42986 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 
AGENCY : Environmental Protection 
Agency and National Highway Traffic Safety Administration. 
ACTION : Notice of proposed rulemaking. 
SUMMARY : 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 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. 
ADDRESSES : 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: 
ÆEPA: 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. 
ÆNHTSA: 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: ÆEPA: 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. 
ÆNHTSA: 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: otaqpublicweb@epa.gov. 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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1NHTSA 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 CO
2standards under 
the Clean Air Act (CAA). 
249 U.S.C. 32902. 342 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’’). 
4See, 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 CO
2 
emissions is a very direct and close one. The amount of those CO
2emissions 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 CO
2it 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 CO
2. 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 CO
2. Thus, there 
is a single pool of technologies for addressing these twin problems, i.e., those that reduce fuel 
consumption and thereby reduce CO
2emissions as 
well.’’) 540 CFR 86.1818–12(h)(1); see also 77 FR 62624 
(Oct. 15, 2012). 
681 FR 49217 (Jul. 27, 2016). 
781 FR 87927 (Dec. 6, 2016). 
8Docket 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). 
9See https://www.whitehouse.gov/briefings- 
statements/remarks-president-trump-american- center-mobility-detroit-mi/. 
1082 FR 14671 (Mar. 22, 2017). SUPPLEMENTARY INFORMATION : 
I. Overview of Joint NHTSA/EPA Proposal 
II. Technical Foundation for NPRM Analysis III. Proposed CAFE and CO
2Standards 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 CO
2 
Standards 
VIII. Impacts of Alternative CAFE and CO 2 
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 
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 (CO
2) 
emissions standards, respectively, for passenger cars and light trucks manufactured for sale in the United States in model years (MYs) 2021 through 2026.
1CAFE and CO 2standards 
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, CO
2—‘‘cause[s] or contribute[s] to air 
pollution which may reasonably be anticipated to endanger public health or welfare.’’
3NHTSA and EPA are 
proposing these standards concurrently because tailpipe CO
2emissions 
standards are directly and inherently related to fuel economy standards,
4and 
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 CO
2standards for 
MYs 2017 and beyond; in that rulemaking action, EPA set CO
2 
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 CO
2 
standards for those model years. EPA’s CO
2standards for MYs 2022–2025 were 
subject to a ‘‘mid-term evaluation,’’ by which EPA bound itself through regulation to re-evaluate the CO
2 
standards for those model years and to undertake to develop new CO
2 
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 light- duty vehicles remain appropriate under section 202(a) of the Clean Air Act.
5The 
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.
7EPA 
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.’’
9In 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 
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1182 FR 39551 (Aug. 21, 2017). 
1282 FR 39976 (Aug. 23, 2017). 
13The public comments, public hearing 
transcript, and other information relevant to the 
Mid-term Evaluation are available in docket EPA– HQ–OAR–2015–0827. 
1483 FR 16077 (Apr. 2, 2018). 
1549 U.S.C. 32902. 16See FCC v. Fox Television, 556 U.S. 502 (2009). 
17Note: 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 CO
2standards, 
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 CO
2curves 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 CO
2standards as a set ‘‘mpg,’’ ‘‘g/mi,’’ 
or ‘‘mpg-e’’ number, attempting to define those values today will end up being inaccurate. January 2017 Determination.11EPA held 
a public hearing in Washington DC on 
September 6, 2017.12EPA 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.
15NHTSA 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 suggest that the CAFE standards 
previously set for MY 2021 are no longer maximum feasible, and the CO
2 
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 CO 2 
standards applicable in MY 2020 for MYs 2021–2026, while taking comment on a wide range of alternatives, including different stringencies and retaining existing CO
2standards and the 
augural CAFE standards.17Table I–4 below presents those alternatives. We 
note further that prior to MY 2021, CO 2 
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 CO
2 
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 CO
2curves 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. 
BILLING CODE 4910–59–P 
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18Carbon 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. 
19Beginning 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 CO
2for compliance with tailpipe 
CO 2standards. BILLING CODE 4910–59–C 
In the tables above, estimated 
required CO 2increases between MY 
2020 and MY 2021 because, again, EPA 
is proposing to exclude CO 2-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 alternatives and have specifically modeled eight alternatives (including the proposed alternative) and the current requirements (i.e., baseline/no- 
action). The modeled alternatives are provided below: 
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 
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20Mankiw, N. Gregory, Principles of 
Macroeconomics, Sixth Edition, 2012, at 4. 
21In 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-proposed- 
settlement-for-ford-powershift-owners/ (last 
accessed Jul. 2, 2018). 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 CO
2purposes. 
Technologies have played out 
differently in the fleet from what the agencies assumed in 2012. 
The technology to improve fuel 
economy and reduce CO
2emissions 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 CO
2emissions. 
Even when used in combination, technologies that can improve fuel economy and reduce CO
2emissions 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 CO
2emissions. 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 CO
2emissions. 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/emissions- reducing’’ 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.
21The 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 CO
2standards 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 CO
2emissions 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: 
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22A 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. 
23The 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 CO
2per mile 
as well. If a vehicle emits 300 g/mi CO 2, 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. 
24The 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 CO 2rulemaking 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/. 
25Today 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. 
For purposes of illustration, assume a 
vehicle owner who drives a light vehicle 
15,000 miles per year (a typical assumption for analytical purposes).
22If 
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.
23Automakers, who must 
nonetheless continue adding technology to improve fuel economy and reduce CO
2emissions, 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 
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26In 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.). 
27Carey, 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-york- suvs/lured-by-rising-suv-sales-automakers-flood- market-with-models-idUSKBN1H50KI (last accessed 
Jun. 13, 2018). Many commentators have recently argued that manufacturers are deliberately increasing vehicle footprint size in order to get ‘‘easier’’ CAFE and CO
2standards. This 
misunderstands, somewhat, how the footprint- based standards work. While it is correct that larger- footprint 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 larger- footprint 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. 
28The 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. 
29Annual Energy Outlook 2018, U.S. Energy 
Information Administration, at 53 (Feb. 6, 2018), https://www.eia.gov/outlooks/aeo/pdf/ AEO2018.pdf. 
30See id. 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 ever- higher 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,
26information 
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.
27Some consumers plainly value fuel economy and low CO 2 
emissions above other attributes, and 
thanks in part to CAFE and CO 2 
standards, they have a plentiful selection of high-fuel economy and low CO
2-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.
28Things 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 increase the overall average CO
2 
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 CO
2emission 
rates. 
The Consequence of Unreasonable 
Fuel Economy and CO 2Standards: 
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 more- stringent 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 in- service fleet has been increasing, and when fleet turnover slows, not only does it take longer for fleet-wide fuel economy and CO
2emissions 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 CO
2emissions standards 
(especially considering that, on a fleet- wide 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 
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31See, 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-Average- New-Car-Prices-Rise-Nearly-4-Percent-For-January- 2018-On-Shifting-Sales-Mix-According-To-Kelley- Blue-Book (last accessed Jun. 15, 2018). 
32Bell, 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-affordability- survey/ (last accessed Jun. 15, 2018). 
33Average Auto Loan Interest Rates: 2018 Facts 
and Figures, ValuePenguin, available at https://www.valuepenguin.com/auto-loans/average-auto- loan-interest-rates (last accessed Jun. 15, 2018). 
3477 FR 62624, 62666 (Oct. 15, 2012). 
35The $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 CO
2standards accounts explicitly for multiyear planning and credit 
banking. 
36While 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. recently exceeding $36,000—up by 
more than $3,000 since 2014 alone.31In 
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.
32That analysis used 
the historically accepted approach that consumers should make a down- payment 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.
33Longer financing terms may 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 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. 
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37Data 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). 
38Over the lifetime of vehicles through MY 2029. 39Passenger 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. 
40See, 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-older- average-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.’’). 
Preferred Alternative 
For all of these reasons, the agencies 
are proposing to maintain the MY 2020 
fuel economy and CO 2emissions 
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. 
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 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
38and 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 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.
39As the 
average car on the road is approaching 
12 years old, apparently the oldest in our history,
40major safety benefits will 
occur by reducing fleet age. Other safety benefits will occur from other areas such as avoiding the increased driving 
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41The 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 CO
2targets. That specific assumption has been 
removed for today’s analysis. 
42The reduction in annual fatalities varies each 
calendar year, averaging 894 fewer fatalities annually for the CAFE program and 1,150 fewer 
fatalities for the CO 2program over calendar years 
2036–2045. 
43Counter-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. 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.
41Together these 
and other factors lead to estimated 
annual fatalities under the proposed standards that are significantly reduced
42relative 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 CO 2 
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 CO
2basis, the results are similarly minimal. The following graph compares 
the estimated atmospheric CO 2 
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: 
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Net Benefits From Preferred Alternative 
Maintaining the MY 2020 curves for 
MYs 2021–2026 will save American 
consumers, the auto industry, and the public a considerable amount of money as compared to if EPA retained the 
previously-set CO
2standards and 
NHTSA finalized the augural standards. This was identified as the preferred alternative, in part, because it maximizes net benefits compared to the 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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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 three- percent 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 three- percent 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 CO
2standards 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. Compliance Flexibilities 
This proposal also seeks comment on 
a variety of changes to NHTSA’s and 
EPA’s compliance programs for CAFE and CO
2as 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 fleet- wide 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 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 emissions- reducing 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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4478 FR 2112 (Jan. 9, 2013). 
45CAA Section 177, 42 U.S.C. 7507. 46See 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). The agencies seek comment as to 
whether some adjustments and non- statutory 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. 
One National Standard 
Setting appropriate and maximum 
feasible fuel economy and tailpipe CO
2 
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,
44and, as allowed under the 
CAA, a number of other States adopted California’s standards.
45The CAA states 
that EPA shall not grant a waiver of preemption if EPA finds that California’s determination that its 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 relatively- straightforward 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.
46In 
the past decade, California has disproportionately focused on GHG emissions. Parts of California have a real and significant local air pollution problem, but CO
2is not part of that local 
problem. 
California’s Tailpipe CO 2Emissions 
Standards and ZEV Mandate Conflict 
With EPCA 
Moreover, California regulation of 
tailpipe CO 2emissions, both through its 
GHG standards and ZEV program, conflicts directly and indirectly with EPCA and the CAFE program. EPCA expressly preempts State standards related to fuel economy. Tailpipe CO
2 
standards, whether in the form of fleet- wide CO
2limits or in the form of 
requirements that manufacturers selling vehicles in California sell a certain number of low- and no-tailpipe-CO
2 
emissions vehicles as part of their overall sales, are unquestionably related to fuel economy standards. Standards that control tailpipe CO
2emissions are 
de facto fuel economy standards because CO
2is a direct and inevitable 
byproduct of the combustion of carbon- based 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 CO
2per 
unit of distance; therefore, improving fuel economy necessarily reduces tailpipe CO
2emissions, and reducing 
CO 2emissions necessarily improves fuel 
economy. EPCA therefore necessarily preempts California’s Advanced Clean Cars program to the extent that it regulates or prohibits tailpipe CO
2 
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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47While 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). 
48Docket No. NHTSA–2018–0067. 
49See 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 ). 
50Information 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. 
51As part of the Argonne simulation effort, 
individual technology combinations simulated in Autonomie were paired with Argonne’s BatPAC 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/. 
52Additionally, 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-suite- applications/propulsion-systems/gt-power-engine- simulation-software. 
5382 FR 39533 (Aug. 21, 2017). 
54Since its earliest Title II regulations, EPA has 
considered the safety of pollution control technologies. See 45 FR 14496, 14503 (1980). 
