.0

-62.5
-11.9
-4.7

-0.7
-102.0
-90.7
11.3

-0.6
-114.0
-92.7
20.9

-0.2
-96.8
-56.2
40.7

0.3
-130.0
-65.3
64.4

0.4
-121.0
-57.7
63.5

0.0
0.0
0.0
0.0

-0.8
-563.8
-362.6
200.8

Table VII-52- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, GHG
P ro !ram, 3%0 n·IS COUllt R at e, M·lr
I lOllS 0 f$2016

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MY
2022
0.685

MY
2023
-717

MY
2024
-48.0

MY
2025
-679

MY
2026
0.00

0.00
-10.4

0.00
0.0803

0.00
-63.9

-114
-187

-13.3
-175

0.00
0.00

-127
-436

-59.5

0.766

-781

-349

-867

0.00

-2060

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-1490

EP24AU18.222

Retrievable Electrification
Costs
Electrification Tax Credits
Irretrievable Electrification
Costs
Total Electrification costs

TOTAL

MY
2021
-49.1

EP24AU18.221

sradovich on DSK3GMQ082PROD with PROPOSALS2

Model Year Standards Through

43314

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

(a) What are the impacts on producers
of new vehicles?

EP24AU18.224

(b) CAFE Standards

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sradovich on DSK3GMQ082PROD with PROPOSALS2

2. What are the private costs and
benefits of the proposed standards,
relative to the no-action alternative?

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

43315

Table VII-55- Combined Light-Duty CAFE Compliance Impacts and Cumulative Industry Costs
th roue1h MY 2029
MY
2021

MY
2022

MY
2023

MY
2024

MY
2025

MY
2026

sradovich on DSK3GMQ082PROD with PROPOSALS2

Fuel Economy
Average Required Fuel
37.0
37.0
37.0
37.0
37.0
37.0
Economy - MY 2026+ (mpg)
Percent Change in Stringency
-20.6% -26.0% -26.0%
-5.4% -10.2%
-15.3%
from Baseline
Average Achieved Fuel
39.7
39.7
39.7
39.7
39.7
39.7
Economy - MY 2030 (mpg)
Average Achieved Fuel
37.2
37.2
37.2
37.2
37.2
37.2
Economy - MY 2020 (mpg)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
-31.0
-39.0
-56.5
-41.9
0.0
Total Technology Costs ($b)
-23.9
Total Civil Penalties ($b)
-0.7
-0.6
-0.6
-0.1
-0.1
0.0
-56.6
-41.9
Total Regulatory Costs ($b)
-24.5
-31.6
-39.6
0.0
Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
Sales Change (millions)
0.1
0.2
0.2
0.3
0.2
0.0
Revenue Change ($b)
-23.8
-30.2
-36.8
-52.7
-38.9
0.0

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TOTAL

N/A
N/A
N/A
N/A
-192.3
-2.1
-194.2
1.0
-182.4

EP24AU18.225

Model Year Standards Through

43316

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Tabl e VII- 56 - C om b.me d L.Iglht- D uty Fl eet P enetratwn f or MY 2030 CAFE P rog ram
MY
MY
MY
Model Year Standards Through
2021
2022
2023
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction (percent change
4.3%
4.3%
4.3%
from MY 20 16)
High Compression Ratio Non-Turbo
17.2%
17.2%
17.2%
Engines
Turbocharged Gasoline Engines
51.1%
51.1%
51.1%
Dynamic Cylinder Deactivation
0.0%
0.0%
0.0%
Advanced Transmissions
92.9%
92.9%
92.9%
Stop-Start 12V (Non-Hybrid)
13.7%
13.7%
13.7%
Mild Hybrid Electric Systems (48v)
0.4%
0.4%
0.4%
Strong Hybrid Electric Systems
2.3%
2.3%
2.3%

'

MY
2024

MY
2025

MY
2026

4.3%

4.3%

4.3%

17.2%

17.2%

17.2%

51.1%
0.0%
92.9%
13.7%
0.4%
2.3%

51.1%
0.0%
92.9%
13.7%
0.4%
2.3%

51.1%
0.0%
92.9%
13.7%
0.4%
2.3%

Plug-In Hybrid Electric Vehicles (PHEVs)

0.5%

0.5%

0.5%

0.5%

0.5%

0.5%

Dedicated Electric Vehicles (EV s)
Fuel Cell Vehicles (FCVs)

0.5%
0.0%

0.5%
0.0%

0.5%
0.0%

0.5%
0.0%

0.5%
0.0%

0.5%
0.0%

Table VII-57 -Light Truck CAFE Compliance Impacts and Cumulative Industry Costs through MY
2029
MY
2021

MY
2022

MY
2023

MY
2024

MY
2025

MY
2026

sradovich on DSK3GMQ082PROD with PROPOSALS2

Fuel Economy
Average Required Fuel
31.3
31.3
31.3
31.3
31.3
31.3
Economy - MY 2026+ (mpg)
Percent Change in Stringency
-6.6% -11.7% -17.0% -22.6% -28.3% -28.3%
from Baseline
Average Achieved Fuel
33.6
33.6
33.6
33.6
33.6
33.6
Economy - MY 2030 (mpg)
Average Achieved Fuel
31.6
31.6
31.6
31.6
31.6
31.6
Economy - MY 2020 (mpg)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
Total Technology Costs ($b)
-13.1
-20.1
-18.9
-35.8
-20.2
0.0
Total Civil Penalties ($b)
-0.3
-0.3
-0.4
0.0
-0.1
0.0
Total Regulatory Costs ($b)
-13.4
-20.4
-19.2
-35.8
-20.2
0.0
Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
Sales Change (millions)
-0.4
-0.4
-0.2
-0.1
-0.1
0.0
Revenue Change ($b)
-20.6
-27.1
-23.0
-37.0
-21.7
0.0

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TOTAL

N/A
N/A
N/A
N/A
-108.1
-1.0
-109.0
-1.1
-129.4

EP24AU18.226

Model Year Standards Through

43317

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Tabl e VII- 58 - L.Iglht T rue k Fl eet P enet ra f IOn f or MY 2030 CAFE P rogram

'

MY
MY
MY
MY
Model Year Standards Through
2021
2022
2023
2024
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction (percent
4.4%
4.4%
4.4%
4.4%
change from MY 20 16)
High Compression Ratio Non-Turbo
8.1%
8.1%
8.1%
8.1%
Engines
Turbocharged Gasoline Engines
53.1%
53.1%
53.1%
53.1%
Dynamic Cylinder Deactivation
0.0%
0.0%
0.0%
0.0%
Advanced Transmissions
98.3%
98.3%
98.3%
98.3%
Stop-Start 12V (Non-Hybrid)
12.3%
12.3%
12.3%
12.3%
Mild Hybrid Electric Systems (48v)
0.0%
0.0%
0.0%
0.0%
Strong Hybrid Electric Systems
0.9%
0.9%
0.9%
0.9%
Plug-In Hybrid Electric Vehicles
0.3%
0.3%
0.3%
0.3%
(PHEVs)
0.3%
0.3%
0.3%
0.3%
Dedicated Electric Vehicles (EV s)
Fuel Cell Vehicles (FCVs)
0.0%
0.0%
0.0%
0.0%

MY
2025

MY
2026

4.4%

4.4%

8.1%

8.1%

53.1%
0.0%
98.3%
12.3%
0.0%
0.9%

53.1%
0.0%
98.3%
12.3%
0.0%
0.9%

0.3%

0.3%

0.3%
0.0%

0.3%
0.0%

Table VII-59- Passenger Car CAFE Compliance Impacts and Cumulative Industry Costs through
MY 2029
MY
2021

MY
2022

MY
2023

MY
2024

MY
2025

MY
2026

sradovich on DSK3GMQ082PROD with PROPOSALS2

Fuel Economy
Average Required Fuel
43.7
43.7
43.7
43.7
43.7
43.7
Economy - MY 2026+ (mpg)
Percent Change in Stringency
-4.3%
-9.2% -14.3% -19.6% -25.2% -25.2%
from Baseline
Average Achieved Fuel
46.7
46.7
46.7
46.7
46.7
46.7
Economy - MY 2030 (mpg)
Average Achieved Fuel
43.9
43.9
43.9
43.9
43.9
43.9
Economy - MY 2020 (mpg)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
Total Technology Costs ($b)
-10.8
-10.9
-20.1
-20.7
-21.6
0.0
Total Civil Penalties ($b)
-0.4
-0.4
-0.2
-0.1
0.0
0.0
Total Regulatory Costs ($b)
-11.1
-11.3
-20.4
-20.8
-21.7
0.0
Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
0.5
0.4
0.4
0.3
0.0
Sales Change (millions)
0.5
Revenue Change ($b)
-3.3
-3.1
-13.7
-15.7
-17.2
0.0

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TOTAL

N/A
N/A
N/A
N/A
-84.1
-1.0
-85.3
2.1
-52.9

EP24AU18.227

Model Year Standards Through

43318

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Tabl e VII-60 - P assen~er Car Fl eet P enet ra fIOn ~or MY 2030 CAFE P ro ~ram

'

MY
MY
MY
MY
Model Year Standards Through
2021
2022
2023
2024
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction
4.1%
4.1%
4.1%
4.1%
(percent change from MY 20 16)
High Compression Ratio Non24.7%
24.7%
24.7%
24.7%
Turbo Engines
Turbocharged Gasoline Engines
49.5%
49.5%
49.5%
49.5%
Dynamic Cylinder Deactivation
0.0%
0.0%
0.0%
0.0%
Advanced Transmissions
88.5%
88.5%
88.5%
88.5%
Stop-Start 12V (Non-Hybrid)
15.0%
15.0%
15.0%
15.0%
Mild Hybrid Electric Systems
0.7%
0.7%
0.7%
0.7%
(48v)
Strong Hybrid Electric Systems
3.5%
3.5%
3.5%
3.5%
Plug-In Hybrid Electric
0.7%
0.7%
0.7%
0.7%
Vehicles (PHEV s)
Dedicated Electric Vehicles
0.7%
0.7%
0.7%
0.7%
(EVs)
0.0%
0.0%
0.0%
0.0%
Fuel Cell Vehicles (FCVs)

MY
2025

MY
2026

4.1%

4.1%

24.7%

24.7%

49.5%
0.0%
88.5%
15.0%

49.5%
0.0%
88.5%
15.0%

0.7%

0.7%

3.5%

3.5%

0.7%

0.7%

0.7%

0.7%

0.0%

0.0%

Table VII-61- Domestic Car CAFE Compliance Impacts and Cumulative Industry Costs through MY
2029
MY
2021

MY
2022

MY
2023

MY
2024

MY
2025

MY
2026

sradovich on DSK3GMQ082PROD with PROPOSALS2

Fuel Economy
Average Required Fuel
43.2
43.2
43.2
43.2
43.2
43.2
Economy - MY 2026+ (mpg)
Percent Change in Stringency
-4.3%
-9.1% -14.2%
-19.6%
-25.2% -25.2%
from Baseline
Average Achieved Fuel
46.5
46.5
46.5
46.5
46.5
46.5
Economy -MY 2030 (mpg)
Average Achieved Fuel
43.6
43.6
43.6
43.6
43.6
43.6
Economy - MY 2020 (mpg)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
Total Technology Costs ($b)
-6.1
-9.1
-13.9
-12.6
-14.3
0.0
Total Civil Penalties ($b)
-0.1
-0.1
0.0
0.0
0.2
0.0
Total Regulatory Costs ($b)
-6.2
-9.3
-14.0
-12.5
-14.3
0.0
Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
Sales Change (millions)
0.3
0.3
0.3
0.2
0.2
0.0
Revenue Change ($b)
-1.8
-4.7
-10.3
-9.7
-ll.8
0.0

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TOTAL

N/A
N/A
N/A
N/A
-56.1
0.0
-56.3
1.2
-38.4

EP24AU18.228

Model Year Standards Through

43319

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

T abl e VII-62 - D omes fIC Car Fl eet P ene tra fIOn f or MY 2030 CAFE P rogram

'

MY
MY
MY
MY
Model Year Standards Through
2021
2022
2023
2024
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction (percent
4.8%
4.8%
4.8%
4.8%
change from MY 20 16)
High Compression Ratio Non12.7%
12.7%
12.7%
12.7%
Turbo Engines
Turbocharged Gasoline Engines
61.9%
61.9%
61.9%
61.9%
Dynamic Cylinder Deactivation
0.0%
0.0%
0.0%
0.0%
Advanced Transmissions
91.1%
91.1%
91.1%
91.1%
Stop-Start 12V (Non-Hybrid)
11.5%
11.5%
11.5%
11.5%
Mild Hybrid Electric Systems
0.1%
0.1%
0.1%
0.1%
(48v)
Strong Hybrid Electric Systems
1.0%
1.0%
1.0%
1.0%
Plug-In Hybrid Electric Vehicles
0.6%
0.6%
0.6%
0.6%
(PHEVs)
Dedicated Electric Vehicles
0.6%
0.6%
0.6%
0.6%
(EVs)
0.0%
0.0%
0.0%
0.0%
Fuel Cell Vehicles (FCVs)

MY
2025

MY
2026

4.8%

4.8%

12.7%

12.7%

61.9%
0.0%
91.1%
11.5%

61.9%
0.0%
91.1%
11.5%

0.1%

0.1%

1.0%

1.0%

0.6%

0.6%

0.6%

0.6%

0.0%

0.0%

Table VII-63- Imported Car CAFE Compliance Impacts and Cumulative Industry Costs through MY
2029
MY
2021

MY
2022

MY
2023

MY
2024

MY
2025

MY
2026

sradovich on DSK3GMQ082PROD with PROPOSALS2

Fuel Economy
Average Required Fuel
44.2
44.2
44.2
44.2
44.2
44.2
Economy - MY 2026+ (mpg)
Percent Change in Stringency
-4.3%
-9.2% -14.3%
-19.6% -25.3% -25.3%
from Baseline
Average Achieved Fuel
47.0
47.0
47.0
47.0
47.0
47.0
Economy -MY 2030 (mpg)
Average Achieved Fuel
44.1
44.1
44.1
44.1
44.1
44.1
Economy - MY 2020 (mpg)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
Total Technology Costs ($b)
-4.6
-1.8
-6.2
-8.1
-7.3
0.0
Total Civil Penalties ($b)
-0.2
-0.3
-0.2
-0.2
-0.1
0.0
Total Regulatory Costs ($b)
-4.9
-2.0
-6.4
-8.3
-7.4
0.0
Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
Sales Change (millions)
0.2
0.2
0.2
0.1
0.1
0.0
Revenue Change ($b)
-1.4
1.6
-3.4
-6.0
-5.4
0.0

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TOTAL

N/A
N/A
N/A
N/A
-27.9
-1.0
-29.0
0.9
-14.6

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43320

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

T a bl e VII-64 - I mporte dC ar Fl eet P enetratwn f or MY 2030 CAFE P rogram
MY
Model Year Standards Through
2021
Technology Use Under CAFE Alternative in MY
Curb Weight Reduction (percent
3.2%
change from MY 20 16)
High Compression Ratio Non39.0%
Turbo Engines
Turbocharged Gasoline Engines
34.7%
Dynamic Cylinder Deactivation
0.0%
Advanced Transmissions
85.4%
Stop-Start 12V (Non-Hybrid)
19.1%
Mild Hybrid Electric Systems (48v)
1.3%
Strong Hybrid Electric Systems
6.5%
Plug-In Hybrid Electric Vehicles
0.8%
(PHEVs)
0.9%
Dedicated Electric Vehicles (EV s)
Fuel Cell Vehicles (FCVs)
0.0%

'

MY
MY
MY
2022
2023
2024
2030 (total fleet penetration)

MY
2025

MY
2026

3.2%

3.2%

3.2%

3.2%

3.2%

39.0%

39.0%

39.0%

39.0%

39.0%

34.7%
0.0%
85.4%
19.1%
1.3%
6.5%

34.7%
0.0%
85.4%
19.1%
1.3%
6.5%

34.7%
0.0%
85.4%
19.1%
1.3%
6.5%

34.7%
0.0%
85.4%
19.1%
1.3%
6.5%

34.7%
0.0%
85.4%
19.1%
1.3%
6.5%

0.8%

0.8%

0.8%

0.8%

0.8%

0.9%
0.0%

0.9%
0.0%

0.9%
0.0%

0.9%
0.0%

0.9%
0.0%

(c) CO2 Standards

Table VII-65- Combined Light-Duty C02 Compliance Impacts and Cumulative Industry Costs
t h rouglhMY2029
Model Year Standards Through

MY
2021

MY
2022

MY
2023

MY
2024

MY
2025

MY
2026

N/A
N/A
N/A
-195.6
N/A
-195.6

EP24AU18.231

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-185.1

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Average C0 2 Emission Rate
Average Required C0 2 - MY
240.0
240.0
240.0
240.0
240.0
240.0
2026+ (g/mi)
Percent Change in Stringency
-13.3% -18.5% -24.4% -30.5% -36.9% -36.9%
from Baseline
Average Achieved C0 2 - MY
229.0
229.0
229.0
229.0
229.0
229.0
2030 (g/mi)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
-48.9
-44.1
0.0
Total Technology Costs ($b)
-32.8
-34.9
-34.9
N/A
N/A
Total Civil Penalties ($b)
N/A
N/A
N/A
N/A
Total Regulatory Costs ($b)
-32.8
-34.9
-34.9
-48.9
-44.1
0.0
Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
Sales Change (millions)
0.2
0.2
0.2
0.2
0.2
0.0
Revenue Change ($b)
-31.1
-34.2
-32.4
-45.8
-41.6
0.0

TOTAL

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

43321

T a bl e VII-66 - C om b.me d L.Iglht-D my
t Fl eet P enet ra f IOn ~or MY 2030 CO2 P ro gram
'
MY
MY
MY
MY
MY
MY
Model Year Standards Through
2021
2022
2023
2024
2025
2026
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction (percent
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
change from MY 20 16)
High Compression Ratio Non-Turbo
12.4%
12.4%
12.4%
12.4%
12.4%
12.4%
Engines
Turbocharged Gasoline Engines
40.8%
40.8%
40.8%
40.8%
40.8%
40.8%
Dynamic Cylinder Deactivation
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Advanced Transmissions
93.6%
93.6%
93.6%
93.6%
93.6%
93.6%
Stop-Start 12V (Non-Hybrid)
11.1%
11.1%
11.1%
11.1%
11.1%
11.1%
Mild Hybrid Electric Systems (48v)
1.5%
1.5%
1.5%
1.5%
1.5%
1.5%
Strong Hybrid Electric Systems
1.8%
1.8%
1.8%
1.8%
1.8%
1.8%
Plug-In Hybrid Electric Vehicles
0.3%
0.3%
0.3%
0.3%
0.3%
0.3%
(PHEVs)
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
Dedicated Electric Vehicles (EV s)
Fuel Cell Vehicles (FCVs)
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%

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Table VII-67- Light Truck C02 Compliance Impacts and Cumulative Industry Costs through MY
2029
MY
MY
MY
MY
MY
MY
Model Year Standards Through
TOTAL
2021
2022
2023
2024
2025
2026
Average C0 2 Emission Rate
Average Required C0 2 - MY
284.0
284.0
284.0
284.0
284.0
284.0
N/A
2026+ (g/mi)
Percent Change in Stringency
-14.1% -19.8% -25.7% -32.1% -39.2% -39.2%
N/A
from Baseline
Average Achieved C0 2 - MY
268.0
268.0
268.0
268.0
268.0
268.0
N/A
2030 (g/mi)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
Total Technology Costs ($b)
-16.2
-18.9
-17.0
-29.3
-22.1
0.0
-103.5
Total Civil Penalties ($b)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Total Regulatory Costs ($b)
-16.2
-18.9
-17.0
-29.3
-22.1
0.0
-103.5
Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
Sales Change (millions)
-0.4
-0.7
-0.2
-0.1
-0.1
0.0
-1.5
Revenue Change ($b)
-23.6
-31.8
-21.1
-31.9
-24.1
0.0
-132.5

43322

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Tabl e VII-68 - L.Iglht T rue k Fl eet P ene tra fIOn f or MY 2030 CO2 P rogram

'

MY
MY
MY
MY
Model Year Standards Through
2021
2022
2023
2024
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction (percent
4.4%
4.4%
4.4%
4.4%
change from MY 20 16)
High Compression Ratio Non-Turbo
6.3%
6.3%
6.3%
6.3%
Engines
Turbocharged Gasoline Engines
42.1%
42.1%
42.1%
42.1%
0.0%
0.0%
0.0%
0.0%
Dynamic Cylinder Deactivation
Advanced Transmissions
98.6%
98.6%
98.6%
98.6%
10.2%
10.2%
10.2%
10.2%
Stop-Start 12V (Non-Hybrid)
Mild Hybrid Electric Systems (48v)
3.1%
3.1%
3.1%
3.1%
Strong Hybrid Electric Systems
0.7%
0.7%
0.7%
0.7%
Plug-In Hybrid Electric Vehicles
0.1%
0.1%
0.1%
0.1%
(PHEVs)
0.3%
0.3%
0.3%
0.3%
Dedicated Electric Vehicles (EV s)
Fuel Cell Vehicles (FCVs)
0.0%
0.0%
0.0%
0.0%

MY
2025

MY
2026

4.4%

4.4%

6.3%

6.3%

42.1%
0.0%
98.6%
10.2%
3.1%
0.7%

42.1%
0.0%
98.6%
10.2%
3.1%
0.7%

0.1%

0.1%

0.3%
0.0%

0.3%
0.0%

Table VII-69- Passenger Car C02 Compliance Impacts and Cumulative Industry Costs through MY
2029
MY
2021

MY
2022

MY
2023

MY
2024

MY
2025

MY
2026

sradovich on DSK3GMQ082PROD with PROPOSALS2

Average C0 2 Emission Rate
Average Required C0 2 - MY
204.0
204.0
204.0
204.0
204.0
204.0
2026+ (g/mi)
Percent Change in Stringency
-12.7% -17.9% -24.4% -30.8% -36.9% -36.9%
from Baseline
Average Achieved C0 2 - MY
197.0
198.0
198.0
198.0
198.0
198.0
2030 (g/mi)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
Total Technology Costs ($b)
-16.6
-16.1
-17.9
-19.5
-22.0
0.0
Total Civil Penalties ($b)
N/A
N/A
N/A
N/A
N/A
N/A
Total Regulatory Costs ($b)
-16.6
-16.1
-17.9
-19.5
-22.0
0.0
Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
Sales Change (millions)
0.6
0.9
0.4
0.4
0.3
0.0
Revenue Change ($b)
-7.4
-2.4
-11.4
-13.9
-17.5
0.0

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TOTAL

N/A
N/A
N/A
-92.1
N/A
-92.1
2.6
-52.7

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43323

(d) What are the impacts on buyers of
new vehicles?
(e) CAFE Standards

EP24AU18.235

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(f) CO2 Standards

43324

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table VII-72 - Impacts to the Average Consumer of a MY 2030 Vehicle under CAFE Program, 7%
Discount Rate
Model Year Standards
MY
MY
MY
Through
2021
2022
2023
Per Vehicle Consumer Impacts for MY 2030 ($)
-200
-280
-380
Average Price Increase
-50
-70
-90
Ownership Costs
-220
-160
-240
Fuel Savings
-80
-70
-90
Mobility Benefit
-10
-10
-10
Refueling Benefit
-250
-350
-470
Total Costs
-320
-230
-340
Total Benefits
-60
Net Benefits
110
120

MY
2024

MY
2025

MY
2026

TOTAL

-500
-120
-310
-100
-10
-620
-430
210

-490
-110
-280
-90
-10
-610
-370
220

0
0
0
0
0
0
0
0

-1,850
-440
-1,210
-430
-50
-2,300
-1,690
600

Table VII-73 - Impacts to the Average Consumer of a MY 2030 Vehicle under C02 Program, 3%
Discount Rate
Model Year Standards
MY
MY
MY
Through
2021
2022
2023
Per Vehicle Consumer Impacts for MY 2030 ($)
-240
-340
-420
Average Price Increase
-70
-90
-110
Ownership Costs
-320
-410
-300
Fuel Savings
-100
-130
-90
Mobility Benefit
-10
-20
-10
Refueling Benefit
-310
-430
-530
Total Costs
-430
-550
-410
Total Benefits
-120
-130
Net Benefits
130

MY
2024
-580
-160
-380
-110
-10
-740
-510
230

MY
2025

MY
2026

-680
-180
-420
-110
-20
-860
-540
320

TOTAL

0
0
0
0
0
0
0
0

-2,260
-610
-1,830
-540
-70
-2,870
-2,440
430

D. What are the Energy and
Environmental Impacts?
Today’s proposal directly involves the
fuel economy and average CO2
emissions of light-duty vehicles, and the
proposal is expected to most directly
and significantly impact national fuel
consumption and CO2 emissions. Fuel
economy and CO2 emissions are so
closely related that it is expected the

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MY
2024

MY
2025

MY
2026

TOTAL

-580
-140
-320
-110
-10
-730
-440
280

-680
-160
-340
-110
-20
-840
-470
370

0
0
0
0
0
0
0
0

-2,260
-550
-1,510
-540
-70
-2,810
-2,120
690

impacts on national fuel consumption
and national CO2 emissions will track in
virtual lockstep with each other.
Today’s proposal does not directly
involve pollutants such as carbon
monoxide, smog-forming pollutants
(nitrogen oxides and unburned
hydrocarbons), final particles, or ‘‘air
toxics’’ (e.g., formaldehyde,
acetaldehyde, benzene). While today’s

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proposal is expected to indirectly
impact such emissions (by reducing
travel demand and accelerating fleet
turnover to newer and cleaner vehicles
on one hand while, on the other,
increasing activity at refineries and in
the fuel distribution system), it is
expected that these impacts will be
much smaller than impacts on fuel use
and CO2 emissions because standards

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Model Year Standards
MY
MY
MY
Through
2021
2022
2023
Per Vehicle Consumer Impacts for MY 2030 ($)
-240
-340
-420
Average Price Increase
-60
-90
-100
Ownership Costs
-260
-340
-250
Fuel Savings
-100
-130
-90
Mobility Benefit
-10
-20
-10
Refueling Benefit
-300
-420
-520
Total Costs
-370
-480
-360
Total Benefits
-70
-60
Net Benefits
170

EP24AU18.237

Table VII-7 4 - Impacts to the Average Consumer of a MY 2030 Vehicle under C02 Program, 7%
Discount Rate

43325

for these other pollutants are
independent of those for CO2 emissions.
Following decades of successful
regulation of criteria pollutants and air
toxics, modern vehicles are already

vastly cleaner than in the past, and it is
expected that new vehicles will
continue to improve. For example, the
following chart shows trends in new
vehicles’ emission rates for volatile

organic compounds (VOCs) and
nitrogen oxides (NOX) — the two motor
vehicle criteria pollutants that
contribute to the formation of smog.

Because new vehicles are so much
cleaner than older models, it is expected
that under any of the alternatives
considered here for fuel economy and
CO2 standards, emissions of smog-

forming pollutants would continue to
decline nearly identically over the next
two decades. The following chart shows
estimated total fuel consumption, CO2
emissions, and smog-forming emissions

under the baseline and proposed
standards (CAFE standards — trends for
CO2 standards would be very similar),
using units that allow the three to be
shown together:

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

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While the differences in fuel use and
CO2 emissions trends under the baseline
and proposed standards are clear, the

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corresponding difference in smogforming emissions trends is too small to
discern. For these three measures, the

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following table shows percentage
differences between the amounts shown
above:

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43326

As indicated, for most of the coming
two decades, it is estimated that, even
as fuel consumption and CO2 emissions
would increase under the proposed
standards (compared to fuel
consumption and CO2 emissions under
the baseline standards), smog-forming
pollution would actually decrease.
During the two decades shown above, it
is estimated that the proposed standards
would increase aggregate fuel
consumption and CO2 emissions by
about four percent but would decrease
aggregate smog-forming pollution by
about 0.1% (because impacts of the
reduced travel and accelerated fleet
turnover would outweigh those of
increased refining and fuel distribution).
As the analysis affirms, while fuel
economy and CO2 emissions are two
sides (or, arguably, the same side) of the
same coin, fuel economy and CO2 are
only incidentally related to pollutants

1. Energy and Warming Impacts
Section V discusses, among other
things, the need of the Nation to
conserve energy, providing context for
the estimated impacts on national-scale
fuel consumption summarized below.
Corresponding to these changes in fuel
consumption, the agencies estimate that
today’s proposal will impact CO2
emissions. CO2 is one of several
greenhouse gases that absorb infrared
radiation, thereby trapping heat and
making the planet warmer. The most
important greenhouse gases directly

597 Impacts and U.S. emissions of GHGs are
discussed at greater length in EPA’s 2018 Inventory

of U.S. Greenhouse Gas Emissions and Sinks (EPA
430–R–18–003) (Apr. 12, 2018), available at https://

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such as smog, and any positive or
negative impacts of today’s notice on
these other air quality problems would
most likely be far too small to observe.
The remainder of this section
summarizes the impacts on fuel
consumption and emissions for both the
proposed CAFE standards and the
proposed CO2 standards.

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43327

emitted by human activities include
carbon dioxide (CO2), methane (CH4),
nitrous oxide (N2O), and several
fluorine-containing halogenated
substances. Although CO2, CH4, and
N2O occur naturally in the atmosphere,
human activities have changed their
atmospheric concentrations. From the
pre-industrial era (i.e., ending about
1750) to 2016, concentrations of these
greenhouse gases have increased
globally by 44, 163, and 22%,
respectively.597 The Draft
Environmental Impact Analysis (DEIS)
accompanying today’s notice discusses
potential impacts of greenhouse gases at
greater length, and also summaries
analysis quantifying some of these
impacts (e.g., average temperatures) for
each of the considered regulatory
alternatives.
(a) CAFE Standards

www.epa.gov/sites/production/files/2018-01/
documents/2018_complete_report.pdf.

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43328

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table VII-76- Cumulative Changes in Fuel Consumption and GHG Emissions for MY's 1977-2029
V n d er CAFEP rogram
Model Year Standards
Through
Upstream Emissions
C0 2 (million metric tons)
c~ (thousand metric tons)
N 20 (thousand metric tons)
Tailpipe Emissions
C0 2 (million metric tons)
c~ (thousand metric tons)
N 20 (thousand metric tons)
Total Emissions
C02 (million metric tons)
c~ (thousand metric tons)
N20 (thousand metric tons)
Fuel Consumption (billion
Gallons)

MY 2021

MY 2022

MY 2023

MY 2024

MY 2025

MY 2026

TOTAL

37.2
330
5.0

23.8
214
3.2

30.4
274
4.1

37.8
358
5.4

21.9
251
3.9

0.0
0.0
0.0

151
1,430
21.5

149
-2.5
-2.2

97
-1.9
-1.7

125
-2.4
-2.1

165
-2.9
-2.5

122
-2.4
-2.0

0.0
0.0
0.0

658
-12.0
-10.6

186
327
2.8
16.7

121
212
1.5
10.9

156
272
2.0
14.1

203
355
2.9
18.3

144
249
1.9
13.1

0.0
0.0
0.0
0.0

810
1,420
11.0
73.1

Table VII-77- Cumulative Changes in Criteria Pollutant Emissions for MY's 1977-2029 Under CAFE
p rogram
MY
2021

MY
2022

MY
2023

MY
2024

MY
2025

MY
2026

TOTAL

0.0
48.7
27.4
20.3
2.1

0.0
31.6
17.5
12.6
1.3

0.0
41.2
22.7
15.8
1.7

0.0
53.8
28.7
18.2
2.2

0.0
39.4
18.7
6.8
1.5

0.0
0.0
0.0
0.0
0.0

0.1
215
115
73.7
8.8

-1.0
-64.2
-56.4
-0.6
-2.2

-0.8
-52.2
-42.1
-0.4
-1.8

-1.0
-65.9
-53.1
-0.5
-2.3

-1.3
-84.8
-66.7
-0.6
-2.9

-1.1
-64.7
-52.2
-0.5
-2.4

0.0
0.0
0.0
0.0
0.0

-5.2
-332
-271
-2.5
-11.7

-1.0
-15.5
-29.0
19.7
-0.1

-0.8
-20.6
-24.5
12.2
-0.5

-1.0
-24.7
-30.4
15.3
-0.6

-1.3
-31.0
-38.1
17.7
-0.7

-1.1
-25.3
-33.5
6.4
-1.0

0.0
0.0
0.0
0.0
0.0

-5.2
-117
-156
71.3
-2.9

EP24AU18.242

(b) CO2 Standards

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Model Year Standards
Through
Upstream Emissions
CO (million metric tons)
VOC (thousand metric tons)
NOx (thousand metric tons)
so2 (thousand metric tons)
PM (thousand metric tons)
Tailpipe Emissions
CO (million metric tons)
VOC (thousand metric tons)
NOx (thousand metric tons)
so2 (thousand metric tons)
PM (thousand metric tons)
Total Emissions
CO (million metric tons)
VOC (thousand metric tons)
NOx (thousand metric tons)
so2 (thousand metric tons)
PM (thousand metric tons)

43329

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table VII-78 - Cumulative Changes in Fuel Consumption and GHG Emissions for MY's 1977-2029
Under C02 Proeram
Model Year Standards
MY
MY
MY
MY2024 MY 2025 MY 2026 TOTAL
Through
2021
2022
2023
Upstream Emissions
C02 (million metric tons)
c~ (thousand metric tons)
N20 (thousand metric tons)
Tailpipe Emissions
C02 (million metric tons)
c~ (thousand metric tons)
N20 (thousand metric tons)
Total Emissions
C02 (million metric tons)
c~ (thousand metric tons)
N20 (thousand metric tons)
Fuel Consumption (billion
Gallons)

45.2
398
6.0

45.4
403
6.0

26.4
234
3.5

24.5
268
4.1

17.6
234
3.7

0.0
0.0
0.0

159
1,540
23.3

180
-2.8
-2.5

182
-3.2
-3.0

106
-2.5
-2.2

128
-3.1
-2.6

117

-2.7
-2.3

0.0
0.0
0.0

713
-14.2
-12.6

225
396
3.5
20.3

228
400
3.1
20.5

133
232
1.3
12.0

153
265
1.5
13.8

134
231
1.4
12.3

0.0
0.0
0.0
0.0

873
1,520
10.7
78.9

2. How would the proposal impact
emissions of criteria and toxic
pollutants?
Although this proposal focuses on
standards for fuel economy and CO2, it
will also have an impact on criteria and
air toxic pollutant emissions, although
as discussed above, it is expected that

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0.0
59.1
33.2
24.6
2.5

0.0
59.8
33.1
24.0
2.5

0.0
34.9
19.5
14.2
1.5

0.0
41.3
19.9
8.5
1.6

0.0
37.3
16.2
2.6
1.3

0.0
0.0
0.0
0.0
0.0

0.1
232
122
73.9
9.4

-1.2
-74.6
-63.0
-0.6
-2.5

-1.3
-76.2
-65.3
-0.7
-2.9

-1.0
-62.1
-51.7
-0.5
-2.4

-1.4
-84.9
-69.8
-0.6
-3.1

-1.2
-74.5
-61.9
-0.5
-2.9

0.0
0.0
0.0
0.0
0.0

-6.1
-372
-312
-3.0
-13.8

-1.1
-15.5
-29.8
24.0
0.0

-1.2
-16.5
-32.2
23.3
-0.4

-1.0
-27.2
-32.2
13.7
-0.9

-1.4
-43.6
-49.9
7.9
-1.6

-1.2
-37.2
-45.7
2.1
-1.6

0.0
0.0
0.0
0.0
0.0

-6.0
-140
-190
71.0
-4.4

incremental impacts on criteria and air
toxic pollutant emissions would be too
small to observe under any of the
regulatory alternatives under
consideration. Nevertheless, the
following sections detail the criteria
pollutant and air toxic inventory
impacts of this proposal; the

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methodology used to calculate those
impacts; the health and environmental
effects associated with the criteria and
toxic air pollutants that are being
impacted by this proposal; the potential
impact of this proposal on
concentrations of criteria and air toxic
pollutants in the ambient air; and other

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Upstream Emissions
CO (million metric tons)
VOC (thousand metric tons)
NOx (thousand metric tons)
so2 (thousand metric tons)
PM (thousand metric tons)
Tailpipe Emissions
CO (million metric tons)
VOC (thousand metric tons)
NOx (thousand metric tons)
so2 (thousand metric tons)
PM (thousand metric tons)
Total Emissions
CO (million metric tons)
VOC (thousand metric tons)
NOx (thousand metric tons)
so2 (thousand metric tons)
PM (thousand metric tons)

EP24AU18.244

Table VII-79- Cumulative Changes in Criteria Pollutant Emissions for MY's 1977-2029 Under GHG
p roeram
Model Year Standards
MY
MY
MY
MY
MY
MY
TOTAL
Through
2021
2022
2023
2024
2025
2026

43330

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

unquantified health and environmental
effects.
Today’s analysis reflects the
combined result of several underlying
impacts, all discussed above. CAFE and
CO2 standards are estimated to impacts
new vehicle prices, fuel economy levels,
and CO2 emission rates. These changes
are estimated to impact the size and
composition of the new vehicle fleet
and to impact the retention of older
vehicles (i.e., vehicle survival and
scrappage) that tend to have higher
criteria and toxic pollutant emission
rates. Along with the rebound effect,
these lead to changes in the overall
amount of highway travel and the
distribution among different vehicles in
the on-road fleet. Vehicular emissions
depend on the overall amount of
highway travel and the distribution of
that travel among different vehicles, and
emissions from ‘‘upstream’’ processes
(e.g., petroleum refining, electricity
generation) depend on the total
consumption of different types of fuels
for light-duty vehicles.

sradovich on DSK3GMQ082PROD with PROPOSALS2

(a) Impacts
In addition to affecting fuel
consumption and emissions of
greenhouse gases, this rule would
influence ‘‘non-GHG’’ pollutants, i.e.,
‘‘criteria’’ air pollutants and their
precursors, and air toxics. The proposal
would affect emissions of carbon
monoxide (CO), fine particulate matter
(PM2.5), sulfur dioxide (SOX), volatile
organic compounds (VOC), nitrogen
oxides (NOX), benzene, 1,3-butadiene,
formaldehyde, acetaldehyde, and
acrolein. Consistent with the evaluation
conducted for the Environmental Impact
Statement accompanying this NPRM,
the agency analyzed criteria air
pollutant impacts in 2025 and 2035 (as
a representation of future program
impacts). Estimates of these non-GHG
emission impacts are shown by
pollutant in Table VII–80 through Table
VII–87 and are broken down by the two
drivers of these changes: (a)
‘‘downstream’’ emission changes,
reflecting the estimated effects of VMT
rebound (discussed in Chapter 8.7 of the
PRIA), changes in vehicle fleet age,
changes in vehicle emission standards,
and changes in fuel consumption; and
(b) ‘‘upstream’’ emission increases
because of increased refining and
distribution of motor vehicle gasoline
relative to the baseline. Program impacts
on criteria and toxics emissions are
discussed below, followed by individual
discussions of the methodology used to

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calculate each of these three sources of
impacts.598
As shown in Table VII–80, it is
estimated in 2025 the light duty vehicle
CAFE scenarios would result in
reductions of NOX, VOC, and CO, and
increases in PM2.5 and SOx.599 For NOx,
VOC, and CO, it is estimated net
reductions result from lower
downstream, or tailpipe emissions in
the scenarios evaluated. This is a result
of reduced VMT rebound as well as
fewer older vehicles in the scenarios as
compared to the baseline. Because the
scenarios result in greater fuel
consumption than the baseline,
however, upstream emissions associated
with fuel refining and distribution
increase for all pollutants in all
scenarios as compared to the baseline.
Tailpipe emissions reductions for NOx,
VOC, and CO more than compensate for
this increase in 2025. PM2.5 and SOx,
tailpipe emissions reductions are not
598 The agencies have employed the same
methodology in this rulemaking to estimate the
effect of each alternative on emissions of PM and
other criteria pollutants emissions as they have
previously applied in the other rulemakings under
the National Program. Briefly, emissions from
vehicle use are estimated for each calendar year of
the analysis period by applying emission rates per
vehicle-mile of travel to estimates of VMT for cars
and light trucks produced during each model year
making up the vehicle fleet. These emission rates
are derived from EPA’s Motor Vehicle Emissions
Simulator (MOVES); they reflect normal increases
in vehicles’ emission rates as they age and
accumulate mileage, as well as adopted and
pending vehicle emission standards and regulations
on fuel composition. ‘‘Upstream’’ emissions from
crude oil production, fuel refining, and fuel
distribution are estimated from the total energy
content of fuels produced and consumed (gasoline,
diesel, ethanol, and electricity), using separate
emission factors per unit of fuel energy for each
phase of fuel production and distribution derived
from Argonne National Laboratories’ Greenhouse
Gases and Regulated Emissions in Transportation
(GREET) fuel cycle model. This procedure accounts
for differences in domestic emissions associated
with refining fuel from imported and domesticallysupplied crude petroleum, as well as from
importing fuel that has been refined outside the
U.S. Economic damages caused by emissions from
vehicle use and from fuel production and
distribution are monetized using different per-ton
values, which reflect differences in the locations
where emissions occur and resulting variation in
population exposure to their potential adverse
health effects. However, we note that in some other
rules affecting tailpipe emissions of criteria
pollutants, EPA has employed more detailed
methods for estimating emissions associated with
different phases of fuel production and distribution,
and has also used more detailed estimates of their
per-ton health damage costs that reflect variation in
population exposure to emissions occurring during
different phases of fuel production and distribution.
The agencies will consider whether to employ these
more detailed procedures in their analysis
supporting the final rule.
599 While estimates for CY 2025 and 2035 are
shown here, estimates through 2050 are shown in
PRIA Chapter 5.

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great enough to compensate for
increased emissions from fuel refining
and distribution and therefore an overall
increase in total PM2.5 and SOx is seen
in 2025. Similar results can be seen in
Table VII–81 which shows results for
the CO2 target scenarios.
In 2035, Table VII–82 shows
decreases in total CO result from all
CAFE scenarios, while NOX, VOC, SO2,
and PM2.5 increase. Tailpipe CO
emissions reductions more than offset
increases in upstream CO emissions. For
NOX, VOC, SO2, and PM2.5 however,
upstream emissions increases are not
offset by tailpipe NOX, VOC, SO2, and
PM2.5 emissions reductions. Similar
results can be seen in the CO2 target
scenarios for 2035 shown in Table VII–
83, with the exception that NOX
emission decrease for scenarios 1–4 and
increase for scenarios 5–8. For all
criteria pollutants, the overall impact of
the proposed program would be small
compared to total U.S. inventories
across all sectors.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Pollutant

co

voc
NOx

so2

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PM2s

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Table VII-81- Criteria Emissions
Alt. 2
Alt. 1
tailpipe
-140.738
-133.545
upstream
2.528
2.430
total
-138.210
-131.115
tailpipe
-11.916
-11.283
upstream
9.242
8.879
total
-2.674
-2.404
tailpipe
-9.160
-8.650
upstream
5.104
4.905
total
-4.057
-3.745
tailpipe
-0.064
-0.061
upstream
3.504
3.370
total
3.440
3.309
tailpipe
-0.247
-0.234
upstream
0.384
0.369
total
0.137
0.135

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metric tons) under Fuel Economy Targets
Alt. 7
Alt.8
Alt. 4
Alt. 5
Alt. 6
-136.685 -102.784
-98.207
-71.136
-58.049
2.396
1.723
1.720
1.299
1.083
-134.289 -101.061
-96.487
-69.837
-56.966
-12.117
-9.260
-8.862
-6.460
-5.285
9.020
6.595
6.566
5.009
4.269
-3.097
-2.664
-2.295
-1.451
-1.016
-8.980
-6.708
-6.550
-4.810
-3.786
4.886
3.532
3.522
2.668
2.241
-4.094
-3.176
-3.027
-2.141
-1.546
-0.054
-0.037
-0.035
-0.025
-0.020
3.074
2.104
2.119
1.553
1.202
3.021
2.067
2.084
1.528
1.182
-0.235
-0.175
-0.167
-0.120
-0.098
0.370
0.268
0.267
0.203
0.171
0.135
0.093
0.100
0.082
0.073

in 2025 (1,000 metric tons) under C02 Targets
Alt. 3
Alt. 4
Alt. 5
Alt. 6
Alt. 7
-127.227
-99.668
-55.956
-60.866
-39.908
2.276
1.784
1.006
1.078
0.725
-124.951
-97.884
-54.949
-59.788
-39.183
-10.812
-8.599
-4.906
-5.447
-3.636
8.331
6.571
3.793
4.043
2.638
-2.481
-2.028
-1.114
-1.404
-0.999
-8.280
-6.440
-3.547
-3.923
-2.607
4.596
3.609
2.049
2.193
1.451
-3.684
-2.832
-1.497
-1.730
-1.157
-0.057
-0.043
-0.022
-0.023
-0.014
3.143
2.428
1.290
1.397
0.849
3.086
2.385
1.268
1.374
0.836
-0.223
-0.173
-0.096
-0.104
-0.068
0.346
0.272
0.155
0.166
0.115
0.123
0.099
0.059
0.062
0.047

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Alt.8
-27.145
0.501
-26.644
-2.492
1.960
-0.532
-1.724
1.030
-0.694
-0.009
0.573
0.564
-0.045
0.078
0.033

EP24AU18.245

Table VII-80 - Criteria Emissions in 2025 (1,000
Alt.1
Alt. 2
Alt. 3
t
tailpipe -174.789 -163.704
-155.704
co
upstream
3.087
2.901
2.771
total -171.703 -160.802
-152.933
tailpipe
-15.250
-14.308
-13.596
upstream
11.485
10.825
10.346
voc
total
-3.765
-3.482
-3.249
tailpipe
-11.506
-10.732
-10.220
NOx
upstream
6.275
5.900
5.636
total
-5.231
-4.832
-4.584
tailpipe
-0.073
-0.068
-0.064
upstream
4.078
3.806
3.630
so2
total
4.005
3.738
3.566
tailpipe
-0.303
-0.283
-0.270
upstream
0.474
0.446
0.426
PM2s
total
0.171
0.162
0.156

43331

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

As shown in Table VII–84 through
Table VII–87, it is estimated that the
proposed program would result in small
changes for air toxic emissions
compared to total U.S. inventories
across all sectors. In 2025, it is
estimated the scenarios evaluated would
reduce total acetaldehyde, acrolein,
benzene, butadiene, and formaldehyde,

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Jkt 244001

toxics as compared to the baseline. This
result is caused by greater VMT rebound
miles assumed in the augural scenario
and fewer rebound VMT in scenarios 1–
8, and fewer older vehicles in the
scenarios as compared to the baseline.
Similarly, in 2035, acetaldehyde,
benzene, butadiene, acrolein, and
formaldehyde would all be reduced as

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compared to the baseline. As is the case
with criteria emissions, upstream toxic
emissions generally increase in the
evaluated scenarios as compared to the
baseline because of the greater amount
of gasoline and diesel being refined and
distributed.

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43332

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

43333

Table VII-84- Toxic Emissions in 2025 1,000 metric tons ) under Fuel Economy Targets
Pollutant
Alt.l
Alt. 2
Alt. 3
Alt. 4
Alt. 5
Alt. 6
Alt. 7
Alt.8

Acrolein

Benzene

Butadiene

Formaldehyde

tailpipe

-0.117

-0.109

-0.104

-0.091

-0.067

-0.064

-0.046

-0.038

upstream

0.002

0.002

0.002

0.002

0.001

0.001

0.001

0.001

total

-0.114

-0.107

-0.102

-0.089

-0.066

-0.063

-0.046

-0.037

tailpipe

-0.006

-0.006

-0.005

-0.005

-0.004

-0.003

-0.002

-0.002

upstream

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

total

-0.006

-0.005

-0.005

-0.005

-0.003

-0.003

-0.002

-0.002

tailpipe

-0.457

-0.428

-0.407

-0.361

-0.274

-0.263

-0.192

-0.156

upstream

0.044

0.041

0.040

0.034

0.025

0.025

0.019

0.016

total

-0.413

-0.387

-0.368

-0.327

-0.249

-0.238

-0.172

-0.140

tailpipe

-0.054

-0.051

-0.048

-0.043

-0.032

-0.031

-0.022

-0.018

upstream

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

total

-0.054

-0.050

-0.048

-0.042

-0.032

-0.031

-0.022

-0.018

tailpipe

-0.092

-0.086

-0.082

-0.072

-0.055

-0.052

-0.038

-0.031

upstream

0.016

0.015

0.015

0.013

0.009

0.009

0.007

0.006

total

-0.076

-0.071

-0.068

-0.060

-0.045

-0.043

-0.031

-0.025

Table VII-85- Toxic Emissions in 2025 (1,000 metric tons) under C02 Tar~ets
Pollutant
Alt.l
Alt. 2
Alt. 3
Alt. 4
Alt. 5
Alt. 6
Alt. 7
Acetaldehyde

Acrolein

Benzene

Butadiene

sradovich on DSK3GMQ082PROD with PROPOSALS2

Formaldehyde

VerDate Sep<11>2014

Alt.8

tailpipe

-0.095

-0.090

-0.086

-0.067

-0.037

-0.040

-0.026

-0.018

upstream

0.002

0.002

0.002

0.001

0.001

0.001

0.000

0.000

total

-0.093

-0.088

-0.084

-0.065

-0.036

-0.039

-0.025

-0.017

tailpipe

-0.005

-0.005

-0.004

-0.004

-0.002

-0.002

-0.001

-0.001

upstream

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

total

-0.005

-0.005

-0.004

-0.003

-0.002

-0.002

-0.001

-0.001

tailpipe

-0.361

-0.341

-0.327

-0.258

-0.146

-0.161

-0.107

-0.073

upstream

0.035

0.034

0.032

0.025

0.015

0.015

0.010

0.008

total

-0.325

-0.308

-0.295

-0.233

-0.132

-0.146

-0.097

-0.066

tailpipe

-0.043

-0.041

-0.039

-0.031

-0.018

-0.019

-0.012

-0.009

upstream

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

total

-0.043

-0.041

-0.039

-0.031

-0.017

-0.019

-0.012

-0.009

tailpipe

-0.074

-0.070

-0.067

-0.052

-0.029

-0.032

-0.021

-0.015

upstream

0.013

0.013

0.012

0.009

0.005

0.006

0.004

0.003

total

-0.061

-0.057

-0.055

-0.043

-0.024

-0.026

-0.017

-0.012

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Acetaldehyde

43334

sradovich on DSK3GMQ082PROD with PROPOSALS2

(b) Methodology
For the downstream analysis,
emission factors in grams per mile for
VOC, CO, NOX, PM2.5, and air toxics by
vehicle model year and age were taken
from the current version of the EPA
‘‘Motor Vehicle Emission Simulator’’
(MOVES2014a) and multiplied in the
CAFE model by assumed VMT to
estimate mass VOC, CO, NOX, PM2.5,
and air toxics emissions. Additional

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emissions from light duty cars and
trucks attributable to the rebound effect
were also calculated using the CAFE
model. A more complete discussion of
the inputs, methodology, and results is
contained in PRIA Chapter 6. This
proposal also assumes implementation
of EPA’s Tier 3 emission standards.600

For a more detailed description of the
method used to estimate emissions,
please refer to pages 104–106 of the
CAFE model documentation.
For the purposes of this emission
analysis, it is assumed that all gasoline
in the timeframe of the analysis is
blended with 10% ethanol (E10). While
electric vehicles have zero tailpipe

600 See 79 FR 23414 (April 28, 2014). EPA’s Tier
3 emissions standards included standards for

vehicle emissions and the sulfur content of
gasoline.

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emissions, it is assumed that
manufacturers will plan for these
vehicles in their regulatory compliance
strategy for non-GHG emissions
standards, and will not over-comply
with those standards. Because the Tier
3 emissions standards are fleet-average
standards (for all pollutants except
formaldehyde and PM2.5), it is assumed
that if a manufacturer introduces EVs
into its fleet, that it would
correspondingly compensate through
changes to vehicles elsewhere in its
fleet, rather than meet an overall lower
fleet-average emissions level.
Consequently, no tailpipe pollutant
benefit (other than CO2, formaldehyde,
and PM2.5) is assumed. The analysis
does not estimate evaporative emissions
from light-duty vehicles. Other factors
which may impact downstream nonGHG emissions, but are not estimated in
this analysis, include the potential for
decreased criteria pollutant emissions
because of increased air conditioner
efficiency; reduced refueling emissions
because of less frequent refueling events
and reduced annual refueling volumes
resulting from the CO2 standards; and
increased hot soak evaporative
emissions because of the likely increase
in number of trips associated with VMT
rebound modeled in this proposal. In
all, these additional analyses would
likely result in small changes relative to
the national inventory.
To determine the impacts of increased
fuel production on upstream emissions,
the impact of increased gasoline
consumption by light-duty vehicles on
the extraction and transportation of
crude oil, refining of crude oil, and
distribution and storage of finished
gasoline was estimated. To assess the
resulting increases in domestic
emissions, the fraction of increased
gasoline consumption that would be
supplied by additional domestic
refining of gasoline, and the fraction of
that gasoline that would be refined from
domestic crude oil was estimated. Using
NEMS, it was estimated that 50% of
increased gasoline consumption would
be supplied by increased domestic
refining and that 90% of this additional
refining would use imported crude
petroleum. Emission factors for most
upstream emission sources are based on
the DOE Argonne National Laboratory’s
GREET 2017 model,601 but emission
factors developed by EPA were relied on
for the air toxics estimated in this
analysis: benzene, 1,3-butadiene,
acetaldehyde, acrolein, and
601 Greenhouse Gas, Regulated Emissions, and
Energy Use in Transportation model (GREET), U.S.
Department of Energy, Argonne National
Laboratory, https://greet.es.anl.gov/.

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formaldehyde. These emission factors
came from the MOVES 2014a model and
were incorporated into the CAFE model.
Emission factors for electricity
upstream emissions were also based on
GREET 2017. GREET allows the user to
either select a region of the country for
the electricity upstream emissions or to
use the U.S. average of electricity
emissions. The regional emission factors
reflect the specific mix of fuels used to
generate electricity in the selected
region. The U.S. mix provides an
average of electricity-related emissions
(in grams per million Btu) in the U.S. in
a given calendar year. The GREET 2017
U.S. mix emission factors were used for
the analysis. In order to capture
projected changes in upstream
emissions over time, upstream emission
factors for gasoline, diesel, and
electricity were taken from the GREET
2017 model in five year increments,
beginning in 1995 and ending in 2040.
For the downstream analysis of
emissions, there are a number of
uncertainties associated with the
method, such as: Emission factors are
based on samples of tested vehicles and
these samples may not represent average
emissions for the full in-use fleet; and
there is considerable uncertainty in
estimating total vehicle use (VMT). For
the upstream analysis of emissions,
there are uncertainties related to the
projection of emissions associated with
fossil fuel extraction, refining, and mode
split for transportation of fuels. In
addition, projections for electricityrelated upstream emissions are based on
assumptions about the fuels and
technologies used to generate electricity
which may not represent actual
conditions through 2050.
E. Health Effects of Non-GHG Pollutants
This section discusses health effects
associated with exposure to some of the
criteria and air toxic pollutants
impacted by the proposed vehicle
standards.
1. Particulate Matter
(a) Background
Particulate matter is a highly complex
mixture of solid particles and liquid
droplets distributed among numerous
atmospheric gases which interact with
solid and liquid phases. Particles range
in size from those smaller than 1
nanometer (10–9 meter) to more than
100 micrometers (mm, or 10–6 meter) in
diameter (for reference, a typical strand
of human hair is 70 mm in diameter and
a grain of salt is approximately 100 mm).
Atmospheric particles can be grouped
into several classes according to their
aerodynamic and physical sizes.

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Generally, the three broad classes of
particles include ultrafine particles
(UFPs, generally considered as
particulates with a diameter less than or
equal to 0.1 mm [typically based on
physical size, thermal diffusivity or
electrical mobility]), ‘‘fine’’ particles
(PM2.5; particles with a nominal mean
aerodynamic diameter less than or equal
to 2.5 mm), and ‘‘thoracic’’ particles
(PM10; particles with a nominal mean
aerodynamic diameter less than or equal
to 10 mm).602 Particles that fall within
the size range between PM2.5 and PM10,
are referred to as ‘‘thoracic coarse
particles’’ (PM10–2.5, particles with a
nominal mean aerodynamic diameter
less than or equal to 10 mm and greater
than 2.5 mm). EPA currently has
standards that regulate PM2.5 and
PM10.603
Particles span many sizes and shapes
and may consist of hundreds of different
chemicals. Particles are emitted directly
from sources and are also formed
through atmospheric chemical
reactions; the former are often referred
to as ‘‘primary’’ particles, and the latter
as ‘‘secondary’’ particles. Particle
concentration and composition varies
by time of year and location, and, in
addition to differences in source
emissions, is affected by several
weather-related factors, such as
temperature, clouds, humidity, and
wind. A further layer of complexity
comes from particles’ ability to shift
between solid/liquid and gaseous
phases, which is influenced by
concentration and meteorology,
especially temperature.
Fine particles are produced primarily
by combustion processes and by
transformations of gaseous emissions
(e.g., sulfur oxides (SOX), oxides of
nitrogen, and volatile organic
compounds (VOC)) in the atmosphere.
The chemical and physical properties of
PM2.5 may vary greatly with time,
region, meteorology, and source
category. Thus, PM2.5 may include a
complex mixture of different
components including sulfates, nitrates,
organic compounds, elemental carbon
and metal compounds. These particles
can remain in the atmosphere for days
602 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F. Figure 3–1.
603 Regulatory definitions of PM size fractions,
and information on reference and equivalent
methods for measuring PM in ambient air, are
provided in 40 CFR parts 50, 53, and 58. With
regard to national ambient air quality standards
(NAAQS) which provide protection against health
and welfare effects, the 24-hour PM10 standard
provides protection against effects associated with
short-term exposure to thoracic coarse particles
(i.e., PM10–2.5).

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to weeks and travel hundreds to
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(b) Health Effects of PM
Scientific studies show exposure to
ambient PM is associated with a broad
range of health effects. These health
effects are discussed in detail in the
2009 Integrated Science Assessment for
Particulate Matter (PM ISA), which was
used as the basis of the 2012 NAAQS.604
The PM ISA summarizes health effects
evidence for short- and long-term
exposures to PM2.5, PM10–2.5, and
ultrafine particles.605 The PM ISA
concludes that human exposures to
ambient PM2.5 are associated with a
number of adverse health effects and
characterizes the weight of evidence for
broad health categories (e.g.,
cardiovascular effects, respiratory
effects, etc.).606 The discussion below
highlights the PM ISA’s conclusions
pertaining to health effects associated
with both short- and long-term PM
exposures. Further discussion of health
effects associated with PM can also be
found in the rulemaking documents for
the most recent review of the PM
NAAQS completed in 2012.607 608
EPA has concluded that ‘‘a causal
relationship exists’’ between both longand short-term exposures to PM2.5 and
premature mortality and cardiovascular
effects and that ‘‘a causal relationship is
likely to exist’’ between long- and shortterm PM2.5 exposures and respiratory
effects. Further, there is evidence
‘‘suggestive of a causal relationship’’
between long-term PM2.5 exposures and
other health effects, including
developmental and reproductive effects
(e.g., low birth weight, infant mortality)
and carcinogenic, mutagenic, and
genotoxic effects (e.g., lung cancer
mortality).609
604 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F.
605 The ISA also evaluated evidence for
individual PM components but did not reach causal
determinations for components.
606 The causal framework draws upon the
assessment and integration of evidence from across
epidemiological, controlled human exposure, and
toxicological studies, and the related uncertainties
that ultimately influence our understanding of the
evidence. This framework employs a five-level
hierarchy that classifies the overall weight of
evidence and causality using the following
categorizations: Causal relationship, likely to be
causal relationship, suggestive of a causal
relationship, inadequate to infer a causal
relationship, and not likely to be a causal
relationship (U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F, Table 1–3).
607 78 FR 3103–3104 (Jan. 15, 2013).
608 77 FR 38906–38911 (June 29, 2012).
609 These causal inferences are based not only on
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As summarized in the final rule
promulgating the 2012 PM NAAQS, and
discussed extensively in the 2009 PM
ISA, the available scientific evidence
significantly strengthens the link
between long- and short-term exposure
to PM2.5 and mortality, while providing
indications that the magnitude of the
PM2.5-mortality association with longterm exposures may be larger than
previously estimated.610 611 The
strongest evidence comes from recent
studies investigating long-term exposure
to PM2.5 and cardiovascular-related
mortality. The evidence supporting a
causal relationship between long-term
PM2.5 exposure and mortality also
includes consideration of studies that
demonstrated an improvement in
community health following reductions
in ambient fine particles.
The 2009 PM ISA examined the
association between cardiovascular
effects and long-term PM2.5 exposures in
multi-city epidemiological studies
conducted in the U.S. and Europe.
These studies have provided new
evidence linking long-term exposure to
PM2.5 with an array of cardiovascular
effects such as heart attacks, congestive
heart failure, stroke, and mortality. This
evidence is coherent with
epidemiological studies of effects
associated with short-term exposure to
PM2.5 that have observed associations
with a continuum of effects ranging
from subtle changes in indicators of
cardiovascular health to serious clinical
events, such as increased
hospitalizations and emergency
department visits due to cardiovascular
disease and cardiovascular mortality.612
As detailed in the 2009 PM ISA,
extended analyses of seminal
epidemiological studies, as well as more
recent epidemiological studies
conducted in the U.S. and abroad,
provide strong evidence of respiratoryrelated morbidity effects associated with
long-term PM2.5 exposure. The strongest
evidence for respiratory-related effects
available in this review but also reflect
consideration of important progress that has been
made to advance our understanding of a number of
potential biologic modes of action or pathways for
PM-related cardiovascular and respiratory effects
(U.S. EPA. (2009). Integrated Science Assessment
for Particulate Matter (Final Report). U.S.
Environmental Protection Agency, Washington, DC,
EPA/600/R–08/139F, Chapter 5).
610 78 FR 3103–3104 (Jan. 15, 2013).
611 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F, Chapter 6
(Section 6.5) and Chapter 7 (Section 7.6).
612 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F, Chapter 2
(Section 2.3.1 and 2.3.2) and Chapter 6.

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is from studies that evaluated
decrements in lung function growth (in
children), increased respiratory
symptoms, and asthma development.
The strongest evidence from short-term
PM2.5 exposure studies has been
observed for increased respiratoryrelated emergency department visits and
hospital admissions for chronic
obstructive pulmonary disease (COPD)
and respiratory infections.613
The body of scientific evidence
detailed in the 2009 PM ISA is still
limited with respect to associations
between long-term PM2.5 exposures and
developmental and reproductive effects
as well as cancer, mutagenic, and
genotoxic effects. The strongest
evidence for an association between
PM2.5 and developmental and
reproductive effects comes from
epidemiological studies of low birth
weight and infant mortality, especially
due to respiratory causes during the
post-neonatal period (i.e., 1 month to 12
months of age).614 With regard to cancer
effects, ‘‘[m]ultiple epidemiologic
studies have shown a consistent
positive association between PM2.5 and
lung cancer mortality, but studies have
generally not reported associations
between PM2.5 and lung cancer
incidence.’’ 615
In addition to evaluating the health
effects attributed to short- and long-term
exposure to PM2.5, the 2009 PM ISA also
evaluated whether specific components
or sources of PM2.5 are more strongly
associated with specific health effects.
The 2009 PM ISA concluded that ‘‘many
[components] of PM can be linked with
differing health effects, and the
evidence is not yet sufficient to allow
differentiation of those [components] or
sources that are more closely related to
specific health outcomes.’’ 616
For PM10–2.5, the 2009 PM ISA
concluded that available evidence was
‘‘suggestive of a causal relationship’’
between short-term exposures to
PM10–2.5 and cardiovascular effects (e.g.,
hospital admissions and Emergency
Department (ED) visits, changes in
613 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F, Chapter 2
(Section 2.3.1 and 2.3.2) and Chapter 6.
614 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F, Chapter 2
(Section 2.3.1 and 2.3.2) and Chapter 7.
615 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F. pg 2–13.
616 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F. pg 2–26.

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cardiovascular function), respiratory
effects (e.g., ED visits and hospital
admissions, increase in markers of
pulmonary inflammation), and
premature mortality. The scientific
evidence was ‘‘inadequate to infer a
causal relationship’’ between long-term
exposure to PM10–2.5 and various health
effects.617 618 619
For UFPs, the 2009 PM ISA
concluded that the evidence was
‘‘suggestive of a causal relationship’’
between short-term exposures and
cardiovascular effects, including
changes in heart rhythm and vasomotor
function (the ability of blood vessels to
expand and contract). It also concluded
that there was evidence ‘‘suggestive of a
causal relationship’’ between short-term
exposure to UFPs and respiratory
effects, including lung function and
pulmonary inflammation, with limited
and inconsistent evidence for increases
in ED visits and hospital admissions.
Scientific evidence was ‘‘inadequate to
infer a causal relationship’’ between
short-term exposure to UFPs and
additional health effects including
premature mortality as well as long-term
exposure to UFPs and all health
outcomes evaluated.620 621
The 2009 PM ISA conducted an
evaluation of specific groups within the
general population potentially at
increased risk for experiencing adverse
health effects related to PM
exposures.622 623 624 625 The evidence
detailed in the 2009 PM ISA expands
our understanding of previously
identified at-risk populations and
lifestages (i.e., children, older adults,
and individuals with pre-existing heart
and lung disease) and supports the
identification of additional at-risk
populations (e.g., persons with lower
socioeconomic status, genetic
differences). Additionally, there is
emerging, though still limited, evidence
617 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F. Section 2.3.4
and Table 2–6.
618 78 FR 3167–3168 (Jan. 15, 2013).
619 77 FR 38947–38951 (June 29, 2012).
620 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F. Section 2.3.5
and Table 2–6.
621 78 FR 3121 (Jan. 15, 2013).
622 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F. Chapter 8
and Chapter 2.
623 77 FR 38890 (June 29, 2012).
624 78 FR 3104 (Jan. 15, 2013).
625 U.S. EPA. (2011). Policy Assessment for the
Review of the PM NAAQS. U.S. Environmental
Protection Agency, Washington, DC, EPA/452/R–
11–003. Section 2.2.1.

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for additional potentially at-risk
populations and lifestages, such as those
with diabetes, people who are obese,
pregnant women, and the developing
fetus.626
2. Ozone
(a) Background
Ground-level ozone pollution is
typically formed through reactions
involving VOC and NOX in the lower
atmosphere in the presence of sunlight.
These pollutants, often referred to as
ozone precursors, are emitted by many
types of sources, such as highway and
nonroad motor vehicles and engines,
power plants, chemical plants,
refineries, makers of consumer and
commercial products, industrial
facilities, and smaller area sources.
The science of ozone formation,
transport, and accumulation is complex.
Ground-level ozone is produced and
destroyed in a cyclical set of chemical
reactions, many of which are sensitive
to temperature and sunlight. When
ambient temperatures and sunlight
levels remain high for several days and
the air is relatively stagnant, ozone and
its precursors can build up and result in
more ozone than typically occurs on a
single high-temperature day. Ozone and
its precursors can be transported
hundreds of miles downwind from
precursor emissions, resulting in
elevated ozone levels even in areas with
low local VOC or NOX emissions.
(b) Health Effects of Ozone
This section provides a summary of
the health effects associated with
exposure to ambient concentrations of
ozone.627 The information in this
section is based on the information and
conclusions in the February 2013
Integrated Science Assessment for
Ozone (Ozone ISA), which formed the
basis for EPA’s revision to the primary
and secondary standards in 2015.628
The Ozone ISA concludes that human
exposures to ambient concentrations of
ozone are associated with a number of
adverse health effects and characterizes
626 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F. Chapter 8
and Chapter 2 (Section 2.4.1).
627 Human exposure to ozone varies over time
due to changes in ambient ozone concentration and
because people move between locations which have
notable different ozone concentrations. Also, the
amount of ozone delivered to the lung is not only
influenced by the ambient concentrations but also
by the individuals breathing route and rate.
628 U.S. EPA. Integrated Science Assessment of
Ozone and Related Photochemical Oxidants (Final
Report). U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–10/076F, 2013. The
ISA is available at http://cfpub.epa.gov/ncea/isa/
recordisplay.cfm?deid=247492#Download.

