From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop.gov> 

To: William Charmley/AA/USEPA/US@EPA, "'Steve.Wood@dot.gov'" <Steve.Wood@dot.gov>, 

"'Neil.Eisner@dot.gov'" <Neil.Eisner@dot.gov>, "''james.tamm@dot.gov' (james.tamm@dot.gov)'" 

<james.tamm@dot.gov>, "'rebecca.yoon@dot.gov'" <rebecca.yoon@dot.gov>, Robin 

Moran/AA/USEPA/US@EPA, Ed Nam/AA/USEPA/US@EPA 

Date: 11/13/2011 06:07 PM 

Subject: Summary of additional interagency working comments received on draft rule under EO 12866 

review 

 

 

Please find attached additional interagency working comments received on draft NPRM under EO 12866 

and EO 13563 review. They are for your consideration and are subject to further policy review. 

 

Thanks. 

Chandana 

Summary of interagency working comments received on draft NPRM under EO 12866 review.pdf 
Subject: Summary of additional interagency working comments received on draft rule under EO 12866 review 

Sent: 11/13/2011 11:06 PM EST

From: "Achanta, Chandana L." [Chandana_L._Achanta@omb.eop.gov]

To: William Charmley; "'Steve.Wood@dot.gov'" <Steve.Wood@dot.gov>; "'Neil.Eisner@dot.gov'" 

<Neil.Eisner@dot.gov>; "''james.tamm@dot.gov' (james.tamm@dot.gov)'" <james.tamm@dot.gov>; 

"'rebecca.yoon@dot.gov'" <rebecca.yoon@dot.gov>; Robin Moran; Ed Nam

Hello All, 

 

Please find attached additional interagency working comments received on draft NPRM under EO 12866 

and EO 13563 review. They are for your consideration and are subject to further policy review. 

 

Thanks. 

Chandana 

Summary of working interagency comments received on draft rule under EO 12866 review.pdf 

 
Subject: Summary of additional interagency working comments received on draft rule under EO 12866 

review 

Date: 11/14/2011 10:46 AM 

From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop.gov> 

To: William Charmley/AA/USEPA/US@EPA, "'Steve.Wood@dot.gov'" <Steve.Wood@dot.gov>, 

"'Neil.Eisner@dot.gov'" <Neil.Eisner@dot.gov>, "''james.tamm@dot.gov' (james.tamm@dot.gov)'" 

<james.tamm@dot.gov>, "'rebecca.yoon@dot.gov'" <rebecca.yoon@dot.gov>, Robin 

Moran/AA/USEPA/US@EPA, Ed Nam/AA/USEPA/US@EPA, Ann 

Wolverton/DC/USEPA/US@EPA, Alex Barron/DC/USEPA/US@EPA 

 

Please find attached additional interagency working comments received on draft NPRM under EO 12866 

and EO 13563 review. They are for your consideration and are subject to further policy review. 

 

Thanks. 

Chandana 

Summary of working interagency comments received on draft rule under EO 12866 review.pdf 

 
Subject: Revised draft of EPA RIA Chapter 8 in response to recent interagency comments. 

Date: 11/14/2011 03:24 PM 

From: William Charmley/AA/USEPA/US 

To: Chandana_L._Achanta@omb.eop.gov 

Cc: Ed Nam/AA/USEPA/US@EPA, Robin Moran/AA/USEPA/US@EPA, rebecca.yoon@dot.gov, 

james.tamm@epa.gov 

Chandana - 

Here is a revised draft of EPA's draft RIA Chapter 8, which shows only the updates compared to the draft 

EPA sent on Sunday. 

This reflects our suggested changes in response to the 1.5 page suggested text we received from the 

interagency commenters on Sunday night at 11pm, and the slightly revised version of that text which we

receive this morning at about 10:45am. 

thanks 

Bill 

RIA Chp 8 11_14_11 from 11_13.doc 

 
Subject: Summary of additional interagency working comments received on draft rule under EO 12866 review 

Sent: 11/14/2011 08:01 PM EST

From: "Achanta, Chandana L." [Chandana_L._Achanta@omb.eop.gov]

To: William Charmley; "'Steve.Wood@dot.gov'" <Steve.Wood@dot.gov>; "'Neil.Eisner@dot.gov'" 

<Neil.Eisner@dot.gov>; "''james.tamm@dot.gov' (james.tamm@dot.gov)'" <james.tamm@dot.gov>; 

"'rebecca.yoon@dot.gov'" <rebecca.yoon@dot.gov>; Robin Moran; Ed Nam

 

Please find attached additional interagency working comments received on draft NPRM under EO 12866 

and EO 13563 review. They are for your consideration and are subject to further policy review. 

 

Thanks. 

Chandana 

Summary of interagency working comments received on draft NPRM under EO 12866 reviewa.pdf 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

review. Subject to further policy review. 

Page 1 of 33

Below is a summary of interagency working comments received during EO 12866 and 

EO 13563 review of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle 

Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards”. They 

are for your consideration and are subject to further policy review. 

1) Preamble Costs and Benefits Discussion: 

a. Interagency commenters recommend that the agencies note in the Preamble 

Section I discussion of the costs and benefits of this rule that there is the 

possibility that car sales will decrease. 

b. Interagency commenters also recommend that the agencies include a sensitivity 

analysis of car sales decreasing. 

c. Interagency commenters recommend that the agencies note the sensitivity 

analysis in the Preamble and seek comment on the analysis and agency 

assumptions. 

2) Interagency commenters suggest the following edits to section H below: 

H. Safety Considerations in Establishing CAFE/GHG Standards 

1. Why do the agencies consider safety?

The primary goals of the proposed CAFE and GHG standards are to reduce fuel 

consumption and GHG emissions from the on-road light-duty vehicle fleet, but in addition to 

these intended effects, the agencies also consider the potential of the standards to affect vehicle 

safety.1

 As a safety agency, NHTSA has long considered the potential for adverse safety 

consequences when establishing CAFE standards,2

 and under the CAA, EPA considers factors 

related to public health and human welfare, and safety, in regulating emissions of air pollutants 

from mobile sources.3

 Safety trade-offs associated with fuel economy increases have occurred in 

the past (particularly before NHTSA CAFE standards were attribute-based), and the agencies 

must be mindful of the possibility of future ones. These past safety trade-offs may have occurred 

 1

 In this rulemaking document, “vehicle safety” is defined as societal fatality rates per vehicle miles traveled (VMT), 

which include fatalities to occupants of all the vehicles involved in the collisions, plus any pedestrians. 2

 This practice is recognized approvingly in case law. As the United States Court of Appeals for the D.C. Circuit 

stated in upholding NHTSA’s exercise of judgment in setting the 1987-1989 passenger car standards, “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 (“CEI I”), 901 

F.2d 107, 120 at n. 11 (D.C. Cir. 1990). 

3

 See NRDC v. EPA, 655 F. 2d 318, 332 n. 31 (D.C. Cir. 1981).(EPA may consider safety in developing standards 

under section 202 (a) and did so appropriately in the given instance).
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

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Page 2 of 33

because manufacturers chose, at the time, to build smaller and lighter vehicles—partly in 

response to CAFE standards—rather than adding more expensive fuel-saving technologies (and 

maintaining vehicle size and safety), and the smaller and lighter vehicles did not fare as well in 

crashes as larger and heavier vehicles. Historically, as shown in FARS data analyzed by 

NHTSA, the safest cars generally have been heavy and large, while the cars with the highest 

fatal-crash rates have been light and small. The question, then, is whether past is necessarily 

prologue when it comes to potential changes in vehicle size (both footprint and “overhang”) and 

mass in response to these proposed future CAFE and GHG standards. Manufacturers have stated 

that they will reduce vehicle mass as one of the cost-effective means of increasing fuel economy 

and reducing CO2 emissions in order to meet the proposed standards, and the agencies have 

incorporated this expectation into our modeling analysis supporting the proposed standards. 

Because the agencies discern a historical relationship between vehicle mass, size, and safety, it is 

reasonable to assume that these relationships will continue in the future. The question of 

whether vehicle design can mitigate the adverse effects of mass reduction is discussed below. 

Manufacturers are less likely than they were in the past to reduce vehicle footprint in 

order to reduce mass for increased fuel economy. The primary mechanism in this rulemaking for 

mitigating the potential negative effects on safety is the application of footprint-based standards, 

which create a disincentive for manufacturers to produce smaller-footprint vehicles. See section 

II. C.1, above. This is because, as footprint decreases, the corresponding fuel economy/GHG 

emission target becomes more stringent. We also believe that the shape of the footprint curves 

themselves is approximately “footprint-neutral,” that is, that it should neither encourage 

manufacturers to increase the footprint of their fleets, nor to decrease it. Upsizing footprint is 

also discouraged through the curve “cut-off” at larger footprints.4

 However, the footprint-based 

standards do not discourage downsizing the portions of a vehicle in front of the front axle and to 

the rear of the rear axle, or of other areas of the vehicle outside the wheels. The crush space 

provided by those portions of a vehicle can make important contributions to managing crash 

energy. Additionally, simply because footprint-based standards create no incentive to downsize 

vehicles does not mean that manufacturers will not downsize if doing so makes it easier to meet 

the overall CAFE/GHG standard, as for example if the smaller vehicles are so much lighter that 

they exceed their targets by much greater amounts. On balance, however, we believe the target 

 4

 The agencies recognize that at the other end of the curve, manufacturers who make small cars and trucks below 41 

square feet (the small footprint cut-off point) have some incentive to downsize their vehicles to make it easier to 

meet the constant target. That cut-off may also create some incentive for manufacturers who do not currently offer 

models that size to do so in the future. However, at the same time, the agencies believe that there is a limit to the 

market for cars and trucks smaller than 41 square feet: most consumers likely have some minimum expectation 

about interior volume, for example, among other things. Additionally, vehicles in this segment are the lowest price 

point for the light-duty automotive market, with several models in the $10,000-$15,000 range. Manufacturers who 

find themselves incentivized by the cut-off will also find themselves adding technology to the lowest price segment 

vehicles, which could make it challenging to retain the price advantage. Because of these two reasons, the agencies 

believe that the incentive to increase the sales of vehicles smaller than 41 square feet due to this rulemaking, if any, 

is small. See Section II.C.1 above and Chapter 1 of the draft Joint TSD for more information on the agencies’ 

choice of “cut-off” points for the footprint-based target curves. 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

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Page 3 of 33

curves and the incentives they provide generally will not encourage down-sizing (or up-sizing) in 

terms of footprint reductions (or increases). Consequently, all of our analyses are based on the 

assumption that this rulemaking, in and of itself, will not result in any differences in the salesweighted distribution of vehicle sizes.5

Given that we expect manufacturers to reduce vehicle mass in response to the proposed 

standards, and do not expect manufacturers to reduce vehicle footprint in response to the 

proposed standards, the agencies must attempt to predict the safety effects, if any, of the 

proposed standards based on the best information currently available. This section explained 

why the agencies consider safety; the following section discusses how the agencies consider 

safety. 

2. How do the agencies consider safety? 

Assessing the effects of vehicle mass reduction and size on societal safety is a complex 

issue. One part of estimating potential safety effects involves trying to understand better the 

relationship between mass and vehicle design. The extent of mass reduction that manufacturers 

may be considering to meet more stringent fuel economy and GHG standards may raise different 

safety concerns from what the industry has previously faced. The principal difference between 

the heavier vehicles, especially truck-based LTVs, and the lighter vehicles, especially passenger 

cars, is that 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 higher in the lighter 

vehicle, similar to the mass ratio proportion. As a result of the higher change in velocity, the 

fatality risk may also increase. Removing more mass from the heavier vehicle than in the lighter 

vehicle by amounts that bring the mass ratio closer to 1.0 reduces the delta V in the lighter 

vehicle, possibly resulting in a net societal benefit. 

Another complexity is that if a vehicle is made lighter, adjustments must be made to the 

vehicle’s structure such that it will be able to manage the energy in a crash while limiting 

intrusion into the occupant compartment after adopting materials that may be stiffer. To maintain 

an acceptable occupant compartment deceleration, the effective front end stiffness has to be 

managed such that the crash pulse does not increase as stiffer yet lighter materials are utilized. If 

the energy is not well managed, the occupants may have to “ride down” a more severe crash 

pulse, putting more burdens on the restraint systems to protect the occupants. There may be 

technological and physical limitations to how much the restraint system may mitigate these 

effects. 

The agencies must attempt to estimate now, based on the best information currently 

available to us, how the assumed levels of mass reduction without additional changes (i.e. 

 5

 This statement makes no prediction of how consumer choices of vehicle size will change in the future, independent 

of this proposal. 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

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Page 4 of 33

footprint, performance, functionality) might affect the safety of vehicles, and how lighter 

vehicles might affect the safety of drivers and passengers in the entire on-road fleet, as we are 

analyzing potential future CAFE and GHG standards. The agencies seek to ensure that the 

standards are designed to encourage manufacturers to pursue a path toward compliance that is 

both cost-effective and safe. 

To estimate the possible safety effects of the MY 2017-2025 standards, then, the agencies 

have undertaken research that approaches this question from several angles. First, we are using a 

statistical approach to study the effect of vehicle mass reduction on safety historically, as 

discussed in greater detail in section C below. Statistical analysis is performed using the most 

recent historical crash data available, and is considered as the agencies’ best estimate of potential 

mass-safety effects. The agencies recognize that negative safety effects estimated based on the 

historical relationships could potentially be tempered with safety technology advances in the 

future, and may not represent the current or future fleet. Second, we are using an engineering 

approach to investigate what amount of mass reduction is affordable and feasible while 

maintaining vehicle safety and other major functionalities such as NVH and acceleration 

performance. Third, we are also studying the new challenges these lighter vehicles might bring 

to vehicle safety and potential countermeasures available to manage those challenges effectively. 

The sections below discuss more specifically the state of the research on the mass-safety 

relationship, and how the agencies integrate that research into our assessment of the potential 

safety effects of the MY 2017-2025 CAFE and GHG standards. 

3. What is the current state of the research on statistical analysis of historical crash 

data? 

a. Background 

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 

over time. In the MY 2012-2016 final rule, the agencies stated that 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 help 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/GHG rulemaking in regards to statistical analysis of historical data. 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

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Page 5 of 33

First, NHTSA would contract with an independent institution to review the statistical 

methods that NHTSA and DRI have used to analyze historical data related to mass, size and 

safety, and to provide recommendation on whether the existing methods or other methods should 

be used for future statistical analysis of historical data. This study will 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 was also peer reviewed by two other experts in the safety 

field - Charles Farmer (Insurance Institute for Highway Safety) and Anders Lie (Swedish 

Transport Administration).6

 

Second, NHTSA and EPA, in consultation with DOE, would update the MYs 1991–1999 

database on which the 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 help address 

concerns that differences in data were leading to different results in statistical analyses by 

different researchers. 

And third, in order to assess if the design of recent model year vehicles that incorporate 

various mass reduction methods affect the relationships among vehicle mass, size and safety, the 

agencies sought to identify vehicles that are using material substitution and smart design, and to 

try 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. 

Significant progress has been made on these tasks since the MY 2012-2016 final rule, as 

follows: The independent review of recent and updated statistical analyses of the relationship 

between vehicle mass, size, and crash fatality rates has been completed. NHTSA contracted with 

the University of Michigan Transportation Research Institute (UMTRI) to conduct this review, 

and the UMTRI team led by Paul Green evaluated over 20 papers, including studies done by 

NHTSA’s Charles Kahane, Tom Wenzel of the US Department of Energy’s Lawrence Berkeley 

National Laboratory, Dynamic Research, Inc., and others. UMTRI’s basic findings will be 

discussed below. Some commenters in recent CAFE rulemakings, including some vehicle 

manufacturers, suggested that the designs and materials of more recent model year vehicles may 

have weakened the historical statistical relationships between mass, size, and safety. The 

agencies agree that the statistical analysis would be improved by using an updated database that 

reflects more recent safety technologies, vehicle designs and materials, and reflects changes in 

the overall vehicle fleet. The agencies also believe, as UMTRI also found, that different 

statistical analyses may have had different results because they each used slightly different 

 6

 All three of the peer reviews are in docket, NHTSA-2010-0152. You can access the docket at 

http://www.regulations.gov/#!home by typing ‘NHTSA-2010-0152’ where it says “enter keyword or ID” and then 

clicking on “Search.”
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

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Page 6 of 33

datasets for their analyses. In order to try to mitigate this problem and to support the current 

rulemaking, NHTSA has created a common, updated database for statistical analysis that consists 

of crash data of model years 2000-2007 vehicles in calendar years 2002-2008, as 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 is 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 has made the new databases available to the public,7

 enabling 

other researchers to analyze the same data and hopefully minimizing discrepancies in the results 

that would have been due to inconsistencies across databases.8

 The agencies recognize, 

however, that the updated database may not represent the future fleet, because vehicles have 

continued and will continue to change. 

The agencies are aware that several studies have been initiated using NHTSA’s 2011 

newly established safety database. In addition to a new Kahane study, which is discussed in 

section II.G.4, other on-going studies include two by Wenzel at Lawrence Berkeley National 

Laboratory (LBNL) under contract with the U.S. DOE, and one by Dynamic Research, Inc. 

(DRI) contracted by the International Council on Clean Transportation (ICCT). These studies 

may take somewhat different approaches to examine the statistical relationship between fatality 

risk, vehicle mass and size. In addition to a detailed assessment of the NHTSA 2011 report, 

Wenzel is expected to consider the effect of mass and footprint reduction on casualty risk per 

crash, using data from thirteen states. Casualty risk includes both fatalities and serious or 

incapacitating injuries. DRI is expected to use a two-stage approach to separate the effect of 

mass reduction on two components of fatality risk, crash avoidance and crashworthiness. The 

LBNL assessment of the NHTSA 2011 report is available in the docket for this NPRM.9

 The 

casualty risk effect study was not available in time to inform this NPRM. The completed final 

peer reviewed-report on both assessments will be available prior to the final rule. DRI has also 

indicated that it expects its study to be publicly available prior to the final rule. The agencies will 

consider these studies and any others that become available, and the results may influence the 

safety analysis for the final rule. 

Other researchers are free to download the database from NHTSA’s website, and we 

expect to see additional papers in the coming months and as comments to the rulemaking that 

may also inform our consideration of these issues for the final rule. Kahane’s updated study for 

2011 is currently undergoing peer-review, and is available in the docket for this rulemaking for 

review by commenters. 

 7

 The new databases are available at http://www.nhtsa.gov/fuel-economy (look for “Download Crash Databases for 

Statistical Analysis of Relationships Between Vehicles’ Fatality Risk, Mass, and Footprint.” 8

 75 Fed. Reg. 25324 (May 7, 2010); the discussion of planned statistical analyses is on pp. 25395-25396. 

9

 –Wenzel, T.P. (2011b). Assessment of NHTSA’s Report “Relationships between Fatality Risk, Mass, and 

Footprint in Model Year 2000-2007 Passenger Cars and LTVs”, available at... 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

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Page 7 of 33

Finally, EPA and NHTSA with DOT’s Volpe Center, part of the Research and Innovative 

Technology Administration (RITA), attempted to investigate the implications of “Smart Design,” 

by identifying and describing the types of “Smart Design” and methods for using “Smart 

Design” to result in vehicle mass reduction, selecting analytical pairs of vehicles, and using the 

appropriate crash database to analyze vehicle crash data. The analysis identified several onevehicle and two-vehicle crash datasets with the potential to shed light on the issue, but the 

available data for specific crash scenarios was insufficient to produce consistent results that 

could be used to support conclusions regarding historical performance of “smart designs.” 

Undertaking these tasks has helped the agencies come closer to resolving some of the 

ongoing debates in statistical analysis research of historical crash data. We intend to apply these 

conclusions going forward, and we believe that the public discussion of the issues will be 

facilitated by the research conducted. The following sections discuss the findings from these 

studies and others in greater detail, to present a more nuanced picture of the current state of the 

statistical research. 

b. NHTSA workshop on vehicle mass, size and safety 

On February 25, 2011, NHTSA hosted a workshop on mass reduction, vehicle size, and 

fleet safety at the Headquarters of the U.S. Department of Transportation in Washington, DC.10 

The purpose of the workshop was to provide the agencies with a broad understanding of current 

research in the field and provide stakeholders and the public with an opportunity to weigh in on 

this issue. NHTSA also created a public docket to receive comments from interested parties that 

were unable to attend. 

The speakers included Charles Kahane of NHTSA, Tom Wenzel of Lawrence Berkeley 

National Laboratory, R. Michael Van Auken of Dynamic Research Inc. (DRI), Jeya Padmanaban 

of JP Research, Inc., Adrian Lund of the Insurance Institute for Highway Safety, Paul Green of 

the University of Michigan Transportation Research Institute (UMTRI), Stephen Summers of 

NHTSA, Gregg Peterson of Lotus Engineering, Koichi Kamiji of Honda, John German of the 

International Council on Clean Transportation (ICCT), Scott Schmidt of the Alliance of 

Automobile Manufacturers, Guy Nusholtz of Chrysler, and Frank Field of the Massachusetts 

Institute of Technology. 

The wide participation in the workshop allowed the agencies to hear from a broad range 

of experts and stakeholders. The contributions were particularly relevant to the agencies’ 

analysis of the effects of weight reduction for this proposed rule. The presentations were divided 

into two sessions that addressed the two expansive sets of issues – statistical evidence of the 

 10 A video recording, transcript, and the presentations from the NHTSA workshop on mass reduction, vehicle size 

and fleet safety is available at http://www.nhtsa.gov/fuel-economy (look for “NHTSA Workshop on Vehicle MassSize-Safety on Feb. 25”) 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

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Page 8 of 33

roles of mass and size on safety, and engineering realities – structural crashworthiness, occupant 

injury and advanced vehicle design. 

The first session focused on previous and ongoing statistical studies of crash data that 

attempt to identify the relative effects of vehicle mass and size on fleet safety. There was 

consensus that there is a complicated relationship with many confounding influences in the data. 

Wenzel summarized a recent study he conducted comparing four types of risk (fatality or 

casualty risk, per vehicle registration-years or per crash) using police-reported crash data from 

five states.11 He showed that the trends in risk for various classes of vehicles (e.g., non-sports 

car passenger cars, vans, SUVs, crossover SUVs, pickups) were similar regardless of what risk 

was being measured (fatality or casualty) or what exposure metric was used (e.g., registration 

years, police-reported crashes, etc.). In general, most trends showed a lower risk for drivers of 

larger, heavier vehicles. 

Although Wenzel’s analysis was focused on differences in the four types of risk on the 

relative risk by vehicle type, he cautioned that, when analyzing casualty risk per crash, analysts 

should control for driver age and gender, crash location (urban vs. rural), and the state in which 

the crash occurred (to account for crash reporting biases). 

Several participants pointed out that analyses must also control for individual 

technologies with significant safety effects (e.g., Electronic Stability Control, airbags).It was not 

always conclusive whether a specialty vehicle group (e.g., sports cars, two-door cars, early 

crossover SUVs) were outliers that confound the trend or unique datasets that isolate specific 

vehicle characteristics. Unfortunately, specialty vehicle groups are usually adopted by specific 

driver groups, often with outlying vehicle usage or driver behavior patterns. Green, who 

conducted an independent review of the previous statistical analyses, suggested that evaluating 

residuals will give an indication of whether or not a data subset can be legitimately removed 

without inappropriately affecting the analytical results. 

It was recognized that the physics of a two-vehicle crash require that the lighter vehicle 

experience a greater change in velocity, which often leads to disproportionately more injury risk. 

Lund noted persistent historical trends that, in any time period, occupants of the smallest and 

lightest vehicles had, on average, fatality rates approximately twice those of occupants of the 

largest and heaviest vehicles but predicted “the sky will not fall” as the fleet downsizes, we will 

not see an increase in absolute injury risk because smaller cars will become increasingly 

protective of their occupants. Padmanaban also noted in her research of the historical trends that 

mass ratio and vehicle stiffness are significant predictors with mass ratio consistently the 

dominant parameter when correlating harm. Reducing the mass of any vehicle may have 

 11 Wenzel, T.P. (2011a). Analysis of Casualty Risk per Police-Reported Crash for Model Year 2000 to 2004 

Vehicles, using Crash Data from Five States, March 2011, LBNL-4897E, available at: 

http://eetd.lbl.gov/EA/teepa/pub.html#Vehicle 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

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Page 9 of 33

competing societal effects as it increases the injury risk in the lightened vehicle and decreases 

them in the partner vehicle 

The separation of key parameters was also discussed as a challenge to the analyses, as 

vehicle size has historically been highly correlated with vehicle mass. Presenters had varying 

approaches for dealing with the potential multicollinearity between these two variables. Van 

Auken of DRI stated that there was latitude in the value of Variance Inflation Factor (VIF, a 

measure of multicollinearity) that would call results into question, and suggested that the large 

value of VIF for curb weight might imply “perhaps the effect of weight is too small in 

comparison to other factors.” Green, of UMTRI, stated that highly correlated variables may not 

be appropriate for use in a predictive model and that “match[ing] on footprint” (i.e., conducting 

multiple analyses for data subsets with similar footprint values) may be the most effective way to 

resolve the issue. 

There was no consensus on the overall effect of the maneuverability of smaller, lighter 

vehicles. German noted that lighter vehicles should have improved handling and braking 

characteristics and “may be more likely to avoid collisions”. Lund presented crash involvement 

data that implied that, among vehicles of similar function and use rates, crash risk does not go 

down for more “nimble” vehicles. Several presenters noted the difficulties of projecting past 

data into the future as new technologies will be used that were not available when the data were 

collected. The advances in technology through the decades have dramatically improved safety 

for all weight and size classes. A video of IIHS’s 50th anniversary crash test of a 1959 Chevrolet 

Bel Air and 2009 Chevrolet Malibu graphically demonstrated that stark differences in design and 

technology that can possibly mask the discrete mass effects, while videos of compatibility crash 

tests between smaller, lighter vehicles and contemporary larger, heavier vehicles graphically 

showed the significance of vehicle mass and size. 

Kahane presented results from his 2010 report12 that found that a scenario which took 

some mass out of heavier vehicles but little or no mass out of the lightest vehicles did not impact 

safety in absolute terms. Kahane noted that if the analyses were able to consider the mass of 

both vehicles in a two-vehicle crash, the results may be more indicative of future crashes. There 

is apparent consistency with other presentations (e.g., Padmanaban, Nusholtz) that reducing the 

overall ranges of masses and mass ratios seems to reduce overall societal harm. That is, the effect 

of mass reduction exclusively does not appear to be a “zero sum game” in which any increase in 

harm to occupants of the lightened vehicle is precisely offset by a decrease in harm to the 

occupants of the partner vehicle. If the mass of the heavier vehicle is reduced by a larger 

 12 Kahane, C. J. (2010). “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 1991-1999 and 

Other Passenger Cars and LTVs,” Final Regulatory Impact Analysis: Corporate Average Fuel Economy for MY 

2012-MY 2016 Passenger Cars and Light Trucks. Washington, DC: National Highway Traffic Safety 

Administration, pp. 464-542, available at http://www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/CAFE_2012-

2016_FRIA_04012010.pdf. 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

review. Subject to further policy review. 

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percentage, the changes in velocity from the collision are more nearly equal and the injuries 

suffered in the lighter vehicle are likely to be reduced more than the injuries in the heavier 

vehicle are increased. Alternatively, a fixed mass reduction (say, 100 lbs) in all vehicles could 

increase societal harm whereas a fixed percentage mass reduction is more likely to be neutral. 

Padmanaban described a series of studies conducted in recent years. She included 

numerous vehicle parameters including bumper height and several measures of vehicle size and 

stiffness and also commented on previous analyses that using weight and wheelbase together in a 

logistic model distorts the estimates, resulting in inflated variance with wrong signs and 

magnitudes in the results. Her results consistently showed that vehicle mass ratio was a more 

important parameter than those describing vehicle geometry or stiffness. Her ultimate 

conclusion was that removing mass (e.g., 100 lbs.) from all passenger cars would cause an 

overall increase in fatalities in truck-to-car crashes while removing the same amount from light 

trucks would cause an overall decrease in fatalities. 

c. Report by Green et al., UMTRI – “Independent Review: Statistical Analyses of 

Relationship Between Vehicle Curb Weight, Track Width, Wheelbase and 

Fatality Rates,” April 2011. 

As explained above, NHTSA contracted with the University of Michigan Transportation 

Research Institute (UMTRI) to conduct an independent review13 of a set of statistical analyses of 

relationships between vehicle curb weight, the footprint variables (track width, wheelbase) and 

fatality rates from vehicle crashes. The purpose of this review was to examine analysis methods, 

data sources, and assumptions of the statistical studies, with the objective of identifying the 

reasons for any differences in results. Another objective was to examine the suitability of the 

various methods for estimating the fatality risks of future vehicles. 

 UMTRI reviewed a set of papers, reports, and manuscripts provided by NHTSA 

(listed in Appendix A of UMTRI’s report, which is available in the docket to this rulemaking) 

that examined the statistical relationships between fatality or casualty rates and vehicle properties 

such as curb weight, track width, wheelbase and other variables. 

 It is difficult to summarize a study of that length and complexity for purposes of this 

discussion, but fundamentally, the UMTRI team concluded the following: 

 Differences in data may have complicated comparisons of earlier analyses, but if the 

methodology is robust, and the methods were applied in a similar way, small changes in 

data should not lead to different conclusions. The main conclusions and findings should 

be reproducible. The data base created by Kahane appears to be an impressive collection 

of files from appropriate sources and the best ones available for answering the research 

 13 The review is independent in the sense that it was conducted by an outside third party without any interest in the 

reported outcome. 
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questions considered in this study. 

 In statistical analysis simpler models generally lead to improved inference, assuming the 

data and model assumptions are appropriate. In that regard, the disaggregate logistic 

regression model used by NHTSA in the 2003 report14 seems to be the most appropriate 

model, and valid for the analysis in the context that it was used: finding general 

associations between fatality risk and mass – and the general directions of the reported 

associations are correct. 

 The two-stage logistic regression model in combination with the two-step aggregate 

regression used by DRI seems to be more complicated than is necessary based on the data 

being analyzed, and summing regression coefficients from two separate models to arrive 

at conclusions about the effects of reductions in weight or size on fatality risk seems to 

add unneeded complexity to the problem. 

 One of the biggest issues regarding this work is the historical correlation between curb 

weight, wheelbase, and track width. Including three variables that are highly correlated 

in the same model can have adverse effects on the fit of the model, especially with 

respect to the parameter estimates, as discussed by Kahane. UMTRI makes no 

conclusions about multicollinearity, other than to say that inferences made in the 

presence of multicollinearity should be judged with great caution. At the NHTSA 

workshop on size, safety and mass, Paul Green suggested that a matched analysis, in 

which regressions are run on the relationship between mass reduction and risk separately 

for vehicles of similar footprint, could be undertaken to investigate the effect of 

multicollinearity between vehicle mass and size. Kahane has combined wheelbase and 

track width into one variable (footprint) to compare with curb weight. NHTSA believes 

that the 2011 Kahane analysis has done all it can to lessen concerns about 

multicollinearity, but a concern still exists. In considering other studies provided by 

NHTSA for evaluation by the UMTRI team: 

o Papers by Wenzel, and Wenzel and Ross, addressing associations between fatality 

risk per vehicle registration-year, weight, and size by vehicle model contribute to 

understanding some of the relationships between risk, weight, and size. However, 

least squares linear regression models, without modification, are not exposurebased risk models and inference drawn from these models tends to be weak since 

they do not account for additional differences in vehicles, drivers, or crash 

conditions that could explain the variance in risk by vehicle model. 

o A 2009 J.P. Research paper focused on the difficulties associated with separating 

out the contributions of weight and size variables when analyzing fatality risk 

properly recognized the problem arising from multicollinearity and included a 

clear explanation of why fatality risk is expected to increase with increasing mass 

ratio. UMTRI concluded that the increases in fatality risk associated with a 100-

pound reduction in weight allowing footprint to vary with weight as estimated by 

Kahane and JP Research, are broadly more convincing than the 6.7 percent 

reduction in fatality risk associated with mass reduction while holding footprint 

constant, as reported by DRI. 

 
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o A paper by Nusholtz et al. focused on the question of whether vehicle size can 

reasonably be the dominant vehicle factor for fatality risk, and finding that 

changing the mean mass of the vehicle population (leaving variability unchanged) 

has a stronger influence on fatality risk than corresponding (feasible) changes in 

mean vehicle dimensions, concluded unequivocally that reducing vehicle mass 

while maintaining constant vehicle dimensions will increase fatality risk. UMTRI 

concluded that if one accepts the methodology, this conclusion is robust against 

realistic changes that may be made in the force vs. deflection characteristics of the 

impacting vehicles. 

o Two papers by Robertson, one a commentary paper and the other a peer-reviewed 

journal article, were reviewed. The commentary paper did not fit separate models 

according to crash type, and included passenger cars, vans, and SUVs in the same 

model. UMTRI concluded that some of the claims in the commentary paper 

appear to be overstated, and intermediate results and more documentation would 

help the reader determine if these claims are valid. The second paper focused 

largely on the effects of electronic stability control (ESC), but generally followed 

on from the first paper except that curb weight is not fit and fuel economy is used 

as a surrogate. 

 

The UMTRI study provided a number of useful suggestions that Kahane considered in 

updating his 2011 analysis, and that have been incorporated into the safety effects estimates for 

the current rulemaking. 

d. Report by Dr. Charles Kahane, NHTSA – “Relationships Between Fatality Risk, 

Mass, and Footprint in Model Year 2000-2007 Passenger Cars and LTVs,” 2011 

The relationship between a vehicle’s mass, size, and fatality risk is complex, and it varies 

in different types of crashes. NHTSA, along with others, has been examining this relationship 

for over a decade. The safety chapter of NHTSA’s April 2010 final regulatory impact analysis 

(FRIA) of CAFE standards for MY 2012-2016 passenger cars and light trucks included a 

statistical analysis of relationships between fatality risk, mass, and footprint in MY 1991-1999 

passenger cars and LTVs (light trucks and vans), based on calendar year (CY) 1995-2000 crash 

and vehicle-registration data.15 The 2010 analysis used the same data as the 2003 analysis, but 

included vehicle mass and footprint in the same regression model. 