55See 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). 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 
A. Basics of CAFE and CO
2Standards 
Analysis 
The agencies’ analysis of CAFE and 
CO 2standards 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., CO
2emissions 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 CO
2standards 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 the 2016 rulemaking regarding heavy- duty pickup and van fuel consumption 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,
49and 
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 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 CO
2standards for 
light-duty vehicles. EPA weighs many factors when determining appropriate levels for CO
2standards, 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,
54and energy 
impacts associated with use of the technology.
55Using the CAFE model 
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56From Docket Number EPA–HQ–OAR–2015– 
0827, see Comment by Global Automakers, Docket 
ID EPA–HQ–OAR–2015–0827–9728, at 34. 
57The 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 CO
2 
ratings of vehicles in manufacturers’ fleets and to monitor year-by-year CAFE (or average CO
2ratings), 
costs, avoided fuel outlays, and avoided CO 2-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. 
58For 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 
Continued 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 CO
2 
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 CO
2standards. 
There are sound reasons for the 
agencies to use the CAFE model going forward in this rulemaking. First, the CAFE and CO
2fact 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 year- by-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 simply some single year well into the 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 economy- improving/CO
2emissions-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 fuel- saving 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 full- vehicle 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 CO
2standards. 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 CO
2emission 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). Considering the advantages of DOE/Argonne’s Autonomie model, it is reasonable and appropriate to use Autonomie to estimate fuel economy levels and CO
2 
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.
56For 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 CO
2 
standards.57Although 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 
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OMEGA model’s focus on direct technological 
inputs and costs—as opposed to industry self- reported 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. 
59In 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. 
60NRDC’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. 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. 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 decision- making 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 decision- 
making 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 CO
2 
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 decision may be informed by non-EPA- created models does not, in any way, constitute a delegation of its statutory power to set standards or decision- making authority.
61Arguing 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 CO
2standards 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 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), CO
2 
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 hybrid electric vehicles (HEVs), plug-in 
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62While 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. 63The 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. 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-by- year basis, 
•Year-by-year simulation of how 
manufacturers could comply with EPA’s CO
2standards, including 
ÆCalculation of vehicle models’ CO 2 
emission rates before and after application of fuel-saving (and, therefore, CO
2-reducing) technologies, 
ÆCalculation of manufacturers’ fleet 
average CO 2emission rates, 
ÆCalculation of manufacturers’ fleet 
average CO 2emission rates under 
attribute-based CO 2standards, 
ÆAccounting for adjustments to 
average CO 2emission rates reflecting 
reduction of air conditioner refrigerant leakage, 
ÆAccounting for the treatment of 
alternative fuel vehicles for CO
2 
compliance, 
ÆAccounting for production 
‘‘multipliers’’ for PHEVs, BEVs, compressed natural gas (CNG) vehicles, and fuel cell vehicles (FCVs), 
ÆAccounting for transfer of CO
2 
credits between regulated fleets, 
ÆAccounting for carried-forward 
(a.k.a. ‘‘banked’’) CO 2credits, including 
credits from model years earlier than modeled explicitly. 
2. Sensitivity Cases and Why We 
Examine Them 
Today’s notice presents estimated 
impacts of the proposed CAFE and CO
2 
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 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 long- standing 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 CO
2standards 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 CO
2emission 
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 CO
2standards; 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 CO
2emissions. 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 
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64Considering 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. 
65Available 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. 66Shea, 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-to- develop-new-models/. 67Available 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. them.64Additionally, 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.
65For 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, 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 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 
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68CO 2emissions rates are directly related to fuel 
economy levels, and the CAFE model uses the latter 
to calculate the former. 6982 FR 39551 (Aug. 21, 2017). 
70For 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). 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 CO
2standards 
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 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, 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.
70On 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 
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71For 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). 
72Comments 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.’’ 73These 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. fuel economy rather than continuing to 
improve vehicle performance and utility.
71Because manufacturers change 
and improve product offerings over time, conducting analysis with an older analysis fleet (or with a fleet using fuel economy levels and CO
2emissions 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 CO
2 
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 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 CO
2standards 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 economy and reduce CO
2emissions.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 CO
2emissions. To improve how the 
observed vehicle fleet ‘‘fits into’’ the analysis, each vehicle model/ configuration is ‘‘mapped’’ to the full- 
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74Full-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. 75Mapping 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 manufacturer- reported data or CBI to help map the vehicle to a simulation). vehicle simulation modeling74by 
Argonne National Laboratory that is 
used to estimate the effectiveness of the fuel economy-improving/CO
2 
emissions-reducing technologies considered. Argonne produces full- vehicle 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. 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 CO
2emission 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 observed technologies and equipment 
on that vehicle.
75This 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. 
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76The 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. 
77The 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. 
78For instance, manufacturers may use different 
piston strokes on a common engine block or bore 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. 
79For 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. 
80Engine family is referred to in the analysis as 
an ‘‘engine code.’’ 
81Specifically, 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. 82This 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. 
83Manufacturers 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. 
84Transmission configurations are referred to in 
the analysis as ‘‘transmission codes.’’ 
85Similar 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. BILLING CODE 4910–59–C 
(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.
77Similar to vehicle 
platforms, manufacturers create engines that share parts.
78One engine family 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.
79Engine families for each 
manufacturer were tabulated and assigned
80based 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.
81By grouping engines together, 
the CAFE model controls future engine 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, rear- wheel-drive, all-wheel-drive based on a front-wheel-drive platform, or all-wheel- drive 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
84will 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/ CO
2emissions-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 over- simplified 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 
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86In 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. 
87In 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). 
88NRDC, et al., at 32. 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 
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 CO
2emissions 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. 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 cycles (e.g., four to five years), and this 
approach was considered for the analysis.
87Some 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.
88Also, 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. 
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89Technology class, or tech class, refers to a 
group of fuel-economy simulations of similar vehicles. As explained, each vehicle is assigned to a representative simulation to estimate technology 
effectiveness for purposes of the analysis. 
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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 refresh and redesign cycles. Table II–3 below summarizes the average time between redesigns, by manufacturer, by vehicle technology class.
89Dashes 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. 
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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 between vehicle redesigns). Across the 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 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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9049 U.S.C. 32902(a)(3)(A). 91Such 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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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. 
C. Development of Footprint-Based 
Curve Shapes 
As in the past four CAFE rulemakings, 
the most recent two of which included related standards for CO
2emissions, 
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 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.
90While 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.
91Section 
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 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? 
Under attribute-based standards, 
every vehicle model has fuel economy and CO
2targets, 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: 
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92If 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. 
93The 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-corporate- average-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. 
94Bento, 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). 
952002 NAS Report at 4–5, finding 10. 
Here, i represents a given model92in 
a manufacturer’s fleet, Production i 
represents the U.S. production of that 
model, and Target irepresents 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, attribute- based standards reduce the incentive for manufacturers to respond to CAFE standards by reducing vehicle size in ways harmful to safety.
93Larger 
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.
94Because each 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 fleet- wide 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 size- related, 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.
95A 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. 2. Why footprint as the attribute? 
It is important that the CAFE and CO
2 
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 CO
2 
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 footprint- based standards than there would be by increasing vehicle mass under weight- based 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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96See 74 FR at 14359 (Mar. 30, 2009). 
97See 74 FR 14196, 14363–14370 (Mar. 30, 2009) 
for NHTSA discussion of curve fitting in the MY 
2011 CAFE final rule. 98The 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). 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,
96it 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 CO
2levels. 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. 
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 CO
2regulation, and EPA has continued 
to harmonize this aspect of its CO 2 
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 fuel- saving 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 CO
2 
reduction benefits will be forfeited artificially. 
•Given the same industry-wide 
average required fuel economy or CO
2 
standard, flatter standards tend to place greater compliance burdens on full-line manufacturers. •Given the same industry-wide 
average required fuel economy or CO
2 
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 CO
2 
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 CO
2emissions) better accommodates the 
design requirements of larger vehicles — especially large pickups — and extends the size range over which downsizing is discouraged. 
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.
97The 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 
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 
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99See 74 FR 14196, 14363–14370 (Mar. 30, 2009) 
for NHTSA discussion of curve fitting in the MY 
2011 CAFE final rule. 100See 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. 
10175 FR at 25362. 
102See generally 74 FR at 49491–96; 75 FR at 
25357–62. 103The 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. 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) 
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,
99but 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 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 CO
2standards 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.
101A 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 CO
2basis, 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.
102These 
transformations are typically presented 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 full- line 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 sales- weighted, 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-to- footprint. 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 CO
2 
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 
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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 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 methods, weighting schemes, and 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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104Thomas, 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). 
105The 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. (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 
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 CO
2emissions 
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 technology- neutral relationship between footprint 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 third- party simulation work form Argonne National Laboratories (ANL) was developed. Estimating physics-based curves better ensures that technology and performance are held constant for 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
105and ‘‘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. 
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106Available at https://www.nhtsa.gov/staticfiles/ 
rulemaking/pdf/cafe/Draft-TAR-Final.pdf (last 
accessed June 15, 2018). 
10777 FR 62624 (Oct. 15, 2012). 
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 3- box. 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 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 CO
2 
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 fuel- saving 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 CO
2emissions 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)
106as 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.
107In some cases, 
manufacturers have nearly universally adopted a technology in today’s new vehicle fleet (for example, electric power steering). In other cases, 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 CO
2status 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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108FEV 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. 
109For 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. 
110For 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. 111ANL’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. 
112At 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.’’ 
113See e.g., Automotive News ‘‘CAFE math gets 
trickier as industry innovates’’ (Kulisch), March 26, 2018. 114The Alliance of Automobile Manufacturers 
commented that ‘‘the OMEGA model is over- 
optimized 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). 
115For 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). 
11649 U.S.C. 32902(b)(2)(B). 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.
108As 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, peer- 
reviewed 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 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 fuel- savings 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,
112especially as the tool 
was used to evaluate increasingly heterogeneous combinations of technologies in the baseline fleet.
113For 
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 Model or ALPHA), and applied cost 
effective technologies to manufacturers’ fleets in response to potential standards. Many stakeholders commented that the OMEGA model oversimplified fleet- wide analysis, resulting in significant shortcomings.
114For 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
116and 
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. 
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117For 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. 
118The analysis referenced studies from private 
businesses and business analysts for emerging technologies and for off-the-shelf technologies that were commercially mature. 119‘‘Baseline’’ here refers to a reference part, 
piece of equipment, or engineering system that efficiency improvements and costs are relative to. 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 CO
2 
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. 
(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 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.
118Due 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 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.’’
119The 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 fuel- saving 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 
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120See, 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-10- speed-transmissions-for-2017. 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,
120and 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 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 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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121More information about Autonomie is 
available at https://www.anl.gov/technology/ 
project/autonomie-automotive-system-design (last 
accessed June 21, 2018). 
122ANL Model Documentation, ‘‘A Detailed 
Vehicle Simulation Process To Support CAFE Standards’’ ANL/ESD–18/6. 
123National 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-effectiveness- 
and-deployment-of-fuel-economy-technologies-for- light-duty-vehicles (last accessed June 21, 2018). 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 
(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 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 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 estimates for fuel efficiency 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 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,
121a physics- 
based 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.
122Real-world use has 
contributed significantly to aspects of Autonomie important to producing realistic estimates of fuel economy and CO
2emission 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 full- vehicle simulation to improve the analysis method of estimating technology effectiveness.
123The 
committee acknowledged that developing and executing tens or hundreds of thousands of constantly changing vehicle packages models in 
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124Pannone, 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). 125Separate technology classes were created for 
high performance and low performance vehicles to better account for performance diversity across the fleet. 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 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 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 fuel- savings 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.
125Baseline 
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. 
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126The vehicle glider is defined here as the 
vehicle without the engine, transmission, and 
driveline. See PRIA Chapter 6.3 for further information. 