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the weight of evidence for these health
effects.629 The discussion below
highlights the Ozone ISA’s conclusions
pertaining to health effects associated
with both short-term and long-term
periods of exposure to ozone.
For short-term exposure to ozone, the
Ozone ISA concludes that respiratory
effects, including lung function
decrements, pulmonary inflammation,
exacerbation of asthma, respiratoryrelated hospital admissions, and
mortality, are causally associated with
ozone exposure. It also concludes that
cardiovascular effects, including
decreased cardiac function and
increased vascular disease, and total
mortality are likely to be causally
associated with short-term exposure to
ozone, and that evidence is suggestive of
a causal relationship between central
nervous system effects and short-term
exposure to ozone.
For long-term exposure to ozone, the
Ozone ISA concludes that respiratory
effects, including new onset asthma,
pulmonary inflammation and injury, are
likely to be causally related with ozone
exposure. The Ozone ISA characterizes
the evidence as suggestive of a causal
relationship for associations between
long-term ozone exposure and
cardiovascular effects, reproductive and
developmental effects, central nervous
system effects and total mortality. The
evidence is inadequate to infer a causal
relationship between chronic ozone
exposure and increased risk of lung
cancer.
Finally, inter-individual variation in
human responses to ozone exposure can
result in some groups being at increased
risk for detrimental effects in response
to exposure. In addition, some groups
are at increased risk of exposure due to
their activities, such as outdoor workers
or children. The Ozone ISA identified
several groups that are at increased risk
for ozone-related health effects. These
groups are people with asthma, children
and older adults, individuals with
reduced intake of certain nutrients (i.e.,
Vitamins C and E), outdoor workers,
and individuals having certain genetic
variants related to oxidative metabolism
or inflammation. Ozone exposure
during childhood can have lasting
effects through adulthood. Such effects
include altered function of the
629 The ISA evaluates evidence and draws
conclusions on the causal nature of relationship
between relevant pollutant exposures and health
effects, assigning one of five ‘‘weight of evidence’’
determinations: Causal relationship, likely to be a
causal relationship, suggestive of, but not sufficient
to infer, a causal relationship, inadequate to infer
a causal relationship, and not likely to be a causal
relationship. For more information on these levels
of evidence, please refer to Table II in the Preamble
of the ISA.

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respiratory and immune systems.
Children absorb higher doses
(normalized to lung surface area) of
ambient ozone, compared to adults, due
to their increased time spent outdoors,
higher ventilation rates relative to body
size, and a tendency to breathe a greater
fraction of air through the mouth.
Children also have a higher asthma
prevalence compared to adults.
3. Nitrogen Oxides

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(a) Background
Oxides of nitrogen (NOX) refers to
nitric oxide and nitrogen dioxide (NO2).
For the NOX NAAQS, NO2 is the
indicator. Most NO2 is formed in the air
through the oxidation of nitric oxide
(NO) emitted when fuel is burned at a
high temperature. NOX is also a major
contributor to secondary PM2.5
formation. NOX and VOC are the two
major precursors of ozone.
(b) Health Effects of Nitrogen Oxides
The most recent review of the health
effects of oxides of nitrogen completed
by EPA can be found in the 2016
Integrated Science Assessment for
Oxides of Nitrogen—Health Criteria
(Oxides of Nitrogen ISA).630 The
primary source of NO2 is motor vehicle
emissions, and ambient NO2
concentrations tend to be highly
correlated with other traffic-related
pollutants. Thus, a key issue in
characterizing the causality of NO2health effect relationships was
evaluating the extent to which studies
supported an effect of NO2 that is
independent of other traffic-related
pollutants. EPA concluded that the
findings for asthma exacerbation
integrated from epidemiologic and
controlled human exposure studies
provided evidence that is sufficient to
infer a causal relationship between
respiratory effects and short-term NO2
exposure. The strongest evidence
supporting an independent effect of NO2
exposure comes from controlled human
exposure studies demonstrating
increased airway responsiveness in
individuals with asthma following
ambient-relevant NO2 exposures. The
coherence of this evidence with
epidemiologic findings for asthma
hospital admissions and ED visits as
well as lung function decrements and
increased pulmonary inflammation in
children with asthma describe a
plausible pathway by which NO2
exposure can cause an asthma
exacerbation. The 2016 ISA for Oxides
630 U.S. EPA. Integrated Science Assessment for
Oxides of Nitrogen—Health Criteria (2016 Final
Report). U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–15/068, 2016.

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of Nitrogen also concluded that there is
likely to be a causal relationship
between long-term NO2 exposure and
respiratory effects. This conclusion is
based on new epidemiologic evidence
for associations of NO2 with asthma
development in children combined with
biological plausibility from
experimental studies.
In evaluating a broader range of health
effects, the 2016 ISA for Oxides of
Nitrogen concluded evidence is
‘‘suggestive of, but not sufficient to
infer, a causal relationship’’ between
short-term NO2 exposure and
cardiovascular effects and mortality and
between long-term NO2 exposure and
cardiovascular effects and diabetes,
birth outcomes, and cancer. In addition,
the scientific evidence is inadequate
(insufficient consistency of
epidemiologic and toxicological
evidence) to infer a causal relationship
for long-term NO2 exposure with
fertility, reproduction, and pregnancy,
as well as with postnatal development.
A key uncertainty in understanding the
relationship between these nonrespiratory health effects and short- or
long-term exposure to NO2 is
copollutant confounding, particularly
by other roadway pollutants. The
available evidence for non-respiratory
health effects does not adequately
address whether NO2 has an
independent effect or whether it
primarily represents effects related to
other or a mixture of traffic-related
pollutants.
The 2016 ISA for Oxides of Nitrogen
concluded that people with asthma,
children, and older adults are at
increased risk for NO2-related health
effects. In these groups and lifestages,
NO2 is consistently related to larger
effects on outcomes related to asthma
exacerbation, for which there is
confidence in the relationship with NO2
exposure.
4. Sulfur Oxides

Science Assessment for Sulfur Oxides—
Health Criteria (SOX ISA).631 Short-term
peaks (5–10 minutes) of SO2 have long
been known to cause adverse respiratory
health effects, particularly among
individuals with asthma. In addition to
those with asthma (both children and
adults), potentially at-risk lifestages
include all children and the elderly.
During periods of elevated ventilation,
asthmatics may experience symptomatic
bronchoconstriction within minutes of
exposure. Following an extensive
evaluation of health evidence from
epidemiologic and laboratory studies,
EPA concluded that there is a causal
relationship between respiratory health
effects and short-term exposure to SO2.
Separately, based on an evaluation of
the epidemiologic evidence of
associations between short-term
exposure to SO2 and mortality, EPA
concluded that the overall evidence is
suggestive of a causal relationship
between short-term exposure to SO2 and
mortality.
5. Carbon Monoxide
(a) Background
Carbon monoxide is a colorless,
odorless gas emitted from combustion
processes. Nationally, particularly in
urban areas, the majority of CO
emissions to ambient air come from
mobile sources.632
(b) Health Effects of Carbon Monoxide
Information on the health effects of
CO can be found in the January 2010
Integrated Science Assessment for
Carbon Monoxide (CO ISA) associated
with the 2010 evaluation of the
NAAQS.633 The CO ISA presents
conclusions regarding the presence of
causal relationships between CO
exposure and categories of adverse
health effects. This section provides a
summary of the health effects associated
with exposure to ambient
concentrations of CO, along with the
ISA conclusions.634

(a) Background
Sulfur dioxide (SO2), a member of the
sulfur oxide (SOX) family of gases, is
formed from burning fuels containing
sulfur (e.g., coal or oil derived),
extracting gasoline from oil, or
extracting metals from ore. SO2 and its
gas phase oxidation products can
dissolve in water droplets and further
oxidize to form sulfuric acid which
reacts with ammonia to form sulfates,
which are important components of
ambient PM.
(b) Health Effects of SO2
Information on the health effects of
SO2 can be found in the 2008 Integrated

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631 U.S. EPA. (2008). Integrated Science
Assessment (ISA) for Sulfur Oxides—Health
Criteria (Final Report). EPA/600/R–08/047F.
Washington, DC: U.S. Environmental Protection
Agency.
632 U.S. EPA, (2010). Integrated Science
Assessment for Carbon Monoxide (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–09/019F, 2010.
Available at http://cfpub.epa.gov/ncea/cfm/
recordisplay.cfm?deid=218686. See Section 2.1.
633 U.S. EPA, (2010). Integrated Science
Assessment for Carbon Monoxide (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–09/019F, 2010.
Available at http://cfpub.epa.gov/ncea/cfm/
recordisplay.cfm?deid=218686.
634 Personal exposure includes contributions from
many sources and in many different environments.
Total personal exposure to CO includes both

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Controlled human exposure studies of
subjects with coronary artery disease
show a decrease in the time to onset of
exercise-induced angina (chest pain)
and electrocardiogram changes
following CO exposure. In addition,
epidemiologic studies observed
associations between short-term CO
exposure and cardiovascular morbidity,
particularly increased emergency room
visits and hospital admissions for
coronary heart disease (including
ischemic heart disease, myocardial
infarction, and angina). Some
epidemiologic evidence is also available
for increased hospital admissions and
emergency room visits for congestive
heart failure and cardiovascular disease
as a whole. The CO ISA concludes that
a causal relationship is likely to exist
between short-term exposures to CO and
cardiovascular morbidity. It also
concludes that available data are
inadequate to conclude that a causal
relationship exists between long-term
exposures to CO and cardiovascular
morbidity.
Animal studies show various
neurological effects with in-utero CO
exposure. Controlled human exposure
studies report central nervous system
and behavioral effects following lowlevel CO exposures, although the
findings have not been consistent across
all studies. The CO ISA concludes the
evidence is suggestive of a causal
relationship with both short- and longterm exposure to CO and central
nervous system effects.
A number of studies cited in the CO
ISA have evaluated the role of CO
exposure in birth outcomes such as
preterm birth or cardiac birth defects.
There is limited epidemiologic evidence
of a CO-induced effect on preterm births
and birth defects, with weak evidence
for a decrease in birth weight. Animal
toxicological studies have found
perinatal CO exposure to affect birth
weight, as well as other developmental
outcomes. The CO ISA concludes the
evidence is suggestive of a causal
relationship between long-term
exposures to CO and developmental
effects and birth outcomes.
Epidemiologic studies provide
evidence of associations between shortterm CO concentrations and respiratory
morbidity such as changes in
pulmonary function, respiratory
symptoms, and hospital admissions. A
limited number of epidemiologic
studies considered copollutants such as
ozone, SO2, and PM in two-pollutant
models and found that CO risk estimates
ambient and nonambient components; both
components may contribute to adverse health
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were generally robust, although this
limited evidence makes it difficult to
disentangle effects attributed to CO
itself from those of the larger complex
air pollution mixture. Controlled human
exposure studies have not extensively
evaluated the effect of CO on respiratory
morbidity. Animal studies at levels of
50–100 ppm CO show preliminary
evidence of altered pulmonary vascular
remodeling and oxidative injury. The
CO ISA concludes that the evidence is
suggestive of a causal relationship
between short-term CO exposure and
respiratory morbidity, and inadequate to
conclude that a causal relationship
exists between long-term exposure and
respiratory morbidity.
Finally, the CO ISA concludes that
the epidemiologic evidence is
suggestive of a causal relationship
between short-term concentrations of
CO and mortality. Epidemiologic
evidence suggests an association exists
between short-term exposure to CO and
mortality, but limited evidence is
available to evaluate cause-specific
mortality outcomes associated with CO
exposure. In addition, the attenuation of
CO risk estimates which was often
observed in copollutant models
contributes to the uncertainty as to
whether CO is acting alone or as an
indicator for other combustion-related
pollutants. The CO ISA also concludes
that there is not likely to be a causal
relationship between relevant long-term
exposures to CO and mortality.
6. Diesel Exhaust
(a) Background
Diesel exhaust consists of a complex
mixture composed of particulate matter,
carbon dioxide, oxygen, nitrogen, water
vapor, carbon monoxide, nitrogen
compounds, sulfur compounds and
numerous low-molecular-weight
hydrocarbons. A number of these
gaseous hydrocarbon components are
individually known to be toxic,
including aldehydes, benzene and 1,3butadiene. The diesel particulate matter
present in diesel exhaust consists
mostly of fine particles (<2.5 mm), of
which a significant fraction is ultrafine
particles (< 0.1 mm). These particles
have a large surface area which makes
them an excellent medium for adsorbing
organics, and their small size makes
them highly respirable. Many of the
organic compounds present in the gases
and on the particles, such as polycyclic
organic matter, are individually known
to have mutagenic and carcinogenic
properties.
Diesel exhaust varies significantly in
chemical composition and particle sizes
between different engine types (heavy-

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duty, light-duty), engine operating
conditions (idle, acceleration,
deceleration), and fuel formulations
(high/low sulfur fuel). Also, there are
emissions differences between on-road
and nonroad engines because the
nonroad engines are generally of older
technology. After being emitted in the
engine exhaust, diesel exhaust
undergoes dilution as well as chemical
and physical changes in the atmosphere.
The lifetime for some of the compounds
present in diesel exhaust ranges from
hours to days.
(b) Health Effects of Diesel Exhaust
In EPA’s 2002 Diesel Health
Assessment Document (Diesel HAD),
exposure to diesel exhaust was
classified as likely to be carcinogenic to
humans by inhalation from
environmental exposures, in accordance
with the revised draft 1996/1999 EPA
cancer guidelines.635 636 A number of
other agencies (National Institute for
Occupational Safety and Health, the
International Agency for Research on
Cancer, the World Health Organization,
California EPA, and the U.S.
Department of Health and Human
Services) made similar hazard
classifications prior to 2002. EPA also
concluded in the 2002 Diesel HAD that
it was not possible to calculate a cancer
unit risk for diesel exhaust due to
limitations in the exposure data for the
occupational groups or the absence of a
dose-response relationship.
In the absence of a cancer unit risk,
the Diesel HAD sought to provide
additional insight into the significance
of the diesel exhaust cancer hazard by
estimating possible ranges of risk that
might be present in the population. An
exploratory analysis was used to
characterize a range of possible lung
cancer risk. The outcome was that
environmental risks of cancer from longterm diesel exhaust exposures could
plausibly range from as low as 10–5 to
as high as 10–3. Because of
uncertainties, the analysis
acknowledged that the risks could be
lower than 10–5, and a zero risk from
diesel exhaust exposure could not be
ruled out.
Non-cancer health effects of acute and
chronic exposure to diesel exhaust
emissions are also of concern to EPA.
EPA derived a diesel exhaust reference
635 U.S. EPA. (March 2005). Guidelines for
Carcinogen Risk Assessment EPA/630/P–03/001F,
https://www.epa.gov/risk/guidelines-carcinogenrisk-assessment (Last accessed July 2018).
636 U.S. EPA (2002). Health Assessment
Document for Diesel Engine Exhaust. EPA/600/8–
90/057F Office of Research and Development,
Washington, DC. Retrieved on March 17, 2009 from
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?
deid=29060 (last accessed July 2018). pp. 1–1 1–2.

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concentration (RfC) from consideration
of four well-conducted chronic rat
inhalation studies showing adverse
pulmonary effects. The RfC is 5 mg/m3
for diesel exhaust measured as diesel
particulate matter. This RfC does not
consider allergenic effects such as those
associated with asthma or immunologic
or the potential for cardiac effects. There
was emerging evidence in 2002,
discussed in the Diesel HAD, that
exposure to diesel exhaust can
exacerbate these effects, but the
exposure-response data were lacking at
that time to derive an RfC based on
these then-emerging considerations. The
EPA Diesel HAD states, ‘‘With [diesel
particulate matter] being a ubiquitous
component of ambient PM, there is an
uncertainty about the adequacy of the
existing [diesel exhaust] noncancer
database to identify all of the pertinent
[diesel exhaust]-caused noncancer
health hazards.’’ The Diesel HAD also
notes ‘‘that acute exposure to [diesel
exhaust] has been associated with
irritation of the eye, nose, and throat,
respiratory symptoms (cough and
phlegm), and neurophysiological
symptoms such as headache,
lightheadedness, nausea, vomiting, and
numbness or tingling of the
extremities.’’ The Diesel HAD noted that
the cancer and noncancer hazard
conclusions applied to the general use
of diesel engines then on the market and
as cleaner engines replace a substantial
number of existing ones, the
applicability of the conclusions would
need to be reevaluated.
It is important to note that the Diesel
HAD also briefly summarizes health
effects associated with ambient PM and
discusses EPA’s then-annual PM2.5
NAAQS of 15 mg/m3. In 2012, EPA
revised the annual PM2.5 NAAQS to 12
mg/m3. There is a large and extensive
body of human data showing a wide
spectrum of adverse health effects
associated with exposure to ambient
PM, of which diesel exhaust is an
important component. The PM2.5
NAAQS is designed to provide
protection from the noncancer health
effects and premature mortality
attributed to exposure to PM2.5. The
contribution of diesel PM to total
ambient PM varies in different regions
of the country and also, within a region,
from one area to another. The
contribution can be high in nearroadway environments, for example, or
in other locations where diesel engine
use is concentrated.
Since 2002, several new studies have
been published which continue to
report increased lung cancer risk with
occupational exposure to diesel exhaust
from older engines. Of particular note

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since 2011 are three new epidemiology
studies which have examined lung
cancer in occupational populations, for
example, truck drivers, underground
nonmetal miners and other diesel
motor-related occupations. These
studies reported increased risk of lung
cancer with exposure to diesel exhaust
with evidence of positive exposureresponse relationships to varying
degrees.637 638 639 These newer studies
(along with others that have appeared in
the scientific literature) add to the
evidence EPA evaluated in the 2002
Diesel HAD and further reinforces the
concern that diesel exhaust exposure
likely poses a lung cancer hazard. The
findings from these newer studies do
not necessarily apply to newer
technology diesel engines b the newer
engines have large reductions in the
emission constituents compared to older
technology diesel engines.
In light of the growing body of
scientific literature evaluating the health
effects of exposure to diesel exhaust, in
June 2012 the World Health
Organization’s International Agency for
Research on Cancer (IARC), a
recognized international authority on
the carcinogenic potential of chemicals
and other agents, evaluated the full
range of cancer-related health effects
data for diesel engine exhaust. IARC
concluded that diesel exhaust should be
regarded as ‘‘carcinogenic to
humans.’’ 640 This designation was an
update from its 1988 evaluation that
considered the evidence to be indicative
of a ‘‘probable human carcinogen.’’
7. Air Toxics
(a) Background
Light-duty vehicle emissions
contribute to ambient levels of air toxics
that are known or suspected human or
animal carcinogens, or that have
noncancer health effects. The
population experiences an elevated risk
of cancer and other noncancer health
effects from exposure to the class of
637 Garshick, E., Laden, F., Hart, J.E., Davis, M.E.,
Eisen, E.A., & Smith T.J. 2012. Lung cancer and
elemental carbon exposure in trucking industry
workers. Environmental Health Perspectives.
120(9): 1301–1306.
638 Silverman, D.T., Samanic, C.M., Lubin, J.H.,
Blair, A.E., Stewart, P.A., Vermeulen, R., & Attfield,
M.D. (2012). The diesel exhaust in miners study: a
nested case-control study of lung cancer and diesel
exhaust. Journal of the National Cancer Institute.
639 Olsson, A.C., et al. ‘‘Exposure to diesel motor
exhaust and lung cancer risk in a pooled analysis
from case-control studies in Europe and Canada.’’
American Journal of Respiratory and Critical Care
Medicine 183(7). (2011): 941–948.
640 IARC [International Agency for Research on
Cancer]. (2013). Diesel and gasoline engine exhausts
and some nitroarenes. IARC Monographs Volume
105. [Online at http://monographs.iarc.fr/ENG/
Monographs/vol105/index.php].

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pollutants known collectively as ‘‘air
toxics.’’ 641 These compounds include,
but are not limited to, benzene, 1,3butadiene, formaldehyde, acetaldehyde,
acrolein, polycyclic organic matter, and
naphthalene. These compounds were
identified as national or regional risk
drivers or contributors in the 2011
National-scale Air Toxics Assessment
and have significant inventory
contributions from mobile sources.642
(b) Benzene
EPA’s Integrated Risk Information
System (IRIS) database lists benzene as
a known human carcinogen (causing
leukemia) by all routes of exposure and
concludes that exposure is associated
with additional health effects, including
genetic changes in both humans and
animals and increased proliferation of
bone marrow cells in mice.643 644 645
EPA states in its IRIS database that data
indicate a causal relationship between
benzene exposure and acute
lymphocytic leukemia and suggest a
relationship between benzene exposure
and chronic non-lymphocytic leukemia
and chronic lymphocytic leukemia.
EPA’s IRIS documentation for benzene
also lists a range of 2.2 x 10–6 to 7.8 x
10–6 per mg/m3 as the unit risk estimate
(URE) for benzene.646 647 The
International Agency for Research on
Cancer (IARC) has determined that
benzene is a human carcinogen and the
U.S. Department of Health and Human
Services (DHHS) has characterized
benzene as a known human
carcinogen.648 649
641 U.S. EPA. (2015) Summary of Results for the
2011 National-Scale Assessment. http://
www3.epa.gov/sites/production/files/2015-12/
documents/2011-nata-summary-results.pdf.
642 U.S. EPA (2015) 2011 National Air Toxics
Assessment. http://www3.epa.gov/national-airtoxics-assessment/2011-national-air-toxicsassessment.
643 U.S. EPA. (2000). Integrated Risk Information
System File for Benzene. This material is available
electronically at: https://www.epa.gov/iris (Last
accessed July 2018)
644 International Agency for Research on Cancer,
IARC monographs on the evaluation of carcinogenic
risk of chemicals to humans, Volume 29, some
industrial chemicals and dyestuffs, International
Agency for Research on Cancer, World Health
Organization, Lyon, France 1982.
645 Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.;
Henry, V.A. (1992). Synergistic action of the
benzene metabolite hydroquinone on myelopoietic
stimulating activity of granulocyte/macrophage
colony-stimulating factor in vitro, Proc. Natl. Acad.
Sci. 89:3691–3695.
646 A unit risk estimate is defined as the increase
in the lifetime risk of an individual who is exposed
for a lifetime to 1 mg/m3 benzene in air.
647 U.S. EPA. (2000). Integrated Risk Information
System File for Benzene. This material is available
electronically at: http://www3.epa.gov/iris/subst/
0276.htm.
648 International Agency for Research on Cancer
(IARC). (1987). Monographs on the evaluation of
carcinogenic risk of chemicals to humans, Volume

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A number of adverse noncancer
health effects including blood disorders,
such as pre-leukemia and aplastic
anemia, have also been associated with
long-term exposure to benzene. The
most sensitive noncancer effect
observed in humans, based on current
data, is the depression of the absolute
lymphocyte count in blood. EPA’s
inhalation reference concentration (RfC)
for benzene is 30 mg/m3. The RfC is
based on suppressed absolute
lymphocyte counts seen in humans
under occupational exposure
conditions. In addition, recent work,
including studies sponsored by the
Health Effects Institute, provides
evidence that biochemical responses are
occurring at lower levels of benzene
exposure than previously known.650 651
652 653 EPA’s IRIS program has not yet
evaluated these new data. EPA does not
currently have an acute reference
concentration for benzene. The Agency
for Toxic Substances and Disease
Registry (ATSDR) Minimal Risk Level
(MRL) for acute exposure to benzene is
29 mg/m3 for 1–14 days exposure.
(c) 1,3-Butadiene

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EPA has characterized 1,3-butadiene
as carcinogenic to humans by
inhalation.654 655 The IARC has
determined that 1,3-butadiene is a
human carcinogen and the U.S. DHHS
has characterized 1,3-butadiene as a
29, Supplement 7, Some industrial chemicals and
dyestuffs, World Health Organization, Lyon, France.
649 NTP. (2014). 13th Report on Carcinogens.
Research Triangle Park, NC: U.S. Department of
Health and Human Services, Public Health Service,
National Toxicology Program.
650 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.;
Cohen, B.; Melikian, A.; Eastmond, D.; Rappaport,
S.; Li, H.; Rupa, D.; Suramaya, R.; Songnian, W.;
Huifant, Y.; Meng, M.; Winnik, M.; Kwok, E.; Li, Y.;
Mu, R.; Xu, B.; Zhang, X.; Li, K. (2003). HEI Report
115, Validation & Evaluation of Biomarkers in
Workers Exposed to Benzene in China.
651 Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B.
Cohen, et al. (2002). Hematological changes among
Chinese workers with a broad range of benzene
exposures. American. Journal of Industrial
Medicine. 42: 275–285.
652 Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et
al. (2004). Hematotoxically in Workers Exposed to
Low Levels of Benzene. Science 306: 1774–1776.
653 Turtletaub, K.W. and Mani, C. (2003). Benzene
metabolism in rodents at doses relevant to human
exposure from Urban Air. Research Reports Health
Effect Inst. Report No.113.
654 U.S. EPA. (2002). Health Assessment of 1,3Butadiene. Office of Research and Development,
National Center for Environmental Assessment,
Washington Office, Washington, DC. Report No.
EPA600–P–98–001F. This document is available
electronically at http://www3.epa.gov/iris/supdocs/
buta-sup.pdf.
655 U.S. EPA. (2002). ‘‘Full IRIS Summary for 1,3butadiene (CASRN 106–99–0)’’ Environmental
Protection Agency, Integrated Risk Information
System (IRIS), Research and Development, National
Center for Environmental Assessment, Washington,
DC http://www3.epa.gov/iris/subst/0139.htm.

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known human carcinogen.656 657 658
There are numerous studies consistently
demonstrating that 1,3-butadiene is
metabolized into genotoxic metabolites
by experimental animals and humans.
The specific mechanisms of 1,3butadiene-induced carcinogenesis are
unknown; however, the scientific
evidence strongly suggests that the
carcinogenic effects are mediated by
genotoxic metabolites. Animal data
suggest that females may be more
sensitive than males for cancer effects
associated with 1,3-butadiene exposure;
there are insufficient data in humans
from which to draw conclusions about
sensitive subpopulations. The URE for
1,3-butadiene is 3 × 10¥5 per mg/m3.659
1,3-butadiene also causes a variety of
reproductive and developmental effects
in mice; no human data on these effects
are available. The most sensitive effect
was ovarian atrophy observed in a
lifetime bioassay of female mice.660
Based on this critical effect and the
benchmark concentration methodology,
an RfC for chronic health effects was
calculated at 0.9 ppb (approximately 2
mg/m3).
(d) Formaldehyde
In 1991, EPA concluded that
formaldehyde is a carcinogen based on
nasal tumors in animal bioassays.661 An
Inhalation URE for cancer and a
Reference Dose for oral noncancer
effects were developed by the agency
and posted on the IRIS database. Since
that time, the National Toxicology
656 International Agency for Research on Cancer
(IARC). (1999). Monographs on the evaluation of
carcinogenic risk of chemicals to humans, Volume
71, Re-evaluation of some organic chemicals,
hydrazine and hydrogen peroxide and Volume 97
(in preparation), World Health Organization, Lyon,
France.
657 International Agency for Research on Cancer
(IARC). (2008). Monographs on the evaluation of
carcinogenic risk of chemicals to humans, 1,3Butadiene, Ethylene Oxide and Vinyl Halides
(Vinyl Fluoride, Vinyl Chloride and Vinyl Bromide)
Volume 97, World Health Organization, Lyon,
France.
658 NTP. (2014). 13th Report on Carcinogens.
Research Triangle Park, NC: U.S. Department of
Health and Human Services, Public Health Service,
National Toxicology Program.
659 U.S. EPA. (2002). ‘‘Full IRIS Summary for 1,3butadiene (CASRN 106–99–0)’’ Environmental
Protection Agency, Integrated Risk Information
System (IRIS), Research and Development, National
Center for Environmental Assessment, Washington,
DC https://cfpub.epa.gov/ncea/iris2/chemical
Landing.cfm?substance_nmbr=139 (Last accessed
July 10, 2018).
660 Bevan, C.; Stadler, J.C.; Elliot, G.S.; et al.
(1996). Subchronic toxicity of 4-vinylcyclohexene
in rats and mice by inhalation. Fundamental
Applied Toxicology. 32:1–10.
661 EPA. Integrated Risk Information System.
Formaldehyde (CASRN 50–00–0) https://
cfpub.epa.gov/ncea/iris/iris_documents/
documents/subst/0419_summary.pdf (Last accessed
July 2018).

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Program (NTP) and International
Agency for Research on Cancer (IARC)
have concluded that formaldehyde is a
known human carcinogen.662 663
The conclusions by IARC and NTP
reflect the results of epidemiologic
research published since 1991 in
combination with previous animal,
human and mechanistic evidence.
Research conducted by the National
Cancer Institute reported an increased
risk of nasopharyngeal cancer and
specific lymph hematopoietic
malignancies among workers exposed to
formaldehyde.664 665 666 A National
Institute of Occupational Safety and
Health study of garment workers also
reported increased risk of death due to
leukemia among workers exposed to
formaldehyde.667 Extended follow-up of
a cohort of British chemical workers did
not report evidence of an increase in
nasopharyngeal or lymph hematopoietic
cancers, but a continuing statistically
significant excess in lung cancers was
reported.668 Finally, a study of
embalmers reported formaldehyde
exposures to be associated with an
increased risk of myeloid leukemia but
not brain cancer.669
Health effects of formaldehyde in
addition to cancer were reviewed by the
Agency for Toxics Substances and
662 NTP. (2014). 13th Report on Carcinogens.
Research Triangle Park, NC: U.S. Department of
Health and Human Services, Public Health Service,
National Toxicology Program.
663 IARC Monographs on the Evaluation of
Carcinogenic Risks to Humans Volume 100F (2012):
Formaldehyde.
664 Hauptmann, M., Lubin, J. H., Stewart, P. A.,
Hayes, R. B., & Blair, A. 2003. Mortality from
lymphohematopoetic malignancies among workers
in formaldehyde industries. Journal of the National
Cancer Institute 95: 1615–1623.
665 Hauptmann, M., Lubin, J. H., Stewart, P. A.,
Hayes, R. B., & Blair, A. 2004. Mortality from solid
cancers among workers in formaldehyde industries.
American Journal of Epidemiology 159: 1117–1130.
666 Beane Freeman, L. E., Blair, A., Lubin, J. H.,
Stewart, P. A., Hayes, R. B., Hoover, R. N., &
Hauptmann, M. 2009. Mortality from lymph
hematopoietic malignancies among workers in
formaldehyde industries: The National Cancer
Institute cohort. Journal of the National Cancer
Institute. 101: 751–761.
667 Pinkerton, L. E. 2004. Mortality among a
cohort of garment workers exposed to
formaldehyde: an update. Occupational
Environmental Medicine 61: 193–200.
668 Coggon, D., Harris, E. C. Poole, J., & Palmer,
K. T. 2003. Extended follow-up of a cohort of
British chemical workers exposed to formaldehyde.
Journal of the National Cancer Institute. 95:1608–
1615.
669 Hauptmann, M., Stewart P. A., Lubin J. H.,
Beane Freeman, L. E., Hornung, R. W., Herrick, R.
F., Hoover, R. N., Fraumeni, J. F., & Hayes, R. B.
2009. Mortality from lymph hematopoietic
malignancies and brain cancer among embalmers
exposed to formaldehyde. Journal of the National
Cancer Institute 101:1696–1708.

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Disease Registry in 1999,670
supplemented in 2010,671 and by the
World Health Organization.672 These
organizations reviewed the scientific
literature concerning health effects
linked to formaldehyde exposure to
evaluate hazards and dose response
relationships and defined exposure
concentrations for minimal risk levels
(MRLs). The health endpoints reviewed
included sensory irritation of eyes and
respiratory tract, reduced pulmonary
function, nasal histopathology, and
immune system effects. In addition,
research on reproductive and
developmental effects and neurological
effects were discussed along with
several studies that suggest that
formaldehyde may increase the risk of
asthma, particularly in the young.
EPA released a draft Toxicological
Review of Formaldehyde—Inhalation
Assessment through the IRIS program
for peer review by the National Research
Council (NRC) and public comment in
June 2010.673 The draft assessment
reviewed more recent research from
animal and human studies on cancer
and other health effects. The NRC
released their review report in April
2011.674 EPA is currently developing a
revised draft assessment in response to
this review.

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(e) Acetaldehyde
Acetaldehyde is classified in EPA’s
IRIS database as a probable human
carcinogen, based on nasal tumors in
rats, and is considered toxic by the
inhalation, oral, and intravenous
routes.675 The URE in IRIS for
acetaldehyde is 2.2 × 10 6 per mg/
m3.676 Acetaldehyde is reasonably
670 ATSDR. 1999. Toxicological Profile for
Formaldehyde, U.S. Department of Health and
Human Services (HHS), July 1999.
671 ATSDR. 2010. Addendum to the Toxicological
Profile for Formaldehyde. U.S. Department of
Health and Human Services (HHS), October 2010.
672 IPCS. 2002. Concise International Chemical
Assessment Document 40. Formaldehyde. World
Health Organization.
673 EPA (U.S. Environmental Protection Agency).
2010. Toxicological Review of Formaldehyde (CAS
No. 50–00–0)—Inhalation Assessment: In Support
of Summary Information on the Integrated Risk
Information System (IRIS). External Review Draft.
EPA/635/R–10/002A. U.S. Environmental
Protection Agency, Washington DC [online].
Available: http://cfpub.epa.gov/ncea/irs_drats/
recordisplay.cfm?deid=223614.
674 NRC (National Research Council). 2011.
Review of the Environmental Protection Agency’s
Draft IRIS Assessment of Formaldehyde.
Washington DC: National Academies Press. http://
books.nap.edu/openbook.php?record_id=13142.
675 U.S. EPA (1991). Integrated Risk Information
System File of Acetaldehyde. Research and
Development, National Center for Environmental
Assessment, Washington, DC. This material is
available electronically at http://www3.epa.gov/iris/
subst/0290.htm.
676 U.S. EPA (1991). Integrated Risk Information
System File of Acetaldehyde. This material is

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anticipated to be a human carcinogen by
the U.S. DHHS in the 13th Report on
Carcinogens and is classified as possibly
carcinogenic to humans (Group 2B) by
the IARC.677 678 Acetaldehyde is
currently listed on the IRIS Program
Multi-Year Agenda for reassessment
within the next few years.
The primary noncancer effects of
exposure to acetaldehyde vapors
include irritation of the eyes, skin, and
respiratory tract.679 In short-term (four
week) rat studies, degeneration of
olfactory epithelium was observed at
various concentration levels of
acetaldehyde exposure.680 681 Data from
these studies were used by EPA to
develop an inhalation reference
concentration of 9 mg/m3. Some
asthmatics have been shown to be a
sensitive subpopulation to decrements
in functional expiratory volume (FEV1
test) and bronchoconstriction upon
acetaldehyde inhalation.682
(f) Acrolein
EPA most recently evaluated the
toxicological and health effects
literature related to acrolein in 2003 and
concluded that the human carcinogenic
potential of acrolein could not be
determined because the available data
were inadequate. No information was
available on the carcinogenic effects of
acrolein in humans and the animal data
provided inadequate evidence of
carcinogenicity.683 The IARC
determined in 1995 that acrolein was
available electronically at http://www3.epa.gov/iris/
subst/0290.htm.
677 NTP. (2014). 13th Report on Carcinogens.
Research Triangle Park, NC: U.S. Department of
Health and Human Services, Public Health Service,
National Toxicology Program.
678 International Agency for Research on Cancer
(IARC). (1999). Re-evaluation of some organic
chemicals, hydrazine, and hydrogen peroxide. IARC
Monographs on the Evaluation of Carcinogenic Risk
of Chemical to Humans, Vol 71. Lyon, France.
679 U.S. EPA (1991). Integrated Risk Information
System File of Acetaldehyde. This material is
available electronically at http://www3.epa.gov/iris/
subst/0290.htm.
680 U.S. EPA. (2003). Integrated Risk Information
System File of Acrolein. Research and
Development, National Center for Environmental
Assessment, Washington, DC. This material is
available electronically at http://www3.epa.gov/iris/
subst/0364.htm.
681 Appleman, L.M., Woutersen, R. A., & Feron,
V. J. (1982). Inhalation toxicity of acetaldehyde in
rats. I. Acute and subacute studies. Toxicology. 23:
293–297.
682 Myou, S., Fujimura, M., Nishi, K., Ohka, T.,
& Matsuda, T. (1993) Aerosolized acetaldehyde
induces histamine-mediated bronchoconstriction in
asthmatics. Am. Rev. Respir. Dis. 148(4 Pt 1): 940–
943.
683 U.S. EPA. (2003). Integrated Risk Information
System File of Acrolein. Research and
Development, National Center for Environmental
Assessment, Washington, DC. This material is
available at http://www3.epa.gov/iris/subst/
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not classifiable as to its carcinogenicity
in humans.684
Lesions to the lungs and upper
respiratory tract of rats, rabbits, and
hamsters have been observed after
subchronic exposure to acrolein.685 The
agency has developed an RfC for
acrolein of 0.02 mg/m3 and an RfD of 0.5
mg/kg-day.686
Acrolein is extremely acrid and
irritating to humans when inhaled, with
acute exposure resulting in upper
respiratory tract irritation, mucus
hypersecretion and congestion. The
intense irritancy of this carbonyl has
been demonstrated during controlled
tests in human subjects, who suffer
intolerable eye and nasal mucosal
sensory reactions within minutes of
exposure.687 These data and additional
studies regarding acute effects of human
exposure to acrolein are summarized in
EPA’s 2003 Toxicological Review of
Acrolein.688 Studies in humans indicate
that levels as low as 0.09 ppm (0.21 mg/
m3) for five minutes may elicit
subjective complaints of eye irritation
with increasing concentrations leading
to more extensive eye, nose and
respiratory symptoms. Acute exposures
in animal studies report bronchial
hyper-responsiveness. Based on animal
data (more pronounced respiratory
irritancy in mice with allergic airway
disease in comparison to non-diseased
mice 689) and demonstration of similar
effects in humans (e.g., reduction in
684 International Agency for Research on Cancer
(IARC). (1995). Monographs on the evaluation of
carcinogenic risk of chemicals to humans, Volume
63. Dry cleaning, some chlorinated solvents and
other industrial chemicals, World Health
Organization, Lyon, France.
685 U.S. EPA. (2003). Integrated Risk Information
System File of Acrolein. Office of Research and
Development, National Center for Environmental
Assessment, Washington, DC. This material is
available at http://www3.epa.gov/iris/subst/
0364.htm.
686 U.S. EPA. (2003). Integrated Risk Information
System File of Acrolein. Office of Research and
Development, National Center for Environmental
Assessment, Washington, DC. This material is
available at http://www3.epa.gov/iris/subst/
0364.htm.
687 U.S. EPA. (2003) Toxicological review of
acrolein in support of summary information on
Integrated Risk Information System (IRIS) National
Center for Environmental Assessment, Washington,
DC. EPA/635/R–03/003. p. 10. Available online at:
http://www3.epa.gov/ncea/iris/toxreviews/
0364tr.pdf.
688 U.S. EPA. (2003) Toxicological review of
acrolein in support of summary information on
Integrated Risk Information System (IRIS) National
Center for Environmental Assessment, Washington,
DC. EPA/635/R–03/003. Available online at: https://
cfpub.epa.gov/ncea/risk/recordisplay.cfm?
deid=51977 (Last accessed July 10 2018).
689 Morris, J. B., Symanowicz, P. T., Olsen, J. E.,
et al. (2003). Immediate sensory nerve-mediated
respiratory responses to irritants in healthy and
allergic airway-diseased mice. Journal of Applied
Physiology. 94(4):1563–1571.

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respiratory rate), individuals with
compromised respiratory function (e.g.,
emphysema, asthma) are expected to be
at increased risk of developing adverse
responses to strong respiratory irritants
such as acrolein. EPA does not currently
have an acute reference concentration
for acrolein. The available health effect
reference values for acrolein have been
summarized by EPA and include an
ATSDR MRL for acute exposure to
acrolein of 7 mg/m3 for 1–14 days’
exposure; and Reference Exposure Level
(REL) values from the California Office
of Environmental Health Hazard
Assessment (OEHHA) for one-hour and
8-hour exposures of 2.5 mg/m3 and 0.7
mg/m3, respectively.690

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(g) Polycyclic Organic Matter
The term polycyclic organic matter
(POM) defines a broad class of
compounds that includes the polycyclic
aromatic hydrocarbon compounds
(PAHs). One of these compounds,
naphthalene, is discussed separately
below. POM compounds are formed
primarily from combustion and are
present in the atmosphere in gas and
particulate form. Cancer is the major
concern from exposure to POM.
Epidemiologic studies have reported an
increase in lung cancer in humans
exposed to diesel exhaust, coke oven
emissions, roofing tar emissions, and
cigarette smoke; all of these mixtures
contain POM compounds.691 692 Animal
studies have reported respiratory tract
tumors from inhalation exposure to
benzo[a]pyrene and alimentary tract and
liver tumors from oral exposure to
benzo[a]pyrene.693 In 1997 EPA
classified seven PAHs (benzo[a]pyrene,
benz[a]anthracene, chrysene,
benzo[b]fluoranthene,
benzo[k]fluoranthene,
dibenz[a,h]anthracene, and
690 U.S. EPA. (2009). Graphical Arrays of
Chemical-Specific Health Effect Reference Values
for Inhalation Exposures (Final Report). U.S.
Environmental Protection Agency, Washington, DC,
EPA/600/R–09/061, 2009. http://cfpub.epa.gov/
ncea/cfm/recordisplay.cfm?deid=211003 (last
accessed July 10 2018).
691 Agency for Toxic Substances and Disease
Registry (ATSDR). (1995). Toxicological profile for
Polycyclic Aromatic Hydrocarbons (PAHs). Atlanta,
GA: U.S. Department of Health and Human
Services, Public Health Service. Available
electronically at http://www.atsdr.cdc.gov/
ToxProfiles/TP.asp?id=122&tid=25.
692 U.S. EPA (2002). Health Assessment
Document for Diesel Engine Exhaust. EPA/600/8–
90/057F Office of Research and Development,
Washington DC. http://cfpub.epa.gov/ncea/cfm/
recordisplay.cfm?deid=29060 (last accessed July 10
2018).
693 International Agency for Research on Cancer
(IARC). (2012). Monographs on the Evaluation of
the Carcinogenic Risk of Chemicals for Humans,
Chemical Agents and Related Occupations. Vol.
100F. Lyon, France.

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indeno[1,2,3-cd]pyrene) as Group B2,
probable human carcinogens.694 Since
that time, studies have found that
maternal exposures to PAHs in a
population of pregnant women were
associated with several adverse birth
outcomes, including low birth weight
and reduced length at birth, as well as
impaired cognitive development in
preschool children (three years of
age).695 696 These and similar studies are
being evaluated as a part of the ongoing
IRIS reassessment of health effects
associated with exposure to
benzo[a]pyrene.
(h) Naphthalene
Naphthalene is found in small
quantities in gasoline and diesel fuels.
Naphthalene emissions have been
measured in larger quantities in both
gasoline and diesel exhaust compared
with evaporative emissions from mobile
sources, indicating it is primarily a
product of combustion. Acute (shortterm) exposure of humans to
naphthalene by inhalation, ingestion, or
dermal contact is associated with
hemolytic anemia and damage to the
liver and the nervous system.697
Chronic (long term) exposure of workers
and rodents to naphthalene has been
reported to cause cataracts and retinal
damage.698 EPA released an external
review draft of a reassessment of the
inhalation carcinogenicity of
naphthalene based on a number of
694 U.S. EPA (1997). Integrated Risk Information
System File of indeno (1,2,3-cd) pyrene. Research
and Development, National Center for
Environmental Assessment, Washington, DC. This
material is available electronically at https://
cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=
2776 (Last accessed July 10 2018).
695 Perera, F. P., Rauh, V., Tsai, W. Y., et al.
(2002). Effect of transplacental exposure to
environmental pollutants on birth outcomes in a
multiethnic population. Environmental Health
Perspectives. 111: 201–205.
696 Perera, F. P., Rauh, V., Whyatt, R. M., Tsai, W.
Y., Tang, D., Diaz, D., Hoepner, L., Barr, D., Tu, Y.
H., Camann, D., & Kinney, P. (2006). Effect of
prenatal exposure to airborne polycyclic aromatic
hydrocarbons on neurodevelopment in the first 3
years of life among inner-city children.
Environmental Health Perspectives. 114: 1287–
1292.
697 U. S. EPA. 1998. Toxicological Review of
Naphthalene (Reassessment of the Inhalation
Cancer Risk), Environmental Protection Agency,
Integrated Risk Information System, Research and
Development, National Center for Environmental
Assessment, Washington, DC. This material is
available electronically at https://cfpub.epa.gov/
ncea/iris/iris_documents/documents/toxreviews/
0436tr.pdf (last accessed July 10 2018).
698 U. S. EPA. 1998. Toxicological Review of
Naphthalene (Reassessment of the Inhalation
Cancer Risk), Environmental Protection Agency,
Integrated Risk Information System, Research and
Development, National Center for Environmental
Assessment, Washington, DC. This material is
available electronically at https://cfpub.epa.gov/
ncea/iris/iris_documents/documents/toxreviews/
0436tr.pdf (last accessed July 10 2018)

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recent animal carcinogenicity studies.
The draft reassessment completed
external peer review.699 Based on
external peer review comments
received, a revised draft assessment that
considers all routes of exposure, as well
as cancer and noncancer effects, is
under development. The external
review draft does not represent official
agency opinion and was released solely
for the purposes of external peer review
and public comment. The National
Toxicology Program listed naphthalene
as ‘‘reasonably anticipated to be a
human carcinogen’’ in 2004 on the basis
of bioassays reporting clear evidence of
carcinogenicity in rats and some
evidence of carcinogenicity in mice.700
California EPA has released a new risk
assessment for naphthalene, and the
IARC has reevaluated naphthalene and
re-classified it as Group 2B: Possibly
carcinogenic to humans.701
Naphthalene also causes a number of
chronic non-cancer effects in animals,
including abnormal cell changes and
growth in respiratory and nasal tissues.
The current EPA IRIS assessment
includes noncancer data on hyperplasia
and metaplasia in nasal tissue that form
the basis of the inhalation RfC of 3 mg/
m3.702 The ATSDR MRL for acute
exposure to naphthalene is 0.6 mg/kg/
day.
(i) Other Air Toxics
In addition to the compounds
described above, other compounds in
gaseous hydrocarbon and PM emissions
from motor vehicles will be affected by
this action. Mobile source air toxic
compounds that will potentially be
impacted include ethylbenzene,
propionaldehyde, toluene, and xylene.
Information regarding the health effects
of these compounds can be found in
EPA’s IRIS database.703
699 Oak Ridge Institute for Science and Education.
(2004). External Peer Review for the IRIS
Reassessment of the Inhalation Carcinogenicity of
Naphthalene. August 2004. http://cfpub.epa.gov/
ncea/cfm/recordisplay.cfm?deid=84403.
700 NTP. (2014). 13th Report on Carcinogens. U.S.
Department of Health and Human Services, Public
Health Service, National Toxicology Program.
701 International Agency for Research on Cancer
(IARC). (2002). Monographs on the Evaluation of
the Carcinogenic Risk of Chemicals for Humans.
Vol. 82. Lyon, France.
702 U.S. EPA. (1998). Toxicological Review of
Naphthalene. Environmental Protection Agency,
Integrated Risk Information System (IRIS), Research
and Development, National Center for
Environmental Assessment, Washington, DC
https://cfpub.epa.gov/ncea/iris/iris_documents/
documents/toxreviews/0436tr.pdf (last accessed
July 10 2018).
703 U.S. EPA Integrated Risk Information System
(IRIS) database is available at: https://www.epa.gov/
iris (last accessed July 10 2018)

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(j) Exposure and Health Effects
Associated With Traffic
Locations in close proximity to major
roadways generally have elevated
concentrations of many air pollutants
emitted from motor vehicles. Hundreds
of such studies have been published in
peer-reviewed journals, concluding that
concentrations of CO, NO, NO2,
benzene, aldehydes, particulate matter,
black carbon, and many other
compounds are elevated in ambient air
within approximately 300–600 meters
(approximately 1,000–2,000 feet) of
major roadways. Highest concentrations
of most pollutants emitted directly by
motor vehicles are found at locations
within 50 meters (approximately 165
feet) of the edge of a roadway’s traffic
lanes.
A large-scale review of air quality
measurements in the vicinity of major
roadways between 1978 and 2008
concluded that the pollutants with the
steepest concentration gradients in
vicinities of roadways were CO,
ultrafine particles, metals, elemental
carbon (EC), NO, NOX, and several
VOCs.704 These pollutants showed a
large reduction in concentrations within
100 meters downwind of the roadway.
Pollutants that showed more gradual
reductions with distance from roadways
included benzene, NO2, PM2.5, and
PM10. In the review article, results
varied based on the method of statistical
analysis used to determine the trend.
For pollutants with relatively high
background concentrations relative to
near-road concentrations, detecting
concentration gradients can be difficult.
For example, many aldehydes have high
background concentrations as a result of
photochemical breakdown of precursors
from many different organic
compounds. This can make detection of
gradients around roadways and other
primary emission sources difficult.
However, several studies have measured
aldehydes in multiple weather
conditions and found higher
concentrations of many carbonyls
downwind of roadways.705 706 These
704 Karner, A. A., Eisinger, D. S., & Niemeier, D.
A. (2010). Near-roadway air quality: synthesizing
the findings from real-world data. Environmental
Science Technology. 44: 5334–5344.
705 Liu, W., Zhang, J., Kwon, J. et al. (2006).
Concentrations and source characteristics of
airborne carbonyl comlbs measured outside urban
residences. Journal of the Air Waste Management
Assocication 56: 1196–1204.
706 Cahill, T. M., Charles, M. J., & Seaman, V. Y.
(2010). Development and application of a sensitive
method to determine concentrations of acrolein and
other carbonyls in ambient air. Health Effects

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findings suggest a substantial roadway
source of these carbonyls.
In the past 15 years, many studies
have been published with results
reporting that populations who live,
work, or go to school near high-traffic
roadways experience higher rates of
numerous adverse health effects,
compared to populations far away from
major roads.707 In addition, numerous
studies have found adverse health
effects associated with spending time in
traffic, such as commuting or walking
along high-traffic roadways; however, it
is difficult to fully control for
confounding in such
studies.708 709 710 711 The health
outcomes with the strongest evidence
linking them with traffic-associated air
pollutants are respiratory effects,
particularly in asthmatic children, and
cardiovascular effects.
Numerous reviews of this body of
health literature have been published as
well. In 2010, an expert panel of the
Health Effects Institute (HEI) published
a review of hundreds of exposure,
epidemiology, and toxicology
studies.712 The panel rated how the
evidence for each type of health
outcome supported a conclusion of a
causal association with trafficassociated air pollution as either
‘‘sufficient,’’ ‘‘suggestive but not
Institute Research Report 149.Available at https://
www.healtheffects.org/publication/developmentand-application-sensitive-method-determineconcentrations-acrolein-and-other (last accessed
July 10 2018)
707 In the widely-used PubMed database of health
publications, between January 1, 1990 and August
18, 2011, 605 publications contained the keywords
‘‘traffic, pollution, epidemiology,’’ with
approximately half the studies published after 2007.
708 Laden, F., Hart, J. E., Smith, T. J., Davis, M.
E., & Garshick, E. (2007) Cause-specific mortality in
the unionized U.S. trucking industry.
Environmental Health Perspectives 115:1192–1196.
709 Peters, A., von Klot, S., Heier, M.,
Trentinaglia, I., Ho¨rmann, A., Wichmann, H. E., &
Lo¨wel, H. (2004) Exposure to traffic and the onset
of myocardial infarction. New England Journal of
Medicine. 351: 1721–1730.
710 Zanobetti, A., Stone, P. H., Spelzer, F. E.,
Schwartz, J. D., Coull, B. A., Suh, H. H., Nearling,
B. D., Mittleman, M. A., Verrier, R. L., & Gold, D.
R. (2009) T-wave alternans, air pollution and traffic
in high-risk subjects. American Journal of
Cardiology. 104: 665–670.
711 Dubowsky Adar, S., Adamkiewicz, G., Gold,
D. R., Schwartz, J., Coull, B. A., & Suh, H. (2007)
Ambient and microenvironmental particles and
exhaled nitric oxide before and after a group bus
trip. Environmental Health Perspectives. 115: 507–
512.
712 Health Effects Institute Panel on the Health
Effects of Traffic-Related Air Pollution. (2010).
Traffic-related air pollution: A critical review of the
literature on emissions, exposure, and health
effects. HEI Special Report 17. Available at http://
www.healtheffects.org.

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sufficient,’’ or ‘‘inadequate and
insufficient.’’ The panel categorized
evidence of a causal association for
exacerbation of childhood asthma as
‘‘sufficient.’’ The panel categorized
evidence of a causal association for new
onset asthma as between ‘‘sufficient’’
and ‘‘suggestive but not sufficient.’’
‘‘Suggestive of a causal association’’ was
how the panel categorized evidence
linking traffic-associated air pollutants
with exacerbation of adult respiratory
symptoms and lung function decrement.
It categorized as ‘‘inadequate and
insufficient’’ evidence of a causal
relationship between traffic-related air
pollution and health care utilization for
respiratory problems, new onset adult
asthma, chronic obstructive pulmonary
disease (COPD), nonasthmatic
respiratory allergy, and cancer in adults
and children. Other literature reviews
have been published with conclusions
generally similar to the HEI
panel’s.713 714 715 716 However, in 2014,
researchers from the U.S. Centers for
Disease Control and Prevention (CDC)
published a systematic review and
meta-analysis of studies evaluating the
risk of childhood leukemia associated
with traffic exposure and reported
positive associations between
‘‘postnatal’’ proximity to traffic and
leukemia risks, but no such association
for ‘‘prenatal’’ exposures.717
Health outcomes with few
publications suggest the possibility of
other effects still lacking sufficient
evidence to draw definitive conclusions.
Among these outcomes with a small
number of positive studies are
neurological impacts (e.g., autism and
reduced cognitive function) and
reproductive outcomes (e.g., preterm
713 Boothe, V. L. & Shendell, D. G. (2008).
Potential health effects associated with residential
proximity to freeways and primary roads: review of
scientific literature, 1999–2006. Journal of
Environmental Health. 70: 33–41.
714 Salam, M. T., Islam, T., & Gilliland, F. D.
(2008). Recent evidence for adverse effects of
residential proximity to traffic sources on asthma.
Curr Opin Pulm Med 14: 3–8.
715 Sun, X., Zhang, S., & Ma, X. (2014) No
association between traffic density and risk of
childhood leukemia: a meta-analysis. Asia Pacific
Journal of Cancer Prevention. 15: 5229–5232.
716 Raaschou-Nielsen, O. & Reynolds, P. (2006).
Air pollution and childhood cancer: A review of the
epidemiological literature. International Journal of
Cancer. 118: 2920–9.
717 Boothe, V. L., Boehmer, T. K., Wendel, A. M.,
& Yip, F. Y. (2014) Residential traffic exposure and
childhood leukemia: a systematic review and metaanalysis. American Journal of Preventative
Medicine. 46: 413–422.

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birth, low birth weight).718 719 720 721
In addition to health outcomes,
particularly cardiopulmonary effects,
conclusions of numerous studies
suggest mechanisms by which trafficrelated air pollution affects health.
Numerous studies indicate that nearroadway exposures may increase
systemic inflammation, affecting organ
systems, including blood vessels and
lungs.722 723 724 725 Long-term exposures
in near-road environments have been
associated with inflammation-associated
conditions, such as atherosclerosis and
asthma.726 727 728
Several studies suggest that some
factors may increase susceptibility to
the effects of traffic-associated air
pollution. Several studies have found
718 Volk, H. E., Hertz-Picciotto, I., Delwiche, L., et
al. (2011). Residential proximity to freeways and
autism in the CHARGE study. Environmental
Health Perspectives. 119: 873–877.
719 Franco-Suglia, S., Gryparis, A., Wright, R. O.,
et al. (2007). Association of black carbon with
cognition among children in a prospective birth
cohort study. American Journal of Epidemiology.
doi: 10.1093/aje/kwm308. [Online at http://
dx.doi.org].
720 Power, M. C., Weisskopf, M. G., Alexeef, S. E.,
et al. (2011). Traffic-related air pollution and
cognitive function in a cohort of older men.
Environmental Health Perspectives. 2011: 682–687.
721 Wu, J., Wilhelm, M., Chung, J., et al. (2011).
Comparing exposure assessment methods for trafficrelated air pollution in and adverse pregnancy
outcome study. Environmental Research. 111: 685–
6692.
722 Riediker, M. (2007). Cardiovascular effects of
fine particulate matter components in highway
patrol officers. Inhal Toxicol 19: 99–105. doi:
10.1080/08958370701495238 Available at http://
dx.doi.org.
723 Alexeef, S. E., Coull, B. A., Gryparis, A., et al.
(2011). Medium-term exposure to traffic-related air
pollution and markers of inflammation and
endothelial function. Environmental Health
Perspectives. 119: 481–486. doi:10.1289/
ehp.1002560 Available at http://dx.doi.org.
724 Eckel, S. P., Berhane, K., Salam, M. T., et al.
(2011). Traffic-related pollution exposure and
exhaled nitric oxide in the Children’s Health Study.
Environmental Health Perspectives. (IN PRESS).
doi:10.1289/ehp.1103516. Available at http://
dx.doi.org.
725 Zhang, J., McCreanor, J. E., Cullinan, P., et al.
(2009). Health effects of real-world exposure diesel
exhaust in persons with asthma. Res Rep Health
Effects Inst 138. [Online at http://
www.healtheffects.org].
726 Adar, S. D., Klein, R., Klein, E. K., et al.
(2010). Air pollution and the microvasculatory: a
cross-sectional assessment of in vivo retinal images
in the population-based Multi-Ethnic Study of
Atherosclerosis. PLoS Med 7(11): E1000372.
doi:10.1371/journal.pmed.1000372. Available at
http://dx.doi.org.
727 Kan, H., Heiss, G., Rose, K. M., et al. (2008).
Prospective analysis of traffic exposure as a risk
factor for incident coronary heart disease: the
Atherosclerosis Risk in Communities (ARIC) study.
Environmental Health Perspectives. 116: 1463–
1468. doi:10.1289/ehp.11290. Available at http://
dx.doi.org.
728 McConnell, R., Islam, T., Shankardass, K., et
al. (2010). Childhood incident asthma and trafficrelated air pollution at home and school.
Environmental Health Perspectives. 1021–1026.

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stronger respiratory associations in
children experiencing chronic social
stress, such as in violent neighborhoods
or in homes with high family
stress.729 730 731
The risks associated with residence,
workplace, or schools near major roads
are of potentially high public health
significance due to the large population
in such locations. According to the 2009
American Housing Survey, more than
22 million homes (17% of all U.S.
housing units) were located within 300
feet of an airport, railroad, or highway
with four or more lanes. This
corresponds to a population of more
than 50 million U.S. residents in close
proximity to high-traffic roadways or
other transportation sources. Based on
2010 Census data, a 2013 publication
estimated that 19% of the U.S.
population (more than 59 million
people) lived within 500 meters of roads
with at least 25,000 annual average
daily traffic (AADT), while about 3.2%
of the population lived within 100
meters (about 300 feet) of such roads.732
Another 2013 study estimated that 3.7%
of the U.S. population (about 11.3
million people) lived within 150 meters
(about 500 feet) of interstate highways
or other freeways and expressways.733
On average, populations near major
roads have higher fractions of minority
residents and lower socioeconomic
status. Furthermore, on average,
Americans spend more than an hour
traveling each day, bringing nearly all
residents into a high-exposure
microenvironment for part of the day.
In light of these concerns, EPA has
required through the NAAQS process
that air quality monitors be placed near
high-traffic roadways for determining
concentrations of CO, NO2, and PM2.5
(in addition to those existing monitors
located in neighborhoods and other
locations farther away from pollution
729 Islam, T., Urban, R., Gauderman, W. J., et al.
(2011). Parental stress increases the detrimental
effect of traffic exposure on children’s lung
function. American Journal of Respiratory Critical
Care Medicine. (In press).
730 Clougherty, J. E., Levy, J. I., Kubzansky, L. D.,
et al. (2007). Synergistic effects of traffic-related air
pollution and exposure to violence on urban asthma
etiology. Environmental Health Perspectives. 115:
1140–1146.
731 Chen, E., Schrier, H. M., Strunk, R. C., et al.
(2008). Chronic traffic-related air pollution and
stress interact to predict biologic and clinical
outcomes in asthma. Environmental Health
Perspectives. 116: 970–5.
732 Rowangould, G. M. (2013) A census of the U.S.
near-roadway population: public health and
environmental justice considerations.
Transportation Research Part D 25: 59–67.
733 Boehmer, T. K., Foster, S. L., Henry, J. R.,
Woghiren-Akinnifesi, E. L., & Yip, F. Y. (2013)
Residential proximity to major highways—United
States, 2010. Morbidity and Mortality Weekly Report
62 (3); 46–50.

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sources). Near-roadway monitors for
NO2 begin operation between 2014 and
2017 in Core Based Statistical Areas
(CBSAs) with population of at least
500,000. Monitors for CO and PM2.5
begin operation between 2015 and 2017.
These monitors will further our
understanding of exposure in these
locations.
EPA and DOT continue to research
near-road air quality, including the
types of pollutants found in high
concentrations near major roads and
health problems associated with the
mixture of pollutants near roads.
8. Environmental Justice
Environmental justice (EJ) is a
principle asserting that all people
deserve fair treatment and meaningful
involvement with respect to
environmental laws, regulations, and
policies. EPA seeks to provide the same
degree of protection from environmental
health hazards for all people. DOT
shares this goal and is informed about
the potential environmental impacts of
its rulemakings through its NEPA
process (see NHTSA’s DEIS). As
referenced below, numerous studies
have found that some environmental
hazards are more prevalent in areas
where non-white, Hispanic and people
with low socioeconomic status (SES)
represent a higher fraction of the
population compared with the general
population. In addition, compared to
non-Hispanic whites, some
subpopulations defined by race and
ethnicity have been shown to have
greater levels of some health conditions
during some life stages. For example, in
2014, about 13% of Black, non-Hispanic
and 24% of Puerto Rican children were
estimated to currently have asthma,
compared with eight percent of white,
non-Hispanic children.734
As discussed in the DEIS,
concentrations of many air pollutants
are elevated near high-traffic roadways.
If minority populations and low-income
populations disproportionately live near
such roads, then an issue of EJ may be
present. We reviewed existing scholarly
literature examining the potential for
disproportionate exposure among
people with low SES, and we conducted
our own evaluation of two national
datasets: The U.S. Census Bureau’s
American Housing Survey for calendar
year 2009 and the U.S. Department of
Education’s database of school
locations.
Publications that address EJ issues
generally report that populations living
near major roadways (and other types of
734 http://www.cdc.gov/asthma/most_recent_
data.htm.

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transportation infrastructure) tend to be
composed of larger fractions of
nonwhite residents. People living in
neighborhoods near such sources of air
pollution also tend to be lower in
income than people living elsewhere.
Numerous studies evaluating the
demographics and socioeconomic status
of populations or schools near roadways
have found that they include a greater
percentage of minority residents, as well
as lower SES (indicated by variables
such as median household income).
Locations in these studies include Los
Angeles, CA; Seattle, WA; Wayne
County, MI; Orange County, FL; and
California 735 736 737 738 739 740 Such
disparities may be due to multiple
factors.741
People with low SES often live in
neighborhoods with multiple
environmental stressors and higher rates
of health risk factors, including reduced
health insurance coverage rates, higher
smoking and drug use rates, limited
access to fresh food, visible
neighborhood violence, and elevated
rates of obesity and some diseases such
as asthma, diabetes, and ischemic heart
disease. Although questions remain,
several studies find stronger
associations between air pollution and
health in locations with such chronic
neighborhood stress, suggesting that
populations in these areas may be more
susceptible to the effects of air
pollution.742 743 744 745 Household-level
735 Marshall, J. D. (2008) Environmental
inequality: air pollution exposures in California’s
South Coast Air Basin.
736 Su, J. G., Larson, T., Gould, T., Cohen, M., &
Buzzelli, M. (2010) Transboundary air pollution
and environmental justice: Vancouver and Seattle
compared. GeoJournal 57: 595–608. doi:10.1007/
s10708–009–9269–6 [Online at http://dx.doi.org].
737 Chakraborty, J. & Zandbergen, P. A. (2007)
Children at risk: measuring racial/ethnic disparities
in potential exposure to air pollution at school and
home. Journal of Epidemiol Community Health 61:
1074–1079. doi: 10.1136/jech.2006.054130 [Online
at http://dx.doi.org].
738 Green, R. S., Smorodinsky, S., Kim, J. J.,
McLaughlin, R., & Ostro, B. (2003) Proximity of
California public schools to busy roads.
Environmental Health Perspectives. 112: 61–66.
doi:10.1289/ehp.6566 [http://dx.doi.org].
739 Wu, Y. & Batterman, S. A. (2006) Proximity of
schools in Detroit, Michigan to automobile and
truck traffic. Journal of Exposure Science &
Environmental Epidemiology. doi:10.1038/
sj.jes.7500484 [Online at http://dx.doi.org].
740 Su, J. G., Jerrett, M., de Nazelle, A., & Wolch,
J. (2011) Does exposure to air pollution in urban
parks have socioeconomic, racial, or ethnic
gradients? Environmental Research. 111: 319–328.
741 Depro, B. & Timmins, C. (2008) Mobility and
environmental equity: do housing choices
determine exposure to air pollution? North Caroline
State University Center for Environmental and
Resource Economic Policy
742 Clougherty, J. E. & Kubzansky, L. D. (2009) A
framework for examining social stress and
susceptibility to air pollution in respiratory health.
Environmental Health Perspectives. 117: 1351–

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stressors such as parental smoking and
relationship stress also may increase
susceptibility to the adverse effects of
air pollution.746 747
Two national databases were analyzed
that allowed evaluation of whether
homes and schools were located near a
major road and whether disparities in
exposure may be occurring in these
environments. The American Housing
Survey (AHS) includes descriptive
statistics of over 70,000 housing units
across the nation. The study survey is
conducted every two years by the U.S.
Census Bureau. The second database we
analyzed was the U.S. Department of
Education’s Common Core of Data,
which includes enrollment and location
information for schools across the U.S.
In analyzing the 2009 AHS, the focus
was on whether or not a housing unit
was located within 300 feet of ‘‘4-ormore lane highway, railroad, or
airport.’’ 748 Whether there were
differences between households in such
locations compared with those in
locations farther from these same
transportation facilities was
analyzed.749 Other variables, such as
1358. Doi:10.1289/ehp.0900612 [Online at http://
dx.doi.org].
743 Clougherty, J. E., Levy, J. I., Kubzansky, L. D.,
Ryan, P. B., Franco Suglia, S., Jacobson Canner, M.,
& Wright, R. J. (2007) Synergistic effects of trafficrelated air pollution and exposure to violence on
urban asthma etiology. Environmental Health
Perspectives. 115: 1140–1146. doi:10.1289/ehp.9863
[Online at http://dx.doi.org].
744 Finkelstein, M. M., Jerrett, M., DeLuca, P.,
Finkelstein, N., Verma, D. K., Chapman, K., & Sears,
M. R. (2003) Relation between income, air pollution
and mortality: A cohort study. Canadian Medical
Association Journal. 169: 397–402.
745 Shankardass, K., McConnell, R., Jerrett, M.,
Milam, J., Richardson, J., & Berhane, K. (2009)
Parental stress increases the effect of traffic-related
air pollution on childhood asthma incidence. Proc
National Academy of Science. 106: 12406–12411.
doi:10.1073/pnas.0812910106 [Online at http://
dx.doi.org].
746 Lewis, A. S., Sax, S. N., Wason, S. C. &
Campleman, S. L (2011) Non-chemical stressors and
cumulative risk assessment: an overview of current
initiatives and potential air pollutant interactions.
International Journal of Environmental Research in
Public Health. 8: 2020–2073. Doi:10.3390/
ijerph8062020 [Online at http://dx.doi.org].
747 Rosa, M. J., Jung, K. H., Perzanowski, M. S.,
Kelvin, E. A., Darling, K.W., Camann, D. E.,
Chillrud, S. N., Whyatt, R. M., Kinney, P. L., Perera,
F. P., & Miller, R. L. (2010) Prenatal exposure to
polycyclic aromatic hydrocarbons, environmental
tobacco smoke and asthma. Respiratory Medicine.
(In press). doi:10.1016/j.rmed.2010.11.022 [Online
at http://dx.doi.org].
748 This variable primarily represents roadway
proximity. According to the Central Intelligence
Agency’s World Factbook, in 2010, the United
States had 6,506,204 km or roadways, 224,792 km
of railways, and 15,079 airports. Highways thus
represent the overwhelming majority of
transportation facilities described by this factor in
the AHS.
749 Bailey, C. (2011) Demographic and Social
Patterns in Housing Units Near Large Highways and
other Transportation Sources. Memorandum to
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land use category, region of country,
and housing type were included.
In examining schools near major
roadways, the Common Core of Data
(CCD) from the U.S. Department of
Education, which includes information
on all public elementary and secondary
schools and school districts nationwide,
was examined.750 To determine school
proximities to major roadways, a
geographic information system (GIS) to
map each school and roadways based on
the U.S. Census’s TIGER roadway file
was used.751 Non-white students were
found to be overrepresented at schools
within 200 meters of the largest
roadways, and schools within 200
meters of the largest roadways also had
higher than expected numbers of
students eligible for free or reducedprice lunches. For example, Black
students represent 22% of students at
schools located within 200 meters of a
primary road, whereas Black students
represent 17% of students in all U.S.
schools. Hispanic students represent
30% of students at schools located
within 200 meters of a primary road,
whereas Hispanic students represent
22% of students in all U.S. schools.
Overall, there is substantial evidence
that people who live or attend school
near major roadways are more likely to
be non-white, Hispanic ethnicity, and/
or low SES. The emission reductions
from these proposed standards will
likely result in widespread air quality
improvements, but the impact on
pollution levels in close proximity to
roadways will be most direct. Thus,
these proposed standards will likely
help in mitigating the disparity in racial,
ethnic, and economically based
exposures.
9. Environmental Effects of Non-GHG
Pollutants
(a) Visibility
Visibility can be defined as the degree
to which the atmosphere is transparent
to visible light.752 Visibility impairment
is caused by light scattering and
absorption by suspended particles and
gases. Visibility is important because it
has direct significance to people’s
enjoyment of daily activities in all parts
of the country. Individuals value good
visibility for the well-being it provides
750 http://nces.ed.gov/ccd/.
751 Pedde, M. & Bailey, C. (2011) Identification of
Schools within 200 Meters of U.S. Primary and
Secondary Roads. Memorandum to the docket.
752 National Research Council, (1993). Protecting
Visibility in National Parks and Wilderness Areas.
National Academy of Sciences Committee on Haze
in National Parks and Wilderness Areas. National
Academy Press, Washington, DC. This book can be
viewed on the National Academy Press website at
http://www.nap.edu/books/0309048443/html/.