The principal findings of NHTSA’s 2010 analysis were that mass reduction in lighter 

cars, even while holding footprint constant, would significantly increase societal fatality risk, 

whereas mass reduction in the heavier LTVs would significantly reduce net societal fatality risk, 

 15 Kahane, C. J. (2010). “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 1991-1999 and 

Other Passenger Cars and LTVs,” Final Regulatory Impact Analysis: Corporate Average Fuel Economy for MY 

2012-MY 2016 Passenger Cars and Light Trucks. Washington, DC: National Highway Traffic Safety 

Administration, pp. 464-542, available at http://www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/CAFE_2012-

2016_FRIA_04012010.pdf. 
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because it would reduce the fatality risk of occupants in lighter vehicles which collide with the 

heavier LTVs. NHTSA concluded that, as a result, any reasonable combination of mass 

reductions while holding footprint constant in MY 2012-2016 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’s 2010 report partially agreed and partially disagreed with analyses published 

during 2003-2005 by Dynamic Research, Inc. (DRI). NHTSA and DRI both found a significant 

protective effect for footprint, and that reducing mass and footprint together (downsizing) on 

smaller vehicles was harmful. DRI’s analyses estimated a significant overall reduction in 

fatalities from mass reduction in all light-duty vehicles if wheelbase and track width were 

maintained, whereas NHTSA’s report showed overall fatality reductions only in the heavier 

LTVs, and benefits only in some types of crashes for other vehicle types. Much of NHTSA’s 

2010 report, as well as recent work by DRI, involved sensitivity tests on the databases and 

models, which generated a range of estimates somewhere between the initial DRI and NHTSA 

results.16 

Immediately after issuing the final rule for MYs 2012-2016 CAFE and GHG standards in 

May 2010, NHTSA and EPA began work on the next joint rulemaking to develop CAFE and 

GHG standards for MY 2017 to 2025 and beyond. The preamble to the 2012-2016 final rule 

stated that NHTSA, working closely with EPA and the Department of Energy (DOE), would 

perform a new statistical analysis of the relationships between fatality rates, mass and footprint, 

updating the crash and exposure databases to the latest available model years, refining the 

methodology in response to peer reviews of the 2010 report and taking into account changes in 

vehicle technologies. The previous databases of MY 1991-1999 vehicles in CY 1995-2000 

crashes has become outdated as new safety technologies, vehicle designs and materials were 

introduced. The new databases comprising MY 2000-2007 vehicles in CY 2002-2008 crashes 

with 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 has made the new 

 16 Van Auken, R. M., and Zellner, J. W. (2003). A Further Assessment of the Effects of Vehicle Weight and Size 

Parameters on Fatality Risk in Model Year 1985-98 Passenger Cars and 1986-97 Light Trucks. Report No. DRITR-03-01. Torrance, CA: Dynamic Research, Inc.; Van Auken, R. M., and Zellner, J. W. (2005a). An Assessment of 

the Effects of Vehicle Weight and Size on Fatality Risk in 1985 to 1998 Model Year Passenger Cars and 1985 to 

1997 Model Year Light Trucks and Vans. Paper No. 2005-01-1354. Warrendale, PA: Society of Automotive 

Engineers; Van Auken, R. M., and Zellner, J. W. (2005b). Supplemental Results on the Independent Effects of Curb 

Weight, Wheelbase, and Track on Fatality Risk in 1985-1998 Model Year Passenger Cars and 1986-97 Model Year 

LTVs. Report No. DRI-TR-05-01. Torrance, CA: Dynamic Research, Inc.; Van Auken, R.M., and Zellner, J. W. 

(2011). “Updated Analysis of the Effects of Passenger Vehicle Size and Weight on Safety,” NHTSA Workshop on 

Vehicle Mass-Size-Safety, Washington, February 25, 2011, 

http://www.nhtsa.gov/staticfiles/rulemaking/pdf/MSS/MSSworkshop_VanAuken.pdf 
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databases available to the public17, enabling other researchers to analyze the same data and 

hopefully minimizing discrepancies in the results due to inconsistencies across the data used.18

One way to estimate these effects is via statistical analyses of societal fatality rates per 

vehicle miles traveled (VMT), by vehicles’ mass and footprint, for the current on-road vehicle 

fleet. The basic analytical method used for the 2011 NHTSA report is the same as in NHTSA’s 

2010 report: cross-sectional analyses of the effect of mass and footprint reductions on the 

societal fatality rate per billion vehicle miles of travel (VMT), while controlling for driver age 

and gender, vehicle type, vehicle safety features, crash times and locations, and other factors. 

Separate logistic regression models are run for three types of vehicles and nine types of crashes. 

Societal fatality rates include occupants of all vehicles in the crash, as well as non-occupants, 

such as pedestrians and cyclists. NHTSA’s 2011 Report19 analyzes MY 2000-2007 cars and 

LTVs in CY 2002-2008 crashes. Fatality rates were derived from FARS data, 13 State crash 

files, and registration and mileage data from R.L. Polk. 

The most noticeable change in MY 2000-2007 vehicles from MY 1991-1999 has been 

the increase in crossover utility vehicles (CUV), which are SUVs of unibody construction, often 

but not always built upon a platform shared with passenger cars. CUVs have blurred the 

distinction between cars and trucks. The new analysis treats CUVs and minivans as a separate 

vehicle class, because they differ in some respects from pickup-truck-based LTVs and in other 

respects from passenger cars. In the 2010 report, the many different types of LTVs were 

combined into a single analysis and NHTSA believes that this may have made the analyses too 

complex and might have contributed to some of the uncertainty in the results. 

The new database has accurate VMT estimates, derived from a file of odometer readings 

by make, model, and model year recently developed by R.L. Polk and purchased by NHTSA.20 

For the 2011 report, the relative distribution of crash types has been changed to reflect the 

projected distribution of crashes during the period from 2017 to 2025, based on the estimated 

effectiveness of electronic stability control (ESC) in reduction the number of fatalities in rollover 

crashes and crashes with a stationary object. The annual target population of fatalities or the 

annual fatality distribution baseline21 was not decreased in the period between 2017 and 2025 for 

 17 http://www.nhtsa.gov/fuel-economy 18 75 Fed. Reg. 25324 (May 7, 2010); the discussion of planned statistical analyses is on pp. 25395-25396. 19 Kahane, C. J. (2011). “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2000-2007 

Passenger Cars and LTVs,”, July 2011. The report is available in the NHTSA docket, NHTSA-2010-0152. You can 

access the docket at http://www.regulations.gov/#!home by typing ‘NHTSA-2010-0152’ where it says “enter 

keyword or ID” and then clicking on “Search.” 

20 In the 1991-1999 data base, VMT was estimated only by vehicle class, based on NASS CDS data. 21 MY 2004 -2007 vehicles with fatal crashes occurred in CY 2004-2008 are selected as the annual fatality 

distribution baseline in the Kahane analysis. 
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the safety statistics analysis, but is taken into account later in the Volpe model analysis, since all 

vehicles in the future will be equipped with ESC22. 

For the 2011 report, vehicles are now grouped into five classes rather than four: 

passenger cars (including both 2-door and 4-door cars) are split in half by median weight; CUVs 

and minivans; and truck-based LTVs, which are also split in half by median weight of the model 

year 2000-2007 vehicles. Table Error! No text of specified style in document.-1 presents the 

estimated percent increase in U.S. societal fatality risk per ten billion VMT for each 100-pound 

reduction in vehicle mass, while holding footprint constant, for each of the five classes of 

vehicles. 

Table Error! No text of specified style in document.-1 Results of 2011 NHTSA report 

Fatality Increase (%) per 100-Pound Mass Reduction While Holding Footprint Constant 

MY 2000-2007 

CY 2002-2008 

Fatality Increase (%) Per 100-Pound Mass 

Reduction While Holding Footprint Constant 

Point Estimate 95% Confidence Bounds 

Cars < 3,106 pounds 1.44 + .29 to +2.59 

Cars > 3,106 pounds .47 - .58 to +1.52 

CUVs and minivans - .46 -1.75 to + .83 

Truck-based LTVs < 4,594 pounds .52 - .43 to +1.46 

Truck-based LTVs > 4,594 pounds - .39 -1.06 to + .27 

Only the 1.44 percent risk increase in the lighter cars is statistically significant. There are 

non-significant increases in the heavier cars and the lighter truck-based LTVs, and nonsignificant societal benefits for mass reduction in CUVs, minivans, and the heavier truck-based 

LTVs. Based on these results, potential combinations of mass reductions that maintain footprint 

and are proportionately somewhat higher for the heavier vehicles may be safety-neutral or better 

as point estimates and, in any case, unlikely to significantly increase fatalities. The primarily 

non-significant results are not due to a paucity of data, but because the societal effect of mass 

reduction while maintaining footprint, if any, is small. 

MY 2000-2007 vehicles of all types are heavier and larger than their MY 1991-1999 

counterparts. The average mass of passenger cars increased by 5 percent from 2000 to 2007 and 

the average mass of pickup trucks increased by 19 percent. Other types of vehicles became 

heavier, on the average, by intermediate amounts. There are several reasons for these increases: 

during this time frame, some of the lighter make-models were discontinued; many models were 

redesigned to be heavier and larger; and consumers more often selected stretched versions such 

as crew cabs in their new-vehicle purchases. 

 22 In the Volpe model, NHTSA assumed that the safety trend would result in 12.6 percent reduction between 2007 

and 2020 due to the combination of ESC, new safety standard, and behavior changes anticipated. 
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It is interesting to compare the new results to NHTSA’s 2010 analysis of MY 1991-1999 

vehicles in CY 1995-2000, especially the new point estimate to the “actual regression result 

scenario” in the 2010 report: 

Table Error! No text of specified style in document.-2 2010 Report: MY 1991-1999, 

CY 1995-2000 Fatality Increase (%) per 100-Pound Mass Reduction While Holding 

Footprint Constant 

Actual Regression 

Result Scenario 

Upper-Estimate 

Scenario 

Lower-Estimate 

Scenario 

Cars < 2,950 pounds 2.21 2.21 1.02 

Cars > 2,950 pounds 0.90 0.90 0.44 

LTVs < 3,870 pounds 0.17 0.55 0.41 

LTVs > 3,870 pounds -1.90 -0.62 -0.73 

Table Error! No text of specified style in document.-3 Fatality Increase (%) per 100-

Pound Mass Reduction While Holding Footprint Constant 

NHTSA (2010) NHTSA (2011) 

Lighter cars 2.21% 1.43% 

Heavier cars 0.89% 0.48% 

Lighter LTVs 0.17%* 0.52% 

Heavier LTVs -1.90%* -0.40% 

CUV/ minivan -0.47% 

*Includes CUV/minivan 

The new results are directionally the same as in 2010: fatality increase in the lighter cars, 

safety benefit in the heavier LTVs, but the effects may have become weaker at both ends. (The 

agencies do not consider this conclusion to be definitive because of the relatively wide 

confidence bounds of the estimates.) The fatality increase in the lighter cars tapered off from 

2.21 percent to 1.44 percent while the societal benefit of mass reduction in the heaviest LTVs 

diminished from 1.90 percent to 0.39 percent and is no longer statistically significant. 

The agencies believe that the changes may be due to a combination of both changes in the 

characteristics of newer vehicles and revisions to the analysis. NHTSA believes, above all, that 

several light, small car models with poor safety performance were discontinued by 2000 or 

during 2000-2007. Also, the tendency of light, small vehicles to be driven poorly is not as strong 

as it used to be – perhaps in part because safety improvements in lighter and smaller vehicles 

have made some good drivers more willing to buy them. Both agencies believe that at the other 

end of the weight/size spectrum, blocker beams and other voluntary compatibility improvements 

in LTVs, as well as compatibility-related self-protection improvements to cars, have made the 

heavier LTVs less aggressive in collisions with lighter vehicles (although the effect of mass 
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disparity remains). This report’s analysis of CUVs and minivans as a separate class of vehicles 

may have relieved some inaccuracies in the 2010 regression results for LTVs. Interestingly, the 

new actual-regression results are quite close to the previous report’s “lower-estimate scenario,” 

which was an attempt to adjust for supposed inaccuracies in some regressions and for a 

seemingly excessive trend toward higher crash rates in smaller and lighter cars. 

The principal difference between the heavier vehicles, especially truck-based LTVs, and 

the lighter vehicles, especially passenger cars, is that mass reduction has a different effect in 

collisions with another car or LTV. When two vehicles of unequal mass collide, the delta V is 

higher in the lighter vehicle, in the same proportion as the mass ratio. As a result, the fatality 

risk is also higher. Removing some mass from the heavy vehicle reduces delta V in the lighter 

vehicle, where fatality risk is high, 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: fatality 

increase in the lighter cars, reduction in the heavier LTVs, and little effect in the intermediate 

groups. However, in some types of crashes, especially first event rollovers and impacts with 

fixed objects, mass reduction is usually not harmful and often beneficial, because the lighter 

vehicles respond more quickly to braking and steering and are often more stable because their 

center of gravity is lower. Offsetting that benefit is the continuing historical tendency of lighter 

and smaller vehicles to be driven less well – although it continues to be unknown why that is so, 

and to what extent, if any, the lightness or smallness of the vehicle contributes to people driving 

it less safely. 

The estimates of the model are formulated for each 100-pound reduction in mass; in other 

words, if risk increases by 1 percent for 100 pounds reduction in mass, it would increase by 2 

percent for a 200-pound reduction, and 3 percent for a 300-pound reduction (more exactly, 2.01 

percent and 3.03 percent, because the effects work like compound interest). Confidence bounds 

around the point estimates will grow wider by the same proportions. 

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, the model may become somewhat less accurate and it is difficult to assess 

the sensitivity to additional mass reduction greater than 100 pounds. The agencies recognize that 

the light-duty vehicle fleet in the 2017-2025 timeframe will be different than the 2000-2007 fleet 

analyzed for this study. Nevertheless, one consideration provides some basis for confidence. 

This is NHTSA’s fourth evaluation of the effects of mass reduction and/or downsizing, 

comprising databases ranging from MY 1985 to 2007. The results of the four studies are not 

identical, but they have been consistent up to a point. During this time period, many makes and 
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models have increased substantially in mass, sometimes as much as 30-40 percent.23 If the 

statistical analysis has, over the past years, been able to accommodate mass increases of this 

magnitude, perhaps it will also succeed in modeling the effects of mass reductions on the order 

of 10-20 percent, if they occur in the future. 

e. Report by Tom Wenzel, LBNL, “An Assessment of NHTSA’s Report 

‘Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2000-

2007 Passenger Cars and LTVs’”, 2011 

DOE contracted with Tom Wenzel of Lawrence Berkeley National Laboratory to conduct 

an assessment of NHTSA’s updated 2011 study of the effect of mass and footprint reductions on 

US fatality risk per vehicle miles traveled, and to provide an analysis of the effect of mass and 

footprint reduction on casualty risk per police-reported crash, using independent data from 

thirteen states. The assessment has been completed and reviewed by NHTSA and EPA staff, and 

a draft final version is included in the docket of today’s rulemaking; the separate analysis of 

crash data from thirteen states will be completed and included in the docket shortly. Both reports 

will be peer reviewed by outside experts. 

The LBNL report replicates Kahane’s analysis for NHTSA, using the same data and 

methods, and in many cases using the same SAS programs. The Wenzel report finds that 

although mass reduction in lighter (less than 3,106 lbs) cars leads to a statistically significant 

1.44% increase in fatality risk per vehicle miles travelled (VMT), the increase is small. He tests 

this result for sensitivity to changes in specifications of the regression models and what data are 

used. In addition Wenzel shows that there is a wide range in fatality rates by vehicle model for 

models that have the same mass, even after accounting for differences in drivers’ age and gender, 

safety features installed, and crash times and locations. This section summarizes the results of 

the Wenzel assessment of the most recent NHTSA analysis. 

The LBNL report highlights the effect of the other driver, vehicle, and crash control 

variables, in addition to the effect of mass and footprint reduction, on risk. Some of the other 

variables NHTSA included in its regression models have much larger effects on fatality risk than 

mass or footprint reduction. For example, the models indicate that a 100-lb increase in the mass 

of a lighter car results in a 1.44% reduction in fatality risk; this is the largest estimated effect of 

changes in vehicle mass, and the only one that is statistically significant. For comparison this 

reduction in fatality risk could also be achieved by a 13% increase in 4-door sedans equipped 

with ESC. 

 23 For example, one of the most popular models of small 4-door sedans increased in curb weight from 1,939 pounds 

in MY 1985 to 2,766 pounds in MY 2007, a 43 percent 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 2-door 

cab and rear-wheel drive; and a popular minivan from 2,940 to 3,862 pounds (31%). 
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The 1.44% increase in risk from reducing mass in the lighter cars was tested for 

sensitivity changes in the specification of, or the data used in, the regression models. For 

example, using the current distribution of crashes, rather than adjusting the distribution to that 

expected after full adoption of ESC, reduces the effect to 1.18%; excluding the calendar year 

variables from the model, which may be weakening the modeled benefits of vehicle safety 

technologies, reduces the effect to 1.39%; and including vehicle make in the model increases the 

effect to 1.81%. The results also are sensitive to the selection of data to include in the analysis: 

excluding bad drivers increases the effect to 2.03%, while excluding crashes involving alcohol or 

drugs increases the effect to 1.66%, and including sports, police, and all-wheel drive cars 

increases the effect to 1.64%. Finally, changing the definition of risk also affects the result for 

lighter cars: using the number of fatalities per induced exposure crash reduces the effect to -

0.24% (that is, a 0.24% reduction in risk), while using the number of fatal crashes (rather than 

total fatalities) per VMT increases the effect to 1.84%. These sensitivity tests, except one, 

changed the estimated coefficient by less than 1 percentage point, which is within its statistical 

confidence bounds of 0.29 to 2.59 percent and may be considered compatible with the baseline 

result. Using two or more variables that are strongly correlated in the same regression model 

(referred to as multicollinearity) can lead to inaccurate results. However, the correlation between 

vehicle mass and footprint may not be strong enough to cause serious concern. Experts suggest 

that a correlation of greater than 0.60 (or a variance inflation factor of 2.5) raises concern about 

multicollinearity.24 The correlation between vehicle mass and footprint ranges from over 0.80 

for four-door sedans, pickups, and SUVs, to about 0.65 for two-door cars and CUVs, to 0.26 for 

minivans; when pickups and SUVs are considered together, the correlation between mass and 

footprint is 0.65. Wenzel notes that the 2011 NHTSA report recognizes that the “near” 

multicollinearity between mass and footprint may not be strong enough to invalidate the results 

from a regression model that includes both variables. In addition, NHTSA included several 

analyses to address possible effects of the near-multicollinearity between mass and footprint. 

First, NHTSA ran a sensitivity model specification, where footprint is not held constant, 

but rather allowed to vary as mass varies (i.e. NHTSA ran a regression model which includes 

mass but not footprint). If the multicollinearity was so great that including both variables in the 

same model gave misleading results, removing footprint from the model could give mass 

coefficients five or more percentage points different than keeping it in the model. NHTSA’s 

sensitivity test indicates that when footprint is allowed to vary with mass, the effect of mass 

reduction on risk increases from 1.44% to 2.64% for lighter cars, and from a non-significant 

0.47% to a statistically-significant 1.94% for heaver cars (changes of less than two percentage 

points); however, the effect of mass reduction on light trucks is unchanged, and is still not 

statistically significant for CUVs/minivans. 

 

24 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy 

Standards; Final Rule, April 1, 2010, Section II.G.3., page 139. 
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Second, NHTSA conducted a stratification analysis of the effect of mass reduction on 

risk by dividing vehicles into deciles based on their footprint, and running a separate regression 

model for each vehicle and crash type, for each footprint decile (3 vehicle types times 9 crash 

types times 10 deciles equals 270 regressions). This analysis estimates the effect of mass 

reduction on risk separately for vehicles with similar footprint. The analysis indicates that mass 

reduction does not consistently increase risk across all footprint deciles for any combination of 

vehicle type and crash type. Mass reduction increases risk in a majority of footprint deciles for 

13 of the 27 crash and vehicle combinations, but few of these increases are statistically 

significant. On the other hand, mass reduction decreases risk in a majority of footprint deciles 

for 9 of the 27 crash and vehicle combinations; in some cases these risk reductions are large and 

statistically significant.25 If reducing vehicle mass while maintaining footprint inherently leads 

to an increase in risk, the coefficients on mass reduction should be more consistently positive, 

and with a larger R2

, across the 27 vehicle/crash combinations, than shown in the analysis. 

These findings are consistent with the conclusion of the basic regression analyses, namely, that 

the effect of mass reduction while holding footprint constant, if any, is small. 

One limitation of using logistic regression to estimate the effect of mass reduction on risk 

is that a standard statistic to measure the extent to which the variables in the model explain the 

range in risk, equivalent to the R2

 statistic in a linear regression model, does not exist. (SAS 

does generate a pseudo-R2

 value for logistic regression models; in almost all of the NHTSA 

regression models this value is less than 0.10). For this reason LBNL conducted an analysis of 

risk versus mass by vehicle model. LBNL used the results of the NHTSA logistic regression 

model to predict the number of fatalities expected after accounting for all vehicle, driver, and 

crash variables included in the NHTSA regression model except for vehicle weight and footprint. 

LBNL then plotted expected fatality risk per VMT by vehicle model against the mass of each 

model, and analyzed the change in risk as mass increases, as well as how much of the change in 

risk was explained by all of the variables included in the model. 

The analysis indicates that, after accounting for all the variables, risk does decrease as 

mass increases; however, risk and mass are not strongly correlated, with the R2

 ranging from 

0.33 for CUVs to less than 0.15 for all other vehicle types (as shown in Figure x). This means 

that, on average, risk decreases as mass increases, but the variation in risk among individual 

vehicle models is stronger than the trend in risk from light to heavy vehicles. For fullsize (i.e. 3⁄4- 

and 1-ton) pickups, risk increases as mass increases, with an R2

 of 0.43, consistent with 

NHTSA’s basic regression results for the heavier LTVs (societal risk increases as mass 

increases). LBNL also examined the relationship between residual risk, that is the remaining 

unexplained risk after accounting for all vehicle, driver and crash variables, and mass, and found 

similarly poor correlations. This implies that the remaining factors not included in the regression 

 25 And in 5 of the 27 crash and vehicle combinations, mass reduction increased risk in 5 deciles and decreased risk 

in 5 deciles. 
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Page 21 of 33

model that account for the observed range in risk by vehicle model also are not correlated with 

mass. (LBNL found similar results when the analysis compared risk to vehicle footprint.) 

Figure Error! No text of specified style in document.-1 indicates that some vehicles on 

the road today have the same, or lower, fatality rates than models that weigh substantially more, 

and are substantially larger in terms of footprint. After accounting for differences in driver age 

and gender, safety features installed, and crash times and locations, there are numerous examples 

of different models with similar weight and footprint yet widely varying fatality rates. The 

variation of fatality rates among individual models may reflect differences in vehicle design, 

differences in the drivers who choose such vehicles (beyond what can be explained by 

demographic variables such as age and gender), and statistical variation of fatality rates based on 

limited data for individual models. Differences in vehicle design can, and already do, mitigate 

some safety penalties from reduced mass; this is consistent with NHTSA’s opinion that some of 

the changes in its regression results between the 2003 study and the 2011 study are due to the 

redesign or removal of certain smaller and lighter models of poor design. 

Figure Error! No text of specified style in document.-1 Predicted US fatality risk per 

VMT vs. curb weight by vehicle type, after accounting for all driver, crash, and vehicle 

variables except mass and footprint 

R2 = 0.12 

R2 = 0.04 

R2 = 0.00 

R2 = 0.43 

R2 = 0.05 

R2 = 0.33 

R2 = 0.09 

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

400 

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600 

Predicted US fatality risk (crash fdatalities per 1010 VMT) 

Curb weight (lbs) 

2-dr cars 

4-dr cars 

Small pickups 

Fullsize pickups 

SUVs 

CUVs 

Minivans 
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f. Based on this information, what do the agencies consider to be the current state of 

statistical research on vehicle mass and safety? 

The agencies believe that statistical analysis of historical crash data continues to be an 

informative and important tool in assessing the potential safety impacts of the proposed 

standards. The effect of mass reduction while maintaining footprint is a complicated topic and 

there are open questions whether future designs will reduce the historical correlation between 

weight and size. It is important to note that while the updated database represents more current 

vehicles with technologies more representative of vehicles on the road today, they still do not 

fully represent what vehicles will be on the road in the 2017-2025 timeframe. The vehicles 

manufactured in the 2000-2007 timeframe were not subject to footprint-based fuel economy 

standards. The agencies expect that the attribute-based standards will likely facilitate the design 

of vehicles such that manufacturers may reduce mass while maintaining footprint. Therefore, it is 

possible that the analysis for 2000-2007 vehicles may not be fully representative of the vehicles 

that will be on the road in 2017 and beyond. 

While we recognize that statistical analysis of historical crash data may not be the only 

way to think about the future relationship between vehicle mass and safety, we also recognize 

that other assessment methods are also subject to uncertainties, which makes statistical analysis 

of historical data an important starting point if employed mindfully and recognized for how it 

can be useful and what its limitations may be. 

NHTSA undertook the independent review of statistical studies and held the mass-safety 

workshop in February 2011 in order to help the agencies sort through the ongoing debates over 

what statistical analysis of historical data is actually telling us. Previously, the agencies have 

assumed that differences in results were due in part to inconsistent databases; by creating the 

updated common database and making it publicly available, we are hopeful that that aspect of the 

problem has been resolved, and moreover, the UMTRI review suggested that differences in data 

were probably less significant than the agencies may have thought. Statistical analyses of 

historical crash data should be examined for potential multicollinearity issues. The agencies will 

continue to monitor issues with multicollinearity in our analyses, and hope that outside 

researchers will do the same. And finally, based on the findings of the independent review, the 

agencies continue to be confident that Kahane’s analysis is one of the best for the purpose of 

analyzing potential safety effects of future CAFE and GHG standards. UMTRI concluded that 

Kahane’s approach is valid, and Kahane has continued and refined that approach for the current 

analysis. The NHTSA 2011 statistical fatality report finds directionally similar but less 

statistically significant relationships between vehicle mass, size, and footprint, as discussed 

above. Based on these findings, the agencies believe that in the future, fatalities due to mass 

reduction will be best reduced if mass reduction is concentrated in the heaviest vehicles. 

NHTSA considers part of the reason that more recent historical data shows a dampened effect in 

the relationship between mass reduction and safety is that all vehicles, including traditionally 
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Page 23 of 33

lighter ones, grew heavier during that timeframe (2000s). As lighter vehicles might become 

more prevalent in the fleet again over the next decade, it is possible that the trend could 

strengthen again. On the other hand, extensive use of new lightweight materials and optimized 

vehicle design may weaken the relationship. Future updated analyses will be necessary to 

determine how the effect of mass reduction on risk changes over time. 

Both agencies agree that there are several identifiable safety trends already in place or 

expected to occur in the foreseeable future that are not accounted for in the study, since they 

were not in effect at the time that the vehicles in question were manufactured. For example, 

there are two important new safety standards that have already been issued and will be phasing in 

after MY 2008. FMVSS No. 126 (49 CFR § 571.126) requires electronic stability control in all 

new vehicles by MY 2012, and the upgrade to FMVSS No. 214 (Side Impact Protection, 49 CFR 

§ 571.214) will likely result in all new vehicles being equipped with head-curtain air bags by 

MY 2014. Additionally, we anticipate continued improvements in driver (and passenger) 

behavior, such as higher safety belt use rates. All of these may tend to reduce the absolute 

number of fatalities. On the other hand, as crash avoidance technology improves, future 

statistical analysis of historical data may be complicated by a lower number of crashes. In 

summary, the agencies have relied on the coefficients in the Kahane 2011 study for estimating 

the potential safety effects of the proposed CAFE and GHG standards for MYs 2017-2025, based 

on our assumptions regarding the amount of mass reduction that could be used to meet the 

standards in a cost-effective way without adversely affecting safety. Section E below discusses 

the methodology used by the agencies in more detail; while the results of the safety effects 

analysis are less significant than the results in the MY 2012-2016 final rule, the agencies still 

believe that any statistically significant results warrant careful consideration of the assumptions 

about appropriate levels of mass reduction on which to base future CAFE and GHG standards, 

and have acted accordingly in developing the proposed standards. 

4. How do the agencies think technological solutions might affect the safety estimates 

indicated by the statistical analysis? 

As mass reduction becomes a more important technology option for manufacturers in 

meeting future CAFE and GHG standards, manufacturers will invest more and more resources in 

developing increasingly lightweight vehicle designs that meet their needs for manufacturability 

and the public’s need for vehicles that are also safe, useful, affordable, and enjoyable to drive. 

There are many different ways to reduce mass, as discussed in Chapter 3 of this TSD and in 

Sections II, III, and IV of the preamble, and a considerable amount of information is available 

today on lightweight vehicle designs currently in production and that may be able to be put into 

production in the rulemaking timeframe. Discussion of lightweight material designs from 

NHTSA’s workshop is presented below. 
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Besides “lightweighting” technologies themselves, though, there are a number of 

considerations when attempting to evaluate how future technological developments might affect 

the safety estimates indicated by the statistical analysis. As discussed in the first part of this 

chapter, for example, careful changes in design and/or materials used might mitigate some of the 

potential decrease in safety from mass reduction – through improved distribution of crash pulse 

energy, etc. – but these techniques can sometimes cause other problems, such as increased crash 

forces on vehicle occupants that have to be mitigated, or greater aggressivity against other 

vehicles in crashes. Manufacturers may develop new and better restraints – air bags, seat belts, 

etc. – to protect occupants in lighter vehicles in crashes, but NHTSA’s current safety standards 

for restraint systems are designed based on the current fleet, not the yet-unknown future fleet. 

The agency will need to monitor trends in the crash data to see whether changes to the safety 

standards (or new safety standards) become necessary. Manufacturers are also increasingly 

investigating a variety of crash avoidance technologies – ABS, electronic stability control (ESC), 

lane departure warnings, vehicle-to-vehicle (V2V) communications – that, as they become more 

prevalent in the fleet, are expected to reduce the number of overall crashes, and fatal, crashes. 

Until these technologies are present in the fleet in greater numbers, however, it will be difficult 

to assess whether they can mitigate the observed relationship between vehicle mass and safety in 

the historical data. 

Along with the California Air Resources Board (CARB), the agencies have initiated 

several projects to estimate the maximum potential for advanced materials and improved designs 

to reduce mass in the MY 2017-2021 timeframe, while continuing to meeting safety regulations 

and maintaining functionality of vehicles. Another NHTSA-sponsored study will estimate the 

effects of these design changes on overall fleet safety. 

A. NHTSA has awarded a contract to Electricore, with EDAG and George Washington 

University (GWU) as subcontractors, to study the maximum feasible amount of mass 

reduction for a mid-size car – specifically, a Honda Accord. The study tore down a MY 

2011 Honda Accord, studied each component and sub-system, and then redesigned each 

component and sub-system trying to maximize the amount of mass reduction with 

technologies that are considered feasible for 200,000 units per year production volume 

during the time frame of this rulemaking. Electricore and its sub-contractors are 

consulting industry leaders and experts for each component and sub-system when 

deciding which technologies are feasible. Electricore and its sub-contractors are also 

building detailed CAD/CAE/powertrain models to validate vehicle safety, stiffness, NVH, 

durability, drivability and powertrain performance. For OEM-supplied parts, a detailed 

cost model is being built based on a Technical Cost Modeling (TCM) approach developed 

by the Massachusetts Institute of Technology (MIT) Materials Systems Laboratory’s 

research26 to estimate the costs to OEMs for manufacturing parts. The cost will be broken 

 26 Frank Field, Randolph Kirchain and Richard Roth, Process cost modeling: Strategic engineering and economic 

evaluation of materials technologies, JOM Journal of the Minerals, Metals and Materials Society, Volume 59, 

Number 10, 21-32. Available at http://msl.mit.edu/pubs/docs/Field_KirchainCM_StratEvalMatls.pdf (last accessed 

Aug. 22, 2011). 
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down into each of the operations involved in the manufacturing; for example, for a sheet 

metal part, production costs will be estimated from the blanking of the steel coil to the 

final operation to fabricate the component. Total costs are then categorized into fixed 

cost, such as tooling, equipment, and facilities; and variable costs such as labor, material, 

energy, and maintenance. These costs will be assessed through an interactive process 

between the product designer, manufacturing engineers, and cost analysts. For OEMpurchased parts, the cost will be estimated by consultation with experienced cost analysts 

and Tier 1 system suppliers. This study will help to inform the agencies about the feasible 

amount of mass reduction and the cost associated with it. NHTSA intends to have this 

study completed and peer reviewed before July 2012, in time for it to play an integral role 

in informing the final rule. 

B. EPA has awarded a similar contract to FEV, with EDAG and Monroe & Associates, Inc. 

as subcontractors, to study the maximum feasible amount of mass reduction for a mid-size 

CUV (cross over vehicle) specifically, a Toyota Venza. The study tears down a MY 2010 

vehicle, studies each component and sub-system, and then redesigns each component and 

sub-system trying to maximize the amount of mass reduction with technologies that are 

considered feasible for high volume production for a 2017 MY vehicle. FEV in 

coordination with EDAG is building detailed CAD/CAE/powertrain models to validate 

vehicle safety, stiffness, NVH, durability, drivability and powertrain performance to 

assess the safety of this new design. This study builds upon the low development (20% 

mass reduction) design in the 2010 Lotus Engineering study “An Assessment of Mass 

Reduction Opportunities for a 2017-2020 Model Year Vehicle Program”. This study 

builds upon the low development (20% mass reduction) design in the 2010 Lotus 

Engineering study “An Assessment of Mass Reduction Opportunities for a 2017-2020 

Model Year Vehicle Program”. This study will undergo a peer review. EPA intends to 

have this study completed and peer reviewed before July 2012, in time for it to play an 

integral role in informing the final rule. 

C. California Air Resources Board (CARB) has awarded a contract to Lotus Engineering, to 

study the maximum feasible amount of mass reduction for a mid-size CUV (cross over 

vehicle) specifically, a Toyota Venza. The study will concentrate on the Body-in-White 

and closures in the high development design (40% mass reduction) in the Lotus 

Engineering study cited above. The study will provide an updated design with crash 

simulation, detailed costing and manufacturing feasibility of these two systems for a 

MY2020 high volume production vehicle. This study will undergo a peer review. EPA 

intends to have this study completed and peer reviewed before July 2012, in time for it to 

play an integral role in informing the final rule. 

D. NHTSA has contracted with George Washington University (GWU) to build a fleet 

simulation model to study the impact and relationship of light-weight vehicle design and 

injuries and fatalities. This study will also include an evaluation of potential 

countermeasures to reduce any safety concerns associated with lightweight vehicles. 