127Some 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, 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. 
128The simulation database, or summary of 
simulation outputs, includes all of the estimated performance metrics for each combination of technology as modeled. 129For 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. 
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.
127The analysis resized 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.
128The 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 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 CO
2standards for each 
manufacturer that sells vehicles in the United States. The model begins with a 
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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), CO
2reductions, 
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. •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 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 dual- clutch transmissions and manual transmissions were applied to vehicles already equipped with these transmission architectures. 
Please provide comments on all 
assumptions for fuel economy and CO
2 
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 CO
2over 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 5- speed automatic transmission. For hybrid or electrification technologies that complement other advanced engine 
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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 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 range of estimated effectiveness values. 
Figure II–3 below shows a hierarchy of technologies discussed. 
BILLING CODE 4910–59–P 
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4. Engine Technologies 
There are a number of engine 
technologies that manufacturers can use 
to improve fuel economy and CO 2. 
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’’ 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 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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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. 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 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 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. 
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BILLING CODE 4910–59–C Many types of production powertrains 
were reviewed and tested for this analysis, and engine maps were 
developed for each combination of 
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131See 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). 
132Hakariya et al., ‘‘The New Toyota Inline 4- 
Cylinder 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/. 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. 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 in- 
cylinder 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 noise- vibration-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 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.
131Another recently introduced 
high compression ratio engine uses a sophisticated laser cladding process to manufacture valve seats and improve airflow.
132To 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 
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133Boost refers to the degree to which the 
turbocharger compresses the intake air for the 
engine, which may affect the specific power of the engine. 
134Knock 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. 
135Turbo 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. 
136Specifically, 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. 137In particular, the step from a naturally- 
aspirated V6 to a turbo I4 was particularly cost effective in agency simulations. 138Some 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-wheel- drive vehicles may pose challenges, especially if the engine has two banks and would therefore require room for two such exhaust manifolds. 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,
134especially 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.
136In the Draft TAR, turbo engine 
maps were developed assuming premium fuel. For this rulemaking analyses, pathways to improving fuel economy and CO
2are 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 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 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 high- performance 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., late- 
intake-valve-closing or LIVC) to reduce the effective compression ratio while maintaining the expansion ratio. This approach allows a reduction in top- dead-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 
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139Ellies, 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/. 
140Lee, S., Schenk, C., and McDonald, J., ‘‘Air 
Flow Optimization and Calibration in High- Compression-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/2016-01- 0565/. 141At 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. 
142At 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. 
143Thomas, 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/. 
144For instance, the MY 2018 2.5L Camry engine 
that uses HCR technology also reduces parasitic losses with a variable capacity oil pump. 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 
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 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 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.
144The 
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. 
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145Mazda Next-Generation Technology—Press 
Information, Mazda USA (Oct. 24, 2017), https://
insidemazda.mazdausa.com/press-release/mazda- 
next-generation-technology-press-information/ (last 
visited Apr. 13, 2018). 
146Mazda introduces updated 2019 CX–3 at 2018 
New York International Auto Show, Mazda USA (Mar. 28, 2018), https://insidemazda.mazdausa.com/press-release/mazda- introduces-2019-cx-3-2018-new-york-auto-show/ (last visited Apr. 13, 2018). 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. 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. 
(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.
145Soon 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 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. 
(d) Diesel Engines 
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 NO
Xadsorption 
catalyst system or a urea/ammonia selective catalytic reduction system for control of NO
Xemissions 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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147U.S. Energy Information Administration, What 
is Octane?, https://www.eia.gov/energyexplained/ 
index.cfm?page=gasoline_ home#tab2 (last visited 
Mar. 19, 2018). 
148Id. 
149See 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 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. 
150Nebraska Ethanol Board, Oil Refiners Change 
Nebraska Fuel Components, Nebraska.gov, http://
ethanol.nebraska.gov/wordpress/oil-refiners- change-nebraska-fuel-components/ (last visited 
Mar. 19, 2018). 
151Additionally, 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. 152Fact 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-940- august-29-2016-diverging-trends-engine- compression-ratio-and-gasoline-octane (last visited 
Mar. 21, 2018). 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 low- friction 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. 
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.
147Spontaneous 
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).
148At higher 
elevations, the lowest octane rating available can drop to 85 AKI.
149 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 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 
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153Mark 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-promises- 
lower-emissions-higher-mpg-and-cost-octane- society-of-automotive-engineers/100716174/. 
154The 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-auto- industry-lobbies-for-95-as-new-regular. 155Null, TRX11, TRX12, TRX21, TRX22. 
156Draft TAR, p. 5–297 through 5–298 
summarizes effectiveness values previously 
assumed for stepping between transmission technologies (Null, TRX11, TRX12, TRX21, TRX22). 
157Draft 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).’’ 
158See PRIA Chapter 6.3. 
159Ehsan, 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. improvement in the design of spark- 
ignition 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 CO
2. 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 on- road 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 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 non- petroleum 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 CO
2 
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 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
155by vehicle class in 
their analysis, and often EPA represented upgrades among manual, automatic, continuously variable, and dual clutch transmissions with the same effectiveness
156and cost values157 
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 full- vehicle 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. 
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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 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. (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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160Ford Powershift Transmission Settlement, 
http://fordtransmissionsettlement.com/ (last visited 
June 21, 2018). 161Mass reduction costs are in $/lb. 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 
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 odd- numbered gears, so the next expected 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.
160Most 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 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 
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162See 76 FR 57106, at 57207, 57229 (Sep. 15, 
2011). 
(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 CO
2 
emissions. New for this proposal, and also marking an advance over low rolling resistance tires considered during the heavy duty greenhouse gas rulemaking,
162is 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% 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. (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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163EPA 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. 164Moawad 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%20Consumption%20and%20Cost%20through%20Large%20Scale%20Simulation%20of%20Advanced%20Vehicle%20Technologies%20- %201603.pdf. 
165Notably all power split hybrids, and all plug- 
in hybrid vehicles were assumed to be paired with a high compression ratio internal combustion engine for this analysis. 
166Note: 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. 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.
163For 
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 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. (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.
164ANL’s 
assumptions regarding all hybrid systems, including belt-integrated starter generators, strong parallel and series hybrids, plug-in hybrids,
165and 
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 
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(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. 
(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- 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 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 50- mile 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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167For further discussion of accessory 
technologies, see Chapter 6 of the PRIA 
accompanying this NPRM. (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. (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 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.
167Table II–17 
and Table II–18 below shows the estimated absolute costs including learning effects and retail price equivalent utilized in today’s analysis. 
(a) Electric Power Steering (EPS) 
Electric power steering (EPS)/ 
Electrohydraulic power steering (EHPS) 
is an electrically-assisted steering system that has advantages over traditional hydraulic power steering 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 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. 
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168See 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-vc- turbo-the-worlds-first-production-ready-variable- compression-ratio-engine. 169Murphy, T. Achates: Opposed-Piston Engine 
makers tooling up, Wards Auto (Jan. 23, 2017), http://wardsauto.com/engines/achates-opposed- piston-engine-makers-tooling. 
170Our Formula, Achates Power, http://
achatespower.com/our-formula/opposed-piston/ (last visited June 21, 2018). 
171Robert 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. 
172EfficientDynamics—The intelligent route to 
lower emissions, BMW Group (2007), https://www.bmwgroup.com/content/dam/bmw-group- websites/bmwgroup_ com/responsibility/downloads/ 
en/2007/Alex_ ED__englische_ Version.pdf. 
173Volkswagen at the 37th Vienna Motor 
Symposium, Volkswagen (Apr. 28, 2016), https://
www.volkswagen-media-services.com/en/ detailpage/-/detail/Volkswagen-at-the-37th-Vienna- Motor-Symposium/view/3451577/ 5f5a4dcc90111ee56bcca439f2dcc518?p_ p_
auth=M2yfP3Ze. 
174These engines may be considered in the 
analysis supporting the final rule, but these engine maps were not available in time for the NPRM analysis. Please see Chapter 6.3 of the PRIA accompanying this proposal for more information. 
175Schmitt, 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-charging- solid-state-ev-batteries-around-the-corner-toyota- confirms/#5736630244bb. 
176Moving toward clean mobility, Robert Bosch 
GmbH, https://www.bosch.com/explore-and- 
experience/moving-toward-clean-mobility/ (last 
visited June 21, 2018). 
177Reuters 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-ev- batteries-idUSKBN1EF0FM. 
178Burke, 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. 
179Buckland, K. & Sano, N. Toyota Readies 
Cheaper Electric Motor by Halving Rare Earth Use, 
Continued (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. 
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 opposed- cylinder (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 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 NO
Xand 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 E- boost.
173 174  (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: 
ÆLower-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 
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Bloomberg (Feb, 20, 2018), https://
www.bloomberg.com/news/articles/2018-02-20/ toyota-readies-cheaper-electric-motor-by-halving- rare-earth-use. 
180Burkhardt, 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. 
181Patel, 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. 
182Murphy, T. Planets Aligning for Dana’s 
VariGlide Beltless CVT, Wards Auto (Aug. 22, 2017), http://wardsauto.com/technology/planets- 
aligning-dana-s-variglide-beltless-cvt. 
183Faid, 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. 
184Orr 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). 185Patel, P. Powering Your Car with Waste Heat, 
MIT Technology Review (May 25, 2011), https://
www.technologyreview.com/s/424092/powering- your-car-with-waste-heat/. 
186Baeuml, 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). 
187Advanced Suspension, Tenneco, http://
www.tenneco.com/overview/rc_ advanced_
suspension/ (last visited June 21, 2018). 
188Audi A8 Active Chassis, Audi, https://
www.audi.com/en/innovation/design/more_
personal_ comfort_ a8_active_ chassis.html (last 
visited June 21, 2018). 
18977 FR 62624, 62720 (Oct. 15, 2012). 
19040 CFR 86.1868–12 (2016). 19175 FR 25324, 25431 (May 7, 2010). The A/C 
CO 2Idle 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. 
19277 FR 62624, 62723 (Oct. 15, 2012). 
193Id. 
194Id. at 62725. 
195Lifetime 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- ÆIntegrated multi-phase integrated 
electric vehicle drivetrains. Research 
has been conducted on 6-phase and 9- phase 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. Two- speed transmission designs are available but not currently in production.
183 
(d) Energy-Harvesting Technology 
•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.
184Thermoelectric 
generators, often made of semiconductors, have been tested by automakers but have traditionally not been implemented due to low efficiency and high cost.
185These 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 Off- Cycle 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.
189Table 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 CO
2emissions from the 
technology.190NHTSA 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 about which off-cycle technologies are present on which vehicles (see Section X for further discussion of reporting off- cycle 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 CO
2 
generated at idle when the A/C system is operated.
191The AC17 test is a four- 
part performance test that combines the existing SC03 driving cycle, the fuel economy highway test cycle, and a pre- conditioning cycle, and solar soak period.
192Manufacturers 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
195to use the pre-defined 
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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. 
19640 CFR 86.1868–12(b)(2) (2016). 197See e.g., 77 FR 62623, 62803–62806 (Oct. 15, 
2012). 
198See 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. 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 of the standards.
197Many 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,
19815 auto 
manufacturers included A/C efficiency credits as part of their compliance demonstration in the 2016 MY. These 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 off- cycle program. 
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199For 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). 200See 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) . 
(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 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 than during just idle (as measured in the 
old idle test procedure). As described above and in the 2012 final rule,
200the 
AC17 test is a four-part performance test that combines the existing SC03 driving 
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201See Comment by FCA US LLC, Docket ID 
NHTSA 2016–0068–0082, at 123–124. 