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them directly, where they live and
work, and in places where they enjoy
recreational opportunities. Visibility is
also highly valued in significant natural
areas, such as national parks and
wilderness areas, and special emphasis
is given to protecting visibility in these
areas. For more information on visibility
see the final 2009 PM ISA.753
EPA is working to address visibility
impairment. Reductions in air pollution
from implementation of various
programs associated with the Clean Air
Act Amendments of 1990 (CAAA)
provisions have resulted in substantial
improvements in visibility and will
continue to do so in the future. Because
trends in haze are closely associated
with trends in particulate sulfate and
nitrate due to the relationship between
their concentration and light extinction,
visibility trends have improved as
emissions of SO2 and NOX have
decreased over time due to air pollution
regulations such as the Acid Rain
Program.754
In the Clean Air Act Amendments of
1977, Congress recognized visibility’s
value to society by establishing a
national goal to protect national parks
and wilderness areas from visibility
impairment caused by manmade
pollution.755 In 1999, EPA finalized the
regional haze program to protect the
visibility in Mandatory Class I Federal
areas.756 There are 156 national parks,
forests and wilderness areas categorized
as Mandatory Class I Federal areas.757
These areas are defined in CAA Section
162 as those national parks exceeding
6,000 acres, wilderness areas and
memorial parks exceeding 5,000 acres,
and all international parks which were
in existence on August 7, 1977.
EPA has also concluded that PM2.5
can cause adverse effects on visibility in
other areas that are not targeted by the
Regional Haze Rule, such as urban
areas, depending on PM2.5
concentrations and other factors such as
dry chemical composition and relative
humidity (i.e., an indicator of the water
composition of the particles).758 In
December 2012, EPA revised the
primary (health-based) PM2.5 standards
in order to increase public health
protection. As part of that same review,
753 U.S. EPA. (2009). Integrated Science
Assessment for Particulate Matter (Final Report).
U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/139F.
754 U.S. EPA. 2009 Final Report: Integrated
Science Assessment for Particulate Matter. U.S.
Environmental Protection Agency, Washington, DC,
EPA/600/R–08/139F, 2009.
755 See Section 169(a) of the Clean Air Act.
756 64 FR 35714 (July 1, 1999).
757 62 FR 38680–38681 (July 18, 1997).
758 78 FR 3226, January 15, 2013.

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the EPA generally retained the
secondary (welfare-based) PM2.5
standards, concluding that the target
level of protection against PM-related
visibility impairment would be
achieved in areas meeting the existing
secondary standards for PM2.5.
(b) Plant and Ecosystem Effects of
Ozone
The welfare effects of ozone can be
observed across a variety of scales, i.e.
subcellular, cellular, leaf, whole plant,
population and ecosystem. Ozone can
produce both acute and chronic injury
in sensitive species depending on the
concentration level and the duration of
the exposure.759 In those sensitive
species,760 effects from repeated
exposure to ozone throughout the
growing season of the plant tend to
accumulate, so that even low
concentrations experienced for a longer
duration have the potential to create
chronic stress on vegetation.761 Ozone
damage to sensitive species includes
impaired photosynthesis and visible
injury to leaves. The impairment of
photosynthesis, the process by which
the plant makes carbohydrates (its
source of energy and food), can lead to
reduced crop yields, timber production,
and plant productivity and growth.
Impaired photosynthesis can also lead
to a reduction in root growth and
carbohydrate storage below ground,
resulting in other, more subtle plant and
ecosystems impacts.762 These latter
impacts include increased susceptibility
of plants to insect attack, disease, harsh
weather, interspecies competition and
overall decreased plant vigor. The
adverse effects of ozone on areas with
sensitive species could potentially lead
to species shifts and loss from the
affected ecosystems,763 resulting in a
loss or reduction in associated
ecosystem goods and services.
Additionally, visible ozone injury to
leaves can result in a loss of aesthetic
759 73

FR 16486 (Mar. 27, 2008).
FR 16491 (Mar. 27, 2008). Only a small
percentage of all the plant species growing within
the U.S. (over 43,000 species have been catalogued
in the USDA PLANTS database) have been studied
with respect to ozone sensitivity.
761 The concentration at which ozone levels
overwhelm a plant’s ability to detoxify or
compensate for oxidant exposure varies. Thus,
whether a plant is classified as sensitive or tolerant
depends in part on the exposure levels being
considered. Chapter 9, Section 9.3.4 of U.S. EPA,
2013 Integrated Science Assessment for Ozone and
Related Photochemical Oxidants. Office of Research
and Development/National Center for
Environmental Assessment. U.S. Environmental
Protection Agency. EPA 600/R–10/076F.
762 73 FR 16492 (Mar. 27, 2008).
763 73 FR 16493–16494 (Mar. 27, 2008). Ozone
impacts could be occurring in areas where plant
species sensitive to ozone have not yet been studied
or identified.
760 73

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value in areas of special scenic
significance like national parks and
wilderness areas and reduced use of
sensitive ornamentals in landscaping.764
The most recent Integrated Science
Assessment (ISA) for Ozone presents
more detailed information on how
ozone affects vegetation and
ecosystems.765 The ISA concludes that
ambient concentrations of ozone are
associated with a number of adverse
welfare effects and characterizes the
weight of evidence for different effects
associated with ozone.766 The ISA
concludes that visible foliar injury
effects on some vegetation, reduced
vegetation growth, reduced productivity
in terrestrial ecosystems, reduced yield
and quality of some agricultural crops,
and alteration of below-ground
biogeochemical cycles are causally
associated with exposure to ozone. It
also concludes that reduced carbon
sequestration in terrestrial ecosystems,
alteration of terrestrial ecosystem water
cycling, and alteration of terrestrial
community composition are likely to be
causally associated with exposure to
ozone.
(c) Atmospheric Deposition
Wet and dry deposition of ambient
particulate matter delivers a complex
mixture of metals (e.g., mercury, zinc,
lead, nickel, aluminum, and cadmium),
organic compounds (e.g., polycyclic
organic matter, dioxins, and furans), and
inorganic compounds (e.g., nitrate,
sulfate) to terrestrial and aquatic
ecosystems. The chemical form of the
compounds deposited depends on a
variety of factors including ambient
conditions (e.g., temperature, humidity,
oxidant levels) and the sources of the
material. Chemical and physical
transformations of the compounds occur
in the atmosphere as well as the media
onto which they deposit. These
transformations in turn influence the
fate, bioavailability and potential
toxicity of these compounds.
Adverse impacts to human health and
the environment can occur when
particulate matter is deposited to soils,
764 73

FR 16490–16497 (Mar. 27, 2008).
EPA. Integrated Science Assessment of
Ozone and Related Photochemical Oxidants (Final
Report). U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–10/076F, 2013. The
ISA is available at http://cfpub.epa.gov/ncea/isa/
recordisplay.cfm?deid=247492#Download.
766 The Ozone ISA evaluates the evidence
associated with different ozone related health and
welfare effects, assigning one of five ‘‘weight of
evidence’’ determinations: Causal relationship,
likely to be a causal relationship, suggestive of a
causal relationship, inadequate to infer a causal
relationship, and not likely to be a causal
relationship. For more information on these levels
of evidence, please refer to Table II of the ISA.
765 U.S.

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water, and biota.767 Deposition of heavy
metals or other toxics may lead to the
human ingestion of contaminated fish,
impairment of drinking water, damage
to terrestrial, freshwater and marine
ecosystem components, and limits to
recreational uses. Atmospheric
deposition has been identified as a key
component of the environmental and
human health hazard posed by several
pollutants including mercury, dioxin
and PCBs.768
The ecological effects of acidifying
deposition and nutrient enrichment are
detailed in the Integrated Science
Assessment for Oxides of Nitrogen and
Sulfur-Ecological Criteria.769
Atmospheric deposition of nitrogen and
sulfur contributes to acidification,
altering biogeochemistry and affecting
animal and plant life in terrestrial and
aquatic ecosystems across the United
States. The sensitivity of terrestrial and
aquatic ecosystems to acidification from
nitrogen and sulfur deposition is
predominantly governed by geology.
Prolonged exposure to excess nitrogen
and sulfur deposition in sensitive areas
acidifies lakes, rivers, and soils.
Increased acidity in surface waters
creates inhospitable conditions for biota
and affects the abundance and
biodiversity of fishes, zooplankton,
macroinvertebrates, and ecosystem
function. Over time, acidifying
deposition also removes essential
nutrients from forest soils, depleting the
capacity of soils to neutralize future
acid loadings and negatively affecting
forest sustainability. Major effects in
forests include a decline in sensitive
tree species, such as red spruce (Picea
rubens) and sugar maple (Acer
saccharum). In addition to the role
nitrogen deposition plays in
acidification, nitrogen deposition also
leads to nutrient enrichment and altered
biogeochemical cycling. In aquatic
systems increased nitrogen can alter
species assemblages and cause
eutrophication. In terrestrial systems
nitrogen loading can lead to loss of
nitrogen-sensitive lichen species,
decreased biodiversity of grasslands,
meadows and other sensitive habitats,
767 U.S. EPA. Integrated Science Assessment for
Particulate Matter (Final Report). U.S.
Environmental Protection Agency, Washington, DC,
EPA/600/R–08/139F, 2009.
768 U.S. EPA. (2000). Deposition of Air Pollutants
to the Great Waters: Third Report to Congress.
Office of Air Quality Planning and Standards. EPA–
453/R–00–0005.
769 NO and SO secondary ISA U.S. EPA.
X
X
Integrated Science Assessment (ISA) for Oxides of
Nitrogen and Sulfur Ecological Criteria (Final
Report). U.S. Environmental Protection Agency,
Washington, DC, EPA/600/R–08/082F, 2008.

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and increased potential for invasive
species.
Building materials including metals,
stones, cements, and paints undergo
natural weathering processes from
exposure to environmental elements
(e.g., wind, moisture, temperature
fluctuations, sunlight, etc.). Pollution
can worsen and accelerate these effects.
Deposition of PM is associated with
both physical damage (materials damage
effects) and impaired aesthetic qualities
(soiling effects). Wet and dry deposition
of PM can physically affect materials,
adding to the effects of natural
weathering processes, by potentially
promoting or accelerating the corrosion
of metals, by degrading paints and by
deteriorating building materials such as
stone, concrete and marble.770 The
effects of PM are exacerbated by the
presence of acidic gases and can be
additive or synergistic due to the
complex mixture of pollutants in the air
and surface characteristics of the
material. Acidic deposition has been
shown to have an effect on materials
including zinc/galvanized steel and
other metal, carbonate stone (as
monuments and building facings), and
surface coatings (paints).771 The effects
on historic buildings and outdoor works
of art are of particular concern because
of the uniqueness and irreplaceability of
many of these objects.
(d) Environmental Effects of Air Toxics
Emissions from producing,
transporting, and combusting fuel
contribute to ambient levels of
pollutants that contribute to adverse
effects on vegetation. Volatile organic
compounds, some of which are
considered air toxics, have long been
suspected to play a role in vegetation
damage.772 In laboratory experiments, a
wide range of tolerance to VOCs has
been observed.773 Decreases in
harvested seed pod weight have been
reported for the more sensitive plants,
and some studies have reported effects
770 U.S. Environmental Protection Agency (U.S.
EPA). 2009. Integrated Science Assessment for
Particulate Matter (Final Report). EPA–600–R–08–
139F. National Center for Environmental
Assessment—RTP Division. December. Available on
the internet at http://cfpub.epa.gov/ncea/cfm/
recordisplay.cfm?deid=216546.
771 Irving, P.M., e.d. 1991. Acid Deposition: State
of Science and Technology, Volume III, Terrestrial,
Materials, Health, and Visibility Effects, The U.S.
National Acid Precipitation Assessment Program,
Chapter 24, page 24–76.
772 U.S. EPA. (1991). Effects of organic chemicals
in the atmosphere on terrestrial plants. EPA/600/3–
91/001.
773 Cape J. N., Leith, I. D., Binnie, J., Content, J.,
Donkin, M., Skewes, M., Price, D. N., Brown, A. R.,
& Sharpe, A. D. (2003). Effects of VOCs on
herbaceous plants in an open-top chamber
experiment. Environmental Pollution. 124:341–343.

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on seed germination, flowering and fruit
ripening. Effects of individual VOCs or
their role in conjunction with other
stressors (e.g., acidification, drought,
temperature extremes) have not been
well studied. In a recent study of a
mixture of VOCs including ethanol and
toluene on herbaceous plants,
significant effects on seed production,
leaf water content, and photosynthetic
efficiency were reported for some plant
species.774
Research suggests an adverse impact
of vehicle exhaust on plants, which has
in some cases been attributed to
aromatic compounds and in other cases
to nitrogen oxides.775 776 777
F. Air Quality Impacts of Non-GHG
Pollutants
Changes in emissions of non-GHG
pollutants due to these rules will impact
air quality. Information on current air
quality and the results of our air quality
modeling of the projected impacts of
these rules are summarized in the
following section.
1. Current Concentrations of Non-GHG
Pollutants
Nationally, levels of PM2.5, ozone,
NOX, SOX, CO, and air toxics have
declined significantly in the last 30
years and are continuing to drop as
previously promulgated regulations
come into full effect. However, as of
April 22, 2016, more than 125 million
people lived in counties designated
nonattainment for one or more of the
NAAQS, and this figure does not
include the people living in areas with
a risk of exceeding a NAAQS in the
future. Many Americans continue to be
exposed to ambient concentrations of air
toxics at levels which have the potential
to cause adverse health effects. In
addition, populations who live, work, or
attend school near major roads
experience elevated exposure
concentrations to a wide range of air
pollutants.
774 Cape, J. N., Leith, I. D., Binnie, J., Content, J.,
Donkin, M., Skewes, M., Price, D. N., Brown, A. R.,
& Sharpe, A. D. (2003). Effects of VOCs on
herbaceous plants in an open-top chamber
experiment. Environmental Pollution. 124:341–343.
775 Viskari E. L. (2000). Epicuticular wax of
Norway spruce needles as indicator of traffic
pollutant deposition. Water, Air, and Soil Pollution.
121:327–337.
776 Ugrekhelidze, D., Korte, F., & Kvesitadze, G.
(1997). Uptake and transformation of benzene and
toluene by plant leaves. Ecotox. Environ. Safety
37:24–29.
777 Kammerbauer H., Selinger, H, on Rommelt, R.,
Ziegler-Jons, A., Knoppik, D., & Hock, B. (1987).
Toxic components of motor vehicle emissions for
the spruce Picea abies. Environmental Pollution.
48:235–243.

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
dates for areas designated
nonattainment for the 2008 eight-hour
There are two primary NAAQS for
ozone NAAQS are in the 2015 to 2032
PM2.5: An annual standard (12.0
micrograms per cubic meter (mg/m3)) set timeframe, depending on the severity of
the problem in each area.
in 2012 and a 24-hour standard (35 mg/
Nonattainment area attainment dates
m3) set in 2006, and two secondary
associated with areas designated for the
NAAQS for PM2.5: An annual standard
2015 NAAQS will be in the 2020–2037
(15.0 mg/m3) set in 1997 and a 24-hour
timeframe, depending on the severity of
standard (35 mg/m3) set in 2006.
the problem in each area.
There are many areas of the country
that are currently in nonattainment for
EPA has already adopted many
the annual and 24-hour primary PM2.5
emission control programs that are
NAAQS. As of April 22, 2016, more
expected to reduce ambient ozone
than 23 million people lived in the
levels. As a result of these and other
seven areas that are still designated as
federal, state and local programs, eightnonattainment for the 1997 annual
hour ozone levels are expected to
PM2.5 NAAQS. These PM2.5
improve in the future. However, even
nonattainment areas are comprised of 33
with the implementation of all current
full or partial counties. As of April 22,
state and federal regulations, there are
2016, nine areas aredesignated as
projected to be counties violating the
nonattainment for the 2012 annual
PM2.5 NAAQS; these areas are composed ozone NAAQS well into the future.
of 20 full or partial counties with a
(b) Nitrogen Dioxide
population of more than 23 million. As
On April 6, 2018, based on a review
of April 22, 2016, 16 areas are
of the full body of scientific evidence,
designated as nonattainment for the
2006 24-hour PM2.5 NAAQS, these areas EPA issued a decision to retain the
current national ambient air quality
are composed of 46 full or partial
counties with a population of more than standards (NAAQS) for oxides of
32 million. In total, there are currently
nitrogen (NOX). The EPA has concluded
24 PM2.5 nonattainment areas with a
that the current NAAQS protect the
population of more than 39 million
public health, including the at-risk
people.
populations of older adults, children
The EPA has already adopted many
and people with asthma, with an
mobile source emission control
adequate margin of safety. The NAAQS
programs that are expected to reduce
for nitrogen oxides are a one-hour
ambient PM concentrations. As a result
standard at a level of 100 ppb based on
of these and other federal, state and
the three-year average of 98th percentile
local programs, the number of areas that of the yearly distribution of one-hour
fail to meet the PM2.5 NAAQS in the
daily maximum concentrations, and an
future is expected to decrease. However, annual standard at a level of 53 ppb.
even with the implementation of all
(c) Sulfur Dioxide
current state and federal regulations,
there are projected to be counties
The EPA is currently reviewing the
violating the PM2.5 NAAQS well into the
future. States will need to meet the 2006 primary SO2 NAAQS and has proposed
to retain the current primary standard
24-hour standards in the 2015–2019
timeframe and the 2012 primary annual (83 FR 26752, June 8, 2018), which is a
one-hour standard of 75 ppb established
standard in the 2021–2025 timeframe.
in June 2010. The EPA has been
Ozone
finalizing the initial area designations
The primary and secondary NAAQS
for the 2010 SO2 NAAQS in phases and
for ozone are eight-hour standards with
completed designations for most of the
a level of 0.07 ppm. The most recent
country in December 2017. The EPA is
revision to the ozone standards was in
under a court order to finalize initial
2015; the previous eight-hour ozone
designations by December 31, 2020, for
primary standard, set in 2008, had a
level of 0.075 ppm. As of April 22, 2016, a remaining set of about 50 areas where
states have deployed new SO2
there were 44 ozone nonattainment
monitoring networks. As of July 2018,
areas for the 2008 ozone NAAQS,
composed of 216 full or partial counties, the EPA has designated 42 areas as
nonattainment for the 2010 SO2 NAAQS
with a population of more than 120
million.
in actions taken in 2013, 2016, and
States with ozone nonattainment
2017.778 There also remain nine
areas are required to take action to bring nonattainment areas for the primary
those areas into attainment. The
annual SO2 NAAQS set in 1971.
attainment date assigned to an ozone
nonattainment area is based on the
778 78 FR 47191, 81 FR 45049, 81 FR 89870, 83
FR 1098, and 83 FR 14597.
area’s classification. The attainment

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(a) Particulate Matter

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43349

(d) Carbon Monoxide
There are two primary NAAQS for
CO: An eight-hour standard (9 ppm) and
a one-hour standard (35 ppm). The
primary NAAQS for CO were retained
in August 2011. There are currently no
CO nonattainment areas; as of
September 27, 2010, all CO
nonattainment areas have been
redesignated to attainment.
The past designations were based on
the existing community-wide
monitoring network. EPA is making
changes to the ambient air monitoring
requirements for CO. The new
requirements are expected to result in
approximately 52 CO monitors
operating near roads within 52 urban
areas by January 2015 (76 FR 54294,
August 31, 2011).
(e) Diesel Exhaust PM
Because DPM is part of overall
ambient PM and cannot be easily
distinguished from overall PM, we do
not have direct measurements of DPM
in the ambient air. DPM concentrations
are estimated using ambient air quality
modeling based on DPM emission
inventories. DPM emission inventories
are computed as the exhaust PM
emissions from mobile sources
combusting diesel or residual oil fuel.
DPM concentrations were recently
estimated as part of the 2011 NATA.
Areas with high concentrations are
clustered in the Northeast, Great Lake
States, California, and the Gulf Coast
States and are also distributed
throughout the rest of the U.S. The
median DPM concentration calculated
nationwide is 0.76 mg/m3.
(f) Air Toxics
The most recent available data
indicate that the majority of Americans
continue to be exposed to ambient
concentrations of air toxics at levels
which have the potential to cause
adverse health effects. The levels of air
toxics to which people are exposed vary
depending on where people live and
work and the kinds of activities in
which they engage, as discussed in
detail in EPA’s most recent Mobile
Source Air Toxics Rule. According to
the National Air Toxic Assessment
(NATA) for 2015, mobile sources were
responsible for 50% of outdoor
anthropogenic toxic emissions and were
the largest contributor to cancer and
noncancer risk from directly emitted
pollutants. Mobile sources are also large
contributors to precursor emissions
which react to form air toxics.
Formaldehyde is the largest contributor
to cancer risk of all 71 pollutants
quantitatively assessed in the 2011

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NATA. Mobile sources were responsible
for more than 25% of primary
anthropogenic emissions of this
pollutant in 2011 and are major
contributors to formaldehyde precursor
emissions. Benzene is also a large
contributor to cancer risk, and mobile
sources account for almost 80% of
ambient exposure. Over the years, EPA
has implemented a number of mobile
source and fuel controls which have
resulted in VOC reductions, which also
reduced formaldehyde, benzene and
other air toxic emissions.
2. Air Quality Impacts of Non-GHG
Pollutants

sradovich on DSK3GMQ082PROD with PROPOSALS2

(a) Impacts of Proposed Standards on
Future Ambient Concentrations of
PM2.5, Ozone and Air Toxics
Full-scale photochemical air quality
modeling is necessary to accurately
project levels of criteria pollutants and
air toxics. For the final rule, a nationalscale air quality modeling analysis will
be performed to analyze the impacts of
the standards on PM2.5, ozone, and
selected air toxics (i.e., benzene,
formaldehyde, acetaldehyde, acrolein
and 1,3-butadiene). The length of time
needed to prepare the necessary

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emissions inventories, in addition to the
processing time associated with the
modeling itself, has precluded us from
performing air quality modeling for this
proposal.
Section VI.D.2 of the preamble
present projections of the changes in
criteria pollutant and air toxics
emissions because of the proposed
vehicle standards; the basis for those
estimates is set out in Chapter 10 of the
PRIA. The atmospheric chemistry
related to ambient concentrations of
PM2.5, ozone and air toxics is very
complex, and making predictions based
solely on emissions changes is
extremely difficult.
3. Other Unquantified Health and
Environmental Effects
In addition, the agencies seek
comment on whether there are any other
health and environmental impacts
associated with advancements in
technologies that should be considered.
For example, the use of technologies
and other strategies to reduce fuel
consumption and/or GHG emissions
could have effects on a vehicle’s lifecycle impacts (e.g., materials usage,
manufacturing, end of life disposal),

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beyond the issues regarding fuel
production and distribution (upstream)
GHG emissions discussed in Section
VI.D.2. The agencies seek comment on
any studies or research in this area that
should be considered in the future to
assess a fuller range of health and
environmental impacts from the lightduty vehicle fleet shifting to different
technologies and/or materials. At this
point, it is unclear whether there is
sufficient information about the
lifecycle impacts of the myriad of
available technologies, materials, and
cradle-to-grave pathways to conduct the
type of detailed assessments that would
be needed in a regulatory context, but
the agencies seek comment on any
current or future studies and research
underway on this topic, and how such
analysis could practicably and in a
balanced way be integrated in the
modeling, especially considering the
characterization of specific vehicles in
the analysis fleet and the
characterization of specific technology
options.
G. What are the impacts on the total
fleet size, usage, and safety?
1. CAFE Standards

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43351

Table VII-88- Cumulative Changes in Fleet Size, Usage and Fatalities for MY's 1977-2029
U nder CAFEP ro~ ram
Model Year Standards
MY
MY
MY
MY
MY
Through
2021
2022
2023
2024
2025
Cumulative Changes in Fleet Size, Usage and Fatalities Through MY 2029
-31
-28
-38
-48
-46
Fleet Size (millions)
45%
45%
45%
45%
45%
Share LT, CY 2040
VMT, Fatalities, and Fuel Consumption for MY's 2017-2029
-222
-149
-200
-236
-219
VMT, with rebound
(billion miles)
-48
-29
-43
-46
-70
VMT, without rebound
(billion miles)
Fatalities, with rebound
-1,840
-1,160
-1,740
-2,010
-1,880
-420
-175
-452
-442
-666
Fatalities, without
rebound
Fuel Consumption, with
20
14
18
23
17
rebound (billion gallons)
Fuel Consumption,
26
18
23
29
21
without rebound (billion
gallons)
VMT, Fatalities, and Fuel Consumption for MY's 1977-2016
-115
VMT, with rebound
-76.6
-70.4
-88.0
-91.4
(billion miles)
-119
VMT, without rebound
-79.3
-72.8
-91.0
-94.5
(billion miles)
-711
-646
-804
-829
Fatalities, with rebound
-1,060
-832
-856
-737
-669
Fatalities, without
-1,090
rebound
Fuel Consumption, with
-3.33
-2.87
-3.58
-4.65
-3.65
rebound (billion gallons)
Fuel Consumption,
-3.46
-2.98
-3.71
-4.82
-3.78
without rebound (billion
gallons)

MY
2026

TOTAL

0
45%

-190
N/A

0

-1,030

0

-235

0
0

-8,630
-2,160

0

91

0

116

0

-441

0

-457

0
0

-4,050
-4,180

0

-18.1

0

-18.8

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2. CO2 Standards

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

H. What other impacts (quantitative and
unquantifiable) will these proposed
standards have?

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1. Sensitivity Analysis
As discussed at the beginning of this
section, results presented today reflect
the agencies’ best judgments regarding
many different factors. Based on
analyses in past rulemakings, the
agencies recognize that some analytical
inputs are especially uncertain, some
are likely to exert considerable
influence over specific types of

779 The CAFE model and all inputs and outputs
supporting today’s proposal are available at https://

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estimated impacts, and some are likely
to do so for the bulk of the analysis. To
explore the sensitivity of estimated
impacts to changes in model inputs,
analysis was conducted using
alternative values for a range of different
inputs. Results of this sensitivity
analysis are summarized below, and
detailed model inputs and outputs are
available on NHTSA’s website.779
Regulatory alternatives are identical
across all cases, except that one case
includes an increase in civil penalty rate

www.nhtsa.gov/corporate-average-fuel-economy/
compliance-and-effects-modeling-system.

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starting in MY 2019; NHTSA may
consider changing the civil penalty rate
in a separate regulatory action, and
depending on the timing of any such
action, the final rule to follow today’s
proposal could reflect the change.780
The following table lists the cases
included in the sensitivity analysis. The
final rule could adopt any
combination—or none—of these
alternatives as reference case inputs,
and the agencies invite comment on all
of them.

780 83

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FR 13904 (Apr. 2, 2018).

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43353

·t A nalySIS
Ta bl e VII-90 - C ases I ncldd.
u e Ill Sens•·rIvHy
Description

High Oil Price

Reference case
Assume 50% loss in consumer surplus- equivalent to
the assumption that consumers will only value the
calculated benefits they receive at 50 percent of the
analysis estimates
75% loss in consumer surplus
New vehicle sales will remain at levels specified for MY
2016 in the market data input file
Keeps average new vehicle prices at MY 2016 levels
within the scrappage model throughout the model
simulation; this disables the effect of slower scrappage
when new vehicle prices increase across more stringent
scenanos.
Disables both the scrappage price effect and the fleet
share and sales response.
High fuel price estimates

High Oil Price with 60 Month Payback

High fuel price estimates and a 60-mo. payback period

Low Oil Price

Low fuel price estimates

Low Oil Price with 12 Month Payback

Low fuel price estimates and a 12-mo. payback period

High GDP
High GDP with High Oil Price

High GDP growth rate
High GDP growth rate and high fuel price estimates

High GDP with Low Oil Price

High GDP growth rate and low fuel price estimates

LowGDP

Low GDP growth rate

Low GDP with High Oil Price

Low GDP growth rate and high fuel price estimates

Low GDP with Low Oil Price

Low GDP growth rate and low fuel price estimates
On-road gap (difference between rated fuel economy
and observed fuel economy) is set to 0 .1.
On-road gap is set to 0.3
12-month payback period (i.e., voluntary application of
technologies paying back within first year of vehicle
ownership)

Consumer Benefit at 50%
Consumer Benefit at 75%
Fleet Share and Sales Response Disabled

Disable Scrappage Price Effect

Scrappage and Fleet Share Disabled

On Road Gap 0.10
On Road Gap 0.30

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12 Month Payback Period
24 Month Payback Period

24-month payback period

36 Month Payback Period

36-month payback period

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Sensitivity Case
Reference Case

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Rebound Effect at 10%

Rebound effect, the increase miles traveled as the cost of
travel decreases, is set to 10%

Rebound Effect at 30%

Rebound effect set to 30%

High Social Cost of Carbon

Redesign cadence (schedule of major technology
upgrades for vehicles, engines, etc.) is extended to 1.2
times that of the reference case (rounded to nearest MY)
Redesign cadence shortened to a 0.8 times that of the
reference case (rounded to nearest MY)
Lower bounds of confidence interval of safety
coefficients
Upper bounds of confidence interval of safety
coefficients
Improvements in successive MY vehicles stabilize 5
years earlier than central case
Improvements in successive MY vehicles stabilize 5
years later than central case
High social cost of carbon

Low Social Cost of Carbon

Low social cost of carbon

High HEV Battery Costs

HEV battery costs 1/3 more than in reference case

Low HEV Battery Costs

HEV battery costs 1/3 less than in reference case

Exclude Strong Hybrids

Strong hybrids are excluded from the analysis

Include HCR2 Engines

HCR2 (advanced high compression ratio engine) is
included in the analysis

Fines at $14 in 2019

CAFE compliance fines are set to $14 beginning in 2019

Technology Cost Markup 1.10

Technology retail price equivalent (RPE) of 1.10 (i.e.,
10% markup of direct costs)

Technology Cost Markup 1.19

Technology retail price equivalent (RPE) of 1.19 (i.e.,
19% markup of direct costs)

Long Fleet Redesign Cadence
Short Fleet Redesign Cadence
Safety Coefficient at 5th Percentile
Safety Coefficient at 95th Percentile
Fatalities Flat Earlier

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Fatalities Flat Later

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43355

781 Climate-related economic damages caused by
emissions of GHGs other than CO2 were estimated
by converting those emissions to their (mass)
equivalents in CO2 emissions and applying the perton damage costs used to monetize CO2 emissions.
Specifically, emissions of methane (CH4) and
nitrous oxide (N2O) were converted to their
equivalent in CO2 emissions using the 100-year
Global Warming Potentials (GWPs) for those gases,

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which are 25 for CH4 and 298 for N2O. These GWPs
were estimated by the United Nations
Intergovernmental Panel on Climate Change in its
4th Assessment Report (available at https://
www.ipcc.ch/publications_and_data/ar4/wg1/en/
ch2s2-10-2.html; last accessed July 19, 2018). An
alternative approach would be to develop direct
estimates of the climate damage costs for these
GHGs derived using the same process that was used
to estimate the SCC, described previously in PRIA

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Chapter 8.11.2 and the Appendix to Chapter 8. For
comparison, using the alternative approach results
in estmates which average $256 per (metric) ton for
CH4 and $2,820 for N2O over the analysis period,
or about 22% and 13% higher than the values used
in this sensitivity case. A detailed description of the
methods used to construct these alternative values
is available in the docket for this rule. The agency
will consider using this alternative approach in its
analysis supporting the final rule.

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The remaining tables in the section
summarize various estimated impacts as
estimated for all of the cases included
in the sensitivity analysis.

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Sensitivity Case

Average
Required
CAFE
Standard
(mpg)

Average
Achieved
CAFE
Level
(mpg)

Vehicle
Sales
(xl,OOO)

Employment
Hours (xl,OOO)

37.0
37.0
37.0

39.7
39.7
39.7

18,006
18,006
18,006

2,527,497
2,527,497
2,527,497

36.9

39.5

16,578

2,339,120

37.0
36.9
38.3
38.2
36.0
36.0
37.0
38.3
36.0
37.0
38.3
36.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0
37.0

39.7
39.5
43.2
48.4
37.7
37.6
39.7
43.2
37.7
39.7
43.2
37.7
39.5
39.9
38.8
39.3
40.0
39.7
39.7
39.9
39.8
39.7
39.7
39.7
39.7
39.7
39.7
39.7
39.7
39.8
41.1
39.8
40.5

18,006
16,578
18,003
17,960
18,006
18,000
18,092
18,089
18,092
17,457
17,454
17,457
18,004
18,005
18,004
18,006
18,005
18,006
18,006
18,000
18,003
18,006
18,006
18,006
18,006
18,006
18,006
18,006
18,006
18,006
18,012
18,007
18,012

2,527,497
2,339,120
2,486,835
2,550,397
2,565,428
2,568,164
2,539,507
2,498,657
2,577,619
2,450,393
2,410,837
2,487,169
2,527,780
2,529,090
2,523,931
2,525,462
2,529,575
2,527,497
2,527,497
2,533,310
2,537,370
2,527,497
2,527,497
2,527,497
2,527,497
2,527,497
2,527,497
2,527,497
2,527,634
2,527,741
2,523,575
2,528,506
2 530 142

Reference Case
Consumer Benefit at 50%
Consumer Benefit at 75%
Fleet Share and Sales Response
Disabled
Scrappage Price Effect Disabled
Scrappage and Fleet Share Disabled
High Oil Price
High Oil Price with 60 Month Payback
Low Oil Price
Low Oil Price with 12 Month Payback
HighGDP
LowGDP
High GDP with High Oil Price
High GDP with Low Oil Price
Low GDP with High Oil Price
Low GDP with Low Oil Price
On Road Gap 0.10
On Road Gap 0.30
12 Month Payback Period
24 Month Payback Period
36 Month Payback Period
Rebound Effect at 10%
Rebound Effect at 30%
Long Fleet Redesign Cadence
Short Fleet Redesign Cadence
Safety Coefficient at 5th Percentile
Safety Coefficient at 95th Percentile
Fatalities Flat Earlier
Fatalities Flat Later
High Social Cost of Carbon
Low Social Cost of Carbon
High HEV Battery Costs
Low HEV Battery Costs
Exclude Strong Hybrids
Include HCR2 Engines
Fines at $14 in 2019
Technology Cost Markup 1.10

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Table VII-91 - Average Required and Achieved CAFE Levels, Vehicle Sales,
and Employment Hours under Proposed CAFE Standards (MY 2029 Combined Fleet)

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

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37.0
37.0
37.2
37.0
37.0

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40.3
39.8
39.4
39.2
39.8
39.7
39.7

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18,011
18,009
18,010
18,001
17,995
18,007
18,006
18,006

E:\FR\FM\24AUP2.SGM

2,528,548
2,529,575
2,526,972
2,527,326
2,528,328
2,520,290
2,527,497
2,527,497

24AUP2

EP24AU18.255

sradovich on DSK3GMQ082PROD with PROPOSALS2

Technology Cost Markup 1.19
Technology Cost Markup 1.24
Technology Cost Markup 1.37
Technology Cost Markup 1.75
Technology Cost Markup 2.00
AE020 18 Fuel Prices
Utility Value Loss in HEV s
Nonzero Valuation ofC~ and N 2 0

43357

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Table VII-92- Average Required and Achieved C02 Levels, Vehicle Sales, and
Employment Hours under Proposed C02 Standards (MY 2029 Combined Fleet)
Average
Average
Vehicle
Employment
Required C02
Achieved
Sensitivity Case
Hours
Sales
Standard
C02 Rating
(xl,OOO)
(xl,OOO)
(g/mile)
(g/mile)
Reference Case
240.1
229.6
18,016
2,519,524
Consumer Benefit at 50%
240.1
229.6
18,016
2,519,524
Consumer Benefit at 75%
240.1
229.6
18,016
2,519,524
Fleet Share and Sales Response
241.3
230.7
16,578
2,331,605
Disabled
Scrappage Price Effect Disabled
240.1
229.6
18,016
2,519,524
Scrappage and Fleet Share Disabled
241.3
230.7
16,578
2,331,605
High Oil Price
231.8
207.3
18,006
2,485,426
232.7
186.6
17,965
2,547,313
High Oil Price with 60 Month Payback
Low Oil Price
246.2
242.8
18,019
2,554,288
246.1
243.9
18,018
2,554,045
Low Oil Price with 12 Month Payback
HighGDP
240.1
230.1
18,102
2,530,790
LowGDP
231.8
207.3
18,092
2,497,237
High GDP with High Oil Price
246.2
242.8
18,105
2,566,418
240.1
230.1
17,468
2,442,039
High GDP with Low Oil Price
Low GDP with High Oil Price
231.8
207.3
17,457
2,409,607
Low GDP with Low Oil Price
246.2
242.4
17,469
2,476,916
On Road Gap 0.10
240.1
230.6
18,015
2,518,279
On Road Gap 0.30
240.2
227.7
18,014
2,520,876
12 Month Payback Period
239.8
237.2
18,019
2,511,392
24 Month Payback Period
240.0
232.5
18,018
2,515,942
240.2
226.2
18,012
2,523,599
36 Month Payback Period
Rebound Effect at 10%
240.1
229.6
18,016
2,519,524
Rebound Effect at 30%
240.1
229.6
18,016
2,519,524
240.0
227.6
18,012
2,525,628
Long Fleet Redesign Cadence
Short Fleet Redesign Cadence
240.3
227.8
18,014
2,524,315
Safety Coefficient at 5th Percentile
240.1
229.6
18,016
2,519,524
Safety Coefficient at 95th Percentile
240.1
229.6
18,016
2,519,524
Fatalities Flat Earlier
240.1
229.6
18,016
2,519,524
240.1
229.6
18,016
2,519,524
Fatalities Flat Later
240.1
229.6
18,016
2,519,524
High Social Cost of Carbon
240.1
229.6
18,016
2,519,524
Low Social Cost of Carbon
240.1
229.6
18,016
2,519,524
High HEV Battery Costs
Low HEV Battery Costs
240.0
230.0
18,017
2,517,939
Exclude Strong Hybrids
240.1
229.1
18,016
2,519,640
Include HCR2 Engines
240.1
220.0
18,016
2,516,858
240.2
222.1
18,017
2,523,878
Technology Cost Markup 1.10
Technology Cost Markup 1.19
240.2
224.6
18,018
2,521,079
Technology Cost Markup 1.24
240.1
226.6
18,019
2,518,399

VerDate Sep<11>2014

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

43360

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.

---

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I

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I

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Reference Case
Consumer Benefit at 50%
Consumer Benetlt at 75%
Fleet Share and Sales Response
Disabled
Scrappage Price Effect Disabled
Scrappage and Fleet Share Disabled
High Oil Price
High Oil Price with 60 Month Payback
Low Oil Price
Low Oil Price with 12 Month Payback
High GDP
LowGDP
High GDP with High Oil Price
High GDP with Low Oil Price
Low GOP with High Oil Price
Low GDP with Low Oil Price
On Road Gap 0.10
On Road Gap 0.30
12 Month Payback Period
24 Month Payback Period
36 Month Payback Period
Rebom1d Effect at 10%

.

----------------------------------------.------

Initial
Average
Vehicle
MSRP
Model Year
2016
32,048
32,048
32,04X

- - - - - - - - - - - - - .. ·--------------

I

CAFE Pro2ram

,-,

GHG Pro2ram

Average
Vehicle
MSRP Model
Year 2029

Average Vehicle
MSRP Model Year
2029, No-Action
Alternative

Average
Vehicle
MSRPModel
Year 2016

Average
Vehicle
MSRP Model
Year 2029

Average Vehicle
MSRP Model Year
2029, No-Action
Alternative

32,774
32,774
32,774

34,813
34,813
34,X 13

32,048
32,048
32,04X

32,550
32,550
32,550

35,031
35,031
35,031

32,04X

32,904

34,7XX

32,04X

32,700

34,942

32,04X
32,048
32,048
32,048
32,048
32,04X
32,048
32,048
32,048
32,048
32,04X
32,048
32,048
32,048
32,048
32,04X
32,048
32,048

32,774
32,904
32,133
33,234
33,357
33,393
32,774
32,133
33,357
32,774
32,131
33,357
32,774
32,804
32,720
32,745
32,811
32,774

34,Xl3
34,788
33,709
33,833
35,634
35,645
34,813
33,709
35,634
34,813
33,711
35,634
34,816
34,772
34,833
34,X23
34,767
34,813

32,04X
32,048
32,048
32,048
32,048
32,04X
32,048
32,048
32,048
32,048
32,04X
32,048
32,048
32,048
32,048
32,04X
32,048
32,048

32,550
32,700
32,069
33,147
33,083
33,07X
32,541
32,069
33,084
32,542
32,069
33,091
32,531
32,592
32,421
32,496
32,636
32,550

35,031
34,942
33,811
33,681
35,909
35,933
35,038
33,812
35,910
35,032
33,XII
35,912
35,075
35,004
35,161
35,07X
34,996
35,031

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.258

-------

sradovich on DSK3GMQ082PROD with PROPOSALS2

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32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
n/a
32,048

32,774
32,848
32,854
32,774
32,774
32,774
32,774
32,774
32,774
32,774
32,770
32,775
32,686
32,787
32,654
32,676
32,712
32,716
32,864
32,954
32,663
32,774
n/a
32,774

34,813
34,755
34,850
34,813
34,813
34,813
34,813
34,813
34,813
34,813
34,625
34,606
34,136
34,825
34,084
34,240
34)28
34,570
35,253
35,640
34,691
34,813
n/a
34,813

32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
n/a
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048
32,048

32,550
32,651
32,658
32,550
32,550
32,550
32,550
32,550
32,550
32,550
32,527
32,555
32,527
n/a
32,525
32,511
32,483
32,520
32,595
32,616
32,450
32,550
32,395
32,550

35,031
34,905
35,021
35,031
35,031
35,031
35,031
35,031
35,031
35,031
34,778
34,821
34,177
n/a
34,205
34,375
34,471
34,771
35,560
36,067
34,885
35,031
34,861
35,031

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Rebound Effect at 30%
Long Fleet Redesign Cadence
Short Fleet Redesign Cadence
Safety Coefficient at 5th Percentile
Safety Coefficient at 95th Percentile
Fatalities Flat Earlier
Fatalities Flat Later
High Social Cost of Carbon
Low Social Cost of Carbon
High HEV Battery Costs
Low HEY Battery Costs
Exclude Strong Hybrids
Include HCR2 Engines
Fines at $14 in 2019
Technology Cost Markup 1.10
Technology Cost Markup 1.19
Technology Cost Markup 1.24
Technology Cost Markup 1.37
Technology Cost Markup 1.75
Technology Cost Markup 2.00
AE02018 Fuel Prices
Utility Value Loss in HEVs
Perfect Trading of C0 2 Credits
Nonzero Valuation ofCIL and N 2 0

43361

EP24AU18.259

sradovich on DSK3GMQ082PROD with PROPOSALS2

43362

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Consumption with Rebound

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Reference Case
Consumer Benefit at 50%
Consumer Benefit at 75%
Fleet Share and Sales Response
Disabled
Scrappage Price Effect
Disabled
Scrappage and Fleet Share
Disabled
High Oil Price
High Oil Price with 60 Month
Payback
Low Oil Price
Low Oil Price with 12 Month
Payback
HighGDP
LowGDP
High GD P with High Oil Price
High GDP with Low Oil Price
Low GDP with High Oil Price
Low GDP with Low Oil Price
On Road Gap 0.10
On Road Gap 0.30

2040

C02
(mmt)

-190
-190
-190

(%)
4538%
4538%
4538%

VMT
(billion
Miles)

809
809
809

-1,470
-1,470
-1,470

-12,680
-12,680
-12,680

Fuel
Cons.
(billion
gallons)
73.1
73.1
73.1

-202

4627%

718

-1,550

-13,370

64.9

-830

-7,440

88

-44

4572%

986

-920

-7,820

89.1

-140

-1,490

114

-59

4663%

894

-1,010

-8,560

80.8

-280

-2,640

104

-174

3383%

138

-1,510

-13,140

12.7

-680

-6,590

51

-51

3541%

65

-490

-4,300

6.2

-270

-2,720

23

-185

5364%

1,297

-1,250

-10,920

117.1

-630

-5,770

126

-181

5338%

1,293

-1,240

-10,810

116.7

-610

-5,650

126

-191
-174
-185
-186
-170
-180
-192
-181

4540%
3380%
5368%
4532%
3388%
5351%
4537%
4549%

803
136
1,288
787
135
1,260
747
889

-1,460
-1,510
-1,250
-1,430
-1,470
-1,220
-1,500
-1,390

-12,660
-13,100
-10,910
-12,340
-12,800
-10,670
-12,980
-12,000

72.6
12.5
116.3
71.2
12.4
113.8
67.6
80.4

-690
-680
-630
-670
-670
-610
-700
-650

-6,350
-6,580
-5,780
-6,180
-6,400
-5,650
-6,440
-5,950

97
51
126
95
50
123
90
108

Fleet Size
(millions)

Share
LT,CY

VMT, Fatalities and Fuel
Consumption without Rebound

Fatalities

VMT
(billion
Miles)

Fatalities

-690
-690
-690

-6,340
-6,340
-6,340

Fuel
Cons.
(billion
gallons)
98
98
98

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.260

Table VII-94 - Cumulative Changes in Fleet Size, Travel (VMT), Fatalities, Fuel Consumption and C02 Emissions through
1\'IY 2029 under Prooosed CAFE Standard

sradovich on DSK3GMQ082PROD with PROPOSALS2

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-210
-202
-179
-190
-190
-175
-182

4493%
4525%
4550%
4538%
4538%
4508%
4541%

901
854
762
945
673
827
631

-1,670
-1,570
-1,370
-1,080
-1,860
-1,390
-1,330

-14,470
-13,600
-11,840
-9,510
-15,850
-12,280
-11,730

81.4
77.2
69.0
85.4
60.8
75.1
56.9

-780
-750
-640
-690
-690
-630
-680

-7,270
-6,860
-5,900
-6,340
-6,340
-6,080
-6,390

109
103
92
98
98
98
77

-190

4538%

809

-1,470

-10,830

73.1

-690

-4,630

98

-190

4538%

809

-1,470

-14,520

73.1

-690

-8,050

98

-190
-190
-190
-190
-190
-180
-184
-140
-194
-142
-152
-154
-175
-214
-236
-196
-190

4538%
4538%
4538%
4538%
4538%
4539%
4542%
4551%
4538%
4566%
4560%
4559%
4545%
4524%
4509%
4418%
45.4

809
809
809
809
809
835
751
623
766
695
723
715
802
837
850
768
809

-1,470
-1,470
-1,470
-1,470
-1,470
-1,450
-1,420
-1,140
-1,460
-1,190
-1,250
-1,250
-1,400
-1,580
-1,660
-1,530
-1,470

-12,680
-12,680
-12,680
-12,680
-12,680
-12,520
-12,210
-9,900
-12,580
-10,310
-10,810
-10,770
-12,110
-13,650
-14,420
-13,180
-12,680

73.1
73.1
73.1
73.1
73.1
75.5
68.7
56.3
69.2
62.9
65.4
64.7
72.5
75.7
76.8
69.5
73.1

-690
-1,470
-690
-690
-690
-670
-690
-530
-710
-540
-570
-570
-640
-760
-820
-720
-690

-6,340
-12,680
-6,340
-6,340
-6,340
-6,090
-6,300
-4,940
-6,470
-4,950
-5,220
-5,240
-5,910
-7,000
-7,600
-6,620
-6,340

98
73
98
98
98
100
90
74
93
84
87
86
97
101
103
96
98

-190

45.4

809

-1,470

-12,680

73.1

-690

-6,340

98

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

12 Month Payback Period
24 Month Payback Period
36 Month Payback Period
Rebound Effect at 10%
Rebound Effect at 30%
Long Fleet Redesign Cadence
Short Fleet Redesign Cadence
Safety Coefficient at 5th
Percentile
Safety Coefficient at 95th
Percentile
Fatalities Flat Earlier
Fatalities Flat Later
High Social Cost of Carbon
Low Social Cost of Carbon
High HEV Battery Costs
Low HEV Batterv Costs
Exclude Strong Hybrids
Include HCR2 Engines
Fines at $14 in 2019
Technology Cost Markup 1.10
Technology Cost Markup 1.19
Technology Cost Markup 1.24
Technology Cost Markup 1.37
Technology Cost Markup 1.75
Technology Cost Markup 2.00
AE02018 Fuel Prices
Utility Value Loss in HEV s
Nonzero Valuation ofCH4 and
N20

43363

EP24AU18.261

sradovich on DSK3GMQ082PROD with PROPOSALS2

43364

VerDate Sep<11>2014
--------

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Consumption with Rebound

Jkt 244001

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E:\FR\FM\24AUP2.SGM
24AUP2

Reference Case
Consumer Benefit at 50%
Consumer Benefit at 75%
Fleet Share and Sales Response
Disabled
Scrappage Price Effect Disabled
Scrappage and Fleet Share
Disabled
High Oil Price
High Oil Price with 60 Month
Payback
Low Oil Price
Low Oil Price with 12 Month
Payback
HighGDP
LowGDP
High GDP with High Oil Price
High GDP with Low Oil Price
Low GDP with High Oil Price
Low GDP with Low Oil Price
On Road Gap 0.10
On Road Gap 0.30
12 Month Payback Period
24 Month Payback Period

VMT, Fatalities and Fuel
Consumption without Rebound

Fleet Size
(millions)

Share LT,
CY 2040
(%)

C02
(mmt)

VMT
(billion
Miles)

Fatalities

Fuel Cons.
(billion
gallons)

-190
-190
-190

4538%
4538%
4538%

809
809
809

-1,470
-1,470
-1,470

-12,680
-12,680
-12,680

73.1
73.1
73.1

VMT
(billion
Miles)
-690
-690
-690

Fatalities

Fuel Cons.
(billion
gallons)

-6,340
-6,340
-6,340

98
98
98

-202

4627%

718

-1,550

-13,370

64.9

-830

-7,440

88

-44

4572%

986

-920

-7,820

89.1

-140

-1,490

114

-59

4663%

894

-1,010

-8,560

80.8

-280

-2,640

104

-174

3383%

138

-I ,510

-13,140

12.7

-680

-6,590

51

-51

3541%

65

-490

-4,300

6.2

-270

-2,720

23

-185

5364%

1,297

-1,250

-10,920

117.1

-630

-5,770

126

-181

5338%

1,293

-1,240

-10,810

116.7

-610

-5,650

126

-191
-174
-185
-186
-170
-180
-192
-181
-210
-202

4540%
3380%
5368%
4532%
3388%
5351%
4537%
4549%
4493%
4525%

803
136
1,288
787
135
1,260
747
889
901
854

-1,460
-1,510
-1,250
-I ,430
-1,470
-1,220
-1,500
-1,390
-1,670
-1,570

-12,660
-13,100
-10,910
-12,340
-12,800
-10,670
-12,980
-12,000
-14,470
-13,600

72.6
12.5
116.3
71.2
12.4
113.8
67.6
80.4
81.4
77.2

-690
-680
-630
-670
-670
-610
-700
-650
-780
-750

-6,350
-6,580
-5,780
-6,180
-6,400
-5,650
-6,440
-5,950
-7,270
-6,860

97
51
126
95
50
123
90
108
109
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23:42 Aug 23, 2018
EP24AU18.262

Table VII-95- Cumulative Changes in Fleet Size, Travel (VMT), Fatalities, Fuel Consumption and C0 2 Emissions through
MY 2029 under Pronosed co, Standard

sradovich on DSK3GMQ082PROD with PROPOSALS2

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-179
-190
-190
-175
-182

4550%
4538%
4538%
4508%
4541%

762
945
673
827
631

-1,370
-1,080
-1,860
-1,390
-1,330

-11,840
-9,510
-15,850
-12,280
-11,730

69.0
85.4
60.8
75.1
56.9

-640
-690
-690
-630
-680

-5,900
-6,340
-6,340
-6,080
-6,390

92
98
98
98
77

-190

4538%

809

-1,470

-10,830

73.1

-690

-4,630

98

-190

4538%

809

-1,470

-14,520

73.1

-690

-8,050

98

-190
-190
-190
-190
-190
-180
-184
-140
-142
-152
-154
-175
-214
-236
-196
-190
-242

4538%
4538%
4538%
4538%
4538%
4539%
4542%
4551%
4566%
4560%
4559%
4545%
4524%
4509%
4418%
4538%
44.9

809
809
809
809
809
835
751
623
695
723
715
802
837
850
768
809
848

-1,470
-1,470
-1,470
-1,470
-1,470
-1,450
-1,420
-1,140
-1,190
-1,250
-1,250
-1,400
-1,580
-1,660
-1,530
-1,470
-1,860

-12,680
-12,680
-12,680
-12,680
-12,680
-12,520
-12,210
-9,900
-10,310
-10,810
-10,770
-12,110
-13,650
-14,420
-13,180
-12,680
-16,460

73.1
73.1
73.1
73.1
73.1
75.5
68.7
56.3
62.9
65.4
64.7
72.5
75.7
76.8
69.5
73.1
76.3

-690
-1,470
-690
-690
-690
-670
-690
-530
-540
-570
-570
-640
-760
-820
-720
-690
-950

-6,340
-12,680
-6,340
-6,340
-6,340
-6,090
-6,300
-4,940
-4,950
-5,220
-5,240
-5,910
-7,000
-7,600
-6,620
-6,340
-9,060

98
73
98
98
98
100
90
74
84
87
86
97
101
103
96
98
106

-232

45.2

876

-1,780

-15,560

79.1

-880

-8,260

108

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

36 Month Payback Period
Rebound Effect at 10%
Rebound E±Icct at 30%
Long Fleet Redesign Cadence
Short Fleet Redesign Cadence
Safety Coefficient at 5th
Percentile
Safety Coefficient at 95th
Percentile
Fatalities Flat Earlier
Fatalities Flat Later
High Social Cost of Carbon
Low Social Cost of Carbon
High HEY Battery Costs
Low HEV Batterv Costs
Exclude Strong Hybrids
Include HCR2 Engines
Technology Cost Markup 1.10
Technology Cost Markup 1.19
Technology Cost Markup 1.24
Technology Cost Markup 1.37
Technology Cost Markup 1. 75
Technology Cost Markup 2.00
AE020 18 Fuel Prices
Utility Value Loss in HEV s
Perfect Trading of C02 Credits
Nonzero Valuation ofC~ and
N20

43365

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43366

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Table VII-96- Change in Total Regulatory Costs during MYs 2017-2029 under Proposed
CAFE and C02 Standards

Reference Case
Consumer Benefit at 50%
Consumer Benefit at 75%
Fleet Share and Sales Response Disabled
Disable Scrappage Price Effect
Disable Scrappage Price Effect and Fleet
Share and Sales Response
High Oil Price
High Oil Price with 60 Month Payback
Low Oil Price
Low Oil Price with 12 Month Payback
High GDP
High GDP with High Oil Price
High GDP with Low Oil Price
LowGDP
Low GDP with High Oil Price
Low GDP with Low Oil Price
On Road Gap 0.10
On Road Gap 0.30
12 Month Payback Period
24 Month Payback Period
36 Month Payback Period
Rebound Effect at 10%
Rebound Effect at 30%
Long Fleet Redesign Cadence
Short Fleet Redesign Cadence
Safety Coefficient at 5th Percentile
Safety Coefficient at 95th Percentile
Fatalities Flat Earlier
Fatalities Flat Later
High Social Cost of Carbon
Low Social Cost of Carbon
High HEV Battery Costs
Low HEV Battery Costs
Exclude Strong Hybrids
Include HCR2 Engines
Fines at $14 in 2019
Technology Cost Markup 1.10

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C02 Standards
Percent
Total
Change from
Regulatory
Reference
Costs ($b)
Case
-325.7
n/a
-325.7
0.0
-325.7
0.0
-299.4
-8.1
-325.7
0.0

-299.5

-6.2

-299.4

-8.1

-244.4
-88.3
-354.5
-353.1
-319.4
-244.5
-354.8
-307.9
-236.1
-342.0
-321.4
-311.7
-328.7
-325.4
-309.4
-319.1
-319.1
-306.7
-259.6
-319.1
-319.1
-319.1
-319.1
-319.1
-319.1
-319.1
-283.5
-280.7
-209.0
-310.7
-219.3

-23.4
-72.3
11.1
10.6
0.1
-23.4
11.2
-3.5
-26.0
7.2
0.7
-2.3
3.0
2.0
-3.1
0.0
0.0
-3.9
-18.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-11.2
-12.1
-34.5
-2.6
-31.3

-219.1
-65.7
-371.5
-388.1
-327.2
-220.0
-371.9
-314.8
-211.5
-358.0
-332.1
-311.0
-356.7
-335.8
-301.7
-325.7
-325.7
-321.6
-310.2
-325.7
-325.7
-325.7
-325.7
-325.7
-325.7
-325.7
-297.3
-295.7
-191.4
n/a
-209.3

-32.7
-79.8
14.1
19.2
0.5
-32.5
14.2
-3.3
-35.1
9.9
2.0
-4.5
9.5
3.1
-7.4
0.0
0.0
-1.2
-4.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-8.7
-9.2
-41.2
n/a
-35.7

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Sensitivity Case

CAFE Standards
Percent
Total
Change from
Regulatory
Reference
Costs ($b)
Case
-319.1
n/a
-319.1
0.0
-319.1
0.0
-299.5
-6.2
-319.1
0.0

43367

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

-241.2
-250.1
-288.3
-377.8
-429.0
-318.1
-319.1
n/a
-319.1

-24.4
-21.6
-9.7
18.4
34.4
-0.3
0.0
n/a
0.0

-234.2
-248.8
-290.1
-391.7
-454.3
-317.0
-325.7
-284.5
-325.7

-28.1
-23.6
-10.9
20.3
39.5
-2.7
0.0
-12.7
0.0

Table VII-97- Incremental Costs and Benefits- Cumulative over Useful Life ofMYs 20172029 un d er P ropose d CAFE Stan d ar d s
Social
Total
Private
Total
Net
Sensitivity Case
Benefits
Benefits
Benefits
Costs
Costs
Reference Case
-51.9
-502.1
-176.4
-325.8
176.3
Consumer Benefit at 50%
-51.9
-502.1
-176.4
-259.3
242.8
Consumer Benefit at 75%
-51.9
-502.1
-176.4
-292.5
209.5
Fleet Share and Sales
-56.4
-503.2
-164.5
-296.8
206.4
Response Disabled
Scrappage Price Effect
-33.5
-416.7
-176.9
-357.5
59.2
Disabled
Scrappage and Fleet Share
-38.1
-418.1
-165.0
-328.7
89.4
Disabled
High Oil Price
-54.8
-456.3
-274.1
-325.3
131.0
High Oil Price with 60 Month
-80.4
-105.8
49.9
-17.9
-155.7
Payback
Low Oil Price
-43.2
-490.9
-121.0
-270.4
220.5
Low Oil Price with 12 Month
-42.9
-487.7
-121.1
-269.9
217.8
Payback
HighGDP
-51.9
-502.1
-175.8
-324.3
177.7
LowGDP
-54.7
-455.9
-273.0
-323.6
132.3
High GDP with High Oil
-43.2
-491.0
-120.6
-269.3
221.7
Price
High GDP with Low Oil
-50.4
-486.0
-171.3
-316.4
169.6
Price
Low GDP with High Oil Price
-53.3
-442.2
-266.8
-316.9
125.2
Low GDP with Low Oil Price
-42.2
-476.0
-117.5
-262.5
213.5
On Road Gap 0.10
-53.4
-510.8
-174.5
-311.8
199.0
On Road Gap 0.30
-343.1
-49.2
-483.1
-178.3
140.0
12 Month Payback Period
-58.9
-544.9
-199.9
-366.1
178.7
24 Month Payback Period
-55.6
-525.5
-187.6
-345.4
180.1
36 Month Payback Period
-48.4
-477.4
-165.7
-306.4
171.1
Rebound Effect at 10%
-37.0
-433.7
-93.5
-268.7
165.0
Rebound Effect at 30%
-66.9
-570.5
-259.2
-382.8
187.7
Long Fleet Redesign Cadence
-49.5
-487.0
-172.2
-323.7
163.3

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Technology Cost Markup 1.19
Technology Cost Markup 1.24
Technology Cost Markup 1.37
Technology Cost Markup 1.75
Technology Cost Markup 2.00
AE020 18 Fuel Prices
Utility Value Loss in HEV s
Perfect Trading of C0 2 Credits
Nonzero Valuation ofC~ and N 2 0

43368

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

-45.4

-422.9

-145.5

-261.9

161.0

-51.9

-471.9

-174.2

-323.6

148.3

-51.9

-532.2

-178.5

-327.9

204.3

-51.9
-51.9
-51.9
-51.9
-51.9
-51.5
-49.7
-40.2
-51.1
-42.1
-44.2
-44.0
-49.6
-55.7
-58.3
-54.1
-51.9

-502.1
-502.1
-502.1
-502.1
-502.1
-471.2
-460.0
-357.7
-485.5
-375.8
-403.2
-409.0
-466.7
-567.1
-621.5
-511.5
-547.9

-176.4
-69.5
-176.4
-176.4
-176.4
-178.9
-164.0
-135.3
-169.9
-149.0
-155.1
-153.4
-172.3
-185.1
-190.3
-187.8
-176.4

-325.8
-218.9
-327.5
-322.2
-325.8
-333.0
-299.1
-250.1
-311.4
-276.7
-288.0
-284.9
-319.9
-339.7
-347.8
-339.3
-325.8

176.3
283.1
174.5
179.9
176.3
138.3
160.9
107.6
174.2
99.1
115.2
124.1
146.8
227.3
273.7
172.2
222.2

-51.9

-502.1

-176.4

-326.0

176.1

Table VII-98- Incremental Costs and Benefits- Cumulative over Useful Life ofMYs 20172029 un d er P ropose d CO2 Stan d ar d s
Social
Total
Private
Total
Net
Sensitivity Case
Benefits
Benefits
Benefits
Costs
Costs
Reference Case
-62.1
-560.8
-201.7
-363.6
197.2
Consumer Benefit at 50%
-62.1
-560.8
-201.7
-291.4
269.4
Consumer Benefit at 75%
-62.1
-560.8
-201.7
-327.5
233.3
Fleet Share and Sales Response
-65.5
-550.6
-186.1
-329.3
221.3
Disabled
Scrappage Price Effect Disabled
-40.6
-461.9
-202.1
-399.9
62.0
Scrappage and Fleet Share
-44.5
-453.8
-186.5
-365.1
88.7
Disabled
High Oil Price
-55.5
-439.3
-259.6
-293.0
146.3
High Oil Price with 60 Month
-14.9
-122.5
-73.3
-89.5
33.0
Payback
Low Oil Price
-52.0
-550.7
-138.9
-302.7
248.0
Low Oil Price with 12 Month
-53.5
-572.0
-143.7
-313.5
258.5
Payback
HighGDP
-62.4
-563.6
-201.6
-362.4
201.2
LowGDP
-55.5
-440.4
-259.7
-292.9
147.5

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Short Fleet Redesign Cadence
Safety Coefficient at 5th
Percentile
Safety Coefficient at 95th
Percentile
Fatalities Flat Earlier
Fatalities Flat Later
High Social Cost of Carbon
Low Social Cost of Carbon
High HEV Battery Costs
Low HEV Battery Costs
Exclude Strong Hybrids
Include HCR2 Engines
Fines at $14 in 2019
Technology Cost Markup 1.10
Technology Cost Markup 1.19
Technology Cost Markup 1.24
Technology Cost Markup 1.37
Technology Cost Markup 1.75
Technology Cost Markup 2.00
AE020 18 Fuel Prices
Utility Value Loss in HEV s
Nonzero Valuation ofC~
and N20

sradovich on DSK3GMQ082PROD with PROPOSALS2

VIII. Impacts of Alternative CAFE and
CO2 Standards Considered for MYs
2021/22–2026
As discussed above, a range of
regulatory alternatives are being
considered. Section III defines the
proposed preferred alternative, and
Section IV defines the no-action
alternative as well as the other seven
alternatives. The potential impacts of
each alternative in each case relative to
the no-action alternative were
estimated. For the preferred alternative,
these impacts are presented above on an
incremental basis, such that the impacts
attributed separately to standards
proposed in each model year. To
facilitate comparison of different

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Jkt 244001

alternatives, total estimated impacts
(i.e., summing impacts attributable to all
model years’ standards) were calculated
under each alternative.
Tables in the remaining section
summarize these estimated impacts for
each alternative, considering the same
measures as shown above for the
preferred alternative. As for the
preferred alternative, social costs and
benefits, private costs and benefits, and
environmental and energy impacts were
evaluated, and were done so separately
for CAFE and CO2 standards defining
each regulatory alternative. Also, as for
the preferred alternative, the
compliance-related private costs and
benefits were evaluated separately for

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43369

domestic and imported passenger cars
under CAFE standards but not under
CO2 standards because EPCA/EISA’s
requirement for separate compliance
applies only to CAFE standards.
This analysis does not explicitly
identify ‘‘co-benefits’’ from its proposed
action to change fuel economy
standards, as such a concept would
include all benefits other than cost
savings to vehicle buyers. Instead, it
distinguishes between private benefits—
which include economic impacts on
vehicle manufacturers, buyers of new
cars and light trucks, and owners (or
users) of used cars and light trucks—and
external benefits, which represent
indirect benefits (or costs) to the

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43370

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

remainder of the U.S. economy that
stem from the proposal’s effects on the
behavior of vehicle manufacturers,
buyers, and users. In this accounting
framework, changes in fuel use and
safety impacts resulting from the
proposal’s effects on the number of used
vehicles in use represent an important
component of its private benefits and
costs, despite the fact that previous
analyses have failed to recognize these
effects. The agency’s presentation of
private costs and benefits from its
proposed action clearly distinguishes
between those that would be
experienced by owners and users of cars
and light trucks produced during
previous model years, and those that

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would be experienced by buyers and
users of cars and light trucks produced
during the model years it would affect.
Moreover, it clearly separates these into
benefits related to fuel consumption and
those related to safety consequences of
vehicle use. This is more meaningful
and informative than simply identifying
all impacts other than changes in fuel
savings to buyers of new vehicles as
‘‘co-benefits.’’
Like the preferred alternative, all
other alternatives involve standards less
stringent than the no-action alternative.
Therefore, as discussed above,
incremental benefits and costs for each
alternative are negative—in other words,
each alternative involves foregone
benefits and avoided costs.