NHTSA will include three light-weighted vehicle designs in this study: the one from 

Electricore/EDAG/GWU mentioned above, one from Lotus Engineering funded by 

California Air Resource Board for the second phase of the study, evaluating mass 
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Page 26 of 33

reduction levels around 35 percent of total vehicle mass, and two funded by EPA and the 

International Council on Clean Transportation (ICCT). This study will help to inform the 

agencies about the possible safety implications for light-weight vehicle designs and the 

appropriate counter-measures,27 if applicable, for these designs, as well as the feasible 

amounts of mass reduction. All of these analyses are expected to be finished and peerreviewed before July 2012, in time to inform the final rule. 

a. NHTSA workshop on vehicle mass, size and safety 

As stated above, in section C.2, on February 25, 2011, NHTSA hosted a workshop on 

mass reduction, vehicle size, and fleet safety at the Headquarters of the US Department of 

Transportation in Washington, DC. The purpose of the workshop was to provide the agencies 

with a broad understanding of current research in the field and provide stakeholders and the 

public with an opportunity to weigh in on this issue. The agencies also created a public docket 

to receive comments from interested parties that were unable to attend. The presentations were 

divided into two sessions that addressed the two expansive sets of issues. The first session 

explored statistical evidence of the roles of mass and size on safety, and is summarized in section 

C.2. The second session explored the engineering realities of structural crashworthiness, 

occupant injury and advanced vehicle design, and is summarized here. The speakers in the 

second session included Stephen Summers of NHTSA, Gregg Peterson of Lotus Engineering, 

Koichi Kamiji of Honda, John German of the International Council on Clean Transportation 

(ICCT), Scott Schmidt of the Alliance of Automobile Manufacturers, Guy Nusholtz of Chrysler, 

and Frank Field of the Massachusetts Institute of Technology. 

The second session explored what degree of weight reduction and occupant protection are 

feasible from technical, economic, and manufacturing perspectives. Field emphasized that 

technical feasibility alone does not constitute feasibility in the context of vehicle mass reduction. 

Sufficient material production capacity and viable manufacturing processes are essential to 

economic feasibility. Both Kamiji and German noted that both good materials and good designs 

will be necessary to reduce fatalities. For example, German cited the examples of hexagonally 

structured aluminum columns, such as used in the Honda Insight, that can improve crash 

absorption at lower mass, and of high-strength steel components that can both reduce weight and 

improve safety. Kamiji made the point that widespread mass reduction will reduce the kinetic 

energy of all crashes which should produce some beneficial effect. 

Summers described NHTSA’s plans for a model to estimate fleetwide safety effects 

based on an array of vehicle-to-vehicle computational crash simulations of current and 

anticipated vehicle designs. In particular, three computational models of lightweight vehicles are 

under development. They are based on current vehicles that have been modified to substantially 

reduce mass. The most ambitious was the “high development” derivative of a Toyota Venza 

 27 Countermeasures could potentially involve improved front end structure, knee bags, seat ramps, buckle 

pretensioners, and others. 
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developed by Lotus Engineering and discussed by Mr. Peterson. Its structure currently contains 

about 75% aluminum, 12% magnesium, 8% steel, and 5% advanced composites. Peterson 

expressed confidence that the design had the potential to meet federal safety standards. Nusholtz 

emphasized that computational crash simulations involving more advanced materials were less 

reliable than those involving traditional metals such as aluminum and steel. 

Nusholtz presented a revised data-based fleet safety model in which important vehicle 

parameters were modeled based on trends from current NCAP crash tests. For example, crash 

pulses and potential intrusion for a particular size vehicle were based on existing distributions. 

Average occupant deceleration was used to estimate injury risk. Through a range of simulations 

of modified vehicle fleets, he was able to estimate the net effects of various design strategies for 

lighter weight vehicles, such as various scaling approaches for vehicle stiffness or intrusion. The 

approaches were selected based on engineering requirements for modified vehicles. Transition 

from the current fleet was considered. He concluded that protocols resulting in safer transitions 

(e.g., removing more mass from heavier vehicles with appropriate stiffness scaling according to a 

3/2 power law) were not generally consistent with those that provide the greatest reduction in 

GHG production. 

German discussed several important points on the future of mass reduction. Similar to 

Kahane’s discussion of the difficulties of isolating the impact of weight reduction, German stated 

that other important variables, such as vehicle design and compatibility factors, must be held 

constant in order for size or weight impacts to be quantified in statistical analyses. He presented 

results that, compared to driver, driving influences, and vehicle design influences, the safety 

impacts of size and weight are small and difficult to quantify. He noted that several scenarios, 

such as rollovers, greatly favored the occupants of smaller and lighter cars once a crash occurred. 

He pointed out that if size and design are maintained, lower weight should translate into a lower 

total crash force. He thought that advanced material designs have the potential to “decouple” the 

historical correlation between vehicle size and weight, and felt that effective design and driver 

attributes may start to dominate size and weight issues in future vehicle models. 

Other presenters noted industry’s perspective of the effect of incentivizing weight 

reduction. Field highlighted the complexity of institutional changes that may be necessitated by 

weight reduction, including redesign of material and component supply chains and 

manufacturing infrastructure. Schmidt described an industry perspective on the complicated 

decisions that must be made in the face of regulatory change, such as evaluating goals, gains, and 

timing. 

Field and Schmidt noted that the introduction of technical innovations is generally an 

innate development process involving both tactical and strategic considerations that balance 

desired vehicle attributes with economic and technical risk. In the absence of challenging 

regulatory requirements, a substantial technology change is often implemented in stages, starting 

with lower volume pilot production before a commitment is made to the infrastructure and 
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Page 28 of 33

supply chain modifications necessary for inclusion on a high-volume production model. Joining, 

damage characterization, durability, repair, and significant uncertainty in final component costs 

are also concerns. Thus, for example, the widespread implementation of high-volume composite 

or magnesium structures might be problematic in the short or medium term when compared to 

relatively transparent aluminum or high strength steel implementations. Regulatory changes will 

affect how these tradeoffs are made and these risks are managed. 

Koichi Kamiji presented data showing in increased use of high strength steel in their 

Honda product line to reduced vehicle mass and increase vehicle safety. He stated that mass 

reduction is clearly a benefit in 42% of all fatal crashes because absolute energy is reduced. He 

followed up with slides showing the application of certain optimized it designs can improve 

safety even when controlling for weight and size. 

A philosophical theme developed that explored the ethics of consciously allowing the 

total societal harm associated with mass reduction to approach the anticipated benefits of 

enhanced safety technologies. Although some participants agreed that there may eventually be 

specific fatalities that would not have occurred without downsizing, many also agreed that safety 

strategies will have to be adapted to the reality created by consumer choices, and that “We will 

be ok if we let data on what works – not wishful thinking – guide our strategies.” 

5. How have the agencies estimated safety effects for the proposed standards? 

a. What was the agencies’ methodology for estimating safety effects for the 

proposed standards? 

As explained above, the agencies consider the 2011 statistical analysis of historical crash 

data by NHTSA to represent the best estimates of the potential relationship between mass 

reduction and fatality increases in the future fleet. This section discusses how the agencies used 

NHTSA’s 2011 analysis to calculate specific estimates of safety effects of the proposed 

standards, based on the analysis of how much mass reduction manufacturers might use to meet 

the proposed standards. 

Neither the proposed CAFE/GHG standards nor the agencies’ analysis mandates mass 

reduction, or mandates that mass reduction occur in any specific manner. However, mass 

reduction is one of the technology applications available to the manufacturers and a degree of 

mass reduction is used by both agencies’ models to determine the capabilities of manufacturers 

and to predict both cost and fuel consumption/emissions impacts of improved CAFE/GHG 

standards. We note that the amount of mass reduction selectedwe assume will result from for 

this rulemaking is based on our assumptions about how much is technologically feasible without 

compromising safety. While we are confident that manufacturers will build safe vehicles, we 

cannot predict with certainty that they will choose to reduce mass in exactly the ways that the 

agencies have analyzed in response to the standards. In the event that manufacturers ultimately 
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choose to reduce mass and/or footprint in ways not analyzed or anticipated by the agencies, the 

safety effects of the rulemaking may likely differ from the agencies’ estimates. 

NHTSA utilized the 2011 Kahane study relationships between weight and safety, 

expressed as percent changes in fatalities per 100-pound weight reduction while holding 

footprint constant. However, as mentioned previously, there are several identifiable safety trends 

already occurring, or expected to occur in the foreseeable future, that are not accounted for in the 

study. For example, the two important new safety standards that were discussed above for 

electronic stability control and head curtain airbags, have already been issued and began phasing 

in after MY 2008. The recent shifts in market shares from pickups and SUVs to cars and CUVs 

may continue, or accelerate, if gasoline prices remain high, or rise further. The growth in vehicle 

miles travelled may continue to stagnate if the economy does not improve, or gasoline prices 

remain high. And improvements in driver (and passenger) behavior, such as higher safety belt 

use rates, may continue. All of these will tend to reduce the absolute number of fatalities in the 

future. The agency estimated the overall change in fatalities by calendar year after adjusting for 

ESC, Side Impact Protection, and other Federal safety standards and behavioral changes 

projected through this time period. The smaller percent changes in risk from mass reduction 

(from the 2011 NHTSA analysis), coupled with the reduced number of baseline fatalities, results 

in smaller absolute increases in fatalities than those predicted in the 2010 rulemaking. 

NHTSA examined the impacts of identifiable safety trends over the lifetime of the 

vehicles produced in each model year. An estimate of these impacts was contained in a previous 

agency report.28 The impacts were estimated on a year-by-year basis, but could be examined in a 

combined fashion. Using this method, we estimate a 12.6 percent reduction in fatality levels 

between 2007 and 2020 for the combination of safety standards and behavioral changes 

anticipated (ESC, head-curtain air bags, and increased belt use). Since the same safety standards 

are taking effect in the same years, the estimates derived from applying NHTSA fatality 

percentages to a baseline of 2007 fatalities were thus multiplied by 0.874 to account for changes 

that NHTSA believes will take place in passenger car and light truck safety between the 2007 

baseline on-road fleet used for this particular safety analysis and year 2025. 

To estimate the amount of mass reduction we expect manufacturers to [assume 

manufacturers will?] apply in the rulemaking analysis, the agencies considered fleet safety 

effects for mass reduction. As previously discussed and shown in Error! Reference source not 

 ls technologies, JOM Journal of the Minerals, Metals and Materials Society, Volume 59, Number 10, 21-32. 

Available at http://msl.mit.edu/pubs/docs/Field_KirchainCM_StratEvalMatls.pdf (last accessed Aug. 22, 2011). 28 Countermeasures could potentially involve improved front end structure, knee bags, seat ramps, buckle 

pretensioners, and others. 

28 Blincoe, L. and Shankar, U, “The Impact of Safety Standards and Behavioral Trends on Motor Vehicle Fatality 

Rates,” DOT HS 810 777, January 2007. See Table 4 comparing 2020 to 2007 (37,906/43,363 = 12.6% reduction 

(1-.126 = .874). Since 2008 was a recession year, it does not seem appropriate to use that as a baseline. We believe 

this same ratio should hold for this analysis which should compare 2025 to 2008. Thus, we are inclined to continue 

to use the same ratio. 
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found., the Kahane 2011 study shows that applying mass reduction to CUVs and light duty 

trucks will generally decrease societal fatalities, while applying mass reduction to passenger cars 

will increase fatalities. The CAFE model uses coefficients from the Kahane study along with the 

mass reduction level applied to each vehicle model to project societal fatality effects in each 

model year. NHTSA used the CAFE model and conducted iterative modeling runs varying the 

maximum amount of mass reduction applied to each subclass in order to identify a combination 

that achieved a high level of overall fleet mass reduction while not adversely affecting overall 

fleet safety. These maximum levels of mass reduction for each subclass were then used in the 

CAFE model for the rulemaking analysis. The agencies believe that mass reduction of up to 20 

percent is feasible on light trucks, CUVs and minivans, 29 but that less mass reduction should be 

implemented on other vehicle types to avoid increases in societal fatalities.. For this proposal, 

NHTSA used the mass reduction levels shown in Table Error! No text of specified style in 

document.-4. 

Table Error! No text of specified style in document.-15 Mass Reduction Levels Applied 

in CAFE Model 

Absolute 

% 

Subcompact 

and 

Subcompact 

Perf. PC

Compact 

and 

Compact 

Perf. PC

Midsize PC 

and Midsize 

Perf. PC

Large PC 

and Large 

Perf. PC

Minivan LT

Small, 

Midsize and 

Large LT

MR1* 0.0% 2.0% 1.5% 1.5% 1.5% 1.5%

MR2 0.0% 0.0% 5.0% 7.5% 7.5% 7.5%

MR3 0.0% 0.0% 0.0% 10.0% 10.0% 10.0%

MR4 0.0% 0.0% 0.0% 0.0% 15.0% 15.0%

MR5 0.0% 0.0% 0.0% 0.0% 20.0% 20.0%

Notes:

*MR1-MR5: different levels of mass reduction used in CAFE model 

After applying the mass reduction levels in the CAFE model, Table Error! No text of 

specified style in document.-5 shows the results of NHTSA’s safety analysis separately for each 

model year.30 These 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 (indicated by parentheses) means that fatalities are projected to decrease. The results are 

 

29 When applying mass reduction, NHSTA capped the maximum amount of mass reduction to 20 percent 

for any individual vehicle class. The 20 percent cap is the maximum amount of mass reduction the agencies believe 

to be feasible in MYs 2017-2025 time frame. 

30 NHTSA has changed the definitions of a passenger car and light truck for fuel economy purposes between the 

time of the Kahane 2003 analysis and this final rule. About 1.4 million 2 wheel drive SUVs have been redefined as 

passenger cars instead of light trucks. The Kahane 2011 analysis continues with the definitions used in the Kahane 

2003 analysis. Thus, there are different definitions between Tables IX-1 and IX-2 (which use the old definitions) 

and Table IX-3 (which uses the new definitions). 

Comment [A1]: Interagency commenters request 

agencies to explain what determines when each MR 

level is applied. Also, commenters request agencies 

to translate each MR level into mass for at least the 

averave vehicle in each category, either in this table 

or in a new one. 
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significantly affected by the assumptions put into the Volpe model to take more weight out of the 

heavy LTVs, CUVs, and minivans than out of other vehicles. As the negative coefficients only 

appear for LTVs greater than 4,594 lbs., CUVs, and minivans, a statistically improvement in 

safety can only occur if more weight is taken out of these vehicles than passenger cars or smaller 

light trucks. Combining passenger car and light truck safety estimates for the proposed standards 

results in an increase in fatalities over the lifetime of the nine model years of MY 2017-2025 of 4 

fatalities, broken up into an increase of 61 fatalities in passenger cars and 56 decrease in fatalities 

in light trucks. NHTSA also analyzed the results for different regulatory alternatives in Chapter 

IX of its PRIA; 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 that the weight reduction 

applies to. 

Table Error! No text of specified style in document.-5 NHTSA Calculated MassSafety-Related Fatality Impacts of the Proposed Standards over the Lifetime of the 

Vehicles Produced in each Model Year 

MY 

2017

MY 

2018

MY 

2019

MY 

2020

MY 

2021

MY 

2022

MY 

2023

MY 

2024

MY 

2025 Total

Passenger 

cars 

(2) (0) 11 16 15 13 12 1 (2) 63 

Light 

trucks 3 (7) 0 (13) (8) (15) (12) (8) 0 (56) 

Total 1 (8) 12 3 7 (2) 0 (8) (2) 4 

Using the same coefficients from the 2011 Kahane study, EPA used the OMEGA model 

to conduct a similar analysis. After applying these percentage increases to the estimated weight 

reductions per vehicle size by model year assumed in the Omega model, Table Error! No text of 

specified style in document.-6 shows the results of EPA’s safety analysis separately for each 

model year. These 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 means that fatalities are projected to decrease. For details, see the EPA RIA Chapter 3. 

Table Error! No text of specified style in document.-6 EPA Calculated Mass-SafetyRelated Fatality Impacts of the Proposed Standards over the Lifetime of the Vehicles 

Produced in each Model Year 

MY 

2017

MY 

2018

MY 

2019

MY 

2020

MY 

2021

MY 

2022

MY 

2023

MY 

2024

MY 

2025 Total

Passenger 

cars -3 -5 -8 -11 -14 8 32 58 86 143 

Light 

trucks -1 -1 -2 -2 -4 -35 -67 -99 -133 -343 

Total -3 -7 -10 -13 -18 -27 -34 -41 -47 -201 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

review. Subject to further policy review. 

Page 32 of 33

b. Why might the real-world effects be less than or greater than what the agencies 

have calculated? 

As discussed above the ways in which future technological advances could potentially 

mitigate the safety effects estimated for this rulemaking: lightweight vehicles could be designed 

to be both stronger and not more aggressive; restraint systems could be improved to deal with 

higher crash pulses in lighter vehicles; crash avoidance technologies could reduce the number of 

overall crashes; roofs could be strengthened to improve safety in rollovers. As also stated above, 

however, while we are confident that manufacturers will strive to build safe vehicles, it will be 

difficult for both the agencies and the industry to know with certainty ahead of time how crash 

trends will change in the future fleet as lightweighted vehicles become more prevalent. Going 

forward, Wwe will have to continue to monitor the crash data as well as changes in vehicle 

weight relative to what we expect.going forward.

Additionally, we note that the total amount of mass reduction used in the agencies’ 

analysis for this rulemaking were chosen based on our assumptions about how much is 

technologically feasible without compromising safety. Again, while we are confident that 

manufacturers are motivated to build safe vehicles, we cannot predict with certainty that they 

will choose to reduce mass in exactly the ways that the agencies have analyzed in response to the 

standards. In the event that manufacturers ultimately choose to reduce mass and/or footprint in 

ways not analyzed by the agencies, the safety effects of the rulemaking may likely differ from 

the agencies’ estimates. 

The agencies acknowledge it is possible that a manufacturer could theoretically “game” a 

footprint-based standard by redesigning a vehicle to increase wheelbase and/or track width. 

However, as NHTSA explained in promulgating MY 2008-2011 light truck CAFE standards and 

MY 2011 passenger car and light truck CAFE standards, and as the agencies jointly explained in 

promulgating MY 2012-2016 CAFE and GHG standards, the agencies believe that such 

engineering changes are significant enough to be unattractive as a measure to undertake solely to 

reduce compliance burdens. Similarly, the agencies acknowledge that a manufacturer could, 

without actually reengineering specific vehicles to increase footprint, shift production toward 

those that perform well compared to their respective footprint-based targets. However, as 

NHTSA and, more recently NHTSA and EPA have previously explained, because such 

production shifts would run counter to market demands, they would also be competitively 

unattractive. Based on this regulatory design, the analysis assumes this proposal will not have 

either of the effects described above. 

As discussed in Chapter 2 of the Draft Joint TSD, the agencies note that the standard is 

flat for vehicles smaller than 41 square feet and that downsizing in this category could help 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 

review. Subject to further policy review. 

Page 33 of 33

achieve overall compliance, if the vehicles are desirable to consumers. The agencies note that 

only xxxx percent of MY2008 passenger cars were below 41 square feet, and due to the overall 

lower level of utility of these vehicles, and the engineering challenges involved in ensuring that 

these vehicles meet all applicable federal motor vehicle safety standards (FMVSS), we expect a 

significant increase in this segment of the market in the future is unlikely. Please see Chapter 2 

of the Draft Joint TSD for additional discussion. 

We seek comment on the appropriateness of the overall analytic assumption that the 

attribute-based aspect of the proposed standards will have no effect on the overall distribution of 

vehicle footprints. Notwithstanding the agencies’ current judgment that such deliberate 

reengineering or production shift are unlikely as pure compliance strategies, both agencies are 

considering the potential future application of vehicle choice models, and anticipate that doing so 

could result in estimates that market shifts induced by changes in vehicle prices and fuel 

economy levels could lead to changes in fleet’s footprint distribution. However, neither agency 

is currently able to include vehicle choice modeling in our analysis. 

c. Do the agencies plan to make any changes in these estimates for the final rule? 

As discussed above, the agencies have based our estimates of safety effects due to the 

proposed standards on Kahane’s 2011 report. That report is currently undergoing peer review 

and is docketed for public review;31 the peer review comments and response to peer review 

comments, along with any revisions to the report in response to that review, will also be 

docketed there. Depending on the results of the peer review, our calculation of safety effects for 

the final rule will also be revised accordingly. The agencies will also consider any comments 

received on the proposed rule, and determine at that time whether and how our estimates should 

be changed in response to those comments. Additional studies published by the agencies or 

other independent researchers as previously discussed will also be considered, along with any 

other relevant information. 

 31 Kahane, C. J. (2011). “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2000-2007 

Passenger Cars and LTVs,”, July 2011. The report is available in the NHTSA docket, NHTSA-2010-0152. You can 

access the docket at http://www.regulations.gov/#!home by typing ‘NHTSA-2010-0152’ where it says “enter 

keyword or ID” and then clicking on “Search.”
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

1 

Below is a summary of interagency comments received during EO 12866 and EO 13563 review 

of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission 

Standards and Corporate Average Fuel Economy Standards”. They are for your consideration 

and are subject to further policy review.

Interagency commenters recommend that the agencies incorporate the following language into 

Chapter 8 of the EPA RIA, and relevant sections of the Joint Preamble, NHTSA RIA and TSD 

chapters: 

“For years, consumers have been learning about the benefits that accrue to them from 

owning and operating vehicles with greater fuel efficiency. Consumer demand has thus 

shifted towards such vehicles, not only because of higher fuel prices but also because 

consumers are getting better at making purchases based on the total cost of owning and 

operating a vehicle over its lifetime. This type of learning is expected to continue before 

and during the model years affected by this rule, particularly given the new fuel economy 

labels that should reinforce that learning. Therefore, some increase in the demand for, 

and production of, more fuel efficient vehicles is incorporated in the [alternative] baseline 

(i.e., without these rules) [developed by NHTSA]. The agency requests comment on the 

appropriateness of using a flat or rising baseline after 2016. 

The rule combined with the new and easier-to-understand fuel economy label required to 

be on all new vehicles beginning in 2012 may increase sales above baseline levels by 

hastening this very type of consumer learning: as more consumers experience the savings 

in time and expense from owning more fuel efficient vehicles, as a result of the rule, 

demand may shift yet further in the direction of the vehicles mandated under the rule. 

This learning can take place both within and across households. Most directly, the time 

and fuel savings associated with operating more fuel efficient vehicles will be more 

salient to individuals who own them, causing their subsequent purchase decisions to shift 

closer to minimizing the total cost of ownership over the lifetime of the vehicle. Second, 

this appreciation may spread across households through word of mouth and other forms 

of communications. Third, as more motorists value fuel efficiency, the price of used cars 

will better reflect fuel efficiency, further reducing the cost of owning more efficient 

vehicles for the buyers of new vehicles (since the resale price will go up). 

If these induced learning effects are strong, the rule could even increase total vehicle 

sales over time. These increased sales would not occur in the model years first affected 

by the rule, but they could occur once the induced learning takes place It is not possible 

to quantify these learning effects years in advance and that effect may be speeded or 

slowed by other factors that enter into a consumer’s valuation of fuel efficiency in 

selecting vehicles. 

The possibility that the rule will (after a lag for consumer learning) increase sales does 

not rest on the assumption that automobile manufacturers are failing to pursue profitable 

opportunities to supply the vehicles consumers demand. Without the rule, no individual 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

2 

automobile manufacturer would find it profitable to produce the more efficient vehicles 

mandated under the rule. In particular, no individual company can internalize the future 

boost to demand resulting from the rule. If one company were to make more efficient 

vehicles, counting on consumer learning to enhance demand in the future, that company 

would capture only a fraction of the extra sales so generated, because the learning at issue 

is not specific to any one company's fleet. Many of the extra sales would accrue to that 

company's competitors. In the language of economics, consumer learning about the 

benefits of fuel efficient vehicles involves positive externalities (spillovers) from one 

company to the others. [Footnote: Industry-wide positive spillovers of this type are 

hardly unique to this situation. In many industries companies form trade associations to 

promote industry-wide public goods. For example, merchants in a given locale may band 

together to promote tourism in that locale. Antitrust law recognizes that this type of 

coordination can increase output.] 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

1 

Below is a summary of interagency comments received during EO 12866 and EO 13563 review 

of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission 

Standards and Corporate Average Fuel Economy Standards”. They are for your consideration 

and are subject to further policy review.

Interagency commenters recommend that the agencies incorporate the following language into 

Chapter 8 of the EPA RIA, and into all relevant sections of the Joint Preamble, NHTSA RIA and 

TSD chapters: 

“For years, consumers have been learning about the benefits that accrue to them from owning 

and operating vehicles with greater fuel efficiency. Consumer demand has thus shifted towards 

such vehicles, not only because of higher fuel prices but also because many consumers are 

learning about the value of purchases based not only on initial costs but also on the total cost of 

owning and operating a vehicle over its lifetime. This type of learning is expected to continue 

before and during the model years affected by this rule, particularly given the new fuel economy 

labels that clarify potential economic effects and should therefore reinforce that 

learning. Therefore, some increase in the demand for, and production of, more fuel efficient 

vehicles is incorporated in the [alternative] baseline (i.e., without these rules) [developed by 

NHTSA]. The agency requests comment on the appropriateness of using a flat or rising baseline 

after 2016. 

Today’s proposed rule, combined with the new and easier-to-understand fuel economy label 

required to be on all new vehicles beginning in 2012, may increase sales above baseline levels by 

hastening this very type of consumer learning. As more consumers experience, as a result of the 

rule, the savings in time and expense from owning more fuel efficient vehicles, demand may 

shift yet further in the direction of the vehicles mandated under the rule. This social learning can 

take place both within and across households, as consumers learn from one another. 

First and most directly, the time and fuel savings associated with operating more fuel efficient 

vehicles will be more salient to individuals who own them, causing their subsequent purchase 

decisions to shift closer to minimizing the total cost of ownership over the lifetime of the 

vehicle. Second, this appreciation may spread across households through word of mouth and 

other forms of communications. Third, as more motorists experience the time and fuel savings 

associated with greater fuel efficiency, the price of used cars will better reflect such efficiency, 

further reducing the cost of owning more efficient vehicles for the buyers of new vehicles (since 

the resale price will increase). 

If these induced learning effects are strong, the rule could potentially increase total vehicle sales 

over time. These increased sales would not occur in the model years first affected by the rule, 

but they could occur once the induced learning takes place . It is not possible to quantify these 

learning effects years in advance and that effect may be speeded or slowed by other factors that 

enter into a consumer’s valuation of fuel efficiency in selecting vehicles. 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

2 

The possibility that the rule will (after a lag for consumer learning) increase sales need not rest 

on the assumption that automobile manufacturers are failing to pursue profitable opportunities to 

supply the vehicles that consumers demand. In the absence of the rule, no individual automobile 

manufacturer would find it profitable to move toward the more efficient vehicles mandated under 

the rule. In particular, no individual company can fully internalize the future boost to demand 

resulting from the rule. If one company were to make more efficient vehicles, counting on 

consumer learning to enhance demand in the future, that company would capture only a fraction 

of the extra sales so generated, because the learning at issue is not specific to any one company's 

fleet. Many of the extra sales would accrue to that company's competitors. 

In the language of economics, consumer learning about the benefits of fuel efficient vehicles 

involves positive externalities (spillovers) from one company to the others. [Footnote: Industrywide positive spillovers of this type are hardly unique to this situation. In many industries, 

companies form trade associations to promote industry-wide public goods. For example, 

merchants in a given locale may band together to promote tourism in that locale. Antitrust law 

recognizes that this type of coordination can increase output.] These positive externalities may 

lead to benefits for manufacturers as a whole. 

We emphasize that this discussion has been tentative and qualified. To be sure, social learning of 

related kinds has been identified in a number of contexts. [Footnote: See Hunt Alcott, Social 

Norms and Energy Conservation, Journal of Public Economics (forthcoming 2011), available at 

http://web.mit.edu/allcott/www/Allcott%202011%20JPubEc%20-

%20Social%20Norms%20and%20Energy%20Conservation.pdf; Christophe Chamley, Rational 

Herds: Economic Models of Social Learning (Cambridge, 2003).] Comments are invited on the 

discussion offered here, with particular reference to any relevant empirical findings.” 
2017 Regulatory Impact Analysis 

8-1 

Chapter 8: Vehicle Sales and Employment Impacts 

8.1 Vehicle Sales Impacts 

8.1.1 Vehicle Sales Impacts and Payback Period 

Predicting the effects of this rule on vehicles entails comparing two competing effects. 

On the one hand, as a result of this rule, the vehicles will become more expensive, which 

would, by itself, be expected to discourage sales. On the other hand, the vehicles will have 

improved fuel economy and thus lower operating costs, which makes them more attractive to 

consumers. Which of these effects dominates for potential vehicle buyers when they are 

considering a purchase will determine the expected net costs to consumers of the regulation. 

However, assessing the net effect of these two competing effects is complex and uncertain, as 

it rests on how consumers value fuel savings at the time of purchase and the extent to which 

manufacturers and dealers reflect them in the purchase price. The empirical literature does 

not provide clear evidence on whether consumers fully consider the value of fuel savings at 

the time of purchase. It also generally does not speak to the efficiency of manufacturing and 

dealer pricing decisions. Thus, for the proposal we do not provide quantified estimates of 

potential sales impacts. Rather, we solicit comment on the issues raised here and on methods 

for estimating the effect of this rule on vehicle sales. 

If consumers do not accurately compare the value of fuel savings with the increased 

cost of fuel economy technology in their vehicle purchase decisions, they may continue to 

behave in this way for 2017 and beyond as well. If auto makers have accurately gauged how 

consumers consider fuel economy when purchasing vehicles and have provided the levels of 

improvement that consumers desire, then consumers should not be expected to want the more 

fuel-efficient vehicles beyond what manufacturers may offer in the future absent this rule. 

After all, auto makers would be expected to provide as much fuel economy as consumers 

want, and vehicle sales would not be expected to increase due to possible consumer 

undervaluation of fuel economy alone. 

If, on the other hand, auto makers underestimated consumer demand for fuel economy, 

or the rule motivates an increase in consumer demand for fuel economy, then this rule could 

lead to production of more desirable vehicles and a commensurate increase in sales. 

However, this would only manifest itself in increased sales if auto makers have missed some 

profit-making opportunities by not offering consumers the actual future levels of fuel 

economy for which they would be willing to pay. 

It is possible that previous greenhouse gas/fuel economy rulemakings and the new fuel 

economy label, as well as this proposal, might increase consumer awareness of the benefits of 

improved fuel economy. The ability of consumers to learn from improved fuel economy 

could be a positive result of auto makers providing improved fuel economy. In the absence of 

the rule, no individual company can fully internalize the future boost to demand resulting 

from consumer learning. If one company were to provide more information on the consumer 

benefits of improved vehicle efficiency, the company would capture only a fraction of the 

extra sales, because the learning isn't specific to that company's fleet. Many of the sales might 

Deleted: what this means for vehicle sales 

Deleted: more 

Deleted: are likely to 

Deleted: are 

Deleted: ing

Deleted: have 

Deleted: d

Deleted: . Thus, 

Deleted: are unlikely 
Chapter 8 

8-2 

accrue to the company's competitors. Thus, consumer education efforts may be underprovided. Alternatively, with the rule is in place, the benefits of firms' educational efforts will 

be fully captured by the whole industry. Thus, the level of consumer education will expand 

across the whole industry. As more consumers experience the savings in time and expense 

from owning more fuel efficient vehicles as a result of these rules, demand may shift yet 

further in the direction of the fuel economy/GHG improvements mandated under the rule. If 

these induced learning effects are strong, the rule could even increase total vehicle sales over 

time. We do not have data to support the impacts of the label or existing fuel economy 

standards on consumer attitudes toward fuel economy.

We note that automakers are currently putting more fuel-saving technologies into 

vehicles and are advertising based in part on the fuel efficiency of their vehicles. We cannot 

assess whether these improvements in fuel economy are due to higher gasoline prices in 

recent years, to increased consumer demand for fuel economy, to auto makers realizing that 

fuel economy is a valuable attribute to consumers, or to EPA’s and NHTSA’s recent 

rulemakings on fuel economy and greenhouse gases. If adding more fuel economy to 

vehicles, even with the added costs, does make the vehicles more attractive to consumers, 

then we would expect auto makers to add this fuel economy in the absence of the rule. If so, 

the benefits and costs of that fuel economy would not be attributable to the rule, and both the 

benefits and costs estimated for this rule are overstated. 

In addition to uncertainty over the expected offerings of fuel economy in new vehicles 

in the absence of this rule, we note the uncertainty over the role of fuel economy in vehicle 

purchase decisions. As discussed in Chapter 8.1.2.5 below, how consumers consider fuel 

economy when buying cars is not well understood; it is likely to vary substantially among 

individuals, and possibly across vehicle classes. In addition, consumer interest in fuel 

economy may change over time, in response to changes in fuel prices, to marketing efforts by 

automakers, to more experience with fuel-efficient vehicles, and to better understanding of the 

costs associated with fuel savings, not to mention changing market conditions and other shifts 

in the U.S. economy that could affect vehicle demand and national driving behavior. As 

noted in Preamble III.H.1.a, EPA and NHTSA recently revised the fuel economy label for 

new vehicles, to improve consumer information on fuel economy. We do not have data on 

the effect of the label on consumer vehicle purchase decisions. If consumers put more 

emphasis on fuel economy in their purchase decisions, then automakers would be expected to 

respond to that interest independent of the standards proposed here, and an increase in vehicle 

sales resulting from improved fuel economy would be a result of market forces instead of this 

rulemaking. Preamble III.D. discusses EPA’s reasoning in assuming that the reference fleet 

will comply with the standards for MY 2016 vehicles but will not over-comply. Adding to the 

uncertainty is the dynamic effect that the proposed rule may have on other relevant aspects of 

the vehicle sales market aside from purchase price. For example, the proposed rule may 

induce technological change that would not have occurred otherwise, and introduce 

consumers to new vehicle attributes that impact vehicle demand in a positive way. Similarly, 

improvements in vehicle fuel economy might accompany other changes in vehicle 

characteristics that are relevant for purchase decisions. While these factors are always present, 

they are particularly acute in the current context, in which the rule will not take effect until 

2017. 
2017 Regulatory Impact Analysis 

8-3 

In previous rulemakings, EPA and NHTSA conducted vehicle sales analyses by 

comparing the up-front costs of the vehicles with the present value of five years’ worth of fuel 

savings. We assumed that the costs for the fuel-saving technologies would be passed along 

fully to vehicle buyers in the vehicle prices. The up-front vehicle costs were adjusted to take 

into account several factors that would affect consumer costs: the increased sales tax that 

consumers would pay, the increase in insurance premiums, the increase in loan payments that 

buyers would face, and a higher resale value, with all of these factors due to the higher upfront cost of the vehicle. Those calculations resulted in an adjusted increase in costs to 

consumers. We then assumed that consumers considered the present value of five years of 

fuel savings in their vehicle purchase, which is consistent with the length of a typical new 

light-duty vehicle loan, and is similar to the average time that a new vehicle purchaser holds 

onto the vehicle.A The present value of fuel savings was subtracted from technology costs to 

get a net effect on vehicle cost of ownership. We then used a short-run demand elasticity of -

1 to convert a change in price into a change in quantity demanded of vehicles.B

 An elasticity 

of -1 means that a 1% increase in price leads to a 1% reduction in quantity sold. In the 

vehicle sales analyses, if five years of fuel savings outweighed the adjusted technology costs, 

then vehicle sales were predicted to increase; if the fuel savings were smaller than the 

adjusted technology costs, sales would decrease, compared to a world without the standards. 