202Id. at 124. 203See Comment by Toyota (revised), Docket ID 
NHTSA–2016–0068–0088, at 23. 204See Comment by Alliance of Automobile 
Manufacturers, Docket ID EPA–HQ–OAR–2015– 
0827–4089 and NHTSA–2016–0068–0072, at 160. 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 CO
2effectiveness 
(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 
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 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 off- cycle application for the Denso SAS A/ 
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205EPA 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. 
206Alternative 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). 
207Id. 
208Comment by Alliance of Automobile 
Manufacturers, Docket ID EPA–HQ–OAR–2015– 0827–4089 and NHTSA–2016–0068–0072, at 152. 
209Comment by Toyota (revised), Docket ID 
NHTSA–2016–0068–0088, at 23. 21040 C.F.R § 86.1868–12(b)(2) (2016). 
211Joint 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). 21277 FR 62624, 62832 (Oct. 15, 2012). 
213Id. at 62839. 
214For a description of each technology and the 
derivation of the pre-defined credit levels, see Chapter 5 of the Joint Technical Support Document: C compressor with variable crankcase 
suction valve technology, requesting an off-cycle GHG credit of 1.1 grams CO
2 
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.’’
208Other 
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 off- cycle 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 off- cycle 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 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 menu- 
based 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.
210Several 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 off- 
cycle 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 values and are not part of the off-cycle regulation (40 CFR 86.1869–12). However, it should be noted that off- cycle 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 off- cycle technology credits in the MY 2012–2016 rule and revised the program in the MY 2017–2025 rule.
212NHTSA 
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 CO
2 
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.
214A 
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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). 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 fleet- wide 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 
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215EPA Decision Document: Mercedes-Benz Off- 
cycle Credits for MYs 2012–2016, U.S. EPA (Sept. 
2014), available at https://nepis.epa.gov/Exe/ ZyPDF.cgi/P100KB8U.PDF?Dockey=P100KB8U.PDF. 
216EPA 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 . 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 in- use 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 determine these off-cycle credits in 
September 2014.
215Subsequently, 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.
216Note, however, that 
although EPA granted the use of 
alternative methodologies to determine 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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217Thus 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 gasoline- electric hybrid vehicles, an auxiliary electric pump, which keeps engine coolant circulating in cold 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. 
In 2016, manufacturers generated the 
vast majority of credits using the pre- 
defined menu.217Although MY 2014 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 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 
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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 
(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 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 CO
2emissions standards for model 
years 2021–26 at three percent and seven percent discount rates in a format that is intended to meet these objectives. 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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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 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 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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218Note: 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. 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 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. 
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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The analysis simulates compliance 
through MY 2032 explicitly and must 
consider the full useful lives of those vehicles, approximately 40 years, in order to estimate their lifetime mileage accumulation and fuel consumption. This means that any macroeconomic forecast influencing those factors must 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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219The 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 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, 
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. 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.
219The projections by 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. 
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3. New Vehicle Sales and Employment 
Assumptions 
In all previous CAFE and GHG 
rulemaking analyses, static fleet forecasts that were based on a 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 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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220See e.g., Comment by Alliance of Automobile 
Manufacturers, Docket ID EPA–HQ–OAR–2015– 
0827–4089 and NHTSA–2016–0068–0072. 221In 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. 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.
220In 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 will fully reflect future fuel cost savings 
consumers would realize from owning— and potentially re-selling—more fuel- efficient 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 economy, and whether it implies over- , under- or full-valuation of the expected fuel savings from purchasing a model 
with higher fuel economy. Most studies have relied on car buyers’ purchasing behavior to estimate their willingness- to-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.
221Empirical 
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 cross- sectional 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 
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222These 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. 223Killian & 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. 
224Each 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 
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. 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 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 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 over- valuation, while lower discount rates imply less complete valuation in all three studies. 
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225Allcott & 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. 
226In fact, those earlier analyses assumed that 
new car and light truck buyers attach relatively 
Continued 
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 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. 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 higher- mpg 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.
226The evidence 
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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. 227There 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. 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. 
(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 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.
227But annual 
vehicle sales did not grow at a similar pace –even accounting for the cyclical nature of the industry. Total new light- duty 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 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 time- series 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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228Effects on the used car market are accounted 
for separately. 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.
228The 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 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 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, b
1and b2, respectively 
must satisfy three conditions. Their sum must be less than one, b
2¥ b1<1, and 
the absolute value of b2must 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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229Out 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. 
230U.S. Total Sales by Make, Automotive News, 
http://www.autonews.com/section/datalist18 (last 
visited June 22, 2018). 
231Mislinski, 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-latest- look-at-the-long-term-trend. 
232For more detail about the compliance 
simulation and manufacturer fleet representation, see Section II.G. 
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 per- capita sales of new vehicles peaked in 1986 and has declined more than 25% from this peak to today’s level.
231While 
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 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 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 CO
2standards 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).
232While 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 
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23367 FR 77015, 77021 (Dec. 16, 2002). 
234See 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). 
235See 77 FR 62624, 62952, 63102 (Oct. 15, 2012). 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-to- pay 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. 
(f) Industry Employment Baseline 
(Including Multiplier Effect) and Data Description 
In the first two joint CAFE/CO
2 
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 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.
234Both 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.
235Notwithstanding 
these uncertainties, CAFE and CO 2 
standards do impact industry labor hours, and providing the best analysis practicable better informs stakeholders 
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236Regulatory 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). 
237The agencies recognize a few local production 
facilities may contribute meaningfully to local economies, but the analysis reported only on national effects. 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.
236Today’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,
237in 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. 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 fuel- savings 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 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, ride- sharing, 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 full- employment 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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238NADA 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). 
239NAICS Code 3361, 3363. 240The 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. 
241The analysis assumed incremental OEM 
revenue as the retail price equivalent for technologies, adjusting for changes in sales volume. 
242The analysis assumed incremental supplier 
revenue as the technology cost for technologies before retail price equivalent mark-up, adjusting for 
changes in sales volume. 
243The 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. 
244The analysis made vehicle specific 
assumptions about percent U.S. content and U.S. assembly employment hours. 
245The analysis estimated technology cost for 
each vehicle, for each year based on the technology content applied in the CAFE model, year-by-year. 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. 
(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.
238The 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. 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,
239and 
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 fuel- savings 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 analysis looked at financial reports from 
publicly traded automotive businesses.
240Based 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.
242These 
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 fuel- economy 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,
243but other 
constants were vehicle specific,244or 
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 
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246See 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. 
247Zabritski, 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-SAFM- revised.pdf (last visited June 22, 2018). 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. 
(a) Sales Taxes 
The analysis took auto sales taxes by 
state
246and 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 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 registration- based 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. 
(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.
247The 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 non- financed 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 
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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. 
(c) Insurance Costs 
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 value- related insurance will decline as the vehicle depreciates. This is represented in the model as the following stream of expected collision and comprehensive insurance payments: 
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. 
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248Fitch 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). 249Based 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. 250The 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. 
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%.
248It 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. 
(d) Consumer Acceptance of Specific 
Technologies 
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.
249Additionally, 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).
250In 
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 
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251See e.g., Comment by Alliance of Automobile 
Manufacturers, Docket ID EPA–HQ–OAR–2015– 
0827–4089 and NHTSA–2016–0068–0072. 
252See Edmunds, https://www.edmunds.com/ 
(last visited June 22, 2018). Edmunds publishes 
automotive data, reviews, and advice. 
253See 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. 254It 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. 
255In 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 FE
Nameplate ). 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-to- pay for electrified vehicles. 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, plug- in 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.
254The 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.
255The 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 = ,b
1(ICE * Age) + b 2(HEV * 
Age) + b 3(PHEV * Age) + b 4(BEV * 
Age) + b 5(HEV) + b 6(PHEV) + 
b7(BEV) + FE Nameplate  
For each observation in the dataset, 
the ‘‘Value’’ at age zero is determined by 
setting the age variable to zero and solving. 
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256The 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. 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.
256As 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 
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257Often 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. 258See https://www.transportation.gov/sites/ 
dot.gov/files/docs/ValueofTravelTime
Memorandum.pdf (last accessed July 3, 2018). 259Total 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). 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-to- pay 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-and- harshness, interior space,
257and 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 re- estimation 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 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.
258The 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 $29.68.
259Table–II–39 below 
demonstrates the approach to estimating the value of travel time ($/hour) for both urban and rural (intercity) driving. This approach relies on the use of DOT- recommended weights that assign a lesser valuation to personal travel time than to business travel time, as well as 
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260Time spent on personal travel during rural 
(intercity) travel is valued at a greater rate than that 
of urban travel. There are several reasons behind the divergence in these values: (1) Time is scarcer on a long trip; (2) a long trip involves complementary expenditures on travel, lodging, food, and entertainment because time at the 
destination is worth such high costs. weights that adjust for the distribution 
between personal and business travel. 
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 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 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. 
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261See 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. 
262Source: 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. 
263TPMS 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. 
264The data collection period for the TPMS study 
ranged from October 10, 2010, through April 15, 2011. 
265Approximately 60% of respondents indicated 
‘‘gas tank low’’ as the primary reason for the refueling trip in question. 
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.
265This 
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 
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2662009 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. 
267See The Economics Daily, The compensation- 
productivity 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. 
268Note: Here, as elsewhere in the analysis, 
discounting is applied on an annual basis from CY 2017. 269Peer review materials, peer reviewer 
backgrounds, comments, and NHTSA responses for this prior assessment are available at Docket NHTSA–2012–0001. 
270Estimate 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. 271See 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/home- 
charging-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/2018- 
Chevrolet-Bolt-EV-Product-Guide.pdf (last visited 
June 22, 2018). 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,
266this 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
267and to apply a discount rate to 
determine the net present value of time saved.
268A 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 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.
269Beyond 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
270results 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 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—CO
2in 
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)
271for 
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 
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272Type here refers to the following body styles: 
Pickups, vans/SUVs, and other cars. 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. 
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
272is 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 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 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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273Though not included in today’s analysis, 
corresponding schedules for heavy-duty pickups and vans were developed using the same 
methodology. 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 in- sample 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. (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.
273In 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: 
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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 one- year-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. 
(3) Comparison to other VMT 
projections (2012 FR, AEO average lifetime miles, totals?) 
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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274In 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. 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.
274There 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 inter- annual 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. 
(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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275Differentiated 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. 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 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
275in 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 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 on- road 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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276Continued high inflation combined with high 
unemployment and slow economic growth. 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 vehicle prices to maintenance and 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’
276of 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 ‘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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277The 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). 
278Note: 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. 279Light-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. 
280Note: 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. 
281Work 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. 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 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 light- duty 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 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.
279The 
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.
281It 
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 
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282The 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. 
283See e.g., Edmunds January 2017 Lease Market 
Report, Edmunds (Jan. 2017), https://dealers.edmunds.com/static/assets/articles/lease- 
report-jan-2017.pdf. 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.
282The 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 analysis. 
(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
283so 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: 
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284Based on data from FHWA and IHS/Polk. 
285Waker (1968); Park (1977); Feeney & 
Cardebring (1988); Hamilton & Macauley (1999); 
and Bento, Ruth, & Zhuo (2016) note that vehicles change in durability over time. 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. 
(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 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.
285In fact, when 
the CAFE model is exercised in a way that keeps average new vehicle prices at 
(approximately) MY 2016 levels, the on- road fleet grows from an initial level of 228 million in 2016 to 340 million in 2050, an increase of 49% over the 35- year 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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286There 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%. 
287Based on calculations using Polk’s National 
Vehicle Population Profile (NVPP). 2015.286In the 35 years between 2016 
and 2050, our simulation shows the on- 
road 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 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 on- road 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 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 inter- annual 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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288Complete 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. 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 current owner will only supply a 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 long- run. 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 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 well- documented 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.