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Environmental and energy impacts are
correspondingly negative, involving
foregone avoided CO2 emissions and
foregone avoided fuel consumption. For
consistency with past rulemakings,
these are reported as negative values
rather than as additional CO2 emissions
and additional fuel consumption.
As discussed above, more detailed
results are available in the PRIA and
DEIS accompanying today’s notice, as
well as in underlying model output files
posted on NHTSA’s website.
A. What are the social costs and benefits
of each alternative, relative to the noaction alternative?
1. CAFE Standards

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sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
-

---

-

---·-

----

---

--

-·---

--

----1

--------

------,

Und'

d

-----------------

Alternative

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Model Years

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Increase

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AC/Off-Cycle Procedures

No
Action
20212025
MY
20172021
Augural
MY
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

1

2

3

4

5

6

7

X

20212026
O.O%Nea
rPC
O.O%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
1.0%Nea
rPC
2.0%Nea
rLT

20222026
l.O%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

2022-2026

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-303
-184
-87.5
-11.7
-57.7

-284
-174
-81.7
-11.1
-53.5

-261
-152
-68.7
-9.8
-44.1

-210
-114
-50.3
-7.4
-32.9

-188
-109
-45.2
-7.1
-26.0

-112
-76
-28.2
-5.1
-10.4

-124
-71.6
-28.6
-4.7
-15.0

-59.0
-59.0

-55.7
-55.7

-48.0
-48.0

-35.7
-35.7

-32.9
-32.9

-21.8
-21.8

-21.5
-21.5

-90.2

-83.7

-69.0

-51.5

-40.7

-16.2

-23.5

-92.3

-87.1

-75.1

-55.9

-51.5

-34.0

-33.6

Societal Costs and Benefits Through MY 2029 ($b)
-315
Technology Costs
Pre-tax Fuel Savings
-194
Mobility Benefit
-93.6
Refueling Benefit
-12.3
Non-Rebound Fatality
-62.8
Costs
-62.7
Rebound Fatality Costs
Benefits Offsetting
-62.7
Rebound Fatality Costs
-98.2
Non-Rebound Non-Fatal
Crash Costs
Rebound Non-Fatal Crash
-98.1
Costs

2.0%Near
PC
3.0%Near
LT

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table
Combined
Benefits for MYs 1977-2029. CAFE P
--·--- VIII-1.
- -- LDV Societal
----------Net
-----------

43371

EP24AU18.268

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43372

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I Net Benefits

-

-98.1

-92.3

-87.1

-75.1

-55.9

-51.5

-34.0

-33.6

-

-85.3

-79.4

-74.2

-62.5

-46.7

-40.3

-22.0

-25.3

-

-16.0
-6.4

-15.1
-6.0

-14.4
-5.7

-12.6
-5.0

-9.5
-3.8

-9.1
-3.6

-6.4
-2.5

-6.0
-2.4

-

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

-

-0.8

-0.9

-0.9

-0.9

-0.5

-0.9

-1.0

-0.5

-

-722
-484

-682
-456

-638
-431

-560
-372

-433
-278

-379
-260

-216
-175

-242
-169

-

238

225

207

187

156

119

40.9

73.5

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.269

Benefits Offsetting
Rebound Non-Fatal Crash
Costs
Additional Congestion and
Noise (Costs)
Energy Security Benefit
A voided C0 2 Damages
(Benefits)
Other A voided GHG
Damages (Benefits)
Other A voided Pollutant
Damages (Benefits)
Total Costs
Total Benefits

sradovich on DSK3GMQ082PROD with PROPOSALS2

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AC/Off-Cycle Procedures

E:\FR\FM\24AUP2.SGM
24AUP2

Retrievable Electrification
Costs
Electrification Tax Credits
Irretrievable Electrification
Costs
Total Electrification costs

1

2

3

4

5

6

7

X

20212026
O.O%Nea
rPC
O.O%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
1.0%Nea
rPC
2.0%Nea
rLT

20222026
l.O%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

2022-2026

No
Change

No
Change

No
Change

No
Change

No
Change

-1,500

-1,470

-1,470

-1,470

-1,240

Phaseout
20222026
-940

No
Change

1,540

Phaseout
20222026
-1,470

99.0
440

-35.1
-379

-35.1
-376

0.76
-338

-35.1
-376

0.46
-316

0.53
-318

0.52
-256

0.34
-256

2,080

-1,910

-1,880

-1,810

-1,880

-1,790

-1,560

-1,200

-1,190

No
Action
20212025
MY
20172021
Augural
MY
20222025
No
Change

2.0%Near
PC
3.0%Near
LT

-939

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-2- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, CAFE Program, Undiscounted,
Millions of $2016

43373

EP24AU18.270

sradovich on DSK3GMQ082PROD with PROPOSALS2

43374

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-

----

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PO 00000

---

--

-·---

--

-- --

7

-----

--

-

-

-----7

3%D.--------- -----R
-

-

-

Alternative

Model Years
Annual Rate of Stringency
Increase

Frm 00390
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24AUP2
EP24AU18.271

---·-

AC/Off-Cycle Procedures

No
Action
20212025
MY
20172021
Augural
MY
20222025
No
Change

1

2

3

4

5

6

7

X

20212026
O.O%Nea
rPC
O.O%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
1.0%Nea
rPC
2.0%Nea
rLT

20222026
1.0%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

2022-2026

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No Change

-243
-125
-57.0
-8.0
-32.4

-228
-119
-53.3
-7.7
-30.1

-209
-104
-44.9
-6.8
-24.9

-169
-77.5
-32.7
-5.1
-18.5

-151
-74.5
-29.8
-4.9
-14.8

-91.4
-51.8
-18.9
-3.5
-6.3

-99.5
-48.2
-18.7
-3.2
-8.4

-39.2
-39.2

-37.0
-37.0

-31.9
-31.9

-23.7
-23.7

-22.1
-22.1

-14.8
-14.8

-14.3
-14.3

-50.7

-47.1

-39.0

-29.0

-23.2

-9.8

-13.2

-61.3

-57.9

-50.0

-37.0

-34.6

-23.2

-22.4

Societal Costs and Benefits Through MY 2029 ($b)
-253
Technology Costs
-133
Pre-tax Fuel Savings
Mobility Benefit
-61.0
-8.5
Refueling Benefit
-35.4
Non-Rebound Fatality
Costs
-41.7
Rebound Fatality Costs
-41.7
Benefits Offsetting
Rebound Fatality Costs
Non-Rebound Non-Fatal
-55.3
Crash Costs
Rebound Non-Fatal Crash
-65.2
Costs

2.0%Near
PC
3.0%Near
LT

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table
- ------ LDV Societal
- - --------Net
- - Benefits
- -------- for MYs 1977-2029. CAFE P
--·- -- VIII-3- Combined

sradovich on DSK3GMQ082PROD with PROPOSALS2

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-

-65.2

-61.3

-57.9

-50.0

-37.0

-34.6

-23.2

-22.4

-

-51.9

-48.4

-45.3

-38.3

-28.5

-25.1

-14.3

-15.7

-

-10.9
-4.3

-10.3
-4.1

-9.8
-3.9

-8.6
-3.4

-6.4
-2.5

-6.2
-2.4

-4.3
-1.7

-4.1
-1.6

-

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

-

-1.2

-1.2

-1.2

-1.0

-0.7

-0.9

-0.8

-0.5

-

-502
-326
176

-475
-307
168

-445
-290
155

-394
-250
143

-306
-186
120

-271
-175
95.9

-160
-119
40.8

-173
-113
60.5

-

-

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Benefits Offsetting
Rebound Non-Fatal Crash
Costs
Additional Congestion and
Noise (Costs)
Energy Security Benefit
A voided C0 2 Damages
(Benefits)
Other A voided GHG
Damages (Benefits)
Other A voided Pollutant
Damages (Benefits)
Total Costs
Total Benefits
Net Benefits

43375

EP24AU18.272

sradovich on DSK3GMQ082PROD with PROPOSALS2

43376

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Increase

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E:\FR\FM\24AUP2.SGM
24AUP2
EP24AU18.273

1

2

3

4

5

6

7

X

20212026
O.O%Nea
rPC
O.O%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
1.0%Nea
rPC
2.0%Nea
rLT

20222026
l.O%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

2022-2026

20222025
No
Change

No
Change

No
Change

No
Change

No
Change

No
Change

-1,200

-1,180

-1,180

-1,180

-1,010

Phaseout
20222026
-775

No
Change

1,230

Phaseout
20222026
-1,180

85.8
365

-28.6
-315

-28.6
-312

0.62
-285

-28.6
-312

0.37
-268

0.43
-269

0.42
-219

0.27
-219

1,680

-1,540

-1,520

-1,460

-1,520

-1,450

-1,280

-994

-993

No
Action
20212025
MY

20172021
Augural

2.0%Near
PC
3.0%Near
LT

MY

AC/Off-Cycle Procedures

Retrievable Electrification
Costs
Electrification Tax Credits
Irretrievable Electrification
Costs
Total Electrification costs

-774

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-4- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, CAFE Program, 3% Discount
Rate, Millions of $2016

sradovich on DSK3GMQ082PROD with PROPOSALS2

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No
Action
20212025
MY
20172021
Augural
MY
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

1

2

3

4

5

6

7

8

20212026
O.O%Nea
rPC
O.O%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
l.O%Nea
rPC
2.0%Nea
rLT

20222026
l.O%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20222026
2.0%Nea
rPC
3.0%Nea
rLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-185
-79.3
-34.6
-5.1
-16.9
-24.3
-24.3

-173
-75.3
-32.4
-4.9
-15.7
-22.9
-22.9

-160
-65.5
-27.3
-4.3
-13.1
-19.8
-19.8

-129
-48.5
-19.8
-3.2
-9.7
-14.6
-14.6

-116
-47.2
-18.4
-3.2
-8.0
-13.9
-13.9

-71.3
-32.8
-11.9
-2.3
-3.7
-9.5
-9.5

-76.1
-30.0
-11.4
-2.0
-4.5
-8.9
-8.9

-26.4

-24.5

-20.5

-15.2

-12.5

-5.7

-7.0

-38.0

-35.9

-31.0

-22.8

-21.7

-14.9

-13.9

Societal Costs and Benefits Through MY 2029 ($b)
Technology Costs
-192
Pre-tax Fuel Savings
-84.3
Mobility Benefit
-37.1
Refueling Benefit
-5.4
Non-Rebound Fatality Costs
-18.4
Rebound Fatality Costs
-25.8
Benefits Offsetting Rebound
-25.8
Fatality Costs
Non-Rebound Non-Fatal
-28.8
Crash Costs
Rebound Non-Fatal Crash
-40.4
Costs

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

7%D.

Table VIII-5- Combined LDV Societal Net Benefits for MYs 1977-2029. CAFE P

43377

EP24AU18.274

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43378

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-

-40.4

-38.0

-35.9

-31.0

-22.8

-21.7

-14.9

-13.9

-

-29.6

-27.6

-25.9

-22.0

-16.2

-14.7

-8.9

-9.1

-

-6.9
-2.7

-6.5
-2.6

-6.2
-2.5

-5.4
-2.1

-4.0
-1.6

-3.9
-1.5

-2.8
-1.1

-2.5
-1.0

-

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

-

-1.1

-1.1

-1.1

-0.9

-0.6

-0.7

-0.6

-0.4

-

-335
-204

-318
-191

-298
-181

-266
-156

-207
-115

-187
-110

-

132

126

117

110

92.1

76.6

-114
-75.7
38.3

-119
-70.2
49.2

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.275

Benefits Offsetting Rebound
Non-Fatal Crash Costs
Additional Congestion and
Noise (Costs)
Energy Security Benefit
A voided C0 2 Damages
(Benefits)
Other A voided GHG
Damages (Benefits)
Other A voided Pollutant
Damages (Benefits)
Total Costs
Total Benefits
Net Benefits

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Frm 00395

Annual Rate of Stringency
Increase

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AC/Off-Cycle Procedures

E:\FR\FM\24AUP2.SGM
24AUP2

Retrievable Electrification
Costs
Electrification Tax Credits
Irretrievable Electrification
Costs
Total Electrification costs

1

2

3

4

5

6

7

X

20212026
O.O%Nea
rPC
O.O%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
1.0%Nea
rPC
2.0%Nea
rLT

20222026
l.O%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

2022-2026

No
Change

No
Change

No
Change

No
Change

No
Change

-911

-898

-898

-897

-782

Phaseout
20222026
-612

No
Change

938

Phaseout
20222026
-897

71.9
290

-22.0
-251

-22.0
-249

0.47
-231

-22.0
-249

0.28
-218

0.33
-219

0.32
-181

0.21
-181

1,300

-1,180

-1,170

-1,130

-1,170

-1,110

-1,000

-793

-793

No
Action
20212025
MY
20172021
Augural
MY
20222025
No
Change

2.0%Near
PC
3.0%Near
LT

-612

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-6- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, CAFE Program, 7% Discount
Rate, Millions of $2016

43379

EP24AU18.276

sradovich on DSK3GMQ082PROD with PROPOSALS2

43380

VerDate Sep<11>2014

ted

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Increase

Frm 00396
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AC/Off-Cycle Procedures

No
Action
20212025
MY
20172021
Augural
MY
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM

1

2

3

4

5

6

7

8

20212026
O.O%Nea
rPC
O.O%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
l.O%Nea
rPC
2.0%Nea
rLT

20222026
l.O%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20222026
2.0%Nea
rPC
3.0%Nea
rLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-318
-197
-101

-299
-185

-266
-155

-191
-101

-12.9
-79.8
-68.6

-92.4
-12.1
-69.7
-62.8

-75.6
-10.2
-57.0
-52.0

-202
-98.6
-50.0
-6.7
-43.2
-34.5

-46.6
-6.7
-33.9
-32.3

-123
-71.5
-28.7
-4.8
-16.3
-20.9

-121
-62.5
-28.2
-4.2
-21.5
-19.9

Societal Costs and Benefits Through MY 2029 ($b)
-327
Technology Costs
-208
Pre-tax Fuel Savings
-107
Mobility Benefit
Refueling Benefit
-13.6
-82.6
Non-Rebound Fatality Costs
-72.2
Rebound Fatality Costs

24AUP2

Benefits Offsetting Rebound
Fatality Costs
Non-Rebound Non-Fatal
Crash Costs
Rebound Non-Fatal Crash
Costs

-

-72.2

-68.6

-62.8

-52.0

-34.5

-32.3

-20.9

-19.9

-

-129

-125

-109

-89.1

-67.6

-53.1

-25.4

-33.7

-

-113

-107

-98.2

-81.3

-53.9

-50.5

-32.7

-31.2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.277

Und·

Table VIII-7- Combined LDV Societal Net Benefits for MYs 1977-2029. CO,- P

sradovich on DSK3GMQ082PROD with PROPOSALS2

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-

-113

-107

-98.2

-81.3

-53.9

-50.5

-32.7

-31.2

-

-104

-99.3

-88.5

-73.2

-52.4

-44.5

-24.5

-28.2

-

-17.3
-6.8
0.0

-16.4
-6.4
0.0

-15.5
-6.1
0.0

-13.0
-5.1
0.0

-8.5
-3.2
0.0

-8.6
-3.3
0.0

-6.1
-2.4
0.0

-5.4
-2.1
0.0

-

0.1

0.2

-0.1

0.1

0.9

0.2

-0.3

0.3

-

-828
-538
290

-797
-509
288

-728
-472
255

-619
-392
227

-454
-254
199

-405
-248
157

-243
-167
76

-256
-153
102

-

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Benefits Offsetting Rebound
Non-Fatal Crash Costs
Additional Congestion and
Noise (Costs)
Energy Security Benefit
C0 2 Damages (Benefits)
Other A voided GHG
Damages (Benefits)
Other A voided Pollutant
Damages (Benefits)
Total Costs
Total Benefits
Net Benefits

43381

EP24AU18.278

sradovich on DSK3GMQ082PROD with PROPOSALS2

43382

VerDate Sep<11>2014
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24AUP2
EP24AU18.279

1

2

3

4

5

6

7

X

20212026
O.O%Near
PC
O.O%Near
LT

20212026
0.5%Near
PC
0.5%Near
LT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
1.0%Nea
rPC
2.0%Nea
rLT

20222026
1.0%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

2022-2026

20222025
No
Change

No
Change

No
Change

No
Change

No
Change

No
Change

-1,900

-1,900

-1,840

-1,840

-1,600

Phaseout
20222026
-822

No
Change

1,900

Phaseout
20222026
-1,900

149
532

-149
-532

-149
-532

-149
-532

-149
-519

-149
-519

-149
-521

-14.9
-201

-15.5
-289

2,580

-2,580

-2,580

-2,580

-2,500

-2,500

-2,270

-1,040

-1,690

No
Action
20212025
MY

20172021
Augural

2.0%Near
PC
3.0%Near
LT

MY

AC/Off-Cycle Procedures

Retrievable Electrification
Costs
Electrification Tax Credits
Irretrievable Electrification
Costs
Total Electrification costs

-1,390

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-8- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, GHG Program, Undiscounted,
Millions of $2016

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
-

-

I Net Benefits for MYs 1977-2029. CO, P
~

-

-

~

3%D.

R

Alternative

Jkt 244001

Model Years
Annual Rate of Stringency Increase

PO 00000

1
20212026
0.0%/Year
PC
O.Oo/o/Year

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-252
-136
-65.7
-8.9
-44.6
-45.3
-45.3

-238
-127
-60.2
-8.3
-39.2
-41.6
-41.6

-212
-107
-49.2
-7.0
-32.0
-34.4
-34.4

-160
-68.6
-32.4
-4.7
-23.9
-22.7
-22.7

-153
-69.1
-30.6
-4.6
-19.2
-21.5
-21.5

-99.6
-48.7
-19.1
-3.3
-9.7
-14.2
-14.2

-96.9
-43.1
-18.5
-2.9
-12.1
-13.3
-13.3

-69.7

-61.3

-50.0

-37.3

-30.0

-15.1

-18.9

-70.8
-70.8

-65.0
-65.0

-53.9
-53.9

-35.6
-35.6

-33.7
-33.7

-22.1
-22.1

-20.8
-20.8

-59.6

-53.5

-44.2

-31.1

-27.1

-15.6

-17.1

-11.3

-10.6

-8.9

-5.9

-5.9

-4.2

-3.7

Societal Costs and Benefits Tiuough MY 2029 ($b)
-260
Technology Costs
-144
Pre-tax Fuel Savings
Mobility Benefit
-69.5
Refueling Benefit
-9.4
Non-Rebound Fatality Costs
-46.2
Rebound Fatality Costs
-47.8
-47.8
Benefits Offsetting Rebound
Fatality Costs
Non-Rebound Non-Fatal Crash
-72.3
Costs
-74.7
Rebound Non-Fatal Crash Costs
Bendits O±Isetting Rebound Non-74.7
Fatal Crash Costs
-62.4
Additional Congestion and Noise
(Costs)
-11.9
Energy Security Benefit

Sfmt 4725

AC/Off-Cyclc Procedures

Fmt 4701

LT

2
20212026
0.5%/Year
PC
0.5%/Year
LT

Frm 00399

No Action
20212025
MY 20172021
Augural
MY 20222025
No
Change

3
20212026
0.5%/Year
PC
0.5%/Year

4
20212026
1.0%/Year
PC
2.0%/Year
LT

5
20222026
1.0%/Year
PC
2.0%/Year
LT

6
20212026
2.0%/Year
PC
3.0%/Year
LT

LT

7
20212026
2.0%/Year
PC
3.0%/Year
LT

8
20222026
2.0%/Year
PC
3.0%/Year
LT

E:\FR\FM\24AUP2.SGM
24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-9- Combined LDV S

43383

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43384

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-

-4.7

-4.4

-4.2

-3.5

-2.2

-2.3

-1.6

-1.4

-

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

-

-0.8

-0.7

-0.7

-0.5

0.1

-0.2

-0.4

00

-

-563
-363
201

-542
-343
199

-499
-318
181

-426
-264
162

-311
-172
139

-285
-168
117.0

-176
-114

-179
-104
75.3

-

62.6

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.281

Avoided C0 2 Damages (Benefits)
Other A voided GHG Damages
(Benefits)
Other Avoided Pollutant Damages
(Benefits)
Total Costs
Total Benefits
Net Benefits

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Model Years

Frm 00401

Annual Rate of Stringency
Increase

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E:\FR\FM\24AUP2.SGM
24AUP2

1

2

3

4

5

6

7

X

20212026
O.O%Near
PC
O.O%Near
LT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
1.0%Nea
rPC
2.0%Nea
rLT

20222026
l.O%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

2022-2026

20222025
No
Change

No
Change

No
Change

No
Change

No
Change

No
Change

-1,490

-1,490

-1,440

-1,440

-1,270

Phaseout
20222026
-663

No
Change

1,490

Phaseout
20222026
-1,490

127
436

-127
-436

-127
-436

-127
-436

-127
-426

-127
-426

-127
-427

-12.3
-171

-12.9
-244

2,060

-2,060

-2,060

-2,060

-2,000

-2,000

-1,830

-847

-1,370

No
Action
20212025
MY

20172021
Augural

2.0%Near
PC
3.0%Near
LT

MY

AC/Off-Cycle Procedures

Retrievable Electrification
Costs
Electrification Tax Credits
Irretrievable Electrification
Costs
Total Electrification costs

-1,120

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-10- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, GHG Program, 3% Discount
Rate, Millions of $2016

43385

EP24AU18.282

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-

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Increase

Frm 00402
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E:\FR\FM\24AUP2.SGM
24AUP2
EP24AU18.283

-

AC/Off-Cycle Procedures

No
Action
20212025
MY
20172021
Augural
MY
20222025
No
Change

1

2

3

4

5

6

7

X

20212026
O.O%Nea
rPC
O.O%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
l.O%Nea
rPC
2.0%Nea
rLT

20222026
l.O%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20222026
2.0%Nea
rPC
3.0%Nea
rLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-190
-86.4
-39.6
-5.7
-22.9
-27.8
-27.8

-180
-81.0
-36.5
-5.3
-20.4
-25.7
-25.7

-160
-67.7
-29.8
-4.5
-16.6
-21.3
-21.3

-121
-43.9
-19.6
-3.0
-12.1
-14.0
-14.0

-116
-44.0
-18.7
-3.0
-10.1
-13.4
-13.4

-76.8
-30.9
-11.9
-2.1
-5.5
-9.0
-9.0

-73.6
-27.4
-11.3
-1.9
-6.3
-8.3
-8.3

-35.8

-31.8

-25.9

-19.0

-15.9

-8.5

-9.9

Societal Costs and Benefits Through MY 2029 ($b)
-196
Technology Costs
Pre-tax Fuel Savings
-91.5
Mobility Benefit
-42.0
Refueling Benefit
-6.0
Non-Rebound Fatality Costs
-23.8
Rebound Fatality Costs
-29.4
-29.4
Benefits Offsetting Rebound
Fatality Costs
Non-Rebound Non-Fatal
-37.3
Crash Costs

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-11- Combined LDV Societal Net Benefits for MYs 1977-2029. CO,""" P

sradovich on DSK3GMQ082PROD with PROPOSALS2

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-

-46.0

-43.5

-40.1

-33.3

-21.9

-21.0

-14.1

-12.9

-

-46.0

-43.5

-40.1

-33.3

-21.9

-21.0

-14.1

-12.9

-

-35.0

-33.3

-30.2

-24.9

-17.2

-15.5

-9.3

-9.7

-

-7.6
-3.0

-7.2
-2.8

-6.7
-2.6

-5.7
-2.2

-3.7
-1.4

-3.7
-1.4

-2.6
-1.0

-2.4
-0.9

-

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

-

-1.0

-0.9

-0.9

-0.7

-0.2

-0.3

-0.3

-0.2

-

-367
-226
141

-353
-214
139

-328
-199
129

-282
-165
117

-205
-108
97.0

-192
-106
86.8

-123
-72.0
51.2

-121
-65.2
55.4

-

-

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Rebound Non-Fatal Crash
Costs
Benefits Offsetting Rebound
Non-Fatal Crash Costs
Additional Congestion and
Noise (Costs)
Energy Security Benefit
A voided C0 2 Damages
(Benefits)
Other A voided GHG
Damages (Benefits)
Other A voided Pollutant
Damages (Benefits)
Total Costs
Total Benefits
Net Benefits

43387

EP24AU18.284

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Annual Rate of Stringency
Increase

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E:\FR\FM\24AUP2.SGM
24AUP2
EP24AU18.285

AC/Off-Cycle Procedures

Retrievable Electrification
Costs
Electrification Tax Credits
Irretrievable Electrification
Costs
Total Electrification costs

1

2

3

4

5

6

7

X

20212026
O.O%Near
PC
O.O%Near
LT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
l.O%Nea
rPC
2.0%Nea
rLT

20222026
l.O%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20222026
2.0%Near
PC
3.0%Near
LT

No
Change

No
Change

No
Change

No
Change

No
Change

-1,110

-1,110

-1,070

-1,070

-958

Phaseout
20222026
-512

No
Change

1,110

Phaseout
20222026
-1,110

104
342

-104
-342

-104
-342

-104
-342

-104
-334

-104
-334

-104
-335

-9.7
-142

-10.1
-198

1,560

-1,560

-1,560

-1,560

-1,510

-1,510

-1,400

-663

-1,060

No
Action
20212025
MY
20172021
Augural
MY
20222025
No
Change

-853

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-12- Combined LDV Estimated Electrification Cost Coverage for MYs 2017-2029, GHG Program, 3% Discount
Rate, Millions of $2016

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
B. What are the private costs and
benefits of each alternative, relative to
the no-action alternative?
1. What are the impacts on producers of
new vehicles?

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(a) CAFE Standards

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23:42 Aug 23, 2018

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VerDate Sep<11>2014
---

--

--

---

-

-----

----

f
---------

I

----

dC

lative- ----Industrv
Costs
- -- -- th
--- --

---- t-- ------ - ------------

----

- --

hMY2029
-- -

-

-

-

- --

Alternative

Jkt 244001

Model Years
Annual Rate of
Stringency Increase

PO 00000
Frm 00406

AC/Off-Cycle
Procedures

No Action
2021-2025
Final
2017-2021,
Aug ural
2022-2025
No Change

1
2021-2026

O.O%Near
PC
O.O%Near
LT
No Change

2
2021-2026
0.5%Near
PC
0.5%Near
LT
No Change

3
2021-2026
0.5%Near
PC
0.5%Near
LT
Phaseout
2022-2026

Fmt 4701
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E:\FR\FM\24AUP2.SGM

4
2021-2026
l.O%Near
PC
2.0%Near
LT
No Change

5
2022-2026
l.O%Near
PC
2.0%Near
LT
No Change

6
2021-2026
2.0%Near
PC
3.0%Near
LT
No Change

7
2021-2026
2.0%Near
PC
3.0%Near
LT
Phaseout
2022-2026

8
2022-2026
2.0%Near
PC
3.0%Near
LT
No Change

40.5

42.1

43.0

43.0

44.2

-15.2%

-10.9%

-8.5%

-8.6%

-5.6%

41.3

42.4

43.1

42.9

44.2

37.7

38.2

38.0

38.3

38.6

Fuel Economy
Average Required Fuel
46.7
37.0
38.1
38.1
Economy - MY 2026+
(mpg)
-26.0%
-22.4%
-22.5%
Percent Change in
Stringency from
Baseline
Average Achieved Fuel
46.4
39.7
40.1
39.2
Economy- MY 2030
(mpg)
Average Achieved Fuel
39.4
37.2
37.4
37.5
Economy - MY 2020
(mpg)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)

24AUP2

Total Technology Costs
($b)
Total Civil Penalties
($b)
Total Regulatory Costs
($b)

-

-192

-185

-173

-160

-129

-116

-71.3

-76.1

-

-2.1

-1.9

-1.8

-1.5

-0.8

-1.0

-11

-0.7

-

-194

-186

-175

-161

-130

-117

-72.4

-76.7

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.286

Table
Combined
---·--- VIII-13.
------------- Li2:ht-Dutv
--- CAFE C
-

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Sales Change (millions)

-

1.0

1.0

1.0

0.9

0.7

0.6

0.4

0.4

Revenue Change ($b)

-

-182

-175

-164

-150

-120

-109

-67.0

-70.8

E:\FR\FM\24AUP2.SGM
24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)

43391

EP24AU18.287

sradovich on DSK3GMQ082PROD with PROPOSALS2

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VerDate Sep<11>2014
23:42 Aug 23, 2018

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Model Years

PO 00000

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Stringency Increase

Frm 00408

AC/Off-Cycle Procedures

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No
Action
20212025
Final
20172021
Augural
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

1

2

3

4

5

6

7

8

20212026
0.0%/Year
PC
0.0%/Year
LT

20212026
0.5%/Year
PC
0.5%/Year
LT

20212026
0.5%/Year
PC
0.5%/Year
LT

20212026
1.0%/Year
PC
2.0%/Year
LT

20222026
1.0%/Year
PC
2.0%/Year
LT

20212026
2.0%/Year
PC
3.0%/Year
LT

20212026
2.0%/Year
PC
3.0%/Year
LT

20222026
2.0%/Year
PC
3.0%/Year
LT

No
Change

No
Change

Phaseout
20222026

No
Change

5.4%

5.7%

5.8%

5.9%

20.9%

20.9%

20.9%

20.9%

58.7%

61.2%

62.7%

62.2%

0.0%

0.0%

6.9%

3.5%

93.0%
16.2%

91.7%
16.0%

83.5%
13.5%

88.7%
17.1%

12.7%

20.5%

31.5%

30.1%

Phaseout
No
2022Change
2026
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction
5.7%
4.3%
4.4%
4.6%
5.1%
(percent change from MY
2016)
High Compression Ratio
26.2%
17.2%
17.2%
17.1%
17.1%
Non-Turbo Engines
56.1%
Turbocharged Gasoline
63.6%
51.1%
53.7%
53.8%
Engines
0.0%
0.0%
0.0%
0.0%
Dynamic Cylinder
6.3%
Deactivation
92.9%
Advanced Transmissions
71.7%
92.9%
92.9%
92.9%
Stop-Start 12V (Non14.1%
13.7%
13.8%
15.7%
16.1%
Hybrid)
Mild Hybrid Electric
32.5%
0.4%
0.3%
2.2%
2.7%
Systems (48v)
No
Change

No
Change

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Jkt 244001
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Table VIII-14- Combined Li2:ht-Dutv Fleet Penetration for MY 2030. CAFE P

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23.6%

2.3%

2.4%

2.4%

2.4%

2.4%

3.8%

12.2%

6.9%

1.1%

0.5%

0.5%

0.5%

0.5%

0.5%

0.5%

0.6%

0.5%

0.5%

0.5%

0.5%

0.5%

0.5%

0.5%

0.5%

0.5%

0.5%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

E:\FR\FM\24AUP2.SGM
24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Strong Hybrid Electric
Systems
Plug-In Hybrid Electric
Vehicles (PHEV s)
Dedicated Electric
Vehicles (EV s)
Fuel Cell Vehicles
(FCVs)

43393

EP24AU18.501

sradovich on DSK3GMQ082PROD with PROPOSALS2

43394

VerDate Sep<11>2014

I

t

lative Industrv Costs th

dC

h MY 2029

Alternative

Jkt 244001

Model Years

PO 00000

Annual Rate of Stringency
Increase

Frm 00410
Fmt 4701

AC/Off-Cycle Procedures

Sfmt 4725

Fuel Economy

No
Action
20212025
Final
20172021,
Augural
20222025
No
Change

E:\FR\FM\24AUP2.SGM

1

2

3

4

5

6

7

8

20212026
O.O%Ne
arPC
O.O%Ne
arLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
l.O%Ne
arPC
2.0%Ne
arLT

20222026
l.O%Ne
arPC
2.0%Ne
arLT

20212026
2.0%Ne
arPC
3.0%Ne
arLT

20212026
2.0%Ne
arPC
3.0%Ne
arLT

20222026
2.0%Ne
arPC
3.0%Ne
arLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

32.2

35.3

36.9

37.5

37.5

38.8

-24.5%

-13.7%

-8.7%

-6.8%

-6.8%

-3.4%

33.4

35.7

36.9

37.5

37.4

38.6

32.0

32.3

32.7

32.7

33.1

33.2

24AUP2

31.3
32.2
Ave rage Required Fuel Economy 40.1
- MY 2026+ (mpg)
Percent Change in Stringency
-28.3%
-24.5%
from Baseline
Average Achieved Fuel Economy 40.0
33.6
34.1
-MY 2030 (mpg)
Average Achieved Fuel Economy 33.7
31.6
31.8
- MY 2020 (mpg)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
Total Technology Costs ($b)
Total Civil Penalties ($b)
Total Regulatory Costs ($b)

EP24AU18.502

r

-

-108

-103

-95.1

-83.5

-65.1

-55.7

-24.8

-27.9

-1.0

-1.0

-0.9

-0.7

-0.3

-0.5

-0.4

-0.3

-109

-103

-95.9

-84.1

-65.4

-56.1

-25.3

-28.1

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-15 - Li2:ht Truck CAFE C

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
Jkt 244001
PO 00000
Frm 00411
Fmt 4701

Sales Change (millions)

Sfmt 4725

Revenue Change ($b)

-

-1.1

-1.0

-1.0

-0.7

-0.4

-0.3

-0.3

-0.2

-129

-123

-114

-97.9

-72.1

-62.4

-31.2

-31.1

E:\FR\FM\24AUP2.SGM
24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)

43395

EP24AU18.503

sradovich on DSK3GMQ082PROD with PROPOSALS2

43396

VerDate Sep<11>2014

Alternative

Jkt 244001

Model Years

PO 00000

Annual Rate of
Stringency Increase

Frm 00412
Fmt 4701
Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2
EP24AU18.504

AC/Off-Cycle
Procedures

No
Action
20212025
Final
20172021
Augural
20222025
No
Change

1

2

3

4

5

6

7

8

20212026
0.0%/Year
PC
0.0%/Year
LT

20212026
0.5%/Year
PC
0.5%/Year
LT

20212026
0.5%/Year
PC
0.5%/Year
LT

20212026
1.0%/Year
PC
2.0%/Year
LT

20222026
1.0%/Year
PC
2.0%/Year
LT

20212026
2.0%/Year
PC
3.0%/Year
LT

20212026
2.0%/Year
PC
3.0%/Year
LT

20222026
2.0%/Year
PC
3.0%/Year
LT

No
Change

No
Change

Phaseout
20222026

No
Change

6.3%

6.7%

6.8%

6.8%

10.8%

10.8%

10.8%

10.8%

66.9%

67.3%

69.0%

67.3%

0.0%

0.0%

14.1%

6.8%

98.3%
17.7%

97.5%
19.1%

86.7%
7.6%

92.9%
12.1%

19.8%

34.9%

55.4%

55.4%

Phaseout
No
2022Change
2026
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction
6.6%
4.4%
4.6%
4.9%
5.8%
(percent change from
MY 2016)
High Compression Ratio 11.9%
8.1%
8.1%
8.1%
8.1%
Non-Turbo Engines
Turbocharged Gasoline
69.9%
53.1%
58.4%
58.4%
62.8%
Engines
Dynamic Cylinder
12.7%
0.0%
0.0%
0.0%
0.0%
Deactivation
Advanced Transmissions 75.3%
98.3%
98.3%
98.3%
98.3%
Stop-Start 12V (Non11.4%
12.3%
12.4%
13.2%
14.0%
Hybrid)
Mild Hybrid Electric
45.9%
0.0%
0.0%
1.8%
5.2%
Systems (48v)
No
Change

No
Change

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-16- Li2:ht Truck Fleet Penetration for MY 2030. CAFE P

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
Jkt 244001
PO 00000
Frm 00413
Fmt 4701
Sfmt 4725
E:\FR\FM\24AUP2.SGM

23.5%

0.9%

0.9%

0.9%

0.9%

0.9%

1.7%

12.6%

6.4%

0.8%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

Fuel Cell Vehicles
(FCVs)

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Strong Hybrid Electric
Systems
Plug-In Hybrid Electric
Vehicles (PHEV s)
Dedicated Electric
Vehicles (EVs)

43397

EP24AU18.505

sradovich on DSK3GMQ082PROD with PROPOSALS2

43398

VerDate Sep<11>2014

r

lative Ind

dC

I

c

h

hMY 2029

Alternative

Jkt 244001

Model Years

PO 00000

Annual Rate of Stringency Increase

Frm 00414
Fmt 4701

AC/Off-Cycle Procedures

Sfmt 4725

Fuel Economy

E:\FR\FM\24AUP2.SGM
24AUP2

Average Required Fuel Economy MY 2026+ (mpg)
Percent Change in Stringency from
Baseline
Average Achieved Fuel Economy MY 2030 (mpg)
Average Achieved Fuel Economy MY 2020 (mpg)
Total Regulatory Costs Through MY
Total Technology Costs ($b)
Total Civil Penalties ($b)
Total Regulatory Costs ($b)

EP24AU18.506

Car CAFE C
1

2

3

4

5

6

7

8

20212026
0.0%/Ye
arPC
0.0%/Ye
arLT

20212026
0.5%/Ye
arPC
0.5%/Ye
arLT

20212026
0.5%/Ye
arPC
0.5%/Ye
arLT

20212026
1.0%/Ye
arPC
2.0%/Ye
arLT

20222026
1.0%/Ye
arPC
2.0%/Ye
arLT

20212026
2.0%/Ye
arPC
3.0%/Ye
arLT

20212026
2.0%/Ye
arPC
3.0%/Ye
arLT

20222026
2.0%/Ye
arPC
3.0%/Ye
arLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

54.7

43.7

45.0

45.0

46.4

47.9

49.3

49.3

50.4

-

-25.2%

-21.5%

-21.6%

-17.9%

-14.2%

-10.9%

-10.9%

-8.6%

54.2

46.7

46.9

45.9

47.7

48.7

49.7

49.3

50.6

45.9

43.9

43.9

43.9

44.0

44.6

44.1

44.2

44.7

No
Action
20212025
Final
20172021,
Augural
20222025
No
Change

2029 Vehicles (7% discount rate)

-

-84.1

-81.9

-77.9

-76.1

-63.6

-60.6

-46.5

-48.2

-1.0

-0.9

-0.9

-0.8

-0.5

-0.5

-0.6

-0.4

-85.3

-83.0

-78.8

-77.0

-64.2

-61.2

-47.1

-48.6

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-17- P

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
Jkt 244001
PO 00000
Frm 00415
Fmt 4701
Sfmt 4725

Sales Change (millions)

-

2.1

2.0

1.9

1.6

1.0

0.9

0.7

0.6

Revenue Change ($b)

-

-53.0

-52.1

-49.4

-52.5

-48.4

-46.4

-35.8

-39.7

E:\FR\FM\24AUP2.SGM
24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)

43399

EP24AU18.507

sradovich on DSK3GMQ082PROD with PROPOSALS2

43400

VerDate Sep<11>2014

Car Fleet P

for MY 2030. CAFE P

Alternative

Jkt 244001

Model Years

PO 00000

Annual Rate of
Stringency Increase

Frm 00416
Fmt 4701

AC/Off-Cycle
Procedures

No
Action
20212025
Final
20172021
Augural
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

1

2

3

4

5

6

7

8

20212026
O.O%Near
PC
O.O%Near

20212026
0.5%Near
PC
0.5%Near

20212026
0.5%Near
PC
0.5%Near

20212026
l.O%Near
PC
2.0%Near

20222026
l.O%Near
PC
2.0%Near

20212026
2.0%Near
PC
3.0%Near

20212026
2.0%Near
PC
3.0%Near

20222026
2.0%Near
PC
3.0%Near

LT

LT

LT

LT

LT

LT

LT

LT

No
Change

No
Change

Phaseout
20222026

No
Change

5.1%

5.5%

5.8%

5.8%

29.7%

29.8%

29.8%

29.8%

51.5%

55.9%

57.1%

57.7%

0.0%

0.0%

0.5%

0.5%

88.3%
15.0%

86.6%
13.3%

80.6%
18.7%

85.1%
21.5%

6.5%

7.9%

10.2%

7.7%

Phaseout
No
2022Change
2026
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction
5.9%
4.1%
4.3%
4.4%
4.7%
(percent change from
MY 2016)
High Compression Ratio 39.0%
24.7%
24.7%
24.7%
24.7%
Non-Turbo Engines
Turbocharged Gasoline
57.8%
49.5%
49.9%
49.9%
50.4%
Engines
Dynamic Cylinder
0.5%
0.0%
0.0%
0.0%
0.0%
Deactivation
Advanced Transmissions 68.4%
88.5%
88.4%
88.3%
88.3%
Stop-Start 12V (Non16.5%
15.0%
15.0%
17.8%
17.8%
Hybrid)
Mild Hybrid Electric
20.4%
0.7%
0.5%
2.6%
0.5%
Systems (48v)
No
Change

No
Change

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.508

Table VIII-18- P

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
Jkt 244001
PO 00000
Frm 00417
Fmt 4701
Sfmt 4725
E:\FR\FM\24AUP2.SGM

23.6%

3.5%

3.6%

3.7%

3.7%

3.8%

5.7%

11.9%

7.3%

1.4%

0.7%

0.7%

0.7%

0.7%

0.7%

0.8%

0.8%

0.8%

0.7%

0.7%

0.7%

0.7%

0.7%

0.7%

0.7%

0.7%

0.7%

Fuel Cell Vehicles
(FCVs)

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Strong Hybrid Electric
Systems
Plug-In Hybrid Electric
Vehicles (PHEV s)
Dedicated Electric
Vehicles (EVs)

43401

EP24AU18.509

sradovich on DSK3GMQ082PROD with PROPOSALS2

43402

VerDate Sep<11>2014

r

I

lative Ind

dC

c

h

h MY 2029

Alternative

Jkt 244001

Model Years

PO 00000

Annual Rate of Stringency Increase

Frm 00418
Fmt 4701

AC/Off-Cycle Procedures

No
Action
20212025
Final
20172021,
Augural
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM

1

2

3

4

5

6

7

8

20212026
0.0%/Ye
arPC
0.0%/Ye
arLT

20212026
0.5%/Ye
arPC
0.5%/Ye
arLT

20212026
0.5%/Y
ear PC
0.5%/Y
earLT

20212026
1.0%/Ye
arPC
2.0%/Ye
arLT

20222026
1.0%/Ye
arPC
2.0%/Ye
arLT

20212026
2.0%/Ye
arPC
3.0%/Ye
arLT

20212026
2.0%/Ye
arPC
3.0%/Ye
arLT

20222026
2.0%/Ye
arPC
3.0%/Ye
arLT

No
Change

No
Change

Phaseou
t 20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

44.5

45.9

47.4

48.8

48.8

49.9

-21.6%

-17.9%

-14.2%

-10.9%

-10.9%

-8.6%

45.8

47.7

49.0

50.2

49.9

51.2

43.7

43.8

44.9

44.0

44.1

45.0

Fuel Economy

24AUP2

Average Required Fuel Economy 54.1
43.2
44.5
MY 2026+ (mpg)
Percent Change in Stringency from
-25.2%
-21.6%
Baseline
Average Achieved Fuel Economy 55.1
46.5
46.8
MY 2030 (mpg)
43.7
Average Achieved Fuel Economy 45.9
43.6
MY 2020 (mpg)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
Total Technology Costs ($b)
Total Civil Penalties ($b)
Total Regulatory Costs ($b)

EP24AU18.510

. Car CAFE C

-

-56.2

-54.8

-51.6

-50.9

-42.5

-39.7

-28.9

-31.3

0.0

0.0

-0.1

0.0

0.1

0.0

-0.2

0.0

-56.3

-54.9

-51.7

-51.0

-42.5

-39.8

-29.0

-31.3

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-19 - D

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
Jkt 244001
PO 00000
Frm 00419
Fmt 4701
Sfmt 4725

Sales Change (millions)

-

1.3

1.2

1.1

0.9

0.6

0.5

0.4

0.3

Revenue Change ($b)

-

-38.4

-37.8

-35.4

-37.5

-33.8

-31.7

-22.7

-26.4

E:\FR\FM\24AUP2.SGM
24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)

43403

EP24AU18.511

sradovich on DSK3GMQ082PROD with PROPOSALS2

43404

VerDate Sep<11>2014

tic Car Fleet Penetration for MY 2030. CAFE P

Alternative

Jkt 244001

Model Years

PO 00000

Annual Rate of
Stringency Increase

Frm 00420

AC/Off-Cycle Procedures

Fmt 4701

No
Action
20212025
Final
20172021
Augural
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

1

2

3

4

5

6

7

8

20212026
O.O%Near
PC
O.O%Near
LT

20212026
0.5%Near
PC
0.5%Near
LT

20212026
0.5%Near
PC
0.5o/o/Year
LT

20212026
l.O%Near
PC
2.0%Near
LT

20222026
1.0%Near
PC
2.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20222026
2.0%Near
PC
3.0%Near
LT

No
Change

No
Change

Phaseout
20222026

No
Change

5.8%

6.3%

6.6%

6.6%

17.5%

17.5%

17.4%

17.4%

64.3%

71.2%

72.0%

74.6%

0.0%

0.0%

1.0%

1.0%

91.2%
15.9%

89.3%
12.8%

81.7%
23.1%

88.0%
26.6%

6.1%

9.3%

17.2%

8.7%

Phaseout
No
2022Change
2026
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction
6.4%
4.8%
5.1%
5.1%
5.3%
(percent change from MY
2016)
High Compression Ratio
22.7%
12.7%
12.7%
12.6%
12.6%
Non-Turbo Engines
Turbocharged Gasoline
75.2%
61.9%
62.5%
62.6%
63.6%
Engines
Dynamic Cylinder
1.0%
0.0%
0.0%
0.0%
0.0%
Deactivation
Advanced Transmissions
63.0%
91.1%
91.1%
91.1%
91.2%
Stop-Start 12V (Non11.2%
11.5%
11.5%
16.1%
17.1%
Hybrid)
Mild Hybrid Electric
23.3%
0.1%
0.1%
3.9%
0.1%
Systems (48v)
No
Change

No
Change

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.512

Table VIII-20- D

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
Jkt 244001
PO 00000
Frm 00421
Fmt 4701
Sfmt 4725

29.2%

1.0%

1.0%

1.0%

1.0%

1.0%

3.1%

10.7%

4.4%

0.8%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.6%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

E:\FR\FM\24AUP2.SGM
24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Strong Hybrid Electric
Systems
Plug-In Hybrid Electric
Vehicles (PHEV s)
Dedicated Electric
Vehicles (EV s)
Fuel Cell Vehicles
(FCVs)

43405

EP24AU18.513

sradovich on DSK3GMQ082PROD with PROPOSALS2

43406

VerDate Sep<11>2014

r

I

t

dC

lative Industrv Costs th

hMY2029

Alternative

Jkt 244001

Model Years

PO 00000

Annual Rate of Stringency
Increase

Frm 00422
Fmt 4701

AC/Off-Cycle Procedures

Sfmt 4725

Fuel Economy

No
Action
20212025
Final
20172021,
Augural
20222025
No
Change

E:\FR\FM\24AUP2.SGM

1

2

3

4

5

6

7

8

20212026
O.O%Ne
arPC
O.O%Ne
arLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
l.O%Ne
arPC
2.0%Ne
arLT

20222026
l.O%Ne
arPC
2.0%Ne
arLT

20212026
2.0%Ne
arPC
3.0%Ne
arLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20222026
2.0%Ne
arPC
3.0%Ne
arLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

46.9

48.5

49.9

49.9

51.0

-17.9%

-14.2%

-11.0%

-11.0%

-8.6%

47.6

48.4

49.0

48.6

49.8

44.1

44.3

44.3

44.3

44.4

24AUP2

Average Required Fuel
55.3
44.2
45.5
45.5
Economy - MY 2026+ (mpg)
Percent Change in Stringency
-25.3%
-21.5%
-21.5%
from Baseline
Average Achieved Fuel
53.3
47.0
47.1
46.0
Economy- MY 2030 (mpg)
Average Achieved Fuel
45.8
44.1
44.1
44.1
Economy - MY 2020 (mpg)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)
Total Technology Costs ($b)
Total Civil Penalties ($b)
Total Regulatory Costs ($b)

EP24AU18.514

ted Car CAFE C

-

-27.9

-27.1

-26.3

-25.3

-21.1

-20.8

-17.7

-16.9

-1.0

-0.9

-0.8

-0.8

-0.6

-0.5

-0.5

-0.4

-29.0

-28.1

-27.1

-26.0

-21.7

-21.4

-18.1

-17.3

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-21 - I

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Sales Change (millions)

Sfmt 4725

Revenue Change ($b)

-

0.9

0.8

0.8

0.7

0.4

0.4

0.3

0.2

-14.6

-14.3

-14.0

-15.1

-14.6

-14.7

-13.0

-13.3

E:\FR\FM\24AUP2.SGM
24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)

43407

EP24AU18.515

sradovich on DSK3GMQ082PROD with PROPOSALS2

43408

VerDate Sep<11>2014

ted Car Fleet Penetration for MY 2030.' CAFE P
-

-

-

Altemative

Jkt 244001

Model Years

PO 00000

Annual Rate of Stringency
Increase

Frm 00424

AC/Off-Cycle Procedures

Fmt 4701

No
Action
20212025
Final
20172021
Augural
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

1

2

3

4

5

6

7

8

20212026
O.O%Near
PC
O.O%Near
LT

20212026
0.5%Near
PC
0.5%Near
LT

20212026
0.5%Near
PC
0.5%Near
LT

20212026
l.O%Near
PC
2.0%Near
LT

20222026
l.O%Near
PC
2.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20222026
2.0%Near
PC
3.0%Near
LT

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

4.1%

4.2%

4.6%

4.8%

4.7%

39.2%

44.1%

44.3%

44.4%

44.4%

34.8%

36.4%

37.7%

39.4%

37.7%

0.0%

0.0%

0.0%

0.0%

0.0%

84.9%
18.7%

84.9%
14.0%

83.4%
13.9%

79.3%
13.5%

81.6%
15.4%

1.1%

7.0%

6.2%

1.9%

6.5%

Phaseout
20222026
Technology Use Under CAFE Altemative in MY 2030 (total fleet penetration)
Curb Weight Reduction
5.2%
3.2%
3.3%
3.5%
(percent change from MY
2016)
High Compression Ratio
58.3%
39.0%
39.0%
39.1%
Non-Turbo Engines
Turbocharged Gasoline
37.3%
34.7%
34.9%
34.8%
Engines
Dynamic Cylinder
0.0%
0.0%
0.0%
0.0%
Deactivation
Advanced Transmissions
74.7%
85.4%
85.1%
85.0%
Stop-Start 12V (Non22.8%
19.1%
19.1%
19.9%
Hybrid)
Mild Hybrid Electric
17.0%
1.3%
1.1%
1.1%
Systems (48v)
No
Change

No
Change

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.516

Table VIII-22 - I

sradovich on DSK3GMQ082PROD with PROPOSALS2

Jkt 244001
PO 00000
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Fmt 4701
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17.1%

6.5%

6.8%

6.8%

7.0%

7.1%

8.8%

13.4%

10.8%

2.0%

0.8%

0.8%

0.9%

0.8%

0.9%

0.9%

1.1%

1.0%

Dedicated Electric Vehicles
(EVs)

0.9%

0.9%

0.9%

0.9%

0.9%

0.9%

0.9%

0.9%

0.9%

Fuel Cell Vehicles (FCVs)

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

(b) CO2 Standards

VerDate Sep<11>2014

Strong Hybrid Electric
Systems
Plug-In Hybrid Electric
Vehicles (PHEV s)

43409

EP24AU18.517

sradovich on DSK3GMQ082PROD with PROPOSALS2

43410

VerDate Sep<11>2014

I

t

dC

lative Industrv Costs th

hMY2029

Alternative

Jkt 244001

Model Years

PO 00000

Annual Rate of Stringency
Increase

Frm 00426

AC/Off-Cycle Procedures

Fmt 4701

No
Action
20212025
Final
20172021,
Augural
20222025
No
Change

1

2

3

4

5

6

7

8

20212026

20212026

20212026

20212026

20222026

20212026

20212026

20222026

O.O%Ne

0.5%Ne

0.5%Ne

l.O%Ne

l.O%Ne

2.0%Ne

2.0%Ne

2.0%Ne

arPC

arPC

arPC

arPC

arPC

arPC

arPC

arPC

Sfmt 4725
E:\FR\FM\24AUP2.SGM

O.O%Ne

0.5%Ne

0.5%Ne

2.0%Ne

2.0%Ne

3.0%Ne

3.0%Ne

3.0%Ne

arLT

arLT

arLT

arLT

arLT

arLT

arLT

arLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

220.0

212.0

207.0

207.0

201.0

-25.2%

-20.7%

-17.9%

-18.1%

-14.7%

216.0

209.0

206.0

205.0

200.0

Average C0 2 Emission Rate
Average Required C0 2 - MY
175.0
240.0
233.0
233.0
2026+ (g/mi)
Percent Change in Stringency
-36.9%
-33.0%
-33.1%
from Baseline
Average Achieved C0 2 - MY
174.0
229.0
228.0
230.0
2030 (g/mi)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)

24AUP2

Total Technology Costs ($b)

-

-196.0

-190.0

-180.0

-160.0

-121.0

-116.0

-76.8

-73.6

Total Civil Penalties ($b)

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

Total Regulatory Costs ($b)

-

-196.0

-190.0

-180.0

-160.0

-121.0

-116.0

-76.8

-73.6

Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
Sales Change (millions)
Revenue Change ($b)

EP24AU18.518

-

1.1

1.0

1.0

0.8

0.6

0.6

0.4

0.4

-185.0

-179.0

-170.0

-151.0

-113.0

-109.0

-71.4

-68.7

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

r

Table VIII-23- Combined Li2:ht-Dutv CO,- C

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

Fleet P

for MY 2030. CO?- P

Jkt 244001
PO 00000
Frm 00427
Fmt 4701
Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

3

4

5

6

7

8

20212026
0.5%Near
PC
0.5%Near
LT

20212026
l.O%Near
PC
2.0%Near
LT

20222026
l.O%Near
PC
2.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20222026
2.0%Near
PC
3.0%Near
LT

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

5.0%

5.5%

6.2%

6.4%

6.5%

13.1%

22.5%

22.5%

22.8%

22.4%

55.3%

56.6%

58.4%

60.9%

60.5%

0.0%

0.0%

0.0%

6.9%

0.0%

93.0%
11.5%

92.1%
7.8%

91.0%
8.7%

84.1%
7.3%

88.0%
14.6%

5.1%

13.6%

16.5%

30.2%

26.2%

Phaseout
20222026
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
Curb Weight Reduction
6.8%
4.0%
4.1%
4.4%
(percent change from MY
2016)
High Compression Ratio
26.2%
12.4%
12.4%
13.1%
Non-Turbo Engines
61.8%
40.8%
41.8%
48.2%
Turbocharged Gasoline
Engines
0.0%
0.0%
0.0%
Dynamic Cylinder
6.5%
Deactivation
Advanced Transmissions
74.8%
93.6%
93.6%
93.4%
Stop-Start 12V (Non14.6%
11.1%
11.1%
10.1%
Hybrid)
Mild Hybrid Electric
37.3%
1.5%
1.7%
3.7%
Systems (48v)

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-24- Combined Li!!ht-D
Alternative
1
2
No
Action
202120212021Model Years
2025
2026
2026
Annual Rate of Stringency Final
O.O%Near 0.5%Near
Increase
2017PC
PC
2021
O.O%Near 0.5%Near
Augural LT
LT
20222025
AC/Off-Cycle Procedures
No
No
No
Change
Change
Change

43411

EP24AU18.519

sradovich on DSK3GMQ082PROD with PROPOSALS2

43412

VerDate Sep<11>2014
Jkt 244001
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Sfmt 4725
E:\FR\FM\24AUP2.SGM

20.7%

1.8%

1.8%

2.1%

2.7%

3.9%

5.1%

11.9%

8.2%

0.9%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

1.0%

0.5%

0.5%

0.5%

0.5%

0.5%

0.5%

1.0%

0.6%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.520

Strong Hybrid Electric
Systems
Plug-In Hybrid Electric
Vehicles (PHEVs)
Dedicated Electric
Vehicles (EVs)
Fuel Cell Vehicles (FCVs)

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

r

I

dC

c

lative Ind

h

h MY 2029

Alternative

Jkt 244001

Model Years
Annual Rate of Stringency Increase

PO 00000
Frm 00429

AC/Off-Cycle Procedures

Fmt 4701

No
Action
20212025
Final
20172021,
Augural
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM

1

2

3

4

5

6

7

8

20212026
O.O%Ne
arPC
O.O%Ne
arLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
l.O%Ne
arPC
2.0%Ne
arLT

20222026
l.O%N
ear PC
2.0%N
earLT

20212026
2.0%Ne
arPC
3.0%Ne
arLT

20212026
2.0%Ne
arPC
3.0%Ne
arLT

20222026
2.0%Ne
arPC
3.0%Ne
arLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

276.0

252.0

241.0

237.0

237.0

229.0

-35.3%

-23.5%

-18.1%

-16.2%

-16.2%

-12.3%

268.0

251.0

243.0

238.0

237.0

231.0

Average C0 2 Emission Rate
Average Required C0 2 - MY 2026+ 204.0
284.0
276.0
(g/mi)
-39.2%
-35.3%
Percent Change in Stringency from
Baseline
203.0
268.0
266.0
Average Achieved C0 2 - MY 2030
(g/mi)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)

24AUP2

Total Technology Costs ($b)

-

-103.0

-100.0

-95.8

-84.7

-64.0

-61.3

-38.7

-38.8

Total Civil Penalties ($b)

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

Total Regulatory Costs ($b)

-

-103.0

-100.0

-95.8

-84.7

-64.0

-61.3

-38.7

-38.8

Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
Sales Change (millions)
Revenue Change ($b)

-

-1.5

-1.4

-1.3

-1.1

-0.5

-0.5

-0.4

-0.2

-132.0

-127.0

-121.0

-105.0

-74.0

-70.3

-45.7

-42.2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-25- Li2:ht Truck C01- C

43413

EP24AU18.521

sradovich on DSK3GMQ082PROD with PROPOSALS2

43414

VerDate Sep<11>2014
23:42 Aug 23, 2018

Alternative

Model Years

PO 00000

Annual Rate of
Stringency Increase

Frm 00430

AC/Off-Cycle Procedures

Fmt 4701

No
Action
20212025
Final
20172021
Augural
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

1

2

3

4

5

6

7

8

20212026
O.O%Near
PC
O.O%Near
LT

20212026
0.5%Near
PC
0.5%Near
LT

20212026
0.5%Near
PC
0.5o/o/Year
LT

20212026
l.O%Near
PC
2.0%Near
LT

20222026
1.0%Near
PC
2.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20222026
2.0%Near
PC
3.0%Near
LT

No
Change

No
Change

Phaseout
20222026

No
Change

6.3%

7.4%

7.8%

7.9%

10.9%

10.9%

10.9%

10.9%

61.5%

61.5%

64.7%

63.9%

0.0%

0.0%

13.8%

0.0%

96.0%
3.2%

95.2%
5.7%

89.8%
3.9%

94.0%
8.9%

22.4%

27.0%

46.5%

45.4%

Phaseout
No
2022Change
2026
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
4.8%
5.7%
Curb Weight Reduction
8.1%
4.4%
4.5%
(percent change from MY
2016)
High Compression Ratio
12.0%
6.3%
6.3%
6.3%
6.3%
Non-Turbo Engines
Turbocharged Gasoline
68.0%
42.1%
44.2%
50.8%
61.5%
Engines
Dynamic Cylinder
12.7%
0.0%
0.0%
0.0%
0.0%
Deactivation
Advanced Transmissions
81.5%
98.6%
98.6%
98.1%
97.0%
Stop-Start 12V (Non9.0%
10.2%
9.9%
7.9%
7.3%
Hybrid)
Mild Hybrid Electric
55.8%
3.1%
3.7%
7.8%
10.2%
Systems (48v)
No
Change

No
Change

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Jkt 244001
EP24AU18.522

Table VIII-26 - Li2ht Truck Fleet Penetration for MY 2030. CO,- P

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
Jkt 244001
PO 00000
Frm 00431
Fmt 4701
Sfmt 4725

17.4%

0.7%

0.7%

1.2%

2.3%

3.5%

4.2%

9.1%

5.4%

0.8%

0.1%

0.1%

0.1%

0.1%

0.1%

0.1%

0.2%

0.4%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.3%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.2%

0.0%

E:\FR\FM\24AUP2.SGM
24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Strong Hybrid Electric
Systems
Plug-In Hybrid Electric
Vehicles (PHEV s)
Dedicated Electric
Vehicles (EV s)
Fuel Cell Vehicles
(FCVs)

43415

EP24AU18.523

sradovich on DSK3GMQ082PROD with PROPOSALS2

43416

VerDate Sep<11>2014

Alternative

Jkt 244001

Model Years

PO 00000

Annual Rate of Stringency
Increase

Frm 00432

AC/Off-Cycle Procedures

Fmt 4701

No
Action
20212025
Final
20172021,
Augural
20222025
No
Change

-

r

I

t

lative Industrv Costs th

dC

h MY 2029

Sfmt 4725
E:\FR\FM\24AUP2.SGM

1

2

3

4

5

6

7

8

20212026
O.O%Ne
arPC
O.O%Ne
arLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
l.O%Ne
arPC
2.0%Ne
arLT

20222026
l.O%Ne
arPC
2.0%Ne
arLT

20212026
2.0%Ne
arPC
3.0%Ne
arLT

20212026
2.0%Ne
arPC
3.0%Ne
arLT

20222026
2.0%Ne
arPC
3.0%Ne
arLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

198.0

192.0

186.0

180.0

180.0

176.0

-32.9%

-28.9%

-24.8%

-20.8%

-20.8%

-18.1%

198.0

187.0

180.0

177.0

177.0

172.0

Average C0 2 Emission Rate
Average Required C0 2 - MY
149.0
204.0
198.0
2026+ (g/mi)
-36.9%
-32.9%
Percent Change in Stringency from Baseline
148.0
198.0
196.0
Average Achieved C0 2 - MY
2030 (g/mi)
Total Regulatory Costs Through MY 2029 Vehicles (7% discount rate)

24AUP2

Total Technology Costs ($b)

-

-92.1

-89.3

-84.2

-75.5

-56.5

-55.1

-38.1

-34.8

Total Civil Penalties ($b)

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

Total Regulatory Costs ($b)

-

-92.1

-89.3

-84.2

-75.5

-56.5

-55.1

-38.1

-34.8

Sales and Revenue Impacts Through MY 2029 Vehicles (7% discount rate for Revenue Change)
Sales Change (millions)
Revenue Change ($b)

EP24AU18.524

-

2.6

2.5

2.3

1.9

1.2

1.1

0.8

0.5

-52.6

-51.9

-49.4

-46.6

-39.2

-38.8

-25.7

-26.5

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-27 - P ass en ger Car CO? C

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

Car Fleet P

for MY 2030. CO?- P

Alternative

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Annual Rate of
Stringency Increase

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AC/Off-Cycle Procedures

No
Action
20212025
Final
20172021
Augural
20222025
No
Change

Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

1

2

3

4

5

6

7

8

20212026
O.O%Near
PC
O.O%Near
LT

20212026
0.5%Near
PC
0.5%Near
LT

20212026
0.5%Near
PC
0.5o/o/Year
LT

20212026
l.O%Near
PC
2.0%Near
LT

20222026
1.0%Near
PC
2.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20222026
2.0%Near
PC
3.0%Near
LT

No
Change

No
Change

Phaseout
20222026

No
Change

5.4%

5.8%

6.1%

6.1%

32.5%

32.8%

33.6%

32.8%

52.4%

55.6%

57.4%

57.5%

0.0%

0.0%

0.7%

0.0%

88.8%
11.8%

87.2%
11.4%

79.0%
10.5%

82.6%
19.7%

Phaseout
No
2022Change
2026
Technology Use Under CAFE Alternative in MY 2030 (total fleet penetration)
4.6%
Curb Weight Reduction
6.8%
3.4%
3.6%
4.0%
(percent change from MY
2016)
High Compression Ratio
39.2%
17.4%
17.4%
18.8%
18.9%
Non-Turbo Engines
Turbocharged Gasoline
56.1%
39.8%
39.8%
46.1%
49.9%
Engines
0.0%
0.0%
0.0%
0.0%
Dynamic Cylinder
0.8%
Deactivation
89.6%
89.5%
Advanced Transmissions
68.7%
89.5%
89.5%
Stop-Start 12V (Non19.7%
11.9%
12.1%
11.9%
15.0%
Hybrid)
No
Change

No
Change

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-28- P

43417

EP24AU18.525

sradovich on DSK3GMQ082PROD with PROPOSALS2

43418

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E:\FR\FM\24AUP2.SGM

20.3%

0.1%

0.1%

0.4%

0.7%

5.9%

7.3%

15.5%

8.9%

23.7%

2.8%

2.7%

2.8%

3.0%

4.2%

5.8%

14.5%

10.7%

1.7%

0.4%

0.4%

0.4%

0.4%

0.4%

0.4%

0.4%

0.4%

1.1%

0.7%

0.7%

0.7%

0.7%

0.7%

0.7%

1.0%

1.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

0.0%

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.526

Mild Hybrid Electric
Systems (48v)
Strong Hybrid Electric
Systems
Plug-In Hybrid Electric
Vehicles (PHEV s)
Dedicated Electric
Vehicles (EV s)
Fuel Cell Vehicles
(FCVs)

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
2. What are the impacts on buyers of
new vehicles?

sradovich on DSK3GMQ082PROD with PROPOSALS2

(a) CAFE Standards

VerDate Sep<11>2014

23:42 Aug 23, 2018

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24AUP2

43419

sradovich on DSK3GMQ082PROD with PROPOSALS2

43420

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Model Years

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Annual Rate of Stringency
Increase

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E:\FR\FM\24AUP2.SGM
24AUP2
EP24AU18.527

AC/Off-Cycle Procedures

c

ts to the A
Alternative
1
No
Action
202120212025
2026
Final
O.O%Nea
2017rPC
2021
O.O%Nea
Augura rLT
I 20222025
No
No
Change Change

Per Vehicle Consumer Impacts for MY 2030 ($)
-1,850
Average Price Increase
-490
Ownership Costs
-1,470
Fuel Savings
-430
Mobility Benefit
-50
Refueling Benefit
Total Costs
-2,340
-1,950
Total Benefits
390
Net Benefits

-

fa MY 2030 Vehicl

-

derCAFEP
-

'

3%D.

tRat

2

3

4

5

6

7

8

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
1.0%Nea
rPC
2.0%Nea
rLT

20222026
1.0%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20222026
2.0%Nea
rPC
3.0%Nea
rLT

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-1,770
-470
-1,370
-400
-50
-2,240
-1,830
420

-1,650
-430
-1,290
-370
-50
-2,080
-1,700
380

-1,450
-380
-1,090
-300
-40
-1,830
-1,430
390

-1,150
-290
-850
-230
-30
-1,450
-1,110
340

-950
-240
-690
-180
-30
-1,190
-890
290

-450
-110
-350
-90
-10
-560
-460
110

-620
-150
-470
-120
-20
-770
-610
170

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-29 - I

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Model Years

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Annual Rate of Stringency
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AC/Off-Cycle Procedures

ts to the A

c
-

Alternative
1
No
Action
202120212025
2026
Final
O.O%Nea
2017rPC
2021
O.O%Nea
Augura rLT
I 20222025
No
No
Change Change

E:\FR\FM\24AUP2.SGM
24AUP2

Per Vehicle Consumer Impacts for MY 2030 ($)
-1,850
Average Price Increase
-440
Ownership Costs
-1,210
Fuel Savings
-430
Mobility Benefit
Refueling Benefit
-50
Total Costs
-2,300
-1,690
Total Benefits
600
Net Benefits

fa MY 2030 Vehicl

-

7%D.

derCAFEP
-

tRat

2

3

4

5

6

7

8

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
1.0%Nea
rPC
2.0%Nea
rLT

20222026
1.0%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20222026
2.0%Nea
rPC
3.0%Nea
rLT

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-1,770
-420
-1,130
-400
-50
-2,200
-1,580
610

-1,650
-390
-1,060
-370
-50
-2,040
-1,480
560

-1,450
-340
-900
-300
-40
-1,790
-1,240
550

-1,150
-270
-700
-230
-30
-1,420
-960
460

-950
-220
-570
-180
-30
-1,170
-770
390

-450
-100
-290
-90
-10
-550
-390
160

-620
-140
-390
-120
-20
-760
-520
230

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

(b) CO2 Standards

VerDate Sep<11>2014

Table VIII-30- I

43421

EP24AU18.528

sradovich on DSK3GMQ082PROD with PROPOSALS2

43422

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Frm 00438

Annual Rate of Stringency
Increase

Fmt 4701
Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2
EP24AU18.529

AC/Off-Cycle Procedures

Alternative
1
No
Action
202120212025
2026
Final
O.O%Nea
2017rPC
2021
O.O%Nea
Augura rLT
120222025
No
No
Change Change

Per Vehicle Consumer Impacts for MY 2030 ($)
-2,260
Average Price Increase
-610
Ownership Costs
Fuel Savings
-1,830
-540
Mobility Benefit
-70
Refueling Benefit
Total Costs
-2,870
-2,440
Total Benefits
Net Benefits
430

2

3

4

5

6

7

8

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
0.5%Nea
rPC
0.5%Nea
rLT

20212026
l.O%Nea
rPC
2.0%Nea
rLT

20222026
l.O%Nea
rPC
2.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20212026
2.0%Nea
rPC
3.0%Nea
rLT

20222026
2.0%Nea
rPC
3.0%Nea
rLT

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-2,210
-590
-1,770
-520
-70
-2,800
-2,350
450

-2,000
-530
-1,540
-440
-60
-2,540
-2,040
500

-1,770
-470
-1,260
-350
-50
-2,240
-1,660
580

-1,410
-370
-890
-250
-40
-1,780
-1 '180
600

-1 '140
-300
-730
-190
-30
-1,440
-950
490

-570
-150
-340
-80
-10
-710
-440
280

-750
-190
-480
-120
-20
-950
-620
330

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-31 -Impacts to the Average Consumer of a MY 2030 Vehicle under C02 Program, 3% Discount Rate

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
Jkt 244001
PO 00000

Model Years

Frm 00439

Annual Rate of Stringency
Increase

Fmt 4701
Sfmt 4725

AC/Off-Cycle Procedures

E:\FR\FM\24AUP2.SGM
24AUP2

Average Price Increase
Ownership Costs
Fuel Savings
Mobility Benefit
Refueling Benefit
Total Costs
Total Benefits
Net Benefits

ts to the A

c
-

Alternative
1
No
Action
202120212025
2026
Final
0.0%/Yea
2017rPC
2021
0.0%/Yea
Augura rLT
I 20222025
No
Change

-

-

-2,260
-550
-1,510
-540
-70
-2,810
-2,120
690

fa MY 2030 Vehicl

-

der CO, P
-

-

'

~

7%D'

tRat

2

3

4

5

6

7

8

20212026
0.5%/Yea
rPC
0.5%/Yea
rLT

20212026
0.5%/Yea
rPC
0.5%/Yea
rLT

20212026
1.0%/Yea
rPC
2.0%/Yea
rLT

20222026
1.0%/Yea
rPC
2.0%/Yea
rLT

20212026
2.0%/Yea
rPC
3.0%/Yea
rLT

20212026
2.0%/Yea
rPC
3.0%/Yea
rLT

20222026
2.0%/Yea
rPC
3.0%/Yea
rLT

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-2,210
-540
-1,460
-520
-70
-2,740
-2,040
700

-2,000
-480
-1,270
-440
-60
-2,490
-1,770
720

-1,770
-420
-1,040
-350
-50
-2,200
-1,440
750

-1,410
-330
-740
-250
-40
-1,750
-1,020
720

-1,140
-270
-600
-190
-30
-1,410
-820
590

-570
-130
-280
-80
-10
-700
-380
320

-750
-170
-400
-120
-20
-930
-540
390

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-32- I

43423

EP24AU18.530

43424

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

C. What are the energy and
environmental impacts?

sradovich on DSK3GMQ082PROD with PROPOSALS2

1. CAFE Standards

VerDate Sep<11>2014

23:42 Aug 23, 2018

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24AUP2

sradovich on DSK3GMQ082PROD with PROPOSALS2

p

Jkt 244001

Model Years

PO 00000

Annual Rate of Stringency
Increase

Frm 00441
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Sfmt 4702
E:\FR\FM\24AUP2.SGM
24AUP2

Upstream Emissions
C0 2 (million metric tons)
c~ (thousand metric tons)
N 20 (thousand metric tons)
Tailpipe Emissions
C0 2 (million metric tons)
c~ (thousand metric tons)
N20 (thousand metric tons)
Total Emissions
C0 2 (million metric tons)
c~ (thousand metric tons)
N 20 (thousand metric tons)
Fuel Consumption (billion
Gallons)

Alternative
No
Action
20212025
Final
20172021
Augural
20222025
No
Change

1

2

3

4

5

6

7

8

20212026
0.0%/Yea
rPC
0.0%/Yea
rLT

20212026
0.5%/Yea
rPC
0.5%/Yea
rLT

20212026
0.5%/Yea
rPC
0.5%/Yea
rLT

20212026
1.0%/Yea
rPC
2.0%/Yea
rLT

20222026
1.0%/Yea
rPC
2.0%/Yea
rLT

20212026
2.0%/Yea
rPC
3.0%/Yea
rLT

20212026
2.0%/Yea
rPC
3.0%/Yea
rLT

20222026
2.0%/Yea
rPC
3.0%/Yea
rLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-

151
1,430
21.6

142
1,350
20.4

135
1,280
19.4

116
1,120
16.9

84.8
836
12.7

81.3
803
12.2

55.1
560
8.6

49.9
521
8.0

-

658
-12.0
-10.6

623
-11.1
-9.8

592
-10.4
-9.1

518
-8.6
-7.5

391
-6.3
-5.4

375
-5.4
-4.6

263
-2.7
-2.3

247
-3.1
-2.6

-

809
1,410
11.0
73.1

765
1,340
10.6
69.1

726
1,270
10.3
65.7

634
1,110
9.5
57.4

475
830
7.3
43.1

456
797
7.7
41.3

318
557
6.4
28.9

297
518
5.3
27.0

-

-

-

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

2. CO2 Standards

VerDate Sep<11>2014

Table VIII-33- Cumulative Changes in Fuel Consumption and GHG Emissions for MYs 1977-2029 Under CAFE

43425

EP24AU18.531

sradovich on DSK3GMQ082PROD with PROPOSALS2

43426

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Increase

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AC/Off-Cycle Procedures

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E:\FR\FM\24AUP2.SGM
24AUP2

Upstream Emissions
C0 2 (million metric tons)
c~ (thousand metric tons)
N 20 (thousand metric tons)
Tailpipe Emissions
C0 2 (million metric tons)
c~ (thousand metric tons)
N 20 (thousand metric tons)
Total Emissions
C02 (million metric tons)
c~ (thousand metric tons)
N 20 (thousand metric tons)
Fuel Consumption (billion
Gallons)

No
Action
20212025
Final
20172021
Augural
20222025
No
Change

dGHGE . .