We do not here present a vehicle sales analysis using this approach. This rule takes 

effect for MY 2017-2025. In the intervening years, it is possible that the assumptions 

underlying this analysis – the failures in the market for fuel economy, how consumers 

consider fuel savings when buying new vehicles, how auto makers decide how much fuel 

economy to provide in new vehicles, other market conditions – might change. Instead, we 

present a payback period analysis to estimate the number of years of fuel savings needed to 

recover the up-front costs of the new technologies. In other words, the payback period 

identifies the break-even point for new vehicle buyers. If auto makers would not provide 

these technologies in the absence of the rule, and if new vehicle buyers take into account more 

than the break-even number of years, then vehicle sales would be expected to increase as a 

result of the rule. On the other hand, if auto makers plan to provide improved fuel economy 

in the absence of the rule, then vehicle sales will increase, but not due to the proposed 

standards except to the extent the increased consumer demand being met by automakers is 

attributable, at least in part, to this rule. Finally, if consumers take into account less than the 

break-even number of years in their buying decisions, then vehicle sales could decrease. In 

all cases, we would expect the impact of this rule on vehicle sales to be modest relative to 

other factors influencing future vehicle sales such as fuel prices and overall economic 

conditions, which are strong determinants of overall sales in any given year. 

 

A

 In this proposal, the 5-year payback assumption corresponds to an assumption that vehicle buyers take into 

account between 30 and 50 percent of the present value of lifetime vehicle fuel savings (with the variation 

depending on discount rate, model year, and car vs. truck). 

B

 For a durable good such as an auto, the elasticity may be smaller in the long run: though people may be able to 

change the timing of their purchase when price changes in the short run, they must eventually make the

investment. We request comment on whether or when a long-run elasticity should be used for a rule that phases 

in over time, as well as how to find good estimates for the long –run elasticity. 

Deleted: 
Chapter 8 

8-4 

The calculation of the payback period is discussed in DRIA Chapter 5.3. Table 1 

shows the estimated payback period for MY 2021 and 2025. We present MY 2021 because it 

is the last year before the mid-term review impacts, if any, will take place, and MY 2025 

because it is the last year of the program. The payback period in 2021 is shorter than that in 

2025, because the technologies required to meet the proposed MY 2021 standards are more 

cost-effective than those for MY 2025. In all cases, the payback periods are less than the 5-

year payback period that EPA and NHTSA have previously used in the vehicle sales analyses. 

Given the relatively short time frames for the payback periods, these results suggest that 

vehicle sales could increase as a result of improved fuel economy. Alternatively, if 

consumers have shorter time frames for payback periods, then the rule may have a negative 

impact on vehicle sales. In either situation we would expect the impact of this rule on sales to 

be modest. As noted above, though, it is possible that some or all of this increase in fuel 

economy would occur in the absence of the rule, and it is also possible that consumers may 

revise how they think about fuel economy in their vehicle purchase decisions. 

Table 1 Estimated Payback Period for Model Years 2021 and 2025 (Years) 

Model 

Year 

Estimated 

Payback Period 

for Cash 

Purchase, 3% 

Discount Rate 

Estimated 

Payback Period 

for Cash 

Purchase, 7% 

Discount Rate 

Estimated 

Payback Period 

for Purchase on 

Credit, 3% 

Discount Rate 

Estimated 

Payback Period 

for Purchase on 

Credit, 7% 

Discount Rate 

2021 2.7 2.9 2.9 2.8

2025 3.7 3.9 3.9 3.9

We welcome comments on all aspects of this discussion, including assumptions on 

consumer and producer behavior; how consumers value future fuel savings at the time of 

purchase; factors that should be taken into account when assessing the impact of fuel 

economy standard on aggregate vehicle sales; and sources of uncertainty in estimating vehicle 

sales impacts. We welcome comments on the methodology described here for quantitative 

estimates of the effects of this proposal on sales and its appropriateness for this rulemaking; 

we also welcome proposals for other methods. 

We here provide further detail on some of the assumptions included in the payback 

analysis. We seek comment on all these factors as well. The analysis starts with the increase 

in costs estimated by the OMEGA model. We assume that these costs are fully passed along 

to consumers. This assumption is appropriate for cost increases in perfectly competitive 

markets. In less than perfectly competitive markets, though, it is possible that the cost 

increase is split between consumers and automakers, and possibly suppliers, and the price 

may not increase as much as costs.1

 Thus, the assumption of full cost pass-through is possibly 

an overestimate. 
2017 Regulatory Impact Analysis 

8-5 

The next step in the analysis is to adjust this cost increase for other effects on the 

consumer. The higher vehicle price is likely to lead to an increase in sales tax, insurance, and 

vehicle financing costs. 

The increase in insurance costs is estimated from the average value of collision plus 

comprehensive insurance as a proportion of average new vehicle price. Collision plus 

comprehensive insurance is the portion of insurance costs that depend on vehicle value. The 

Insurance Information Institute2 provides the average value of collision plus comprehensive 

insurance in 2008 as $432. The average value of a new vehicle in 2008, according to the U.S. 

Department of Energy, was $23,334.3 Dividing the insurance cost by the average price of a 

new vehicle gives the proportion of comprehensive plus collision insurance as 1.85 percent of 

the price of a vehicle. If this same proportion holds for the increase in price of a vehicle, then 

insurance costs should go up by 1.85 percent of the increase in vehicle cost. We use 

information on depreciation of vehicle value from the same U.S. Department of Energy report 

to estimate a reduction in insurance costs, due to reduction in the estimated value of the 

vehicle, over 9 years. 

Calculating the average increase in sales tax starts with the vehicle sales tax for each 

state in 2006, the most recent source identified.4 The sales tax per state was then multiplied 

by the 2010 population of the state;5 those values were summed and divided by total U.S.

population, to give a population-weighted sales tax. That estimate of the state sales taxes for 

vehicles in the U.S. is 5.3 percent. This value is assumed to be a one-time cost incurred when 

the vehicle is purchased. 

As of July, 2011, the national average interest rate for a 5 year new car loan was 5.51 

percent.6

 We use this loan rate to calculate the increase in vehicle costs due to financing a 

loan. 

8.1.2 Consumer Vehicle Choice ModelingC

An alternative to the vehicle sales analysis discussed above is the use of consumer 

vehicle choice models. In this section we describe some of the consumer vehicle choice 

models EPA has reviewed in the literature, and we describe the models’ results and 

limitations that we have identified. The evidence from consumer vehicle choice models 

indicates a huge range of estimates for consumers’ willingness to pay for additional fuel 

economy. Because consumer surplus estimates from consumer vehicle choice models depend 

critically on this value, we would consider any consumer surplus estimates of the effect of our 

rule from such models to be unreliable. In addition, the predictive ability of consumer vehicle 

choice models may be limited. While vehicle choice models are based on sales of existing 

vehicles, vehicle models are likely to change, both independently and in response to this rule. 

The models may not predict well in response to these changes. Instead, we compare the value 

of the fuel savings associated with this rule with the increase in technology costs. EPA will 

 

C

 This section is drawn heavily from Helfand, Gloria, and Ann Wolverton, “Evaluating the Consumer Response 

to Fuel Economy: A Review of the Literature.” International Review of Environmental and Resource 

Economics 5 (2011): 103-146. 
Chapter 8 

8-6 

continue its efforts to review the literature, but, given the known limitations and uncertainties 

of vehicle choice models, EPA has not conducted an analysis using these models for this 

proposal. 

This rule will lead automakers to change characteristics – in particular, the fuel 

economy -- of the vehicles they produce. These changes will affect the cost of manufacturing 

the vehicle; as a result, the prices of the vehicles will also change. 

In response to these changes, the number and types of vehicles sold is likely to change. 

When consumers buy vehicles, they consider both their personal characteristics (such as age, 

family composition, income, and their vehicle needs) and the characteristics of vehicles (e.g., 

vehicle size, fuel economy, and price). In response to the changes in vehicle characteristics, 

consumers will reconsider their purchases. Increases in fuel economy are likely to be 

attractive to consumers, but increases in price, as well as any detrimental changes in other 

vehicle characteristics, may be deterrents to purchase. As a result, consumers may choose a 

different vehicle than they would have purchased in the absence of the rule. The changes in 

prices and vehicle characteristics are likely to influence consumers on multiple market scales: 

the total number of new vehicles sold; the mix of new vehicles sold; and the effects of the 

sales on the used vehicle market. 

Consumer vehicle choice modeling (CCM) is a method used to predict what vehicles 

consumers will purchase based on vehicle characteristics and prices. In principle, it should 

produce more accurate estimates of compliance costs compared to models that hold fleet mix 

constant, since it predicts changes in the fleet mix that can affect compliance costs. It can also 

be used to measure changes in consumer surplus, the benefit that consumers perceive from a 

good over and above the purchase price. (Consumer surplus is the difference between what 

consumers would be willing to pay for a good, represented by the demand curve, and the 

amount they actually pay. For instance, if a consumer were willing to pay $30,000 for a new 

vehicle, but ended up paying $25,000, the $5000 difference is consumer surplus.) 

A number of consumer vehicle choice models have been developed. They vary in the 

methods used, the data sources, the factors included in the models, the research questions they 

are designed to answer, and the results of the models related to the effects of fuel economy on 

consumer decisions. This section will give some background on these differences among the 

models. 

8.1.2.1 Methods 

Consumer choice models (CCMs) of vehicle purchases typically use a form of discrete 

choice modeling. Discrete choice models seek to explain discrete rather than continuous 

decisions. An example of a continuous decision is how many pounds of food a farm might 

grow: the pounds of food can take any numerical value. Discrete decisions can take only a 

limited set of values. The decision to purchase a vehicle, for instance, can only take two 

values, yes or no. Vehicle purchases are typically modeled as discrete choices, where the 

choice is whether to purchase a specified vehicle. The result of these models is a prediction 

of the probability that a consumer will purchase a specified vehicle. A minor variant on 

discrete choice models estimates the market share (a continuous variable between 0 and 1) for 
2017 Regulatory Impact Analysis 

8-7 

each vehicle. Because the market share is, essentially, the probability that consumers will 

purchase a specific vehicle, these approaches are similar in process; they differ mostly in the 

kinds of data that they use. 

The primary methods used to model vehicle choices are nested logit and mixed logit.D 

In a nested logit, the model is structured in layers. For instance, the first layer may be the 

choice of whether to buy a new or used vehicle. Given that the person chooses a new vehicle, 

the second layer may be whether to buy a car or a truck. Given that the person chooses a car, 

the third layer may be the choice among an economy, midsize, or luxury car. Examples of 

nested logit models include Goldberg,7 Greene et al.,8 and McManus.9

In a mixed logit, personal characteristics of consumers play a larger role than in nested 

logit. While nested logit can look at the effects of a change in average consumer 

characteristics, mixed logit allows consideration of the effects of the distribution of consumer 

characteristics. As a result, mixed logit can be used to examine the distributional effects on 

various socioeconomic groups, which nested logit is not designed to do. Examples of mixed 

logit models include Berry, Levinsohn, and Pakes,10 Bento et al.,11 and Train and Winston.12

 

While discrete choice modeling appears to be the primary method for consumer choice 

modeling, others (such as Kleit13 and Austin and Dinan14) have used a matrix of demand 

elasticities to estimate the effects of changes in cost. The discrete choice models can produce 

such elasticities. Kleit as well as Austin and Dinan used the elasticities from an internal GM 

vehicle choice model. 

8.1.2.2 Data Sources 

The predictions of vehicle purchases from CCMs are based on consumer and vehicle 

characteristics. The CCMs identify the effects of changing the characteristics on the purchase 

decisions. These effects are typically called the parameters or coefficients of the models. For 

instance, the model parameters might predict that an increase in a person’s income of 10% 

would increase the probability of her purchasing vehicle A by 5%, and decrease the 

probability of her purchasing vehicle B by 10%. 

The parameters in CCMs can be developed either from original data sources 

(estimated models), or using values taken from other studies (calibrated models). 

Estimated models use datasets on consumer purchase patterns, consumer 

characteristics, and vehicle characteristics to develop their original sets of parameters. The 

datasets used in these studies sometimes come from surveys of individuals’ behaviors.15 

Because they draw on the behavior of individuals, they provide what is sometimes called 

micro-level data. Other studies, that estimate market shares instead of discrete purchase 

decisions, use aggregated data that can cover long time periods.16 

 

D

 Logit refers to a statistical analysis method used for analyzing the factors that affect discrete choices (i.e., 

yes/no decisions or the choice among a countable number of options). 
Chapter 8 

8-8 

Calibrated models rely on existing studies for their parameters. Researchers may draw 

on results from a number of estimated models, or even from research other than CCM, to 

choose the parameters of the models. The Fuel Economy Regulatory Analysis Model 

developed for the Energy Information Administration17 and the New Vehicle Market Model 

developed by NERA Economic Consulting18 are examples of calibrated models. 

8.1.2.3 Factors Included in the Models 

Consumer choice models vary in their complexity and levels of analysis. Some focus 

only on the new vehicle market;19 others consider the choice between new vehicles and an 

outside good (possibly including a used vehicle);20 others explicitly consider the relationship 

between the new and used vehicle markets.21 Some models include consideration of vehicle 

miles traveled,22 though most do not. 

The models vary in their inclusion of both consumer and vehicle information. One 

model includes only vehicle price and the distribution of income in the population influencing 

choice;23 others include varying numbers and kinds of vehicle and consumer attributes. 

Some models include only the consumer side of the vehicle market;24 others seek to 

represent both consumer and producer decisions.25 Models that include only the consumer 

side are suitable for reflecting consumer choices, but they do not allow for revisions of 

vehicle characteristics in response to consumer preferences. Including producer behavior 

allows for vehicle characteristics such as price and fuel economy to be the result of market 

forces rather than characteristics of the existing fleet. For instance, in the context of 

“feebates” (subsidizing fuel-efficient cars with revenue collected by taxing fuel-inefficient 

vehicles), Greene et al. estimated that 95% of the increase in fuel economy was due to 

addition of technology rather than changes in vehicles sold.26 Including auto maker response 

is a complex exercise. Auto makers are commonly considered to have market power; they 

can influence the prices that consumers pay to increase their profits. As a result, the price 

increases that consumers face may reflect strategic factors that could make them higher or 

lower than the technology costs. In addition, auto makers may seek to influence consumer 

preferences through marketing and advertising.27 Even those vehicle choice models that 

include a producer model may not include much detail, due to computational limits: it is 

unusual for models to allow both buyers and producers to choose one vehicle characteristic, 

much less multiple characteristics.28 

8.1.2.4 Research Questions for the Models 

Consumer choice models have been developed to analyze many different research and 

policy questions. In part, these models have been developed to advance the state of economic 

modeling. The work of Berry, Levinsohn, and Pakes,29 for instance, is often cited outside the 

motor vehicle context for its incorporation of multiple new modeling issues into its 

framework. In addition, because the vehicle sector is a major part of the U.S. economy and 

involved in many public policy discussions, research questions cover a wide gamut. These 

topics have included the effects of voluntary export restraints on Japanese vehicles compared 

to tariffs and quotas,30 the market acceptability of alternative-fuel vehicles,31 the effects of 

introduction and exit of vehicles from markets,32 causes of the decline in market shares of 
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U.S. automakers,33 and the effects of gasoline taxes34 and “feebates”35 (subsidizing fuelefficient cars with revenue collected by taxing fuel-inefficient vehicles). 

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions 

Consumer vehicle choice models typically consider the effect of fuel economy on 

vehicle purchase decisions. It can appear in various forms. 

Some models36 incorporate fuel economy through its effects on the cost of owning a 

vehicle. With assumptions on the number of miles traveled per year and the cost of fuel, it is 

possible to estimate the fuel savings (and perhaps other operating costs) associated with a 

more fuel-efficient vehicle. Those savings are considered to reduce the cost of owning a 

vehicle: effectively, they reduce the purchase price. This approach relies on the assumption 

that, when purchasing vehicles, consumers can estimate the fuel savings that they expect to 

receive from a more fuel-efficient vehicle and consider the savings equivalent to a reduction 

in purchase price. Turrentine and Kurani37 question this assumption; they find, in fact, that

consumers do not make this calculation when they purchase a vehicle. The question remains, 

then, how or whether consumers take fuel economy into account when they purchase their 

vehicles. 

Most estimated consumer choice models, instead of making assumptions about how 

consumers incorporate fuel economy into their decisions, use data on consumer behavior to 

identify that effect. In some models, miles per gallon is one of the vehicle characteristics 

included to explain purchase decisions. Other models use fuel consumption per mile, the 

inverse of miles per gallon, as a measure:38 since consumers pay for gallons of fuel, then this 

measure can assess fuel savings relatively directly.

39 Yet other models multiply fuel 

consumption per mile by the cost of fuel to get the cost of driving a mile,40 or they divide fuel 

economy by fuel cost to get miles per dollar.41 It is worth noting that these last two measures 

assume that consumers respond the same way to an increase in fuel economy as they do to a 

decrease in the price of fuel when each has the same effect on cost per mile driven.E On the 

one hand, while this assumption does not rely on as complex a calculation as the present value 

of fuel savings that Turrentine and Kurani examined, it suggests a calculating consumer. On 

the other hand, using a form of cost per mile is a way to recognize the role of fuel prices in 

consumers’ purchase of fuel economy: recent research42 presents results that higher fuel 

prices play a major role in that decision. 

Greene and Liu,43 in a paper published in 1988, reviewed 10 papers using consumer 

vehicle choice models and estimated for each one how much consumers would be willing to 

pay at time of purchase to reduce vehicle operating costs by $1 per year. They found that 

people were willing to pay between $0.74 and $25.97 for a $1 decrease in annual operating 

 

E

 Likewise, these measures assume consumers respond the same way to increases and decreases in cost per mile 

of driving, as well as if those increases and decreases are large shocks rather than small, gradual changes. The 

issue of potential asymmetric consumer response to increased fuel efficiency compared to other types of changes 

to the cost of driving also arises and is discussed in the context of the VMT rebound effect (see Section III.H.4 of 

the Preamble and Chapter 4.2.5.2 of the TSD). 
Chapter 8 

8-10 

costs for a vehicle. This is clearly a very wide range: while the lowest estimate suggests that 

people are not willing to pay $1 once to get $1 per year reduced costs of operating their 

vehicles, the maximum suggests a willingness to pay 35 times as high. For comparison, the 

present value of saving $1 per year for 15 years at a 3% discount rate is $11.94, while a 7% 

discount rate produces a present value of $8.78. While this study is quite old, it suggests that, 

at least as of that time, consumer vehicle choice models produced widely varying estimates of 

the value of reduced vehicle operating costs. 

A newer literature review from David Greene44 suggests continued lack of 

convergence on the value of increased fuel economy to consumers. Of 27 studies, willingness 

to pay for fuel economy as a percent of the expected value of fuel savings varied from highly 

positive to highly negative. Significant numbers of studies found that consumers overvalued 

fuel economy, undervalued fuel economy, or roughly valued fuel economy correctly relative 

to fuel savings. Part of the difficulty may be, as these papers note, that fuel economy may be 

correlated (either positively or negatively) with other vehicle attributes, such as size, power, 

or quality, not all of which may be included in the analyses; as a result, “fuel economy” may 

in fact represent several characteristics at the same time. Indeed, Gramlich45 includes both 

fuel cost (dollars per mile) and miles per gallon in his analysis, with the argument that miles 

per gallon measures other undesirable quality attributes, while fuel cost picks up the 

consumer’s demand for improved fuel economy. Greene finds that, while some of the 

variation may be explainable due to issues in some of the studies, the variation shows up in 

studies that appear to be well conducted. As a result, further work needs to be conducted 

before it is possible to identify the role of fuel economy in consumer purchase decisions. 

Some studies46 argue that automakers could increase profits by increasing fuel 

economy because the amount that consumers are willing to pay for increased fuel economy 

outweighs the costs of that improvement. Other studies47 have found that increasing fuel 

economy standards imposes welfare losses on consumers and producers, because consumers 

should already be buying as much fuel economy as they want. In the course of reaching this 

result, though, at least one of these studies48 notes that its baseline model implies that 

consumers are willing to buy more fuel economy than producers have provided; they have to 

adjust their model to eliminate these “negative-cost” fuel economy improvements. 

The models do not appear to yield very consistent results on the role of fuel economy 

in consumer and producer decisions. 

8.1.2.6 Why Consumers May Not Buy, and Producers May Not 

Provide, Fuel Economy that Pays for Itself 

A detailed and wide ranging literature attempts to explain why consumers appear to 

under-invest in energy saving technologies that – at least using a present value calculation 

based on engineering estimates – appear to pay for themselves. Existing research does not 

provide a definitive answer to this question. Potential explanations are bounded by two 

scenarios. On the one hand, purely private benefits of fuel economy (fuel savings, time 

savings, increases in driving time) must be accompanied by private losses of the same 

magnitude. However, if there is no such private loss, or if it is small or insignificant, then 

there is a market or behavioral failure. 
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On the consumer side, this disconnect between net present value estimates of energyconserving cost savings and what consumers actually spend on energy conservation is often 

referred to as the Energy Paradox,49 since consumers appear to undervalue a wide range of 

investments in energy conservation. There are many possible explanations for the paradox 

discussed in the literature.50 Some explanations point to costs or aspects of consumer 

decision-making unaccounted for in a simple present value calculation, while others point to 

potential behavioral or market failures. There is little empirical literature to help the analyst 

determine which combination of hypothesis offers the most credible explanation. Possibilities 

include: 

• Consumers put little weight on benefits from fuel economy in the future and show 

high discount rates; 

• Consumers do not find the benefits from fuel economy to be sufficiently salient at 

the time of purchase, even if it would be in consumers’ economic interest to take 

account of those benefits; 

• Consumers consider other attributes more important than fuel economy at the time 

of vehicle purchase, especially if fuel economy is a relatively “shrouded” attribute; 

• Consumers have difficulty in calculating expected fuel savings; 

• Consumers may use imprecise rules of thumb when deciding how much fuel 

economy to purchase; 

• Consumers might associate higher fuel economy with inexpensive, less well 

designed vehicles; 

• Fuel savings in the future are uncertain, while at the time of purchase the increased 

costs of fuel-saving technologies are certain and immediate; 

• Consumers may not be able to find the vehicles they want with improved fuel 

economy; 

• The level of cost savings may be affected by the underlying reasons for the gap: 

factors such as transactions costs and differences in quality may not be adequately 

measured;51

• There is likely to be variation among consumers in the benefits they get from 

improved fuel economy, due to different miles driven and driving styles. 

The producer side of this paradox is much less studied. We don’t know how 

producers incorporate information from consumers on their apparent willingness to pay for 

fuel economy when making production decisions. There is a general expectation that 

producers maximize profits; some but not all possible explanations are consistent with this 

expectation. Hypotheses for under provision of fuel economy by producers include: 
Chapter 8 

8-12 

• Producers put more effort into attributes that consumers have regularly sought in 

the past, such as size and power, than into fuel and time savings with uncertain 

future returns; 

• In selecting a limited number of vehicle attributes among which consumers can 

choose, producers may aim to provide choices related to characteristics (such as 

numbers of doors or transmission types) that strongly influence what vehicle a 

consumer will buy, and fuel economy and time savings may not make that list; 

• While consumer preferences for fuel economy may change rapidly as fuel prices 

fluctuate, producers cannot change their design or production decisions as rapidly; 

as a result, vehicle designs may end up not satisfying consumer desires at a 

particular time; 

• Producers may have underestimated the value that consumers place on fuel 

economy. 

How consumers buy, and producers provide, fuel economy involves complex 

decisions on both sides of the market. Both sides of the market rely heavily in their 

calculations on the uncertain benefits of savings from fuel economy improvements. In 

addition, consumers trade off fuel economy with many other vehicle attributes, and producers 

do not (and perhaps cannot) provide the full range of choices for all attributes of interest to 

consumers. From this perspective, it may not be a surprise that, at a given point in time, 

consumer preferences for fuel economy may not match up with producer provision of it. 

8.1.2.7 Modeling Electric Vehicles and Other New Vehicles 

Modeling the introduction of new vehicles can be a greater challenge than modeling 

the existing vehicle market, because the modeler does not have data on how many of the new 

vehicles consumers buy. Nevertheless, it can be possible to estimate the effects of new 

vehicle introduction by identifying characteristics for the new vehicles and using those in a 

vehicle choice model. For instance, as discussed above, the models can estimate effects on 

the vehicle market when vehicles change their fuel economy or price. If the model 

incorporates other vehicle attributes important to the new vehicles, such as size, performance, 

or range, then the effect of the introduction can be modeled by applying the parameters for 

those features to the new vehicle characteristics. 

As discussed above, some models rely on vehicle price as the primary or only 

explanatory variable. Even in these models, it is possible, with some additional information, 

to consider the effects of new vehicle introduction. The first step is to find a vehicle similar 

on as many dimensions as possible to the new vehicle. For instance, if the change is to create 

an electric vehicle (EV) version of an existing model, then the existing model serves as the 

base vehicle. Next, it is necessary to measure the changes in vehicle attributes of interest to 

potential vehicle buyers. For an EV, changes in vehicle driving range and cost of fueling may 

be two such attributes. The next requirement is information on the value to consumers of the 

attributes that change between the new and the base vehicle. Multiplying the value for that 

attribute by the change in the attribute provides an estimate of the benefit or cost associated 
2017 Regulatory Impact Analysis 

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with changing that characteristic. That amount can then be added to or subtracted from the 

vehicle purchase price to give an adjusted purchase price reflecting the changed characteristic. 

This procedure is just an extension of the approach, discussed above, used to incorporate fuel 

economy improvements into vehicle choice models, by calculating future fuel savings and 

subtracting them (either in whole or a fraction) from vehicle purchase price. 

Incorporating new vehicles into a vehicle choice model, then, requires estimates of the 

changes in key attributes from conventional vehicles, and estimates of the value, also called 

the willingness to pay (WTP), that consumers put on those attributes. 

Electric vehicles (EVs) will have a number of changes in vehicle characteristics from 

any baseline model. EVs are likely to have a smaller driving range between refuelings than 

conventional vehicles, due to the large battery capacity needed to increase range. The ability 

to recharge at home may be a convenient, desirable feature for people who have garages with 

electric hookups, but not for people who park on the street. If an infrastructure develops for 

recharging vehicles with the convenience approaching that of buying gasoline, range or home 

recharging may become less of a barrier to purchase. The reduced tailpipe emissions and 

reduced noise may be attractive features to some consumers.F They may have different 

performance or storage capacity. If sufficient data were available, the changes in these 

attributes, combined with WTP for each of the attributes, could be used to adjust the purchase 

price of the baseline vehicle to estimate consumers’ WTP for the electric version of a vehicle. 

Greene (2001), for instance, used this approach for a model that simulates choice, not only for 

EVs, but also for other alternative-fuel vehicles.52 In that model, he considers only one base 

vehicle, a passenger car, but considers the effect on WTP of fuel cost per mile, range, 

acceleration, and several other vehicle attributes.

Vehicle driving range has received attention because of the current paucity of 

recharging infrastructure: if the driver of an EV gets low on fuel, it may be difficult to find a 

place to recharge. Because range appears to be a major factor in EV acceptability, it is 

starting to draw attention in the research community. 

In several studies, researchers have used stated preference conjoint analysis to 

estimate the effect of vehicle range on consumer vehicle choice. In a conjoint analysis, 

consumers are given a choice between several vehicles with different attributes. One choice 

might be, for instance, between a baseline car and another car with higher range and a higher 

purchase price. The choices that consumers make (e.g., how much higher does the purchase 

price have to be for the consumer not to choose more range?) provide data that can be used to 

estimate the role of vehicle attributes in the consumer’s choice. Stated preference analysis is 

sometimes considered less reliable than actual market behavior, because what people say they 

will do in hypothetical situations may not match what they would do in actual situations. On 

the other hand, stated preference methods can be used to study goods where market data do 

 

F

 For instance, Hidrue et al. (Hidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. 

“Willingness to Pay for Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 

686-705) find that some consumers are willing to pay $5100 more for vehicles with 95% lower emissions than 

the vehicles they otherwise aim to purchase. 
Chapter 8 

8-14 

not exist, such as future market products undergoing development (marketing studies often 

use stated preference methods), or environmental goods. Because electric vehicles are not in 

widespread enough use for market studies, stated preference studies are, at this point, one of 

the few options to examine consumer behavior relating to these vehicles. 

Table 8.1-2 summarizes results from several conjoint studies that include the effects of 

extending range (in the table, from 150 to 300 miles, to present standardized results). 

Variation of results in the table is from income or other demographic factors, not from 

confidence intervals. The results suggest that the value of additional range varies among 

consumers, and the amount of that variation is changing (perhaps shrinking) in more recent 

studies. 

Table 8.1-2Willingness to Pay for Increasing Range Calculated from Various Studies 

Study (Date) Value of extending range 

from 150 to 300 miles (dollar 

year) 

Value of additional 

range in 2009$a 

Bunch et al. (1993)b $7,600 (1991$) $11,100

Kavelek (1996) for California 

Energy Commissionc 

$2600 - $41,900 (1993$) $3700 - $58,700

Resource Systems Group (2009) for 

California Energy Commissiond 

$2900 - $7500 (2009$) $2900 - $7500

Hess et al. (2009), using the same 

data as Resource Systems Group 

(2009)e 

$2400 - $8500 (2009$) $2400 - $8500

Hidrue et al. (2011)f $3776 - $10,399 (2009$) $3776 - $10,399

aValues adjusted to 2009$ using the Bureau of Economic Analysis GDP deflator. 

bBunch, David S., Mark Bradley, Thomas F. Golob, and Ryuichi Kitamura. “Demand for Clean-Fuel Vehicles

in California: A Discrete-Choice Stated Preference Pilot Project.” Transportation Research Part A 27A(3) 

(1993): 237-253. The value of range was, in their model, assumed to be the same for all people. 

cKavelek, Chris. “CALCARS: The California Conventional and Alternative Fuel Response Simulator.” 

Demand Analysis Office, California Energy Commission, April 1996. The variation in values is due to 

willingness to pay (WTP) varying by income levels and for one-car and two-car households. The coefficient on 

range for one-car households was not statistically significantly different from zero (t-statistic = 1.5), but it was 

for 2-car households (t-statistic = 3.02). The minima and maxima presented here represent the values across 

both ownership and income categories. 

dResource Systems Groups, Inc. “Transportation Fuel Demand Forecast Household and Commercial Fleet 

Survey Task 8 Report: Logistic Regression Analysis and Results.” Prepared for California Energy Commission, 

June 2009. 

Deleted: Table 8.1-3

Deleted: 4
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eHess, S., T. Adler, M. Fowler and A. Bahreinian “The Use of Cross-nested Logit Models for Multi-Dimensional 

Choice Processes: The Case of the Demand for Alternative Fuel Vehicles,” Proceedings of the 2009 European 

Transport Conference, Leiden, Netherlands, 2009. This study uses the same data as the Resource Systems 

Group study. The coefficient on range was not statistically significantly different from zero in these regressions: 

t-statistics varied from 1.29 to 1.52. The variation in values is due to willingness to pay (WTP) varying by 

income levels and statistical specification. The minima and maxima presented here represent the values across 

both income categories and specifications. 

fHidrue, Michael K., George R. Parsons, Willett Kempton, and Meryl P. Gardner. “Willingness to Pay for

Electric Vehicles and their Attributes.” Resource and Energy Economics 33(3) (2011): 686-705. The range of 

values is due to the model separating consumers into “gasoline vehicle-oriented” and “electric vehicle-oriented” 

groups. The EV-oriented group has higher WTP for additional range. 

Driving range may be a major factor in consumers’ decisions on EVs, but it is not the 

only attribute that may be important to potential buyers (e.g., as noted, Hidrue et al. find that 

some consumers appear willing to pay substantially for reduced tailpipe emissions). A model 

that does not incorporate the other factors important to consumers’ decisions may not perform 

well in predicting vehicle purchases. In addition, as mentioned above, and as seen in Table 

8.1-2, it is likely that the WTP values for attributes of EVs will change over time, particularly 

if EVs are used more widely, the infrastructure to fuel the vehicles becomes more accessible, 

and consumers develop more familiarity and understanding of the vehicles. Thus, challenges 

associated with predicting market shares for EVs are even more serious than those already 

serious challenges associated with predicting market shares for conventional vehicles. 

8.1.2.8 EPA Exploration of Vehicle Choice Modeling 

In order to develop greater understanding of these models, EPA is in the process of 

developing a vehicle choice model. In its current form, the model assumes that the vehicle 

fleet and all characteristics of each vehicle, except vehicle prices and fuel economy, stay the 

same. The model will predict changes in the vehicle fleet, at the individual-configuration 

level and at more aggregated levels, in response to changes in vehicle fuel economy and price. 

The draft EPA model uses a nested logit structure common in the vehicle choice 

modeling literature, as discussed above in Chapter 8.1.2.1. “Nesting” refers to the decisiontree structure of the model, and “logit” refers to the fact that the choices are discrete (i.e., 

yes/no decisions about which vehicles to purchase, instead of continuous values). 

The nesting involves a hierarchy of choices. In its current form, at the initial decision 

node, consumers choose between buying a new vehicle or not. Conditional on choosing a 

new vehicle, consumers then choose between passenger vehicles, cargo vehicles, and ultraluxury vehicles. The next set of choices subdivides each of these categories into vehicle type 

(e.g., standard car, minivan, SUV, etc.). Next, the vehicle types are divided into classes 

(small, medium, and large SUVs, for instance), and then, at the bottom, are the individual 

vehicle configurations. 

At this bottom level, vehicles that are similar to each other (such as standard 

subcompacts, or prestige large vehicles) end up in the same “nest.” Substitution within a nest 

is considered much more likely than substitution across nests, because the vehicles within a 

nest are more similar to each other than vehicles in different nests. For instance, a person is 

more likely to substitute between a Chevrolet Aveo and a Toyota Yaris than between an Aveo 

Deleted: Table 8.1-3
Chapter 8 

8-16 

and a pickup truck. In addition, substitution is greater at low decision nodes (such as 

individual configurations) than at higher decision nodes (such as the buy/no buy decision), 

because there are more choices at lower levels than at higher levels. 

Parameters for the model (including demand elasticities and the value of fuel economy 

in purchase decisions) are being selected based on a review of values found in the literature 

on vehicle choice modeling. As discussed above, a number of studies have estimated these 

parameters. Those estimates, combined with some theoretical requirements,G assist in 

assigning values for the parameters. The model will allow individual users to change those 

parameters. 

The fuel economy of a vehicle is used to adjust the price of the vehicle, using a 

version of the procedure discussed in Chapter 8.1.2.7: the value that the consumer places on 

fuel economy is multiplied by the change in fuel economy and incorporated into the “effective 

price” of the vehicle. In practice, implementing this calculation involves calculating the 

change in expenditures on fuel based on schedules of VMT, vehicle survival, and fuel prices 

in the future consistent with those in OMEGA. As discussed in Chapter 8.1.2.5, there is no 

consensus value for consumers’ willingness to pay for improved fuel economy: estimates 

vary tremendously. The model assumes that consumers will use some years of discounted 

fuel savings, with the modeler able to input both the number of years and the discount rate to 

be used in the analysis. 