288After 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%). 
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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 recently- 
published 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 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 provincial- 
level 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 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 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’ 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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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 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 efficiency indirectly, since heavier 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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289DeBorger 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. 290As 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. 291This 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. prices.289These 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 fuel- efficient 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 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, low- MPG vehicles they replaced. They attributed this apparent absence of a fuel economy rebound effect—which they described as an ‘‘attribute- 
adjusted’’ 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.
290While 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 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 higher- income 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 
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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. 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 newly- purchased 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 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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292NHTSA included a quantification of rebound- 
associated 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. 
293DOT 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. 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. 
(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 (NO
X), fine particulate 
matter (PM 2.5), and sulfur oxides (SO X). 
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. The social damage costs associated 
with changes in the emissions of criteria 
pollutants and CO
2was 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 CO
2emissions from a global 
perspective, accumulating social costs for CO
2emissions based on adverse 
outcomes attributable to climate change in any country. In this analysis, however, the costs of CO
2emissions 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 CO
2and 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 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, footprint- based 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.
292In 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 
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294It 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. 
295Drivers 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. 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 higher vehicle prices are directly attributed to CAFE standards.
294This 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 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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296In 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. 
297All 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 . 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 
The primary goals of CAFE and CO 2 
standards are reducing fuel 
consumption and CO 2emissions 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.
296As 
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 attribute- based 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 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 CO
2to 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/CO
2 
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 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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298Those databases are available at ftp://
ftp.nhtsa.dot.gov/CAFE/. 
299See 75 FR 25324, 25395–25396 (May 7, 2010) 
(for a discussion of planned statistical analyses). 
300Cost, Effectiveness and Deployment of Fuel 
Economy Technologies for Light-Duty Vehicles, 
National Academy of Sciences (2015). 301A complete discussion of the historical 
analysis of vehicle mass and safety is located in Chapter 10 of the PRIA accompanying this proposed rulemaking. 302Kahane, 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. 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 CO
2rulemaking. In 
addition, the National Academy of Sciences published a new report in this area.
300Throughout 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 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 light- duty 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– 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 vehicle- registration data;
302this 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 
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303Kahane, 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. 304Van 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-Workshop- on-Vehicle-Mass%E2%80%93Size%E2%80%93Safety. 305Greene, 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. 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. 
(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 four- door 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 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 partner- vehicle 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 ESC- equipped, 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
303and voiced concern about 
inaccurately estimated regression coefficients.
304High 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- 
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306Median 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. pound reduction in vehicle mass, while holding footprint constant, for each of 
the five vehicle classes: 
None of the estimated effects have 95- 
percent 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 (VMT- weighted) 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). 
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307Items 0035 (Lie), 0036 (Farmer) and 0037 
(Green) in Docket No. NHTSA–2010–0152. 
308Wenzel, 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://eta- publications.lbl.gov/sites/default/files/lbnl- 5695e.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. 
309Comment by International Council on Clean 
Transportation, Docket ID NHTSA–2010–0131– 0258. 310As 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/ViewPublication/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. 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 peer- reviewed the 2011 Kahane report.
307In 
preparing his 2012 report (along with the 2016 Puckett and Kindelberger report and Draft TAR), Kahane also took into account Wenzel’s
308assessment 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.
309These 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,
310comprising 
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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-mass- footprint-2003-10.pdf. 
311For 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%). 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%.
311If 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. (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. 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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For all light-duty vehicles, mass 
changes are estimated to lead to a decrease in fatalities over the 
cumulative lifetime of MY 1977–2029 vehicles in all alternatives evaluated. The effects of mass changes on fatalities 
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312Blincoe, 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/ViewPublication/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. 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 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.
312If 
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) 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 three- percent discount rate and by between $49 million and $1.3 billion at a seven- percent 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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For all light-duty vehicles, mass 
changes are estimated to lead to an average annual decrease in fatalities in 
all alternatives evaluated for CYs 2035– 2045. The effects of mass changes on fatalities range from a combined 
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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 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 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 seven- percent 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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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 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 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 
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estimated to be concentrated among MY 
2023 through MY 2029 vehicles (146 out of 165, or 91% of net fatalities mitigated). 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, 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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Lifetime social safety costs are 
estimated to decrease generally by model year, with decreases associated 
with passenger cars generally offset partially by increases associated with light trucks. At a three-percent discount 
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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 seven- percent discount rate range from $7 million to $136 million. At a three- percent 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 seven- percent 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 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 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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313Wenzel, 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. 314Samaha, 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-fleet- compatibility-research (accessed by clicking on the 
.zip file for DOT HS 812 051). 
315Samaha, 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/vehicle- aggressivity-and-fleet-compatibility-research (accessed by clicking on the .zip file for DOT HS 812 051 [appendices are Part 2]). 
316Singh, 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. 317Light-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. 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.’’
313In 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 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 light- weighted 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 light- weighted vehicle occupant simulations. Any modifications to restraint systems to improve occupant injury risks or societal injury risks in the light- weighted 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
314to study the 
impact and relationship of light- weighted 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 •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 
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318The 2012 Kahane study considered only 
fatalities, whereas, the fleet simulation study 
considered severe (AIS 3+) injuries and fatalities (DOT HS 811 665). 319The risk assessment for CUV in the regression 
model combined CUVs and minivans in all crash 
modes and included belted and unbelted 
occupants. 
320The fleet simulation considered only frontal 
crashes. 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 two- vehicle 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 mid- size CUV model was more sensitive to leg injuries. 
This study only looked at lightweight 
designs for a midsize sedan and a mid- size 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,
318which 
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
320and 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. 
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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321National 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/ViewPublication/811825. 
322Based on data acquired from Ward’s 
Automotive. 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 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 micro- scale 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. 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,
321NHTSA’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.
322This 
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 
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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 on- road 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 light- duty 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 x- axis) 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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323Note: 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. 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 
The coefficient estimates and model 
summary are in Table II–67. 
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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 on- road 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 on- road 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 mid- 2020s 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 mid- 2020s. 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 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 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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324Blincoe, 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. 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
324that 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. 
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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 mid- term 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 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 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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325Kahane, 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/ViewPublication/812069. 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. 
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326Note: 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. 
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.
326NHTSA’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. 
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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 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 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-scale- ais/. 
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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327Press 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://mediaroom.kbb.com/2017-02-01-New-Car- 
Transaction-Prices-Remain-High-Up-More-Than-3- 
Percent-Year-Over-Year-In-January-2017- According-To-Kelley-Blue-Book. 328Edmunds Used Vehicle Market Report, 
Edmunds (Feb. 2017), https://dealers.edmunds.com/static/assets/articles/2017_Feb_ Used_ Market_ Report.pdf. 
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 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 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. 
The total property damage cost 
reduction was then calculated as a 
function of the number of fatalities reduced or increased by CAFE as follows: 
Where: 
S = total property damage savings from 
retaining used vehicles longer 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 
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329Note: 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. n = the 8 injury severity categories 
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. 
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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 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. 
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330It 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. 
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. G. How the Model Analyzes Different 
Potential CAFE and CO
2Standards 
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 CO
2standards can influence 
the design of new vehicles offered for sale by requiring manufacturers to produce increasingly fuel efficient vehicles in order to meet program 
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331For the purpose of estimating their 
contribution to CAFE compliance, the grams CO 2/ 
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). 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. (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 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 off- cycle 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 off- 
cycle 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 off- cycle FCIVs for each manufacturer and fleet, denominated in grams CO
2per 
mile,331are provided in Table II–79. 
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33249 U.S.C. 32902(h) (2007). 
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 CO
2compliance. 
(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 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 non- compliance 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 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.
332As 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, 
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333Id. 
334Dedicated 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. 335When 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. 
336In 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 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). 
337Comment 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. 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 CO
2 
credit provisions, today’s analysis includes simulation of carried-forward and transferred CO
2credits 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,
333taking 
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 cost- effective 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 CO
2standards. 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 CO
2standards. In particular, the CAFE 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. 
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 CO
2 
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 CO
2standards. 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: 
(1) The manufacturer’s fleet achieves 
compliance335with 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;
336or (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 cost- effective (from the manufacturer’s perspective) than adding further technology. 
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.
337Moreover, 
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 
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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 CO
2/mile, and utilizing the set of 
specific credit provisions defined under EPA’s program. 
(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 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 
CO
2standards, 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 CO
2requirements, 
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 CO
2credits in 
some model years and use those credits 
in later model years, thereby providing 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 mid- cycle 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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3382015 NAS Report, at pg. 258–259. 339Comment 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. 
340CAFE Model Peer Review, p. 19. 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.
338The 
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 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 product- redesign 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.’’
340The 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 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 power- split 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 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 light- 
duty 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 newly- introduced 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 vehicle- technology 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 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 CO
2standards 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 CO
2credits 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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341Unlike 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 effectiveness. 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 CO
2regulations, 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 regarding CAFE standards for passenger cars and light trucks. Both analyses reflected integrated analysis of the light- 
duty 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 CO
2standards, 
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.
341In 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. 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 II–81. 
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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 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 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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342Inputs are specified to assign each vehicle in 
the analysis fleet to one of these technology classes, 
as discussed in Section II.B. 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 large- scale 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.
342Like 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. 
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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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343More detail about how the Argonne simulation 
database was integrated into the CAFE model can 
be found in PRIA Chapter 6. 
344This is true for all combinations other than 
those containing manual transmissions. Because the model does not convert automatic transmissions to 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. technology combination.343For 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 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 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 6- speed 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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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 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 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 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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345The only notable exception to this rule occurs 
whenever SHEVP2 technology is applied on a 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. 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).
345This implicitly forces vehicles to always move in the direction of 
increasing technological sophistication each time they are reevaluated by the model. 
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 MY 2016 analysis fleet contains 
information about each manufacturer’s: 
•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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346The 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. 347As 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. •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 production- 
weighted 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.
346In 
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 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.
347The 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 cost- effective technologies regardless of compliance status (essentially any 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 CO
2 
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 multi- 
year 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 CO
2 
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 CO
2standards 
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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348This 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. 349The 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. 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 cost- effective 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 cost- effective (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 CO
2standard compliance 
simulation is similar, but without the option of penalty payment. 
(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. The Chevrolet Equinox is represented 
in the model inputs as a single 
nameplate, with five model variants distinguished by the presence of all- wheel 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.
348This 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 and XTS,
349the 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 6- speed 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. 
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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 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 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 over- compliance 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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350See, e.g., 75 FR 62844, 75 FR 63105. 
(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 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 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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351Draft TAR, p. 13–10, available at https://
www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/ 
Draft-TAR-Final.pdf (last accessed June 15, 2018). 
352NHTSA, 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). 
353Ibid. Additional data available at https://
one.nhtsa.gov/cafe_ pic/CAFE_ PIC_ Mfr_ LIVE.html 
(last accessed June 27, 2018). 
354By 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, and future efforts will focus consideration on more plausible imperfect trading. 
355Having 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. 
356This 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. 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.
352In fact, a number of 
manufacturers that traditionally paid CAFE civil penalties even reached compliance during years with sufficiently high fuel prices.
353The 
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 CO
2standards, the effective 
cost calculation applied when simulating compliance with CO
2 
standards uses an estimate of the potential value of CO
2credits. Including 
a valuation of CO 2credits in the 
effective cost metric provides a potential basis for future explicit modeling of credit trading.
354Manufacturers, though, have thus far declined to 
disclose the actual terms of CAFE or CO
2credit 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 CO
2credits 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 CO
2credits 
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 CO
2credits.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 CO
2emissions. 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 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 2
1⁄2years 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 2
1⁄2years) 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. 