· Fuel C

lative Ch
Alternative

for MYs 1977-2029 Under CO,- P

1

2

3

4

5

6

7

8

2021-2026

2021-2026

2021-2026

0.0%/Year
PC
0.0%/Year
LT

0.5%/Year
PC
0.5%/Year
LT

0.5%/Year
PC
0.5%/Year
LT

20212026
1.0%/Yea
rPC
2.0%/Yea
rLT

20222026
1.0%/Yea
rPC
2.0%/Yea
rLT

20212026
2.0%/Yea
rPC
3.0%/Yea
rLT

20212026
2.0%/Yea
rPC
3.0%/Yea
rLT

20222026
2.0%/Yea
rPC
3.0%/Yea
rLT

No
Change

No
Change

Phaseout
2022-2026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-

159
1,540
23.3

149
1,450
22.0

140
1,370
20.8

114
1,140
17.4

67.6
730
11.2

69.0
742
11.4

48.4
527
8.1

40.4
462
7.2

-

713
-14.2
-12.6

675
-13.6
-12.0

636
-12.1
-10.6

535
-9.8
-8.6

348
-6.8
-5.8

354
-5.7
-4.8

251
-3.0
-2.4

223
-3.4
-2.8

-

872
1,520
10.7
78.9

825
1,440

775
1,350

649
1,130

10.0

10.2

X.9

74.6

70.2

58.8

416
723
5.4
37.8

422
736
6.7
38.3

300
524
5.7
27.2

264
458
4.4
24.0

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.532

Table VIII-34 - C

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014

lative Ch

-

Jkt 244001

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Stringency Increase

Frm 00443
Fmt 4701

AC/Off-Cycle
Procedures

Sfmt 4725
E:\FR\FM\24AUP2.SGM
24AUP2

Upstream Emissions
CO (million metric tons)
VOC (thousand metric
tons)
NOx (thousand metric
tons)
so2 (thousand metric
tons)
PM (thousand metric
tons)
Tailpipe Emissions
CO (million metric tons)
VOC (thousand metric
tons)
NOx (thousand metric
tons)

-

Alternative
No
Action
20212025
Final
20172021
Augural
20222025
No
Change

· Criteria Pollutant E · ·
-

for MYs 1977-2029 Under CAFE P
-

-

1

2

3

4

5

6

7

8

20212026
O.O%Near
PC
O.O%Near
LT

20212026
0.5%Near
PC
0.5%Near
LT

20212026
0.5o/o!Year
PC
0.5o/o!Year
LT

20212026
l.O%Near
PC
2.0%Near
LT

20222026
1.0%Near
PC
2.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20212026
2.0%Near
PC
3.0%Near
LT

20222026
2.0%Near
PC
3.0%Near
LT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-

0.1
215

0.1
203

0.1
193

0.0
169

0.0
127

0.0
122

0.0
85.6

0.0
80.4

-

115

108

103

89.4

66.2

63.5

43.6

40.3

-

73.7

68.8

65.2

55.0

38.2

36.8

23.5

20.0

-

8.8

8.3

7.9

6.9

5.1

4.9

3.4

3.1

-

-5.2
-332

-4.8
-310

-4.5
-291

-3.8
-251

-2.9
-190

-2.5
-171

-1.3
-100

-1.5
-103

-

-270

-251

-235

-200

-148

-132

-75.2

-77.8

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-35 - C

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-

-2.5

-2.3

-2.2

-1.8

-1.2

-1.1

-0.5

-0.6

-

-11.7

-10.8

-10.1

-8.5

-6.3

-5.4

-2.8

-3.2

-

-5.2
-117

-4.8
-107

-4.5
-97.8

-3.8
-82.2

-2.8
-62.3

-2.5
-48.8

-1.3
-14.7

-1.5
-22.7

-

-155

-142

-132

-110

-81.4

-68.9

-31.5

-37.4

-

71.2

66.5

63.0

53.2

36.9

35.7

23.0

19.4

-

-2.9

-2.6

-2.3

-1.6

-1.2

-0.5

0.6

-0.1

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.534

so2 (thousand metric
tons)
PM (thousand metric
tons)
Total Emissions
CO (million metric tons)
VOC (thousand metric
tons)
NOx (thousand metric
tons)
so2 (thousand metric
tons)
PM (thousand metric
tons)

sradovich on DSK3GMQ082PROD with PROPOSALS2

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Annual Rate of
Stringency Increase

Frm 00445

AC/Off-Cycle Procedures

Fmt 4701
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E:\FR\FM\24AUP2.SGM
24AUP2

Upstream Emissions
CO (million metric tons)
VOC (thousand metric
tons)
NOx (thousand metric
tons)
so2 (thousand metric
tons)
PM (thousand metric
tons)
Tailpipe Emissions
CO (million metric tons)
VOC (thousand metric
tons)
NOx (thousand metric
tons)

lative Ch

Alternative
No
Action
20212025
Final
20172021
Augural
20222025
No
Change

· Criteria Pollutant E · ·

for MYs 1977-2029 Under GHG P

1

2

3

4

5

6

7

8

20212026
0.0%/Year
PC
0.0%/Year
LT

20212026
0.5%/Year
PC
0.5%/Year
LT

20212026
0.5%/Year
PC
0.5%/Year
LT

20212026
1.0%/Year
PC
2.0%/Year
LT

20222026
1.0%/Y ear
PC
2.0%/Year
LT

20212026
2.0%/Year
PC
3.0%/Year
LT

20212026
2.0%/Year
PC
3.0%/Year
LT

20222026
2.0%/Year
PC
3.0%/Year
LT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-

0.1
232

0.1
220

0.1
207

0.0
174

0.0
113

0.0
114

0.0
81.1

0.0
71.7

-

122

115

108

89.3

55.0

56.0

39.4

33.8

-

74.0

68.7

63.5

49.9

24.7

25.6

17.3

12.5

-

9.4

8.8

8.3

6.9

4.3

4.4

3.1

2.7

-

-6.1
-372

-5.8
-356

-5.2
-327

-4.3
-275

-3.1
-195

-2.7
-178

-1.5
-110

-1.6
-112

-

-312

-297

-270

-224

-158

-140

-83.5

-87.4

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-36- C

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-

-3.0

-2.9

-2.5

-2.0

-1.3

-1.1

-0.5

-0.6

-

-13.7

-13.2

-11.8

-9.8

-7.0

-5.9

-3.2

-3.6

-

-6.0
-140

-5.7
-136

-5.2
-120.0

-4.3
-101.0

-3.1
-82.9

-2.6
-64.2

-1.5
-28.9

-1.6
-39.8

-

-190

-183

-162

-135

-103.0

-84.5

-44.1

-53.7

-

71.0

65.8

60.9

47.8

23.3

24.5

16.8

11.9

-

-4.4

-4.4

-3.5

-2.9

-2.7

-1.5

-0.1

-1.0

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018
EP24AU18.536

so2 (thousand metric
tons)
PM (thousand metric
tons)
Total Emissions
CO (million metric tons)
VOC (thousand metric
tons)
NOx (thousand metric
tons)
so2 (thousand metric
tons)
PM (thousand metric
tons)

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
D. What are the impacts on the total
fleet size, usage, and safety?

sradovich on DSK3GMQ082PROD with PROPOSALS2

1. CAFE Standards

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24AUP2

43431

sradovich on DSK3GMQ082PROD with PROPOSALS2

43432

VerDate Sep<11>2014
-

-

lative Ch
-

· Fleet Size.
-, U

-

-

3

4

5

6

7

8

20212026
O.O%Nea
rPC
O.O%Nea
rLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
l.O%Ne
arPC
2.0%Ne
arLT

20222026
l.O%Ne
arPC
2.0%Ne
arLT

20212026
2.0%Ne
arPC
3.0%Ne
arLT

20212026
2.0%Ne
arPC
3.0%Ne
arLT

20222026
2.0%Ne
arPC
3.0%Ne
arLT

No
Change

No
Change

No
Change

Phaseout
20222026

No
Change

-164

-137

-I 04

-S5

-37

-52

46%

46%

46%

46%

46%

47%

47%

No
Change

Fleet Size (millions)

-

-190

-177

Share LT, CY 2040

47%

45%

VMT, Fatalities, and Fuel Consumption for MYs 2017-2029

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No
Change

Model Years

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Increase

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20222026
Cumulative Changes in Fleet Size, Usage and Fatalities Through MY 2029

AC/Off-Cycle Procedures

24AUP2

VMT, with rebound (billion
miles)
VMT, without rebound (billion
miles)
Fatalities, with rebound

-

-1,030

-949

-885

-728

-530

-450

-235

-281

-

-235

-205

-183

-122

-79

-36

36

-11

-

-8,630

-7,990

-7,460

-6,180

-4,540

-3,800

-1,970

-2,360

Fatalities, without rebound

-

-1,710

-1,230

-844

-398

273

-141

81.6

71.2

53.6

50.8

34.5

32.8

103

89.2

66.6

62.3

41.6

40.0

-2,160

-1,890

Fuel Consumption, with rebound
91.2
86.1
(billion gallons)
Fuel Consumption, without
116
110
rebound (billion gallons)
VMT, Fatalities, and Fuel Consumption for MYs 1977-2016

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

2

E:\FR\FM\24AUP2.SGM

1

No
Action
20212025
Final
20172021
Augural
20222025
No
Change

EP24AU18.537

d Fatalities for MYs 1977-2029 Under CAFE P

Alternative

Sfmt 4725

23:42 Aug 23, 2018

Table VIII-37 - C

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-

-442

-415

-390

-340

-262

-234

-137

-144

-

-457

-429

-403

-352

-271

-242

-142

-149

-

-4,050

-3,800

-3,570

-3,120

-2,400

-2,150

-1,270

-1,330

Fatalities, without rebound
Fuel Consumption, with rebound
(billion gallons)
Fuel Consumption, without
rebound (billion gallons)

-

-4,190
-18.1

-3,930
-16.9

-3,700
-15.9

-3,230
-13.8

-2,480
-10.6

-2,230
-9.50

-1,320
-5.65

-1,370
-5.81

-

-18.7

-17.5

-16.5

-14.3

-10.9

-9.86

-5.86

-6.03

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

2. CO2 Standards

VerDate Sep<11>2014

VMT, with rebound (billion
miles)
VMT, without rebound (billion
miles)
Fatalities, with rebound

43433

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sradovich on DSK3GMQ082PROD with PROPOSALS2

43434

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-

-

lative Ch

-

-

-

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Annual Rate of Stringency Increase

Frm 00450

2

3

4

5

6

7

8

20212026
O.O%Nea
rPC
O.O%Nea
rLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
0.5%Ne
arPC
0.5%Ne
arLT

20212026
l.O%Ne
arPC
2.0%Ne
arLT

20222026
l.O%Ne
arPC
2.0%Ne
arLT

20212026
2.0%Ne
arPC
3.0%Ne
arLT

20212026
2.0%N
ear PC
3.0%N
earLT

20222026
2.0%N
ear PC
3.0%N
earLT

No
Change

No
Change

Phaseout
20222026

No
Change

No
Change

No
Change

Phaseou
t 20222026

No
Change

Sfmt 4725

1

Fmt 4701

No
Action
20212025
Final
20172021
Augural
20222025
No
Change

AC/Off-Cycle Procedures

Cumulative Changes in Fleet Size, Usage and Fatalities Through MY 2029

E:\FR\FM\24AUP2.SGM

Fleet Size (millions)

6,665

-235

-227

-200

-167

-129

-103

-51

-67

Share LT, CY 2040

47%

45%

45%

46%

46%

46%

47%

47%

47%

VMT, Fatalities, and Fuel Consumption for MY s 2017-2029
VMT, with rebound (billion miles)

24AUP2

VMT, without rebound (billion
miles)
Fatalities, with rebound
Fatalities, without rebound

EP24AU18.539

d Fatalities for MYs 1977-2029 Under C01- P

· Fleet Size.
-' U

-

-

-1,300

-1,240

-1,090

-885

-624

-509

-262

-319

-387

-376

-299

-229

-189

-101

0

-68

-

-11,200

-10,700

-9,410

-7,610

-5,380

-4,400

-2,290

-2,730

-3,720

-3,630

-2,930

-2,240

-1,810

-1,050

-129

-664

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Table VIII-38 - C

sradovich on DSK3GMQ082PROD with PROPOSALS2

VerDate Sep<11>2014
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PO 00000
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93.9

88.0

74.0

48.7

48.5

33.7

30.4

121

113

94.0

61.8

60.7

40.9

37.6

Fmt 4701
Sfmt 4725
E:\FR\FM\24AUP2.SGM

VMT, with rebound (billion miles)

-

-489

-470

-435

-372

-270

-250

-158

-159

VMT, without rebound (billion
miles)
Fatalities, with rebound

-

-506

-486

-449

-384

-279

-259

-164

-165

-

-4,470

-4,290

-3,980

-3,400

-2,470

-2,290

-1,460

-1,460

-4,630
-20.2

-4,440
-19.3

-4,110
-17.9

-3,520
-15.2

-2,550
-10.9

-2,370
-10.2

-1,510
-6.51

-1,510
-6.47

-

-20.9

-20.0

-18.5

-15.8

-11.3

-10.5

-6.76

-6.70

Fatalities, without rebound
Fuel Consumption, with rebound
(billion gallons)
Fuel Consumption, without rebound
(billion gallons)

24AUP2

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

23:42 Aug 23, 2018

Fuel Consumption, with rebound
99.0
(billion gallons)
Fuel Consumption, without rebound
128
(billion gallons)
VMT, Fatalities, and Fuel Consumption for MYs 1977-2016

43435

EP24AU18.540

43436

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

E. What are the Impacts on
Employment?

sradovich on DSK3GMQ082PROD with PROPOSALS2

As discussed in Section II.E, the
analysis includes estimates of impacts
on U.S. auto industry labor, considering
the combined impact of changes in sales
volumes and changes in outlays for
additional fuel-saving technology. Note:
This analysis does not consider the
possibility that potential new jobs and
plants attributable to increased
stringency will not be located in the
United States, or that increased
stringency will not lead to the relocation
of current jobs or plants to foreign
countries. Compared to the no-action
alternative (i.e., the baseline standards),
the proposed standards (alternative 1)
and other regulatory alternatives under
consideration all involve reduced
regulatory costs expected to lead to
reduced average vehicle prices and, in
turn, increased sales. While the
increased sales slightly increase
estimated U.S. auto sector labor,
because producing and selling more
vehicles uses additional U.S. labor, the
reduced outlays for fuel-saving
technology slightly reduce estimated
U.S. auto sector labor, because
manufacturing, integrating, and selling
less technology means using less labor
to do so. Of course, this is technology
that may not otherwise be produced or
deployed were it not for regulatory
mandate, and the additional costs of this
technology would be borne by a reduced
number of consumers given reduction in
sales in response to increased prices.
Today’s analysis shows the negative
impact of reduced mandatory
technology outlays outweighing the
positive impact of increased sales.
However, both of these underlying
factors are subject to uncertainty. For
example, if fuel-saving technology that
would have been applied under the
baseline standards is more likely to have
come from foreign suppliers than
estimated here, less of the foregone
labor to manufacture that technology
would have been U.S. labor. Also, if
sales would be more positively
impacted by reduced vehicle prices than
estimated here, correspondingly
positive impacts on U.S. auto sector

VerDate Sep<11>2014

23:42 Aug 23, 2018

Jkt 244001

labor could be magnified. Alternatively,
if manufacturers are able to deploy
technology to improve vehicle attributes
that new car buyers prefer to fuel
economy improvements, both
technology spending and vehicle sales
would correspondingly increase. As
discussed above, the analysis of sales
and employment may be updated for the
final rule, and it is expected that doing
so could possibly produce incremental
changes opposite in sign from those
presented below. In particular, comment
is sought on the potential for changes in
stringency to result in new jobs and
plants being created in foreign countries
or for current United States jobs and
plants to be moved outside of the
United States.
The employment analysis was
focused on automotive labor because
adjacent employment factors and
consumer spending factors for other
goods and services are uncertain and
difficult to predict. How direct labor
changes may affect the macro economy
and possibly change employment in
adjacent industries were not considered.
For instance, possible labor changes in
vehicle maintenance and repair were
not considered, nor were changes in
labor at retail gas stations considered.
Possible labor changes due to raw
material production, such as production
of aluminum, steel, copper, and lithium
were not considered, nor were possible
labor impacts due to changes in
production of oil and gas, ethanol, and
electricity considered. Effects of how
consumers could spend money saved
due to improved fuel economy were not
analyzed, nor were effects of how
consumers would pay for more
expensive fuel savings technologies at
the time of purchase analyzed; either
could affect consumption of other goods
and services, and hence affect labor in
other industries. The effects of increased
usage of car-sharing, ride-sharing, and
automated vehicles were not analyzed.
How changes in labor from any industry
could affect gross domestic product and
possibly affect other industries as a
result were not estimated.
Also, no assumptions were made
about full-employment or not fullemployment and the availability of

PO 00000

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Sfmt 4702

human resources to fill positions. When
the economy is at full employment, a
fuel economy regulation is unlikely to
have much impact on net overall U.S.
employment; instead, labor would
primarily be shifted from one sector to
another. These shifts in employment
impose an opportunity cost on society,
approximated by the wages of the
employees, as regulation diverts
workers from other activities in the
economy. In this situation, any effects
on net employment are likely to be
transitory as workers change jobs (e.g.,
some workers may need to be retrained
or require time to search for new jobs,
while shortages in some sectors or
regions could bid up wages to attract
workers). On the other hand, if a
regulation comes into effect during a
period of high unemployment, a change
in labor demand due to regulation may
affect net overall U.S. employment
because the labor market is not in
equilibrium. Schmalansee and Stavins
point out that net positive employment
effects are possible in the near term
when the economy is at less than full
employment due to the potential hiring
of idle labor resources by the regulated
sector to meet new requirements (e.g., to
install new equipment) and new
economic activity in sectors related to
the regulated sector longer run, the net
effect on employment is more difficult
to predict and will depend on the way
in which the related industries respond
to the regulatory requirements. For that
reason, this analysis does not include
multiplier effects but instead focuses on
labor impacts in the most directly
affected industries. Those sectors are
likely to face the most concentrated
labor impacts.
The tables presented below
summarize these results for regulatory
alternatives under consideration. While
values are reported as thousands of jobyears, changes in labor utilization
would not necessarily involve the same
number of changes in actual jobs, as
auto industry employers may use a
range of strategies (e.g., shift changes,
overtime) beyond simply adding or
eliminating jobs.
1. CAFE Standards

E:\FR\FM\24AUP2.SGM

24AUP2

43437

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Table VIII-39- Estimated Labor (Hours, as 1000s of Job-Years) under CAFE

I Regulatory Alternative

I
MY
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030

Baseline
1,169
1,208
1,237
1,263
1,293
1,301
1,306
1,306
1,309
1,312
1,315
1,320
1,323
1,325

1
1,166
1,198
1,220
1,236
1,244
1,248
1,249
1,251
1,253
1,257
1,260
1,261
1,264
1,265

2
1,166
1,199
1,221
1,237
1,246
1,249
1,251
1,253
1,255
1,259
1,262
1,264
1,266
1,268

p ro~ram

I
3
1,166
1,200
1,223
1,239
1,249
1,252
1,254
1,256
1,258
1,264
1,265
1,268
1,270
1,<272

4
1,166
1,200
1,224
1,241
1,252
1,256
1,258
1,260
1,263
1,269
1,271
1,275
1,277
1,279

5
1,167
1,203
1,227
1,245
1,260
1,263
1,266
1,269
1,273
1,280
1,281
1,285
1,288
1,290

6
1,167
1,203
1,228
1,247
1,263
1,268
1,271
1,275
1,278
1,287
1,287
1,292
1,295
1,297

7
1,167
1,204
1,231
1,251
1,272
1,279
1,283
1,287
1,292
1,304
1,300
1,307
1,310
1,312

8
1,168
1,205
1,233
1,254
1,275
1,280
1,284
1,286
1,290
1,298
1,297
1,303
1,306
1,308

2. CO2 Standards

Table VIII-40- Estimated Labor (Hours, as 1000s of Job-Years) under C02 Pro gram
Regulatory Alternative
1
1,167
1,198
1,220
1,236
1,247
1,247
1,249
1,251
1,253
1,255
1,259
1,260
1,263
1,264

IX. Vehicle Classification
Vehicle classification, for purposes of
the light-duty CAFE and CO2
programs,782 refers to whether a vehicle
782 See

40 CFR 86.1803–01. For the MYs 2012–
2016 standards, the MYs 2017–2025 standards, and
this NPRM, EPA has agreed to use NHTSA’s

VerDate Sep<11>2014

23:42 Aug 23, 2018

Jkt 244001

2
1,167
1,198
1,220
1,237
1,248
1,247
1,250
1,251
1,254
1,256
1,260
1,261
1,264
1,265

3
1,167
1,198
1,220
1,237
1,249
1,248
1,251
1,253
1,255
1,258
1,262
1,264
1,266
1,267

4
1,167
1,199
1,222
1,240
1,254
1,253
1,255
1,258
1,261
1,266
1,270
1,272
1,276
1,277

5
1,168
1,202
1,227
1,247
1,263
1,260
1,264
1,268
1,271
1,277
1,281
1,286
1,288
1,290

is considered to be a passenger
automobile (car) or a non-passenger
automobile (light truck).783 As
regulatory definitions for determining which
vehicles would be subject to which CO2 standards.
783 EPCA uses the terms ‘‘passenger automobile’’
and ‘‘non-passenger automobile;’’ NHTSA’s
regulation on vehicle classification, 49 CFR part

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1,296

7
1,168
1,201
1,228
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8
1,168
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1,250
1,269
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1,298
1,303
1,307
1,309

discussed above in Section III,
passenger cars and light trucks are
subject to different fuel economy and
CO2 standards as required by EPCA/
523, further clarifies the EPCA definitions and
introduces the term ‘‘light truck’’ as a plainer
language alternative for ‘‘non-passenger
automobile.’’

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1,169
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1,254
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1,314
1,318
1,320

EP24AU18.288

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MY
2017
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2019
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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

sradovich on DSK3GMQ082PROD with PROPOSALS2

EISA and consistent with their different
capabilities.
In EPCA, Congress designated some
vehicles as passenger automobiles and
some as non-passenger automobiles.
Vehicles ‘‘capable of off-highway
operation’’ are, by statute, not passenger
automobiles. Determining ‘‘off-highway
operation’’ is a two-part inquiry: First,
does the vehicle have 4-wheel drive, or
is it over 6,000 pounds gross vehicle
weight rating (GVWR), and second, does
the vehicle (that is either 4-wheel drive
or over 6,000 pounds GVWR) also have
‘‘a significant feature designed for offhighway operation,’’ as defined by DOT
regulations.784 Additionally, vehicles
that DOT ‘‘decides by regulation [are]
manufactured primarily for transporting
not more than 10 individuals’’ are, by
statute, passenger automobiles; that
means that certain vehicles that DOT
decides by regulation are not
manufactured primarily for transporting
not more than 10 passengers are not
passenger automobiles. NHTSA’s
regulation on vehicle classification,785
contains requirements for vehicles to be
classified as light trucks either on the
basis of off-highway capability 786 or on
the basis of having ‘‘truck-like
characteristics.’’ 787 Over time, NHTSA
has refined the light truck vehicle
classification by revising its regulations
and issuing legal interpretations.
However, based on agency observations
of current vehicle design trends,
compliance testing and evaluation, and
discussions with stakeholders, NHTSA
has become aware of vehicle designs
that complicate light truck classification
determinations for the CAFE and CO2
programs. When there is uncertainty as
to how vehicles should be classified,
inconsistency in determining
manufacturers’ compliance obligations
can result, which is detrimental to the
predictability and fairness of the
program. While the agency has not
assessed the magnitude of the
classification issues and is not
proposing any vehicle reclassifications
at this time, NHTSA is interested in
gathering more information from
commenters on several of the light truck
classification criteria, and therefore
seeks comment on the issues discussed
below.
A. Classification Based on ‘‘truck-like
characteristics’’
One of the ‘‘truck-like characteristics’’
that allows manufacturers to classify
vehicles as light trucks is having at least
784 49

U.S.C. 32901(a)(18).
CFR part 523.
786 49 CFR 523.5(b).
787 49 CFR 523.5(a).
785 49

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three rows of seats as standard
equipment, as long as it also ‘‘permit[s]
expanded use of the automobile for
cargo-carrying purposes or other nonpassenger-carrying purposes through the
removal or stowing of foldable or
pivoting seats so as to create a flat,
leveled cargo surface extending from the
forwardmost point of installation of
those seats to the rear of the
automobile’s interior.’’ 788 NHTSA has
identified two issues thus far with this
criterion that various manufacturers
appear to be approaching differently,
which, again, could be causing
unfairness in compliance obligations.
Both relate to how to measure the cargo
area when seats are moved out of the
way. Given that the purpose of this
criterion is to ‘‘permit expanded use of
the automobile for cargo-carrying
purposes or other non-passengercarrying purposes,’’ the less cargo space
the vehicle design can provide, the
harder it is for NHTSA to agree that the
vehicle is properly classified as a light
truck.
The first issue is how to identify the
‘‘forwardmost point of installation’’ and
how the location impacts the available
cargo floor area and volume behind the
seats. Seating configurations have
evolved considerably over the last 20
years, as minivan seats are now very
complex in design providing far more
ergonomic functionality. For example,
the market demand for increased rear
seat leg room and the installation of rear
seat air bag systems has resulted in the
introduction of adjustable second row
seats—second-row seats that remain
upright, unable to articulate and stow
into the vehicle floor. These seats
provide adjustable leg room by sliding
forward or backward on sliding tracks
and aim to provide expanded cargo
carrying room by moving forward
against the back of the front seats.
Earlier seating designs had fixed
attachment points on the vehicle floor,
and it was easy to identify the
‘‘forwardmost point of installation’’
because it was readily observable and
did not change. When seats move
forward and backward on sliding tracks,
the ‘‘forwardmost point of installation’’
is less readily identifiable. Some
manufacturers have argued that the
forwardmost point of installation is the
forwardmost point where the seat
attaches to the sliding track with the
seat positioned at its rearmost position
on the track. This would allow vehicles
with certain second-row seat designs to
be considered as meeting this criterion
(e.g., a second-row seat where the
bottom cushion folds upward toward its
788 49

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seatback, allowing the entire seat to
slide forward up against the back of the
front seat, beyond the identified
forwardmost point of installation).
Other approaches could include
adjusting the seat to a position that can
accommodate a 75-percentile male
dummy. Selecting any of these positions
will change the forwardmost point of
installation and could ultimately impact
the flat floor surface area and cargo
volume, respectively. NHTSA seeks
comment on how to determine the
reference point of the forwardmost point
of installation of these seats for vehicles
to qualify as light trucks using this
provision. Also, should NHTSA
establish a minimum amount of cargo
surface area for seats that remain within
the vehicle?
The second issue is what makes a
surface ‘‘flat and leveled.’’ Many SUVs
have three rows of designated seating
positions, where the second row has
‘‘captain’s seats’’ (i.e., two independent
bucket seats) rather than the traditional
bench-style seating more common when
the provision was added to NHTSA’s
regulation. When captain seats are
folded down, the seatback can form a
flat surface for expanded cargo carrying
purposes, but the surface of the
seatbacks may not be level (i.e., may be
angled at some angle slightly greater
than 0°), or may not be level with the
rest of the cargo area (i.e., horizontal
surface of folded seats is 0° at a different
height from horizontal surface of cargo
area behind the seats). Captain seats,
when folded flat, may also leave
significant gaps around and between the
seats. Some manufacturers have opted
to use plastic panels to level the surface
and to covers the gaps between seats,
while others have left the space open
and the surface non-level. NHTSA
therefore seeks comment on the
following questions related to the
requirement for a flat leveled cargo
surface:
• Does the cargo surface need to be flat and
level in exactly the same plane, or does it
fulfill the intent of the criterion and provide
appropriate cargo-carrying functionality for
the cargo surface to be other than flat and
level in the same plane?
• Does the cargo surface need to be flat and
level across the entire surface, or are
(potentially large) gaps in that surface
consistent with the intent of the criterion and
providing appropriate cargo-carrying
functionality? Should panels to fill gaps be
required?
• Certain third row seats are located on top
the rear axle causing them to sit higher and
closer to the vehicle roof. When these seats
fold flat the available cargo-carrying volume
is reduced. Is cargo-carrying functionality
better ensured by setting a minimum amount

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of useable cargo-carrying volume in a vehicle
when seats fold flat?

B. Issues that NHTSA has Observed
Regarding Classification Based on ‘‘offroad capability’’
1. Measuring Vehicle Characteristics for
Off-Highway Capability
For a vehicle to qualify as off-highway
capable, in addition to either having
4WD or a GVWR more than 6,000
pounds, the vehicle must also have four
out of five characteristics indicative of
off-highway operation. These
characteristics include: 789
• An approach angle of not less than 28
degrees
• A breakover angle of not less than 14
degrees
• A departure angle of not less than 20
degrees
• A running clearance of not less than 20
centimeters
• Front and rear axle clearances of not less
than 18 centimeters each

sradovich on DSK3GMQ082PROD with PROPOSALS2

NHTSA’s regulations require
manufacturers to measure these
characteristics when a vehicle is at its
curb weight, on a level surface, with the
front wheels parallel to the automobile’s
longitudinal centerline, and the tires
inflated to the manufacturer’s
recommended cold inflation
pressure.790 Given that the regulations
describe the vehicle’s physical position
and characteristics at time of
measurement, NHTSA previously
assumed that manufacturers would use
physical measurements of vehicles. In
practice, NHTSA has instead received
from manufacturers a mixture of angles
and dimensions from design models
(i.e., the vehicle as designed, not as
actually produced) and/or physical
vehicle measurements.791 When
appropriate, the agency will verify
reported values by measuring
production vehicles in the field. NHTSA
currently requires that manufacturers
must use physical vehicle
measurements as the basis for values
reported to the agency for purposes of
vehicle classification. NHTSA seeks
comment on whether regulatory changes
are needed with respect to this issue.
2. Approach, Breakover, and Departure
Angles
Approach angle, breakover angle, and
departure angle are relevant to
determining off-highway capability.
789 49

CFR 523.5(b)(2).

790 Id.
791 NHTSA previously encountered a similar
issue when manufacturers reported CAFE footprint
information. In the October 2012 final rule, NHTSA
clarified manufacturers must submit footprint
measurements based upon production values. 77 FR
63138 (October 15, 2012).

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Large approach and departure angles
ensure the front and rear bumpers and
valance panels have sufficient clearance
for obstacle avoidance while driving offroad. The breakover angle ensures
sufficient body clearance from rocks and
other objects located between the front
and rear wheels while traversing rough
terrain. Both the approach and
departure angles are derived from a line
tangent to the front (or rear) tire static
loaded radius arc extending from the
ground near the center of the tire patch
to the lowest contact point on the front
or rear of the vehicle. The term ‘‘static
loaded radius arc’’ is based upon the
definitions in SAE J1100 and J1544. The
term is defined as the distance from
wheel axis of rotation to the supporting
surface (ground) at a given load of the
vehicle and stated inflation pressure of
the tire (manufacturer’s recommended
cold inflation pressure).792
The static loaded radius arc is easy to
measure, but the imaginary line tangent
to the static loaded radius arc is difficult
to ascertain in the field. The approach
and departure angles are the angles
between the line tangent to the static
loaded radius arc, as explained above,
and the level ground on which the test
vehicle rests. Simpler measurements,
that provide good approximations for
the approach and departure angles,
involve using a line tangent to the
outside diameter or perimeter of the tire,
or a line that originates at the geometric
center of the tire contact patch, and
extends to the lowest contact point on
the front or rear of the vehicle. The first
method provides an angle slightly
greater than, and the second method
provides an angle slightly less than, the
angle derived from the true static loaded
radius arc. When appropriate, the
agency would like the ability to measure
these angles in the field to verify data
submitted by the manufacturers used to
determine light truck classification
decisions. The agency understands that
the term static loaded radius arc is
unclear to many manufacturers. NHTSA
seeks comment on what the effect
would be if we replaced reference to the
‘‘static loaded arc radius,’’ with simpler
terms like, ‘‘outside perimeter of the
tire,’’ or ‘‘geometric center of the tire
contact patch.’’ NHTSA would consider
using the outside perimeter of the tire as
a reliable method for ensuring
repeatability and reproducibility and
accepts that the approach would
provide slightly larger approach and
departure angles, thereby making it
slightly easier to qualify as ‘‘off-highway
capable.’’
792 49

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3. Running Clearance
NHTSA regulations define ‘‘running
clearance’’ as ‘‘the distance from the
surface on which an automobile is
standing to the lowest point on the
automobile, excluding unsprung
weight.’’ 793 Unsprung weight includes
the components (e.g., suspension,
wheels, axles and other components
directly connected to the wheels and
axles) that are connected and translate
with the wheels. Sprung weight, on the
other hand, includes all components
fixed underneath the vehicle and
translate with the vehicle body (e.g.,
mufflers and subframes). To clarify
these requirements, NHTSA previously
issued a letter of interpretation stating
that certain parts of a vehicle, such as
tire aero deflectors, which are made of
flexible plastic, bend without breaking,
and return to their original position,
would not count against the 20centimeter running clearance
requirement.794 The agency explained
that this does not mean a vehicle with
less than 20-centimeters running
clearance could be elevated by an
upward force bending the deflectors and
then be considered as compliant with
the running clearance criterion, as it
would be inconsistent with the
conditions listed in the introductory
paragraph of 49 CFR 523.5(b)(2).
Further, NHTSA explained that without
a flexible component installed, the
vehicle must meet the 20-centimeter
running clearance along its entire
underside. This 20-centimeter clearance
is required for all sprung weight
components.
The agency is aware of vehicle
designs that incorporate rigid (i.e.,
inflexible) air dams, valance panels,
exhaust pipes, and other components,
equipped as manufacturers’ standard or
optional equipment (e.g., running
boards and towing hitches), that likely
do not meet the 20-centimeter running
clearance requirement. Despite these
rigid features, it appears manufacturers
are not taking these components into
consideration when making
measurements. Additionally, we believe
some manufacturers may provide
dimensions for their base vehicles
without considering optional or various
trim level components that may reduce
the vehicle’s ground clearance.
Consistent with our approach to other
measurements, NHTSA believes that
ground clearance, as well as all the
other suspension criteria for a light
793 Id.
794 See letter to Mark D. Edie, Ford Motor
Company, July 30, 2012. Available online at https://
isearch.nhtsa.gov/files/11-000612%20M.Edie%20
(Part%20523).htm (last accessed February 2, 2018).

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truck determination, should use the
measurements from vehicles with all
standard and optional equipment
installed, at time of first retail sale. The
agency reiterates that the characteristics
listed in 49 CFR 523.5(b)(2) are
characteristics indicative of off-highway
capability. A fixed feature, such as an
air dam, which does not flex and return
to its original state, or an exhaust, which
could detach, inherently interfere with
the off-highway capability of these
vehicles. If manufacturers seek to
classify these vehicles as light trucks
under 49 CFR 523.5(b)(2) and the
vehicles do not meet the four remaining
characteristics to demonstrate offhighway capability, they must be
classified as passenger cars. NHTSA
seeks comment on the incorporation of
air dams, exhaust pipes, and other
hanging component features—especially
those that are inflexible—and whether
the agency should consider amending
its existing regulations to account for
new vehicle designs.
4. Front and Rear Axle Clearance
NHTSA regulations also state that
front and rear axle clearances of not less
than 18 centimeters are another of the
criteria that can be used for designating
a vehicle as off-highway capable.795 The
agency defines ‘‘axle clearance’’ as the
vertical distance from the level surface
on which an automobile is standing to
the lowest point on the axle differential
of the automobile.796
The agency believes this definition
may be outdated because of vehicle
design changes including axle system
components and independent front and
rear suspension components. In the
past, traditional light trucks with and
without 4WD systems had solid rear
axles with center-mounted differentials
on the axle. For these trucks, the rear
axle differential was closer to the
ground than any other axle or
suspension system component. This
traditional axle design still exists today
for some trucks with a solid chassis
(also known as body-on-frame
configuration). Today, many SUVs and
CUVs that qualify as light trucks are
constructed with a unibody frame 797
and have unsprung (e.g., control arms,

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795 49

CFR 523.5(b)(2)(v).
CFR 523.3.
797 Unibody frames integrate the frame and body
components into a combined structure.
796 49

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tie rods, ball joints, struts, shocks, etc.)
and sprung components (e.g., the axle
subframes) connected together as a part
of the axle assembly. These unsprung
and sprung components are located
under the axles, making them lower to
the ground than the axles and the
differential, and were not contemplated
when NHTSA established the definition
and the allowable clearance for axles.
The definition also did not originally
account for 2WD vehicles with GVWRs
greater than 6,000 pounds that had one
axle without a differential, such as the
model year 2018 Ford Expedition.
Vehicles with axle components that are
low enough to interfere with the
vehicle’s ability to perform off-road
would seem inconsistent with the
regulation’s intent of ensuring offhighway capability, as Congress sought.
NHTSA seeks comment on whether
(and if so, how) to revise the definition
of axle clearance in light of these issues.
NHTSA seeks comment on what
unsprung axle components should be
considered when determining a
vehicle’s axle clearance. Should the
definition be modified to account for
axles without differentials? NHTSA also
seeks comment on whether the axle
subframes surrounding the axle
components but affixed directly to the
vehicle unibody, as sprung mass (lower
to the ground than the axles) should be
considered in the allowable running
clearance discussed above. Finally,
should NHTSA consider replacing both
the running and axle clearance criteria
with a single ground clearance criterion
that considers all components
underneath the vehicle that impact a
vehicle’s off road capability?
X. Compliance and Enforcement
A. Overview
The CAFE and CO2 emissions
standards are both fleet-average
standards, but for both programs,
determining compliance begins,
conceptually, by testing vehicles on
dynamometers in a laboratory over predefined test cycles under controlled
conditions.798 A machine is connected
798 For readers unfamiliar with this process, it is
not unlike running a car on a treadmill following
a program—or more specifically, two programs. 49
U.S.C. 32904(c) states that EPA must ‘‘use the same
procedures for passenger automobiles [that EPA]
used for model year 1975 (weighted 55 percent
urban cycle and 45 percent highway cycle), or
procedures that give comparable results.’’ Thus, the

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to the vehicle’s tailpipe while it
performs the test cycle, which collects
and analyzes the resulting exhaust
gases; a vehicle that has no tailpipe
emissions has its performance measured
differently, as discussed below. CO2
quantities, as one of the exhaust gases,
can be evaluated directly for vehicles
that produce CO2 emissions directly.
Fuel economy is determined from the
amount of CO2 emissions, because the
two are directly mathematically
related.799 Manufacturers generally
perform their own testing, and EPA
confirms and validates those results by
testing some number of vehicles at the
National Vehicle and Fuel Emissions
Laboratory (NVFEL) in Ann Arbor,
Michigan. The results of this testing
form the basis for determining a
manufacturer’s compliance in a given
model year: Each vehicle model’s
performance on the test cycles is
calculated; that performance is
multiplied by the number of vehicles of
that model that were produced; that
number, in turn, is averaged with the
performance and production volumes of
the rest of the vehicles in the
manufacturer’s fleet to calculate the
fleet’s overall performance. That
performance is then compared against
the manufacturer’s unique compliance
obligation, which is the harmonic
average of the fuel economy and CO2
targets for the footprints of the vehicles
in the manufacturer’s fleet, also
harmonically averaged and productionweighted. Using fuel economy targets to
illustrate the concept, the following
figure shows two vehicle models
produced in a model year for which
passenger cars are subject to a fuel
economy target function that extends
from about 30 mpg for the largest cars
to about 41 mpg for the smallest cars:
‘‘programs’’ are the ‘‘urban cycle,’’ or Federal Test
Procedure (abbreviated as ‘‘FTP’’) and the ‘‘highway
cycle,’’ or Highway Fuel Economy Test (abbreviated
as ‘‘HFET’’), and they have not changed
substantively since 1975. Each cycle is a designated
speed trace (of vehicle speed versus time) that all
certified vehicles must follow during testing—the
FTP is meant to roughly simulate stop and go city
driving, and the HFET is meant to roughly simulate
steady flowing highway driving at about 50 mph.
799 Technically, for the CAFE program, carbonbased tailpipe emissions (including CO2, CH4, and
CO) are measured and fuel economy is calculated
using a carbon balance equation. EPA uses carbonbased emissions (CO2, CH4, and CO, the same as for
CAFE) to calculate tailpipe CO2 equivalent for the
tailpipe portion of its standards.

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If these are the only two vehicles the
manufacturer produces, the
manufacturer’s required CAFE level is
determined by calculating the salesweighted harmonic average of the
targets applicable at the hatchback and
sedan footprints (about 41 mpg for the
hatchback and about 33 mpg for the
sedan), and the manufacturer’s achieved
CAFE level is determined by calculating
the sales-weighted harmonic average of
the hatchback and sedan fuel economy
levels (48 mpg for the hatchback and 25
mpg for the sedan). Depending on the
relative mix of hatchbacks and sedans
the manufacturer produces, the
manufacturer produces a fleet for which
the required and achieved levels are
equal, or produce a fleet that either
earns (if required CAFE is less than
achieved CAFE) or applies (if required
CAFE is greater than achieved CAFE)
CAFE credits. Although the arithmetic
is different for CO2 standards (which do
not involve harmonic averaging), the
concept is the same.
There are thus two parts to the
foundation of compliance with CAFE
and CO2 emissions standards: First, how
well any given vehicle model performs

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relative to its target, and second, how
many of each vehicle model a
manufacturer sells. While no given
model need precisely meet its target
(and virtually no model exactly meets
its target in the real world), if a
manufacturer finds itself producing and
selling large numbers of vehicles that
fall well short of their targets, it will
have to find a way of offsetting that
shortfall, either by increasing
production of vehicles that exceed their
targets, or by taking advantage of
compliance flexibilities. Given that
manufacturers typically need to sell
vehicles that consumers want to buy,
their options for pursuing the former
approach can often be limited.
The CAFE and CO2 programs both
offer a number of compliance
flexibilities, discussed in more detail
below. Some flexibilities are provided
for by statute, and some have been
implemented voluntarily by the
agencies through regulations.
Compliance flexibilities for the CAFE
and CO2 programs have a great deal of
theoretical attractiveness: If properly
constructed, they can help to reduce
overall regulatory costs while

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43441

maintaining or improving programmatic
benefits. If poorly constructed, they may
create significant potential for market
distortion (for instance, when
manufacturers, in response to an
incentive to deploy a particular type of
technology, produce vehicles for which
there is no natural market, such vehicles
must be discounted below their cost in
order to sell).800 Use of compliance
flexibilities without sufficient
transparency may complicate the ability
to understand manufacturers’ paths to
compliance. Overly-complicated
flexibility programs can result in greater
800 Manufacturers are currently required by the
state of California to produce certain percentages of
their fleets with certain types of technologies, partly
in order to help California meet self-imposed GHG
reduction goals. While many manufacturers
publicly discuss their commitment to these
technologies, consumer interest in them thus far
remains low despite often-large financial incentives
from both manufacturers and the Federal and State
governments in the form of tax credits. It is
questionable whether continuing to provide
significant compliance incentives for technologies
that consumers appear not to want is an efficient
means to achieve either compliance or national
goals (see, e.g., Congress’ phase-out of the AMFA
dual-fueled vehicle incentive in EISA, 49 U.S.C.
32906).

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expenditure of both private sector and
government resources to track, account
for, and manage. Moreover, targeting
flexibilities toward specific technologies
could theoretically distort the market.
By these means, compliance flexibilities
could create an environment in which
entities are encouraged to invest in such
government-favored technologies and,
unless those technologies are
independently supported by market
forces, encourage rent seeking in order
to protect, preserve, and enhance profits
that are parasitic on the distortions
created by government mandate.

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Further, to the extent that there is a
market demand for vehicles with lower
CO2 emissions and higher fuel economy,
compliance flexibilities may create
competitive disadvantages for some
manufacturers if they become overly
reliant on flexibilities rather than
simply improving their vehicles to meet
that market demand.
If standards are set at levels that are
appropriate/maximum feasible, then the
need for extensive compliance
flexibilities should be low. Comment is
sought on whether and how each
agency’s existing flexibilities might be
amended, revised, or deleted to avoid

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these potential negative effects.
Specifically, comment is sought on the
appropriate level of compliance
flexibility, including credit trading, in a
program that is correctly designed to be
both appropriate and feasible. Comment
is sought on allowing all incentivebased adjustments to expire except
those that are mandated by statute,
among other possible simplifications to
reduce market distortion, improve
program transparency and
accountability, and improve overall
performance of the compliance
programs.

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Tabl e X -2 - Incen f 1ves th at add ress
NHTSA

Authority

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A/C
efficiency

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m compnance
r
t es t proce d ures

23:42 Aug 23, 2018

Current
Program
Allows mfrs
to earn "fuel
consumption
improvement
values"
(FCIVs)
equivalent to
EPA credits
starting in
MY 2017

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EPA

NPRM

Authority

No change;
seeking
comment on
eliminating;
seeking
comment on
Alliance/Global
request to allow
retroactive
starting in MY
2012 (propose
to deny)

CAA
202(a)

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Current
Program
"Credits" for
A/C
efficiency
improvements
up to caps of
5.0 g/mi for
cars and 7.2
g/mi for
trucks

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24AUP2

NPRM
Seeking
comment on
combining
A/C efficiency
menu items
and thermal
technologies
menu items;
seeking
comment on
adding
combined caps
of8 g/mi for
cars and 11.5
g/mi for trucks
(thermal
efficiency
technologiues
are currently
capped under
the off-cycle
menu at 10
g/mi)

EP24AU18.292

Regulatory
item

~aps

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Allows mfrs
to earn "fuel
consumption
improvement
values"
(FCIVs)
equivalent to
EPA credits
starting in
MY 2017

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No change;
seeking
comment on
eliminating;
seeking
comment on
Alliance/Global
request to allow
retroactive
starting in MY
2012 (propose
to deny)

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CAA
202(a)

"Menu" of
pre-approved
credits (~ 10),
up to cap of
10 g/mi for
MY 2014 and
beyond; other
pathways
require EPA
approval
through either
5-cycle
testing or
through
public notice
and comment

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24AUP2

Seeking
comment on
expanding to
include: 2 new
techs for menu
(high
efficiency
alternators and
advanced A/C
compressors),
.
.
mcreasmg cap
to 15 g/mi,
'streamlining'
approval
process,
adding other
techs to menu,
updating menu
values,
allowing
suppliers to
seek approval
(rather than
just OEMs)

EP24AU18.293

sradovich on DSK3GMQ082PROD with PROPOSALS2

Off-cycle

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

43445

Ta bl e X -3 - Incenf 1ves th at encourage applr1carIOn oft ec h no og1es
Pickup
trucks

Allows mfrs
to earn
FCIVs
equivalent
to EPA
credits
starting in
MY 2017

No change;
seeking
comment on
extending
availability
of incentive
past current
expiration
date

CAA
202(a)

10 g/mi for
full-size
pickups with
mild hybrids

OR
overperforming
target by 15%
(MYs 20172021 ); 20 g/mi
for full-size
pickups with
strong hybrids

Seeking comment
on
extending/expanding
incentives to all
light trucks and to
passenger cars

OR

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EP24AU18.294

sradovich on DSK3GMQ082PROD with PROPOSALS2

Dedicated
alternative fuel
vehicle

overperforming
target by 20%
(MYs 20172025)
Ta bl e X -4 - Incent1ves t h at encoura ~e a ternative f ue I ve h.ICI es
49
Fuel economy No
CAA
Multiplier
Seeking comment
calculated
change
202(a)
incentives
on
U.S.C.
32905(a) assummg
forEVs,
extending/expanding
and (c)
gallon of
FCVs,
multipliers and on
liquid/gaseous
additional incentives
NGVs
alt fuel= 0.15
(each
forNGVs; seeking
vehicle
comment on
gallons of
gasoline; for
counts as
extending 0 g/mi
Evs,
2.0
factor for upstream
petroleum
vehicles);
emissiOns
equivalency
each EV =
factor
0 g/mi
upstream
emissiOns
through
MY 2021
(then
phases out
based on
per-mfr
production
cap of200k
vehicles)

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sradovich on DSK3GMQ082PROD with PROPOSALS2

BILLING CODE 4910–59–C

It is further noted that compliance is
a measure of how a manufacturer’s fleet
performance compares to its individual
compliance obligation and is generally
not a measure of how the
manufacturer’s fleet performance
compares to other manufacturers’ fleets
or to some industry-wide number.801
This is because the standards are
attribute-based, per Congress (in the
case of CAFE, at least), rather than a
single ‘‘flat’’ mpg or g/mi number which
801 The exception is the CAFE program’s
minimum standard for domestically-manufactured
passenger cars, see Section III and V above and 49
U.S.C. 32902.

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each manufacturer’s fleet must meet.
This means that a manufacturer can
produce, for example, much largerfootprint vehicles than it was expected
to produce when the standards (i.e., the
curves) were set and still be in
compliance because its fleet
performance is better than its
compliance obligation given the
footprints of the vehicles it ended up
producing. This also means that a
manufacturer can produce plenty of
small-footprint vehicles and still fall
short of its compliance obligation if
enough of its vehicles fall below their
targets and the manufacturer has no
other way of making up the shortfall.

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Whether the vehicles a manufacturer
produces are large or small therefore has
no impact on compliance—compliance
depends, instead, on the performance of
a manufacturer’s vehicles relative to
their targets, averaged across the fleet as
a whole.
The following sections discuss
NHTSA’s compliance and enforcement
program, EPA’s compliance and
enforcement program, and seek
comment on a variety of options with
respect to the compliance flexibilities
currently available under each program.
More broadly, the agencies are taking
the opportunity with this rulemaking to
seek comment and suggestions relating

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
to the current flexibilities allowed under
the existing CAFE and tailpipe CO2
programs (including eliminating or
expanding existing flexibilities). The
agencies also seek comment on several
outstanding petitions relating to existing
or newly-proposed flexibilities, and the
current credit trading system.
B. NHTSA Compliance and
Enforcement
NHTSA’s CAFE enforcement program
is largely dictated by statute. As
discussed earlier in this notice, each
vehicle manufacturer is subject to
separate CAFE standards for passenger
cars and light trucks, and for the
passenger car standards, a
manufacturer’s domesticallymanufactured and imported passenger
car fleets are required to comply
separately.802 Additionally,
domestically-manufactured passenger
cars are subject to the statutory
minimum standard.803
EPA calculates the fuel economy level
of each fleet produced by each
manufacturer, and transmits that
information to NHTSA; 804 that
calculation includes adjustments to the
fuel economy of individual vehicles
depending on whether they have certain
incentivized technologies.805
Manufacturers also report early product
projections to NHTSA per EPCA’s
reporting requirements, and NHTSA
relies upon both this manufacturer data
and EPA-validated data to conduct its
own enforcement of the CAFE program.
NHTSA also periodically releases public
reports through its CAFE Public
Information Center (PIC) to share recent
CAFE program data.806
NHTSA then determines the
manufacturer’s compliance with each
applicable standard and notifies
manufacturers if any of their fleets have
fallen short. Manufacturers have the
option of paying civil penalties on any
shortfall or can submit credit plans to
NHTSA. Credits can either be earned or
purchased and can be used either in the
year they were earned or in several
802 49

U.S.C. 32904(b).
U.S.C. 32902(b)(4).
804 49 U.S.C. 32904(c)–(e). EPCA granted EPA
authority to establish fuel economy testing and
calculation procedures; EPA uses a two-year early
certification process to qualify manufacturers to
start selling vehicles, coordinates manufacturer
testing throughout the model year, and validates
manufacturer-submitted final test results after the
close of the model year.
805 For example, alternative fueled vehicles get
special calculations under EPCA (49 U.S.C. 32905–
32906), and fuel economy levels can also be
adjusted to reflect air conditioning efficiency and
‘‘off-cycle’’ improvements, as discussed below.
806 NHTSA CAFE Public Information Center,
https://one.nhtsa.gov/cafe_pic/CAFE_PIC_
Home.htm.

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803 49

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years prior and following, subject to
various statutory constraints.
EPCA and EISA specify several
flexibilities that are available to help
manufacturers comply with CAFE
standards. Some flexibilities are defined
by statute—for example, while Congress
required that NHTSA allow
manufacturers to transfer credits earned
for over-compliance from their car fleet
to their truck fleet and vice versa,
Congress also limited the amount by
which manufacturers could increase
their CAFE levels using those
transfers.807 NHTSA believes Congress
balanced the energy-saving purposes of
the statute against the benefits of certain
flexibilities and incentives and
intentionally placed some limits on
certain statutory flexibilities and
incentives. NHTSA has done its best in
crafting the credit transfer and trading
regulations authorized by EISA to
ensure that total fuel savings are
preserved when manufacturers exercise
their statutorily-provided compliance
flexibilities.
NHTSA and EPA have previously
developed other compliance flexibilities
for the CAFE program under EPA’s
EPCA authority to calculate
manufacturer’s fuel economy levels. As
finalized in the 2012 final rule for MYs
2017 and beyond, EPA provides
manufacturers ‘‘credits’’ under EPA’s
program and fuel economy
‘‘adjustments’’ or ‘‘improvement values’’
under NHTSA’s program for: (1)
Technologies that cannot be measured
on the 2-cycle test procedure, i.e., ‘‘offcycle’’ technologies; and (2) air
conditioning (A/C) efficiency
improvements that also improve fuel
economy that cannot be measured on
the 2-cycle test procedure. Additionally,
the programs give manufacturers
compliance incentives for utilizing
‘‘game changing’’ technologies on
pickup trucks, such as pickup truck
hybridization.
The following sections outline how
NHTSA determines whether
manufacturers are in compliance with
the CAFE standards for each model
year, and how manufacturers may use
compliance flexibilities to comply, or
address non-compliance by paying civil
penalties. As mentioned above, some
compliance flexibilities are prescribed
by statute and some are implemented
through EPA’s EPCA authority to
measure fuel economy, such as fuel
consumption improvement values for
air conditioning efficiency and off-cycle
technologies. This proposal includes
language updating and clarifying
existing regulatory text in this area.
807 See

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43447

Comment is sought on these changes, as
well as on the general efficacy of these
flexibilities and their role in the fuel
economy and GHG programs.
Moreover, the following sections
explain how manufacturers submit data
and information to the agency—NHTSA
is proposing to implement a new
standardized template for manufacturers
to use to submit CAFE data to the
agency, as well as standardized
templates for reporting credit
transactions. Additionally, NHTSA is
proposing to add requirements that
specify the precision of the fuel savings
adjustment factor in 49 CFR 536.4.
These new proposals are intended to
streamline reporting and data collection
from manufacturers, in addition to
helping the agency use the best
available data to inform CAFE program
decision making.
Finally, NHTSA provides an overview
of CAFE compliance data for MYs 2011
through 2018 to demonstrate how
manufacturers have responded to the
progressively increasing CAFE
standards for those years. NHTSA
believes that providing this data is
important because it gives the public a
better understanding of current
compliance trends and the potential
impacts that CAFE compliance in those
model years may have on the future
model years addressed by this
rulemaking.
This is, of course, only an overview
description of CAFE compliance.
NHTSA also granted a petition for
rulemaking in 2016 requesting a number
of changes to compliance-related
topics.808 The responses to those
requests are discussed below. In general,
there is a tentatively decision to deny
most of the Alliance and Global’s
requests as discussed in the sections
that follow. Comment is sought on these
tentative decisions, including what
impact granting any of these individual
requests could have on effective
stringency and compliance pathways.
1. Light-Duty CAFE
(a) How does NHTSA determine
compliance?
(1) Manufacturers Submit Data to
NHTSA and EPA Facilitates CAFE
Testing
EPCA, as amended by EISA, requires
a manufacturer to submit reports to the
Secretary of Transportation explaining
whether the manufacturer will comply
with an applicable CAFE standard for
the model year for which the report is
made; the actions a manufacturer has
taken or intends to take to comply with
808 81

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the standard; and other information the
Secretary requires by regulation.809 A
manufacturer must submit a report
containing the above information during
the 30-day period before the beginning
of each model year, and during the 30day period beginning the 180th day of
the model year.810 When a manufacturer
decides it is unlikely to comply with its
CAFE standard, the manufacturer must
report additional actions it intends to
take to comply and include a statement
about whether those actions are
sufficient to ensure compliance.811
To implement these reporting
requirements, NHTSA issued 49 CFR
part 537, ‘‘Automotive Fuel Economy
Reports,’’ which specifies three types of
CAFE reports that manufacturers must
submit to comply. Manufacturers must
first submit a pre-model year (PMY)
report containing a manufacturer’s
projected compliance information for
that upcoming model year. The PMY
report must be submitted before
December 31st of the calendar year prior
to the corresponding model year.
Manufacturers must then submit a midmodel year (MMY) report containing
updated information from
manufacturers based upon actual and
projected information known midway
through the model year. The MMY
report must be submitted by July 31 of
the given model year. Finally,
manufacturers must submit a
supplementary report anytime the
manufacturer needs to correct
previously submitted information.
Manufacturers submit both nonconfidential and confidential versions of
CAFE reports to NHTSA. Confidential
reports differ in that they include
estimated production sales information
that is withheld from public disclosure
to protect each manufacturer’s
competitive sales strategies.
Manufacturer reports include
information on light-duty automobiles
and medium-duty passenger vehicles for
each model year and describe projected
and actual fuel economy standards, fuel
economy performance values,
production volumes, information on
vehicle design features (e.g., engine
displacement and transmission class),
and other vehicle attribute
characteristics (e.g., track width,
wheelbase, and other off-road features
for light trucks). Beginning with MY
2017, manufacturers may also provide
projected information on any airconditioning (A/C) systems with
improved efficiency, off-cycle
technologies (e.g., stop-start systems),
809 49

U.S.C. 32907(a).

810 Id.
811 Id.

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and any hybrid/electric full-size pickup
truck technologies used each model year
to calculate the average fuel economy
specified in 40 CFR 600.510–12(c).
Manufacturers identify the makes and
model types 812 equipped with each
technology, which compliance category
those vehicles belong to, and the
associated fuel economy adjustment
value for each technology. In some
cases, NHTSA may require
manufacturers to provide supplemental
information to justify or explain the
benefits of these technologies. NHTSA
requires manufacturers to provide
detailed information on the model types
using these technologies to gain fuel
economy benefits. These details are
necessary to facilitate NHTSA’s
technical analyses and to ensure the
agency can perform random
enforcement audits when necessary.
NHTSA uses PMY, MMY, and
supplemental reports to help the agency
and manufacturers anticipate potential
compliance issues as early as possible,
and help manufacturers plan
compliance strategies. NHTSA also uses
the reports for auditing purposes, which
helps manufacturers correct errors prior
to the end of the model year and
accordingly, submit accurate final
reports to EPA. Additionally, NHTSA
issues public reports twice a year that
provide a summary of manufacturers’
final and projected fleet fuel economy
performances values.
Throughout the model year, NHTSA
also conducts vehicle testing as part of
its footprint validation program, to
confirm the accuracy of track width and
wheelbase measurements submitted in
manufacturer’s reports.813 This helps
the agency better understand how
manufacturers may adjust vehicle
characteristics to change a vehicle’s
footprint measurement, and thus its fuel
economy target.
NHTSA ultimately determines a
manufacturer’s compliance based on
CAFE data EPA receives in final model
year reports. EPA verifies the
information, accounting for NHTSA and
EPA testing, and forwards the
information to NHTSA. A
manufacturer’s final model year report
must be submitted to EPA no later than
90 days after December 31 of the model
year.
812 NHTSA collects model type information based
upon the EPA definition for ‘‘modet type’’ in 40
CFR 600.002.
813 U.S. Department of Transportation, NHTSA,
Laboratory Test Procedure for 49 CFR part 537,
Automobile Fuel Economy Attribute Measurements
(Mar. 30, 2009), available at http://www.nhtsa.gov/
DOT/NHTSA/Vehicle%20Safety/
Test%20Procedures/Associated%20Files/TP-53701.pdf.

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(2) Proposed Changes to CAFE
Reporting Requirements
NHTSA is proposing changes to CAFE
reporting requirements with the intent
to streamline reporting and data
collection from manufacturers, in
addition to helping the agency use the
best available data to inform CAFE
program decision-making. The agency
requests comments on the following
reporting requirements.
(i) Standardized CAFE Report
Templates
In a 2015 rulemaking, NHTSA
proposed to amend 49 CFR part 537 to
require a new data format for light-duty
vehicle CAFE reports.814 NHTSA
introduced a new standardized template
for collecting manufacturer’s CAFE
information under 49 CFR 537.7(b) and
(c) in order to ensure the accuracy and
completeness of data collected and to
better align with the final data provided
to EPA. NHTSA explained that for MYs
2013–2015, most manufacturer reports
NHTSA received did not conform to all
of the requirements specified in 49 CFR
part 537. For example, NHTSA
identified several instances where
manufacturers’ CAFE reports included
‘‘yes’’ or ‘‘no’’ values in response to
requests for a vehicle’s numerical
ground clearance values.
Some manufacturers contend that the
changes in reporting requirements may
be one source of confusion. NHTSA is
aware that manufacturers seem to be
confused about what footprint data is
required because of the modification to
the base tire definition 815 in the 2012
final rule for MYs 2017 and beyond.
Specifically, these manufacturers fail to
understand the required reporting
information for model types based upon
footprint values. Beginning in MY 2013,
manufacturers were to provide attributebased target standards in consideration
of the change in the base tire definition
for each unique model type and
footprint combination of the
manufacturer’s automobiles. NHTSA
has found cases where manufacturers
did not aggregate their model types by
each unique footprint combination.
Likewise, NHTSA found other errors in
manufacturers’ vehicle information
submissions. A review of the MY 2015
PMY reports showed that several
manufacturers provided the required
information incorrectly.
Problems with inaccurate or missing
data have become an even greater issue
for manufacturers planning to use the
new procedures for A/C efficiency and
off-cycle technologies, and incentives
814 80
815 49

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for advanced full-sized pickup
trucks.816 Manufacturers seeking to take
advantage of the new procedures and
incentives must provide information on
the model types equipped with the
technologies. However, NHTSA has
identified and contacted several
manufacturers that have failed to submit
the required information in their 2017
and 2018 PMY reports.
Therefore, as part of this rulemaking,
NHTSA is proposing to adopt a
standardized template for reporting all
required data for PMY, MMY, and
supplemental CAFE reports. The
template will be available through the
CAFE Public Information Center (PIC)
website. NHTSA is also proposing to
make the PMY and MMY reports exactly
the same; many manufacturers already
submit PMY reports and then update
the MMY reports with the same type of
information. NHTSA believes that this
approach will further simplify reporting
for manufacturers. Further, NHTSA is
expanding its CAFE reporting
requirements for manufacturers to
provide additional vehicle descriptors,
common EPA carline codes, and more
information on emerging technologies.
Additional data columns will be
included in the reporting template for
manufacturers to identify these
emerging technologies.
NHTSA believes adopting a
standardized template will ensure
manufacturers provide the agency with
all the necessary data in a simpler,
compliant format. The template would
organize the required data in a
standardized and consistent manner,
adopt formats for values consistent with
those provided to EPA, and calculate
manufacturer’s target standards. This
will also help NHTSA code CAFE
electronic data for use in the agency’s
electronic database system. Overall,
these changes are anticipated to
drastically reduce manufacturer and
government burden for reporting under
both EPCA/EISA and the Paperwork
Reduction Act.817
NHTSA seeks comment on the use of
a standardized reporting template, or on
any possible changes to the proposed
standardized template, which is located
in NHTSA’s docket for review.
Information on fuel consumption
improvement technologies (i.e., offcycle) in the template will be collected
at the vehicle model type level. NHTSA
plans to revise the template as part of
the Paperwork Reduction Act process.
816 NHTSA allows manufacturers to use these
incentives for complying with standards starting in
MY 2017.
817 44 U.S.C. 3501 et seq.