The vehicle choice model will take as inputs an initial fleet of vehicles (including the 

initial sales and fuel economy) in the absence of standards, the cost of technologies added to 

each vehicle to comply with standards, and the change in fuel economy. With the initial sales 

mix, for each vehicle, the model calculates a vehicle-specific constant that summarizes the 

value of all attributes of the vehicle other than price and fuel economy. This constant ensures 

that the model will predict changes in consumer response that would result only from changes 

in price and fuel economy. This constant substitutes for estimating the effects of changes in 

all other vehicle characteristics; the underlying assumption is that these other vehicle 

characteristics do not change.H

 For instance, it assumes that a Ford Escape will not change in 

size, power, or accessories; the only changes will be to its cost and its fuel economy. 

The model assumes that the increase in vehicle cost associated with increased 

technology is fully passed through as an increase in vehicle price, and some years of fuel 

savings offset this price increase. It then calculates changes in total fleet size and in sales 

mix, at the individual-configuration level and at the level of vehicle class, due to the changes 

in fuel economy and vehicle prices. It also calculates changes in consumer surplus associated 

with the changes in fuel economy and vehicle prices. 

 

G

 The theory of nested logit requires that the price slopes (the change in utility as vehicle full price changes, a 

measure of consumer responsiveness to price changes) must be higher in absolute value for lower nests. This 

condition reflects the point, discussed above, that substitution is greater at lower decision notes than at higher 

ones. 

H

 As explained in Section III.D of the preamble, as part of the technology cost analysis for the proposed rule, the 

agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions. 
2017 Regulatory Impact Analysis 

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It is possible that the predicted changes in fleet mix will lead to predictions of vehicle 

sales for auto makers that do not meet the proposed standards, because the mix and volume of 

vehicles sold changed from the initial levels. To correct this problem, it would be necessary 

to feed the new fleet mix into OMEGA (which calculates costs and compliance) and get a 

new set of output, which could then be fed back into the vehicle choice model. OMEGA 

would increase technologies, and thus costs, to improve compliance; those adjustments would 

then again affect vehicle demand. We expect that this iterative process would converge to a 

fleet mix that would meet standards. Performing this iteration requires development of an 

interface between the vehicle choice model and OMEGA to ensure accurate transmission of 

data between the models. At this time, the vehicle choice model takes output from OMEGA, 

but the results of the modeling do not feed easily back into OMEGA. Building this interface 

is an expected part of our future modeling work. 

The model is still undergoing development; EPA will seek peer review on it before it 

is utilized. In addition, concerns remain over the ability of any vehicle choice model to make 

reasonable predictions of the response of the vehicle fleet to changes in prices and vehicle 

characteristics. EPA seeks comments on the use of vehicle choice modeling for predicting 

changes in fleet mix due to policies, and on methods to test the ability of a vehicle choice 

model to produce reasonable estimates of changes in fleet mix. 

8.1.2.9 Summary and Additional Considerations 

Consumer vehicle choice modeling in principle can provide a great deal of useful 

information for regulatory analysis, helping to answer some of the central questions about 

relevant effects on consumer welfare. In practice, the advantages depend on the success of 

models in predicting changes in fleet size and mix. 

First, consumer vehicle choice modeling has the potential to describe more accurately 

the impact of a policy, by identifying market shifts. More accurate description of the market 

resulting from a policy can improve other estimates of policy impacts, such as the change in 

total vehicle emissions or vehicle miles traveled. The predictive ability of models, though, is 

not proven. 

Vehicle choice models can incorporate the effects on consumer decisions of changes 

in vehicle characteristics, if there are estimates of the value that consumers put on changes in 

those characteristics. These willingness-to-pay values may, however, be sensitive to the ways 

they are estimated, as indicated in the discussion of the value that consumers place on fuel 

economy in their purchase decisions. Especially for characteristics associated with advanced 

technology vehicles, such as EVs, the willingness-to-pay values may change over time as 

consumers develop more experience with the vehicles and these characteristics. Models 

based on current estimates may not predict well for the future. 

The modeling may improve estimates of the compliance costs of a rule. Consumers 

can either accept the new costs and vehicle characteristics, or they can change which vehicles 

they buy. Using a vehicle choice model is likely to reduce compliance costs: because the 

model allows consumers to choose among accepting the new vehicle, buying a different 
Chapter 8 

8-18 

vehicle, or not buying a vehicle, consumers have additional options, which improves their 

welfare relative to the assumption that consumers will not change their buying behavior. . 

An additional complication associated with consumer choice modeling is accurate 

prediction of producers’ responses to the rule. While it is possible to include auto makers’ 

decisions (for instance, on setting prices) into vehicle choices, computational limits affect the 

richness of these models. Technology costs, while an accurate measure of the opportunity 

cost of resources to society, may overestimate or underestimate the effect on the prices that 

consumers face. 

Consumer choice models can be used to calculate consumer surplus impacts on 

vehicle purchase decisions. Because these values are based on the estimates of changes in 

vehicle sales and fleet mix, consumer surplus measures may not be accurate if the changes in 

vehicle sales and fleet mix are not well estimated.

Principles of welfare analysis can be useful for understanding the role of consumer 

vehicle choice models in benefit-cost analysis. In particular, except for EVs, the technology 

cost estimates developed in this proposal take into account the costs to hold other vehicle 

attributes, such as size and performance, constant. In addition, the analysis assumes that the 

full technology costs are passed along to vehicle buyers. With these assumptions, because 

welfare losses are monetary estimates of how much buyers would have to be compensated to 

be made as well off as in the absence of the change,

I

 the price increase measures the loss to 

the buyer.J

 Assuming that the full technology cost gets passed along to the buyer as an 

increase in price, the technology cost thus measures the welfare loss to the buyer. Increasing 

fuel economy would have to lead to other changes in the vehicles that buyers find undesirable 

for there to be additional losses not included in the technology costs. 

Given the current limitations in modeling the role of fuel economy in vehicle purchase 

decisions, and limitations in modeling market responses to the new regulations, in this 

proposal EPA holds constant the vehicle fleet size and mix in its calculations of the impacts of 

this rule, and compares the fuel and other savings that consumers will receive with the 

technology costs of the vehicles. EPA continues to explore options for including consumer 

and producer choice in modeling the impacts of fuel economy-related regulations. This effort 

includes further review of existing consumer vehicle choice models, the estimates of 

 

I

 This approach describes the economic concept of compensating variation, a payment of money after a change 

that would make a consumer as well off after the change as before it. A related concept, equivalent variation, 

estimates the income change that would be an alternative to the change taking place. The difference between 

them is whether the consumer’s point of reference is her welfare before the change (compensating variation) or 

after the change (equivalent variation). In practice, these two measures are typically very close together. 

J

 Indeed, it is likely to be an overestimate of the loss to the buyer, because the buyer has choices other than 

buying the same vehicle with a higher price; she could choose a different vehicle, or decide not to buy a new 

vehicle. The buyer would choose one of those options only if the alternative involves less loss than paying the 

higher price. Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer 

welfare, unless there are other changes to the vehicle due to the fuel economy improvements that make the 

vehicle less desirable to buyers. 
2017 Regulatory Impact Analysis 

8-19 

consumers’ willingness to pay for increased fuel economy, and overall effects on consumer 

welfare, as well as EPA’s exploration of a vehicle choice model for use in the future. 

8.2 Employment Impacts 

8.2.1 Introduction 

Although analysis of employment impacts is not part of a cost-benefit analysis (except 

to the extent that labor costs contribute to costs), employment impacts of federal rules are of 

particular concern in the current economic climate of sizeable unemployment. When 

President Obama requested that the agencies develop this program, he sought a program that 

would “strengthen the [auto] industry and enhance job creation in the United States.”53,K The 

recently issued Executive Order 13563, “Improving Regulation and Regulatory Review” 

(January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety, 

and our environment while promoting economic growth, innovation, competitiveness, and job 

creation” (emphasis added). EPA is accordingly providing partial estimates of the effects of 

this proposal on domestic employment in the auto manufacturing and parts sectors, while 

qualitatively discussing how it may affect employment in other sectors more generally. 

This proposal is expected to affect employment in the United States through the 

regulated sector – the auto manufacturing industry – and through several related sectors, 

specifically, industries that supply the auto manufacturing industry (e.g., vehicle parts), auto 

dealers, the fuel refining and supply sectors, and the general retail sector. According to the 

U.S. Bureau of Labor Statistics, in 2010, about 677,000 people in the U.S. were employed in 

Motor Vehicle and Parts Manufacturing Sector (NAICS 3361, 3362, and 3363). About 

129,000 people in the U.S. were employed in the Automobile and Light Truck Manufacturing 

Sector (NAICS 33611) in December 2010; this is the directly regulated sector, since it 

encompasses the auto manufacturers that are responsible for complying with the proposed 

standards.54 Changes in light duty vehicle sales, discussed in Chapter 8.1.1, could affect 

employment for auto dealers. The employment effects of this rule are expected to expand 

beyond the regulated sector. Though some of the parts used to achieve the proposed 

standards are likely to be built by auto manufacturers themselves, the auto parts 

manufacturing sector also plays a significant role in providing those parts, and will also be 

affected by changes in vehicle sales. As discussed in Chapter 5.4 of the RIA, this proposal is 

expected to reduce the amount of fuel these vehicles use, and thus affect the petroleum 

refinery and supply industries. Finally, since the net reduction in cost associated with this 

proposal is expected to lead to lower household expenditures on fuel net of vehicle costs, 

consumers then will have additional discretionary income that can be spent on other goods 

and services. 

 

K

 The May 21, 2010 Presidential Memorandum also requested that EPA and NHTSA, in developing the 

technical assessment to inform the rulemaking process (which was issued by the agencies and CARB on 

September 30, 2010), include, among other things, the “impacts on jobs and the automotive manufacturing base 

in the United States.” 
Chapter 8 

8-20 

When the economy is at full employment, an environmental 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.55 In the 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. As Schmalansee and Stavins note, it is possible that the 

magnitude of the effect on employment could vary over time, region, and sector, and positive 

effects on employment in some regions or sectors could be offset by negative effects in other 

regions or sectors. For this reason, they urge caution in reporting partial employment effects 

since it can “paint an inaccurate picture of net employment impacts if not placed in the 

broader economic context.” 

Given the time frame of this proposed rule, the state of the overall economy may have 

improved to the point where the only effect of changes in employment associated with the 

standards proposed here will be to shift workers from one sector to another. For example, to 

the extent that regulation increases the number of workers in the regulated sector (e.g., 

because of higher costs of producing vehicles with higher fuel economy), the opportunity cost 

associated with those shifts in employment will be captured in labor costs, 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). 

A number of different approaches have been used in published literature to conduct 

employment analysis. This section describes some of the common methods, as well as some 

of their limitations. 

8.2.2 Approaches to Quantitative Employment Analysis 

Measuring the employment impacts of a policy depend on a number of inputs and 

assumptions. For instance, as discussed, assumptions about the overall state of 

unemployment in the economy play a major role in measured job impacts. The inputs to the 

models commonly are the changes in quantities or expenditures in the affected sectors; model 

results may vary in different studies depending on the assumptions about the levels of those 

inputs, and which sectors receive those changes. Which sectors are included in the study can 
2017 Regulatory Impact Analysis 

8-21 

also affect the results. For instance, a study of this program that looks only at employment 

impacts in the refinery sector may find negative effects, because consumers will purchase less 

gasoline; a study that looks only at the auto parts sector, on the other hand, may find positive 

impacts, because the program will require redesigned or additional parts for vehicles. In both 

instances, these would only be partial perspectives on the overall change in national 

employment due to Federal regulation. 

8.2.2.1 Conceptual Framework for Employment Impacts in the 

Regulated Sector 

One study by Morgenstern, Pizer, and Shih56 provides a retrospective look at the 

impacts of regulation in employment in the regulated sectors. The paper provides a 

theoretical framework that can be useful for examining the impacts of a regulatory change on 

the regulated sector in the medium to longer term. In particular, it identifies three separate 

ways that employment levels may change in the regulated industry in response to a new (or 

more stringent) regulation. 

• Demand effect: higher production costs due to the regulation will lead to higher 

market prices; higher prices in turn reduce demand for the good, reducing the 

demand for labor to make that good. In the authors’ words, the “extent of this 

effect depends on the cost increase passed on to consumers as well as the demand 

elasticity of industry output.” 

• Cost effect: as costs go up, plants add more capital and labor (holding other 

factors constant), with potentially positive effects on employment. In the authors’ 

words, as “production costs rise, more inputs, including labor, are used to produce 

the same amount of output.” 

• Factor shift effect: post-regulation production technologies may be more or less 

labor-intensive (i.e., more/less labor is required per dollar of output). In the 

authors’ words, “environmental activities may be more labor intensive than 

conventional production,” meaning that “the amount of labor per dollar of output 

will rise,” though it is also possible that “cleaner operations could involve 

automation and less employment, for example.” 

According to the authors, the “demand effect” is expected to have a negative effect on 

employment,L the “cost effect” to have a positive effect on employment, and the “factor shift 

effect” to have an ambiguous effect on employment. Without more information with respect 

to the magnitudes of these competing effects, it is not possible to predict the total effect 

environmental regulation will have on employment levels in a regulated sector. 

 

L

 As will be discussed below, the demand effect in this proposal is potentially an exception to this rule. While 

the vehicles become more expensive, they also produce reduced fuel expenditures; the reduced fuel costs provide 

a countervailing impact on vehicle sales. As discussed in Preamble Section III.H.1, this possibility that vehicles 

may become more attractive to consumers after the program poses a conundrum: why have interactions between 

vehicle buyers and producers not provided these benefits without government intervention? 
Chapter 8 

8-22 

Morgenstern et al. estimated the effects on employment of spending on pollution 

abatement for four highly polluting/regulated U.S. industries (pulp and paper, plastics, steel, 

and petroleum refining) using data for six years between 1979 and 1991. The authors 

conclude that increased abatement expenditures generally have not caused a significant 

change in employment in those sectors. More specifically, their results show that, on average 

across the industries studied, each additional $1 million spent on pollution abatement results 

in a (statistically insignificant) net increase of 1.5 jobs. 

This approach to employment analysis has the advantage of carefully controlling for 

many possibly confounding effects in order to separate the effect of changes in regulatory 

costs on employment. It was, however, conducted for only four sectors. It could also be very 

difficult to update the study for other sectors, because one of the databases on which it relies, 

the Pollution Abatement Cost and Expenditure survey, has been conducted infrequently since 

1994, with the last survey conducted in 2005. The empirical estimates provided by 

Morgenstern et al. are not relevant to the case of fuel economy standards, which are very 

different from the pollution control standards on industrial facilities that were considered in 

that study. In addition, it does not examine the effects of regulation on employment in sectors 

related to but outside of the regulated sector. Nevertheless, the theory that Morgenstern et al. 

developed continues to be useful in this context. 

The following discussion of additional methodologies draws from Berck and 

Hoffmann’s review of employment models.57

 

8.2.2.2 Computable General Equilibrium (CGE) Models 

Computable general equilibrium (CGE) models are often used to assess the impacts of 

policy. These models include a stylized representation of supply and demand curves for all 

major markets in the economy. The labor market is commonly included. CGE models are 

very useful for looking at interaction effects of markets: “they allow for substitution among 

inputs in production and goods in consumption.” Thus, if one market experiences a change, 

such as a new regulation, then the effects can be observed in all other markets. As a result, 

they can measure the employment changes in the economy due to a regulation. Because they 

usually assume equilibrium in all markets, though, they typically lack involuntary 

unemployment. If the total amount of labor changes, it is due to people voluntarily entering 

or leaving the workforce. As a result, these models may not be appropriate for measuring 

effects of a policy on unemployment, because of the assumption that there is no involuntary 

unemployment. In addition, because of the assumptions of equilibrium in all markets and 

forward-looking consumers and firms, they are designed for examining the long-run effects of 

a policy but may offer little insight into its short-run effects. 

8.2.2.3 Input-Output (IO) Models 

Input-output models represent the economy through a matrix of coefficients that 

describe the connections between supplying and consuming sectors. In that sense, like CGE 

models, they describe the interconnections of the economy. These interconnections look at 

how changes in one sector ripple through the rest of the economy. For instance, a 

requirement for additional technology for vehicles requires additional steel, which requires 
2017 Regulatory Impact Analysis 

8-23 

more workers in both the auto and steel sectors; the additional workers in those sectors then 

have more money to spend, which leads to more employment in retail sectors. These are 

known as “multiplier” effects, because an initial impact in one sector gets multiplied through 

the economy. Unlike CGE models, input-output models have fixed, linear relationships 

among the sectors (e.g., substitution among inputs or goods is not allowed), and quantity 

supplied need not equal quantity demanded. In particular, these models do not allow for price 

changes – an increase in the demand for labor or capital does not result in a change in its price 

to help reallocate it to its best use. As a result, these models cannot capture opportunity costs 

from using resources in one area of the economy over another. The multipliers take an initial 

impact and can increase it substantially. 

IO models are commonly used for regional analysis of projects. In a regional analysis, 

the markets are commonly considered small enough that wages and prices are determined 

outside the region, and any excess supply or demand is due to exports and imports (or, in the 

case of labor, emigration or immigration). For national-level employment analysis, the use of 

input-output models requires the assumption that workers flow into or out of the labor market 

perfectly freely. Wages do not adjust; instead, people join into or depart from the labor pool 

as production requires them. For other markets as well, there is no substitution of less 

expensive inputs for more expensive ones. As a result, IO models provide an upper bound on 

employment impacts. As Berck and Hoffmann note, “For the same reason, they can be 

thought of as simulating very short-run adjustment,” in contrast to the CGE’s implicit 

assumption of long-run adjustment. Changes in production processes, introductions of new 

technologies, or learning over time due to new regulatory requirements are also generally not 

captured by IO models, as they are calibrated to already established relationships between 

inputs and outputs. 

8.2.2.4 Hybrid Models 

As Berck and Hoffmann note, input-output models and CGE models “represent a 

continuum of closely related models.” Though not separately discussed by Berck and 

Hoffmann, some hybrid models combine some of the features of CGE models (e.g., prices 

that can change) with input-output relationships. For instance, a hybrid model may include 

the ability to examine disequilibrium phenomena, such as labor being at less than full 

employment. Hybrid models depend on assumptions about how adjustments in the economy 

occur. CGE models characterize equilibria but say little about the pathway between them, 

while IO models assume that adjustments are largely constrained by previously defined 

relationships; the effectiveness of hybrid models depends on their success in overcoming the 

limitations of each of these approaches. Hybrid models could potentially be used to model 

labor market impacts of various vehicle policy options although a number of judgments need 

to be made about the appropriate assumptions underlying the model as well the empirical 

basis for the modeling results. 

8.2.2.5 Single Sectors 

It is possible to conduct a bottom-up analysis of the partial effect of regulation on 

employment in a single sector by estimating the change in output or expenditures in a sector 

and multiplying it by an estimate of the number of workers per unit of output or expenditures, 
Chapter 8 

8-24 

under the assumption that labor demand is proportional to output or expenditures. As Berck 

and Hoffmann note, though, “Compliance with regulations may create additional jobs that are 

not accounted for.” While such an analysis can approximate the effects in that one sector in a 

simple way, it also may miss important connections to related sectors. 

8.2.2.6 Ex-Post Econometric Studies 

A number of ex-post econometric analyses examine the net effect of regulation on 

employment in regulated sectors. Morgenstern, Pizer, and Shih (2002), discussed above, and 

Berman and Bui (2001) are two notable examples that rely on highly disaggregated 

establishment-level time series data to estimate longer-run employment effects.58 While often 

a sophisticated treatment of the issues analyzed, these studies commonly analyze specific 

scenarios or sectors in the past; care needs to be taken in extrapolating their results to other 

scenarios and to the future. For instance, neither of these two studies examines the auto 

industry and are therefore of limited applicability in this context. 

8.2.2.7 Summary 

All methods of estimating employment impacts of a regulation have advantages and 

limitations. CGE models may be most appropriate for long-term impacts, but the usual 

assumption of equilibrium in the employment market means that it is not useful for looking at 

changes in overall employment: overall levels are likely to be premised on full employment. 

IO models, on the other hand, may be most appropriate for small-scale, short-term effects, 

because they assume fixed relationships across sectors and do not require market equilibria. 

Hybrid models, which combine some features of CGEs with IO models, depend upon key 

assumptions and economic relationships that are built into them. Single-sector models are 

simple and straightforward, but they are often based on the assumptions that labor demand is 

proportional to output, and that other sectors are not affected. Finally, econometric models 

have been developed to evaluate the longer-run net effects of regulation on sector 

employment, though these are ex-post analyses commonly of specific sectors or situations, 

and the results may not have direct bearing for the regulation being reviewed. 

8.2.3 Employment analysis of this proposal 

As mentioned above, this program is expected to affect employment in the regulated 

sector (auto manufacturing) and other sectors directly affected by the proposal: auto parts 

suppliers, auto dealers, the fuel supply market (which will face reduced petroleum production 

due to reduced fuel demand but which may see additional demand for electricity or other 

fuels), and consumers (who will face higher vehicle costs and lower fuel expenditures). In 

addition, as the discussion above suggests, each of these sectors could potentially have ripple 

effects in the rest of the economy. These ripple effects depend much more heavily on the 

state of the macroeconomy than do the direct effects. At the national level, employment may 

increase in one industry or region and decrease in another, with the net effect being smaller 

than either individual-sector effect. EPA does not attempt to quantify the net effects of the 

regulation on overall national employment. 
2017 Regulatory Impact Analysis 

8-25 

The discussion that follows provides a partial, bottom-up quantitative estimate of the 

effects of this proposal on the regulated sector (the auto industry; for reasons discussed below, 

we include some quantitative assessment of effects on suppliers to the industry although they 

are not regulated directly). It also includes qualitative discussion of the effects of the proposal 

on other sectors. Focusing quantification of employment impacts on the regulated sector has 

some advantages over quantifying all impacts. First, the analysis relies on data generated as 

part of the rulemaking process, which focuses on the regulated sector; as a result, what is 

presented here is based on internally consistent assumptions and estimates made in this 

proposal. Secondly, as discussed above, net effects on employment in the economy as a 

whole depend heavily on the overall state of the economy when this rule has its effects. 

Focusing on the regulated sector provides insight into employment effects in that sector 

without having to make assumptions about the state of the economy when this rule has its 

impacts. We include a qualitative discussion of employment effects on other sectors to 

provide a broader perspective on the impacts of this rule. 

As noted above, in a full-employment economy, any changes in employment will 

result from people changing jobs or voluntarily entering or exiting the workforce. In a fullemployment economy, employment impacts of this proposal will change employment in 

specific sectors, but it will have small, if any, effect on aggregate employment. This rule 

would take effect in model years 2017 through 2025; by then, the current high unemployment 

may be moderated or ended. For that reason, this analysis does not include multiplier effects, 

but instead focuses on employment impacts in the most directly affected industries. Those 

sectors are likely to face the most concentrated employment impacts. The agencies seek 

comment on other sectors that are likely to be significantly affected and thus warrant further 

analysis in the final rulemaking analysis. 

8.2.3.1 Employment Impacts in the Auto Industry 

Following the Morgenstern et al. conceptual framework for the impacts of regulation 

on employment in the regulated sector, we consider three effects for the auto sector: the 

demand effect, the cost effect, and the factor shift effect. However, we are only able to offer 

quantitative estimates for the cost effect. We note that these estimates, based on 

extrapolations from current data, become more uncertain as time goes on. 

8.2.3.1.1 The Demand Effect 

The demand effect depends on the effects of this proposal on vehicle sales. If vehicle 

sales increase, then more people will be required to assemble vehicles and their components. 

If vehicle sales decrease, employment associated with these activities will unambiguously 

decrease. Unlike in Morgenstern et al.’s study, where the demand effect unambiguously 

decreased employment, there are countervailing effects in the vehicle market due to the fuel 

savings resulting from this program. On one hand, this proposal will increase vehicle costs; 

by itself, this effect would reduce vehicle sales. On the other hand, this proposal will reduce 

the fuel costs of operating the vehicle; by itself, this effect would increase vehicle sales, 

especially if potential buyers have an expectation of higher fuel prices. The sign of demand 

effect will depend on which of these effects dominates. Because, as described in Chapter 8.1, 
Chapter 8 

8-26 

we have not quantified the impact on sales for this proposal, we do not quantify the demand 

effect. 

8.2.3.1.2 The Cost Effect

The demand effect, discussed above, measures employment changes due to new 

vehicle sales only. The cost effect measures employment impacts due to the new or 

additional technologies needed for vehicles to comply with the proposed standards. 

One way to estimate the cost effect, given the cost estimates for complying with the 

rule, is to use the ratio of workers to each $1 million of expenditures in that sector. The use of 

these ratios has both advantages and limitations. It is often possible to estimate these ratios 

for quite specific sectors of the economy: for instance, it is possible to estimate the average 

number of workers in the light-duty vehicle manufacturing sector per $1 million spent in the 

sector, rather than use the ratio from another, more aggregated sector, such as motor vehicle 

manufacturing. As a result, it is not necessary to extrapolate employment ratios from possibly 

unrelated sectors. On the other hand, these estimates are averages for the sectors, covering all 

the activities in those sectors; they may not be representative of the labor required when 

expenditures are required on specific activities, as the factor shift effect (discussed below) 

indicates. For instance, the ratio for the motor vehicle manufacturing sector represents the 

ratio for all vehicle manufacturing, not just for fuel efficiency improvements. In addition, 

these estimates do not include changes in sectors that supply these sectors, such as steel or 

electronics producers. They thus may best be viewed as the effects on employment in the 

auto sector due to the changes in expenditures in that sector, rather than as an assessment of 

all employment changes due to these changes in expenditures. 

Some of the costs of this proposal will be spent directly in the auto manufacturing 

sector, but some of the costs will be spent in the auto parts manufacturing sector. Because we 

do not have information on the proportion of expenditures in each sector, we separately 

present the ratios for both the auto manufacturing sector and the auto parts manufacturing 

sector. These are not additive, but should instead be considered as a range of estimates for the 

cost effect, depending on which sector adds technologies to the vehicles to comply with the 

regulation. 

We use several public sources for estimates of employment per $1 million 

expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment 

Requirements Matrix (ERM),59 which provides direct estimates of the employment per $1 

million in sales of goods in 202 sectors. The most recent estimates, used here, are from 2008 

(adjusted to 2009$). The tables used here are adjusted to remove the employment effects of 

imports through use of the ratio of domestic production to domestic sales, described above, of 

0.667. The values reported are for Motor Vehicle Manufacturing (NAICS 3361) and Motor 

Vehicle Parts Manufacturing (NAICS 3363). 

The Annual Survey of Manufactures60 (ASM) provides another source of estimates 

based on a sample of 50,000 establishments out of a universe of 346,000 manufacturing 

establishments. It includes more sectoral detail than the BLS ERM: for instance, while the 

ERM includes the Motor Vehicle Manufacturing sector, the ASM has detail at the 6-digit 
2017 Regulatory Impact Analysis 

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NAICS code level (e.g., automobile manufacturing vs. light truck and utility vehicle 

manufacturing). While the ERM provides direct estimates of employees/$1 million in 

expenditures, the ASM separately provides number of employees and value shipments; the 

direct employment estimates here are the ratio of those values. The data in the ASM are 

updated annually, except for years when the full Economic Census occurs. The tables 

presented here use data from 2009. As with the ERM, we adjust for the ratio of domestic 

production to domestic sales. The values reported are for Motor Vehicle Manufacturing 

(NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), 

and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Economic Census includes all large companies and a sample of smaller ones. The 

ASM is a subset of the Economic Census; though the Census itself is more complete, it is 

conducted only every 5 years, while the ASM is annual. The values presented here use data 

from 2007 (adjusted to 2009$), with the domestic production-to-sales adjustment. The values 

reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty 

Motor Vehicle Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing 

(NAICS 3363). 

Table 8.2-1 provides the values, either given (BLS) or calculated (ASM, Economic 

Census) for employment per $1 million of expenditures, all based on 2009 dollars, though the 

underlying data come from different years (which may account for some of the differences). 

The different data sources provide similar magnitudes for the estimates for the sectors. Parts 

manufacturing appears to be more labor-intensive than vehicle manufacturing; light-duty 

vehicle manufacturing appears to be slightly less labor-intensive than motor vehicle 

manufacturing as a whole. 

Table 8.2-1 Employment per $1 Million Expenditures (2009$) in the Motor Vehicle 

Manufacturing Sector* 

Source Sector Ratio of 

workers per 

$1 million 

expenditures

Ratio of workers per $1 

million expenditures, 

adjusted for domestic vs. 

foreign production 

BLS ERM Motor Vehicle Mfg 0.834 0.556

ASM Motor Vehicle Mfg 0.824 0.549

ASM Light Duty Vehicle 

Mfg

0.757 0.505

Economic 

Census 

Motor Vehicle Mfg 0.674 0.449

Economic 

Census 

Light Duty Vehicle 

Mfg

0.610 0.407

BLS ERM Motor Vehicle Parts 

Mfg

3.073 2.049

Deleted: Table 8.2-3

Deleted: 3
Chapter 8 

8-28 

ASM Motor Vehicle Parts 

Mfg

3.093 2.063

Economic 

Census 

Motor Vehicle Parts 

Mfg

2.749 1.833

BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix. ASM refers to the 

U.S. Census Bureau’s Annual Survey of Manufactures. Economic Census refers to the U.S. Census Bureau’s 

Economic Census.

Over time, the amount of labor needed in the auto industry has changed: automation 

and improved production methods have led to significant productivity increases. The BLS 

ERM, for instance, provided estimates that, in 1993, 1.52 workers were needed per $1 million 

of 2000$, but only 0.83 workers by 2008 (in 2000$).61 Because the ERM is available 

annually for 1993-2008, we used these data to estimate productivity improvements over time. 

We regressed logged ERM values on year (to estimate percent change per year) for both the 

Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results 

suggest a 4.4 percent per year productivity improvement in the Motor Vehicle Manufacturing 

Sector, and a 3.6 percent per year improvement in the Motor Vehicle Parts Manufacturing 

Sector. We then used the regression relationship to project the ERM through 2025. In the 

results presented below, these projected values (adjusted to 2009$) were used directly for the 

BLS ERM estimates. For the ASM, we used the ratio of the projected value in the future to 

the projected value in 2009 (the base year for the ASM); for the Economic Census estimates, 

we used the ratio of the projected value in the future to the projected value in 2007 (the base 

year for that estimate). As noted above, we adjusted the estimate of workers per vehicle for 

the demand effect in Chapter 8.2.3.1.1, above, using the productivity improvement for the 

Motor Vehicle Manufacturing sector; because the estimate of workers per vehicle for the 

demand effect was based on data from 2001 to 2010, we used the projected value of the ERM 

in 2005 as the denominator of the ratio. 

Table 8.2-2 shows the cost estimates developed for this rule, discussed in Chapter 5. 

The maximum value in Table 8.2-2 for employment impacts per $1 million expenditures

(after accounting for the share of domestic production) is 2.049 in 2009 if all the additional 

costs are in the parts sector; the minimum value is 0.407 in 2009, if all the additional costs are 

in the light-duty vehicle manufacturing sector: that is, the range of employment impacts is 

between 0.4 and 2 additional jobs per $1 million expenditures in the sector in 2009. The 

results in Table 8.2-2 include the productivity adjustment described above. 

While we estimate employment impacts beginning with the first year of the standard 

(2017), some of these job gains may occur earlier as auto manufacturers and parts suppliers 

hire staff in anticipation of compliance with the standard. 

Table 8.2-2 Employment due to Cost Effect in the Motor Vehicle Manufacturing Sector 

Year Costs (before 

adjustment for 

domestic proportion 

of production) 

Minimum 

employment effect 

(if all expenditures 

are in the parts 

Maximum 

employment effect 

(if all expenditures 

are in the light 

Deleted: Table 8.2-4

Deleted: Table 8.2-4

Deleted: Table 8.2-4

Deleted: 4
2017 Regulatory Impact Analysis 

8-29 

($Millions) sector) duty vehicle mfg 

sector) 

2017 $ 2,300 600 3,600 

2018 $ 4,656 1,200 7,000 

2019 $ 6,507 1,600 9,400 

2020 $ 8,467 1,900 11,800 

2021 $ 11,878 2,600 15,900 

2022 $ 19,340 4,100 25,000 

2023 $ 25,036 5,000 31,200 

2024 $ 30,738 5,900 37,000 

2025 $ 33,561 6,200 39,000 

8.2.3.1.3 The Factor Shift Effect 

The factor shift effect looks at the effects on employment due to changes in labor 

intensity associated with a regulation. As noted above, the estimates of the cost effect assume 

constant labor per $1 million in expenditures, though the new technologies may be either 

more or less labor-intensive than the existing ones. An estimate of the factor shift effect 

would either increase or decrease the estimate used for the cost effect. 

We are not quantifying the factor shift effect here, for lack of data on the labor 

intensity of all the possible technologies that manufacturers could use to comply with the 

proposed standards. For a subset of the technologies, though, EPA-sponsored research 

(discussed in Chapter 3.2.1.1 of the Joint TSD) which compared new technologies to existing 

ones at the level of individual components provides some insights into the factor shift effect. 

The comparison involved tearing down the selected technologies to their individual 

components and looking at the differences in materials and labor needs in moving from the 

conventional to the new technologies.62 For instance, the analysis compared all the parts and 

labor associated with an 8-speed automatic transmission to those needed for a 6-speed 

automatic transmission. 

Because labor cost was one of the sources of differences between the technologies, it 

is possible, for those technologies, to see whether labor needs increase or decrease with the 

switch to technologies that might contribute to compliance with the proposed standards. An 

increase in labor cost for the new technology indicates an increase in the labor needed for the 

new technology compared to the baseline technology. For instance, an 8-speed transmission 

requires $15.11 more in labor costs than a 6-speed transmission (as accounted for in EPA’s 

cost estimates for the proposed rule). Dividing the labor cost by a wage per hour estimate 

provides an estimate of the additional hours (and thus the additional labor) needed for the new 

technology compared to the baseline technology. As with labor cost, an increase in labor 

hours per technology indicates greater employment needs for the new technologies. For this 

conversion, a weighted average wage rate (90 percent of the average wage in the Motor 

Vehicle Parts Manufacturing sector, and 10 percent of the average wage in the Motor Vehicle 

Manufacturing Sector) of $46.36/hour in 2015, using 2008 dollars (the unit of analysis for the 
Chapter 8 

8-30 

FEV study). For the change from a 6-speed to an 8-speed transmission, we thus estimate an 

additional 0.33 hours of labor per transmission. 