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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 CO
2emissions 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. 
(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 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-by- year 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 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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357CAFE Public Information Center, http://
www.nhtsa.gov/CAFE_ PIC/CAFE_ PIC_ Home.htm 
(last visited June 22, 2018). 
358GHG credits for EPA’s program are 
denominated in metric tons of CO 2rather than 
gram/mile compliance credits and require no adjustment when traded between manufacturers or 
fleets. 
359The 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. 
(d) Representation of CAFE and CO
2 
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 CO
2 
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 carry- back 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 carry- forward—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 over- compliance 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
357to 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.
358The 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.
359While 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. 
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360In 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. 
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 CO
2credit 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 CO
2program. While the life of 
a CO 2credit, denominated in metric 
tons CO 2, 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.
360The 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: 
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While the CAFE model does not 
simulate the ability to trade credits 
between manufacturers, it does simulate the strategic accumulation and application of compliance credits, as well as the ability to transfer credits between fleets to improve the compliance position of a less efficient fleet by leveraging credits earned by a 
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361In 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. 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-to- be-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 CO
2credits, 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 off- cycle 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 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) CO
2credits 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 CO
2Levels 
The model fully represents the 
required CAFE (and now, CO 2) levels for 
every manufacturer and every fleet. The standard for each manufacturer is based on the harmonic average of footprint 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 CO
2 
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 learning- by-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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The other costs that manufacturers 
incur as a result of CAFE standards are 
civil penalties resulting from non- compliance 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 (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 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 CO
2 
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. 
(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 on- road 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. (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 real- world 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 Federal fuel excise tax is levied on every 
gallon of gasoline and diesel sold in the U.S., with diesel facing a higher per- gallon 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 on- 
road 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 CO
2 
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 CO
2alternative 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. 
(f) Costs and Benefits 
As the CAFE model simulates 
manufacturer compliance with 
regulatory alternatives, it estimates and tracks a number of consequences that generate social costs. The most obvious 
cost associated with the program is the cost of additional fuel economy improving/CO
2emissions 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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362Such 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. 
363EPCA/EISA requires NHTSA to separate 
passenger cars into domestic and import passenger car fleets whereas EPA combines all passenger cars into one fleet. 
364As 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 CO 2Standards 
for MYs 2021–2026 
A. Form of the Standards 
NHTSA and EPA are proposing that 
the form of the CAFE and CO 2standards 
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 CO
2 
standards for light-duty vehicles, EPA has concluded that it is appropriate to set CO
2standards 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 CO 2, standards have 
taken the form of fuel economy and CO 2 
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 CO
2or 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 CO
2average standard for 
each year that is unique to each of its fleets,
363depending 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 CO
2grams/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 CO
2 
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: 
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365EPA 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. 
Where: 
TARGET FEis 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), 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. 
Here, MIN and MAX are functions 
that take the minimum and maximum 
values, respectively, of the set of 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: 
Where: 
TARGET FEis 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. 
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 CO
2targets are expressed as functions that are similar, with coefficients a–h corresponding to those 
listed above.
365For passenger cars, EPA 
is proposing to define CO 2targets as 
follows: 
TARGET CO2= MIN[b,MAX[a,c × 
FOOTPRINT + d]] 
Where: 
TARGET CO2is the CO 2target (in grams per 
mile, or g/mi) applicable to a specific 
vehicle model configuration, 
a is a minimum CO 2target (in g/mi), 
b is a maximum CO 2target (in g/mi), 
c is the slope (in g/mi, per square foot) of a 
line relating CO 2emissions to footprint, 
and 
d is an intercept (in g/mi) of the same line. 
For light trucks, CO 2targets are 
defined as follows: 
TARGET CO2= MIN[MIN[b, MAX[a,c × 
FOOTPRINT + d]], MIN[f,MAX[e, g 
× FOOTPRINT + H]] 
Where: 
TARGET CO2is the CO 2target (in g/mi) 
applicable to a specific vehicle model 
configuration, a, b, c, and d are as for passenger cars, but 
taking values specific to light trucks, 
e is a second minimum CO 2target (in g/mi), 
f is a second maximum CO 2target (in g/mi), 
g is the slope (in g/mi per square foot) of a 
second line relating CO 2emissions 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 CO
2targets, because 
compliance with either CAFE or CO 2 
standards is determined based on corporate average fuel economy or fleet 
average CO
2emission 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: 
CAFE required is the CAFE level the fleet is 
required to achieve, 
i refers to specific vehicle model/ 
configurations in the fleet, PRODUCTION iis the number of model 
configuration i produced for sale in the 
U.S., and 
TARGET FE,ithe fuel economy target (as 
defined above) for model configuration i. Similarly, the required average CO 2 
level applicable to a given fleet in a 
given model year is determined by calculating the production-weighted 
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average (not harmonic) of CO 2targets applicable to specific vehicle model 
configurations in the fleet, as follows: 
Where: 
CO2 required is the average CO 2level the fleet 
is required to achieve, 
i refers to specific vehicle model/ 
configurations in the fleet, 
PRODUCTION iis the number of model 
configuration i produced for sale in the 
U.S., and 
TARGET CO2,i is the CO 2target (as defined 
above) for model configuration i. Today’s action would set standards 
that only apply to fuel economy and 
CO 2. 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 one of the other regulatory alternatives for the final rule. 
B. Passenger Car Standards 
For passenger cars, NHTSA and EPA 
are proposing CAFE and CO
2standards, 
respectively, for MYs 2021–2026 that 
are defined by the following coefficients: 
Section II.C above discusses in detail 
how the coefficients in Table III–1 were developed for this proposal. The coefficients result in the footprint- dependent targets shown graphically below for MYs 2021–2026. The MYs 2017–2020 standards are also shown for 
comparison. 
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366Prior to MY 2021, CO 2targets 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 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; CO
2targets (and resultant fleet average 
requirements) for model years 2021 and beyond do not reflect these adjustments. 367Other 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. 
368Other 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. 
36975 FR 25330, May 7, 2010. 
While we do not know yet with 
certainty what CAFE and CO 2levels 
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 CO
2 
emissions levels for individual manufacturers during MYs 2021–2026. Prior to MY 2021, average required CO
2 
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; CO
2targets and resultant 
fleet average requirements for model 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 CO
2emissions 
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 CO
2 
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.
367As related to methane 
(CH 4) and nitrous oxide (N 2O) 
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 CO
2standard stringency by the average 
anticipated amount of credits when compared to the CAFE stringency requirements.
369For model years 2021– 
2025, the A/C leakage offset, or 
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37077 FR 62805, Oct. 15, 2012. 
37177 FR 62649, Oct. 15, 2012. 
37275 FR 25421–24, May 7, 2010. 
37377 FR 62798, Oct. 15, 2012. 374In 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 CH
4and N 2O standards than 
the rest of the fleet. [See 75 FR 25422, May 7, 2010]. 
37549 U.S.C. 32904(b) (2007). 376Transferred 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). 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 N
2O at 0.010 g/mile 
and CH 4at 0.030 g/mile for MY 2012 
and later vehicles.372However, EPA 
also provided an optional CO 2- 
equivalent approach to address industry concerns about technological feasibility 
and leadtime for the CH
4and N 2O 
standards for MY 2012–2016 vehicles. The CO
2equivalent standard option 
allowed manufacturers to fold all 2- cycle weighted N
2O and CH 4emissions, 
on a CO 2-equivalent basis, along with 
CO 2, into their CO 2emissions fleet 
average compliance level.373EPA 
estimated that on a CO 2equivalent basis, folding in all N 2O and CH 4 
emissions could add up to 3–4 g/mile to 
a manufacturer’s overall CO 2emissions 
level because the equivalent standard must be used for the entire fleet, not just for ‘‘problem vehicles.’’
374To address 
this added difficulty, EPA amended the MY 2012–2016 standards to allow manufacturers to use CO
2credits, on a 
CO 2-equivalent basis, to meet the light- 
duty N 2O and CH 4standards 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 CO
2 
compliance, it would likely re-insert the current A/C leakage offset and increase the stringency levels for CO
2 
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. 
C. Minimum Domestic Passenger Car 
Standards 
EPCA has long required 
manufacturers to meet the passenger car 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.
375In 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.
376According 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 
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37749 U.S.C. 32902(b)(4). 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.
377NHTSA 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 ‘‘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. 
D. Light Truck Standards 
For light trucks, NHTSA and EPA are 
proposing CAFE and CO 2standards, respectively, for MYs 2021–2026 that are defined by the following coefficients: 
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378Prior to MY 2021, average achieved CO 2levels 
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. 
Because EPA is today proposing to exclude air 
conditioning refrigerants and leakage, and nitrous oxide and methane GHGs from average performance calculations after MY 2020, CO 2targets and 
resultant fleet average requirements for MYs 2021 
and beyond do not reflect these adjustments. Section II.C above discusses in detail 
how the coefficients in Table III–4 were 
developed for this proposal. The coefficients result in the footprint- dependent targets shown graphically below for MYs 2021–2026. The MYs 2017–2020 standards are also shown for 
comparison. 
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379As 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. 380For the CAFE program, carbon-based tailpipe 
emissions (including CO 2, CH 4and CO) are 
measured, and fuel economy is calculated using a carbon balance equation. EPA uses carbon-based emissions (CO
2, CH 4and CO, the same as for CAFE) to calculate tailpipe CO 2for its standards. In 
addition, under the no action alternative EPA adds 
CO 2equivalent (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 CO
2, CH 4 
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 CO
2programs. 
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. While we do not know yet with 
certainty what CAFE and CO 2levels 
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 CO
2 
emissions levels for individual manufacturers during MYs 2021–2026. Prior to MY 2021, average required CO
2 
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, CO
2targets 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. 
IV. Alternative CAFE and GHG 
Standards Considered for MYs 2021/ 22–2026 
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 ‘‘no- action’’ 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. 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 percent- increases 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 long- standing 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 CO
2independently 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 CO
2standard.380Like 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 CO
2standard. 
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 
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381Carbon 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. 
382Beginning in MY 2021, air conditioning 
refrigerant leakage, nitrous oxide, and methane emissions may be regulated independently by EPA. 
The GWP equivalent of each of the emissions would no longer be included with the tailpipe CO
2for 
compliance with tailpipe CO 2standards. A 
lengthier discussion of this issue can be found in Section III.B. 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 CO
2 
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. 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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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 draws attention the discussion of 
‘‘enhanced flexibilities’’ in Section X.C. 
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. 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. 
2. Alternative 1 (Proposed) 
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 CO
2for compliance with tailpipe CO 2standards. Section III, 
above, defines this alternative in greater 
detail. 
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 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 CO
2for 
compliance with tailpipe CO 2standards. 
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4. Alternative 3 
Alternative 3 phases out A/C and off- 
cycle 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 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, 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 CO
2 
for compliance with tailpipe CO 2 
standards. 
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5. Alternative 4 
Alternative 4 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 trucks. Beginning in MY 2021, air conditioning refrigerant leakage, nitrous oxide, and methane emissions are no longer included with the tailpipe CO
2for 
compliance with tailpipe CO 2standards. 
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6. Alternative 5 
Alternative 5 increases the stringency 
of targets annually during MYs 2022– 
2026 (on a gallon per mile basis, starting 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 included with the tailpipe CO
2for 
compliance with tailpipe CO 2standards, 
and MY 2021 CO 2targets are adjusted 
accordingly. 
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7. Alternative 6 
Alternative 6 increases the stringency 
of targets annually during MYs 2021– 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 refrigerant leakage, nitrous oxide, and methane emissions are no longer included with the tailpipe CO
2for 
compliance with tailpipe CO 2standards. 