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(ii) Standardized Credit Trade
Documents
A credit trade is defined in 49 CFR
536.3 as the receipt by NHTSA of an
instruction from a credit holder to place
its credits in the account of another
credit holder. Traded credits are moved
from one credit holder to the recipient
credit holder within the same
compliance category for which the
credits were originally earned. If a credit
has been traded to another credit holder
and is subsequently traded back to the
originating manufacturer, it will be
deemed not to have been traded for
compliance purposes. NHTSA does not
administer trade negotiations between
manufacturers and when a trade
document is received the agreement
must be issued jointly by the current
credit holder and the receiving party.
NHTSA does not settle contractual or
payment issues between trading
manufacturers.
NHTSA created its CAFE database to
maintain credit accounts for
manufacturers and to track all credit
transactions. Credit accounts consist of
a balance of credits in each compliance
category and vintage held by the holder.
While maintaining accurate credit
records is essential, it has become a
challenging task for the agency given the
recent increase in credit transactions.
Manufacturers have requested NHTSA
approve trade or transfer requests not
only in response to end-of-model year
shortfalls but also during the model year
when purchasing credits to bank for
future model years.
To reduce the burden on all parties,
encourage compliance, and facilitate
quicker NHTSA credit transaction
approval, the agency is proposing to add
a required template to standardize the
information parties submit to NHTSA in
reporting a credit transaction. Presently,
manufacturers are inconsistent in
submitting the information required by
49 CFR 536.8, creating difficulty for
NHTSA in processing transactions. The
template NHTSA is proposing is a
simple spreadsheet that trading parties
fill out. When completed, parties will be
able to click a button on the spreadsheet
to generate a transaction letter for the
parties to sign and submit to NHTSA,
along with the spreadsheet. Using this
template simplifies the credit
transaction process, and ensures that
trading parties are following the
requirements for a credit transaction in
49 CFR 536.8(a).818
Additionally, the template includes
an acknowledgement of the fraud/error
818 Submitting a properly completed template and
accompanying transaction letter will satisfy the
trading requirements in 49 CFR part 536.

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43449

provisions in 49 CFR 536.8(f), and the
finality provisions of 49 CFR 536.8(g).
NHTSA seeks comment on this
approach, as well as on any changes to
the template that may be necessary to
better facilitate manufacturer credit
transaction requests. The agency’s
proposed template is located in
NHTSA’s docket for review. The
finalized template would be available
on the CAFE PIC site for manufacturers
to use.
(iii) Credit Transaction Information
Though entities are permitted to trade
CAFE credits, there is limited public
information available on credit
transactions.819 As discussed earlier,
NHTSA maintains an online CAFE
database with manufacturer and
fleetwide compliance information that
includes year-by-year accounting of
credit balances for each manufacturer.
While NHTSA maintains this database,
the agency’s regulations currently state
that it does not publish information on
individual transactions,820 and
historically, NHTSA has not required
trading entities to submit information
regarding the compensation (whether
financial, or in terms of other credits)
manufacturers receive in exchange for
credits.821 Thus, NHTSA’s public
database offers sparse information to
those looking to determine the value of
a credit.
The lack of information regarding
credit transactions means entities
wishing to trade credits have little, if
any, information to determine the value
of the credits they seek to buy or sell.
It is widely assumed that the civil
penalty for noncompliance with CAFE
standards largely determines the value
of a credit, because it is logical to
assume that manufacturers would not
purchase credits if it cost less to pay
noncompliance penalties instead, but it
is unknown how other factors affect the
value. For example, a credit nearing the
end of its five-model-year lifespan
would theoretically be worth less than
a credit with its full five-model-year
lifespan remaining. In the latter case,
the credit holder would value the credit
more, as it can be used for a longer
period of time.
In the interest of facilitating a
transparent, efficient credit trading
819 Manufacturers may generate credits, but nonmanufacturers may also hold or trade credits. Thus,
the word ‘‘entities’’ is used to refer to those that
may be a party to a credit transaction.
820 49 CFR 536.5(e)(1).
821 NHTSA understands that not all credits are
exchanged for monetary compensation. If NHTSA
were to require entities to report compensation
exchanged for credits, it would not be limited to
reporting monetary compensation.

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rejected, NHTSA notifies the
manufacturer and requests a revised
plan or payment of the appropriate
penalty. Similarly, if the manufacturer
is delinquent in submitting a response
within 60 days, NHTSA takes action to
immediately collect a civil penalty. If
NHTSA receives and approves a
manufacturer’s plan to carryback future
earned credits within the following
three years in order to comply with
current regulatory obligations, NHTSA
will defer levying fines for noncompliance until the date(s) when the
manufacturer’s approved plan indicates
that the credits will be earned or
acquired to achieve compliance. If the
manufacturer fails to acquire or earn
sufficient credits by the plan dates,
NHTSA will initiate non-compliance
proceedings.823
In the event that a manufacturer does
not comply with a CAFE standard even
after the consideration of credits, EPCA
provides that the manufacturer is liable
for a civil penalty.824 Presently, this
penalty rate is set at $5.50 for each tenth
of a mpg that a manufacturer’s average
fuel economy falls short of the standard
for a given model year multiplied by the
total volume of those vehicles in the
affected compliance category
manufactured for that model year.825 All
penalties are paid to the U.S. Treasury
and not to NHTSA itself.

(3) NHTSA Then Analyzes EPACertified CAFE Values for Compliance
After manufacturers complete
certification testing and submit their

final compliance values to EPA, EPA
verifies the data and issues final CAFE
reports to manufacturers and NHTSA.
NHTSA then identifies the
manufacturers’ compliance categories
(i.e., domestic passenger car, imported
passenger car, and light truck fleets) that
do not meet the applicable CAFE
standards. NHTSA uses EPA-verified
data to compare fleet average standards
with actual fleet performance values in
each compliance category. Each vehicle
a manufacturer produces has a fuel
economy target based on its footprint
(footprint curves are discussed above in
Section II.C), and each compliance
category has a CAFE standard measured
in miles per gallon (mpg). If a vehicle
exceeds its target, it is a ‘‘credit
generator,’’ if it falls short of its target,
it is a ‘‘credit loser.’’ Averaging these
vehicles across a compliance category,
accounting for volume, equals a fleet
average. A manufacturer complies with
NHTSA’s fuel economy standard if its
fleet average performance is greater than
or equal to its required standard, or if
it is able to use available compliance
flexibilities, described below in Section
X.B.1.e., to resolve any shortfall.
If the average fuel economy level of
the vehicles in a compliance category
falls below the applicable fuel economy
standard, NHTSA provides written
notification to the manufacturer that it
has not met that standard. The
manufacturer is required to confirm the
shortfall and must either submit a plan
indicating how it will allocate existing
credits, or if it does not have sufficient
credits available in that fleet, how it will
earn, transfer and/or acquire credits, or
pay the appropriate civil penalty. The
manufacturer must submit a credit
allocation plan or payment within 60
days of receiving agency notification.
NHTSA approves a credit allocation
plan unless it finds the proposed credits
are unavailable or that it is unlikely that
the plan will result in the manufacturer
earning sufficient credits to offset the
projected shortfall. If a plan is approved,
NHTSA revises the manufacturer’s
credit account accordingly. If a plan is

Since the inception of the CAFE
program, NHTSA has collected a total of
$890,427,578 in CAFE civil penalty

payments. Generally, import
manufacturers have paid significantly
more in civil penalties than domestic

manufacturers, with the majority of
payments made by import
manufacturers for passenger cars and

market, NHTSA is considering
modifying its regulations to require
trading parties to submit the amount of
compensation exchanged for credits, in
addition to the parties trading and the
number of credits traded in a
transaction. NHTSA is considering
amending its regulations to permit the
agency to publish information on these
specific transactions. NHTSA seeks
comment on requiring these disclosures
when trades occur.

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(iv) Precision of the CAFE Credit
Adjustment Factor
EPCA, as amended by EISA, required
the Secretary of Transportation to
establish an adjustment factor to ensure
total oil savings are preserved when
manufacturers trade credits.822 The
adjustment factor applies to credits
traded between manufacturers and to
credits transferred across a
manufacturer’s compliance fleets.
In establishing the adjustment factor,
NHTSA did not specify the exact
precision of the output of the equation
in 49 CFR 536.4(b). NHTSA’s standard
practice has been round to the nearest
four decimal places (e.g., 0.0001) for the
adjustment factor. However, in the
absence of a regulatory requirement,
many manufacturers have contacted
NHTSA for guidance, and NHTSA has
had to correct several credit transaction
requests. In some instances,
manufacturers have had to revise signed
credit trade documents and submit
additional trade agreements to properly
address credit shortages.
NHTSA is proposing to add
requirements to 49 CFR 536.4 specifying
the precision of the adjustment factor by
rounding to four decimal places (e.g.,
0.0001). NHTSA has also included
equations for the adjustment factor in its
proposed credit transaction report
template, mentioned above, with the
same level of precision. NHTSA seeks
comment on this approach.

822 49

U.S.C. § 32903(f)(1).
generally 49 CFR part 536.
824 49 U.S.C. § 32912.

825 NHTSA proposed retaining the $5.50 civil
penalty rate in an April 2018 NPRM. See 83 FR
13904 (Apr. 2, 2018).

823 See

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(4) Civil Penalties for Non-Compliance
A manufacturer is liable to the
Federal government for a civil penalty if
it does not comply with its applicable
average fuel economy standard, after
considering credits available to the
manufacturer.826
As previously mentioned, the
potential civil penalty rate is currently
$5.50 for each tenth of a mpg that a
manufacturer’s average fuel economy
falls short of the average fuel economy
standard for a model year, multiplied by
the total volume of those vehicles in the
compliance category.

826 49

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
not light trucks. Import passenger car
manufacturers paid a total of
$890,057,188 in CAFE fines while
domestic manufacturers paid a total of
$370,390.
Prior to the CAFE credit trade and
transfer program, several manufacturers
opted to pay civil penalties instead of
complying with CAFE standards. Since
NHTSA introduced trading and
transferring, manufacturers have largely
traded or transferred credits in lieu of
paying civil penalties. NHTSA assumes
that buying and selling credits is a more
cost-effective strategy for manufacturers
than paying civil penalties, in part
because it seems logical that the price of
a credit is directly related to the civil
penalty rate and decreases as a credit
life diminishes.827 Prior to trading and
transferring, on average, manufacturers
paid $29,075,899 in civil penalty
payments annually (a total of
$814,125,176 from model years 1982 to
2010). Since trading and transferring,
manufacturers now pay an annual
average of $15,260,480 each model year.
The agency notes that five
manufacturers have paid civil penalties
since 2011 totaling $76,302,402, and no
civil penalty payments were made in
2015. However, over the next several
years, as stringency increases,
manufacturers are expected to have
challenges with CAFE standard
compliance.
(b) What Exemptions and Exclusions
does NHTSA allow?

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(a) Emergency and Law Enforcement
Vehicles
Under EPCA, manufacturers are
allowed to exclude emergency vehicles
from their CAFE fleet 828 and all
manufacturers that produce emergency
vehicles have historically done so.
NHTSA is not proposing any changes to
this exclusion.
(b) Small Volume Manufacturers
Per 49 U.S.C. 32902(d), NHTSA
established requirements for exempted
small volume manufacturers in 49 CFR
part 525, ‘‘Exemptions from Average
Fuel Economy Standards.’’ The small
volume manufacturer exemption is
available for any manufacturer whose
projected or actual combined sales
(whether in the United States or not) are
fewer than 10,000 passenger
automobiles in the model year two years
before the model year for which the
manufacturer seeks to comply. The
manufacturer must submit a petition
with information stating that the
827 See 49 CFR 536.4 for NHTSA’s regulations
regarding CAFE credits.
828 49 U.S.C. § 32902(e).

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applicable CAFE standard is more
stringent than the maximum feasible
average fuel economy level that the
manufacturer can achieve. NHTSA must
then issue by Federal Register notice an
alternative average fuel economy
standard for the passenger automobiles
manufactured by the exempted
manufacturer. The alternative standard
is the maximum feasible average fuel
economy level for the manufacturers to
which the alternative standard applies.
NHTSA is not proposing any changes to
the small volume manufacturer
provision or alternative standards
regulations in this rulemaking.
(c) What compliance flexibilities and
incentives are currently available under
the CAFE program and how do
manufacturers use them?
There are several compliance
flexibilities that manufacturers can use
to achieve compliance with CAFE
standards beyond applying fuel
economy-improving technologies. Some
compliance flexibilities are statutorily
mandated by Congress through EPCA
and EISA, specifically program credits,
including the ability to carry-forward,
carry-back, trade and transfer credits,
and special fuel economy calculations
for dual- and alternative-fueled vehicles
(discussed in turn, below). However, 49
U.S.C. 32902(h) expressly prohibits
NHTSA from considering the
availability of statutorily-established
credits (either for building dual- or
alternative-fueled vehicles or from
accumulated transfers or traders) in
determining the level of the standards.
Thus, NHTSA may not raise CAFE
standards because manufacturers have
enough of those credits to meet higher
standards. This is an important
difference from EPA’s authority under
the CAA, which does not contain such
a restriction, and which flexibility EPA
has assumed in the past in determining
appropriate levels of stringency for its
program.
NHTSA also promulgated compliance
flexibilities in response to EPA’s
exercise of discretion under its EPCA
authority to calculate fuel economy
levels for individual vehicles and for
fleets. These compliance flexibilities,
which were first introduced in the 2012
rule for MYs 2017 and beyond, include
air conditioning efficiency improvement
and ‘‘off cycle’’ adjustments, and
incentives for advanced technologies in
full size pick-up trucks, including
incentives for mild and strong hybrid
electric full-size pickup trucks and
performance-based incentives in fullsize pickup trucks. As explained above,
comment is sought on all of these
adjustments and incentives.

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(1) Program Credits and Credit Trading
Generating, trading, transfer, and
applying CAFE credits is fundamentally
governed by statutory mandates defined
by Congress. As discussed above in
Section X.B.1., program credits are
generated when a vehicle
manufacturer’s fleet over-complies with
its determined standard for a given
model year, meaning its vehicle fleet
achieved a higher corporate average fuel
economy value than the amount
required by the CAFE program for that
model year. Conversely, if the fleet
average CAFE level does not meet the
standard, the fleet would incur debits
(also referred to as a shortfall). A
manufacturer whose fleet generates
credits in a given model year has several
options for using those credits,
including credit carry-back, credit carryforward, credit transfers, and credit
trading.
Credit ‘‘carry-back’’ means that
manufacturers are able to use credits to
offset a deficit that had accrued in a
prior model year, while credit ‘‘carryforward’’ means that manufacturers can
bank credits and use them towards
compliance in future model years.
EPCA, as amended by EISA, requires
NHTSA to allow manufacturers to carry
back credits for up to three model years,
and to carry forward credits for up to
five model years.829 EPA also follows
these same limitations under its GHG
program.830
Credit ‘‘transfer’’ means the ability of
manufacturers to move credits from
their passenger car fleet to their light
truck fleet, or vice versa. As part of the
EISA amendments to EPCA, NHTSA
was required to establish by regulation
a CAFE credit transferring program, now
codified at 49 CFR part 536, to allow a
manufacturer to transfer credits between
its car and truck fleets to achieve
compliance with the standards. For
example, credits earned by
overcompliance with a manufacturer’s
car fleet average standard could be used
to offset debits incurred because of that
manufacturer’s not meeting the truck
fleet average standard in a given year.
However, EISA imposed a cap on the
amount by which a manufacturer could
raise its CAFE standards through
transferred credits: 1 mpg for MYs
2011–2013; 1.5 mpg for MYs 2014–
2017; and 2 mpg for MYs 2018 and
829 49

U.S.C. § 32903(a).
part of its 2017–2025 GHG program final
rulemaking, EPA did allow a one-time CO2 carryforward beyond five years, such that any credits
generated from MYs 2010 through 2016 will be able
to be used to comply with light duty vehicle GHG
standards at any time through MY 2021.
830 As

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beyond.831 These statutory limits will
continue to apply to the determination
of compliance with the CAFE standards.
EISA also prohibits the use of
transferred credits to meet the minimum
domestic passenger car fleet CAFE
standard.832
In their 2016 petition for rulemaking,
the Alliance of Automobile
Manufacturers and Global Automakers
(Alliance/Global or Petitioners) asked
NHTSA to amend the definition of
‘‘transfer’’ as it pertains to compliance
flexibilities.833 In particular, Alliance/
Global requested that NHTSA add text
to the definition of ‘‘transfer’’ stating
that the statutory transfer cap in 49
U.S.C. 32903(g)(3) applies when the
credits are transferred. Alliance/Global
assert that adding this text to the
definition is consistent with NHTSA’s
prior position on this issue.
In the 2012–2016 final rule, NHTSA
stated:
NHTSA interprets EISA not to prohibit the
banking of transferred credits for use in later
model years. Thus, NHTSA believes that the
language of EISA may be read to allow
manufacturers to transfer credits from one
fleet that has an excess number of credits,
within the limits specified, to another fleet
that may also have excess credits instead of
transferring only to a fleet that has a credit
shortfall. This would mean that a
manufacturer could transfer a certain number
of credits each year and bank them, and then
the credits could be carried forward or back
‘without limit’ later if and when a shortfall
ever occurred in that same fleet.834

Following that final rule, NHTSA
clarified via interpretation that the
transfer cap from EISA does not limit
how many credits may be transferred in
a given model year, but it does limit the
application of transferred credits to a
compliance category in a model year.835
‘‘Thus, manufacturers may transfer as
many credits into a compliance category
as they wish, but transferred credits may
not increase a manufacturer’s CAFE
level beyond the statutory limits.’’ 836
NHTSA believes the transfer caps in
49 U.S.C. 32903(g)(3) are still properly
read to limit the application of credits
in excess of those values. NHTSA
understands that the language in the
2012–2016 final rule could be read to
831 49

U.S.C. § 32903(g)(3).
U.S.C. § 32903(g)(4).
833 Auto Alliance and Global Automakers Petition
for rulemaking on Corporate Average Fuel Economy
(June 20, 2016) at 13.
834 75 FR 25666 (May 7, 2010).
835 See, letter from O. Kevin Vincent, Chief
Counsel, NHTSA to Tom Stricker, Toyota (July 5,
2011). Available online at https://isearch.nhtsa.gov/
files/10-004142%20--%20Toyota%20CAFE
%20credit%20transfer%20banking%20--%205
%20Jul%2011%20final%20for%20signature.htm
(last accessed Apr. 18, 2018).
836 Id.

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suggest that the transfer cap applies at
the time credits are transferred.
However, NHTSA believes its
subsequent interpretation—that the
transfer cap applies at the time the
credits are used—is a more appropriate,
plain language reading of the statute.
While manufacturers have approached
NHTSA with various interpretations
that would allow them to circumvent
the EISA transfer cap, NHTSA believes
it is improper to ignore a transfer cap
Congress clearly articulated. Therefore,
NHTSA proposes to deny Alliance/
Global’s petition to revise the definition
of ‘‘transfer’’ in 49 CFR 536.3.
Credit ‘‘trading’’ means the ability of
manufacturers to sell credits to, or
purchase credits from, one another.
EISA allowed NHTSA to establish by
regulation a CAFE credit trading
program, also now codified at 49 CFR
part 536, to allow credits to be traded
between vehicle manufacturers. EISA
also prohibits manufacturers from using
traded credits to meet the minimum
domestic passenger car CAFE
standard.837
Under 49 CFR part 536, credit holders
(including, but not limited to
manufacturers) have credit accounts
with NHTSA where they can, as
outlined above, hold credits, use them
to achieve compliance with CAFE
standards, transfer credits between
compliance categories, or trade them. A
credit may also be cancelled before its
expiration date, if the credit holder so
chooses. Traded and transferred credits
are subject to an ‘‘adjustment factor’’ to
ensure total oil savings are preserved, as
required by EISA. EISA also prohibits
credits earned before MY 2011 from
being traded or transferred.
As discussed above, NHTSA is
concerned with the potential for
compliance flexibilities to have
unintended consequences. Given that
the credit trading program is optional
under EISA, comment is sought on
whether the credit trading provisions in
49 CFR part 536 should cease to apply
beginning in MY 2022.
(a) Fuel Savings Adjustment Factor
Under NHTSA’s credit trading
regulations, a fuel savings adjustment
factor is applied when trading occurs
between manufacturers, but not when a
manufacturer carries credits forward or
carries back credits within their own
fleet. The Alliance/Global requested that
NHTSA require manufacturers to apply
the fuel savings adjustment factor when
credits are carried forward or carried
back within the same fleet, including for
existing, unused credits.
837 49

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U.S.C. § 32903(f)(2).

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Per EISA, total oil savings must be
preserved in NHTSA’s credit trading
program.838 The provisions for credit
transferring within a manufacturer’s
fleet 839 do not include the same
requirement; however, NHTSA
prescribed a fuel savings adjustment
factor that applies to both credit trades
between manufacturers and credit
transfers between a manufacturer’s
compliance fleets.840
When NHTSA initially considered the
preservation of oil savings, the agency
explained how one credit is not
necessarily equal to another. For
example, the fuel savings lost if the
average fuel economy of a manufacturer
falls one-tenth of an mpg below the
level of a relatively low standard are
greater than the average fuel savings
gained by raising the average fuel
economy of a manufacturer one-tenth of
a mpg above the level of a relatively
high CAFE standard.841 The effect of
applying the adjustment factor is to
increase the value of credits earned for
exceeding a relatively low CAFE
standard for credits that are intended to
be applied to a compliance category
with a relatively high CAFE standard,
and to decrease the value of credits
earned for exceeding a relatively high
CAFE standard for credits that are
intended to be applied to a compliance
category with a relatively low CAFE
standard.
Alliance/Global stated that while
carry forward and carry back credits
have been used for many years, the
CAFE standards did not change during
the Congressional CAFE freeze, meaning
credits earned during those years were
associated with the same amount of fuel
savings from year to year.842 Alliance/
Global suggest that because there is no
longer a Congressional CAFE freeze,
NHTSA should apply the adjustment
838 49

U.S.C. § 32903(f)(1).
U.S.C. § 32903(g).
840 See 49 CFR 536.5. See also 74 FR 14430 (Mar.
30, 2009) (Per NHTSA’s final rule for MY 2011
Average Fuel Economy Standards for Passenger
Cars and Light Trucks, ‘‘There is no other clear
expression of congressional intent in the text of the
statute suggesting that NHTSA would have
authority to adjust transferred credits, even in the
interest of preserving oil savings. However, the goal
of the CAFE program is energy conservation;
ultimately, the U.S. would reap a greater benefit
from ensuring that fuel oil savings are preserved for
both trades and transfers. Furthermore, accounting
for traded credits differently than for transferred
credits does add unnecessary burden on program
enforcement. Thus, NHTSA will adjust credits both
when they are traded and when they are transferred
so that no loss in fuel savings occurs’’).
841 74 FR 14432 (Mar. 30, 2009).
842 Auto Alliance and Global Automakers Petition
for rulemaking on Corporate Average Fuel Economy
(June 20, 2016) at 10.
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factor when moving credits within a
manufacturer’s fleet.
NHTSA has tentatively decided to
deny Alliance/Global’s request to apply
the fuel savings adjustment factor to
credits that are carried forward or
carried back within the same fleet, to
the extent that the request would impact
credits carried forward or backward
retroactively within manufacturer’s
compliance fleets (i.e., credits that were
generated prior to MY 2021, when this
rule takes effect). NHTSA has
tentatively determined that applying the
adjustment factor to credits earned in
model years past would be inequitable.
Manufacturers planned compliance
strategies based, at least in part, on how
credits could be carried forward and
backward, including the lack of an
adjustment factor when credits are
carried forward or backward within the
same fleet. Thus, retroactively stating
that manufacturers must apply the
adjustment factor in this situation could
disadvantage certain manufacturers, and
result in windfalls for other
manufacturers.
However, NHTSA seeks comment on
whether the agency should apply the
fuel savings adjustment factor to credits
that are carried forward or carried back
within the same fleet beginning with
MY 2021.
(b) VMT Estimates for Fuel Savings
Adjustment Factor
NHTSA uses a vehicle miles traveled
(VMT) estimate as part of its fuel
savings adjustment equation to ensure
that when traded or transferred credits
are used, fuel economy credits are
adjusted to ensure fuel oil savings is
preserved.843 For model years 2017–
2025, NHTSA finalized VMT values of
195,264 miles for passenger car credits,
and 225,865 miles for light truck
credits.844 These VMT estimates
harmonized with those used in EPA’s
GHG program. For model years 2011–
2016, NHTSA estimated different VMTs
by model year.
Alliance/Global requested that
NHTSA apply fixed VMT estimates to
the fuel savings adjustment factor for
MYs 2011–2016, similar to how NHTSA
handles MYs 2017–2021. NHTSA
rejected a similar request from the
Alliance in the 2017 and later
rulemaking, citing lack of scope, and
expressing concern about the potential
loss of fuel savings.845
Alliance/Global argue that data from
MYs 2011–2016 demonstrate that no
fuel savings would have been lost, as

NHTSA had originally been concerned
about. Alliance/Global assert that by not
revising the MY 2012–2016 VMT
estimates, credits earned during that
timeframe were undervalued. Therefore,
Alliance/Global argue that NHTSA
should retroactively revise its VMT
estimates to ‘‘reflect better the real
world fuel economy results.’’ 846
Such retroactive adjustments could
unfairly penalize manufacturers for
decisions they made based on the
regulations as they existed at the time.
As Alliance/Global acknowledge,
adjusting vehicle miles travelled
estimates would disproportionately
affect manufacturers that have a credit
deficit and were part of EPA’s
Temporary Lead-time Allowance
Alternative Standards (TLAAS). The
TLAAS program sunsets for model years
2021 and later. Given some
manufacturers would be
disproportionately harmed were we to
accept Alliance/Global’s suggestion,
NHTSA has tentatively decided to deny
Alliance/Global’s request to
retroactively change the agency’s VMT
schedules for model years 2011–2016.
Alliance/Global’s suggestion that a
TLAAS manufacturer would be allowed
to elect either approach does not change
the fact that manufacturers in the
TLAAS program made production
decisions based on the regulations as
understood at the time.
(2) Special Fuel Economy Calculations
for Dual and Alternative Fueled
Vehicles
As discussed at length in prior
rulemakings, EPCA, as amended by
EISA, encouraged manufacturers to
build alternative-fueled and dual- (or
flexible-) fueled vehicles by providing
special fuel economy calculations for
‘‘dedicated’’ (that is, 100%) alternative
fueled vehicles and ‘‘dual-fueled’’ (that
is, capable of running on either the
alternative fuel or gasoline/diesel)
vehicles.
Dedicated alternative fuel
automobiles include electric, fuel cell,
and compressed natural gas vehicles,
among others. NHTSA’s provisions for
dedicated alternative fuel vehicles in 49
U.S.C. 32905(a) state that the fuel
economy of any dedicated automobile
manufactured after 1992 shall be
measured based on the fuel content of
the alternative fuel used to operate the
automobile. A gallon of liquid
alternative fuel used to operate a
dedicated automobile is deemed to
contain .15 gallon of fuel. Under EPCA,

for dedicated alternative fuel vehicles,
there are no limits or phase-out for this
special fuel economy calculation, unlike
for duel-fueled vehicles, as discussed
below.
EPCA’s statutory incentive for dualfueled vehicles at 49 U.S.C. 32906 and
the measurement methodology for dualfueled vehicles at 49 U.S.C. 32905(b)
and (d) expire in MY 2019; therefore,
NHTSA had to examine the future of
these provisions in the 2017 and later
CAFE rulemaking.847 NHTSA and EPA
concluded that it would be
inappropriate to measure duel-fueled
vehicles’ fuel economy like that of
conventional gasoline vehicles with no
recognition of their alternative fuel
capability, which would be contrary to
the intent of EPCA/EISA. Accordingly,
the agencies proposed that for MY 2020
and later vehicles, the general
provisions authorizing EPA to establish
testing and calculation procedures
would provide discretion to set the
CAFE calculation procedures for those
vehicles.848 The methodology for EPA’s
approach is outlined in the 2012 final
rule for MYs 2017 and beyond at 77 FR
63128 (Oct. 15, 2012). NHTSA seeks
comment on the current approach.
(3) Incentives for Advanced
Technologies in Full Size Pickup Trucks
In the 2012 final rule for MYs 2017
and beyond, EPA finalized criteria that
would provide an adjustment to the fuel
economy of a manufacturer’s full size
pickup trucks if the manufacturer
employed certain defined hybrid
technologies for a significant quantity of
those trucks.849 Additionally, EPA
finalized an adjustment to the fuel
economy of a manufacturer’s full sized
pickup truck if it achieved a fuel
economy performance level
significantly above the CAFE target for
its footprint.850 This performance-based
incentive recognized that not all
manufacturers may have wished to
pursue hybridization, and aimed to
reward manufacturers for applying fuelsaving technologies above and beyond
what they might otherwise have done.
EPA provided the incentive for its GHG
program under its CAA authority, and
for the CAFE program under its EPCA
authority, similar to the A/C efficiency
and off-cycle adjustment values
described below.
EPA established limits on the vehicles
eligible to qualify for these credits; a
truck must meet minimum criteria for
bed size and towing or payload
847 77

843 See

49 CFR § 536.4(c).
844 77 FR 63130 (Oct. 15, 2012).
845 Id.

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846 Auto

Alliance and Global Automakers Petition
for rulemaking on Corporate Average Fuel Economy
(June 20, 2016) at 11.

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FR 62651 (Oct. 15, 2012).
U.S.C. §§ 32904(a), (c).
849 77 FR 62651 (Oct. 15, 2012).
850 Id.
848 49

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capacity, and there are minimum sales
thresholds (in terms of a percentage of
a manufacturer’s full-size pickup truck
fleet) that a manufacturer must satisfy in
order to qualify for the incentives.
Additionally, the incentives phase out
at different rates through 2025—the
mild hybrid incentive phases out in MY
2021, the strong hybrid incentive phases
out in 2025, the 15% performance
incentive (10 g/mi) credit phases out in
MY 2021, and the 20% performance
incentive (20 g/mi) credit is available for
a maximum of five years between MYs
2017–2025, provided the vehicle’s CO2
emissions level does not increase.851
At the time of developing this
proposal, no manufacturer has claimed
these full-size pickup truck credits.
Some vehicle manufacturers have
announced potential collaborations,
research projects, or possible future
introduction these technologies for this
segment.852 Additionally, similar to the
incentive for hybridized pickup trucks,
the agency is not aware of any vehicle
manufacturers currently benefiting from
the performance-based incentive.
Comment is sought on whether to
extend either the incentive for hybrid
full size pickup trucks or the
performance-based incentive past the
dates that EPA specified in the 2012
final rule for MYs 2017 and beyond.
(4) Air Conditioning Efficiency and OffCycle Adjustment Values
A/C efficiency and off-cycle fuel
consumption improvement values
(FCIVs) are compliance flexibilities
made available under NHTSA’s CAFE
program through EPA’s EPCA authority
to calculate fuel economy levels for
individual vehicles and for fleets.
NHTSA modified its regulations in the
2012 final rule for MYs 2017 and
beyond to reflect the fact that certain
flexibilities, including A/C efficiency
improving technologies and off-cycle
technology fuel consumption
improvement values (FCIVs), may be
851 77

FR 62651–2 (Oct. 15, 2012).
the time of this proposal, there is awareness
of some vehicle models that may qualify in future
years should manufacturers choose to claim these
credits. For example, the 2019 Ram 1500 introduces
a mild hybrid ‘‘eTorque’’ system (Sam Abuelsamid,
2019 Ram 1500 Gets 48V Mild Hybrid On All Gas
Engines, Forbes (Jan. 15, 2019), https://
www.forbes.com/sites/samabuelsamid/2018/01/15/
2019-ram-1500-gets-standard-48v-mild-hybrid-onall-gas-engines/#2a0cc967e9e6); Ford is expected to
introduce a hybrid F–150 (Keith Naughton, How
Ford plans to market the gasoline-electric F–150,
Automotive News (November 30, 2017), http://
www.autonews.com/article/20171130/OEM05/
171139990/ford-electric-f150-pickup-marketing;
and the Workhorse W–15 system includes both an
electric battery pack and gasoline range extender
(Workhorse W–15 Pickup, http://workhorse.com/
pickup/ (last accessed April 13, 2018).

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used as part of the determination of a
manufacturers’ CAFE level.853
A/C is a virtually standard automotive
accessory, with more than 95% of new
cars and light trucks sold in the United
States equipped with mobile air
conditioning systems. A/C use places
load on an engine, which results in
additional fuel consumption; the high
penetration rate of A/C systems
throughout the light duty vehicle fleet
means that they can significantly impact
the total energy consumed, as well as
GHG emissions resulting from
refrigerant leakage.854 A number of
methods related to the A/C system
components and their controls can be
used to improve A/C system
efficiencies.855
‘‘Off-cycle’’ technologies are those
that reduce vehicle fuel consumption
and CO2 emissions but for which the
fuel consumption reduction benefits are
not recognized under the 2-cycle test
procedure used to determine
compliance with the fleet average
standards. The CAFE city and highway
test cycles, also commonly referred to
together as the 2-cycle laboratory
compliance tests (or 2-cycle tests), were
developed in the early 1970s when few
vehicles were equipped with A/C
systems. The city test simulates city
driving in the Los Angeles area at that
time. The highway test simulates
driving on secondary roads (not
expressways). The cycles are effective in
measuring improvements in most fuel
economy improving technologies;
however, they are unable to measure or
underrepresent some fuel economy
improving technologies because of
limitations in the test cycles.
853 77 FR 63130–34 (Oct. 15, 2012). Instead of
manufacturers gaining credits as done under the
GHG program, a direct adjustment is made to the
manufacturer’s fuel economy fleet performance
value.
854 Notably, however, manufacturers cannot claim
CAFE-related benefits for reducing A/C leakage or
switching to an A/C refrigerant with a lower global
warming potential, because while these
improvements reduce GHGs consistent with the
purpose of the CAA, they generally do not relate to
fuel economy and thus are not relevant to the CAFE
program.
855 The approach for recognizing potential A/C
efficiency gains is to utilize, in most cases, existing
vehicle technology/componentry but improve the
energy efficiency of the technology designs and
operation. For example, most of the additional air
conditioning-related load on an engine is because
of the compressor, which pumps the refrigerant
around the system loop. The less the compressor
operates, the less load the compressor places on the
engine resulting in less fuel consumption and CO2
emissions. Thus, optimizing compressor operation
with cabin demand using more sophisticated
sensors, controls and control strategies, is one path
to improving the efficiency of the A/C system. For
further discussion of A/C efficiency technologies,
see Section II.D of this NPRM and Chapter 6 of the
accompanying PRIA.

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For example, air conditioning is
turned off during 2-cycle testing. Any
air conditioning system efficiency
improvements that reduce load on the
engine and improve fuel economy
cannot be measured on the tests.
Additionally, the city cycle includes
less time at idle than today’s real world
driving, and the highway cycle is
relatively low speed (average speed of
48 mph and peak speed of 60 mph).
Other off-cycle technologies that
improve fuel economy at idle, such as
stop start, and those that improve fuel
economy to the greatest extent at
expressway speeds, such as active grille
shutters which improve aerodynamics,
receive less than their real-world
benefits in the 2-cycle compliance tests.
Since EPA established its GHG
program for light duty vehicles, NHTSA
and EPA sought to harmonize their
respective standards, despite separate
statutory authorities limiting what the
agencies could and could not consider.
For example, for MYs 2012–2016,
NHTSA was unable to consider
improvements manufacturers made to
passenger car A/C efficiency in
calculating compliance.856 At that time,
NHTSA stated that the agency’s
statutory authority did not allow
NHTSA to provide test procedure
flexibilities that would account for A/C
system and off-cycle fuel economy
improvements.857 Thus, NHTSA
calculated its standards in a way that
allowed manufacturers to comply with
the CAFE standards using 2-cycle
procedures alone.
Of the two agencies, EPA was the first
to establish an off-cycle technology
program. For MYs 2012–2016, EPA
allowed manufacturers to request offcycle credits for ‘‘new and innovative
technologies that achieve GHG
reductions that are not reflected on
current test procedures . . .’’ 858 In the
subsequent 2017 and beyond
rulemaking, off-cycle technology was no
longer required to be new and
innovative, but rather only required to
demonstrate improvements not reflected
on test procedures.
At that time (starting with MY 2017),
NHTSA considered off-cycle
technologies and A/C efficiency
improvements when assessing
compliance with the CAFE program.
Accounting for off-cycle technologies
and A/C efficiency improvements in the
CAFE program allowed manufacturers
to design vehicles with improved fuel
856 74

FR 49700 (Sept. 28, 2009).
that time, NHTSA stated ‘‘[m]odernizing
the passenger car test procedures, or even providing
similar credits, would not be possible under EPCA
as currently written.’’ 75 FR 25557 (May 7, 2010).
858 75 FR 25341 (May 7, 2010).
857 At

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economy, even if the improvements
would not show up on the 2-cycle
compliance test. In adding off-cycle and
A/C efficiency improvements to
NHTSA’s program, the agency was able
to harmonize with EPA, which began
accounting for these features in earlier
GHG regulations.
(a) Distinguishing ‘‘Credits’’ From Air
Conditioning Efficiency and Off-Cycle
Benefits
It is important to note some important
differences between consideration given
to A/C efficiency improvement and offcycle technologies, and other
flexibilities in the CAFE program.
NHTSA accounts for A/C efficiency and
off-cycle improvements through EPA
test procedural changes that determine
fuel consumption improvement values.
While regarded by some as ‘‘credits’’
either as shorthand, or because there are
many terms that overlap between
NHTSA’s CAFE program and EPA’s
GHG program, NHTSA’s CAFE program
does not give manufacturers credits for
implementing more efficient A/C
systems, or introducing off-cycle
technologies.859 That is, there is no
bankable, tradable or transferrable credit
earned by a manufacturer for
implementing more efficient A/C
systems or installing an off-cycle
technology. In fact, the only credits
provided for in NHTSA’s CAFE program
are those earned by overcompliance
with a standard.860 What NHTSA does
for off-cycle technologies and A/C
efficiency improvements is adjust
individual vehicle compliance values
based on the fuel consumption
improvement values of these
technologies. As a result, a
manufacturer’s vehicle as a whole may
exceed its fuel economy target, and be
regarded as a credit-generating vehicle.
Illustrative of this confusion, in the
2016 Alliance/Global petition, the
Petitioners asked NHTSA to avoid
imposing unnecessary restrictions on
the use of credits. Alliance/Global
referenced language from an EPA report
that stated compliance is assessed by
measuring the tailpipe emissions of a
manufacturer’s vehicles, and then
reducing vehicle compliance values
depending on A/C efficiency
improvements and off-cycle
technologies.861 This language is
consistent with NHTSA’s statement in
the 2017 and later final rule, in which
explained how the agencies coordinate
859 This is not to be confused with EPA’s parallel
program, which refers to the GHG’s consideration
of A/C improvements and off-cycle technologies as
‘‘credits.’’
860 49 U.S.C. 32903.
861 See Alliance/Global petition at 15.

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and apply off-cycle and A/C
adjustments. ‘‘There will be separate
improvement values for each type of
credit, calculated separately for cars and
for trucks. These improvement values
are subtracted from the manufacturer’s
2-cycle-based fleet fuel consumption
value to yield a final new fleet fuel
consumption value, which would be
inverted to determine a final fleet fuel
CAFE value.’’ 862
Alliance/Global say because of this
process, ‘‘technology credits earned in
the current model year must be
immediately applied toward any deficits
in the current model year. This
approach forces manufacturers to use
their credits in a sub-optimal way, and
can result in stranded credits.’’ 863 As
explained in this section, NHTSA does
not issue credits to manufacturers for
improving A/C efficiency, nor does it
issue credits for implementing off-cycle
technologies. EPA does adjust fuel
economy compliance values on a
vehicle level for those vehicles that
implement A/C efficiency
improvements and off-cycle
technologies.
NHTSA therefore proposes to deny
Alliance/Global’s request because what
the petitioners 864 refer to as
‘‘technology credits’’ are actually fuel
economy adjustment values applied to
the fuel economy measurement of
individual vehicles. Thus, these
adjustments are not actually ‘‘credits,’’
per the definition of a ‘‘credit’’ in EPCA/
EISA and are not subject to the ‘‘carry
forward’’ and ‘‘carry back’’ provisions in
49 U.S.C. 32903.
To alleviate confusion, and to ensure
consistency in nomenclature, NHTSA is
proposing to update language in its
regulations to reflect that the use of the
term ‘‘credits’’ to refer to A/C efficiency
and off-cycle technology adjustments—
should actually be termed fuel
consumption improvement values
(FCIVs).
(b) Petition Requests on A/C Efficiency
and Off-Cycle Program Administration
As discussed above, NHTSA and EPA
jointly administer the off-cycle program.
The 2016 Alliance/Global petition
requested that NHTSA and EPA make
various adjustments to the off-cycle
program; specifically, the petitioners
requested that the agencies should:
862 77

FR 62726 (Oct. 15, 2012).
at 16.
864 The agencies also refer to A/C and off cycle
technology adjustment values as ‘‘credits’’
sporadically throughout their regulations. The
agencies propose to amend their respective
regulatory texts to reflect these are adjustments and
not actual credits that can be carried forward or
back. For a further discussion, see above.
863 Id.

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• re-affirm that technologies meeting the
stated definitions are entitled to the off-cycle
credit at the values stated in the regulation;
• re-acknowledge that technologies shown
to generate more emissions reductions than
the pre-approved amount are entitled to
additional credit;
• confirm that technologies not in the null
vehicle set but which are demonstrated to
provide emissions reductions benefits
constitute off-cycle credits; and
• modify the off-cycle program to account
for unanticipated delays in the approval
process by providing that applications based
on the 5-cycle methodology are to be deemed
approved if not acted upon by the agencies
within a specified timeframe (for instance 90
days), subject to any subsequent review of
accuracy and good faith.

With respect to Alliance/Global’s
request regarding off-cycle technologies
that demonstrate emissions reductions
greater than what is allowable from the
menu, today’s preferred alternative
retains this capability. As was the case
for model years 2017–2021, a
manufacturer is still eligible for a fuel
consumption improvement value other
than the default value provided for in
the menu, provided the manufacturer
demonstrates the fuel economy
improvement.865 This would include
the two-tiered process for demonstrating
the CO2 reductions and fuel economy
improvement.866
The Alliance/Global’s requests to
streamline aspects of the A/C efficiency
and off-cycle programs in response to
the issues outlined above have been
considered. Among other things, the
Alliance/Global requested the agencies
consider providing for a default
acceptance of petitions for off-cycle
credits, provided that all required
information has been provided, to
accelerate the processing of off-cycle
credit requests. While it is agreed that
any continuation of the A/C efficiency
and off-cycle program should
incorporate programmatic
improvements, there are significant
concerns with the concept of default
accepting petition requests that do not
address program issues like uncertainty
in quantifying program benefits, or
general program administration.
Comment is requested comment on
these issues.
Additionally, for a discussion of the
consideration of inclusion of the offcycle program in future CAFE and GHG
standards, see Section X.D.
865 77
866 40

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(c) Petition Requests on Including AirConditioning Efficiency Improvements
in the CAFE Calculations for MYs 2010–
2016
For model years 2012 through 2016,
NHTSA was unable 867 to consider
improvements manufacturers made to
passenger car A/C efficiency in
calculating CAFE compliance. 868
However, EPA did consider passenger
car improvements to A/C efficiency for
this timeframe. To allow manufacturers
to build one fleet that complied with
both EPA and NHTSA standards,
NHTSA adjusted its standards to
account for the differences borne out of
A/C efficiency improvements.
Specifically, the agencies converted
EPA’s g/mi standards to NHTSA mpg
(CAFE) standards. Then, EPA then
estimated the average amount of
improvement manufacturers were
expected to earn via improved A/C
efficiency. From there, NHTSA took
EPA’s converted mpg standard and
subtracted the average improvement
attributable to improvement in A/C
efficiency. NHTSA set its standard at
this level to allow manufacturers to
comply with both standards with
similar levels of technology.869
In the Alliance/Global petition for
rulemaking, the Petitioners requested
that NHTSA and EPA revisit the average
efficiency benefit calculated by EPA
applicable to model years 2012 through
2016. The Alliance/Global argued that
A/C efficiency improvements were not
properly acknowledged in the CAFE
program, and that manufacturers that
exceeded the A/C efficiency
improvements estimated by the
agencies. The Petitioners request that
EPA amend its regulations such that
manufacturers would be entitled to
additional A/C efficiency improvement
benefits retroactively.
NHTSA has tentatively decided to
retain the structure of the existing A/C
efficiency program, and not extend it to
model years 2010 through 2016.
Likewise, EPA has tentatively decided
not to modify its regulations to change
the way A/C efficiency improvements
are accounted for. It is believed this is
appropriate as manufacturers decided
what fuel economy-improving
technologies to apply to vehicles based
on the standards as finalized in 2010.870
867 At that time, NHTSA stated ‘‘[m]odernizing
the passenger car test procedures, or even providing
similar credits, would not be possible under EPCA
as currently written.’’ 75 FR 25557 (May 7, 2010).
868 74 FR 49700 (Sept. 28, 2009).
869 Id.
870 In the MY 2017 and beyond rulemaking,
NHTSA reaffirmed its position it would not extend
A/C efficiency improvement benefits to earlier
model years. 77 FR 62720 (Oct. 15, 2012).

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This included deciding whether to
apply traditional tailpipe technologies,
or A/C efficiency improvements, or
both. Granting A/C efficiency
adjustments to manufacturers
retroactively could result in arbitrarily
varying levels of adjustments granted to
manufacturers, similar to the Alliance/
Global request regarding retroactive offcycle adjustments. Thus, it is tentatively
believed the existing A/C efficiency
improvement structure for model years
2010 through 2016 should remain
unchanged.
(d) Petition Requests on Including OffCycle Improvements in the CAFE
Calculations for MYs 2010–2016
As described above, NHTSA first
allowed manufacturers to generate offcycle technology fuel consumption
improvement values equivalent to CO2
off-cycle credits in MY 2017.871 In
finalizing the rule covering MYs 2017
and beyond, NHTSA declined to
retroactively extend its off-cycle
program to apply to model years 2012
through 2016,872 explaining ‘‘NHTSA
did not take [off-cycle credits] into
account when adopting the CAFE
standards for those model years. As
such, extending the credit program to
the CAFE program for those model years
would not be appropriate.’’ 873
The Alliance/Global petition for
rulemaking asked NHTSA to reconsider
calculating fuel economy for model
years 2010 through 2016 to include offcycle adjustments allowed under EPA’s
program during that period. The
Petitioners argued that NHTSA
incorrectly stated the agency had taken
off-cycle adjustments into consideration
when setting standards for model years
2017 through 2025, but not for model
years 2010–2016. The Alliance/Global
also argued that because neither NHTSA
nor EPA considered off-cycle
adjustments in formulating the
stringency of the 2012–2016 standards,
NHTSA should retroactively grant
manufacturers off-cycle adjustments for
those model years as EPA did. Doing so,
they say, would maintain consistency
between the agencies’ programs.
Pursuant to the Alliance/Global
request, NHTSA has reconsidered the
idea of granting retroactive credits for
model years 2010 through 2016. For the
reasons that follow, NHTSA has
tentatively decided that manufacturers
should not be granted retroactive off871 77

FR 62840 (Oct. 15, 2012).
id.; EPA decided to extend provisions
from its MY 2017 and beyond off-cycle program to
the 2012–2016 model years.
873 Id.
872 See

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cycle adjustments for model years 2010
through 2016.
Of the two agencies, EPA was the first
to establish an off-cycle technology
program. For model years 2012 through
2016, EPA allowed manufacturers to
request off-cycle credits for ‘‘new and
innovative technologies that achieve
GHG reductions that are not reflected on
current test procedures. . .’’ 874 In the
subsequent 2017 and beyond
rulemaking, NHTSA joined EPA and
included an off-cycle program for CAFE
compliance.
The Alliance/Global petition cites a
statement in the 2012–2016 final rule as
affirmation that NHTSA took off-cycle
adjustments into account in formulating
the 2012–2016 stringencies, and
therefore should allow manufacturers
earn off-cycle benefits in model years
that have already passed. In particular,
Alliance/Global point to a general
statement where NHTSA, while
discussing consideration of the effect of
other motor vehicle standards of the
Government on fuel economy, stated
that that rulemaking resulted in
consistent standards across the
program.875 The Alliance/Global
petition appears to take this statement
as a blanket assertion that NHTSA’s
consideration of all ‘‘relevant
technologies’’ included off-cycle
technologies. To the contrary, as quoted
above, NHTSA explicitly stated it had
not considered these off-cycle
technologies.876
The fact that NHTSA had not taken
off-cycle adjustments into consideration
in setting its 2012–2016 standards
makes granting this request
inappropriate. Doing so would result in
a question as to whether the 2012–2016
standards were maximum feasible under
49 U.S.C. 32902(b)(2)(B). If NHTSA had
not considered industry’s ability to earn
off-cycle adjustments—an incentive that
allows manufacturers to utilize
technologies other than those that were
being modeled as part of NHTSA’s
analysis—the agency could have
concluded more stringent standards
were maximum feasible. Additionally,
granting off-cycle adjustments to
manufacturers retroactively raises
questions of equity. NHTSA issued its
2012–2016 standards without an offcycle program, and manufacturers had
874 75 FR 25341, 25344 (May 7, 2010). EPA had
also provided an option for manufacturers to claim
‘‘early’’ off-cycle credits in the 2009–2011 time
frame.
875 Id.
876 Likewise, EPA stated it had not considered offcycle technologies in finalizing the 2012–2016 rule.
‘‘Because these technologies are not nearly so well
developed and understood, EPA is not prepared to
consider them in assessing the stringency of the
CO2 standards.’’ Id. at 25438.

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no reason to suspect that NHTSA would
allow the use off-cycle technologies to
meet fuel economy standards.
Therefore, manufacturers made fuel
economy compliance decisions with the
expectation that they would have to
meet fuel economy standards using oncycle technologies. Generating off-cycle
adjustments retroactively would
arbitrarily reward (and potentially
disadvantage other) manufacturers for
compliance decisions they made
without the knowledge such
technologies would be eligible for
NHTSA’s off-cycle program. Thus,
NHTSA has tentatively decided to deny
Alliance/Global’s request for retroactive
off-cycle adjustments.
It is worth noting that in the model
years 2017 and later rulemaking,
NHTSA and EPA did include off-cycle
technologies in establishing the
stringency of the standards. As
Alliance/Global note, NHTSA and EPA
limited their consideration to start-stop
and active aerodynamic features,
because of limited technical information
on these technologies. At that time, the
agencies stated they ‘‘have virtually no
data on the cost, development time
necessary, manufacturability, etc [sic] of
these technologies. The agencies thus
cannot project that some of these
technologies are feasible within the
2017–2025 timeframe.’’ 877

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877 Draft Joint Technical Support Document:
Rulemaking for 2017–2025 Light-Duty Vehicle
Greenhouse Gas Emission Standards and Corporate
Average Fuel Economy Standards (November 2011).
P. 5–57.

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(d) Light-Duty CAFE Compliance Data
for MYs 2011–2018
This proposal examines how
manufacturers could respond to
potential future CAFE and CO2
standards. For the reader’s reference,
this section provides a brief overview of
how manufacturers have responded to
the progressively increasing CAFE
standards for MYs 2011–2018. NHTSA
uses data from CAFE reports submitted
by manufacturers to EPA or directly to
NHTSA to evaluate compliance with the
CAFE program. The data for model
years 2011 through 2016 include
manufacturers’ final compliance data
that has been verified by EPA.878 The
data for model years 2017 and 2018
include the most recent estimated
projections from manufacturers’ preand mid-model year (PMY and MMY)
reports required by 49 CFR part 537.
Because the PMY and MMY data do not
reflect final vehicle production levels,
the final CAFE values may be different
than the manufacturers’ PMY and MMY
estimates. Model year 2011 was selected
as the start of the data because it
represents the first compliance model
year where manufacturers are permitted
to trade and transfer credits. The
overview of the data for model years
2011 to 2018 is important because it
gives the public an understanding of
current compliance trends and the
potential impacts that these years may
have on the future model years
addressed by this rulemaking.
878 Volkswagen’s model year 2016 final EPA
verified compliance data is excluded due to
ongoing enforcement activites by EPA and NHTSA
for Volkswagen diesel vehicles.

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Figure X–2 through Figure X–5
provide a graphical overview of fuel
economy performance and standards for
model years 2011 to 2018. There are
separate graphs for the total overall
industry fleet and each of the three
compliance categories, domestic and
import passenger cars and light trucks.
Fuel economy performance is compared
against the overall industry fuel
economy standards for each model year.
Fuel economy performance values
include any increases from dual-fueled
vehicles and for vehicles equipped with
fuel consumption improving
technologies.879 880 Compliance reflects
the actual fuel economy performance of
the fleet, and does not include the
application of prior model year or future
model year credits for overcompliance.
879 Congress established the Alternative Motor
Fuels Act (AMFA) which allows manufacturers to
increase their fleet fuel economy performance
values by producing dual fueled vehicles.
Incentives are allowed for building advanced
technology vehicles such as hybrids and electric
vehicles, compressed natural gas vehicles and
building vehicles able to run on dual fuels such as
E85 and gasoline. For model years 1993 through
2014, the maximum increase in CAFE performance
for a manufacturer attributable to dual fueled
vehicles is 1.2 miles per gallon for each model year
and thereafter decreases by 0.2 miles per gallon
each model year until ending in 2019 (see 49 U.S.C.
32906).
880 Under EPA’s authoirity, NHTSA established
provisions starting in model year 2017 allowing
manufacturers to increase fuel economy
performance using the fuel consumption benefits
gained by technolongies not accounted for during
normal 2-cycle EPA compliance testing (i.e, called
off-cycle technologies for technologies such as stopstart systems) as well as for AC systems with
improved efficiencies and for hybrid or electric full
size pickup trucks.

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Figure X-2 Total Fleet Compliance Overview for MYs 2011 to 2018

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Figure X-3 Domestic Passenger Car Compliance Overview for MYs 2011 to 2018

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Figure X-4- Import Passenger Car Compliance Overview for MYs 2011 to 2018

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MYs 2011 to 2015. Comparatively,
domestic and import passenger cars
exceeded standards on average by 2.1
mpg and 2.3 mpg, respectively. On
aveage, light truck manufacturers fell
short of standards by 0.3 mpg on
average over MYs 2011–2015.
For MYs 2016–2018 the overall
industry is or is estimated to fall short

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of CAFE standards for the overall fleet
and for light trucks and for import
passenger cars fleets individually. For
MYs 2016–2018, the total fleet has an
average shortfall of 0.5 mpg. The largest
individual shortfalls are 1.4 mpg for the
light truck fleet in MY 2016 and 2.8 mpg
for the import passenger car fleet in MY
2018. Domestic passenger car fleets are

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As shown in the figures,
manufacturers fuel economy
performance for the total fleet (the
combination of all vehicles produced for
sale during the model year) and for each
compliance fleet are better than CAFE
standards through MY 2015. On
average, the total fleet exceeds CAFE
standards by approximately 0.9 mpg for

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Figure X-5- Light Truck Compliance Overview for MYs 2011 to 2018

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

expected to continue to exceed CAFE
standards. NHTSA expects that on an
overall industry basis, manufacturers
will apply carry forward and traded
CAFE credits to cover the MY 2016–
2018 noncompliances.
Figure X–6 provides a historical
overview of the industry’s use of CAFE
compliance flexibilities for addressing
shortfalls. MY 2015 is the latest model
year for which CAFE compliance is

complete. Historically, manufacturers
have generally resolved credit shortfalls
first by carrying forward any earned
credits and then applying traded credits.
In model years 2014 and 2015, the
amount of credit shortfalls are almost
the same as the amount of carryforward
and traded credits. Manufacturers
occastionally carryback credits or opt to
transfer earned credits between their
fleets to resolve compliance shortfalls.

Trading credits from another
manufacturer and transferring them
across fleets occurs far more frequently.
Also, credit trading has taken the place
of civil penalty payments for resolving
compliance shortfalls. Only a handful of
manufacturers have had to make civil
penalty payments since the
implementation of the credit trading
program.881

2. Medium- and Heavy-Duty Technical
Amendments

2. The CO2 to gasoline conversion
factor. In 49 CFR 535.6(a)(4)(ii) and
(d)(5)(ii), NHTSA provides the
methodology and equations for
converting the CO2 FELs/FCLs for
heavy-duty pickups vans (gram per
mile) and for engines (grams per hp-hr)
to their gallon-of-gasoline equivalence.
In each equation, NHTSA is proposing
to change the conversion factor to 8,887
grams per gallon of gasoline fuel instead
of a factor of 8,877 as currently existing.
3. Curb weight definition. In 40 CFR
523.2, the reference in the definition for
curb weight is incorrect. NHTSA is
proposing to correct the definition to
incorporate the EPA reference in 40 CFR
86.1803 instead of 49 CFR 571.3.
EPA is requesting comment on a
variety of ‘‘enhanced flexibilities’’
whereby EPA would make adjustments
to current incentives and credits

provisions and potentially add new
flexibility opportunities to broaden the
pathways manufacturers would have to
meet standards. Such an approach
would support the increased application
of technologies that the automotive
industry is developing and deploying
that could potentially lead to further
long-term emissions reductions and
allow manufacturers to comply with
standards while reducing costs.
One category of flexibilities such as
off-cycle credits and credit banking
involve credits that are based on real
world emissions reductions and do not
represent a loss of overall emissions
benefits or a reduction in program
stringency, yet offer manufacturers with
potentially lower-cost or more efficient
paths to compliance. Another category
of flexibilities described below as
incentives, such as incentives for
advanced technologies, hybrid
technologies, and alternative fuels, do

largest amount of civil penalties, followed by Volvo.
See Summary of CAFE Civil Penalties Collected,
CAFE Public Information Center, https://

one.nhtsa.gov/cafe_pic/CAFE_PIC_Fines_
LIVE.html.
882 81 FR 73478 (Oct. 25, 2016).

In today’s rule, NHTSA is proposing
to make minor technical revisions to
correct typographical mistakes and
improper references adopted in the
agency’s 2016 Phase 2 medium- and
heavy-duty fuel efficiency
rulemaking.882 The proposed changes
are as follows:
1. NHTSA heavy-duty vehicles and
engine fuel consumption credit
equations. In each credit equation in 49
CFR 535.7, the minus-sign in each
multiplication factor was omitted in the
final version of the rule sent to the
Federal Register. For example, the
credit equation in Part 535.7(b)(1)
should be specified as, Total MY Fleet
FCC (gallons) = (Std¥Act) × (Volume) ×
(UL) × (10¥2) instead of (102) as
currently existing. NHTSA is proposing
to correct these omissions.
881 Only five manufacturers have paid CAFE civil
penalties since credit trading began in 2011.
Predominately, Jaguar Land Rover has paid the

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
result in a loss of emissions benefit and
represent a reduction in the effective
stringency of the standards to the extent
the incentives are used by
manufacturers. These incentives would
help manufacturers meet a numerically
more stringent standard but would not
reduce real-world CO2 emissions
compared to a lower stringency option
with fewer such incentives in the short
term. A policy rationale for providing
such incentives, as EPA articulated in
the 2012 rulemakings,883 is that such
provisions could incentivize advanced
technologies with the potential to lead
to greater GHG emissions reductions in
the longer-term, where such
technologies today are limited by higher
costs, market barriers, infrastructure,
and consumer awareness. Such
incentive approaches would also result
in rewarding automakers who invest in
certain technological pathways, rather
than being technology neutral.
Automakers and other stakeholders
have expressed support for this type of
approach. For example, Ford recently
stated ‘‘[w]e support increasing clean
car standards through 2025 and are not
asking for a rollback. We want one set
of standards nationally, along with
additional flexibility to help us provide
more affordable options for our
customers.’’ 884 Honda also recently
stated their support for an approach that
would retain the existing standards
while extending the advanced
technology multipliers for electrified
vehicles, eliminate automakers’
responsibility for the impact of
upstream emissions from the electric
grid, and accommodate more off-cycle
technologies.885
EPA has received input from
automakers and other stakeholders,
including suppliers and alternative fuels
industries, supporting a variety of
program flexibilities.886 EPA requests
comments on the following and other
flexibility concepts, including the scope
of the flexibilities and the range of
model years over which such provisions
would be appropriate.
The concepts include but are not
limited to:
883 See

77 FR 62810–62826, October 15, 2012.
Measure of Progress’’ By Bill Ford,
Executive Chairman, Ford Motor Company, and Jim
Hackett, President and CEO, Ford Motor Company,
March 27, 2018, https://medium.com/
cityoftomorrow/a-measure-of-progressbc34ad2b0ed.
885 Honda Release ‘‘Our Perspective—Vehicle
Greenhouse Gas and Fuel Economy Standards,’’
April 20, 2018, http://news.honda.com/
newsandviews/pov.aspx?id=10275-en.
886 Memorandum to docket EPA–HQ–OAR–2018–
0283 regarding meetings with the Alliance of
Automobile Manufacturers on April 16, 2018 and
Global Automakers on April 17, 2018.

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884 ‘‘A

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Advanced Technology Incentives: The
current EPA GHG program provides
incentives for electric vehicles, fuel cell
vehicles, plug-in hybrid vehicles, and
natural gas vehicles. Currently,
manufacturers are able to use a 0 g/mile
emissions factor for all electric powered
vehicles rather than having to account
for the GHG emissions associated with
upstream electricity generation up to a
per-manufacturer cumulative
production cap for MYs 2022–2025. The
program also includes multiplier
incentives that allow manufacturers to
count advanced technology vehicles as
more than one vehicle in the
compliance calculations. The current
multipliers begin with MY 2017 and
end after MY 2021.887 Stakeholders
have suggested that these incentives
should be expanded to further support
the production of advanced
technologies by allowing manufacturers
to continue to use the 0 g/mile
emissions factor for electric powered
vehicles rather than having to account
for upstream electricity generation
emissions and by extending and
potentially increasing the multiplier
incentives. EPA is considering a range
of incentives to further encourage
advanced technology vehicles.
Examples of possible incentives and an
estimate of their impact on the
stringency of the standards is provided
below. Global Automakers recently
recommended a multiplier of 3.5 for
EVs and fuel cell vehicles which falls
within the range of the examples
provided below.888 EPA requests
comments on extending or increasing
advanced technology incentives
including the use of 0 g/mile emissions
factor for electric powered vehicles and
multiplier incentives, including
multipliers in the range of 2–4.5.
Hybrid Incentives: The current
program includes incentives for
automakers to use strong and mild
hybrids (or technologies that provide
similar emissions benefits) in full size
pick-up truck vehicles, provided the
manufacturer meets specified
production thresholds. Currently, the
strong hybrid per vehicle credit is 20 g/
mile, available through MY 2025, and
the technology must be used on at least
10% of a company’s full-size pickups to
receive the credit for the model year.
The program also includes a credit for
mild hybrids of 10 g/mi during MYs
887 The current multipliers are for EV/FCVs:
2017–2019—2.0, 2020—1.75, 2021—1.5; for PHEVs
and dedicated and dual fuel CNG vehicles: 2017–
2019—1.6, 2020—1.45, 2021—1.3.
888 Memorandum to docket EPA–HQ–OAR–2018–
0283 regarding meetings with the Alliance of
Automobile Manufacturers on April 16, 2018 and
Global Automakers on April 17, 2018.

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2017–2021. To be eligible a
manufacturer would have to show that
the mild hybrid technology is utilized in
a specified portion of its truck fleet
beginning with at least 20% of a
company’s full-size pickup production
in MY 2017 and ramping up to at least
80% in MY 2021.
EPA received input from automakers
that these incentives should be
extended and available to all light-duty
trucks (e.g., cross-over vehicles,
minivans, sport utility vehicles, smallersized pick-ups) and not only full size
pick-up trucks. Automakers also
recommended that the program’s
production thresholds should be
removed because they discourage the
application of technology since
manufacturers cannot be confident of
achieving the sales thresholds. Some
stakeholders have also suggested an
additional credit for strong and mild
hybrid passenger cars. EPA seeks
comment on whether these incentives
should be expanded along the lines
suggested by stakeholders. For example,
Global Automakers recommends a 20 g/
mile credit for strong hybrid light trucks
and a 10 g/mile credit for strong hybrid
passenger cars. These incentives could
lead to additional product offerings of
strong hybrids, and technologies that
offer similar emissions reductions,
which could enable manufacturers to
achieve additional long-term GHG
emissions reductions.
Off-cycle Emission Credits: Starting
with MY 2008, EPA started employing
a ‘‘five-cycle’’ test methodology to
measure fuel economy for the fuel
economy label.889 However, for GHG
and CAFE compliance, EPA continues
to use the established ‘‘two-cycle’’ (city
and highway test cycles, also known as
the FTP and HFET) test methodology.
As learned through development of the
‘‘five-cycle’’ methodology and prior
rulemakings, there are technologies that
provide real-world GHG emissions and
fuel consumption improvements, but
those improvements are not fully
reflected on the ‘‘two-cycle’’ test. EPA
established the off-cycle credit program
to provide an incentive for technologies
that achieve CO2 reductions but
normally would not be chosen as a GHG
control strategy, as their GHG benefits
are not measured on the specified 2cycle test. Automakers as well as auto
suppliers have recommended several
changes to the current off-cycle credits
program to help it achieve that goal.890
889 https://www.epa.gov/vehicle-and-fuelemissions-testing/dynamometer-drive-schedules.
890 ‘‘Petition for Direct Final Rule with Regard to
Various Aspects of the Corporate Average Fuel

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Automakers and suppliers have
suggested changes including:
• Streamlining the program in ways
that would give auto manufacturers
more certainty and make it easier for
manufacturers to earn credits;
• Expanding the current pre-defined
off-cycle credit menu to include
additional technologies and increasing
credit levels where appropriate;
• Eliminating or increasing the credit
cap on the pre-defined list of off-cycle
technologies and revising the thermal
technology credit cap; and
• A role for suppliers to seek
approval of their technologies.
Under EPA’s existing regulations,
there are three pathways by which a
manufacturer may accrue off-cycle
technology credits. The first is a
predetermined list or ‘‘menu’’ of credit
values for specific off-cycle technologies
that may be used beginning for MY
2014.891 This pathway allows
manufacturers to use conservative credit
values established by EPA for a wide
range of off-cycle technologies, with
minimal data submittal or testing
requirements. In cases where additional
laboratory testing can demonstrate
emission benefits, a second pathway
allows manufacturers to use 5-cycle
testing to demonstrate and justify offcycle CO2 credits.892 The additional
emission tests allow emission benefits
to be demonstrated over some elements
of real-world driving not captured by
the GHG compliance tests, including
high speeds, rapid accelerations, and
cold temperatures. Under this pathway,
manufacturers submit test data to EPA,
and EPA decides whether to approve
the off-cycle credits without soliciting
public comment on the data. The third
and last pathway allows manufacturers
to seek EPA approval, through a notice
and comment process, to use an
alternative methodology other than the
menu of 5-cycle methodology for
determining the off-cycle technology
CO2 credits.893
EPA requests comments on changes to
the off-cycle process that would
streamline the program. Currently,
under the third pathway, manufacturers
submit an application that includes
their methodology to be used to
determine the off-cycle credit value and
data that then undergoes a public
review and comment process prior to an
EPA decision regarding the application.
Each manufacturer separately submits
Economy Program and the Greenhouse Gas
Program,’’ Auto Alliance and Global Automakers,
June 20, 2016.
891 See 40 CFR 86.1869–12(b).
892 See 40 CFR 86.1869–12(c).
893 See 40 CFR 86.1869–12(d).

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an application to EPA that must go
through a public review and comment
process even if the manufacturer uses a
methodology previously approved by
EPA. For example, under the current
program, multiple manufacturers have
submitted applications for high
efficiency alternators and advanced air
conditioning compressors using similar
methodologies and producing similar
levels of credits.
EPA requests comment on revising
the regulations to allow all auto
manufacturers to make use of a
methodology once it has been approved
by EPA without the subsequent
applications from other manufacturers
undergoing the public review process.
This would reduce redundancy present
in the current program. Manufacturers
would need to provide EPA with at least
the same level of data and detail for the
technology and methodology as the firm
that went through the public comment
process.
EPA also requests comment on
revising the regulations to allow EPA to,
in effect, add technologies to the preapproved credit menu without going
through a subsequent rulemaking. For
example, if one or more manufacturers
submit applications with sufficient
supporting data for the same or similar
technology, the data from that
application(s) could potentially be used
by EPA as the basis for adding
technologies to the menu. EPA is
requesting comment on revising the
regulations to allow EPA to establish
through a decision document a credit
value, or scalable value as appropriate,
and technology definitions or other
criteria to be used for determining
whether a technology qualifies for the
new menu credit. This streamlined
process of adding a technology to the
menu would involve an opportunity for
public review but not a formal
rulemaking to revise the regulations,
allowing EPA to add technologies to the
menu in a timely manner, where EPA
believes that sufficient data exists to
estimate an appropriate credit level for
that technology across the fleet. In this
process, EPA could issue a decision
document, after considering public
comments, making the new menu
credits available to all manufacturers
(effectively adding the technology to the
menu without changing the regulations
each time). By adding technologies to
the menu, EPA would eliminate the
need for manufacturers to subsequently
submit individual applications for the
technologies after the first application
was approved.
In addition, EPA requests comments
on modifying the menu through this
current rulemaking to add technologies.