Table 8.2-3 shows the changes in labor hours in moving from baseline to new fuelsaving technologies for technologies in the FEV study. It indicates that, in switching from the 

baseline to the new technologies, labor use per technology increased: the fuel-saving 

technologies use more labor than the baseline technologies. For a subset of the technologies 

likely to be used to meet the standards in this proposal, then, the factor shift effect increases 

labor demand, at least in the short run; in the long run, as with all technologies, the cost 

structure is likely to change due to learning, economies of scale, etc. The technologies 

examined in this research are, however, only a subset of the technologies that auto makers 

may use to comply with the standards proposed in this program. As a result, these results 

cannot be considered definitive evidence that the factor shift effect increases employment for 

this rule. We therefore do not quantify the factor shift effect for this proposal.

Table 8.2-3 Estimated Change in Labor for Selected Compliance Technologies 

Technology FEV 

Case 

Study 

Vehicle Class Labor 

Costs 

 Total 

Costs 

 Hours/ 

Technology 

Downsized 

Turbo GDI 4 

0101 Compact C $72.58 $537.70 1.57 

Downsized 

Turbo GDI V6 

0102 Mid/Large C $25.76 $87.38 0.56

Downsized 

Turbo GDI V6 

0104 SUV/Trucks $84.19 $789.53 1.82 

Electric A/C 

compressor 

0602 $4.68 $167.54 0.10

Power split 

hybrid 

0502 Mid/Large C $395.85 $3,435.01 8.54 

6- to 8-speed 

transmission 

0803 Mid/Large C $15.11 $61.84 0.33

8.2.3.1.4 Summary of Employment Effects in the Auto Sector 

While we are not able to quantify the demand or factor shift effects, the cost effect 

results show that the employment effects of the increased spending in the regulated sector 

(and, possibly, the parts sector) are expected to be positive and on the order of a few thousand 

in the initial years of the program. As noted above, motor vehicle and parts manufacturing 

sectors employed about 677,000 people in 2010, with automobile and light truck 

manufacturing accounting for about 129,000 of that total. 

8.2.4 Effects on Employment for Auto Dealers 

The effects of the proposed standards on employment for auto dealers depend 

principally on the effects of the standards on light duty vehicle sales. In addition, auto dealers 

Deleted: Table 8.2-5

Deleted: 5
2017 Regulatory Impact Analysis 

8-31 

may be affected by changes in maintenance and service costs. Increases in those costs are 

likely to increase labor demand in dealerships. 

Although this proposal predicts very small penetration of advanced technology 

vehicles, the uncertainty on consumer acceptance of such technology vehicles is even greater. 

As discussed in Chapter 8.1.2.7, consumers may find some characteristics of electric vehicles 

and plug-in hybrid electric vehicles, such as the ability to fuel with electricity rather than 

gasoline, attractive; they may find other characteristics, such as the limited range for electric 

vehicles, undesirable. As a result, some consumers will find that EVs will meet their needs, 

but other buyers will choose more conventional vehicles. Auto dealers may play a major role 

in explaining the merits and disadvantages of these new technologies to vehicle buyers. There 

may be a temporary need for increased employment to train sales staff in the new 

technologies as the new technologies become available. 

8.2.5 Effects on Employment in the Auto Parts Sector 

As discussed in the context of employment in the auto industry, some vehicle parts are 

made in-house by auto manufacturers; others are made by independent suppliers who are not 

directly regulated, but who will be affected by the proposed standards as well. The additional 

expenditures on technologies are expected to have a positive effect on employment in the 

parts sector as well as the manufacturing sector; the breakdown in employment between the 

two sectors is difficult to predict. The effects on the parts sector also depend on the effects of 

the proposed standards on vehicle sales and on the labor intensity of the new technologies, 

qualitatively in the same ways as for the auto manufacturing sector. 

8.2.6 Effects on Employment for Fuel Suppliers 

In addition to the effects on the auto manufacturing and parts sectors, these rules will 

result in changes in fuel use that lower GHG emissions. Fuel saving, principally reductions in 

liquid fuels such as gasoline and diesel, will affect employment in the fuel suppliers industry 

sectors throughout the supply chain, from refineries to gasoline stations. To the extent that 

the proposed standards result in increased use of electricity or other new fuels, employment 

effects will result from providing these fuels and developing the infrastructure to supply them 

to consumers. 

Expected petroleum fuel consumption reductions can be found in Chapter 5.3. While 

this reduced consumption represents fuel savings for purchasers of fuel, it represents a loss in 

value of output for the petroleum refinery industry, fuel distributors, and gasoline stations. 

The loss of expenditures to petroleum fuel suppliers throughout the petroleum fuel supply 

chain, from the petroleum refiners to the gasoline stations, is likely to result in reduced 

employment in these sectors. 

This rule is also expected to lead to increases in electricity consumption by vehicles, 

as discussed in Chapter 5.3. This new fuel may require additional infrastructure, such as 

electricity charging locations. Providing this infrastructure will require some increased 

employment. In addition, the generation of electricity is likely to require some additional 

labor. We have insufficient information at this time to predict whether the increases in labor 
Chapter 8 

8-32 

associated with increased infrastructure provision and generation for electricity will be greater 

or less than the employment reductions associated with reduced demand for petroleum fuels. 

8.2.7 Effects on Employment due to Impacts on Consumer Expenditures 

As a result of these proposed standards, consumers will pay a higher up-front cost for 

the vehicles, but they will recover those costs in a fairly short payback period (see Preamble 

Section III.H.10.b); indeed, people who finance their vehicles are expected to find that their 

fuel savings per month exceed the increase in the loan cost (though this depends on the 

particular loan rate a consumer receives). As a result, consumers will have additional money 

to spend on other goods and services, though, for those who do not finance their vehicles, it 

will occur after the initial payback period. These increased expenditures will support 

employment in those sectors where consumers spend their savings. 

These increased expenditures will occur in 2017 and beyond. If the economy returns 

to full employment by that time, any change in consumer expenditures would primarily 

represent a shift in employment among sectors. If, on the other hand, the economy still has 

substantial unemployment, these expenditures would contribute to employment through 

increased consumer demand. 

Economy-wide CGE modeling of this rule could provide additional insight into the 

effects of this proposed rule on consumer expenditures, in addition to impacts on the overall 

market economy.M

 As an exploratory exercise, EPA used a CGE model to conduct an 

analysis of the impacts of the proposed GHG standards on GDP and household 

consumption.N,O We conducted similar economy-wide impact analyses for both the 2012-

2016 light-duty GHG final rule and the recent heavy-duty GHG final rule.P 

 

N

 See Memorandum to Docket, “Economy-Wide Impacts of Proposed Light-Duty Vehicle Greenhouse Gas 

Standards for Model Years 2017-2025,” November, 2011, Docket EPA-HQ-OAR-2010-0799. The results of 

this analysis were not included in the analysis of overall costs and benefits of the proposed standards. 

N

 See Memorandum to Docket, “Economy-Wide Impacts of Proposed Light-Duty Vehicle Greenhouse Gas 

Standards for Model Years 2017-2025,” November, 2011, Docket EPA-HQ-OAR-2010-0799. The results of 

this analysis were not included in the analysis of overall costs and benefits of the proposed standards. 

O

GDP is one widely-used measure of overall economic activity. It measures the market value of all goods and 

services produced in a given year and reflects the sum of its four components: consumption, investment, 

government spending, and net exports. Consumption, one component of GDP, may provide a more explicit 

indicator of households’ well being by showing the value of marketed goods and services that households 

consume in a given year. P

See Memorandum to Docket, "Economy-Wide Impacts of Proposed Greenhouse Gas 

Tailpipe Standards for the Final Rulemaking," March 4, 2010, Document ID: EPA-HQ-OAR-2009-0472-11467 

and Memorandum to Docket, “Economy-Wide Impacts of Heavy-Duty Truck Greenhouse Gas Emissions and 

Fuel Efficiency Standards”, May 20, 2011, Document ID: EPA-HQ-OAR-2010-0162-3342. 

P

See Memorandum to Docket, "Economy-Wide Impacts of Proposed Greenhouse Gas Tailpipe Standards for the 

Final Rulemaking," March 4, 2010, Document ID: EPA-HQ-OAR-2009-0472-11467 and Memorandum to 

Docket, “Economy-Wide Impacts of Heavy-Duty Truck Greenhouse Gas Emissions and Fuel Efficiency 

Standards”, May 20, 2011, Document ID: EPA-HQ-OAR-2010-0162-3342. 

Deleted: We provide general background on CGE 

modeling in Chapter 8.2.2.2.¶

Deleted: ¶
2017 Regulatory Impact Analysis 

8-33 

8.2.8 Summary 

The primary employment effects of this proposal are expected to be found throughout 

several key sectors: auto manufacturers, auto dealers, auto parts manufacturing, fuel 

production and supply, and consumers. We emphasize that the discussion here is a partial 

view of these effects. Whether the economy is in full employment will determine whether the 

discussed changes in employment in these sectors is offset in other sectors of the economy. 

The extent to which employment in the sectors discussed above responds to this program will 

be influenced by how consumers consider fuel savings in their vehicle purchase decisions, 

changes in vehicle manufacturing technologies and locations, and other factors. 

These proposed standards initially take effect in model year 2017, a time period 

sufficiently far in the future that the current sustained high unemployment at the national level 

may be moderated or ended. In an economy with full employment, the primary employment 

effect of a rulemaking is likely to be to move employment from one sector to another, rather 

than to increase or decrease employment. For that reason, we focus our partial quantitative 

analysis on employment in the regulated sector, to examine the impacts on that sector directly. 

We discuss the likely direction of other impacts in the regulated sector as well as in other 

directly related sectors, but we do not quantify those impacts, because they are more difficult 

to quantify with reasonable accuracy, particularly so far into the future. 

For the regulated sector, the cost effect is expected to increase employment by 600 – 

3,600 workers in 2017, depending on the share of that employment that is in the auto 

manufacturing sector compared to the auto parts manufacturing sector. As mentioned above, 

some of these job gains may occur earlier as auto manufacturers and parts suppliers hire staff 

to prepare to comply with the standard. The demand effect is ambiguous and depends on 

changes in vehicle sales, which are not quantified for this proposal. Though we do not have 

estimates of the factor shift effect for all potential compliance technologies, the evidence 

which we do have for some technologies suggests that many of the technologies will have 

increased labor needs. 

Effects in other sectors that are predicated on vehicle sales are also ambiguous. 

Changes in vehicle sales are expected to affect labor needs in auto dealerships and in parts 

manufacturing. Increased expenditures for auto parts are expected to require increased labor 

to build parts, though this effect also depends on any changes in the labor intensity of 

production; as noted, the subset of potential compliance technologies for which data are 

available show increased labor requirements. Reduced fuel production implies less 

employment in the petroleum sectors. Finally, consumer spending is expected to affect 

employment through changes in expenditures in general retail sectors; net fuel savings by 

consumers are expected to increase demand (and therefore employment) in other sectors.

 
Chapter 8 

8-34 

References 

1

 See, for instance, Gron, Ann, and Deborah Swenson, 2000. “Cost Pass-Through in the U.S. 

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2

 Insurance Information Institute, 2008, “Average Expenditures for Auto Insurance By State, 

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4

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 U.S. Census Bureau, "Table 2. Resident Population of the 50 States, the District of 

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7

 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

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9

 McManus, Walter M., “Can Proactive Fuel Economy Strategies Help Automakers Mitigate 

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2017 Regulatory Impact Analysis 

8-35 

10 Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

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Systems from a Combination of Micro and Macro Data: The New Car Market,” Journal of 

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11 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

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12 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

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15 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and 

Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American 

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17 Greene, David L., K.G. Duleep, Doug Elliott, and Sanjana Ahmad, “A Fuel Economy 

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19 E.g., Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining 

Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 

1469-1496 (Docket EPA-HQ-OAR-2010-0799). 
Chapter 8 

8-36 

20 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-2010-

0799). 

21 E.g., Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and 

Roger H. von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American 

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22 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

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25 E.g., Berry, Steven, James Levinsohn, and Ariel Pakes, “Automobile Prices in Market 

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26 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 

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2017 Regulatory Impact Analysis 

8-37 

30 Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly in International 

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34 Bento, Antonio M., Lawrence H. Goulder, Emeric Henry, Mark R. Jacobsen, and Roger H. 

von Haefen, “Distributional and Efficiency Impacts of Gasoline Taxes,” American Economic 

Review 99(3) 2009): 667-699 (Docket EPA-HQ-OAR-2010-0799); Feng, Yi, Don Fullerton, 

and Li Gan, “Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of 

Economic Analysis Working Paper 11553, available at

http://econweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010-

0799). 

35 Greene, David L., Philip D. Patterson, Margaret Singh, and Jia Li, “Feebates, Rebates, and 

Gas-Guzzler Taxes: A Study of Incentives for Increased Fuel Economy,” Energy Policy 33 

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“Vehicle Choices, Miles Driven and Pollution Policies,” National Bureau of Economic 

Analysis Working Paper 11553, available at http://econweb.tamu.edu/gan/w11553.pdf , 

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36 E.g., Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of 

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Chapter 8 

8-38 

37 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy 

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0799). 

42 Busse, Meghan, Christopher R. Knittel, and Florian Zettelmeyer, 2009. “Pain at the Pump: 

How Gasoline Prices Affect Automobile Purchasing in New and Used Markets,” working 

paper, http://web.mit.edu/knittel/www/papers/gaspaper_latest.pdf (accessed 11/1/11) (Docket 

EPA-HQ-OAR-2010-0799); Li, Shanjun, Christopher Timmins, and Roger H. von Haefen, 

2009. “How Do Gasoline Prices Affect Fleet Fuel Economy?” American Economic Journal: 

Economic Policy 1(2): 113-137; Congressional Budget Office (2008) (Docket EPA-HQOAR-2010-0799); Congressional Budget Office (2008). Effects of Gasoline Prices on 

Driving Behavior and Vehicle Markets. The Congress of the United States, Pub. No. 2883

(Docket EPA-HQ-OAR-2010-0799); West. Sarah (2007). “The Effect of Gasoline Prices on 

the Demand for Sport Utility Vehicles.” Working Paper. Macalester College, 

http://www.macalester.edu/~wests/SarahWestMEA2007.pdf, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799). 

43 Greene, David L., and Jin-Tan Liu, 1988. “Automotive Fuel Economy Improvements and 

Consumers’ Surplus,” Transportation Research Part A 22A (3): 203-218 (Docket EPA-HQOAR-2010-0799). 

44 Greene, David L. (2010). “How Consumers Value Fuel Economy: A Literature Review.” 

EPA Report EPA-420-R-10-008 (Docket EPA-HQ-OAR-2010-0799). 

45 Gramlich, Jacob, “Gas Prices, Fuel Efficiency, and Endogenous Product Choice in the U.S. 

Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 

11/1/11 (Docket EPA-HQ-OAR-2010-0799). 

46 Gramlich, Jacob, “Gas Prices and Endogenous Product Selection in the U.S. Automobile 

Industry,” http://faculty.msb.edu/jpg72/Autos_GramlichJacob.pdf , accessed 11/1/11 (Docket 

EPA-HQ-OAR-2010-0799); McManus, Walter, 2007. “The Impact of Attribute-Based 

Corporate Average Fuel Economy (CAFE) Standards: Preliminary Results.” Ann Arbor, MI: 

University of Michigan Transportation Research Institute, Report No. UMTRI-2007-31 

(Docket EPA-HQ-OAR-2010-0799). 
2017 Regulatory Impact Analysis 

8-39 

47 For instance, Kleit, Andrew N., “Impacts of Long-Range Increases in the Fuel Economy 

(CAFE) Standard,” Economic Inquiry 42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Clearing the Air: The Costs and 

Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journal of 

Environmental Economics and Management 50 (2005): 562- (Docket EPA-HQ-OAR-2010-

0799); Klier, Thomas, and Joshua Linn, 2008. “New Vehicle Characteristics and the Cost of 

the Corporate Average Fuel Economy Standard,” Resources for the Future Paper RFF DP 10-

50 (December 2010), http://www.rff.org/RFF/Documents/RFF-DP-10-50.pdf) (Docket EPAHQ-OAR-2010-0799)Jacobsen, Mark, 2010. “Evaluating U.S. Fuel Economy Standards in a 

Model with Producer and Household Heterogeneity,” working paper, at 

http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (accessed 11/1/11) (Docket EPA-HQOAR-2010-0799) . 

48 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher 

CAFE Standards and Increased Gasoline Taxes,” Journal of Environmental Economics and 

Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

49 IEA. 2007. “Mind the Gap: Quantifying Principal-Agent Problems in Energy Efficiency.” 

Paris, France: International Energy Agency (Docket EPA-HQ-OAR-2010-0799); Jaffe, Adam 

B., Richard G. Newell, and Robert N. Stavins (2001). "Energy Efficient Technologies and 

Climate Change Policies: Issues and Evidence." In Climate Change Economics and Policy, 

Toman, Michael A., ed., Washington, D.C.: Resources for the Future. p.171-181 (Docket 

EPA-HQ-OAR-2010-0799); Metcalf, Gilbert E., and Kevin A. Hassett (1999). "Measuring 

the Energy Savings From Home Improvement Investments: Evidence From Monthly Billing 

Data." The Review of Economics and Statistics 81(3): 516-528 (Docket EPA-HQ-OAR-2010-

0799); Tietenberg, T. (2009). “Reflections – Energy Efficiency Policy: Pipe Dream or 

Pipeline to the Future?” Review of Environmental Economics and Policy 3(2): 304-320 

(Docket EPA-HQ-OAR-2010-0799). 

50 Helfand, Gloria, and Ann Wolverton (2011). “Evaluating the Consumer Response to Fuel 

Economy: A Review of the Literature.” International Review of Environmental and 

Resource Economics 5 (2011): 103-146 (Docket EPA-HQ-OAR-2010-0799). 

51 Jaccard, Mark. “Paradigms of Energy Efficiency’s Cost and their Policy Implications: 

Déjà Vu All Over Again.” Modeling the Economics of Greenhouse Gas Mitigation: 

Summary of a Workshop, K. John Holmes, Rapporteur. National Academies Press, 2010. 

http://www.nap.edu/openbook.php?record_id=13023&page=42 (Docket EPA-HQ-OAR2010-0799) 

52 Greene David L., “TAFV Alternative Fuels and Vehicles Choice Model Documentation,” 

prepared by the Oak Ridge National Laboratory for the U.S. Department of Energy, July 2001 

(Docket EPA-HQ-OAR-2010-0799). 

53 President Barack Obama. “Presidential Memorandum Regarding Fuel Efficiency 

Standards. The White House, Office of the Press Secretary, May 21, 2010. 

http://www.whitehouse.gov/the-press-office/presidential-memorandum-regarding-fuelefficiency-standards 
Chapter 8 

8-40 

54 U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages, as accessed 

on August 9, 2011. 

55 Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis 

of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011 

(Docket EPA-HQ-OAR-2010-0799). 

56 Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. “Jobs Versus the 

Environment: An Industry-Level Perspective.” Journal of Environmental Economics and 

Management 43 (2002): 412-436 (Docket EPA-HQ-OAR-2010-0799).

57 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental 

and Natural Resource Policy.” Environmental and Resource Economics 22 (2002): 133-156 

(Docket EPA-HQ-OAR-2010-0799). 

58 Berman, Eli, and Linda T. Bui, (2001) “Environmental Regulation and Labor Demand: 

Evidence from the South Coast Air Basin,” Journal of Public Economics, 79, 265 – 295 

(Docket EPA-HQ-OAR-2010-0799).

59 http://www.bls.gov/emp/ep_data_emp_requirements.htm 

60 http://www.census.gov/manufacturing/asm/index.html

61 http://www.bls.gov/emp/ep_data_emp_requirements.htm; this analysis used data for sectors 

88 (Motor Vehicle Manufacturing) and 90 (Motor Vehicle Parts Manufacturing) from “Chainweighted (2000 dollars) real domestic employment requirements table. . . adjusted to remove 

imports.” 

62FEV, Inc. “Light-Duty Technology Cost Analysis: Power-split and P2 HEV Case Studies,” 

Report FEV07-069-303, October 10, 2011 (Docket EPA-HQ-OAR-2010-0799). 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

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Page 1 of 426

Below is a summary of interagency comments received during EO 12866 and EO 13563 review 

of the draft rule “Joint Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission 

Standards and Corporate Average Fuel Economy Standards”. They are for your consideration 

and are subject to further policy review.

1) Interagency commenters recommend the following edits to EPA’s section seeking 

comment on flexibilities in the Preamble: 

“Further, the Temporary Lead-time Allowance Alternative Standards (TLAAS) 

provisions included in EPA’s MYs 2012-2016 program for manufacturers with MY 2009 

U.S. sales of less than 400,000 vehicles ends after MY 2015 for most eligible 

manufacturers.[1] EPA is not proposing to extend or otherwise replace the TLAAS 

provisions for the proposed MYs 2017-2025 program. However, EPA is inviting comment 

on whether this or some other form of flexibility is warranted for lower volume, limited 

line manufacturers, as further discussed in Section III.B.8. With the exception of the small 

businesses and small volume manufacturers discussed above, the proposed MYs 2017-

2025 standards would apply to all manufacturers.” 

2) Interagency commenters recommend the following edits to NHTSA’s Section IV of the 

Preamble: 

IV 

NHTSA Proposed Rule for Passenger Car and Light Truck CAFE standards for Model 

Years 2017-2025 

A. Executive Overview of NHTSA Proposed Rule 

 1. Introduction 

2. Why does NHTSA set CAFE standards for passenger cars and light 

trucks?

3. Why is NHTSA proposing CAFE standards for MYs 2017-2025 now?

 a. President’s memorandum

 b. Benefits of continuing the National Program

B. Background

1. Chronology of events since the MY 2012-2016 final rule was issued 

2. How has NHTSA developed the proposed CAFE standards since the 

President’s announcement? 

 C. Development and feasibility of the proposed standards

 1. How was the baseline vehicle fleet developed?

 [1] TLAAS ends after MY 2016 for manufacturers with MY 2009 U.S. sales of less than 50,000 vehicles. 
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a. Why do the agencies establish a baseline and reference vehicle 

fleet? 

b. What data did the agencies use to construct the baseline, and 

how did they do so? 

c. How is the development of the baseline fleet for this rule 

different from the baseline fleet that NHTSA used for the MY 

2012-2016 (May 2010) final rule? 

d. How is this baseline different quantitatively from the baseline 

that NHTSA used for the MY 2012-2016 (May 2010) final rule? 

e. How does manufacturer product plan data factor into the 

baseline used in this rule? 

f. What sensitivity analyses is NHTSA conducting on the 

baseline? 

g. How else is NHTSA considering looking at the baseline for the 

final rule?

2. How were the technology inputs developed? 

a. What technologies does NHTSA consider? 

b. How did NHTSA determine the costs and effectiveness of each 

of these technologies for use in its modeling analysis?

c. How does NHTSA use these assumptions in its modeling 

analysis? 

d. Where can readers find more detailed information about 

NHTSA’s technology analysis? 

3. How did NHTSA develop its economic assumptions? 

a. Costs of Fuel Economy-Improving Technologies 

 b. Potential Opportunity Costs of Improved Fuel Economy 

c. The On-Road Fuel Economy “Gap” 

d. Fuel Prices and the Value of Saving Fuel 

e. Consumer Valuation of Fuel Economy and Payback Period 

f. Vehicle Survival and Use Assumptions 

g. Accounting for the Fuel Economy Rebound Effect 

h. Benefits From Increased Vehicle Use 

i. The Value of Increased Driving Range 

j. Added Costs From Congestion, Crashes and Noise 

k. Petroleum Consumption and Import Externalities 

i. Changes in Petroleum Imports 

ii. Benefits from Reducing U.S. Petroleum Imports 

l. Air Pollutant Emissions 

i. Changes in Criteria Air Pollutant Emissions 

ii. Reductions in CO2 Emissions 

iii. Economic Value of Reductions in CO2 Emissions 

m. Discounting Future Benefits and Costs 

n. Accounting for Uncertainty in Benefits and Costs 

o. Where can readers find more information about the economic 

assumptions? 

4. How does NHTSA use the assumptions in its modeling analysis? 
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a. How does the model operate? 

b. Has NHTSA considered other models? 

c. What changes has DOT made to the model? 

d. Does the model set the standards? 

e. How does NHTSA make the model available and transparent? 

D. Statutory Requirements 

1. EPCA, as Amended by EISA 

a. Standard Setting 

i. Statutory Factors Considered in Determining the 

Achievable Level of Average Fuel Economy 

(1) Technological Feasibility 

(2) Economic Practicability 

(3) The Effect of Other Motor Vehicle Standards of 

the Government on Fuel Economy 

(4) The Need of the United States To Conserve 

Energy 

(a) Fuel Prices and the Value of Saving Fuel 

(b) Petroleum Consumption and Import 

Externalities 

(c) Air Pollutant Emissions 

ii. Other Factors Considered by NHTSA 

iii. Factors that NHTSA is Prohibited from 

ConsideringENVIRONMENTAL PROTECTION AGENCY 

40 CFR Parts 85, 86 and 600 

DEPARTMENT OF TRANSPORTATION 

National Highway Traffic Safety Administration 

49 CFR Parts 523, 531, 533, 536 and 537 

 

[EPA–HQ–OAR–2010–0799; FRL_XXXX-X; NHTSA-2010-0131]

RIN 2060-AQ54; RIN 2127-AK79

2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate 

Average Fuel Economy Standards 

AGENCIES: Environmental Protection Agency (EPA) and National Highway Traffic Safety 

Administration (NHTSA). 

ACTION: Proposed Rule. 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

Page 4 of 426

SUMMARY: EPA and NHTSA, on behalf of the Department of Transportation, are issuing this 

joint proposal to further reduce greenhouse gas emissions and improve fuel economy for lightduty vehicles for model years 2017-2025. This proposal extends the National Program beyond 

the greenhouse gas and corporate average fuel economy standards set for model years 2012-

2016. On May 21, 2010, President Obama issued a Presidential Memorandum requesting that 

NHTSA and EPA develop through notice and comment rulemaking a coordinated National 

Program to reduce greenhouse gas emissions of light-duty vehicles for model years 2017-2025. 

This proposal, consistent with the President’s request, responds to the country’s critical need to

address global climate change and to reduce oil consumption. NHTSA is proposing Corporate 

Average Fuel Economy standards under the Energy Policy and Conservation Act, as amended by 

the Energy Independence and Security Act, and EPA is proposing greenhouse gas emissions 

standards under the Clean Air Act. These standards apply to passenger cars, light-duty trucks, 

and medium-duty passenger vehicles, and represent a continued harmonized and consistent 

National Program. Under the National Program for model years 2017-2025, automobile 

manufacturers would be able to continue building a single light-duty national fleet that satisfies 

all requirements under both programs while ensuring that consumers still have a full range of 

vehicle choices. EPA is also proposing a minor change to the regulations applicable to MY 2012-

2016, with respect to air conditioner performance and measurement of nitrous oxides. 

DATES: Comments: Comments must be received on or before [Insert date 60 days after 

publication in the Federal Register], sixty days after date of publication in the Federal 

Register. 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 

[Insert date 30 days after publication in the Federal Register], thirty days after date of 

publication in the Federal Register. See the SUPPLEMENTARY INFORMATION section 

on “Public Participation” for more information about written comments. 

Public Hearings: NHTSA and EPA will jointly hold three public hearings on the following 

dates: January 17, 2012 in Detroit, Michigan; January 19, 2012 in Philadelphia, Pennsylvania; 

and January 24, 2012 in San Francisco, California. EPA and NHTSA will announce the 

addresses for each hearing location in a supplemental Federal Register Notice. The agencies will 

accept comments to the rulemaking documents, and NHTSA will also accept comments to the 

Draft Environmental Impact Statement (EIS). The hearings will start at 9 a.m. local time and 

continue until everyone has had a chance to speak. See the SUPPLEMENTARY 

INFORMATION section on “Public Participation.” for more information about the public 

hearings. 

ADDRESSES: Submit your comments, identified by Docket ID No. EPA-HQ-OAR-2010-0799

and/or NHTSA-2010-0131, by one of the following methods: 

 Online: www.regulations.gov: Follow the on-line instructions for submitting comments. 

 Email: a-and-r-Docket@epa.gov 

 Fax: EPA: (202) 566–9744; NHTSA: (202) 493-2251. 

 Mail: 

o EPA: Environmental Protection Agency, EPA Docket Center (EPA/DC), Air and 

Radiation Docket, Mail Code 28221T, 1200 Pennsylvania Avenue, NW., 

Washington, DC 20460, Attention Docket ID No. EPA-HQ-OAR-2010-0799. In 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

Page 5 of 426

addition, please mail a copy of your comments on the information collection 

provisions 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. 

o NHTSA: Docket Management Facility, M-30, U.S. Department of Transportation, 

West Building, Ground Floor, Rm. W12-140, 1200 New Jersey Avenue, SE, 

Washington, DC 20590. 

 Hand Delivery: 

o EPA: Docket Center, (EPA/DC) EPA West, Room B102, 1301 Constitution 

Ave., NW., Washington, DC, Attention Docket ID No. EPA-HQ-OAR-2010-

0799. Such deliveries are only accepted during the Docket’s normal hours of 

operation, and special arrangements should be made for deliveries of boxed 

information. 

o NHTSA: West Building, Ground Floor, Rm. W12-140, 1200 New Jersey Avenue, 

SE, Washington, DC 20590, between 9 a.m. and 4 p.m. Eastern Time, Monday 

through Friday, except Federal Holidays. 

Instructions: Direct your comments to Docket ID No. EPA-HQ-OAR-2010-0799 and/or 

NHTSA-2010-0131. See the SUPPLEMENTARY INFORMATION section on “Public 

Participation” for more information about submitting written comments.

Docket: All documents in the dockets are listed in the www.regulations.gov index. 

Although listed in the index, some information is not publicly available, e.g., confidential 

business information (CBI) or other information whose disclosure is restricted by statute. Certain 

other material, such as copyrighted material, will be publicly available in hard copy in EPA’s 

docket, and electronically in NHTSA’s online docket. Publicly available docket materials are 

available either electronically in www.regulations.gov or in hard copy at the following locations: 

EPA: EPA Docket Center, EPA/DC, EPA West, Room 3334, 1301 Constitution Ave., NW., 

Washington, DC. 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. 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 5 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: Rebecca Yoon, 

Office of the Chief Counsel, National Highway Traffic Safety Administration, 1200 New Jersey 

Avenue, SE, Washington, DC 20590. Telephone: (202) 366-2992. 

SUPPLEMENTARY INFORMATION: 

A. Does this Action Apply to Me?
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

Page 6 of 426

This action affects companies that manufacture or sell new light-duty vehicles, light-duty 

trucks, and medium-duty passenger vehicles, as defined under EPA’s CAA regulations,1

 and 

passenger automobiles (passenger cars) and non-passenger automobiles (light trucks) as defined 

under NHTSA’s CAFE regulations.2

 Regulated categories and entities include: 

Category NAICS 

CodesA

Examples of Potentially Regulated Entities 

Industry 336111 

336112 

Motor Vehicle Manufacturers 

Industry 811111 

811112 

811198 

423110 

Commercial Importers of Vehicles and Vehicle 

Components 

Industry 335312 

336312 

336399 

811198 

Alternative Fuel Vehicle Converters 

A North American Industry Classification System (NAICS) 

This list is not intended to be exhaustive, but rather provides a guide regarding entities 

likely to be regulated by this action. To determine whether particular activities may be regulated 

by this action, you should carefully examine the regulations. You may direct questions regarding 

the applicability of this action to the person listed in FOR FURTHER INFORMATION 

CONTACT. 

B. Public Participation

NHTSA and EPA request comment on all aspects of this joint proposed rule. This 

section describes how you can participate in this process. 

How Do I Prepare and Submit Comments? 

In this joint proposal, there are many issues common to both EPA’s and NHTSA’s 

proposals. For the convenience of all parties, comments submitted to the EPA docket will be 

considered comments submitted to the NHTSA docket, and vice versa. An exception is that 

comments submitted to the NHTSA docket on NHTSA’s Draft Environmental Impact Statement 

(EIS) will not be considered submitted to the EPA docket. Therefore, the public only needs to 

submit comments to either one of the two agency dockets, although they may submit comments 

 

1

 “Light-duty vehicle,” “light-duty truck,” and “medium-duty passenger vehicle” are defined in 40 CFR 

86.1803-01. Generally, the term “light-duty vehicle” means a passenger car, the term “light-duty truck” means a 

pick-up truck, sport-utility vehicle, or minivan of up to 8,500 lbs gross vehicle weight rating, and “medium-duty 

passenger vehicle” means a sport-utility vehicle or passenger van from 8,500 to 10,000 lbs gross vehicle weight 

rating. Medium-duty passenger vehicles do not include pick-up trucks. 2

 “Passenger car” and “light truck” are defined in 49 CFR Part 523. 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

Page 7 of 426

to both if they so choose. Comments that are submitted for consideration by one agency should 

be identified as such, and comments that are submitted for consideration by both agencies should 

be identified as such. Absent such identification, each agency will exercise its best judgment to 

determine whether a comment is submitted on its proposal. 

Further instructions for submitting comments to either the EPA or NHTSA docket are 

described below.

EPA: Direct your comments to Docket ID No EPA-HQ-OAR-2010-0799. EPA’s policy 

is that all comments received will be included in the public docket without change and may be 

made available online at www.regulations.gov, including any personal information provided, 

unless the comment includes information claimed to be Confidential Business Information (CBI) 

or other information whose disclosure is restricted by statute. Do not submit information that you 

consider to be CBI or otherwise protected through www.regulations.gov or e-mail. The 

www.regulations.gov Web site is an ‘‘anonymous access’’ system, which means EPA will not 

know your identity or contact information unless you provide it in the body of your comment. If 

you send an e-mail comment directly to EPA without going through www.regulations.gov your 

e-mail address will be automatically captured and included as part of the comment that is placed 

in the public docket and made available on the Internet. If you submit an electronic comment, 

EPA recommends that you include your name and other contact information in the body of your 

comment and with any disk or CD–ROM you submit. If EPA cannot read your comment due to 

technical difficulties and cannot contact you for clarification, EPA may not be able to consider 

your comment. Electronic files should avoid the use of special characters, any form of 

encryption, and be free of any defects or viruses. For additional information about EPA’s public 

docket visit the EPA Docket Center homepage at http:// www.epa.gov/epahome/dockets.htm. 

NHTSA: Your comments must be written and in English. To ensure that your comments 

are correctly filed in the Docket, please include the Docket number NHTSA-2010-0131 in your 

comments. Your comments must not be more than 15 pages long.3

 NHTSA established this 

limit to encourage you to write your primary comments in a concise fashion. However, you may 

attach necessary additional documents to your comments, and there is no limit on the length of 

the attachments. If you are submitting comments electronically as a PDF (Adobe) file, we ask 

that the documents submitted be scanned using the Optical Character Recognition (OCR) 

process, thus allowing the agencies to search and copy certain portions of your submissions.4

 

Please note that pursuant to the Data Quality Act, in order for the substantive data to be relied 

upon and used by the agency, it must meet the information quality standards set forth in the 

OMB and Department of Transportation (DOT) Data Quality Act guidelines. Accordingly, we 

encourage you to consult the guidelines in preparing your comments. OMB’s guidelines may be 

accessed at http://www.whitehouse.gov/omb/fedreg/reproducible.html. DOT’s guidelines may be 

accessed at http://www.dot.gov/dataquality.htm.