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8. Alternative 7 
Alternative 7 phases out A/C and off- 
cycle 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 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, 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 CO
2 
for compliance with tailpipe CO 2 
standards. 
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9. Alternative 8 
Alternative 8 increases the stringency 
of targets annually during MYs 2022– 
2026 (on a gallon per mile basis, starting 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 included with the tailpipe CO
2for 
compliance with tailpipe CO 2standards, 
and MY 2021 CO 2targets are adjusted 
accordingly. 
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38349 U.S.C. 32902(b)(1) (2007). 
38449 U.S.C. 32902(a) (2007). 385Id. 
38649 U.S.C. 32902(f) (2007). 
387Both 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. 388Center 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.’’) 
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 
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
383for each model year,384and 
each standard must be the maximum feasible that NHTSA believes the manufacturers can achieve in that model year.
385In 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.
386In 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.
387The 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 
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38949 U.S.C. 32902(a) (2007). 
39049 U.S.C. 32902(g)(2) (2007). 
39149 U.S.C. 32902(b)(1) (2007). 392In the CAFE program, ‘‘domestically- 
manufactured’’ 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. 
39349 U.S.C. § 32902(b)(4) (2007). 39477 FR 62624, 63028 (Oct. 15, 2012). 
395Automobile 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]. several other requirements as explained 
below. 
(a) Lead Time 
EPCA requires that NHTSA prescribe 
new CAFE standards at least 18 months 
before the beginning of each model year.
389For 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.
390EPCA 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.
391NHTSA 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, 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 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 
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39649 U.S.C. 32902(b)(3)(A). 
39749 U.S.C. 32902(b)(3)(B). 
39877 FR 62623, 62630 (Oct. 15, 2012). 
399See 153 Cong. Rec. 2665 (Dec. 28, 2007). 400For example, NHTSA has not considered high- 
speed 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 has considered a range of hybrid vehicle technologies that do so. 
40167 FR 77015, 77021 (Dec. 16, 2002). 
402See, 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). 
403For example, if standards effectively require 
manufacturers to widely apply technologies that attributes related to fuel economy and 
express[ed] . . . in the form of a mathematical function.’’
396NHTSA 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 larger- footprint 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. 
(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.’’
397In 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.’’
398That 
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).
399However, 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. 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. 
(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. 
(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.
400Technological feasibility and economic practicability 
are often conflated, as will be covered further in the following section. To be clear, whether a fuel-economy- improving 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.’’
401In 
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,
402which 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 attribute- based 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,
403the specific fleet mixes of 
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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 practicable. 
40442 FR 63184, 63188 (Dec. 15, 1977). See also 
42 FR 33534, 33537 (June 30, 1977). 405PRIA, Chapter 5. 
406PRIA, Chapter 6. 
40777 FR 62624, 62669 (Oct. 15, 2012). 
408Id. 
409In fact, EPA includes tailpipe CH 4, CO, and 
CO 2in the measurement of tailpipe CO 2for GHG 
compliance using a carbon balance equation so that 
the measurement of tailpipe CO 2exactly aligns with 
the measurement of fuel economy for the CAFE compliance. 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-attribute- based (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 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
404until 
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 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.
405NHTSA 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.’’
407NHTSA 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 CO
2emissions 
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 CO
2emissions, and vice versa), it would 
be absurd not to link their standards.409 
The global warming potential of N 2O, 
CH 4, 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 
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410We 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. 
411This topic is discussed further in Section VI 
below. 412As 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). 
413Section 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. 
414As 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. 415H.R. Rep. No. 103–180, at 583–584, tbl. 2A. 
416See, e.g., 68 FR 16896, 71 FR 17643. 
417See 77 FR 62669. 
418See, 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.’’ 
41942 FR 63184, 63188 (Dec. 15, 1977). 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 CO
2emissions. 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, but is not labeled as one (i.e., a State 
tailpipe CO
2standard or prohibition on 
CO 2emissions). 
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
412for 
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, 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.
416During 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.
417In 
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 
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420See 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.’’ 
421See 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 . 
422For 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 domestically- produced 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/. 
423CAS, 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). 42453 FR 33080, 33096 (Aug. 29, 1988). 
42553 FR 39275, 39302 (Oct. 6, 1988). 
426While 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. 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. 
(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 oil- exporting countries and left the U.S. economically vulnerable.
420As recently 
as 2009, nearly half the U.S. trade deficit was driven by petroleum,
421yet 
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 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 CO
2, the main GHG emitted as a result 
of refining, distribution, and use of transportation fuels. Reducing fuel consumption directly reduces CO
2 
emissions because the primary source of 
transportation-related CO 2emissions 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 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.
424It 
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.
425Since then, 
NHTSA has considered the effects of reducing tailpipe emissions of CO
2in 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.
426Higher 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 
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42749 U.S.C. 32902(h). 42849 U.S.C. 32902(b)(2)(A). 
42949 U.S.C. 32902(b)(2)(C). 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.
427As 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 voluntarily- employed 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.
428And second, between MYs 
2011 and 2020, the standards must ‘‘increase ratably’’ in each model year.
429Neither 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 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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430Ibid., 1181. 
4315 U.S.C. 553. 432NEPA is codified at 42 U.S.C. 4321–47. 
43340 CFR 1502.1. 434‘‘Conserve,’’ Merriam-Webster, available at 
https://www.merriam-webster.com/dictionary/ 
conserve (last visited June 25, 2018). 
435Short-Term Energy Outlook, U.S. Energy 
Information Administration (June 2018), available at https://www.eia.gov/outlooks/steo/pdf/steo _
full.pdf. 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 two- step 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.’’
430The APA also requires 
that agencies provide notice and comment to the public when proposing regulations,
431as 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 considerations be integrated into that 
process.
432To 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 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.’’
434This 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.
435While 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 
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436Today 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. 
437Energy Explained, U.S. Energy Information 
Administration, https://www.eia.gov/energy
explained/index.cfm (last visited June 25, 2018). 
438Review 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 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 . 
439Newell, 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). 
440Ip, 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-impact- of-oil-1519209000 (last visited June 25, 2018). 
441Olson, 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-soaring- u-s-oil-production-1520257595. 442LeBlanc, 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/. 
443Alessi, 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-crude- production-expected-to-surpass-saudi-arabia-in- 2018-1516352405. 
444To 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. 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 cost- 
effective drilling and production technologies.
437Domestic 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.
438And second, 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 long- run 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.’’
439Some 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).
440Some commenters disagree 
and suggest that the best deposits are already known and tapped.
441Other 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.
442Moreover, 
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 per- barrel oil prices rise, U.S. production is now much better able to (and does) ramp up in response, pulling those prices back down. Corresponding per- gallon gas prices may not fall overnight,
444but 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 
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445Annual 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 CO
2 
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/. 
446See 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-the- worlds-largest-oil-producer-by-2023-says-iea- 1520236810 (reporting on remarks at the 2018 
CERAWeek energy conference by IEA Executive Director Fatih Birol). 
447Lynes, 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. 448Depending on the energy source, it may also 
be a byproduct of consumption of electricity by vehicles. 
449Climate 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). case’’ projection.445The 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.
446From 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. Fuel- efficient 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 in the 1970s when making fuel- 
efficiency 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.
447If 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 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 carbon- based fuels in vehicle engines.
448Many 
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 CO
2emissions, 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 CO
2emissions 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-less- rapidly-increasing CAFE standards. The most stringent of the regulatory 
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450The 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.’’) 
45149 CFR 575.401; 40 CFR 600.302–12. 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 fuel- efficient 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 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.
451Even 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 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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452See, 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.’’). 
453This 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. 454See 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. 455As 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. generally tend not to be interested in 
better fuel economy above other attributes, particularly when gasoline prices are low.
452Manufacturers 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.
453NHTSA 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 cost- effective technology is applied with or without CAFE (or CO
2) 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 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,
454because 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 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.
455It 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. 
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Manufacturers have commented to the 
agencies that ‘‘Although automakers are offering more of these models every 
year, with improved technology and options, sales of these vehicles are not growing,’’ noting that even for hybrid 
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456Comment by Global Automakers, Docket ID 
NHTSA–2016–0068–0062, citing IHS Global New 
Vehicle Registration Data for 2013, 2015, and January–June 2016. 
457Id. 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-gas- global-warming.html. 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].’’
456The 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 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 cross- subsidization 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 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 low- income 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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458Using 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/new- vehicle-prices-climb-to-all-time-high-in- december.html. 
459Using average rate of 5.46% (discussed above 
in Section II.E). 
460Using average rate of 4.25% (discussed above 
in Section II.E). 461Using average rate of 1.83% (discussed above 
in Section II.E). 
462Based 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. 
463See, 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 mid- 
2020s]. 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.’’) 
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.
462Some 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. 
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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 low- income 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 consumers, meaning that they must either defer new vehicle purchases or 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 cross- subsidize 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 passed forward to consumers in the form of price increases for that vehicle 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 possible that manufacturers may shift 
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464For the reader’s reference, Alternatives 3 and 
7 phase out A/C and off-cycle procedures, while the 
other alternatives leave those procedures 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. 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-in- time 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: 
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465See Fox v. FCC, 556 U.S. at 514–515; see also 
NAHB v. EPA, 682 F.3d 1032 (D.C. Cir. 2012). 
466If 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). 
46777 FR 62624, 63039 (Oct. 15, 2012). •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.
466If 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 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 then- paramount 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 more- stringent standards. 
Thus, while more stringent standards 
may be possible, insofar as production- ready 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 economy- improving 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 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 societal- benefits 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 No- Action 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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468As 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. 
469CBD 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). 47042 U.S.C. 7521(a). 
47142 U.S.C. 7521(a). 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.
468If 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.
469NHTSA 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 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 CO
2Standards 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)
470and 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).
471Section 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 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 motor- vehicle 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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472Since 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.’’) 
47370 FR 69664, 69676 (Nov. 17, 2005). 
47474 FR 66496 (Dec. 15, 2009). 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,
472and 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 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.
474EPA 
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 (CO
2), 
methane (CH 4), nitrous oxide (N 2O), 
hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF
6). The EPA further 
found under CAA section 202(a) that emissions of the single air pollutant 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 CO
2,CH 4, N2O, and HFC. 
EPA has established standards and other provisions that control emissions of CO
2, HFCs, N 2O, and CH 4. EPA has not 
set any standards for PFCs or SF 6as 
they are not emitted by motor vehicles. 
2. EPA’s Tentative Conclusion That the 
Proposed CO 2Standards 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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475See, e.g., 77 FR 62624, 62673 (Oct. 15, 2012). 
476Section 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). technology cost-effectiveness, and costs 
and other impacts on consumers. As discussed in prior rulemakings setting GHG standards,
475EPA 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),
476does 
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 CO
2Emissions 
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 vehicle fleet of a wide range of 
technologies that both reduce CO
2and 
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 12- Volt 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 CO
2standards 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 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 previously- issued 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 CO
2emissions. For example, as 
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477This preamble and the PRIA document 
estimates annual GHG emissions from light-duty 
vehicles under the baseline CO 2standards, 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 CO
2, 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 CO
2standards 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 CO
2standards 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 CO
2standards. 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 per- vehicle 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 previously- issued 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. 
(d) Consideration of GHG Emissions 
As discussed above, the purpose of 
CO
2standards 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 CO
2standards 
for MYs 2021–2026 would increase vehicle CO
2emissions 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 CO
2reduction 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 CO
2emissions 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 CO
2emissions by 
422 MMT, Alternative 4 by 649 MMT of CO
2, and Alternative 2 by 825 MMT of CO 2.477As 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, lead- time, 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 CO
2 
standards are based on vehicle footprint, 
and in general smaller footprint vehicles have individual CO
2targets 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 emissions averaging, banking, and 
trading program; air-conditioning program credits; flexibility in how to comply with the N
2O and methane 
standard; off-cycle credit program, etc.) was to maintain consumer choice. The EPA standards are designed to require reductions of CO
2emissions 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 CO
2emissions 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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478Light-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 . 