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As noted above, EPA has received data
from multiple manufacturers on high
efficiency alternators and advanced air
conditioning compressors that could
serve as the basis for new menu credits
for these technologies.894 EPA requests
comments on adding these technologies
to the menu including comments on
credit level and appropriate
definitions.895 EPA also requests
comments on other off-cycle
technologies that EPA could consider
adding to the menu including
supporting data that could serve as the
basis for the credit.
In 2014, EPA approved additional
credits for Mercedes-Benz 896 stop-start
system through the off-cycle credit
process based on data submitted by
Mercedes on fleet idle time and its
system’s real-world effectiveness (i.e.,
how much of the time the system turns
off the engine when the vehicle is
stopped). Multiple auto manufacturers
have requested that EPA revise the table
menu value for stop-start technology
based solely on one input value EPA
considered, idle time, in the context of
the Mercedes stop-start system, but no
firms have provided additional data on
any of the other factors which go into
the consideration of a conservative
value for stop-start systems. Systems
vary significantly in hardware, design,
and calibration, leading to wide
variations in how much of the idle time
the engine is actually turned off. EPA
has learned that some stop-start systems
may be less effective in the real world
than the agency estimated in its 2012
rulemaking analysis, for example, due to
systems having a disable switch
available to the driver, or stop-start
systems be disabled under certain
temperature conditions or auxiliary
loads, which would offset the benefits of
the higher idle time estimates. EPA
requests additional data from the OEMs,
suppliers, and other stakeholders
regarding a comprehensive update to
the stop-start off-cycle credit table
value.
The menu currently includes a
fleetwide cap on credits of 10 g/mile 897
to address the uncertainty surrounding
the data and analysis used as the basis
of the menu credits. Some stakeholders
have expressed concern that the current
894 https://www.epa.gov/vehicle-and-enginecertification/compliance-information-light-dutygreenhouse-gas-ghg-standards
895 See EPA Memorandum to Docket EPA–HQ–
OAR–2018–0283 ‘‘Potential Off-cycle Menu Credit
Levels and Definitions for High Efficiency
Alternators and Advanced Air Conditioning
Compressors.’’
896 ‘‘EPA Decision Document: Mercedes-Benz Offcycle Credits for MY 2012–2016,’’ EPA–420–R–14–
025, September 2014.
897 40 CFR 86.1869–12(b)(2).

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
cap may constrain manufacturers ability
in the future to fully utilize the menu
especially if the menu is expanded to
include additional technologies, as
described above. For example, Global
Automakers suggested that the cap be
raised from 10 g/mi to 15 g/mi. EPA
requests comments on increasing the
current cap, for example from the
current 10 g/mile to 15 g/mile to
accommodate increased use of the
menu. EPA also requests comment on a
concept that would replace the current
menu cap with an individual
manufacturer cap that scales with the
manufacturer’s average fleetwide target
levels. The cap would be based on a
percentage of the manufacturer’s
fleetwide 2-cycle emissions
performance, for example at 5–10% of
CO2 a manufacturer’s emissions fleet
wide target. With a cap of five for a
manufacturer with a 2-cycle fleetwide
average CO2 level of 200 g/mile, for
example, the cap would be 10 g/mile.
EPA believes this may be a reasonable
and more technically correct approach
for the caps, recognizing that in many
cases the emissions benefits of off-cycle
technologies correlate with the CO2
levels of the vehicles, providing more or
less emissions reductions depending on
the CO2 levels of the vehicles in the
fleet. For example, applying stop-start to
vehicles with higher vehicle idle CO2
levels provide more emissions
reductions than when applied to
vehicles with lower idle emissions. This
approach also would help account for
the uncertainty associated with the
menu credits and help ensure that offcycle menu credits do not become an
overwhelming portion of the
manufacturers overall emissions
reduction strategy.
The current GHG rule contains a CO2
credit program for improvements to the
efficiency of the air conditioning system
on light-duty vehicles (see § 86.1868–
12). The total of A/C efficiency credits
is calculated by summing the individual
credit values for each efficiency
improving technology used on a vehicle
as specified in the air conditioning
credit menu. The total credit sum for
each vehicle is capped at 5.0 grams/mile
for cars and 7.2 grams/mile for trucks.
Additionally, the off-cycle credit
program (see § 86.1869–12) contains
credit earning opportunities for
technologies that reduce the thermal
loads on the vehicle from environmental
conditions (solar loads, parked interior
ambient air temperature). These menubased thermal control credits have
separate cap limits under the off-cycle
program of 3.0 grams/mile for cars and
4.3 grams/mile for trucks. The AC

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efficiency technologies and the thermal
control technologies directly interact
with each other because improved
thermal control results in reduced air
conditioning loads of the more efficient
air conditioning technologies. Because
of this interaction, an approach that
would remove the thermal control credit
program from the off-cycle credit
program and combine them with the AC
efficiency program would seem
appropriate to quantify the combined
impact. Additionally, a cap that reflects
this combination of these two related
programs may also be appropriate. For
example, if combined, the credit cap for
thermal controls and air conditioning
efficiency could be the combined value
of the current individual program caps
of 8.0 grams/mile for cars and 11.5
grams/mile for trucks. This combined
A/C efficiency and thermal controls cap
would also apply to any additional
thermal control or air conditioning
efficiency technology credit generated
through other off-cycle credit pathways.
Also, by removing the thermal credits
from the off-cycle menu, they would no
longer be counted against the menu cap
discussed above, representing a way to
provide more room under the menu cap
for other off-cycle technologies.
Comment is sought on this approach
and the appropriateness of the described
per vehicle cap limits above.
As mentioned above, EPA has heard
from many suppliers and their trade
associations an interest in allowing
suppliers to have a role in seeking offcycle credits for their technologies. EPA
requests comment on providing a
pathway for suppliers, along with at
least one auto OEM partner, to submit
off-cycle applications for EPA approval.
Auto manufacturers would remain
entirely responsible for the full useful
life emissions performance of the offcycle technology as is currently the
case, including, for example, existing
responsibilities for defect reporting and
the prohibition on defeat devices. Under
such an approach, an application
submitted by a supplier and vehicle
manufacturer would establish a credit
and/or methodology for demonstrating
credits that all auto manufacturers could
then use in their subsequent
applications. This process could include
full-vehicle simulation modeling that is
compatible with EPA’s ALPHA
simulation tool. EPA requests comment
on requiring that the supplier be
partnered in a substantive way with one
or more auto manufacturers to ensure
that there is a practical interest in the
technology prior to investing resources
in the approval process. The supplier
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review and comment prior to an EPA
decision. However, once approved, the
subsequent auto manufacturer
applications requesting credits based on
the supplier methodology would not be
subject to public review. EPA also
requests comments on a concept where
supplier (with at least one auto
manufacturer partner) demonstrated
credits would be available provisionally
for a limited period of time, allowing
manufacturers to implement the
technology and collect data on their
vehicles in order to support a
continuation of credits for the
technology in the longer term. Also, the
provisional credits could be included
under the menu credit cap since they
would be based on a general analysis of
the technology rather than
manufacturer-specific data. EPA
requests comments on all aspects of this
approach.
Incentives for Connected or
Autonomous Vehicles: Connected and
autonomous vehicles have the potential
to significantly impact vehicle
emissions in the future, with their
aggregate impact being either positive or
negative, depending on a large number
of vehicle-specific and system-wide
factors. Currently, connected or
autonomous vehicles would be eligible
for credits under the off-cycle program
if a manufacturer provides data
sufficient to demonstrate the real-world
emissions benefits of such technology.
However, demonstrating the
incremental real-world benefits of these
emerging technologies will be
challenging. Stakeholders have
suggested that EPA should consider an
incentive for these technologies without
requiring individual manufacturers to
demonstrate real world emissions
benefits of the technologies. EPA
believes that any near-term incentive
program should include some
demonstration that the technologies will
be both truly new and have some
connection to overall environmental
benefits. EPA requests comment on such
incentives as a way to facilitate
increased use of these technologies,
including some level of assurance that
they will lead to future additional
emissions reductions.
Among the possible approaches, the
most basic credits could be awarded to
manufacturers that produce vehicles
with connected or automated
technologies. For connected vehicles, a
set amount of credit could be provided
for each vehicle capable of Vehicle-toVehicle (V2V) or Vehicle-toInfrastructure (V2I) communications.
One possible example is to provide a set
amount of credit, using the off-cycle
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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

communicate basic safety messages (as
outlined in SAE J2735) to other
vehicles. The credits provided would be
an incentive to enable future
transportation system efficiencies, as
these technologies on an individual
vehicle are unlikely to impact emissions
in any meaningful way. However, if
these technologies are dispersed widely
across the fleet they could, under some
circumstances, lead to future emission
reductions, and an incentive available to
manufacturers now could help facilitate
that transformation.
The rationale for providing credits for
vehicle automation is similar to that for
connected vehicles. EPA could provide
a set credit for vehicles that achieve
some specific threshold of automation,
perhaps based on the industry standard
SAE definitions (SAE J3016). Individual
autonomous vehicles might achieve
some emissions reductions, but the
impact may increase as larger numbers
of autonomous vehicles are on the road
and can coordinate and provide system
efficiencies. Providing credits for
autonomous vehicles, again through a
set credit, would provide manufacturers
a clear incentive to bring these
technologies to market. It would be
important for any such program to
incentivize only those approaches that
could reasonably be expected to provide
additional contributions to overall
emission reductions, taking system
effects into account. As above, EPA
believes that any near-term incentive
program should include some
demonstration that the technologies are
truly new and have some connection to
environmental benefits overall.
A number of stakeholders have also
requested that EPA consider credits for
automated and connected vehicles that
are placed in ridesharing or other high
mileage applications, where any
potential environmental benefits could
be multiplied due to the high utilization
of these vehicles. That is, credits could
take into account that the per-mile
emission reduction benefits would
accrue across a larger number of miles
for shared-use vehicles. There are likely
many possible approaches that could
accomplish this objective. As one
example, a manufacturer who owns or
partners with a shared-use mobility
entity could receive credit for ensuring
that their autonomous vehicles are used
throughout the life of the vehicle in
shared-use fleets rather than as
personally owned vehicles. Such credits
would be based off of the assumption

that total vehicle miles travelled would
be higher and, therefore, generate more
emission reduction benefits, under the
former case. Credits could be based off
of the CO2 emissions reduction of the
autonomous fleet, taking into account
the higher VMT of the shared-use fleet,
relative to the average.
As suggested by this partial list of
examples, a variety of approaches
would be possible to incentivize the use
of these technologies. EPA seeks
comment on these and related
approaches to incentivize autonomous
and connected vehicle technologies
where they would have the most
beneficial effect on future emissions.
Credit Carry-forward: Currently, CO2
credits may be carried forward, or
banked, for five years, with the
exception that MY 2010–2015 credits
may be carried forward and used
through MY 2021. Automakers have
suggested a variety of ways in which
GHG credit life could be extended under
the Clean Air Act, including the ability
for automakers to carry-forward MY
2010 and later banked credits out to MY
2025, extending the life of credits
beyond five years, or even unlimited
credit life where credits would not
expire. EPA believes longer credit life
would provide manufacturers with
additional flexibility to further integrate
banked credits into their product plans,
potentially reducing costs. EPA requests
comments on extending credit carryforward beyond the current five years,
including unlimited credit life.
Natural Gas Vehicle Credits: Vehicles
that are able to run on compressed
natural gas (CNG) currently are eligible
for an advanced technology multiplier
credit for MYs 2017–2021. Dual-fueled
natural gas vehicles, which can run
either on natural gas or on gasoline, are
also eligible for an advanced technology
multiplier credit if the vehicles meet
minimum CNG range requirements. EPA
received input from several industry
stakeholders who supported expanding
these incentives to further incentivize
vehicles capable of operating on natural
gas, including treating incentives for
natural gas vehicles on par with those
for electric vehicles and other advanced
technologies, and adjusting or removing
the minimum range requirements for
dual-fueled CNG vehicles. EPA requests
comments on these potential additional
incentives for natural gas fueled
vehicles.
High Octane Blends: EPA received
input from renewable fuel industry
stakeholders and from the automotive

industry supporting high octane blends
as a way to enable GHG reducing
technologies such as higher
compression ratio engines. Stakeholders
suggested that mid-level (e.g., E30) high
octane ethanol blends should be
considered and that EPA should
consider requiring that mid-level blends
be made available at service stations.
Higher octane gasoline could provide
manufacturers with more flexibility to
meet more stringent standards by
enabling opportunities for use of lower
CO2 emitting technologies (e.g., higher
compression ratio engines, improved
turbocharging, optimized engine
combustion). EPA requests comment on
if and how EPA could support the
production and use of higher octane
gasoline consistent with Title II of the
Clean Air Act.
To illustrate how additional
flexibilities would translate to a
reduction in the stringency of the
standards, EPA analyzed several
examples as described below.898 The
example flexibilities EPA selected for
this analysis are (1) removing the
requirement to account for upstream
emissions associated with electricity use
(i.e., extending the 0 g/mile emissions
factor), (2) a range of higher multipliers
for electric vehicles, and (3) additional
credits for hybrids sold in the lighttruck fleet. EPA estimated what each
additional flexibility could contribute to
estimate an equivalent percent per year
CO2 standard reduction it would
represent on a fleetwide basis. The
examples and results are provided in
the table below for several example
technology sales penetration values
(three and six percent for battery electric
vehicles, 10 and 20% for mild hybrid
light-trucks, five and 10% for strong
hybrid light-trucks). These examples
were chosen to provide a sense of the
relationship between the additional
flexibility and program stringency. For
each example scenario, EPA made a
number of assumptions regarding the
fleet penetration of the technology, car/
truck mix, and others, which are
documented in the docket. Additional
flexibilities could be structured to
provide a level of overall stringency
equivalent to the full range of the
Alternatives EPA is requesting comment
on in this proposal, from the proposed
standards through more stringent
alternatives described above in this
section, including the ‘‘No Action’’
alternative.

898 Memorandum, ‘‘Spreadsheet tool for the
comparative analysis of program stringencies for

various light-duty vehicle GHG footprint curves and
compliance flexibilities combinations,’’ July 2018,

Kevin Bolon, EPA Office of Air and Radiation.
Docket No. EPA–HQ–OAR–2018–0283.

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reducing the stringency of the
standards. EPA requests comment on
the potential use of enhanced program
flexibilities as an alternative approach
to establishing the appropriate CO2
standards for MY 2021–2025.
EPA solicits comment on the
individual options for flexibilities and
on the potential for combining them as
described in these example scenarios.
For example, EPA solicits comments on
how to take these flexibilities into
account in considering the level of the
standards and whether, for a given level

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of overall stringency, the factors
discussed in Section V above, regarding
EPA Justification for the Proposed GHG
Standards, would support a relatively
less stringent standard with fewer
flexibilities or a relatively more
stringent standard with more
flexibilities. EPA also solicits comment
on whether any flexibilities or
combinations of flexibilities in
particular are more or less consistent
with the Administrator’s rationale for
proposing Alternative 1.

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Table X–6 shows three examples of
scenarios for how enhanced flexibilities
could impact overall program
stringency. Example A reduces the
stringency of the EPA CO2 standard
from 4.7% per year to 4.0% per year.
Example C, which includes the
maximum incentive flexibilities shown
in Table X–5, significantly reduces the
EPA CO2 program stringency from 4.7%
per year to 0.8% per year. Increasing the
BEV multipliers or hybrid credits
beyond those listed in Table XX by EPA
would have the effect of further

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D. Should NHTSA and EPA continue to
account for air conditioning efficiency
and off-cycle improvements?
As stated in the 2012 NPRM and final
rules for MYs 2017 and beyond, the
purpose of the off-cycle improvement
incentive is to encourage the
introduction and market penetration of
off-cycle technologies that achieve realworld benefits.899 In the 2012 NPRM,
NHTSA stated,

sradovich on DSK3GMQ082PROD with PROPOSALS2

. . . because we and EPA do not believe that
we can yet reasonably predict an average
amount by which manufacturers will take
advantage of [the off-cycle FCIV]
opportunity, it did not seem reasonable for
the proposed standards to include it in our
stringency determination at this time. We
expect to re-evaluate whether and how to
include off-cycle credits in determining
maximum feasible standards as the off-cycle
technologies and how manufacturers may be
expected to employ them become better
defined in the future.900

By the 2012 final rule, NHTSA and
EPA had determined that it was
appropriate, under EPA’s EPCA
authority for testing and calculation
procedures, for the agencies to provide
a fuel economy adjustment factor for offcycle technologies.901 NHTSA assessed
some amount of off-cycle credits in the
determination of the maximum feasible

standards for the MYs covered by that
rulemaking.902
The Draft TAR included an extended
discussion of the history and
technological underpinnings of the A/C
efficiency and off-cycle FCIV
measurement procedures; 903 however,
there is a belief that it is also
appropriate to now revisit the basic
question of, and accordingly comment is
sought on, how A/C efficiency and offcycle credits and FCIVs fit in setting
maximum feasible CAFE standards
under EPCA/EISA, and GHG standards
consistent with EPA’s authority under
the CAA. It is believed that it would be
prudent to revisit factors that EPA
identified in their first 2009 NPRM to
establish GHG emissions standards,904
such as how to best ensure that any offcycle credits (and associated FCIVs)
applied for using manufacturer
proposed and agency approved test
procedures are verifiable, reflect realworld reductions, are based on
repeatable test procedures, and are
developed through a transparent process
along with appropriate opportunities for
public comment. Whether the program
is still serving its originally intended
purpose is also a determination to be
made.

899 77

902 77

900 76

903 See

FR 63134 (Oct. 15, 2012).
FR 75226 (Dec. 1, 2011).
901 77 FR 62628, 62649–50 (Oct. 15, 2012).

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FR 62727, 63018 (Oct. 15, 2012).
Draft TAR at 5–207 et seq.
904 See 74 FR 49482 (Sept. 28, 2009).

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1. Why were alternatives that phased
out the A/C efficiency and off-cycle
programs considered?
As part of this rulemaking,
alternatives were considered that phase
out the A/C efficiency and off-cycle
compliance flexibilities to reassess the
benefits and costs of including these
flexibilities in the agencies’ respective
programs. The A/C efficiency and offcycle programs have been the subject of
discussion and debate since the MYs
2017 and beyond final rule. The
Alliance of Automobile Manufacturers
and Global Automakers petitioned the
agencies to streamline aspects of both
agencies’ A/C efficiency and off-cycle
programs as part of a 2016 request to
more broadly harmonize the CAFE and
GHG programs (further discussion of the
Alliance/Global petition is located
above). On the other hand, other
stakeholders have questioned the
purpose and efficacy of the off-cycle
credit program, specifically, whether the
agencies are accurately capturing
technology benefits and whether the
programs are unrealistically inflating
manufacturers’ compliance values.
There are two factors that may be
important to consider at this time, (1)
manufacturer’s increasing use of A/C
efficiency and off-cycle technologies to
achieve compliance in light of the
program’s increasing complexity; and
(2) the questions of whether the
agencies are accurately accounting for

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A/C efficiency and off-cycle benefits. In
response to comments that the programs
in their current form were actually
impeding innovative technology growth,
in particular from manufacturers, the
concept was considered to, instead of
continuing to grow the A/C efficiency
and off-cycle flexibilities, assess two
alternatives that would set standards
without the availability of A/C
efficiency and off-cycle credits for
compliance. Each of these issues will be
expanded upon, in turn.

sradovich on DSK3GMQ082PROD with PROPOSALS2

(a) Manufacturers’ Increasing Reliance
on the A/C Efficiency and Off-cycle
Programs To Achieve Compliance
Since the 2012 final rule for MYs
2017 and beyond and the Draft TAR,
manufacturers have increasingly
utilized A/C efficiency and off-cycle
technology to achieve either credits
under the GHG program, or fuel
consumption improvement values
(FCIVs) under the CAFE program. A/C
efficiency and off-cycle technology use
ranges among manufacturers, from some
manufacturers claiming zero grams/mile
(or the equivalent under the CAFE
program), to some manufacturers
claiming 7 grams/mile in MY 2016.905
Accordingly, with some manufacturers’
potentially reaching the credit cap (10
grams/mile) during the timeframe
contemplated by this rulemaking, if not
before, considerations relating to
manufacturers’ increasing reliance on
the A/C efficiency and off-cycle
programs for compliance, and the
agencies’ administration of the
programs, are presented for discussion.
These issues have not been raised sua
sponte; rather, manufacturers’
comments on the A/C efficiency and offcycle programs have been increasing
recently in volume. Specifically,
manufacturers asserted in their 2016
comments to the Draft TAR that
‘‘[s]ignificant volumes of off-cycle
credits will be essential for the industry
in order to comply with the GHG and
CAFE standards through 2025.’’ 906
905 See Greenhouse Gas Emission Standards for
Light-Duty Vehicles: Manufacturer Performance
Report for the 2016 Model Year (EPA Report 420–
R18–002), U.S. EPA (Jan. 2018), available at https://
nepis.epa.gov/Exe/ZyPDF.cgi?
Dockey=P100TGIA.pdf.
906 Comment by Alliance of Automobile
Manufacturers, Docket ID NHTSA–2016–0068–
0095, at 162. It is important to note the Alliance
submitted this statement in context of the CAFE
and GHG levels set in the 2012 final rule for MYs
2017 and beyond. Specifically, the Alliance
asserted ‘‘[t]he Agencies included off-cycle credits
from only two technologies in their analyses for
setting the stringency of the standards (engine stop
start and active aerodynamic features). However,
because the fuel consumption benefits of many
other technologies were overestimated in the
Agencies’ analyses, and the standards were

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Similarly, in its request for the agencies
to more fully incorporate estimated
costs for A/C efficiency and off-cycle
technologies in their analysis, ICCT
noted that ‘‘companies are clearly
prioritizing [off-cycle] technologies over
more advanced test-cycle efficiency
technologies.’’ 907
Concurrent with the Alliance/Global’s
petition for the agencies to take action
on various aspects of the A/C efficiency
and off-cycle programs, other
stakeholders raised issues about the
programs that could be discussed at this
time. For example, ACEEE commented
on the Draft TAR that ‘‘an off-cycle
technology that is common in current
vehicles and is not reflected in the
stringency of the standards has no place
in the off-cycle credit program. The
purpose of the program is to incentivize
adoption of fuel saving technology, not
to provide loopholes for manufacturers
to achieve the standards on paper.’’ 908
Compare these comments with EPA’s
2017 Light-Duty Automotive
Technology, Carbon Dioxide Emissions,
and Fuel Economy Trends: 1975
Through 2017 report, which estimated
that A/C efficiency and off-cycle credits
could, at most, ‘‘reduce adjusted MY
2016 CO2 tailpipe emission values by
about 7 g/mi, which would translate to
an adjusted fuel economy increase of
approximately 0.5 mpg.’’ 909 A/C and
off-cycle flexibilities allow
manufacturers to optionally apply a
wide array of technologies to improve
fuel economy. While the agencies do not
require or incentivize the adoption of
any particular technologies, the industry
is in fact expanding its use of more costeffective A/C efficiency and off-cycle
technologies rather than other
technology pathways. Accordingly
comment is sought on how large of a
role A/C efficiency and off-cycle
technology should play in manufacturer
compliance. Is an adjusted fuel
economy increase of approximately 0.5
mpg noteworthy?
Next, when manufacturers are
increasingly reliant on A/C efficiency
and off-cycle technology to achieve
compliance, agency administration of
the flexibility becomes more significant.
therefore set at very challenging levels, off-cycle
technologies and the associated GHG and fuel
economy benefits are viewed by the industry as a
critical area that must become a major source of
credits.’’
907 Comment by ICCT, Docket ID EPA–HQ–OAR–
2015–0827–4017, at 10.
908 Comment by ACEEE, Docket ID NHTSA–
2016–0068–0078, at 14.
909 Light-Duty Automotive Technology, Carbon
Dioxide Emissions, and Fuel Economy Trends: 1975
Through 2017, U.S. EPA at 141 (Jan. 2018),
available at https://nepis.epa.gov/Exe/ZyPDF.cgi?
Dockey=P100TGDW.pdf.

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The Alliance commented that the
industry ‘‘needs the off-cycle credit
program to function effectively to fulfill
the significant role that will be needed
for generating large quantities of credits
from [off-cycle] emission reduction.’’ 910
Moreover, the Alliance pointed out that
‘‘[l]imited Agency resources have
delayed the processing of [petitions for
off-cycle credits], and the delay impedes
manufacturers’ ability to plan for
compliance or make investment
decisions.’’ 911 More specifically, the
Alliance commented that:
[c]ase-by-case approvals for off-cycle credit
applications is excessively burdensome due
to slow agency response and unnecessary
testing. The procedures for granting off-cycle
GHG credits are not being implemented per
the provisions of the regulation and are not
functioning to the level necessary for
industry for long-term compliance. Without
timely processing, EPA works against its
stated intent of ‘provid[ing] an incentive for
CO2 and fuel consumption reducing off-cycle
technologies that would otherwise not be
developed because they do not offer a
significant 2-cycle benefit.’ 912

Notably, the agencies’ implementation
of the off-cycle credit provisions has
been described as
‘‘underperforming.’’ 913
The Alliance’s ‘‘primarily regulatory
need’’ as of the 2016 Draft TAR was ‘‘a
renewed focus on removing all obstacles
that are having the unintended result of
slowing investment and implementation
of [credit] technologies.’’ 914 The
Alliance stated generally that ‘‘[w]ith
the pre-approved credit list properly
administered, the off-cycle program can
be expected to grow toward the credit
caps that were established in the
regulation, and these credit caps will
become binding constraints for many or
most automobile manufacturers. At that
point, the credit caps will be
counterproductive since they will
impede greater implementation of the
beneficial off-cycle technologies.’’ 915
Similarly in regards to the agencies’
refusal to grant off-cycle credits for
technologies like driver assistance
systems, the Alliance stated that ‘‘[t]he
unintended consequence of this is that
automakers may not be able to continue
to pursue technologies that do not
910 Comment by Alliance of Automobile
Manufacturers, Docket ID NHTSA–2016–0068–
0095, at 166.
911 Id. at 167.
912 Comment by Alliance of Automobile
Manufacturers, Docket ID EPA–HQ–OA–2017–
0190.
913 Comment by Alliance of Automobile
Manufacturers, Docket ID NHTSA–2016–0068–
0095, at 166.
914 Id. at xiv.
915 Id. at 164.

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sradovich on DSK3GMQ082PROD with PROPOSALS2

provide certainty in supporting vehicle
compliance.’’ 916
These comments highlight the
challenges to assure improvement
values from A/C efficiency and off-cycle
technologies reflect verifiable, realworld fuel economy improvements, are
attributable to specific vehicle models,
are based on repeatable test procedures
and are developed through a transparent
process with appropriate opportunities
for public comment. There is a belief
this process and these considerations
are important to assure the integrity and
fairness of the A/C and off-cycle
procedures. The menu and 5-cycle test
methodologies are predefined and are
not subject to the in-depth review that
proposed new test procedures are
subject to. Comment is sought on
whether and how menu-based A/C and
off-cycle credits should be
implemented.
(b) Potential for Benefits To Be Double
Counted
Next, the potential for technology
benefits to be over-counted is worth
mention, but it is noted that aspects of
this issue are being addressed in this
rulemaking. As stated in the 2012 final
rule for MYs 2017 and beyond, fuel
saving technologies integral to basic
vehicle design (e.g., camless engines,
variable compression ratio engines,
micro air/hydraulic launch assist
devices, advanced transmissions)
should not be eligible for off-cycle
credits. Specifically, ‘‘[b]eing integral,
there is no need to provide an incentive
for their use, and (more important),
these technologies would be
incorporated regardless. Granting
credits would be a windfall.’’ 917
Assumedly, because these technologies
are integral to basic vehicle design, their
benefit would be appropriately captured
on the 2-cycle tests and 5-cycle tests.
Similarly, ICCT commented that, ‘‘[i]n
theory, off-cycle credits are a good idea,
as they encourage real-world fuel
consumption reduction for technologies
that are not fully included on the
official test cycles. However, real-world
benefits only accrue if double-counting
is avoided and the amount of the realworld fuel consumption reduction is
accurately measured.’’ 918
Broadly, there is agreement with the
concept that capturing real-world
driving behavior is essential to
accurately measure the true benefits of
A/C efficiency and off-cycle
technologies. One example where this
916 Id.

at 126.
FR 62732 (Oct. 15, 2012).
918 Comment by ICCT, Docket EPA–HQ–OAR–
2015–0827–4017, at 10.
917 77

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holds true is in particular component
testing as measured with the federal
standardized testing procedure. For
example, the federal test procedures
provide specific guidance on how a
vehicle should be installed on the
dynamometer, if the vehicle’s windows
should be open or closed, and the
vehicle’s tire pressure. On the other
hand, the regulations provide no
specific guidance on how other
components should be tested so the
agencies and manufacturers can most
accurately quantify benefits.
For example, to more accurately
capture the benefit of a high efficiency
alternator on the 2-cycle or 5-cycle test,
the vehicle would need to run more
systems that draw power from the
alternator, like the infotainment system
or temperature controlled seats. There is
not guidance for these additional
components in the tests as they are
currently performed due to the
complexity of systems available in the
light duty vehicle market. Essentially, it
is uncertain how to define in regulations
what component systems need to be on
or off during testing to accurately
capture the benefit of component
synergies. Developing guidance on
specific systems would also likely
require a significant amount of time and
resources. Comment is sought on
specific technologies that may be
receiving more benefit based on the
current test procedures, or more
generally, any other issues related to
integrated component testing.
It is noted, however, that the optional
5-cycle test procedure for determining
A/C and off-cycle improvement values
over-counts benefits. The 5-cycle test
procedure weighs the 2-cycle tests used
for compliance with three additional
test cycles to better represent real-world
factors impacting fuel economy and
GHG emissions, including higher speeds
and more aggressive driving, colder
temperature operation, and the use of
air conditioning. However, the current
regulations erroneously do not require
that the 2-cycle benefit be subtracted
from the 5-cycle benefit, resulting in a
credit calculation that is artificially too
high and not reflecting actual real-world
emission reductions that were intended.
Since the 5-cycle test procedures
include the 2-cycle tests used for
compliance, it is believed the 2-cycle
benefit should be subtracted from the 5cycle benefit to avoid over-counting of
benefits. Manufacturers interested in
generating credits under the 5-cycle
pathway identified this issue to the
agencies, and have asked EPA to clarify
the regulations. This issue is discussed
in Section X.C, above, and comment is

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sought on how to implement this
correction.
2. Why was the phase-out as modeled
(e.g., year over year reductions in
available FCIVs) for certain alternatives
proposed?
The CAFE model was used to assess
the economic, technical, and
environmental impacts of alternatives
that kept the A/C efficiency and offcycle programs as is and alternatives
that phased those programs out. As
described fully in Section II.B, the CAFE
model is a software simulation that
begins with a recently produced fleet of
vehicles and applies cost effective
technologies to each manufacturers’
fleet year-by-year, taking into
consideration vehicle refresh and
redesign schedules and common parts
among vehicles. The CAFE model
outputs technology pathways that
manufacturers could use to comply with
the proposed policy alternatives.
For this NPRM, the modeling analysis
uses the off-cycle credits submitted by
each manufacturer for MY 2017
compliance and carries these forward to
future years with a few exceptions.
Several technologies described in
Section II.D are associated with off-cycle
credits. In particular, stop-start systems,
integrated starter generators, and full
hybrids are assumed to generate offcycle credits when applied to improve
fuel economy. Similarly, higher levels of
aerodynamic improvements are
assumed to require active grille shutters
on the vehicle, which also qualify for
off-cycle credits. The analysis assumes
that any off-cycle credits that are
associated with actions outside of
technologies discussed in Section II.D
(either chosen from the pre-approved
menu or petitioned for separately)
remain at levels identified by
manufacturers in MY 2017. Any
additional off-cycle credits that accrue
as the result of explicit technology
application are calculated dynamically
in each year, for each alternative. This
method allows for the capture of
benefits and costs from A/C efficiency
and off-cycle technologies as compared
to an alternative where those
technologies are not used for
compliance purposes.
In considering potential future actions
regarding the A/C efficiency and offcycle flexibilities, it was recognized that
removing the programs immediately
would present a considerable challenge
for manufacturers. Based on compliance
and mid-model year data for MY 2017,
the first model year that NHTSA
accepted FCIVs for CAFE compliance,
manufacturers have reported A/C
efficiency and off-cycle FCIVs at

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noteworthy levels. EPA’s MY 2016
Performance Report reported wide
penetration of FCIVs from menu
technologies and noted some
technologies widely employed by OEMs
included active grill shutters, glass or
glazing, and stop-start systems.
Additional details of individual
manufacturers’ MY 2016 performance
and individual A/C and off-cycle
technology penetration can be found on
EPA’s website.919 Accordingly, a phase-

out was identified as a reasonable
option for manufacturers to come into
compliance with GHG or fuel economy
standards without using A/C efficiency
and off-cycle improvements for
compliance.
Throughout the joint CAFE and GHG
programs, the agencies have phased out
flexibility and incentive programs rather
than ending those programs abruptly,
such as with the alternative fuel vehicle
program (as mandated by EISA) 920 and

the credit program for advanced
technologies in pickup trucks.921
Accordingly, an incremental decrease in
the maximum A/C efficiency and offcycle FCIVs a manufacturer can receive
starting in MY 2022 and ending in MY
2026 was modeled. Table X–7 below
shows the incremental cap total starting
in MY 2021 and reducing by the
recommended value until MY 2026.

The MY 2016 fleet final compliance
data to identify the starting point for the
FCIV phase-out was reviewed.922 For
A/C efficiency technologies, 6 grams/
mile was used as the starting point,
which was the highest FCIV a single
manufacturer had received in MY 2016.
For off-cycle technologies, the
maximum allowable cap of 10 gram/
mile set in the 2012 final rule for MYs
2017 and beyond was used. Although
no manufacturer had reached the 10
gram/mile cap as of MY 2016, there is
a belief that it is still feasible for some
manufacturers to reach the cap in MYs
prior to 2021. Comment is invited on
this methodology.
3. What do the modeled alternatives
show?
A lower 923 and higher 924 stringency
alternative with and without the A/C
efficiency and off-cycle flexibilities
were modeled to see the impact on
regulatory costs, average vehicle prices,
societal costs and benefits, average
achieved fuel economy, and fuel

consumption, among other attributes.
The alternatives and associated impacts
presented below are compared to a
baseline where EPA’s GHG emissions
standards for MYs 2022–2025 remain in
effect and NHTSA’s augural CAFE
standards would be in place (for further
discussion of the interpretation of what
baseline is appropriate, see preamble
Section II.B and PRIA Chapter 6).
The modeling results indicated no
significant change in the fleet average
achieved fuel economy, which is
expected because the model only
applies technologies to a manufacturers’
fleet until the standard is met. However,
the change in regulatory costs, average
vehicle prices, societal costs, and
societal net benefits is noteworthy.
Without A/C efficiency and off-cycle
technologies available, the CAFE model
applied more costly technologies to the
fleet. This trend was less noticeable
with the low stringency alternative;
however, the advanced technology
required to meet the high stringency

alternative without A/C efficiency or
off-cycle technology was more
expensive. Similarly, although the
CAFE model only applied technology to
the fleet until the fleet met the
standards, alternatives that did not
employ A/C efficiency and off-cycle
technologies saved more fuel and
reduced GHG emissions more than
alternatives that did employ the A/C
efficiency and off-cycle technologies,
and in significantly higher amounts for
the higher stringency alternative. On
average, the modeling shows that
phasing out the A/C efficiency and offcycle programs decreases fuel
consumption over the ‘‘no change’’
scenario but confirms that
manufacturers will have to apply
costlier technology to meet the
standards.
The slight difference in fleet
performance under the different
alternatives confirms how the CAFE
model considers the universe of
applicable technologies and

919 See Greenhouse Gas Emission Standards for
Light-Duty Vehicles: Manufacturer Performance
Report for the 2016 Model Year (EPA Report 420–
R18–002), U.S. EPA (Jan. 2018), available at https://
nepis.epa.gov/Exe/ZyPDF.cgi?
Dockey=P100TGIA.pdf.
920 49 U.S.C. 32906.

921 For further discussion of the advanced
technology pickup truck program, see Section
X.B.1.e.4, above.
922 See Greenhouse Gas Emission Standards for
Light-Duty Vehicles: Manufacturer Performance
Report for the 2016 Model Year (EPA Report 420–
R18–002), U.S. EPA (Jan. 2018), available at https://

nepis.epa.gov/Exe/ZyPDF.cgi?
Dockey=P100TGIA.pdf.
923 Existing standards through MY 2020, then
0.5%/year increases for both passenger cars and
light trucks for MYs 2021–2026.
924 Existing standards through MY 2020, then
2%/year increases for passenger cars and 3%/year
increases for light trucks, for MYs 2021–2026.

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dynamically identifies the most costeffective combination of technologies
for each manufacturer’s vehicle fleet
based on the assumptions about each
technology’s effectiveness, cost, and
interaction with all other technologies.
For further discussion of the technology
pathways employed in the CAFE model,
please refer to Section II.D above.

sradovich on DSK3GMQ082PROD with PROPOSALS2

XI. Public Participation
NHTSA and EPA request comment on
all aspects of this NPRM. This section
describes how you can participate in
this process.
A. How do I prepare and submit
comments?
In this NPRM, there are many issues
common to both NHTSA’s and EPA’s
proposals. For the convenience of all
parties, comments submitted to the
NHTSA docket will be considered
comments to the EPA docket and vice
versa. An exception is that comments
submitted to the NHTSA docket on
NHTSA’s Draft Environmental Impact
Statement (EIS) will not be considered
submitted to the EPA docket. Therefore,
commenters only need to submit
comments to either one of the two
agency dockets, although they may
submit comments to both if they so
choose. Comments that are submitted
for consideration by only one agency
should be identified as such, and
comments that are submitted for
consideration by both agencies should
also be identified as such. Absent such
identification, each agency will exercise
its best judgment to determine whether
a comment is submitted on its proposal.
Further instructions for submitting
comments to either the NHTSA or the
EPA docket are described below.
NHTSA: Your comments must be
written and in English. To ensure that
your comments are correctly filed in the
docket, please include the docket
number NHTSA–2018–0067 in your
comments. Your comments must not be
more than 15 pages long.925 NHTSA
established this limit to encourage you
to write your primary comments in a
concise fashion. However, you may
attach necessary additional documents
to your comments, and there is no limit
on the length of attachments. If you are
submitting comments electronically as a
PDF (Adobe) file, we ask that the
documents please be scanned using the
Optical Character Recognition (OCR)
process, thus allowing the agencies to
search and copy certain portions of your
submissions.926 Please note that
CFR 553.21.
character recognition (OCR) is the
process of converting an image of text, such as a

pursuant to the Data Quality Act, in
order for substantive data to be relied
upon and used by the agency, it must
meet the information quality standards
set forth in the OMB and DOT Data
Quality Act guidelines. Accordingly, we
encourage you to consult the guidelines
in preparing your comments. OMB’s
guidelines may be accessed at https://
www.gpo.gov/fdsys/pkg/FR-2002-02-22/
pdf/R2-59.pdf. DOT’s guidelines may be
accessed at https://
www.transportation.gov/regulations/
dot-information-dissemination-qualityguidelines.
EPA: Direct your comments to Docket
ID No. EPA–HQ–OAR–2018–0283.
EPA’s policy is that all comments
received will be included in the public
docket without change and may be
made available online at http://
www.regulations.gov, including any
personal information provided, unless
the comment includes information
claimed to be Confidential Business
Information (CBI) or other information
whose disclosure is restricted by statute.
Do not submit information that you
consider to be CBI or otherwise
protected through http://
www.regulations.gov or email. The
http://www.regulations.gov website is
an ‘‘anonymous access’’ system, which
means EPA will not know your identity
or contact information unless you
provide it in the body of your comment.
If you send an email comment directly
to EPA without going through http://
www.regulations.gov, your email
address will be automatically captured
and included as part of the comment
that is placed in the public docket and
made available on the internet. If you
submit an electronic comment, EPA
recommends that you include your
name and other contact information in
the body of your comment and with any
disk or CD–ROM you submit. If EPA
cannot read your comment due to
technical difficulties and cannot contact
you for clarification, EPA may not be
able to consider your comment.
Electronic files should avoid the use of
special characters, any form of
encryption, and be free of any defects or
viruses. For additional information
about EPA’s public docket visit the EPA
Docket Center homepage at https://
www.epa.gov/dockets.
B. Tips for Preparing Your Comments
When submitting comments, please
remember to:
• Identify the rulemaking by docket
number and other identifying information

925 49

926 Optical

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(subject heading, Federal Register date and
page number).
• Explain why you agree or disagree,
suggest alternatives, and substitute language
for your requested changes.
• Describe any assumptions and provide
any technical information and/or data that
you used.
• If you estimate potential costs or
burdens, explain how you arrived at your
estimate in sufficient detail to allow for it to
be reproduced.
• Provide specific examples to illustrate
your concerns and suggest alternatives.
• Explain your views as clearly as
possible, avoiding the use of profanity or
personal threats.
• Make sure to submit your comments by
the comment period deadline identified in
the DATES section above.

C. How can I be sure that my comments
were received?
NHTSA: If you submit your comments
to NHTSA’s docket by mail and wish
DOT Docket Management to notify you
upon its receipt of your comments,
please enclose a self-addressed, stamped
postcard in the envelope containing
your comments. Upon receiving your
comments, Docket Management will
return the postcard by mail.
D. How do I submit confidential
business information?
Any confidential business
information (CBI) submitted to one of
the agencies will also be available to the
other agency. However, as with all
public comments, any CBI information
only needs to be submitted to either one
of the agencies’ dockets and it will be
available to the other. Following are
specific instructions for submitting CBI
to either agency:
EPA: Do not submit CBI to EPA
through http://www.regulations.gov or
email. Clearly mark the part or all of the
information that you claim to be CBI.
For CBI information in a disk or CD–
ROM that you mail to EPA, mark the
outside of the disk or CD–ROM as CBI
and then identify electronically within
the disk or CD–ROM the specific
information that is claimed as CBI. In
addition to one complete version of the
comment that includes information
claimed as CBI, a copy of the comment
that does not contain CBI must be
submitted for inclusion in the public
docket. Information so marked will not
be disclosed except in accordance with
the procedures set forth in 40 CFR part
2.
NHTSA: If you wish to submit any
information under a claim of
confidentiality, you should submit three
copies of your complete submission,
including the information you claim to
be confidential business information, to
the Chief Counsel, NHTSA, at the

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address given above under FOR FURTHER
INFORMATION CONTACT. When you send a
comment containing confidential
business information, you should
include a cover letter setting forth the
information specified in 49 CFR part
512.
In addition, you should submit a copy
from which you have deleted the
claimed confidential business
information to the Docket by one of the
methods set forth above.
E. Will the agencies consider late
comments?
NHTSA and EPA will consider all
comments received before the close of
business on the comment closing date
indicated above under DATES. To the
extent practicable, we will also consider
comments received after that date. If
interested persons believe that any
information that the agencies place in
the docket after the issuance of the
NPRM affects their comments, they may
submit comments after the closing date
concerning how the agencies should
consider that information for the final
rule. However, the agencies’ ability to
consider any such late comments in this
rulemaking will be limited due to the
time frame for issuing a final rule.
If a comment is received too late for
us to practicably consider in developing
a final rule, we will consider that
comment as an informal suggestion for
future rulemaking action.

sradovich on DSK3GMQ082PROD with PROPOSALS2

F. How can I read the comments
submitted by other people?
You may read the materials placed in
the dockets for this document (e.g., the
comments submitted in response to this
document by other interested persons)
at any time by going to http://
www.regulations.gov. Follow the online
instructions for accessing the dockets.
You may also read the materials at the
EPA Docket Center or the DOT Docket
Management Facility by going to the
street addresses given above under
ADDRESSES.
G. How do I participate in the public
hearings?
NHTSA and EPA will jointly host two
public hearings on the dates and
locations to be announced in a separate
notice. At all hearings, both agencies
will accept comments on the
rulemaking, and NHTSA will also
accept comments on the EIS.
NHTSA and EPA will conduct the
hearings informally, and technical rules
of evidence will not apply. We will
arrange for a written transcript of each
hearing, to be posted in the dockets as
soon as it is available, and keep the
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30 days following that hearing to allow
you to submit supplementary
information.
XII. Regulatory Notices and Analyses
A. Executive Order 12866, Executive
Order 13563
Executive Order 12866, ‘‘Regulatory
Planning and Review’’ (58 FR 51735,
Oct. 4, 1993), as amended by Executive
Order 13563, ‘‘Improving Regulation
and Regulatory Review’’ (76 FR 3821,
Jan. 21, 2011), provides for making
determinations whether a regulatory
action is ‘‘significant’’ and therefore
subject to the Office of Management and
Budget (OMB) review and to the
requirements of the Executive Order.
Under section 3(f)(1) of Executive Order
12866, this action is an ‘‘economically
significant regulatory action’’ because if
adopted, it is likely to have an annual
effect on the economy of $100 million
or more. Accordingly, EPA and NHTSA
submitted this action to the OMB for
review and any changes made in
response to OMB recommendations
have been documented in the docket for
this action. The benefits and costs of
this proposal are described above and in
the Preliminary Regulatory Impact
Analysis (PRIA), which is located in the
docket and on the agencies’ websites.
B. DOT Regulatory Policies and
Procedures
The rule, if adopted, would also be
significant within the meaning of the
Department of Transportation’s
Regulatory Policies and Procedures. The
benefits and costs of this proposal are
described above and in the PRIA, which
is located in the docket and on
NHTSA’s website.
C. Executive Order 13771 (Reducing
Regulation and Controlling Regulatory
Costs)
This proposed rule is expected to be
an E.O. 13771 deregulatory action.
Details on the estimated cost savings of
this proposed rule can be found in
PRIA, which is located in the docket
and on the agencies’ websites.
D. Executive Order 13211 (Energy
Effects)
Executive Order 13211 applies to any
rule that: (1) Is determined to be
economically significant as defined
under E.O. 12866, and is likely to have
a significant adverse effect on the
supply, distribution, or use of energy; or
(2) that is designated by the
Administrator of the Office of
Information and Regulatory Affairs as a
significant energy action. If the
regulatory action meets either criterion,
the agencies must evaluate the adverse

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energy effects of the proposed rule and
explain why the proposed regulation is
preferable to other potentially effective
and reasonably feasible alternatives
considered.
The proposed rule seeks to establish
passenger car and light truck fuel
economy standards and greenhouse gas
emissions standards. An evaluation of
energy effects of the proposed action
and reasonably feasible alternatives
considered is provided in NHTSA’s
Draft EIS and in the PRIA. To the extent
that EPA’s CO2 standards are
substantially related to fuel economy
and accordingly, petroleum
consumption, the Draft EIS and PRIA
analyses also provide an estimate of
impacts of EPA’s proposed rule.
E. Environmental Considerations
1. National Environmental Policy Act
(NEPA)
Concurrently with this NPRM,
NHTSA is releasing a Draft
Environmental Impact Statement (Draft
EIS), pursuant to the National
Environmental Policy Act, 42 U.S.C.
4321–4347, and implementing
regulations issued by the Council on
Environmental Quality (CEQ), 40 CFR
part 1500, and NHTSA, 49 CFR part
520. NHTSA prepared the Draft EIS to
analyze and disclose the potential
environmental impacts of the proposed
CAFE standards and a range of
alternatives. The Draft EIS analyzes
direct, indirect, and cumulative impacts
and analyzes impacts in proportion to
their significance.
The Draft EIS describes potential
environmental impacts to a variety of
resources. Resources that may be
affected by the proposed action and
alternatives include fuel and energy use,
air quality, climate, land use and
development, hazardous materials and
regulated wastes, historical and cultural
resources, noise, and environmental
justice. The Draft EIS also describes how
climate change resulting from global
GHG emissions (including the U.S. light
duty transportation sector under the
Proposed Action and alternatives) could
affect certain key natural and human
resources. Resource areas are assessed
qualitatively and quantitatively, as
appropriate, in the Draft EIS.
NHTSA has considered the
information contained in the Draft EIS
as part of developing its proposal. The
Draft EIS is available for public
comment; instructions for the
submission of comments are included
inside the document. NHTSA will
simultaneously issue the Final
Environmental Impact Statement and
Record of Decision, pursuant to 49

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U.S.C. 304a(b), and U.S. Department of
Transportation Final Guidance on MAP–
21 Section 1319 Accelerated
Decisionmaking in Environmental
Reviews (http://www.dot.gov/sites/
dot.gov/files/docs/MAP-21_1319_Final_
Guidance.pdf) unless it is determined
that statutory criteria or practicability
considerations preclude simultaneous
issuance. For additional information on
NHTSA’s NEPA analysis, please see the
Draft EIS.
2. Clean Air Act (CAA) as Applied to
NHTSA’s Action
The CAA (42 U.S.C. 7401 et seq.) is
the primary Federal legislation that
addresses air quality. Under the
authority of the CAA and subsequent
amendments, EPA has established
National Ambient Air Quality Standards
(NAAQS) for six criteria pollutants,
which are relatively commonplace
pollutants that can accumulate in the
atmosphere as a result of human
activity. EPA is required to review each
NAAQS every five years and to revise
those standards as may be appropriate
considering new scientific information.
The air quality of a geographic region
is usually assessed by comparing the
levels of criteria air pollutants found in
the ambient air to the levels established
by the NAAQS (taking into account, as
well, the other elements of a NAAQS:
Averaging time, form, and indicator).
Concentrations of criteria pollutants
within the air mass of a region are
measured in parts of a pollutant per
million parts (ppm) of air or in
micrograms of a pollutant per cubic
meter (mg/m3) of air present in repeated
air samples taken at designated
monitoring locations using specified
types of monitors. These ambient
concentrations of each criteria pollutant
are compared to the levels, averaging
time, and form specified by the NAAQS
in order to assess whether the region’s
air quality is in attainment with the
NAAQS.
When the measured concentrations of
a criteria pollutant within a geographic
region are below those permitted by the
NAAQS, EPA designates the region as
an attainment area for that pollutant,
while regions where concentrations of
criteria pollutants exceed Federal
standards are called nonattainment
areas. Former nonattainment areas that
are now in compliance with the NAAQS
are designated as maintenance areas.
Each State with a nonattainment area is
required to develop and implement a
State Implementation Plan (SIP)
documenting how the region will reach
attainment levels within time periods
specified in the CAA. For maintenance
areas, the SIP must document how the

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State intends to maintain compliance
with the NAAQS. When EPA revises a
NAAQS, each State must revise its SIP
to address how it plans to attain the new
standard.
No Federal agency may ‘‘engage in,
support in any way or provide financial
assistance for, license or permit, or
approve’’ any activity that does not
‘‘conform’’ to a SIP or Federal
Implementation Plan after EPA has
approved or promulgated it.927 Further,
no Federal agency may ‘‘approve,
accept, or fund’’ any transportation
plan, program, or project developed
pursuant to title 23 or chapter 53 of title
49, U.S.C., unless the plan, program, or
project has been found to ‘‘conform’’ to
any applicable implementation plan in
effect.928 The purpose of these
conformity requirements is to ensure
that Federally sponsored or conducted
activities do not interfere with meeting
the emissions targets in SIPs, do not
cause or contribute to new violations of
the NAAQS, and do not impede the
ability of a State to attain or maintain
the NAAQS or delay any interim
milestones. EPA has issued two sets of
regulations to implement the conformity
requirements:
(1) The Transportation Conformity
Rule 929 applies to transportation plans,
programs, and projects that are
developed, funded, or approved under
title 23 or chapter 53 of title 49, U.S.C.
(2) The General Conformity Rule 930
applies to all other federal actions not
covered under transportation
conformity. The General Conformity
Rule establishes emissions thresholds,
or de minimis levels, for use in
evaluating the conformity of an action
that results in emissions increases.931 If
the net increases of direct and indirect
emissions are lower than these
thresholds, then the project is presumed
to conform and no further conformity
evaluation is required. If the net
increases of direct and indirect
emissions exceed any of these
thresholds, and the action is not
otherwise exempt, then a conformity
determination is required. The
conformity determination can entail air
quality modeling studies, consultation
with EPA and state air quality agencies,
and commitments to revise the SIP or to
implement measures to mitigate air
quality impacts.
The proposed CAFE standards and
associated program activities are not
927 42

U.S.C. 7506(c)(1).
U.S.C. 7506(c)(2).
929 40 CFR part 51, subpart T, and part 93, subpart
A.
930 40 CFR part 51, subpart W, and part 93,
subpart B.
931 40 CFR 93.153(b).
928 42

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developed, funded, or approved under
title 23 or chapter 53 of title 49, U.S.C.
Accordingly, this action and associated
program activities are not subject to
transportation conformity. Under the
General Conformity Rule, a conformity
determination is required where a
Federal action would result in total
direct and indirect emissions of a
criteria pollutant or precursor
originating in nonattainment or
maintenance areas equaling or
exceeding the rates specified in 40 CFR
93.153(b)(1) and (2). As explained
below, NHTSA’s proposed action results
in neither direct nor indirect emissions
as defined in 40 CFR 93.152.
The General Conformity Rule defines
direct emissions as ‘‘those emissions of
a criteria pollutant or its precursors that
are caused or initiated by the Federal
action and originate in a nonattainment
or maintenance area and occur at the
same time and place as the action and
are reasonably foreseeable.’’ 932 Because
NHTSA’s action would set fuel
economy standards for light duty
vehicles, it would cause no direct
emissions consistent with the meaning
of the General Conformity Rule.933
Indirect emissions under the General
Conformity Rule are ‘‘those emissions of
a criteria pollutant or its precursors (1)
That are caused or initiated by the
federal action and originate in the same
nonattainment or maintenance area but
occur at a different time or place as the
action; (2) That are reasonably
foreseeable; (3) That the agency can
practically control; and (4) For which
the agency has continuing program
responsibility.’’ 934 Each element of the
definition must be met to qualify as
indirect emissions. NHTSA has
determined that, for purposes of general
conformity, emissions that may result
from the proposed fuel economy
standards would not be caused by
NHTSA’s action, but rather would occur
because of subsequent activities the
agency cannot practically control.
‘‘[E]ven if a Federal licensing,
rulemaking, or other approving action is
a required initial step for a subsequent
activity that causes emissions, such
initial steps do not mean that a Federal
agency can practically control any
resulting emissions.’’ 935
932 40

CFR 93.152.
of Transportation v. Public
Citizen, 541 U.S. 752, 772 (2004) (‘‘[T]he emissions
from the Mexican trucks are not ‘direct’ because
they will not occur at the same time or at the same
place as the promulgation of the regulations.’’).
NHTSA’s action is to establish fuel economy
standards for MY 2021–2026 passenger car and
light trucks; any emissions increases would occur
well after promulgation of the final rule.
934 40 CFR 93.152.
935 40 CFR 93.152.
933 Department

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As the CAFE program uses
performance-based standards, NHTSA
cannot control the technologies vehicle
manufacturers use to improve the fuel
economy of passenger cars and light
trucks. Furthermore, NHTSA cannot
control consumer purchasing (which
affects average achieved fleetwide fuel
economy) and driving behavior (i.e.,
operation of motor vehicles, as
measured by VMT). It is the
combination of fuel economy
technologies, consumer purchasing, and
driving behavior that results in criteria
pollutant or precursor emissions. For
purposes of analyzing the
environmental impacts of the proposal
and alternatives under NEPA, NHTSA
has made assumptions regarding all of
these factors. The agency’s Draft EIS
predicts that increases in air toxic and
criteria pollutants would occur in some
nonattainment areas under certain
alternatives. However, the proposed
standards and alternatives do not
mandate specific manufacturer
decisions, consumer purchasing, or
driver behavior, and NHTSA cannot
practically control any of them.936
In addition, NHTSA does not have the
statutory authority to control the actual
VMT by drivers. As the extent of
emissions is directly dependent on the
operation of motor vehicles, changes in
any emissions that result from NHTSA’s
proposed standards are not changes the
agency can practically control or for
which the agency has continuing
program responsibility. Therefore, the
proposed CAFE standards and
alternative standards considered by
NHTSA would not cause indirect
emissions under the General Conformity
Rule, and a general conformity
determination is not required.
3. National Historic Preservation Act
(NHPA)
The NHPA (54 U.S.C. 300101 et seq.)
sets forth government policy and
procedures regarding ‘‘historic
properties’’—that is, districts, sites,
buildings, structures, and objects
included on or eligible for the National
Register of Historic Places. Section 106
of the NHPA requires federal agencies to
‘‘take into account’’ the effects of their
actions on historic properties.937 The
agencies conclude that the NHPA is not
applicable to this proposal because the
promulgation of CAFE and GHG
936 See, e.g., Department of Transportation v.
Public Citizen, 541 U.S. 752, 772–73 (2004); South
Coast Air Quality Management District v. Federal
Energy Regulatory Commission, 621 F.3d 1085,
1101 (9th Cir. 2010).
937 Section 106 is now codified at 54 U.S.C.
306108. Implementing regulations for the Section
106 process are located at 36 CFR part 800.

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emissions standards for light duty
vehicles is not the type of activity that
has the potential to cause effects on
historic properties. However, NHTSA
includes a brief, qualitative discussion
of the impacts of the alternatives on
historical and cultural resources in
Section 7.3 of the Draft EIS.
4. Fish and Wildlife Conservation Act
(FWCA)
The FWCA (16 U.S.C. 2901 et seq.)
provides financial and technical
assistance to States for the development,
revision, and implementation of
conservation plans and programs for
nongame fish and wildlife. In addition,
the Act encourages all Federal
departments and agencies to utilize
their statutory and administrative
authorities to conserve and to promote
conservation of nongame fish and
wildlife and their habitats. The agencies
conclude that the FWCA is not
applicable to this proposal because it
does not involve the conservation of
nongame fish and wildlife and their
habitats.
5. Coastal Zone Management Act
(CZMA)
The Coastal Zone Management Act
(16 U.S.C. 1451 et seq.) provides for the
preservation, protection, development,
and (where possible) restoration and
enhancement of the nation’s coastal
zone resources. Under the statute, States
are provided with funds and technical
assistance in developing coastal zone
management programs. Each
participating State must submit its
program to the Secretary of Commerce
for approval. Once the program has been
approved, any activity of a Federal
agency, either within or outside of the
coastal zone, that affects any land or
water use or natural resource of the
coastal zone must be carried out in a
manner that is consistent, to the
maximum extent practicable, with the
enforceable policies of the State’s
program.938
The agencies conclude that the CZMA
is not applicable to this proposal
because it does not involve an activity
within, or outside of, the nation’s
coastal zones that affects any land or
water use or natural resource of the
coastal zone. NHTSA has, however,
conducted a qualitative review in its
Draft EIS of the related direct, indirect,
and cumulative impacts, positive or
negative, of the alternatives on
potentially affected resources, including
coastal zones.
938 16

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6. Endangered Species Act (ESA)
Under Section 7(a)(2) of the ESA
federal agencies must ensure that
actions they authorize, fund, or carry
out are ‘‘not likely to jeopardize the
continued existence’’ of any federally
listed threatened or endangered species
or result in the destruction or adverse
modification of the designated critical
habitat of these species. 16 U.S.C.
1536(a)(2). If a federal agency
determines that an agency action may
affect a listed species or designated
critical habitat, it must initiate
consultation with the appropriate
Service—the U.S. Fish and Wildlife
Service of the Department of the Interior
and/or the National Oceanic and
Atmospheric Administration’s National
Marine Fisheries Service of the
Department of Commerce, depending on
the species involved—in order to ensure
that the action is not likely to jeopardize
the species or destroy or adversely
modify designated critical habitat. See
50 CFR 402.14. Under this standard, the
federal agency taking action evaluates
the possible effects of its action and
determines whether to initiate
consultation. See 51 FR 19926, 19949
(June 3, 1986).
Pursuant to Section 7(a)(2) of the ESA,
the agencies have considered the effects
of the proposed standards and have
reviewed applicable ESA regulations,
case law, and guidance to determine
what, if any, impact there might be to
listed species or designated critical
habitat. The agencies have considered
issues related to emissions of CO2 and
other GHGs and issues related to nonGHG emissions. Based on this
assessment, the agencies have
determined that the actions of setting
CAFE and GHG emissions standards
does not require consultation under
Section 7(a)(2) of the ESA. Accordingly,
NHTSA and EPA have concluded its
review of this action under Section 7 of
the ESA.
7. Floodplain Management (Executive
Order 11988 and DOT Order 5650.2)
These Orders require Federal agencies
to avoid the long- and short-term
adverse impacts associated with the
occupancy and modification of
floodplains, and to restore and preserve
the natural and beneficial values served
by floodplains. Executive Order 11988
also directs agencies to minimize the
impact of floods on human safety,
health and welfare, and to restore and
preserve the natural and beneficial
values served by floodplains through
evaluating the potential effects of any
actions the agency may take in a
floodplain and ensuring that its program

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planning and budget requests reflect
consideration of flood hazards and
floodplain management. DOT Order
5650.2 sets forth DOT policies and
procedures for implementing Executive
Order 11988. The DOT Order requires
that the agency determine if a proposed
action is within the limits of a base
floodplain, meaning it is encroaching on
the floodplain, and whether this
encroachment is significant. If
significant, the agency is required to
conduct further analysis of the proposed
action and any practicable alternatives.
If a practicable alternative avoids
floodplain encroachment, then the
agency is required to implement it.
In this proposal, the agencies are not
occupying, modifying and/or
encroaching on floodplains. The
agencies, therefore, conclude that the
Orders are not applicable to this action.
NHTSA has, however, conducted a
review of the alternatives on potentially
affected resources, including
floodplains, in its Draft EIS.

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8. Preservation of the Nation’s Wetlands
(Executive Order 11990 and DOT Order
5660.1a)
These Orders require Federal agencies
to avoid, to the extent possible,
undertaking or providing assistance for
new construction located in wetlands
unless the agency head finds that there
is no practicable alternative to such
construction and that the proposed
action includes all practicable measures
to minimize harms to wetlands that may
result from such use. Executive Order
11990 also directs agencies to take
action to minimize the destruction, loss
or degradation of wetlands in
‘‘conducting Federal activities and
programs affecting land use, including
but not limited to water and related land
resources planning, regulating, and
licensing activities.’’ DOT Order 5660.1a
sets forth DOT policy for interpreting
Executive Order 11990 and requires that
transportation projects ‘‘located in or
having an impact on wetlands’’ should
be conducted to assure protection of the
Nation’s wetlands. If a project does have
a significant impact on wetlands, an EIS
must be prepared.
The agencies are not undertaking or
providing assistance for new
construction located in wetlands. The
agencies, therefore, conclude that these
Orders do not apply to this proposal.
NHTSA has, however, conducted a
review of the alternatives on potentially
affected resources, including wetlands,
in its Draft EIS.

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9. Migratory Bird Treaty Act (MBTA),
Bald and Golden Eagle Protection Act
(BGEPA), Executive Order 13186
The MBTA (16 U.S.C. 703–712)
provides for the protection of certain
migratory birds by making it illegal for
anyone to ‘‘pursue, hunt, take, capture,
kill, attempt to take, capture, or kill,
possess, offer for sale, sell, offer to
barter, barter, offer to purchase,
purchase, deliver for shipment, ship,
export, import, cause to be shipped,
exported, or imported, deliver for
transportation, transport or cause to be
transported, carry or cause to be carried,
or receive for shipment, transportation,
carriage, or export’’ any migratory bird
covered under the statute.939
The BGEPA (16 U.S.C. 668–668d)
makes it illegal to ‘‘take, possess, sell,
purchase, barter, offer to sell, purchase
or barter, transport, export or import’’
any bald or golden eagles.940 Executive
Order 13186, ‘‘Responsibilities of
Federal Agencies to Protect Migratory
Birds,’’ helps to further the purposes of
the MBTA by requiring a Federal agency
to develop a Memorandum of
Understanding (MOU) with the Fish and
Wildlife Service when it is taking an
action that has (or is likely to have) a
measurable negative impact on
migratory bird populations.
The agencies conclude that the
MBTA, BGEPA, and Executive Order
13186 do not apply to this proposal
because there is no disturbance, take,
measurable negative impact, or other
covered activity involving migratory
birds or bald or golden eagles involved
in this rulemaking.
10. Department of Transportation Act
(Section 4(f))
Section 4(f) of the Department of
Transportation Act of 1966 (49 U.S.C.
303), as amended, is designed to
preserve publicly owned park and
recreation lands, waterfowl and wildlife
refuges, and historic sites. Specifically,
Section 4(f) provides that DOT agencies
cannot approve a transportation
program or project that requires the use
of any publicly owned land from a
public park, recreation area, or wildlife
or waterfowl refuge of national, State, or
local significance, or any land from a
historic site of national, State, or local
significance, unless a determination is
made that:
(1) There is no feasible and prudent
alternative to the use of land, and
(2) The program or project includes
all possible planning to minimize harm
to the property resulting from the use.
939 16
940 16

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U.S.C. 668(a).

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These requirements may be satisfied if
the transportation use of a Section 4(f)
property results in a de minimis impact
on the area.
NHTSA concludes that Section 4(f) is
not applicable to its proposal because
this rulemaking is not an approval of a
transportation program or project that
requires the use of any publicly owned
land.
11. Executive Order 12898: ‘‘Federal
Actions to Address Environmental
Justice in Minority Populations and
Low-Income Populations’’
Executive Order (E.O.) 12898 (59 FR
7629 (Feb. 16, 1994)) establishes federal
executive policy on environmental
justice. Its main provision directs
federal agencies, to the greatest extent
practicable and permitted by law, to
make environmental justice part of their
mission by identifying and addressing,
as appropriate, disproportionately high
and adverse human health or
environmental effects of their programs,
policies, and activities on minority
populations and low-income
populations in the United States.
With respect to GHG emissions, EPA
has determined that this final rule will
not have disproportionately high and
adverse human health or environmental
effects on minority or low-income
populations because it impacts the level
of environmental protection for all
affected populations without having any
disproportionately high and adverse
human health or environmental effects
on any population, including any
minority or low-income population. The
increases in CO2 and other GHGs
associated with the standards will affect
climate change projections, and EPA has
estimated marginal increases in
projected global mean surface
temperatures and sea-level rise in this
NPRM. Within settlements experiencing
climate change, certain parts of the
population may be especially
vulnerable; these include the poor, the
elderly, those already in poor health, the
disabled, those living alone, and/or
indigenous populations dependent on
one or a few resources. However, the
potential increases in climate change
impacts resulting from this rule are so
small that the impacts are not
considered ‘‘disproportionately high
and adverse’’ on these populations.
For non-GHG co-pollutants such as
ozone, PM2.5, and toxics, EPA has
concluded that reductions in
downstream emissions would have
beneficial human health or
environmental effects on near-road
populations. Therefore, the proposed
rule would not result in
‘‘disproportionately high and adverse’’

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human health or environmental effects
regarding these pollutants on minority
and/or low income populations.
NHTSA has also evaluated whether
its proposal would have
disproportionately high and adverse
human health or environmental effects
on minority or low-income populations.
The agency includes its analysis in
Section 7.5 (Environmental Justice) of
its Draft EIS.

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12. Executive Order 13045: ‘‘Protection
of Children from Environmental Health
Risks and Safety Risks’’
This action is subject to E.O. 13045
(62 FR 19885, April 23, 1997) because
it is an economically significant
regulatory action as defined by E.O.
12866, and the agencies have reason to
believe that the environmental health or
safety risks related to this action may
have a disproportionate effect on
children. Specifically, children are more
vulnerable to adverse health effects
related to mobile source emissions, as
well as to the potential long-term
impacts of climate change. Pursuant to
E.O. 13045, NHTSA and EPA must
prepare an evaluation of the
environmental health or safety effects of
the planned regulation on children and
an explanation of why the planned
regulation is preferable to other
potentially effective and reasonably
feasible alternatives considered by the
agencies. Further, this analysis may be
included as part of any other required
analysis.

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This preamble and NHTSA’s Draft EIS
discuss air quality, climate change, and
their related environmental and health
effects, noting where these would
disproportionately affect children. The
Administrator has also discussed the
impact of climate-related health effects
on children in the Endangerment and
Cause or Contribute Findings for
Greenhouse Gases Under Section 202(a)
of the Clean Air Act (74 FR 66496,
December 15, 2009). Additionally, this
preamble explains why the agencies’
proposal is preferable to other
alternatives considered. Together, this
preamble and NHTSA’s Draft EIS satisfy
the agencies’ responsibilities under E.O.
13045.
F. Regulatory Flexibility Act
Pursuant to the Regulatory Flexibility
Act (5 U.S.C. 601 et seq., as amended by
the Small Business Regulatory
Enforcement Fairness Act (SBREFA) of
1996), whenever an agency is required
to publish a notice of proposed
rulemaking or final rule, it must prepare
and make available for public comment
a regulatory flexibility analysis that
describes the effect of the rule on small
entities (i.e., small businesses, small
organizations, and small governmental
jurisdictions). No regulatory flexibility
analysis is required if the head of an
agency certifies the proposal will not
have a significant economic impact on
a substantial number of small entities.
SBREFA amended the Regulatory

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Flexibility Act to require Federal
agencies to provide a statement of the
factual basis for certifying that a
proposal will not have a significant
economic impact on a substantial
number of small entities.
The agencies considered the impacts
of this notice under the Regulatory
Flexibility Act and certify that this rule
would not have a significant economic
impact on a substantial number of small
entities. The following is the agencies’
statement providing the factual basis for
this certification pursuant to 5 U.S.C.
605(b).
Small businesses are defined based on
the North American Industry
Classification System (NAICS) code.941
One of the criteria for determining size
is the number of employees in the firm.
For establishments primarily engaged in
manufacturing or assembling
automobiles, as well as light duty
trucks, the firm must have less than
1,500 employees to be classified as a
small business. This proposed rule
would affect motor vehicle
manufacturers. There are 14 small
manufacturers of passenger cars and
SUVs of electric, hybrid, and internal
combustion engines.
941 Classified in NAICS under Subsector 336—
Transportation Equipment Manufacturing for
Automobile Manufacturing (336111), Light Truck
(336112), and Heavy Duty Truck Manufacturing
(336120). https://www.sba.gov/document/supporttable-size-standards.