Tips for Preparing Your Comments 

 

3

 See 49 CFR 553.21. 

4

 Optical character recognition (OCR) is the process of converting an image of text, such as a scanned 

paper document or electronic fax file, into computer-editable text. 
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When submitting comments, please remember to: 

• Identify the rulemaking by docket number and other identifying information 

(subject heading, Federal Register date and page number). 

• Explain why you agree or disagree, suggest alternatives, and substitute language 

for your requested changes. 

• Describe any assumptions and provide any technical information and/or data that 

you used. 

• If you estimate potential costs or burdens, explain how you arrived at your 

estimate in sufficient detail to allow for it to be reproduced. 

• Provide specific examples to illustrate your concerns, and suggest alternatives. 

• Explain your views as clearly as possible, avoiding the use of profanity or 

personal threats. 

• Make sure to submit your comments by the comment period deadline identified in 

the DATES section above. 

How Can I Be Sure That My Comments Were Received? 

NHTSA: If you submit your comments by mail and wish Docket Management to notify 

you upon its receipt of your comments, enclose a self-addressed, stamped postcard in the 

envelope containing your comments. Upon receiving your comments, Docket Management will 

return the postcard by mail. 

How Do I Submit Confidential Business Information? 

Any confidential business information (CBI) submitted to one of the agencies will also be 

available to the other agency. However, as with all public comments, any CBI information only 

needs to be submitted to either one of the agencies’ dockets and it will be available to the other. 

Following are specific instructions for submitting CBI to either agency. 

EPA: Do not submit CBI to EPA through www.regulations.gov or e-mail. Clearly mark 

the part or all of the information that you claim to be CBI. For CBI information in a disk or CD 

ROM that you mail to EPA, mark the outside of the disk or CD ROM as CBI and then identify 

electronically within the disk or CD ROM the specific information that is claimed as CBI. In 

addition to one complete version of the comment that includes information claimed as CBI, a 

copy of the comment that does not contain the information claimed as CBI must be submitted for 

inclusion in the public docket. Information so marked will not be disclosed except in accordance 

with procedures set forth in 40 CFR Part 2. 

NHTSA: If you wish to submit any information under a claim of confidentiality, you 

should submit three copies of your complete submission, including the information you claim to 

be confidential business information, to the Chief Counsel, NHTSA, at the address given above 

under FOR FURTHER INFORMATION CONTACT. When you send a comment containing 
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Page 9 of 426

confidential business information, you should include a cover letter setting forth the information 

specified in our confidential business information regulation.5

In addition, you should submit a copy from which you have deleted the claimed 

confidential business information to the Docket by one of the methods set forth above. 

Will the Agencies Consider Late Comments? 

NHTSA and EPA will consider all comments received before the close of business on the 

comment closing date indicated above under DATES. To the extent practicable, we will also 

consider comments received after that date. If interested persons believe that any information 

that the agencies place in the docket after the issuance of the NPRM affects their comments, they 

may submit comments after the closing date concerning how the agencies should consider that 

information for the final rule. However, the agencies’ ability to consider any such late comments 

in this rulemaking will be limited due to the time frame for issuing a final rule. 

If a comment is received too late for us to practicably consider in developing a final rule, 

we will consider that comment as an informal suggestion for future rulemaking action. 

How Can I Read the Comments Submitted By Other People? 

You may read the materials placed in the docket for this document (e.g., the comments 

submitted in response to this document by other interested persons) at any time by going to 

http://www.regulations.gov. Follow the online instructions for accessing the dockets. You may 

also read the materials at the EPA Docket Center or NHTSA Docket Management Facility by 

going to the street addresses given above under ADDRESSES. 

How Do I Participate in the Public Hearings?

 NHTSA and EPA will jointly host three public hearings on the dates and locations 

described in the DATES section above. At all hearings, both agencies will accept comments on 

the rulemaking, and NHTSA will also accept comments on the EIS. 

If you would like to present testimony at the public hearings, we ask that you notify the 

EPA and NHTSA contact persons listed under FOR FURTHER INFORMATION CONTACT

at least ten days before the hearing. Once EPA and NHTSA learn how many people have 

registered to speak at the public hearing, we will allocate an appropriate amount of time to each 

participant, allowing time for lunch and necessary breaks throughout the day. For planning 

purposes, each speaker should anticipate speaking for approximately ten minutes, although we 

may need to adjust the time for each speaker if there is a large turnout. We suggest that you 

bring copies of your statement or other material for the EPA and NHTSA panels. It would also 

be helpful if you send us a copy of your statement or other materials before the hearing. To 

accommodate as many speakers as possible, we prefer that speakers not use technological aids 

(e.g., audio-visuals, computer slideshows). However, if you plan to do so, you must notify the 

contact persons in the FOR FURTHER INFORMATION CONTACT section above. You also 

must make arrangements to provide your presentation or any other aids to NHTSA and EPA in 

 

5

 See 49 CFR Part 512. 
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advance of the hearing in order to facilitate set-up. In addition, we will reserve a block of time 

for anyone else in the audience who wants to give testimony. The agencies will assume that 

comments made at the hearings are directed to the NPRM unless commenters specifically 

reference NHTSA’s EIS in oral or written testimony.

The hearing will be held at a site accessible to individuals with disabilities. Individuals 

who require accommodations such as sign language interpreters should contact the persons listed 

under FOR FURTHER INFORMATION CONTACT section above no later than ten days 

before the date of the hearing. 

NHTSA and EPA will conduct the hearing informally, and technical rules of evidence 

will not apply. We will arrange for a written transcript of the hearing and keep the official 

record of the hearing open for 30 days to allow you to submit supplementary information. You 

may make arrangements for copies of the transcript directly with the court reporter. 

Table of Contents 

I. OVERVIEW OF JOINT EPA/NHTSA PROPOSED 2017-2025 NATIONAL PROGRAM

A. Introduction

1. Continuation of the National Program

2. Additional Background on the National Program

3. California’s Greenhouse Gas Program

4. Stakeholder Engagement

B. Summary of the Proposed 2017-2025 National Program

1. Joint Analytical Approach

2. Level of the Standards

3. Form of the Standards

4. Program Flexibilities for Achieving Compliance

5. Mid-Term Evaluation

6. Coordinated Compliance

7. Additional Program Elements

C. Summary of Costs and Benefits for the Proposed National Program

1. Summary of Costs and Benefits for the Proposed NHTSA CAFE Standards

2. Summary of Costs and Benefits for the Proposed EPA GHG Standards

D. Background and Comparison of NHTSA and EPA Statutory Authority

1. NHTSA Statutory Authority

2. EPA Statutory Authority
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3. Comparing the Agencies’ Authority

II. JOINT TECHNICAL WORK COMPLETED FOR THIS PROPOSAL

A. Introduction

B. Developing the Future Fleet for Assessing Costs, Benefits, and Effects

1. Why Did the Agencies Establish a Baseline and Reference Vehicle Fleet?

2. How Did the Agencies Develop the Baseline Vehicle Fleet?

3. How Did the Agencies Develop the Projected MY 2017-2025 Vehicle Reference Fleet?

C. Development of Attribute-Based Curve Shapes

1. Why are standards attribute-based and defined by a mathematical function?

2. What attribute are the agencies proposing to use, and why?

3. What mathematical functions have the agencies previously used, and why?

4. How have the agencies changed the mathematical functions for the proposed MYs 2017-

2025 standards, and why?

5. What are the agencies proposing for the MYs 2017-2025 curves?

6. Once the agencies determined the appropriate slope for the sloped part, how did the 

agencies determine the rest of the mathematical function?

7. Once the agencies determined the complete mathematical function shape, how did the 

agencies adjust the curves to develop the proposed standards and regulatory alternatives?

D. Joint Vehicle Technology Assumptions

1. What Technologies did the Agencies Consider?

2. How did the Agencies Determine the Costs of Each of these Technologies?

3. How Did the Agencies Determine the Effectiveness of Each of these Technologies?

E. Joint Economic and Other Assumptions

F. Air Conditioning Efficiency CO2 Credits and Fuel Consumption Improvement Values, 

Off-cycle Reductions, and Full-size Pickup Trucks

1. Proposed Air Conditioning CO2 Credits and Fuel Consumption Improvement Values

2. Off-Cycle CO2 Credits

3. Advanced Technology Incentives for Full Sized Pickup Trucks

G. Safety Considerations in Establishing CAFE/GHG Standards

1. Why do the agencies consider safety?

2. How do the agencies consider safety?

3. What is the current state of the research on statistical analysis of historical crash data?

4. How do the agencies think technological solutions might affect the safety estimates 

indicated by the statistical analysis?
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5. How have the agencies estimated safety effects for the proposed standards?

III. EPA PROPOSAL FOR MYS 2017-2025 GREENHOUSE GAS VEHICLE STANDARDS

A. Overview of EPA Rule

1. Introduction

2. Why is EPA Proposing this Rule?

3. What is EPA Proposing?

4. Basis for the GHG Standards under Section 202(a)

5. Other Related EPA Motor Vehicle Regulations

B. Proposed Model Year 2017-2025 GHG Standards for Light-duty Vehicles, Light-duty 

Trucks, and Medium duty Passenger Vehicles

1. What Fleet-wide Emissions Levels Correspond to the CO2 Standards?

2. What Are the Proposed CO2 Attribute-based Standards?

3. Mid-Term Technology Review

4. Averaging, Banking, and Trading Provisions for CO2 Standards

5. Small Volume Manufacturer Standards

6. Nitrous Oxide, Methane, and CO2-equivalent Approaches

7. Small Entity Exemption

8. Additional Leadtime Issues

9. Police and Emergency Vehicle Exemption from CO2 Standards

10. Test Procedures

C. Additional Manufacturer Compliance Flexibilities

1. Air Conditioning Related Credits

2. Incentive for Electric Vehicles, Plug-in Hybrid Electric Vehicles, and Fuel Cell Vehicles

3. Incentives for “Game-Changing” Technologies Including Use of Hybridization and Other 

Advanced Technologies for Full-Size Pickup Trucks

4. Treatment of Plug-in Hybrid Electric Vehicles, Dual Fuel Compressed Natural Gas 

Vehicles, and Ethanol Flexible Fuel Vehicles for GHG Emissions Compliance

5. Off-cycle Technology Credits

D. Technical Assessment of the Proposed CO2 Standards

1. How did EPA develop a reference and control fleet for evaluating standards?

2. What are the Effectiveness and Costs of CO2-reducing Technologies?

3. How were Technologies combined into “Packages” and What is the Cost and 

Effectiveness of Packages?
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4. How does EPA Project how a Manufacturer Would Decide Between Options to Improve 

CO2 Performance to Meet a Fleet Average Standard?

5. Projected Compliance Costs and Technology Penetrations

6. How does the technical assessment support the proposed CO2 standards as compared to 

the alternatives has EPA considered?

7. To What Extent do Any of Today’s Vehicles Meet or Surpass the Proposed MY 2017-

2025 CO2 Footprint-based Targets with Current Powertrain Designs?

E. Certification, Compliance, and Enforcement

1. Compliance Program Overview

2. Compliance with Fleet-Average CO2 Standards

3. Vehicle Certification

4. Useful Life Compliance

5. Credit Program Implementation

6. Enforcement

7. Other Certification Issues

8. Warranty, Defect Reporting, and Other Emission-related Components Provisions

9. Miscellaneous Technical Amendments and Corrections

10. Base Tire Definition

11. Treatment of Driver-Selectable Modes and Conditions

F. How Would This Proposal Reduce GHG Emissions and Their Associated Effects?

1. Impact on GHG Emissions

2. Climate Change Impacts from GHG Emissions

3. Changes in Global Climate Indicators Associated with the Proposal’s GHG Emissions 

Reductions

G. How Would the Proposal Impact Non-GHG Emissions and Their Associated Effects?

1. Inventory

2. Health Effects of Non-GHG Pollutants

3. Environmental Effects of Non-GHG Pollutants

4. Air Quality Impacts of Non-GHG Pollutants

5. Other Unquantified Health and Environmental Effects

H. What are the Estimated Cost, Economic, and Other Impacts of the Proposal?

1. Conceptual Framework for Evaluating Consumer Impacts

2. Costs Associated with the Vehicle Standards

3. Cost per Ton of Emissions Reduced
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4. Reduction in Fuel Consumption and its Impacts

5. CO2 Emission Reduction Benefits

6. Non-Greenhouse Gas Health and Environmental Impacts

7. Energy Security Impacts

8. Additional Impacts

9. Summary of Costs and Benefits

10. U.S. Vehicle Sales Impacts and Payback Period [PLACEHOLDER]

11. Employment Impacts [PLACEHOLDER]

I. Statutory and Executive Order Reviews

J. Statutory Provisions and Legal Authority

 
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iv. Determining the Level of the Standards by Balancing 

the Factors 

v. Other Standards 

(1) Minimum Domestic Passenger Car Standard 

(2) Alternative standards for certain manufacturers 

2. Administrative Procedure Act 

3. National Environmental Policy Act 

E. What are the proposed CAFE standards? 

1. Form of the Standards 

2. Passenger Car Standards for MYs 2017–2025 

3. Minimum Domestic Passenger Car Standards 

4. Light Truck Standards 

F. How do the proposed standards fulfill NHTSA’s statutory obligations?

 1. What are NHTSA’s statutory obligations? 

 2. How did the agency balance the factors for this NPRM? 

G. Impacts of the Proposed CAFE Standards 

1. How will these standards improve fuel economy and reduce GHG 

emissions for MY 2017–2025 vehicles? 

2. How will these standards improve fleet-wide fuel economy and reduce 

GHG emissions beyond MY 2025? 

3. How will these proposed standards impact non-GHG emissions and 

their associated effects? 

4. What are the estimated costs and benefits of these proposed 

standards? 

5. How would these proposed standards impact vehicle sales? 

6. Social Benefits, Private Benefits, and Potential Unquantified 

Consumer Welfare Impacts of the Proposed Standards 

7. What other impacts (quantitative and unquantifiable) will these 

proposed standards have? 

H. Vehicle Classification 

I. Compliance and Enforcement 

1. Overview 

2. How does NHTSA determine compliance? 

a. Manufacturer Submission of Data and CAFE Testing by EPA 

b. NHTSA Then Analyzes EPA-Certified CAFE Values for 

Compliance 

3. What compliance flexibilities are available under the CAFE program 

and how do manufacturers use them? 

a. Dual- and Alternative-Fueled Vehicles 

b. Credit Trading and Transfer 

c. Payment of Civil Penalties 

4. What new incentives are being added to the CAFE program 

for MYs 2017-2025? 
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a. “Game Changing” technologies for full size pick-up trucks 

i. Pickup Truck Hybridization 

ii. Performance-based incentive for full-size pickups 

b. A/C efficiency-improving technologies 

i. What are the proposed testing approaches? 

ii. How are fuel consumption improvement values then 

estimated? 

c. Off-cycle technologies and adjustments 

 5. Other CAFE enforcement issues

a. Electronic Reporting 

b. Reporting of how a vehicle is classified as a light truck 

c. Base Tire Definition 

d. Confirming Target and Fleet Standards 

J. Regulatory notices and analyses

1. Executive Order 12866, Executive Order 13563, and DOT Regulatory 

Policies and Procedures

2. National Environmental Policy Act

3. Regulatory Flexibility Act

4. Executive Order 13132 (Federalism)

5. Executive Order 12988 (Civil Justice Reform)

6. Unfunded Mandates Reform Act

7. Regulation Identifier Number

8. Executive Order 13045

9. National Technology Transfer and Advancement Act

10. Executive Order 13211

11. Department of Energy Review

12. Plain Language

13. Privacy Act

I. NHTSA Proposed Rule for Passenger Car and Light Truck CAFE standards for Model 

Years 2017-2025 

A. Executive Overview of NHTSA Proposed Rule 

1. Introduction 

The National Highway Traffic Safety Administration (NHTSA) is proposing Corporate 

Average Fuel Economy (CAFE) standards for passenger automobiles (passenger cars) and 

nonpassenger automobiles (light trucks) for model years (MY) 2017–2025. NHTSA’s proposed 

CAFE standards would require passenger cars and light trucks to meet an estimated combined 

average of 49.6 mpg in MY 2025. This represents an average annual increase of 4 percent from 

the estimated 34.4 mpg combined fuel economy level expected in MY 2016. Due to these 
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proposed standards, we project total fuel savings of approximately 173 billion gallons over the 

lifetimes of the vehicles sold in model years 2017–2025, with corresponding net societal benefits 

of over $358 billion using a 3 percent discount rate.6

While NHTSA has been setting fuel economy standards since the 1970s, as discussed in 

Section I, NHTSA’s proposed MYs 2017–2025 CAFE standards are part of a National Program 

made up of complementary regulations by NHTSA and the Environmental Protection Agency. 

Today’s proposed standards build upon the success of the first phase of the National Program, 

finalized on May 7, 2010, in which NHTSA and EPA set coordinated CAFE and greenhouse gas 

(GHG) standards for MYs 2012-2016 passenger cars and light trucks. Because of the very close 

relationship between improving fuel economy and reducing carbon dioxide (CO2) tailpipe 

emissions, a large majority of the projected benefits are achieved jointly with EPA’s GHG rule, 

described in detail above in Section III of this preamble. These proposed CAFE standards are 

consistent with the President’s National Fuel Efficiency Policy announcement of May 19, 2009, 

which called for harmonized rules for all automakers, instead of three overlapping and 

potentially inconsistent requirements from DOT, EPA, and the California Air Resources Board. 

And finally, the proposed CAFE standards and the analysis supporting them also respond to 

President’s Obama’s May 2010 memorandum requesting the agencies to develop, through notice 

and comment rulemaking, a coordinated National Program for passenger cars and light trucks for 

MYs 2017 to 2025. 

2. Why does NHTSA set CAFE standards for passenger cars and light trucks? 

Improving vehicle fuel economy has been long and widely recognized as one of the key 

ways of achieving energy independence, energy security, and a low carbon economy.7

 The 

 6

 This value is based on what NHTSA refers to as “Reference Case” inputs, which are based on the assumptions that 

NHTSA has employed for its main analysis (as opposed to sensitivity analyses to examine the effect of variations in 

the assumptions on costs and benefits). The Reference Case inputs include fuel prices based on the AEO 2011 

Reference Case, a 3 percent and a 7 percent discount rate, a 10 percent rebound effect, a value for the social cost of 

carbon (SCC) of $22/metric ton CO2 (in 2010, rising to $45/metric ton in 2050, at a 3 percent discount rate), etc. 

For a full listing of the Reference Case input assumptions, see Section IV.C.3 below. 

7

 Among the reports and studies noting this point are the following: 

John Podesta, Todd Stern and Kim Batten, “Capturing the Energy Opportunity; Creating a Low- Carbon 

Economy,” Center for American Progress (November 2007), pp. 2, 6, 8, and 24–29, available at: 

http://www.americanprogress.org/issues/2007/11/pdf/energy_chapter.pdf (last accessed Sept. 24, 2011). 

Sarah Ladislaw, Kathryn Zyla, Jonathan Pershing, Frank Verrastro, Jenna Goodward, David Pumphrey, 

and Britt Staley, “A Roadmap for a Secure, Low- Carbon Energy Economy; Balancing Energy Security and Climate 

Change,” World Resources Institute and Center for Strategic and International Studies (January 2009), pp. 21–22; 

available at: http://pdf.wri.org/secure_low_carbon_energy_economy_roadmap.pdf (last accessed Sept. 24, 2011). 

Alliance to Save Energy et al., “Reducing the Cost of Addressing Climate Change Through Energy 

Efficiency” (2009), available at: http://www.aceee.org/files/pdf/whitepaper/ReducingtheCostofAddressingClimateChange_synopsis.pdf (last accessed Sept. 24, 2011). 

John DeCicco and Freda Fung, “Global Warming on the Road; The Climate Impact of America’s 

Automobiles,” Environmental Defense (2006) pp. iv- vii; available at: 

http://www.edf.org/sites/default/files/5301_Globalwarmingontheroad_0.pdf (last accessed Sept. 24, 2011). 
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significance accorded to improving fuel economy reflects several factors. Conserving energy, 

especially reducing the nation’s dependence on petroleum, benefits the U.S. in several ways. 

Improving energy efficiency has benefits for economic growth and the environment, as well as 

other benefits, such as reducing pollution and improving security of energy supply. More 

specifically, reducing total petroleum use decreases our economy’s vulnerability to oil price 

shocks. Reducing dependence on oil imports from regions with uncertain conditions enhances 

our energy security. Additionally, the emission of CO2 from the tailpipes of cars and light trucks 

due to the combustion of petroleum is one of the largest sources of U.S. CO2 emissions.8

 Using 

vehicle technology to improve fuel economy, and thereby reducing tailpipe emissions of CO2, is 

one of the three main measures of reducing those tailpipe emissions of CO2.

9

 The two other 

measures for reducing the tailpipe emissions of CO2 are switching to vehicle fuels with lower 

carbon content and changing driver behavior, i.e., inducing people to drive less. 

Reducing petroleum consumption to improve energy security and save the U.S. money 

II. In 1975, Congress enacted the Energy Policy and Conservation Act (EPCA), mandating 

that NHTSA establish and implement a regulatory program for motor vehicle fuel 

economy to meet the various facets of the need to conserve energy, including ones 

having energy independence and security, environmental, and foreign policy 

implications. The need to reduce energy consumption is even more crucial today than it 

was when EPCA was enacted. U.S. energy consumption has been outstripping U.S. 

energy production at an increasing rate. of Joint EPA/NHTSA Proposed 2017-2025 

National Program 

Executive Summary 

EPA and NHTSA are each announcing proposed rules that call for strong and 

coordinated Federal greenhouse gas and fuel economy standards for passenger cars, light-duty 

trucks, and medium-duty passenger vehicles (hereafter light-duty vehicles or LDVs). Together, 

these vehicle categories, which include passenger cars, sport utility vehicles, crossover utility 

vehicles, minivans, and pickup trucks, among others, are presently responsible for approximately 

 “Why is Fuel Economy Important?,” a Web page maintained by the Department of Energy and 

Environmental Protection Agency, available at http://www.fueleconomy.gov/feg/why.shtml (last accessed Sept. 24, 

2011); 

Robert Socolow, Roberta Hotinski, Jeffery B. Greenblatt, and Stephen Pacala, “Solving The Climate 

Problem: Technologies Available to Curb CO2 Emissions,” Environment, volume 46, no. 10, 2004. pages 8–19, 

available at: http://www.princeton.edu/mae/people/faculty/socolow/ENVIRONMENTDec2004issue.pdf (last 

accessed Sept. 24, 2011). 

8

 EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2008 (April 2010), pp. ES–5, ES-8, and 2–17. 

Available at http://www.epa.gov/climatechange/emissions/usgginv_archive.html (last accessed Sept. 25, 2011). 9

 Podesta et al., p. 25; Ladislaw et al. p. 21; DeCicco et al. p. vii; “Reduce Climate Change, a Web page maintained 

by the Department of Energy and Environmental Protection Agency at http:// 

www.fueleconomy.gov/feg/climate.shtml (last accessed Sept. 24, 2011). 
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60 percent of all U.S. transportation-related greenhouse gas (GHG) emissions and fuel 

consumption. This proposal would extend the National Program of Federal light-duty vehicle 

GHG emissions and corporate average fuel economy (CAFE) standards to model years (MYs) 

2017-2025. This proposed coordinated program would achieve important reductions in GHG 

emissions and fuel consumption from the light-duty vehicle part of the transportation sector, 

based on technologies that either are commercially available or that the agencies project will be 

commercially available in the rulemaking timeframe and that can be incorporated at a reasonable 

cost. Higher initial vehicle costs will be more than offset by significant fuel savings for 

consumers over the lives of the vehicles covered by this rulemaking. 

This proposal builds on the success of the first phase of the National Program to regulate 

fuel economy and GHG emissions from U.S. light-duty vehicles, which established strong and 

coordinated standards for model years (MY) 2012-2016. As with the first phase of the National 

Program, collaboration with California Air Resources Board (CARB) and with automobile 

manufacturers and other stakeholders has been a key element in developing the agencies’ 

proposed rules. Continuing the National Program would ensure that all manufacturers can build 

a single fleet of U.S. vehicles that would satisfy all requirements under both programs as well as 

under California’s program, helping to reduce costs and regulatory complexity while providing 

significant energy security and environmental benefits. 

Combined with the standards already in effect for MYs 2012-2016, as well as the MY 

2011 CAFE standards, the proposed standards would result in MY 2025 light-duty vehicles with 

nearly double the fuel economy, and approximately one-half of the GHG emissions compared to 

MY 2010 vehicles -- representing the most significant federal action ever taken to reduce GHG 

emissions and improve fuel economy in the U.S. EPA is proposing standards that are projected 

to require, on an average industry fleet wide basis, 163 grams/mile of carbon dioxide (CO2) in 

model year 2025, which is equivalent to 54.5 mpg if this level were achieved solely through 

improvements in fuel efficiency.10 Consistent with its statutory authority, NHTSA is proposing 

passenger car and light truck standards for MYs 2017-2025 in two phases. The first phase, from 

MYs 2017-2021, includes proposed standards that are projected to require, on an average 

industry fleet wide basis, 40.9 mpg in MY 2021. The second phase of the CAFE program, from 

MYs 2022-2025, represents conditional11 proposed standards that are projected to require, on an 

average industry fleet wide basis, 49.6 mpg in model year 2025. Both the EPA and NHTSA 

 10 Real-world CO2 is typically 25 percent higher and real-world fuel economy is typically 20 percent lower than the 

CO2 and CAFE compliance values discussed here. The reference to CO2 here refers to CO2 equivalent reductions, 

as this included some degree of reductions in greenhouse gases other than CO2, as one part of the air conditioning 

related reductions. 

11 By ”conditional,” NHTSA means to say that the proposed standards for MYs 2022-2025 represent the agency’s 

current best estimate of what levels of stringency would be maximum feasible in those model years, but in order for 

the standards for those model years to be legally reviewable binding a subsequent rulemaking must be undertaken by 

the agency at a later time. See Section IV for more information. 
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standards are projected to be achieved through a range of technologies, including improvements 

in air conditioning efficiency, which reduces both GHG emissions and fuel consumption; the 

EPA standards also are projected to be achieved with the use of air conditioning refrigerants with 

a lower global warming potential (GWP), which reduce GHGs (i.e., hydrofluorocarbons) but do 

not improve fuel economy. The agencies are proposing separate standards for passenger cars 

and trucks, based on a vehicle’s size or “footprint.” For the MYs 2022-2025 standards, EPA and 

NHTSA are proposing a comprehensive mid-term evaluation and agency decision-making 

process, given both the long time frame and NHTSA’s obligation to conduct a separate 

rulemaking in order to establish final standards for vehicles for those model years, 

From a societal standpoint, this second phase of the National Program is projected to save 

approximately 4 billion barrels of oil and 2 billion metric tons of GHG emissions over the 

lifetimes of those vehicles sold in MY 2017-2025. The agencies estimate that fuel savings will 

far outweigh higher vehicle costs, and that the net benefits to society of the MYs 2017-2025 

National Program will be in the range of $311 billion to $421 billion (7 and 3 percent discount 

rates, respectively) over the lifetimes of those vehicles sold in MY 2017-2025. 

These proposed standards would have significant savings for consumers at the pump. 

Higher costs for new vehicle technology will add, on average, about $2000 for consumers who 

buy a new vehicle in MY 2025. Those consumers who drive their MY 2025 vehicle for its entire 

lifetime will save, on average, $5200 to $6600 (7 and 3 percent discount rates, respectively) in 

fuel savings, for a net lifetime savings of $3000 to $4400. For those consumers who purchase 

their new MY 2025 vehicle with cash, the discounted fuel savings will offset the higher vehicle 

cost in an average of less than 4 years, and fuel savings will continue for as long as the consumer 

owns the vehicle. Those consumers that buy a new vehicle with a typical 5-year loan will benefit 

from an average monthly cash flow savings of about $12 during the loan period, or about $140 

per year, on average. So the consumer would benefit beginning at the time of purchase, since the 

increased monthly fuel savings would, on average, more than offset the higher monthly payment 

due to the higher incremental vehicle cost.. 

The agencies have designed the proposed standards to preserve consumer choice -- that 

is, the agencies believe the proposed standards can, and will, be met without affecting 

consumers’ opportunity to purchase the size of vehicle with the performance, utility and safety 

features that meets their needs. The standards are based on a vehicle’s size, or footprint – that is, 

consistent with their general performance and utility needs, larger vehicles have numerically less 

stringent fuel economy/GHG emissions targets and smaller vehicles have more stringent fuel 

economy/GHG emissions targets, although since the standards are fleet average standards, no 

specific vehicle must meet a target. Thus, the agencies believe consumers will be able to 

continue to choose from the same mix of vehicles that are currently in the marketplace. 

Comment [A1]: Interagency commenters note 

that the agencies cannot know with any degree of 

certainty that manufacturers will not comply at the 

expense of any other attributes . 
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The agencies’ believe there is a wide range of technologies available for manufacturers to 

consider in reducing GHG emissions and improving fuel economy. The proposals allow for longterm planning by manufacturers and suppliers for the continued development and deployment 

across their fleets of fuel saving and emissions-reducing technologies. The agencies believe that 

advances in gasoline engines and transmissions will continue for the foreseeable future, and that 

there will be continual improvement in other technologies, including vehicle weight reduction, 

lower tire rolling resistance, improvements in vehicle aerodynamics, diesel engines, and more 

efficient vehicle accessories. The agencies also expect to see increased electrification of the fleet 

through the expanded production of stop/start, hybrid, plug-in hybrid and electric vehicles. 

Finally, the agencies expect that vehicle air conditioners will continue to improve by becoming 

more efficient and by increasing the use of alternative refrigerants. Many of these technologies 

are already available today, and manufacturers will be able to meet the standards through 

significant efficiency improvements in these technologies, as well as a significant penetration of 

these and other technologies across the fleet. Auto manufacturers may also introduce new 

technologies that we have not considered for this rulemaking analysis, which could make 

possible alternative, more cost-effective paths to compliance. 

A. Introduction 

1. Continuation of the National Program 

EPA and NHTSA are each announcing proposed rules that call for strong and 

coordinated Federal greenhouse gas and fuel economy standards for passenger cars, light-duty 

trucks, and medium-duty passenger vehicles (hereafter light-duty vehicles or LDVs). Together, 

these vehicle categories, which include passenger cars, sport utility vehicles, crossover utility 

vehicles, minivans, and pickup trucks, are presently responsible for approximately 60 percent of 

all U.S. transportation-related greenhouse gas emissions and fuel consumption. The proposal 

would extend the National Program of Federal light-duty vehicle greenhouse gas (GHG) 

emissions and corporate average fuel economy (CAFE) standards to model years (MYs) 2017-

2025. The coordinated program being proposed would achieve important reductions of 

greenhouse gas (GHG) emissions and fuel consumption from the light-duty vehicle part of the 

transportation sector, based on technologies that either are commercially available or that the 

agencies project will be commercially available in the rulemaking timeframe and that can be 

incorporated at a reasonable cost. agencies have been developing the basis for these joint 

proposed standards almost since the conclusion of the rulemaking establishing the first phase of 

the National Program. After much research and deliberation by the agencies, along with CARB 

and other stakeholders, President Obama announced plans for these proposed rules on July 29, 

2011 and NHTSA and EPA issued a Supplemental Notice of Intent (NOI) outlining the agencies’ 
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plans for proposing the MY 2017-2025 standards and program.12 This July NOI built upon the 

extensive analysis conducted by the agencies over the past year, including an initial technical 

assessment report and NOI issued in September 2010, and a supplemental NOI issued in 

December 2010 (discussed further below). The State of California and thirteen auto 

manufacturers representing over 90 percent of U.S. vehicle sales provided letters of support for 

the program concurrent with the Supplemental NOI.13 The United Auto Workers (UAW) also 

supported the announcement,14 ,as well as many consumer and environmental groups. As 

envisioned in the Presidential announcement and Supplemental NOI, this proposal sets forth 

proposed MYs 2017-2025 standards as well as detailed supporting analysis for those standards 

and regulatory alternatives for public review and comment. The program that the agencies are 

proposing will spur the development of a new generation of clean cars and trucks through 

innovative technologies and manufacturing that will, in turn, spur economic growth and create 

high-quality domestic jobs, enhance our energy security, and improve our environment. 

Consistent with Executive Order 13563, this proposal was developed with early consultation 

with stakeholders, employs flexible regulatory approaches to reduce burdens, maintains freedom 

of choice for the public, and helps to harmonize federal and state regulations. 

As described below, NHTSA and EPA are proposing a continuation of the National 

Program that the agencies believe represents the appropriate levels of fuel economy and GHG 

emissions standards for model years 2017-2025, given the technologies that the agencies 

anticipate will be available for use on these vehicles and the agencies’ understanding of the cost 

and manufacturers’ ability to apply these technologies during that time frame, and consideration 

of other relevant factors. Under this joint rulemaking, EPA is proposing GHG emissions 

standards under the Clean Air Act (CAA), and NHTSA is proposing CAFE standards under 

EPCA, as amended by the Energy Independence and Security Act of 2007 (EISA). This joint 

rulemaking proposal reflects a carefully coordinated and harmonized approach to implementing 

these two statutes, in accordance with all substantive and procedural requirements imposed by 

law.15

The proposed approach allows for long-term planning by manufacturers and suppliers for 

the continued development and deployment across their fleets of fuel saving and emissionsreducing technologies. NHTSA’s and EPA’s technology assessment indicates there is a wide 

range of technologies available for manufacturers to consider in reducing GHG emissions and 

improving fuel economy. The agencies believe that advances in gasoline engines and 

 12 76 FR 48758 (August 9, 2011). 

13 Commitment letters are available at http://www.epa.gov/otaq/climate/regulations.htm and at 

http://www.nhtsa.gov/fuel-economy (last accessed Aug. 24, 2011). 

14 The UAW’s support was expressed in a statement on July 29, 2011, which can be found at 

http://www.uaw.org/articles/uaw-supports-administration-proposal-light-duty-vehicle-cafe-and-greenhouse-gasemissions-r (last accessed September 19, 2011) 

15 For NHTSA, this includes the requirements of the National Environmental Policy Act (NEPA). 
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transmissions will continue for the foreseeable future, which is a view that is supported in the 

literature and amongst the vehicle manufacturers and suppliers.16 The agencies also believe that 

there will be continual improvement in other technologies including reductions in vehicle 

weight, lower tire rolling resistance, improvements in vehicle aerodynamics, diesel engines, and 

more efficient vehicle accessories. The agencies also expect to see increased electrification of 

the fleet through the expanded production of stop/start, hybrid, plug-in hybrid and electric 

vehicles.17 Finally, the agencies expect that vehicle air conditioners will continue to improve by 

becoming more efficient and by increasing the use of alternative refrigerants. Many of these 

technologies are already available today, and EPA’s and NHTSA’s assessments are that 

manufacturers will be able to meet the standards through significant efficiency improvements in 

these technologies as well as a significant penetration of these and other technologies across the 

fleet. We project that these potential compliance pathways for manufacturers will result in 

significant benefits to consumers and to society, as quantified below. Manufacturers may also 

introduce new technologies that we have not considered for this rulemaking analysis, which 

could make possible alternative, more cost-effective paths to compliance. 