479See 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. 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.
478As 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.
479Through MY 2016, 
these trades have included 12 firms, with five firms trading CO
2credits 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 CO
2have 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, long- term 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. 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 proposed standards, the previously- 
issued 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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48077 FR 62624, 62665 (Oct. 15, 2012). 48167 FR 77025 (December 16, 2002). 
482See Appellants Opening Brief filed on behalf 
Michael P. Kenny in Central Valley Chrysler- 
Plymouth, Inc. et al. v. Michael P. Kenny, No. 02– 
16395, at p. 33 (9th Cir. 2002). 483The 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). 
484Green 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). 
485Public Law 110–140 (2007). 
48673 FR 12156 (Mar. 6, 2008). 
48773 FR 24352 (May 2, 2008). 
488For 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). 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
480increase 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 previously- issued 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 CO
2reductions 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. 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.
481The 
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 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 CO
2tailpipe emissions from 
automobiles, but it did address the relationship between EPA and NHTSA rulemaking obligations.
483Later 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.
484Still 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.
486In 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 CO
2 
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.
488Several days later, on 
January 26, 2009, President Obama issued a memorandum requesting, among other things (including 
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48974 FR 14196 (Mar. 6, 2009). 
49075 FR 25324, 25328 (May 7, 2010). 
49174 FR 32744 (Jul. 8, 2009). 
49274 FR at 32783 (Jul. 8, 2009). 
49375 FR 25324, 25546 (May 7, 2010); see also 74 
FR 49454, 49635 (Sep. 28, 2009). 49476 FR 74854, 74863 (Dec. 1, 2011). 
495While 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 CO
2emissions 
requirements—not to the ZEV program. 
496See 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). 
49749 U.S.C. 32919. 498Shaw v. Delta Airlines, Inc., 463 U.S. 85, 97 
(1983) (ERISA case). 
499504 U.S. 374, 383–84 (1992). 
500Id. at 383. 
501California 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)). 
502S. 1883, 94th Cong., 1st Sess., Section 509. 
503H.R. 7014, 94th Cong., 1st Sess., Section 507 
as introduced, Section 509 as reported. 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.
489Then, 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.
491In 
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.’’
493Later, 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 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.’’
494NHTSA, 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 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.
498The 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.,
499concluding 
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.’’
500Courts 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
502sought to preempt State 
laws only if they were ‘‘inconsistent’’ with Federal fuel economy standards, labeling, or advertising, while the House bill
503sought 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 
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504See 71 FR 17566, 17657 (April 6, 2006). 
50571 FR at 17659, et seq. 50649 U.S.C. 32904(c). 507With the minor exception of regulating the 
carbon intensity of fuels—an activity not preempted 
by EPCA. 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. 
We understand this statutory language 
to prevent EISA from limiting pre- existing 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 CO
2Emissions 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 CO
2emissions, are 
mathematically linked to fuel economy; therefore, regulations limiting tailpipe CO
2emissions are directly related to 
fuel economy.505To summarize, most 
light vehicles are powered by gasoline internal combustion engines. The combustion of gasoline produces CO
2in 
amounts that can be readily calculated. CO
2emissions are always and directly linked to fuel consumption because CO 2 
is a necessary and inevitable byproduct 
of burning gasoline. The more fuel a vehicle burns or consumes, the more CO
2it emits. To the extent that light 
vehicles are not powered by internal 
combustion engines, their use generally involves some release of CO
2or other 
GHG emissions, even if indirectly, associated with the vehicle performing its work of traveling down the road. CNG and LPG vehicles release CO
2 
during combustion. Even for battery- electric 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 CO
2released 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.
506More 
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 CO
2, CO, and 
hydrocarbons from covered vehicles, but primarily on the emission rates of CO
2. 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 CO
2is 
approximately 100 times greater than the combined role of the other two relevant carbon exhaust gases. Given that the amount of CO
2, 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 CO
2emissions and fuel 
economy standards, while in the same piece of legislation expressly 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 CO
2emissions is such a law or 
regulation because it has the direct effect of regulating fuel consumption. 
Given that substantially reducing CO
2 
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 CO
2 
tailpipe emissions. Since there is but one pool of technologies
507for reducing 
tailpipe CO 2emissions and increasing 
fuel economy available now and for the foreseeable future, regulation of CO
2 
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 CO
2 
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 
CO
2, the state would nonetheless 
regulate tailpipe CO 2emissions, since 
CO 2emissions 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 CO
2. 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 CO
2emission limits, some GHG 
emissions from vehicles have no 
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508NHTSA 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, CO
2emissions as well as the 
ZEV mandate. Since EPCA prohibits state regulation of CO
2emissions, 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 California which are in non-attainment for ozone and which contain dense populations of allergy sufferers. 
509U.S. Const. art VI, cl. 2. 
510See Gibbons v. Ogden, 22 U.S. 1 (1824). 
511Ewert v. Bluejacket, 259 U.S. 129, 138 (1922), 
quoting Waskey v. Hammer, 223 U.S. 85, 94 (1912). 
512Waskey, 223 U.S. at 92. 
513Cabazon Band of Mission Indians v. City of 
Indio, Cal., 694 F.2d 634, 637 (9th Cir. 1982). 
514Maryland 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.’’). 
515Antilles Cement Corp. v. Fortuno, 670 F.3d 
310, 323 (1st Cir. 2012) (quoting Gibbons v. Ogden, 22 U.S. (9 Wheat.) 1 (1824)). 51642 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). 
51749 U.S.C. 32919(a) (emphasis added). 
518E.g., 529 F.Supp.2d at 1176. 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 CO
2emissions, 
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 CO
2 
emissions. If, however, a state law sought to limit the combined GHG emissions from a motor vehicle, in a manner that would include tailpipe CO
2 
emissions, EPCA would preempt that portion of the law limiting tailpipe CO
2 
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 CO
2emissions or all tailpipe 
emissions, as such regulations have the effect of regulating CO
2emissions and 
relate to fuel economy standards.508 NHTSA invites comments on the 
extent to which a state standard can 
have some incidental impact on fuel economy or CO
2emissions 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.
509Courts have long 
recognized that the Supremacy Clause of the Constitution gives Congress the power to specifically preempt State law.
510Broadly speaking, the United 
States Supreme Court has long held that ‘‘an act done in violation of a statutory prohibition is void,’’
511and has 
specifically noted that such acts are not merely ‘‘voidable at the instance of the government’’ but void from the outset.
512The 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.’’
514And 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 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.
516EPA 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.’’
517The 
courts in Green Mountain Chrysler and 
Central Valley Chrysler-Jeep recognized the relationship between CO
2emissions 
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.’’
518The 
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 
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519Green Mountain Chrysler, 508 F.Supp.2d at 
398. 
520Central 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’’). 
521See 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). 
522See Proof Brief for the United States as 
Amicus Curiae, 07–4342–cv (2d Cir. filed Apr. 16, 2008). 523Central Valley Chrysler-Jeep, 529 F.Supp.2d at 
1168. 
524Central 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.’’ 
525Id. 
526Id. 
527Id. CAFE standards.519In 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 Chrysler- Jeep 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.’’
520NHTSA 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.
521The 
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
522due to the automotive industry’s withdrawal of 
appeals. As explained above, the withdrawal of those appeals was a pre- condition 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 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.’’
525The 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 
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528Id. at 1170. 
529Public Law 94–163 sec. 502(d), 89 Stat. 904– 
05. 
530See H.R. No. 94–340, at 87. 531Id. § 502(d)(3)(D). 
532The recodification was ‘‘[t]o revise, codify, 
and enact without substantive change’’ laws related 
to transportation. Public Law 103–272 (emphasis 
added). 
53342 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). 
53449 U.S.C. 32919(a). 535This report was prepared in compliance with 
Section 10 of the Energy Supply and Environmental Coordination Act of 1974, Public Law 93–319. 
536See id. at 6–8 and 91–93. 
537See 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. 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.
528In 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 CO
2 
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.’’
529This 
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 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 CO
2regulations 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.
533State 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 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.
535That 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.
536These 
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 CO
2 
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 CO
2emissions 
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 
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538529 F.Supp.2d at 1179. 539See, 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). 
540Docket No. EPA–HQ–OAR–2012–0562, Pp. 
15–16. 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 CO
2emissions is to 
improve fuel economy, it would be impossible for a state tailpipe CO
2 
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 CO
2emissions. 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 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.
538The 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 smog- forming nor CO
2tailpipe 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 feasible means to eliminate tailpipe CO
2 
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,
539ZEV 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 smog- forming 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 (tank- to-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 
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541Cal. Code Regs. tit.13, sec. 1962.2(b). 
542The 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-and- western-states/what-is-zev (projecting ‘‘about 8 
percent of sales to be ZEVs’’ in 2025). 
543These states are Connecticut, Maine, 
Maryland, Massachusetts, New Jersey, New York, Oregon, Rhode Island, and Vermont. 
544See 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). 545California 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. 
54613 Cal. Code of Regulations 1962.2. 547See, 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). 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.
541To 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.
542With 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 CO
2 
emissions within each state that has 
adopted the California ZEV mandate. 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.
545While 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 CO
2emissions (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 cost- effective technologies that better reflect consumer demand.
546This appears to 
conflict directly with Congress’ intent that CAFE standards be performance- based rather than design mandates. 
Moreover, by forcing manufacturers to design, produce, and deliver for sale vehicles that produce no tailpipe CO
2 
emissions, the ZEV mandate forces further expensive investments in fuel- saving technology than NHTSA has determined appropriate to require in setting fuel economy standards.
547We 
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. 
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548Clean Air Act (CAA) section 209(a), 42 U.S.C. 
7543(a). 
549CAA 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 California Air Resources Board). 550As 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.’’ 
551This 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. 
552EPA 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. 553Under 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. 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 
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.
549A 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)].’’ 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 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. 
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554See, e.g., 49 FR 18887 (May 3, 1984). 
555Criteria 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. 556The 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 (NO
X) standards with combined NMOG 
plus NO Xstandards, 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 super- ultra-low-emission vehicles after partial-zero- emission 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. 
55778 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, CO
2equivalent 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 CO
2-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. 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.
554Under 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.
555Under 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 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 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,
556for new passenger cars, 
light-duty trucks, medium-duty passenger vehicles, and certain heavy- duty 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.
557According 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.
55878 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 
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559Available in the docket for the January 2013 
waiver decision, Docket No. EPA–HQ–OAR–2012– 
0562. 560CARB ACC waiver request at EPA–HQ–OAR– 
2012–0562–0004. 
561Under 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. 
562On 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. 
563On 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. 
564In 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. 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 pollutant fleet standard was responsible for those emission reductions.
560CARB 
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).
562For 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.
563A complete description of 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.
564The 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 
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565On 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. 
566Under 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. 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 final action to change a prior action, 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 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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567EPA 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. 
568MEMA I, 627 F.2d at 1122. 
569Id. 
570Id. 571Id. 
57274 FR 32748. 
573MEMA I, 627 F.2d at 1122. 
574Id. at 1126. 
575Id. 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 standards was arbitrary and capricious. 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.
570The court noted that this 
standard of proof ‘‘also accords with the congressional intent to provide 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.
572Moreover, 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.’’
574Therefore, 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 
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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 concentrations of GHGs lie within the 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 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). 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 California- specific causal factors that EPA had considered when reviewing previous waiver applications under section 
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