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NHTSA believes that the rulemaking
would not have a significant economic
impact on the small vehicle
manufacturers because under 49 CFR
part 525, passenger car manufacturers
making less than 10,000 vehicles per
year can petition NHTSA to have
alternative standards set for those
manufacturers. These manufacturers do
not currently meet the 27.5 mpg
standard and must already petition the
agency for relief. If the standard is
raised, it has no meaningful impact on
these manufacturers—they still must go
through the same process and petition
for relief. Given there already is a
mechanism for relieving burden on
small businesses, which is the purpose
of the Regulatory Flexibility Act, a
regulatory flexibility analysis was not
prepared.
EPA believes this rulemaking would
not have a significant economic impact
on a substantial number of small entities
under the Regulatory Flexibility Act, as
amended by the Small Business
Regulatory Enforcement Fairness Act.
EPA is exempting from the CO2
standards any manufacturer, domestic
or foreign, meeting SBA’s size
definitions of small business as
described in 13 CFR 121.201. EPA
adopted the same type of exemption for
942 Number of employees as of March 2018,
source: Linkedin.com.
943 Rough estimate for model year 2017.

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small businesses in the 2017 and later
rulemaking. EPA estimates that small
entities comprise less than 0.1% of total
annual vehicle sales and exempting
them will have a negligible impact on
the CO2 emissions reductions from the
standards. Because EPA is exempting
small businesses from the CO2
standards, we are certifying that the rule
will not have a significant economic
impact on a substantial number of small
entities. Therefore, EPA has not
conducted a Regulatory Flexibility
Analysis or a SBREFA SBAR Panel for
the rule.
EPA regulations allow small
businesses to voluntarily waive their
small business exemption and
optionally certify to the CO2 standards.
This allows small entity manufacturers
to earn CO2 credits under the CO2
program, if their actual fleetwide CO2
performance is better than their
fleetwide CO2 target standard. However,
the exemption waiver is optional for
small entities and thus we believe that
manufacturers opt into the CO2 program
if it is economically advantageous for
them to do so, for example in order to
generate and sell CO2 credits. Therefore,
EPA believes this voluntary option does
not affect EPA’s determination that the
standards will impose no significant
adverse impact on small entities.

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G. Executive Order 13132 (Federalism)
Executive Order 13132 requires
federal agencies to develop an
accountable process to ensure
‘‘meaningful and timely input by State
and local officials in the development of
regulatory policies that have federalism
implications.’’ The Order defines the
term ‘‘Policies that have federalism
implications’’ to include regulations
that have ‘‘substantial direct effects on
the States, on the relationship between
the national government and the States,
or on the distribution of power and
responsibilities among the various
levels of government.’’ Under the Order,
agencies may not issue a regulation that
has federalism implications, that
imposes substantial direct compliance
costs, unless the Federal government
provides the funds necessary to pay the
direct compliance costs incurred by
State and local governments, or the
agencies consult with State and local
officials early in the process of
developing the proposed regulation. The
agencies complied with Order’s
requirements.
See Section VI above for further detail
on the agencies’ assessment of the
federalism implications of this proposal.

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H. Executive Order 12988 (Civil Justice
Reform)
Pursuant to Executive Order 12988,
‘‘Civil Justice Reform,’’ 944 NHTSA has
considered whether this rulemaking
would have any retroactive effect. This
proposed rule does not have any
retroactive effect.

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I. Executive Order 13175 (Consultation
and Coordination With Indian Tribal
Governments)
This proposed rule does not have
tribal implications, as specified in
Executive Order 13175 (65 FR 67249,
November 9, 2000). This rule will be
implemented at the Federal level and
impose compliance costs only on
vehicle manufacturers. Thus, Executive
Order 13175 does not apply to this rule.
J. Unfunded Mandates Reform Act
Section 202 of the Unfunded
Mandates Reform Act of 1995 (UMRA)
requires Federal agencies to prepare a
written assessment of the costs, benefits,
and other effects of a proposed or final
rule that includes a Federal mandate
likely to result in the expenditure by
State, local, or tribal governments, in the
aggregate, or by the private sector, of
more than $100 million in any one year
(adjusted for inflation with base year of
1995). Adjusting this amount by the
implicit gross domestic product price
deflator for 2016 results in $148 million
(111.416/75.324 = 1.48).945 Before
promulgating a rule for which a written
statement is needed, section 205 of
UMRA generally requires NHTSA and
EPA to identify and consider a
reasonable number of regulatory
alternatives and adopt the least costly,
most cost-effective, or least burdensome
alternative that achieves the objective of
the rule. The provisions of section 205
do not apply when they are inconsistent
with applicable law. Moreover, section
205 allows NHTSA and EPA to adopt an
alternative other than the least costly,
most cost-effective, or least burdensome
alternative if the agency publishes with
the proposed rule an explanation of why
that alternative was not adopted.
This proposed rule will not result in
the expenditure by State, local, or tribal
governments, in the aggregate, of more
than $148 million annually, but it will
result in the expenditure of that
magnitude by vehicle manufacturers
and/or their suppliers. In developing
this proposal, NHTSA and EPA
considered a variety of alternative
944 61

FR 4729 (Feb. 7, 1996).
of Economic Analysis, National
Income and Product Accounts (NIPA), Table 1.1.9
Implicit Price Deflators for Gross Domestic Product.
https://bea.gov/iTable/index_nipa.cfm.
945 Bureau

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average fuel economy standards lower
and higher than those proposed. The
proposed fuel economy standards for
MYs 2021–2026 are the least costly,
most cost-effective, and least
burdensome alternative that achieve the
objective of the rule.
K. Regulation Identifier Number
The Department of Transportation
assigns a regulation identifier number
(RIN) to each regulatory action listed in
the Unified Agenda of Federal
Regulations. The Regulatory Information
Service Center publishes the Unified
Agenda in April and October of each
year. You may use the RIN contained in
the heading at the beginning of this
document to find this action in the
Unified Agenda.
L. National Technology Transfer and
Advancement Act
Section 12(d) of the National
Technology Transfer and Advancement
Act (NTTAA) requires NHTSA and EPA
to evaluate and use existing voluntary
consensus standards in its regulatory
activities unless doing so would be
inconsistent with applicable law (e.g.,
the statutory provisions regarding
NHTSA’s vehicle safety authority, or
EPA’s testing authority) or otherwise
impractical.946
Voluntary consensus standards are
technical standards developed or
adopted by voluntary consensus
standards bodies. Technical standards
are defined by the NTTAA as
‘‘performance-based or design-specific
technical specification and related
management systems practices.’’ They
pertain to ‘‘products and processes,
such as size, strength, or technical
performance of a product, process or
material.’’
Examples of organizations generally
regarded as voluntary consensus
standards bodies include the American
Society for Testing and Materials
(ASTM), the Society of Automotive
Engineers (SAE), and the American
National Standards Institute (ANSI). If
the agencies do not use available and
potentially applicable voluntary
consensus standards, we are required by
the Act to provide Congress, through
OMB, an explanation of the reasons for
not using such standards.
For CO2 emissions, EPA is proposing
to collect data over the same tests that
are used for the MY 2012–2016 CO2
standards and for the CAFE program.
This will minimize the amount of
testing done by manufacturers, since
manufacturers are already required to
run these tests. For A/C credits, EPA is
946 15

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proposing to use a consensus
methodology developed by the Society
of Automotive Engineers (SAE) and also
a new A/C test. EPA knows of no
consensus standard available for the A/
C test.
There are currently no voluntary
consensus standards that NHTSA
administers relevant to today’s proposed
CAFE standards.
M. Department of Energy Review
In accordance with 49 U.S.C.
32902(j)(1), NHTSA submitted this
proposed rule to the Department of
Energy for review.
N. Paperwork Reduction Act
The Paperwork Reduction Act (PRA)
of 1995, Public Law 104–13,947 gives the
Office of Management and Budget
(OMB) authority to regulate matters
regarding the collection, management,
storage, and dissemination of certain
information by and for the Federal
government. It seeks to reduce the total
amount of paperwork handled by the
government and the public. The PRA
requires Federal agencies to place a
notice in the Federal Register seeking
public comment on the proposed
collection of information. NHTSA
strives to reduce the public’s
information collection burden hours
each fiscal year by streamlining external
and internal processes.
To this end, NHTSA seeks to continue
to collect information to ensure
compliance with its CAFE program.
NHTSA intends to reinstate its
previously-approved collection of
information for Corporate Average Fuel
Economy (CAFE) reports specified in 49
CFR part 537 (OMB control number
2127–0019), add the additional burden
for reporting changes adopted in the
October 15, 2012 final rule that recently
came into effect (see 77 FR 62623), and
account for the change in burden as
proposed in this rule as well as for other
CAFE reporting provisions required by
Congress and NHTSA. NHTSA is also
changing the name of this collection to
more accurately represent the breadth of
all CAFE regulatory reporting. Although
NHTSA seeks to add additional burden
hours to its CAFE report requirement in
49 CFR 537, the agency believes there
will be a reduction in burden due to the
standardization of data and the
streamlined process. NHTSA is seeking
public comment on this collection.
In compliance with the PRA, this
notice announces that the information
collection request (ICR) abstracted
below has been forwarded to OMB for
review and comment. The ICR describes
947 Codified

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the nature of the information collection
and its expected burden.
Title: Corporate Average Fuel
Economy.
Type of Request: Reinstatement and
amendment of a previously approved
collection.
OMB Control Number: 2127–0019.
Form Numbers: NHTSA Form 1474
(CAFE Projections Reporting Template)
and NHTSA Form 1475 (CAFE Credit
Template).
Requested Expiration Date of
Approval: Three years from date of
approval.
Summary of the collection of
information: As part of this rulemaking,
NHTSA is reinstating and modifying its
previously-approved collection for
CAFE-related collections of information.
NHTSA and EPA have coordinated their
compliance and reporting requirements
in an effort not to impose duplicative
burden on regulated entities. This
information collection contains three
different components: Burden related
NHTSA’s CAFE reporting requirements,
burden related to CAFE compliance, but
not via reporting requirements, and
information gathered by NHTSA to help
inform CAFE analyses. All templates
referenced in this section will be
available in the rulemaking docket for
comment.
1. CAFE Compliance Reports
NHTSA seeks to reinstate 948 its
collection related to the reporting
requirements in 49 U.S.C. 32907
‘‘Reports and tests of manufacturers.’’ In
that section, manufacturers are
statutorily required to submit CAFE
compliance reports to the Secretary of
Transportation.949 The reports must
state if a manufacturer will comply with
its applicable fuel economy standard(s),
what actions the manufacturer intends
to take to comply with the standard(s),
and include other information as
required by NHTSA. Manufacturers are
required to submit two CAFE
compliance reports—a pre-model year
report (PMY) and mid-model year
(MMY) reporter—each year. In the event
a manufacturer needs to correct
previously-submitted information, a
manufacturer may need to file
additional reports.950
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948 This

collection expired on April 30, 2016.
U.S.C. 32907 (delegated to the NHTSA
Administrator at 49 CFR 1.95). Because of this
delegation, for purposes of discussion, statutory
references to the Secretary of Transportation in this
section will discussed in terms of NHTSA or the
NHTSA administrator.
950 Specifically, a manufacturer shall submit a
report containing the information during the 30
days before the beginning of each model year, and
during the 30 days beginning the 180th day of the
model year. When a manufacturer decides that
949 49

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To implement this statute, NHTSA
issued 49 CFR part 537, ‘‘Automotive
Fuel Economy Reports,’’ which adds
additional definition to § 32907. The
first report, the PMY report must be
submitted to NHTSA before December
31 of the calendar year prior to the
corresponding model year and contain
manufacturers’ projected information
for that upcoming model year. The
second report, the MMY report must be
submitted by July 31 of the given model
year and contain updated information
from manufacturers based upon actual
and projected information known
midway through the model year.
Finally, the last report, a supplementary
report, is required to be submitted
anytime a manufacture needs to correct
information previously submitted to
NHTSA.
Compliance reports must include
information on passenger and nonpassenger automobiles (trucks)
describing the projected and actual fuel
economy standards, fuel economy
performance values, production sales
volumes and information on vehicle
design features (e.g., engine
displacement and transmission class)
and other vehicle attribute
characteristics (e.g., track width, wheel
base and other light truck off-road
features). Manufacturers submit
confidential and non-confidential
versions of these reports to NHTSA.
Confidential reports differ by including
estimated or actual production sales
information, which is withheld from
public disclosure to protect each
manufacturer’s competitive sales
strategies. NHTSA uses the reports as
the basis for vehicle auditing and
testing, which helps manufacturers
correct reporting errors prior to the end
of the model year and facilitate
acceptance of their final CAFE report by
the Environmental Protection Agency
(EPA). The reports also help the agency,
as well as the manufacturers who
prepare them, anticipate potential
compliance issues as early as possible,
and help manufacturers plan their
compliance strategies.
Further, NHTSA is modifying this
collection to account for additional
information manufacturers are required
to include in their reports. In the 2017
and beyond final rule,951 NHTSA
allowed for manufacturers to gain
additional fuel economy benefits by
installing certain technologies on their
actions reported are not sufficient to ensure
compliance with that standard, the manufacturer
shall report additional actions it intends to take to
comply with the standard and include a statement
about whether those actions are sufficient to ensure
compliance.
951 77 FR 62623 (Oct. 15, 2012).

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vehicles beginning with MY 2017.952
These technologies include airconditioning systems with increased
efficiency, off-cycle technologies whose
benefits are not adequately captured on
the Federal Test Procedure and/or the
Highway Fuel Economy Test,953 and
hybrid electric technologies installed on
full-size pickup trucks. Prior to MY
2017, manufacturers were unable to
earn a fuel economy benefit for these
technologies, so NHTSA’s reporting
requirements did not include an
opportunity to report them. Now,
manufacturers must provide
information on these technologies in
their CAFE reports. NHTSA requires
manufacturers to provide detailed
information on the model types using
these technologies to gain fuel economy
benefits. These details are necessary to
facilitate NHTSA’s technical analyses
and to ensure the agency can perform
random enforcement audits when
necessary.
In addition to a list of all fuel
consumption improvement technologies
utilized in their fleet, 49 CFR 537
requires manufacturers to report the
make, model type, compliance category,
and production volume of each vehicle
equipped with each technology and the
associated fuel consumption
improvement value (FCIV). NHTSA is
proposing to add the reporting and
enforcement burden hours and cost for
these new incentives to this collection.
Manufacturers can also petition the EPA
and NHTSA, in accordance with 40 CFR
86.1868–12 or 40 CFR 86.1869–12, to
gain additional credits based upon the
improved performance of any of the
new incentivized technologies allowed
for model year 2017. EPA approves
these petitions in collaboration with
NHTSA and any adjustments are taken
into account for both programs. As a
part the agencies’ coordination, NHTSA
provides EPA with an evaluation of
each new technology to ensure its direct
impact on fuel economy and an
assessment on the suitability of each
technology for use in increasing a
manufacturer’s fuel economy
performance. Furthermore, at times,
NHTSA may independently request
additional information from a
manufacturer to support its evaluations.
This information along with any
research conclusions shared with EPA
and NHTSA in the petitions is required
to be submitted in manufacturer’s CAFE
reports.
952 These technologies were not included in the
burden for part 537 at the time as the additional
reporting requirements would not take effect until
years later.
953 E.g., engine idle stop-start systems, active
transmission warmup systems, etc.

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NHTSA is seeking to change the
burden hours for its CAFE reporting
requirements in 49 CFR part 537.
NHTSA plans to reduce the total
amount of time spent collecting the
required reporting information by
standardizing the required data and
streamlining the collection process
using a standardized reporting template.
The standardized template will be used
by manufacturers to collect all the
required CAFE information under 49
CFR 537.7(b) and (c) and provides a
format which ensures accuracy,
completeness and better alignment with
the final data provided to EPA.
2. Other CAFE Compliance Collections
NHTSA is proposing a new
standardized template for manufacturers
buying CAFE credits and for
manufacturers submitting credit
transactions in accordance with 49 CFR
part 536. In 49 CFR part 536.5(d),
NHTSA is required to assess compliance
with fuel economy standards each year,
utilizing the certified and reported
CAFE data provided by the
Environmental Protection Agency for
enforcement of the CAFE program
pursuant to 49 U.S.C. 32904(e). Credit
values are calculated based on the CAFE
data from the EPA. If a manufacturer’s
vehicles in a particular compliance
category performs better than its
required fuel economy standard,
NHTSA adds credits to the
manufacturer’s account for that
compliance category. If a manufacturer’s
vehicles in a particular compliance
category performs worse than the
required fuel economy standard,
NHTSA will add a credit deficit to the
manufacturer’s account and will
provide written notification to the
manufacturer concerning its failure to
comply. The manufacturer will be
required to confirm the shortfall and
must either: Submit a plan indicating
how it will allocate existing credits or
earn, transfer and/or acquire credits or
pay the equivalent civil penalty. The
manufacturer must submit a plan or
payment within 60 days of receiving
notification from NHTSA.
NHTSA is proposing for
manufacturers to use the credit
transaction template any time a credit
transaction request is sent to NHTSA.
For example, manufacturers that
purchase credits and want to apply
them to their credit accounts will use

954 See

the credit transaction template. The
template NHTSA is proposing is a
simple spreadsheet that trading parties
fill out. When completed, parties will be
able to click a button on the spreadsheet
to generate a joint transaction letter for
the parties to sign and submit to
NHTSA, along with the spreadsheet.
NHTSA believes these changes will
significantly reduce the burden on
manufacturers in managing their CAFE
credit accounts.
Finally, NHTSA is accounting for the
additional burden due to existing CAFE
program elements. In 49 CFR part 525,
small volume manufacturers submit
petitions to NHTSA for exemption from
an applicable average fuel economy
standard and to request to comply with
a less stringent alternative average fuel
economy standard. In 49 CFR part 534,
manufacturers are required to submit
information to NHTSA when
establishing a corporate controlled
relationship with another manufacturer.
A controlled relationship exists between
manufacturers that control, are
controlled by, or are under common
control with, one or more other
manufacturers. Accordingly,
manufacturers that have entered into
written contracts transferring rights and
responsibilities to other manufacturers
in controlled relationships for CAFE
purposes are required to provide reports
to NHTSA. There are additional
reporting requirements for
manufacturers submitting carry back
plans and when manufacturers split
apart from controlled relationships and
must designate how credits are to be
allocated between the parties.954
Manufacturers with credit deficits at the
end of the model year, can carry back
future earned credits up to three model
years in advance of the deficit to resolve
a current shortfall. The carryback plan
proving the existence of a manufacturers
future earned credits must be submitted
and approved by NHTSA, pursuant to
49 U.S.C. 32903(b).
3. Analysis Fleet Composition
As discussed in Section II., in setting
CAFE standards, NHTSA creates an
analysis fleet from which to model
potential future economy
improvements. To compose this fleet,
the agency uses a mixture of compliance
data and information from other sources
to best replicate the fleet from a recent
model year. While refining the analysis

fleet, NHTSA occasionally asks
manufacturers for information that is
similar to information submitted as part
of EPA’s final model year report (e.g.,
final model year vehicle volumes).
Periodically, NHTSA may ask
manufacturers for more detailed
information than what is required for
compliance (e.g., what engines are
shared across vehicle models). Often,
NHTSA requests this information from
manufacturers after manufacturers have
submitted their final model year reports
to EPA, but before EPA processes and
releases final model year reports.
Information like this, which is used to
verify and supplement the data used to
create the analysis fleet, is tremendously
valuable to generating an accurate
analysis fleet, and setting maximum
feasible standards. The more accurate
the analysis fleet is, the more accurate
the modeling of what technologies
could be applied will be. Therefore,
NHTSA is accounting for the burden on
manufacturers to provide the agency
with this additional information. In
almost all instances, manufacturers
already have the information NHTSA
seeks, but it might need to be
reformatted or recompiled. Because of
this, NHTSA believes the burden to
provide this information will often be
minimal.
Affected Public: Respondents are
manufacturers of engines and vehicles
within the North American Industry
Classification System (NAICS) and use
the coding structure as defined by
NAICS including codes 33611, 336111,
336112, 33631, 33631, 33632, 336320,
33635, and 336350 for motor vehicle
and parts manufacturing.
Respondent’s obligation to respond:
Regulated entities required to respond
to inquiries covered by this collection.
49 U.S.C. 32907. 49 CFR part 525, 534,
536, and 537.
Frequency of response: Variable,
based on compliance obligation. Please
see PRA supporting documentation in
the docket for more detailed
information.
Average burden time per response:
Variable, based on compliance
obligation. Please see PRA supporting
documentation in the docket for more
detailed information.
Number of respondents: 23.
4. Estimated Total Annual Burden
Hours and Costs

49 CFR part 536.

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Regulatory Text

In accordance with 5 U.S.C. 553(c),
the agencies solicit comments from the
public to better inform the rulemaking
process. These comments are posted,
without edit, to www.regulations.gov, as
described in DOT’s system of records
notice, DOT/ALL–14 FDMS, accessible
through www.transportation.gov/
privacy. In order to facilitate comment
tracking and response, we encourage
commenters to provide their name, or
the name of their organization; however,
submission of names is completely
optional.

In consideration of the foregoing,
under the authority of 49 U.S.C. 32901,
32902, and 32903, and delegation of
authority at 49 CFR 1.95, NHTSA
proposes to amend 49 CFR Chapter V as
follows:

List of Subjects
49 CFR Parts 523, 531, and 533

Fuel economy, Reporting and
recordkeeping requirements.

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Authority: 49 U.S.C 32901, delegation of
authority at 49 CFR 1.95.

2. Amend § 523.2 by revising the
definitions of ‘‘Curb weight’’ and ‘‘Fullsize pickup truck’’ to read as follows:

■

Definitions.

*

49 CFR Parts 536 and 537

23:42 Aug 23, 2018

1. The authority citation for part 523
continues to read as follows:

■

§ 523.2

Fuel economy.

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PART 523—VEHICLE CLASSIFICATION

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*
*
Curb weight has the meaning given in
40 CFR 86.1803.
*
*
*
*
*

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Full-size pickup truck means a light
truck or medium duty passenger vehicle
that meets the requirements specified in
40 CFR 86.1803.
*
*
*
*
*
PART 531—PASSENGER
AUTOMOBILE AVERAGE FUEL
ECONOMY STANDARDS
3. The authority citation for part 531
continues to read as follows:

■

Authority: 49 U.S.C. 32902; delegation of
authority at 49 CFR 1.95.

4. Amend § 531.5 by revising Table III
to paragraph (c), and paragraph (d),
deleting paragraph (e), and
redesignating paragraph (f) as paragraph
(e) to read as follows:

■

§ 531.5

*

Fuel economy standards.

*
*
(c) * * *

*

*

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Table III- Parameters for the Passenger Automobile Fuel Economy Targets, MYs
2012-2026
Parameters
Model year

a (mpg)

c (gal/mi/ft2)

b (mpg)

d (gal/mi)

2012 .........

...............

35.95

27.95

0.0005308

0.006057

36.80

28.46

0.0005308

0.005410

37.75

29.03

0.0005308

0.004725

39.24

29.90

0.0005308

0.003719

41.09

30.96

0.0005308

0.002573

.....

2013 .........

...............
.....

2014 .........

...............
.....

2015 .........

...............
.....

2016 .........

...............

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Parameters
Model year
a (mpg)

2017 .........

b (mpg)

c (gal/milfr)

d (gal/mi)

43.61

32.65

0.0005131

0.001896

45.21

33.84

0.0004954

0.001811

46.87

35.07

0.0004783

0.001729

48.74

36.47

0.0004603

0.001643

48.74

36.47

0.0004603

0.001643

...............
.....

2018 .........

...............
......

2019 .........

...............
......

2020 .........

...............
......

2021 .........

...............

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43483

Parameters
Model year
a (mpg)

2022 .........

b (mpg)

c (gal/milfr)

d (gal/mi)

48.74

36.47

0.0004603

0.001643

48.74

36.47

0.0004603

0.001643

48.74

36.47

0.0004603

0.001643

48.74

36.47

0.0004603

0.001643

48.74

36.47

0.0004603

0.001643

...............
.....

2023 .........

...............
.....

2024 .........

...............
.....

2025 .........

...............
.....

2026 .........

...............

(d) In addition to the requirements of
paragraphs (b) and (c) of this section,

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each manufacturer shall also meet the
minimum fleet standard for

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domestically manufactured passenger
automobiles expressed in Table IV:

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VerDate Sep<11>2014

2011 ............................................ .

27.8

2012 ............................................ .

30.7

2013 ............................................ .

31.4

2014 ............................................ .

32.1

2015 ............................................ .

33.3

2016 ............................................ .

34.7

2017 ............................................ .

36.8

2018 ............................................ .

38.0

2019 ............................................ .

39.4

2020 ............................................ .

40.9

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Table IV- Minimum Fuel Economy Standards for Domestically Manufactured Passenger Automobiles,
MYs 2011-2026
Model year
Minimum standard

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

*

*
*
*
*
■ 5. Amend § 531.6 by revising
paragraphs (a) and (b) to read as follows:

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§ 531.6 Measurement and calculation
procedures.

(a) The fleet average fuel economy
performance of all passenger
automobiles that are manufactured by a
manufacturer in a model year shall be
determined in accordance with
procedures established by the
Administrator of the Environmental
Protection Agency under 49 U.S.C.
32904 and set forth in 40 CFR part 600.
For model years 2017 to 2026, a
manufacturer is eligible to increase the
fuel economy performance of passenger
cars in accordance with procedures
established by EPA set forth in 40 CFR
600, Subpart F, including any
adjustments to fuel economy EPA
allows, such as for fuel consumption
improvements related to air
conditioning efficiency and off-cycle
technologies.
(1) A manufacturer that seeks to
increase its fleet average fuel economy
performance through the use of
technologies that improve the efficiency
of air conditioning systems must follow
the requirements in 40 CFR 86.1868–12.
Fuel consumption improvement values

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resulting from the use of those air
conditioning systems must be
determined in accordance with 40 CFR
600.510–12(c)(3)(i).
(2) A manufacturer that seeks to
increase its fleet average fuel economy
performance through the use of off-cycle
technologies must follow the
requirements in 40 CFR 86.1869–12. A
manufacturer is eligible to gain fuel
consumption improvements for
predefined off-cycle technologies in
accordance with 40 CFR 86.1869–12(b)
or for technologies tested using EPA’s 5cycle methodology in accordance with
40 CFR 86.1869–12(c). The fuel
consumption improvement is
determined in accordance with 40 CFR
600.510–12(c)(3)(ii).
(b) A manufacturer is eligible to
increase its fuel economy performance
through use of an off-cycle technology
requiring an application request made to
EPA in accordance with 40 CFR
86.1869–12(d). The request must be
approved by EPA in consultation with
NHTSA. To expedite NHTSA’s
consultation with EPA, a manufacturer
shall concurrently submit its
application to NHTSA if the
manufacturer is seeking off-cycle fuel
economy improvement values under the
CAFE program for those technologies.

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For off-cycle technologies that are
covered under 40 CFR 86.1869–12(d),
NHTSA will consult with EPA regarding
NHTSA’s evaluation of the specific offcycle technology to ensure its impact on
fuel economy and the suitability of
using the off-cycle technology to adjust
the fuel economy performance. NHTSA
will provide its views on the suitability
of the technology for that purpose to
EPA. NHTSA’s evaluation and review
will consider:
(1) Whether the technology has a
direct impact upon improving fuel
economy performance;
(2) Whether the technology is related
to crash-avoidance technologies, safety
critical systems or systems affecting
safety-critical functions, or technologies
designed for the purpose of reducing the
frequency of vehicle crashes;
(3) Information from any assessments
conducted by EPA related to the
application, the technology and/or
related technologies; and
(4) Any other relevant factors.
*
*
*
*
*
■ 6. Add § 531.7 to read as follows:
§ 531.7

Preemption.

(a) General. When an average fuel
economy standard prescribed under this
chapter is in effect, a State or a political
subdivision of a State may not adopt or

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enforce a law or regulation related to
fuel economy standards or average fuel
economy standards for automobiles
covered by an average fuel economy
standard under this chapter.
(b) Requirements Must Be Identical.
When a requirement under section
32908 of this title is in effect, a State or
a political subdivision of a State may
adopt or enforce a law or regulation on
disclosure of fuel economy or fuel
operating costs for an automobile
covered by section 32908 only if the law
or regulation is identical to that
requirement.
(c) State and Political Subdivision
Automobiles. A State or a political
subdivision of a State may prescribe
requirements for fuel economy for
automobiles obtained for its own use.
■ 7. Redesignate Appendix to Part 531—
Example of Calculating Compliance
under § 531.5(c) as Appendix A to Part
531—Example of Calculating
Compliance under § 531.5(c) and amend
newly redesignated Appendix A by
removing all all references to
‘‘Appendix’’ and adding in their place,
‘‘Appendix A.’’
■ 8. Add Appendix B to Part 531 to read
as follows:
Appendix B to Part 531—Preemption

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(a) Express Preemption:
(1) To the extent that any state law or
regulation regulates or prohibits tailpipe
carbon dioxide emissions from automobiles,

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such a law or regulation relates to average
fuel economy standards within the meaning
of 49 U.S.C. 32919.
(A) Automobile fuel economy is directly
and substantially related to automobile
tailpipe emissions of carbon dioxide;
(B) Carbon dioxide is the natural byproduct of automobile fuel consumption;
(C) The most significant and controlling
factor in making the measurements necessary
to determine the compliance of automobiles
with the fuel economy standards in this Part
is their rate of tailpipe carbon dioxide
emissions;
(D) Almost all technologically feasible
reduction of tailpipe emissions of carbon
dioxide is achievable through improving fuel
economy, thereby reducing both the
consumption of fuel and the creation and
emission of carbon dioxide;
(E) Accordingly, as a practical matter,
regulating fuel economy controls the amount
of tailpipe emissions of carbon dioxide, and
regulating the tailpipe emissions of carbon
dioxide controls fuel economy.
(2) As a state law or regulation related to
fuel economy standards, any state law or
regulation regulating or prohibiting tailpipe
carbon dioxide emissions from automobiles
is expressly preempted under 49 U.S.C.
32919.
(3) A state law or regulation having the
direct effect of regulating or prohibiting
tailpipe carbon dioxide emissions or fuel
economy is a law or regulation related to fuel
economy and expressly preempted under 49
U.S.C. 32919.
(b) Implied Preemption:
(1) A state law or regulation regulating
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that is not attribute-based and does not
separately regulate passenger cars and light
trucks, conflicts with:
(A) The fuel economy standards in this
Part;
(B) The judgments made by the agency in
establishing those standards; and
(C) The achievement of the objectives of
the statute (49 U.S.C. Chapter 329) under
which those standards were established,
including objectives relating to reducing fuel
consumption in a manner and to the extent
consistent with manufacturer flexibility,
consumer choice, and automobile safety.
(2) Any state law or regulation regulating
or prohibiting tailpipe carbon dioxide
emissions from automobiles is impliedly
preempted under 49 U.S.C. Chapter 329.
(3) A state law or regulation having the
direct effect of regulating or prohibiting
tailpipe carbon dioxide emissions or fuel
economy is impliedly preempted under 49
U.S.C. Chapter 329.

PART 533—LIGHT TRUCK FUEL
ECONOMY STANDARDS
9. The authority citation for part 533
continues to read as follows:

■

Authority: 49 U.S.C. 32902; delegation of
authority at 49 CFR 1.95.

10. Amend § 533.5 by revising Table
VII to paragraph (a) to read as follows
and removing paragraph (k).

■

§ 533.5

Requirements.

(a) * * *
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Table VII- Parameters for the Light Truck Fuel Economy Targets for MYs 2017-2026
Parameters

c
a

b

(mpg)

(mpg)

year

g
d

(gal/mi/f
(gal/mi)

e

F

(mpg)

(mpg)

h

(gal/mi/f

t2)

0.00054
2017

2018

2019

2020

2021

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36.26

37.36

38.16

39.11

39.11

39.11

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t2)

0.00509

25.09
84

7

0.00053

0.00479

25.09

0.009851
46

0.00045
35.31

58

7

0.00052

0.00462

25.20

0.009682
46

25.25

0.00045
35.41

25.25

0.009603

65

3

46

0.00051

0.00449

0.00045

40

4

0.00051

0.00449

25.25

35.41

40

4

0.00051

0.00449

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0.009603

0.00045
35.41

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25.25

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35.10

25.20

25.25

(gal/mi)

25.25

0.009603
46

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35.41

25.25

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*

*
*
*
*
■ 11. Amend § 533.6 by revising
paragraphs (b) and (c) as follows:
§ 533.6 Measurement and calculation
procedures.

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*

*
*
*
*
(b) The fleet average fuel economy
performance of all light trucks that are
manufactured by a manufacturer in a
model year shall be determined in
accordance with procedures established
by the Administrator of the
Environmental Protection Agency under
49 U.S.C. 32904 and set forth in 40 CFR
part 600. For model years 2017 to 2026,
a manufacturer is eligible to increase the
fuel economy performance of light
trucks in accordance with procedures
established by EPA set forth in 40 CFR
part 600, subpart F, including any
adjustments to fuel economy EPA
allows, such as for fuel consumption
improvements related to air
conditioning efficiency, off-cycle
technologies, and hybridization and
other performance-based technologies
for full-size pickup trucks that meet the
requirements specified in 40 CFR
86.1803.
(1) A manufacturer that seeks to
increase its fleet average fuel economy

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performance through the use of
technologies that improve the efficiency
of air conditioning systems must follow
the requirements in 40 CFR 86.1868–12.
Fuel consumption improvement values
resulting from the use of those air
conditioning systems must be
determined in accordance with 40 CFR
600.510–12(c)(3)(i).
(2) A manufacturer that seeks to
increase its fleet average fuel economy
performance through the use of off-cycle
technologies must follow the
requirements in 40 CFR 86.1869–12. A
manufacturer is eligible to gain fuel
consumption improvements for
predefined off-cycle technologies in
accordance with 40 CFR 86.1869–12(b)
or for technologies tested using the
EPA’s 5-cycle methodology in
accordance with 40 CFR 86.1869–12(c).
The fuel consumption improvement is
determined in accordance with 40 CFR
600.510–12(c)(3)(ii).
(3) The eligibility of a manufacturer to
increase its fuel economy using
hybridized and other performance-based
technologies for full-size pickup trucks
must follow 40 CFR 86.1870–12 and the
fuel consumption improvement of these
full-size pickup truck technologies must

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be determined in accordance with 40
CFR 600.510–12(c)(3)(iii).
(c) A manufacturer is eligible to
increase its fuel economy performance
through use of an off-cycle technology
requiring an application request made to
EPA in accordance with 40 CFR
86.1869–12(d). The request must be
approved by EPA in consultation with
NHTSA. To expedite NHTSA’s
consultation with EPA, a manufacturer
shall concurrently submit its
application to NHTSA if the
manufacturer is seeking off-cycle fuel
economy improvement values under the
CAFE program for those technologies.
For off-cycle technologies that are
covered under 40 CFR 86.1869–12(d),
NHTSA will consult with EPA regarding
NHTSA’s evaluation of the specific offcycle technology to ensure its impact on
fuel economy and the suitability of
using the off-cycle technology to adjust
the fuel economy performance. NHTSA
will provide its views on the suitability
of the technology for that purpose to
EPA. NHTSA’s evaluation and review
will consider:
(1) Whether the technology has a
direct impact upon improving fuel
economy performance;

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(2) Whether the technology is related
to crash-avoidance technologies, safety
critical systems or systems affecting
safety-critical functions, or technologies
designed for the purpose of reducing the
frequency of vehicle crashes;
(3) Information from any assessments
conducted by EPA related to the
application, the technology and/or
related technologies; and
(4) Any other relevant factors.
*
*
*
*
*
■ 12. Add § 533.7 to read as follows:
§ 533.7

Preemption.

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(a) General. When an average fuel
economy standard prescribed under this
chapter is in effect, a State or a political
subdivision of a State may not adopt or
enforce a law or regulation related to
fuel economy standards or average fuel
economy standards for automobiles
covered by an average fuel economy
standard under this chapter.
(b) Requirements Must Be Identical.
When a requirement under section
32908 of this title is in effect, a State or
a political subdivision of a State may
adopt or enforce a law or regulation on
disclosure of fuel economy or fuel
operating costs for an automobile
covered by section 32908 only if the law
or regulation is identical to that
requirement.
(c) State and Political Subdivision
Automobiles.—A State or a political
subdivision of a State may prescribe
requirements for fuel economy for
automobiles obtained for its own use.
■ 13. Redesignate Appendix to Part
533—Example of Calculating
Compliance under § 533.5(i) as
Appendix A to Part 533—Example of
Calculating Compliance under § 533.5(i)
and amend newly redesignated
Appendix A by removing all references
to ‘‘Appendix’’ and adding in their
place, ‘‘Appendix A’’.
■ 14. Add Appendix B to Part 533 to
read as follows:
Appendix B to Part 533—Preemption
(a) Express Preemption:
(1) To the extent that any state law or
regulation regulates or prohibits tailpipe
carbon dioxide emissions from
automobiles, such a law or regulation
relates to average fuel economy
standards within the meaning of 49
U.S.C. 32919.
(A) Automobile fuel economy is
directly and substantially related to
automobile tailpipe emissions of carbon
dioxide;
(B) Carbon dioxide is the natural byproduct of automobile fuel
consumption;
(C) The most significant and
controlling factor in making the

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measurements necessary to determine
the compliance of automobiles with the
fuel economy standards in this Part is
their rate of tailpipe carbon dioxide
emissions;
(D) Almost all technologically feasible
reduction of tailpipe emissions of
carbon dioxide is achievable through
improving fuel economy, thereby
reducing both the consumption of fuel
and the creation and emission of carbon
dioxide;
(E) Accordingly, as a practical matter,
regulating fuel economy controls the
amount of tailpipe emissions of carbon
dioxide, and regulating the tailpipe
emissions of carbon dioxide controls
fuel economy.
(2) As a state law or regulation related
to fuel economy standards, any state law
or regulation regulating or prohibiting
tailpipe carbon dioxide emissions from
automobiles is expressly preempted
under 49 U.S.C. 32919.
(3) A state law or regulation having
the direct effect of regulating or
prohibiting tailpipe carbon dioxide
emissions or fuel economy is a law or
regulation related to fuel economy and
expressly preempted under 49 U.S.C.
32919.
(b) Implied Preemption:
(1) A state law or regulation regulating
tailpipe carbon dioxide emissions from
automobiles, particularly a law or
regulation that is not attribute-based and
does not separately regulate passenger
cars and light trucks, conflicts with:
(A) The fuel economy standards in
this Part;
(B) The judgments made by the
agency in establishing those standards;
and
(C) The achievement of the objectives
of the statute (49 U.S.C. Chapter 329)
under which those standards were
established, including objectives
relating to reducing fuel consumption in
a manner and to the extent consistent
with manufacturer flexibility, consumer
choice, and automobile safety.
(2) Any state law or regulation
regulating or prohibiting tailpipe carbon
dioxide emissions from automobiles is
impliedly preempted under 49 U.S.C.
Chapter 329.
(3) A state law or regulation having
the direct effect of regulating or
prohibiting tailpipe carbon dioxide
emissions or fuel economy is impliedly
preempted under 49 U.S.C. Chapter 329.
PART 535—MEDIUM- AND HEAVYDUTY VEHICLE FUEL EFFICIENCY
PROGRAM
15. The authority citation for part 535
continues to read as follows:

■

Authority: 49 U.S.C. 32902 and 30101;
delegation of authority at 49 CFR 1.95.

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16. Amend § 535.6 by revising
paragraph (a)(4)(ii) to read as follows:
*
*
*
*
*
(a) * * *
(4) * * *
(ii) Calculate the equivalent fuel
consumption test group results as
follows for spark-ignition vehicles and
alternative fuel spark-ignition vehicles.
CO2 emissions test group result (grams
per mile)/8,887 grams per gallon of
gasoline fuel) × (102) = Fuel
consumption test group result (gallons
per 100 mile).
*
*
*
*
*
■ 16. Amend § 535.6 by revising
paragraphs (a)(4)(ii) and (d)(5)(ii) to read
as follows:
*
*
*
*
*
(a) * * *
(4) * * *
(ii) Calculate the equivalent fuel
consumption test group results as
follows for spark-ignition vehicles and
alternative fuel spark-ignition vehicles.
CO2 emissions test group result (grams
per mile)/8,877 grams per gallon of
gasoline fuel) × (10¥2) = Fuel
consumption test group result (gallons
per 100 mile).
*
*
*
*
*
(d) * * *
(5) * * *
(ii) Calculate equivalent fuel
consumption FCL values for sparkignition engines and alternative fuel
spark-ignition engines. CO2 FCL value
(grams per hp-hr)/8,887 grams per
gallon of gasoline fuel) × (10¥2) = Fuel
consumption FCL value (gallons per 100
hp-hr).
*
*
*
*
*
■ 17. Amend § 535.7 by revising the
equations in paragraphs (b)(1), (c)(1),
(d)(1), (e)(2) and (f)(2)(iii)(E) to read as
follows:
■

§ 535.7 Averaging, banking, and trading
(ABT) credit program.

*

*
*
*
*
(b) * * *
(1) * * *
Total MY Fleet FCC (gallons) =
(Std¥Act) × (Volume) × (UL) × (10¥2)
Where:
Std = Fleet average fuel consumption
standard (gal/100 mile).
Act = Fleet average actual fuel consumption
value (gal/100 mile).
Volume = the total U.S.-directed production
of vehicles in the regulatory subcategory.
UL = the useful life for the regulatory
subcategory. The useful life value for
heavy-pickup trucks and vans
manufactured for model years 2013
through 2020 is equal to the 120,000
miles. The useful life for model years
2021 and later is equal to 150,000 miles.

*

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UL = the useful life for the regulatory
subcategory (miles) as shown in the
following table:

*

Std = the standard for the respective engine
regulatory subcategory (gal/100 hp-hr).
FCL = family certification level for the engine
family (gal/100 hp-hr).
CF = a transient cycle conversion factor in
hp-hr/mile which is the integrated total
cycle horsepower-hour divided by the
equivalent mileage of the applicable test
cycle. For engines subject to sparkignition heavy-duty standards, the

equivalent mileage is 6.3 miles. For
engines subject to compression-ignition
heavy-duty standards, the equivalent
mileage is 6.5 miles.
Volume = the number of engines in the
corresponding engine family.
UL = the useful life of the given engine
family (miles) as shown in the following
table:

Std = the standard for the respective vehicle
family regulatory subcategory (gal/1000
ton-mile).
FEL = family emissions limit for the vehicle
family (gal/1000 ton-mile).
Payload = 10 tons for short box vans and 19
tons for other trailers.
Volume = the number of U.S.-directed
production volume of vehicles in the
corresponding vehicle family.

UL = the useful life for the regulatory
subcategory. The useful life value for
heavy-duty trailers is equal to the
250,000 miles.

*

*

Engine Family FCC (gallons) =
(Std¥FCL) × (CF) × (Volume) × (UL)
× (10¥2)

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Where:

*

*
*
*
*
(e) * * *
(2) * * *
Vehicle Family FCC (gallons) = (Std ¥
FEL) × (Payload) × (Volume) × (UL)
× (10¥3)
Where:

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*

*
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(f) * * *
(2) * * *
(iii) * * *
(E) * * *

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EP24AU18.315

Volume = the number of U.S.-directed
production volume of vehicles in the
corresponding vehicle family.

*
*
(d) * * *
(1) * * *

FEL = family emissions limit for the vehicle
family (gal/1000 ton-mile).
Payload = the prescribed payload in tons for
each regulatory subcategory as shown in
the following table:

EP24AU18.314

Where:
Std = the standard for the respective vehicle
family regulatory subcategory (gal/1000
ton-mile).

EP24AU18.313

(c) * * *
(1) * * *
Vehicle Family FCC (gallons) =
(Std¥FEL) × (Payload) × (Volume) ×
(UL) × (10¥3)

Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules

Where:
CO2 Credits = the credit value in grams per
mile determined in 40 CFR 86.1869–
12(c)(3), (d)(1), (d)(2) or (d)(3).
CF = conversion factor, which for sparkignition engines is 8,887 and for
compression-ignition engines is 10,180.
Production = the total production volume for
the applicable category of vehicles.
VLM = vehicle lifetime miles, which for
2b–3 vehicles shall be 150,000 for the
Phase 2 program.
The term (CO2 Credit/CF) should be
rounded to the nearest 0.0001.

*

*

*

*

*

Where:
A = Adjustment factor applied to traded and
transferred credits when they are applied
to an existing credit shortfall. The
quotient shall be rounded to 4 decimal
places;

*

*
*
*
*
20. Amend § 536.5 by redesignating
paragraphs (c)(1) and (c)(2) as
paragraphs (c)(2) and (c)(3),
respectively, adding paragraph (c)(1),
and revising paragraph (d)(6) to read as
follows:

■

§ 536.5

Trading infrastructure.

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*

*
*
*
*
(c) * * *
(1) Entities trading credits must
generate and submit trade documents
using the NHTSA Credit Template
(OMB Control No. 2127–0019, NHTSA
Form 1475). Entities shall fill out the
NHTSA Credit Template and use it to
generate a credit trade summary and
credit trade confirmation, the latter of
which shall be signed by both trading
entities. The credit trade confirmation
serves as an acknowledgement that the
parties have agreed to trade credits, and
does not dictate terms, conditions, or
other business obligations. Managers
legally authorized to obligate the sale
and purchase of the traded credits must
sign the trade confirmation. The
completed credit trade summary and a
PDF copy of the signed trade
confirmation must be submitted to
NHTSA. The NHTSA Credit Template is
available for download at http://
www.nhtsa.gov.
*
*
*
*
*
(d) * * *

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PART 536—TRANSFER AND TRADING
OF FUEL ECONOMY CREDITS
18. The authority citation for part 536
continues to read as follows:

■

Authority: 49 U.S.C. 32903; delegation of
authority at 49 CFR 1.95.

19. Amend § 536.4 by revising
paragraph (c) to read as follows:

■

§ 536.4

Credits.

*

*
*
*
*
(c) Adjustment factor. When traded or
transferred and used, fuel economy
credits are adjusted to ensure fuel oil
savings is preserved. For traded credits,

(6) Credit allocation plans received
from a manufacturer will be reviewed
and approved by NHTSA. Use the
NHTSA Credit Template (OMB Control
No. 2127–0019, NHTSA Form 1475) to
record the credit transactions requested
in the credit allocation plan. The
template is a fillable form that has an
option for recording and calculating
credit transactions for credit allocation
plans. The template calculates the
required adjustments to the credits. The
credit allocation plan and the completed
transaction template must be submitted
to NHTSA. NHTSA will approve the
credit allocation plan unless it finds that
the proposed credits are unavailable or
that it is unlikely that the plan will
result in the manufacturer earning
sufficient credits to offset the subject
credit shortfall. If the plan is approved,
NHTSA will revise the respective
manufacturer’s credit account
accordingly. If the plan is rejected,
NHTSA will notify the respective
manufacturer and request a revised plan
or payment of the appropriate fine.
*
*
*
*
*
PART 537—AUTOMOTIVE FUEL
ECONOMY REPORTS
21. The authority citation for part 537
continues to read as follows:

■

Authority: 49 U.S.C. 32907, delegation of
authority at 49 CFR 1.95.

24. Amend § 537.5 by revising
paragraph (d) and adding paragraph (e)
to read as follows:

■

§ 537.5

*

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General requirements for reports.

*

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*

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*

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the user (or buyer) must multiply the
calculated adjustment factor by the
number of its shortfall credits it plans to
offset in order to determine the number
of equivalent credits to acquire from the
earner (or seller). For transferred credits,
the user of credits must multiply the
calculated adjustment factor by the
number of its shortfall credits it plans to
offset in order to determine the number
of equivalent credits to transfer from the
compliance category holding the
available credits. The adjustment factor
is calculated according to the following
formula:

(d) Beginning with MY 2019, each
manufacturer shall generate reports
required by this part using the NHTSA
CAFE Projections Reporting Template
(OMB Control No. 2127–0019, NHTSA
Form 1474). The template is a fillable
form.
(1) Select the option to identify the
report as a pre-model year report, midmodel year report, or supplementary
report as appropriate;
(2) Complete all required information
for the manufacturer and for all vehicles
produced for the current model year
required to comply with CAFE
standards. Identify the manufacturer
submitting the report, including the full
name, title, and address of the official
responsible for preparing the report and
a point of contact to answer questions
concerning the report.
(3) Use the template to generate
confidential and non-confidential
reports for all the domestic and import
passenger cars and light truck fleet
produced by the manufacturer for the
current model year. Manufacturers must
submit a request for confidentiality in
accordance with 49 CFR 512 to
withhold projected production sales
volume estimates from public
disclosure. If the request is granted,
NHTSA will withhold the projected
production sales volume estimates from
public disclose until all the vehicles
produced by the manufacturer have
been made available for sale (usually
one year after the current model year).
(4) Submit confidential reports and
requests for confidentiality to NHTSA
on CD–ROM in accordance with Part
537.12. Email copies of non-confidential

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Off-cycle FC credits = (CO2 Credit/CF) ×
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(i.e., redacted) reports to NHTSA’s
secure email address: [email protected].
Requests for confidentiality must be
submitted in a PDF or MS Word format.
Submit 2 copies of the CD–ROM to:
Administrator, National Highway
Traffic Administration, 1200 New Jersey
Avenue SW, Washington, DC 20590,
and submit emailed reports
electronically to the following secure
email address: [email protected];
(5) Confidentiality Requests.
(i) Manufacturers can withhold
information on projected production
sales volumes under 5 U.S.C. 552(b)(4)
and 15 U.S.C. 2005(d)(1). In accordance,
the manufacturer must:
(A) Show that the item is within the
scope of sections 552(b)(4) and
2005(d)(1);
(B) Show that disclosure of the item
would result in significant competitive
damage;
(C) Specify the period during which
the item must be withheld to avoid that
damage; and
(D) Show that earlier disclosure
would result in that damage.
(ii) [Reserved]
(e) Each report required by this part
must be based upon all information and
data available to the manufacturer 30
days before the report is submitted to
the Administrator.
■ 23. Amend § 537.6 by revising
paragraphs (b) and (c) to read as follows:
§ 537.6

General content of reports.

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*

*
*
*
*
(b) Supplementary report. Except as
provided in paragraph (c) of this
section, each supplementary report for
each model year must contain the
information required by and § 537.7(b)
and (c) in accordance with § 537.8(b)(1),
(2), (3), and (4) as appropriate.
(c) Exceptions. The pre-model year
report, mid-model year report, and
supplementary report(s) submitted by
an incomplete automobile manufacturer
for any model year are not required to
contain the information specified in
§ 537.7(c)(4)(xv) through (xviii) and
(c)(5). The information provided by the
incomplete automobile manufacturer
under § 537.7(c) shall be according to
base level instead of model type or
carline.
■ 24. Amend § 537.7 by revising
paragraphs (a)(2) and (3) as follows:
§ 537.7 Pre-model year and mid-model
year reports.

(a) * * *
(2) Provide a report with the
information required by paragraph (a)(1)
of this section by each domestic and
import passenger automobile fleet, as
specified in part 531 of this chapter, and

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by each the light truck fleet, as specified
in part 533 of this chapter, for the
current model year.
(3) Provide the information required
by paragraph (a)(1) for pre- and midmodel year reports using the NHTSA
CAFE Projections Reporting Template,
OMB Control No. 2127–0019, NHTSA
Form 1474. The required reporting
template can be downloaded from
http://www.nhtsa.gov.
*
*
*
*
*
■ 25. Amend § 537.7 by revising
paragraphs (b)(3), (b)(4), (b)(5), (c)(1),
(c)(2), (c)(3) and (c)(7)(i), (c)(7)(ii) and
(c)(7)(iii) to read as follows:
*
*
*
*
*
(b) * * *
(3) State the projected required fuel
economy for the manufacturer’s
passenger automobiles and light trucks
determined in accordance with 49 CFR
531.5(c) and 49 CFR 533.5 and based
upon the projected sales figures
provided under paragraph (c)(2) of this
section. For each unique model type
and footprint combination of the
manufacturer’s automobiles, provide the
information specified in paragraph
(b)(3)(i) and (ii) of this section and the
CAFE Projections Reporting Template,
OMB Control No. 2127–0019, NHTSA
Form 1474.
(i) In the case of passenger
automobiles:
(A) Beginning model year 2013, base
tire as defined in 49 CFR 523.2,
(B) Beginning model year 2013, front
axle, rear axle and average track width
as defined in 49 CFR 523.2,
(C) Beginning model year 2013,
wheelbase as defined in 49 CFR 523.2,
and
(D) Beginning model year 2013,
footprint as defined in 49 CFR 523.2.
(E) The fuel economy target value for
each unique model type and footprint
entry listed in accordance with the
equation provided in 49 CFR parts 531.
(4) State the projected final required
fuel economy that the manufacturer
anticipates having if changes
implemented during the model year will
cause the targets to be different from the
target fuel economy projected under
paragraph (b)(3) of this section.
(5) State whether the manufacturer
believes that the projections it provides
under paragraphs (b)(2) and (b)(4) of this
section, or if it does not provide an
average or target under those
paragraphs, the projections it provides
under paragraphs (b)(1) and (b)(3) of this
section, sufficiently represent the
manufacturer’s average and target fuel
economy for the current model year for
purposes of the Act. In the case of a
manufacturer that believes that the

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projections are not sufficiently
representative for those purposes, state
the specific nature of any reason for the
insufficiency and the specific additional
testing or derivation of fuel economy
values by analytical methods believed
by the manufacturer necessary to
eliminate the insufficiency and any
plans of the manufacturer to undertake
that testing or derivation voluntarily
and submit the resulting data to the
Environmental Protection Agency under
40 CFR 600.509.
(c) * * *
(1) For each model type of the
manufacturer’s automobiles, provide the
information specified in paragraph (c)(2)
of this section in the NHTSA CAFE
Projections Reporting Template (OMB
Control No. 2127–0019, NHTSA Form
1474) and list the model types in order
of increasing average inertia weight
from top to bottom.
(2)(i) Combined fuel economy; and
(ii) Projected sales for the current
model year and total sales of all model
types.
(3) For each vehicle configuration
whose fuel economy was used to
calculate the fuel economy values for a
model type under paragraph (c)(2) of
this section, provide the information
specified in paragraph (c)(4) of this
section in the NHTSA CAFE Projections
Reporting Template (OMB Control No.
2127–0019, NHTSA Form 1474).
*
*
*
*
*
(7) * * *
(i) Provide a list of each air
conditioning efficiency improvement
technology utilized in your fleet(s) of
vehicles for each model year. For each
technology identify vehicles by make
and model types that have the
technology, which compliance category
those vehicles belong to and the number
of vehicles for each model equipped
with the technology. For each
compliance category (domestic
passenger car, import passenger car and
light truck) report the air conditioning
fuel consumption improvement value in
gallons/mile in accordance with the
equation specified in 40 CFR 600.510–
12(c)(3)(i).
(ii) Provide a list of off-cycle
efficiency improvement technologies
utilized in your fleet(s) of vehicles for
each model year that is pending or
approved by EPA. For each technology
identify vehicles by make and model
types that have the technology, which
compliance category those vehicles
belong to, the number of vehicles for
each model equipped with the
technology, and the associated off-cycle
credits (grams/mile) available for each
technology. For each compliance

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Federal Register / Vol. 83, No. 165 / Friday, August 24, 2018 / Proposed Rules
category (domestic passenger car,
import passenger car and light truck)
calculate the fleet off-cycle fuel
consumption improvement value in
gallons/mile in accordance with the
equation specified in 40 CFR 600.510–
12(c)(3)(ii).
(iii) Provide a list of full-size pick-up
trucks in your fleet that meet the mild
and strong hybrid vehicle definitions.
For each mild and strong hybrid type,
identify vehicles by make and model
types that have the technology, the
number of vehicles produced for each
model equipped with the technology,
the total number of full size pick-up
trucks produced with and without the
technology, the calculated percentage of
hybrid vehicles relative to the total
number of vehicles produced and the
associated full-size pickup truck credits
(grams/mile) available for each
technology. For the light truck
compliance category calculate the fleet
Pick-up Truck fuel consumption
improvement value in gallons/mile in
accordance with the equation specified
in 40 CFR 600.510–12(c)(3)(iii).
*
*
*
*
*
■ 26. Amend § 537.8 by revising
paragraphs (a)(3), paragraph (b)(3)(i) and
(ii), and paragraph (c)(1) and adding
paragraphs (a)(4) and (b)(4) to read as
follows:

sradovich on DSK3GMQ082PROD with PROPOSALS2

§ 537.8

Supplementary reports.

(a) * * *
(3) Each manufacturer whose pre- or
mid-model year report omits any of the
information specified in § 537.7(b) or (c)
shall file a supplementary report
containing the information specified in
paragraph (b)(3) of this section.
(4) Each manufacturer whose pre- or
mid-model year report omits any of the
information specified in § 537.5(c) shall
file a supplementary report containing
the information specified in paragraph
(b)(4) of this section.
(b) * * *
(3) * * *
(i) All of the information omitted from
the pre- or mid-model year report under
§ 537.7(b) and (c); and
(ii) Such revisions of and additions to
the information submitted by the
manufacturer in its pre-model year
report regarding the automobiles
produced during the current model year
as are necessary to reflect the
information provided under paragraph
(b)(3)(i) of this section.
(4) The supplementary report required
by paragraph (a)(4) of this section must
contain:
(i) All information omitted from the
pre-model year report under
§ 537.6(c)(2); and

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(ii) Such revisions of and additions to
the information submitted by the
manufacturer in its pre-model year
report regarding the automobiles
produced during the current model year
as are necessary to reflect the
information provided under paragraph
(b)(4)(i) of this section.
(c)(1) Each report required by
paragraph (a)(1), (2), (3), or (4) of this
section must be submitted in
accordance with § 537.5(c) not more
than 45 days after the date on which the
manufacturer determined, or could have
determined with reasonable diligence,
that a report is required under
paragraph (a)(1), (2), (3), or (4) of this
section.
*
*
*
*
*
Environmental Protection Agency
List of Subjects
40 CFR Part 85
Confidential business information,
Imports, Labeling, Motor vehicle
pollution, Reporting and recordkeeping
requirements, Research, Warranties.
40 CFR Part 86
Administrative practice and
procedure, Confidential business
information, Incorporation by reference,
Labeling, Motor vehicle pollution,
Reporting and recordkeeping
requirements.
For the reasons stated in the
preamble, the Environmental Protection
Agency proposes to amend 40 CFR parts
85 and 86 as follows:

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using 298 g CO2 to represent 1 g N2O
and 25 g CO2 to represent 1 g CH4. You
may then subtract the applicable
converted values from the fuel
conversion measured values of CH4 and/
or N2O to demonstrate compliance with
the CH4 and/or N2O standards. This
option may not be used for model year
2021 or later.
(iv) Optionally, through model year
2020, compliance with greenhouse gas
emission requirements may be
demonstrated by comparing emissions
from the vehicle prior to the fuel
conversion to the emissions after the
fuel conversion. This comparison must
be based on FTP test results from the
emission data vehicle (EDV)
representing the pre-conversion test
group. The sum of CO2, CH4, and N2O
shall be calculated for pre- and postconversion FTP test results, where CH4
and N2O are weighted by their global
warming potentials of 25 and 298,
respectively. The post-conversion sum
of these emissions must be lower than
the pre-conversion conversion
greenhouse gas emission results. CO2
emissions are calculated as specified in
40 CFR 600.113–12. If statements of
compliance are applicable and accepted
in lieu of measuring N2O, as permitted
by EPA regulation, the comparison of
the greenhouse gas results also need not
measure or include N2O in the before
and after emission comparisons. This
option may not be used for model year
2021 or later.
*
*
*
*
*

PART 85—CONTROL OF AIR
POLLUTION FROM MOBILE SOURCES

PART 86—CONTROL OF EMISSIONS
FROM NEW AND IN-USE HIGHWAY
VEHICLES AND ENGINES

27. The authority citation for part 85
continues to read as follows:

■

■

Authority: 42 U.S.C. 7401–7671q.

Subpart F—[Amended]
28. Amend § 85.525 by revising
paragraphs (b)(1)(iii) and (b)(1)(iv) to
read as follows:

■

§ 85.525

Applicable standards.

*

*
*
*
*
(b) * * *
(1) * * *
(iii) If the OEM complied with the
nitrous oxide (N2O) and methane (CH4)
standards and provisions set forth in 40
CFR 86.1818–12(f)(1) or (3), and the fuel
conversion CO2 measured value is lower
than the in-use CO2 exhaust emission
standard, you also have the option
through model year 2020 to convert the
difference between the in-use CO2
exhaust emission standard and the fuel
conversion CO2 measured value into
GHG equivalents of CH4 and/or N2O,

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29. The authority citation for part 86
continues to read as follows:
Authority: 42 U.S.C. 7401–7671q.

30. Amend § 86.1818–12 as follows:
a. Revise paragraphs (c)(2)(i)(A)
through (C);
■ b. Revise paragraphs (c)(3)(i)(A), (B)
and (D);
■ c. Revise paragraph (f) introductory
text; and paragraphs (f)(1) through (3).
The revisions read as follows:
■
■

§ 86.1818–12 Greenhouse gas emission
standards for light-duty vehicles, light-duty
trucks, and medium-duty passenger
vehicles.

*

*
*
*
*
(c) * * *
(2) * * *
(i) * * *
(A) For passenger automobiles with a
footprint of less than or equal to 41
square feet, the gram/mile CO2 target
value shall be selected for the

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appropriate model year from Table 1 to
Paragraph (c)(2)(i)(A).

(B) For passenger automobiles with a
footprint of greater than 56 square feet,
the gram/mile CO2 target value shall be

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selected for the appropriate model year
from Table 1 to Paragraph (c)(2)(i)(B).

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grams/mile, except that for any vehicle
footprint the maximum CO2 target value
shall be the value specified for the same
model year in paragraph (c)(2)(i)(B) of
this section:
Target CO2 = [a × ƒ] + b

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Where:
ƒ is the vehicle footprint, as defined in
§ 86.1803; and
a and b are selected from Table 1 to
Paragraph (c)(2)(i)(C):

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(C) For passenger automobiles with a
footprint that is greater than 41 square
feet and less than or equal to 56 square
feet, the gram/mile CO2 target value
shall be calculated using the following
equation and rounded to the nearest 0.1

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*
*
(3) * * *
(i) * * *

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(A) For light trucks with a footprint of
less than or equal to 41 square feet, the
gram/mile CO2 target value shall be
selected for the appropriate model year

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from Table 1 to Paragraph Table 1 to
Paragraph (c)(3)(i)(A):
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rounded to the nearest 0.1 grams/mile,
except that for any vehicle footprint the
maximum CO2 target value shall be the
value specified for the same model year
in paragraph (c)(3)(i)(D) of this section:
Target CO2 = (a × ƒ) + b

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Where:
ƒ is the footprint, as defined in § 86.1803; and
a and b are selected from Table 1 to
Paragraph Table 1 to Paragraph
(c)(3)(i)(B): For the appropriate model
year:

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(B) For light trucks with a footprint
that is greater than 41 square feet and
less than or equal to the maximum
footprint value specified in the table
below for each model year, the gram/
mile CO2 target value shall be calculated
using the following equation and

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*
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*
(D) For light trucks with a footprint
greater than the minimum value

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*

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specified in the table below for each
model year, the gram/mile CO2 target
value shall be selected for the

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appropriate model year from Table 1 to
Paragraph Table 1 to Paragraph
(c)(3)(i)(D):

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*

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(f) Nitrous oxide (N2O) and methane
(CH4) exhaust emission standards for
passenger automobiles and light trucks.
Each manufacturer’s fleet of combined
passenger automobile and light trucks
must comply with N2O and CH4
standards using either the provisions of
paragraph (f)(1), or, through model year
2020, provisions of paragraphs (f)(2) or
(3) of this section. Except with prior
EPA approval, a manufacturer may not
use the provisions of both paragraphs
(f)(1) and (2) of this section in a model
year. For example, a manufacturer may
not use the provisions of paragraph
(f)(1) of this section for their passenger
automobile fleet and the provisions of
paragraph (f)(2) for their light truck fleet
in the same model year. The
manufacturer may use the provisions of
both paragraphs (f)(1) and (through
model year 2020) (3) of this section in
a model year. For example, a
manufacturer may meet the N2O
standard in paragraph (f)(1)(i) of this
section and an alternative CH4 standard
determined under paragraph (f)(3) of
this section. Vehicles certified using the

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N2O data submittal waiver provisions of
§ 86.1829(b)(1)(iii)(G) are not required to
be tested for N2O under the in-use
testing programs required by § 86.1845
and § 86.1846.
(1) Standards applicable to each test
group. (i) Exhaust emissions of nitrous
oxide (N2O) shall not exceed 0.010
grams per mile at full useful life, as
measured according to the Federal Test
Procedure (FTP) described in subpart B
of this part. Through model year 2020,
manufacturers may optionally
determine an alternative N2O standard
under paragraph (f)(3) of this section.
This option may not be used for model
year 2021 or later. (ii) Exhaust emissions
of methane (CH4) shall not exceed 0.030
grams per mile at full useful life, as
measured according to the Federal Test
Procedure (FTP) described in subpart B
of this part. Through model year 2020,
manufacturers may optionally
determine an alternative CH4 standard
under paragraph (f)(3) of this section.
This option may not be used for model
year 2021 or later.
(2) Include N2O and CH4 in fleet
averaging program. Through model year

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2020, manufacturers may elect to not
meet the emission standards in
paragraph (f)(1) of this section. This
option may not be used for model year
2021 or later. Manufacturers making this
election shall include N2O and CH4
emissions in the determination of their
fleet average carbon-related exhaust
emissions, as calculated in 40 CFR part
600, subpart F. Manufacturers using this
option must include both N2O and CH4
full useful life values in the fleet average
calculations for passenger automobiles
and light trucks. Use of this option will
account for N2O and CH4 emissions
within the carbon-related exhaust
emission value determined for each
model type according to the provisions
of 40 CFR part 600. This option requires
the determination of full useful life
emission values for both the Federal
Test Procedure and the Highway Fuel
Economy Test. Manufacturers selecting
this option are not required to
demonstrate compliance with the
standards in paragraph (f)(1) of this
section.
(3) Optional use of alternative N2O
and/or CH4 standards. Through model

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year 2020, manufacturers may select an
alternative standard applicable to a test
group, for either N2O or CH4, or both.
This option may not be used for model
year 2021 or later. For example, a
manufacturer may choose to meet the
N2O standard in paragraph (f)(1)(i) of
this section and an alternative CH4
standard in lieu of the standard in
paragraph (f)(1)(ii) of this section. The
alternative standard for each pollutant
must be greater than the applicable
exhaust emission standard specified in
paragraph (f)(1) of this section.
Alternative N2O and CH4 standards
apply to emissions measured according
to the Federal Test Procedure (FTP)
described in Subpart B of this part for
the full useful life, and become the
applicable certification and in-use
emission standard(s) for the test group.
Manufacturers using an alternative
standard for N2O and/or CH4 must
calculate emission debits according to

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the provisions of paragraph (f)(4) of this
section for each test group/alternative
standard combination. Debits must be
included in the calculation of total
credits or debits generated in a model
year as required under § 86.1865–
12(k)(5). For flexible fuel vehicles (or
other vehicles certified for multiple
fuels) you must meet these alternative
standards when tested on any
applicable test fuel type.
*
*
*
*
*
■ 31. Revise § 86.1867–12 to read as
follows:
§ 86.1867–12 CO2 credits for reducing
leakage of air conditioning refrigerant.

Through model year 2020,
manufacturers may generate credits
applicable to the CO2 fleet average
program described in § 86.1865–12 by
implementing specific air conditioning
system technologies designed to reduce
air conditioning refrigerant leakage over
the useful life of their passenger

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automobiles and/or light trucks.
Manufacturers may not generate these
credits for model year 2021 or later.
Credits shall be calculated according to
this section for each air conditioning
system that the manufacturer is using to
generate CO2 credits. Manufacturers
may also generate early air conditioning
refrigerant leakage credits under this
section for the 2009 through 2011 model
years according to the provisions of
§ 86.1871–12(b).
Issued on August 1, 2018, in Washington,
DC, under authority delegated in 49 CFR 1.95
and 501.5.
Heidi R. King,
Deputy Administrator, National Highway
Traffic Safety Administration.
Andrew R. Wheeler,
Acting Administrator, Environmental
Protection Agency.
[FR Doc. 2018–16820 Filed 8–23–18; 8:45 am]
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