As discussed further below, as with the standards for MYs 2012-2016, the agencies 

believe that the proposed standards would continue to preserve consumer choice, that is, the 

proposed standards should not affect consumers’ opportunity to purchase the size of vehicle that 

meets their needs. NHTSA and EPA are proposing to continue standards based on vehicle 

footprint, where smaller vehicles have relatively more stringent standards, and larger vehicles 

have less stringent standards, so there should not be a significant effect on the relative 

availability of different size vehicles in the fleet. Additionally, as with the standards for MYs 

2012-2016, the agencies believe that the proposed standards should not have a negative effect on 

vehicle safety, as it relates to vehicle footprint and mass as described in Section II.C and II.G 

below, respectively. 

We note that as part of this rulemaking, given the long time frame at issue in setting 

standards for MY 2022-2025 light-duty vehicles, the agencies are discussing a comprehensive 

mid-term evaluation and agency decision-making process. NHTSA has a statutory obligation to 

conduct a separate de novo rulemaking in order to establish final standards for vehicles for the 

2022-2025 model years and would conduct the mid-term evaluation as part of that rulemaking, 

 16 There are a number of competing gasoline engine technologies, with one in particular that the agencies project 

will be common beyond 2016. This is the gasoline direct injection and downsized engines equipped with 

turbochargers and cooled exhaust gas recirculation, which has performance characteristics similar to that of larger, 

less efficient engines. Paired with these engines, the agencies project that advanced transmissions (such as 

automatic and dual clutch transmissions with eight forward speeds) and higher efficiency gearboxes will provide 

significant improvements. Transmissions with eight or more speeds can be found in the fleet today in very limited 

production, and while they are expected to penetrate further by 2016, we anticipate that by 2025 these will be the 

dominant transmissions in new vehicle sales. 

17 For example, while today less than three percent of annual vehicle sales are strong hybrids, plug-in hybrids and all 

electric vehicles, by 2025 we estimate these technologies could represent nearly 15 percent of new sales. 

Comment [A2]: Interagency commenter requests 

clarity on whether size is more appropriate, than 

footprint per se since crush space can change without 

changing the footprint? 
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and EPA is proposing regulations that address the mid-term evaluation. The mid-term evaluation 

will assess the appropriateness of the MY 2022-2025 standards considered in this rulemaking, 

based on an updated assessment of all the factors considered in setting the standards and the 

impacts of those factors on the manufacturers’ ability to comply. NHTSA and EPA fully expect 

to conduct this mid-term evaluation in coordination with the California Air Resources Board, 

given our interest in a maintaining a National Program to address GHGs and fuel economy. 

Further discussion of the mid-term evaluation is found later in this section, as well as in Sections 

III and IV. 

Based on the agencies’ analysis, the National Program standards being proposed are 

currently projected to reduce GHGs by approximately 2 billion metric tons and save 4 billion 

barrels of oil over the lifetime of MYs 2017-2025 vehicles relative to the MY 2016 standard 

curves 18 already in place. The average cost for a MY 2025 vehicle to meet the standards is 

estimated to be about $2,000 compared to a vehicle that would meet the level of the MY 2016 

standards in MY 2025. However, fuel savings for consumers are expected to more than offset 

the higher vehicle costs. The typical driver would save a total of $6,600 in fuel costs over the 

lifetime of a MY 2025 vehicle and, even after accounting for the higher vehicle cost, consumers 

would save a net $4,400 over the vehicle’s lifetime, both values use a 3% discount rate. Further, 

consumers who buy new vehicles with cash would save enough in lower fuel costs after about 4 

years of owning a MY 2025 vehicle to offset the higher upfront vehicle costs, while consumers 

who buy with a 5-year loan would save on average more each month on fuel than the increased 

amount they would spend on the higher monthly loan payment, beginning in the first month of 

ownership. 

Continuing the National Program has both energy security and climate change benefits. 

Climate change is widely viewed as a significant long-term threat to the global environment. 

EPA has found that elevated atmospheric concentrations of six greenhouse gases– carbon 

dioxide, methane, nitrous oxide, hydrofluorocarbons, perflurocarbons, and sulfur hexafluoride -- 

taken in combination endanger both the public health and the public welfare of current and future 

generations. EPA further found that the combined emissions of these greenhouse gases from 

new motor vehicles and new motor vehicle engines contribute to the greenhouse gas air pollution 

that endangers public health and welfare. 74 FR 66496 (Dec. 15, 2009). As summarized in 

EPA's Endangerment and Cause or Contribute Findings under Section 202(a) of the Clear Air 

Act, anthropogenic emissions of GHGs are very likely (90 to 99 percent probability) the cause of 

most of the observed global warming over the last 50 years.19 Mobile sources emitted 31 percent 

of all U.S. GHGs in Improving our energy and national security by reducing our dependence on 

 18 The calculation of GHG reductions and oil savings is relative to a future in which the MY 2016 standards remain 

in place for MYs 2017-2025 and manufacturers comply on average at those levels. 19 74 FR 66,496, - 66,518, December 18, 2009; “Technical Support Document for Endangerment and Cause or 

Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act” Docket: EPA-HQ-OAR2009-0472-11292, http://epa.gov/climatechange/endangerment.html. 

Comment [A3]: Interagency commenter notes 

that the numbers at 7% need to be here with the same 

prominence as those at 3%. Commenters request 

agencies to include both discount rates. 
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foreign oil has been a national objective since the first oil price shocks in the 1970s. Net 

petroleum imports accounted for approximately 51 percent of U.S. petroleum consumption in 

2009.20 World crude oil production is highly concentrated, exacerbating the risks of supply 

disruptions and price shocks as the recent unrest in North Africa and the Persian Gulf highlights. 

The export of U.S. assets for oil imports continues to be an important component of U.S. trade 

deficits. Transportation accounted for about 71 percent of U.S. petroleum consumption in 

2009.21 Light-duty vehicles account for about 60 percent of transportation oil use, which means 

that they alone account for about 40 percent of all U.S. oil consumption. 

Gasoline consumption in the U.S. has historically been relatively insensitive to 

fluctuations in both price and consumer income, and people in most parts of the country tend to 

view gasoline consumption as a non-discretionary expense. Thus, when gasoline’s share in 

consumer expenditures rises, the public experiences fiscal distress. Recent tight global oil 

markets led to prices over $100 per barrel, with gasoline reaching as high as $4 per gallon in 

many parts of the U.S., causing financial hardship for many families and businesses. This fiscal 

distress can, in some cases, have macroeconomic consequences for the economy at large. 

Additionally, since U.S. oil production is only affected by fluctuations in prices over a 

period of years, any changes in petroleum consumption (as through increased fuel economy 

levels for the on-road fleet) largely flow into changes in the quantity of imports. Since 

petroleum imports account for about 2 percent of GDP, increases in oil imports can create a 

discernible fiscal drag. As a consequence, measures that reduce petroleum consumption, like 

fuel economy standards, will directly benefit the balance-of-payments account, and strengthen 

the U.S. economy to some degree. And finally, U.S. foreign policy has been affected by decades 

by rising U.S. and world dependency on crude oil as the basis for modern transportation systems, 

although fuel economy standards have at best an indirect impact on U.S. foreign policy. 

Reducing petroleum consumption to reduce climate change impacts

CO2 is the natural by-product of the combustion of fuel to power motor vehicles. The 

more fuel-efficient a vehicle is, the less fuel it needs to burn to travel a given distance. The less 

fuel it burns, the less CO2 it emits in traveling that distance.22 Since the amount of CO2

emissions is essentially constant per gallon combusted of a given type of fuel, the amount of fuel 

consumption per mile is closely related to the amount of CO2 emissions per mile. Motor 

 20 Energy Information Administration, “How dependent are we on foreign oil?” Available at

http://www.eia.gov/energy_in_brief/foreign_oil_dependence.cfm (last accessed August 28, 2011). 21 Energy Information Administration, Annual Energy Outlook 2011, “Oil/Liquids.” Available at

http://www.eia.gov/forecasts/aeo/MT_liquidfuels.cfm (last accessed August 28, 2011). 

22 Panel on Policy Implications of Greenhouse Warming, National Academy of Sciences, National Academy of 

Engineering, Institute of Medicine, “Policy Implications of Greenhouse Warming: Mitigation, Adaptation, and the 

Science Base,” National Academies Press, 1992, at 287. Available at

http://www.nap.edu/catalog.php?record_id=1605 (last accessed Sept. 25, 2011). 
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vehicles are the second largest GHG-emitting sector in the U.S. after electricity generation, and 

accounted for 27 percent of total U.S. GHG emissions in 2008.23 Concentrations of greenhouse 

gases are at unprecedented levels compared to the recent and distant past, which means that fuel 

economy improvements to reduce those emissions are a crucial step toward addressing the risks 

of global climate change. These risks are well documented in Section III of this notice, and in 

NHTSA’s draft Environmental Impact Statement (DEIS) accompanying these proposed 

standards. 

Fuel economy gains since 1975, due both to the standards and to market factors, have 

resulted in saving billions of barrels of oil and avoiding billions of metric tons of CO2 emissions. 

In December 2007, Congress enacted the Energy Independence and Security Act (EISA), 

amending EPCA to require substantial, continuing increases in fuel economy. NHTSA thus sets 

CAFE standards today under EPCA, as amended by EISA, in order to help the U.S. passenger 

car and light truck fleet save fuel to promote energy independence, energy security, and a low 

carbon economy. 

2. Why is NHTSA proposing CAFE standards for MYs 2017-2025 now? 

a. President’s memorandum 

During the public comment period for the MY 2012-2016 proposed rulemaking, many 

stakeholders encouraged NHTSA and EPA to begin working toward standards for MY 2017 and 

beyond in order to maintain a single nationwide program. After the publication of the final rule 

establishing MYs 2012-2016 CAFE and GHG standards, President Obama issued a 

Memorandum on May 21, 2010 requesting that NHTSA, on behalf of the Department of 

Transportation, and EPA work together to develop a national program for model years 2017-

2025.24 Specifically, he requested that the agencies develop “...a coordinated national program 

under the CAA [Clean Air Act] and the EISA [Energy Independence and Security Act of 2007] to 

improve fuel efficiency and to reduce greenhouse gas emissions of passenger cars and light-duty 

trucks of model years 2017-2025.” The President recognized that our country could take a 

leadership role in addressing the global challenges of improving energy security and reducing 

greenhouse gas pollution, stating that “America has the opportunity to lead the world in the 

development of a new generation of clean cars and trucks through innovative technologies and 

 23 EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2008 (April 2010), p. 2-17. Available at 

http://www.epa.gov/climatechange/emissions/usgginv_archive.html (last accessed Sept. 25, 2011). 24 The Presidential Memorandum is found at: http://www.whitehouse.gov/the-press-office/presidentialmemorandum-regarding-fuel-efficiency-standards. For the reader’s reference, the President also requested the 

Administrators of EPA and NHTSA to issue joint rules under the CAA and EISA to establish fuel efficiency and 

greenhouse gas emissions standards for commercial medium-and heavy-duty on-highway vehicles and work trucks 

beginning with the 2014 model year. The agencies recently promulgated final GHG and fuel efficiency standards 

for heavy duty vehicles and engines for MYs 2014-2018. 76 FR 57106 (September 15, 2011). 
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manufacturing that will spur economic growth and create high-quality domestic jobs, enhance 

our energy security, and improve our environment.” 

The Presidential Memorandum stated “The program should also seek to achieve 

substantial annual progress in reducing transportation sector greenhouse gas emissions and 

fossil fuel consumption, consistent with my Administration's overall energy and climate security 

goals, through the increased domestic production and use of existing, advanced, and emerging 

technologies, and should strengthen the industry and enhance job creation in the United States.” 

Among other things, the agencies were tasked with researching and then developing standards 

for MYs 2017 through 2025 that would be appropriate and consistent with EPA’s and NHTSA’s 

respective statutory authorities, in order to continue to guide the automotive sector along the road 

to reducing its fuel consumption and GHG emissions, thereby ensuring corresponding energy 

security and environmental benefits. During the public comment period for the MY 2012-2016 

proposed rulemaking, many stakeholders, including automakers, encouraged NHTSA and EPA 

to begin working toward standards for MY 2017 and beyond in order to maintain a single 

nationwide program. Several major automobile manufacturers and CARB sent letters to EPA 

and NHTSA in support of a MYs 2017 to 2025 rulemaking initiative as outlined in the 

President’s May 21, 2010 announcement.25 The agencies began working immediately on the 

next phase of the National Program, work which has culminated in the standards proposed in this 

notice for MYs 2017-2025.

b. The President’s memo requested that the agencies, “workBenefits of continuing 

the National Program 

The National Program is both needed and possible because the relationship between 

improving fuel economy and reducing CO2 tailpipe emissions is a very close one. In the real 

world, there is a single pool of technologies for reducing fuel consumption and CO2 emissions. 

Using these technologies in the way that minimizes fuel consumption also minimizes CO2

emissions. While there are emission control technologies that can capture or destroy the 

pollutants that are produced by imperfect combustion of fuel (e.g., carbon monoxide), there are 

at present no such technologies for CO2. In fact, the only way at present to reduce tailpipe 

emissions of CO2 is by reducing fuel consumption. The National Program thus has dual benefits: 

it conserves energy by improving fuel economy, as required of NHTSA by EPCA and EISA; in 

the process, it necessarily reduces tailpipe CO2 emissions consonant with EPA’s purposes and 

responsibilities under the Clean Air Act. 

 25 These commitment letters of support in response to the May 21, 2010 Presidential Memorandum are available at 

http://www.epa.gov/otaq/climate/regulationsproposedregs.htm#prez cl; and 

http://www.nhtsa.gov/Laws+&+Regulations/CAFE+-+Fuel+Economy/Stakeholder+Commitment+Letters (last 

accessed August 28, 2011). 
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Additionally, by setting harmonized Federal standards to regulate both fuel economy and 

greenhouse gas emissions, the agencies are able to provide a predictable regulatory framework 

for the automotive industry while preserving the legal authorities of NHTSA, EPA, and the State 

of California. Consistent, harmonized, and streamlined requirements under the National 

Program, both for MYs 2012-2016 and for MYs 2017-2025, hold out the promise of continuing 

to deliver energy and environmental benefits, cost savings, and administrative efficiencies on a 

nationwide basis that might not be available under a less coordinated approach. The National 

Program makes it possible for the standards of two different Federal agencies and the standards 

of California and other “Section 177” states to act in a unified fashion in providing these 

benefits. A harmonized approach to regulating passenger car and light truck fuel economy and 

GHG emissions is critically important given the interdependent goals of addressing climate 

change and ensuring energy independence and security. Additionally, a harmonized approach 

would help to mitigate the cost to manufacturers of having to comply with multiple sets of 

Federal and State standards. 

One aspect of this phase of the National Program that is unique for NHTSA, however, is 

that the passenger car and light truck CAFE standards for MYs 2022-2025 must be conditional, 

while EPA’s standards for those model years will be legally binding when adopted in this round. 

EISA requires NHTSA to issue CAFE standards for “at least 1, but not more than 5, model 

years.”26 To maintain the harmonization benefits of the National Program, NHTSA will 

therefore propose and adopt standards for all 9 model years from 2017-2025, but the last 4 years 

of standards will not be legally binding as part of this rulemaking. The passenger car and light 

truck CAFE standards for MYs 2022-2025 will be determined with finality in a subsequent, de 

novo notice and comment rulemaking conducted in full compliance with EPCA/EISA and other 

applicable law – beyond simply reviewing the analysis and findings in the present rulemaking to 

see whether they are still accurate and applicable, and taking a fresh look at all relevant factors 

based on the best and most current information available at that future time. 

To facilitate that future effort, NHTSA and EPA will conduct a comprehensive mid-term 

evaluation. Up to date information will be developed and compiled for the evaluation, through a 

collaborative, robust, and transparent process, including notice and comment. The agencies fully 

expect to conduct the mid-term evaluation in close coordination with the California Air 

Resources Board (CARB), consistent with the agencies’ commitment to maintaining a single 

national framework for regulation of fuel economy and GHG emissions.27 Prior to beginning 

NHTSA’s rulemaking process and EPA’s mid-term evaluation, the agencies will jointly prepare 

a draft Technical Assessment Report (TAR) to examine afresh the issues and, in doing so, 

 26 49 U.S.C. 32902(b)(3)(B). 

27 The agencies also fully expect that any adjustments to the standards as a result of the mid-term evaluation process 

from the levels enumerated in the current rulemaking will be made with the participation of CARB and in a manner 

that continues the harmonization of state and Federal vehicle standards. 
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conduct similar analyses and projections as those considered in the current rulemaking, including 

technical and other analyses and projections relevant to each agency’s authority to set standards 

as well as any relevant new issues that may present themselves. The agencies will provide an 

opportunity for public comment on the draft TAR, and appropriate peer review will be 

performed of underlying analyses in the TAR. The assumptions and modeling underlying the 

TAR will be available to the public, to the extent consistent with law. The draft TAR is expected 

to be issued no later than November 15, 2017. After the draft TAR and public comment, the 

agencies will consult and coordinate as NHTSA develops its NPRM. NHTSA will ensure that 

the subsequent final rule will be timed to provide sufficient lead time for industry to make 

whatever changes to their products that the rulemaking analysis deems maximum feasible based 

on the new information available. At the very latest, NHTSA will complete its subsequent 

rulemaking on the standards with at least 18 months lead time as required by EPCA,28 but 

additional lead time may be provided. 

B. Background 

1. Chronology of events since the MY 2012-2016 final rule was issued 

...”. As a first step in respondingIn accordance with the Presidential Memorandum, 

NHTSA and EPA also issued a joint Notice of Intent to Issue a Proposed Rulemaking (NOI).29 

The September 2010 NOI highlighted the results of the analyses contained in the Interim Joint 

TAR, provided an overview of key program design elements, and announced plans for initiating 

the joint rulemaking to improve the fuel efficiency and reduce the GHG emissions of passenger 

cars and light-duty trucks built in MYs 2017-2025. The agencies requested comments on the 

September NOI and accompanying Interim Joint TAR. 

The Interim Joint TAR contained an initial fleet-wide analysis of improvements in overall 

average GHG emissions and equivalent fuel economy levels. For purposes of an initial 

assessment, this range was intended to represent a reasonably broad range of stringency increases 

for potential future GHG emissions standards, and was also consistent with the increases 

suggested by CARB in its letter of commitment in response to the President’s memorandum.30,31 

The TAR evaluated a range of potential stringency scenarios through model year 2025, 

representing a 3, 4, 5, and 6 percent per year estimated decrease in GHG levels from a model 

year 2016 fleet-wide average of 250 gram/mile (g/mi). Thus, the model year 2025 scenarios 

analyzed in the Interim Joint TAR ranged from 190 g/mi on an estimated fleet-wide average 

 28 49 U.S.C. 32902(a). 

29 75 FR 62739, October 13, 2010. 

30 75 FR at 62744-45. 

31 Statement of the California Air Resources Board Regarding Future Passenger Vehicle Greenhouse Gas Emissions 

Standards, California Air Resources Board, May 21, 2010. Available at: 

http://www.epa.gov/otaq/climate/regulations.htm. 
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(calculated to be equivalent to 47 miles per gallon, mpg, if all improvements were made with 

fuel economy-improving technologies) under the 3 percent per year reduction scenario, to 143 

g/mi on an estimated fleet-wide average (calculated to be equivalent to 62 mpg, if all 

improvements were made with fuel economy-improving technologies) under the 6 percent per 

year scenario.32 For each of these scenarios, the TAR also evaluated four pre-defined 

“technological pathways” by which these levels could be attained. These pathways were meant 

to represent ways that the industry as a whole could increase fuel economy and reduce 

greenhouse gas emissions, and did not represent ways that individual manufacturers would be 

required to or necessarily would employ in responding to future standards. Each defined 

technology pathway emphasized a different mix of advanced technologies, by assuming various 

degrees of penetration of advanced gasoline technologies, mass reduction, hybrid electric 

vehicles (HEVs), plug-in hybrids (PHEVs), and electric vehicles (EVs). 

Manufacturers and others commented extensively on the NOI and Interim Joint TAR on a 

variety of topics, including the stringency of the standards, program design elements, the effect 

of potential standards on vehicle safety, and the TAR’s discussion of technology costs, 

effectiveness, and feasibility. In response, the agencies and CARB spent the next several months 

continuing to gather information from the industry and others in response to the agencies’ initial 

analytical efforts. To aid the public’s understanding of some of the key issues facing the 

agencies in developing the proposed rule, EPA and NHTSA also issued a follow-on 

Supplemental NOI in November 2010.33 The Supplemental NOI highlighted many of the key 

comments the agencies received in response to the September NOI and Interim Joint TAR, and 

summarized some of the key themes from the comments and the additional stakeholder meetings. 

We note, as highlighted in the November Supplemental NOI, that there continued to be 

widespread stakeholder support for continuing the National Program for improved fuel economy 

and greenhouse gas standards for model years 2017-2025. The November Supplemental NOI 

also provided an overview of many of the key technical analyses the agencies planned in support 

the proposed rule. 

After issuing the November 2010 Supplemental NOI, EPA, NHTSA and CARB 

continued studies on technology cost and effectiveness and more in-depth and comprehensive 

analysis of the issues. In addition to this work, the agencies continued meeting with 

stakeholders, including with manufacturers, manufacturer organizations, automotive suppliers, a 

labor union, environmental groups, consumer interest groups, and investment organizations. As 

discussed above, on July 29, 2011 President Obama announced plans for these proposed rules 

 32 These levels correspond to on-road values of 37 to 50 mpg, respectively, recognizing that on-road fuel economy 

tends to be about 20 percent worse than calculated mpg values based on the CAFE test cycle. We note, however, 

that because these mpg values are translated from CO2e values that include reductions in hydrofluorocarbon (HFC) 

leakage due to use of advanced refrigerants and leakage improvements, therefore these numbers are not as 

representative of either CAFE test cycle or real-world mpg. 33 75 FR 76337, December 8, 2010. 
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and NHTSA and EPA issued a Supplemental Notice of Intent (NOI) outlining the agencies’ 

plans for proposing the MY 2017-2025 standards and program. 

2. California’s Greenhouse Gas Program 

In 2004, the California Air Resources Board (CARB) approved standards for new lightduty vehicles, regulating the emission of CO2 and other GHGs. Thirteen states and the District 

of Columbia, comprising approximately 40 percent of the light-duty vehicle market, adopted 

California’s standards. On June 30, 2009, EPA granted California’s request for a waiver of 

preemption under the CAA with respect to these standards.34 The granting of the waiver permits 

California and the other states to proceed with implementing the California emission standards 

for MYs 2009-2016. After EPA and NHTSA issued their MYs 2012-2016 standards, CARB 

revised its program such that compliance with the EPA greenhouse gas standards will be deemed 

to be compliance with California’s GHG standards.35 This facilitates the National Program by 

allowing manufacturers to meet all of the standards with a single national fleet. 

As requested by the President and in the interest of maximizing regulatory 

harmonization, NHTSA and EPA have worked closely with CARB throughout the development 

of this proposal to develop a common technical basis. CARB is releasing a proposal for MY 

2017-2025 GHG emissions standards which are consistent with the standards being proposed by 

EPA and NHTSA. CARB recognizes the benefit for the country of continuing the National 

Program and plans an approach similar to the one taken for MYs 2012-2016. CARB has 

committed to propose to revise its GHG emissions standards for MY 2017 and later such that 

compliance with EPA GHG emissions standards shall be deemed compliance with the California 

GHG emissions standards, as long as EPA’s final GHG standards are substantially as described 

in the July 2011 Supplemental NOI.36

 34 74 FR 32744 (July 8, 2009). See also Chamber of Commerce v. EPA, 642 F.3d 192 (D.C. Cir. 2011) (dismissing 

petitions for review challenging EPA’s grant of the waiver). 

35 See “California Exhaust Emission Standards and Test Procedures for 2001 and Subsequent Model Passenger Cars, 

Light-Duty Trucks, and Medium-Duty Vehicles as approved by OAL,” March 29, 2010. Available at

http://www.arb.ca.gov/regact/2010/ghgpv10/oaltp.pdf (last accessed August 28, 2011). 36 See State of California July 28, 2011 letter available at: http://www.epa.gov/otaq/climate/regulations.htm. 
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3. Stakeholder Engagement 

C. On July 29, 2010, President Obama announced the support of thirteen major 

automakers to pursueannounced their support. . and EPAdiscussedSummary of the 

Proposed 2017-2025 National Program 

1. Joint Analytical Approach 

This proposed rulemaking continues the collaborative analytical effort between NHTSA 

and EPA, which began with the MYs 2012-2016 rulemaking. NHTSA and EPA have worked 

together, and in close coordination with CARB, on nearly every aspect of the technical analysis 

supporting these joint proposed rules. The results of this collaboration are reflected in the 

elements of the respective NHTSA and EPA proposed rules, as well as in the analytical work 

contained in the Draft Joint NHTSA and EPA Technical Support Document (Joint TSD). The 

agencies have continued to develop and refine supporting analyses since issuing the NOI and 

Interim Joint TAR last September. The Joint TSD, in particular, describes important details of 

the analytical work that are common, as well as highlighting any key differences in approach. 

The joint analyses include the build-up of the baseline and reference fleets, the derivation of the 

shape of the footprint-based attribute curves that define the agencies’ respective standards, a 

detailed description of the estimated costs and effectiveness of the technologies that are available 

to vehicle manufacturers, the economic inputs used to calculate the costs and benefits of the 

proposed rules, a description of air conditioner and other off-cycle technologies, and the 

agencies’ assessment of the effects of the proposed standards on vehicle safety. This 

comprehensive joint analytical approach has provided a sound and consistent technical basis for 

both agencies in developing their proposed standards, which are summarized in the sections 

below. 

2. Level of the Standards 

 EPA and NHTSA are each proposing two separate sets of standards, each under its 

respective statutory authorities. Both the proposed CO2 and CAFE standards for passenger cars 

and light trucks would be footprint-based, similar to the standards currently in effect through 

model year 2016, and would become more stringent on average in each model year from 2017 

through 2025. The basis for measuring performance relative to standards would continue to be 

based predominantly on the EPA city and highway test cycles (2-cycle test). However, EPA is 

proposing optional air conditioning and off-cycle credits for the GHG program and adjustments 

to calculated fuel economy for the CAFE programs that would be based on test procedures other 

than the 2-cycle tests.

EPA is proposing standards that are projected to require, on an average industry fleet 

wide basis, 163 grams/mile of CO2 in model year 2025. This is projected to be achieved through 

improvements in fuel efficiency with some additional reductions achieved through reductions in 
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non-CO2 GHG emissions from reduced AC system leakage and the use of lower global warming 

potential (GWP) refrigerants. The level of 163 grams/mile CO2 would be equivalent on a mpg 

basis to 54.5 mpg, if this level was achieved solely through improvements in fuel efficiency.37 

For passenger cars, the CO2 compliance values associated with the footprint curves 

would be reduced on average by 5 percent per year from the model year 2016 projected 

passenger car industry-wide compliance level through model year 2025. In recognition of 

manufacturers’ unique challenges in improving the fuel economy and GHG emissions of fullsize pickup trucks as we transition from the MY 2016 standards to MY 2017 and later, while 

preserving the utility (e.g., towing and payload capabilities) of those vehicles, EPA is proposing 

a lower annual rate of improvement for light-duty trucks in the early years of the program. For 

light-duty trucks, the proposed average annual rate of CO2 emissions reduction in model years 

2017 through 2021 is 3.5 percent per year. EPA is also proposing to change the slopes of the 

CO2-footprint curves for light-duty trucks from those in the 2012-2016 rule, in a manner that 

effectively means that the annual rate of improvement for smaller light-duty trucks in model 

years 2017 through 2021 would be higher than 3.5 percent, and the annual rate of improvement 

for larger light-duty trucks over the same time period would be lower than 3.5 percent. For 

model years 2022 through 2025, EPA is proposing an average annual rate of CO2 emissions 

reduction for light-duty trucks of 5 percent per year. 

NHTSA is proposing two phases of passenger car and light truck standards in this 

NPRM. The first phase runs from MYs 2017-2021, with proposed standards that are projected to 

require, on an average industry fleet wide basis, 40.9 mpg in MY 2021. For passenger cars, the 

annual increase in the stringency of the target curves between model years 2017 to 2021 is 

expected to average 4.1 percent. In recognition of manufacturers’ unique challenges in 

improving the fuel economy and GHG emissions of full-size pickup trucks as we transition from 

the MY 2016 standards to MY 2017 and later, while preserving the utility (e.g., towing and 

payload capabilities) of those vehicles, NHTSA is also proposing a slower annual rate of 

improvement for light trucks in the first phase of the program. For light trucks, the proposed 

annual increase in the stringency of the target curves in model years 2017 through 2021 would be 

2.9 percent per year on average. NHTSA is proposing to change the slopes of the fuel economy 

footprint curves for light trucks from those in the MYs 2012-2016 final rule, which would 

effectively make the annual rate of improvement for smaller light trucks in MYs 2017-2021 

higher than 2.9 percent, and the annual rate of improvement for larger light trucks over that time 

period lower than 2.9 percent. 

The second phase of the CAFE program runs from MYs 2022-2025 and represents 

conditional38 proposed standards that are projected to require, on an average industry fleet wide 

 37 Real-world CO2 is typically 25 percent higher and real-world fuel economy is typically 20 percent lower than the 

CO2 and CAFE values discussed here. The reference to CO2 here refers to CO2 equivalent reductions, as this 

included some degree of reductions in greenhouse gases other than CO2, as one part of the AC related reductions. 
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basis, 49.6 mpg in model year 2025. For passenger cars, the annual increase in the stringency of 

the target curves between model years 2022 and 2025 is expected to average 4.3 percent, and for 

light trucks, the annual increase during those model years is expected to average 4.7 percent. For 

the first time, NHTSA is proposing to increase the stringency of standards by the amount (in 

mpg terms) that industry is expected to improve air conditioning system efficiency, and EPA is 

proposing, under EPCA, to allow manufacturers to include air conditioning system efficiency 

improvements in the calculation of fuel economy for CAFE compliance. NHTSA notes that the 

proposed rates of increase in stringency for CAFE standards are lower than EPA’s proposed rates 

of increase in stringency for GHG standards. As in the MYs 2012-2016 rulemaking, this is for 

purposes of harmonization and in reflection of several statutory constraints in EPCA/EISA. As a 

primary example, NHTSA’s proposed standards, unlike EPA’s, do not reflect the inclusion of air 

conditioning system refrigerant and leakage improvements, but EPA’s proposed standards would 

allow consideration of such A/C refrigerant improvements which reduce GHGs but do not affect 

fuel economy. 

As with the MYs 2012-2016 standards, NHTSA and EPA’s proposed MYs 2017-2025 

passenger car and light truck standards are expressed as mathematical functions depending on 

vehicle footprint.39 Footprint is one measure of vehicle size, and is determined by multiplying 

the vehicle’s wheelbase by the vehicle’s average track width. The standards that must be met by 

each manufacturer's fleet would be determined by computing the production-weighted average of 

the targets applicable to each of the manufacturer’s fleet of passenger cars and light trucks.40 

Under these footprint-based standards, the average levels required of individual manufacturers 

will depend, as noted above, on the mix and volume of vehicles the manufacturer produces. The 

values in the tables below reflect the agencies’ projection of the corresponding average fleet 

levels that will result from these attribute-based curves given the agencies’ current assumptions 

about the mix of vehicles that will be sold in the model years covered by the proposed standards. 

As shown in Table II-2, NHTSA’s fleet-wide required CAFE levels for passenger cars 

under the proposed standards are estimated to increase from 40.0 to 56.0 mpg between MY 

Section I above covers the chronology of events in considerable detail, and we refer the reader 

there. 

 38 By ”conditional,” NHTSA means to say that the proposed standards for MYs 2022-2025 represent the agency’s 

current best estimate of what levels of stringency would be maximum feasible in those model years, but in order for 

the standards for those model years to be legally reviewable a subsequent rulemaking must be undertaken by the 

agency at a later time. See Section IV for more information. 39 NHTSA is required to set attribute-based CAFE standards for passenger cars and light trucks. 49 U.S.C. 

32902(b)(3). 

40 For CAFE calculations, a harmonic average is used. 
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3. How has NHTSA developed the proposed CAFE standards since the President’s 

announcement? 

The CAFE standards proposed in this NPRM are based on much more analysis conducted 

by the agencies since July 29, including in-depth modeling analysis by DOT/NHTSA to support 

the proposed CAFE standards, and further refinement of a number of our baseline, technology, 

and economic assumptions used to evaluate the proposed standards and their impacts. This 

NPRM, the draft joint TSD, and NHTSA’s PRIA and EPA’s DRIA contain much more 

information about the analysis underlying these proposed standards. The following sections 

provide the basis for NHTSA’s proposed passenger car and light truck CAFE standards for MYs 

2017-2025, the standards themselves, the estimated impacts of the proposed standards, and much 

more information about the CAFE program relevant to the 2017-2025 timeframe. 

D. Development and feasibility of the proposed standards 

1. How was the baseline vehicle fleet developed? 

a. Why do the agencies establish a baseline and reference vehicle fleet? 

As also discussed in Section II.B above, in order to determine what levels of stringency 

are feasible in future model years, the agencies must project what vehicles will exist in those 

model years, and then evaluate what technologies can feasibly be applied to those vehicles in 

order to raise their fuel economy and lower their CO2 emissions. The agencies therefore 

established a “baseline” vehicle fleet representing those vehicles, based on the best available 

transparent information. The agencies then developed a “reference” fleet, projecting the baseline 

fleet sales into MYs 2017-2025 and accounting for the effect that the MY 2012-2016 CAFE 

standards have on the baseline fleet.41 This reference fleet is then used for comparisons of 

 41 In order to calculate the impacts of the proposed future GHG and CAFE standards, it is necessary to estimate the 

composition of the future vehicle fleet absent those proposed standards in order to conduct comparisons. The first 

step in this process was to develop a baseline fleet based on model year 2008 data. NHTSA and EPA create a 

baseline fleet in order to track the volumes and types of fuel economy-improving and CO2-reducing technologies 

which are already present in the existing vehicle fleet. This 2008-based fleet includes vehicle sales volumes, 

GHG/fuel economy performance, and contains a listing of the base technologies on every 2008 vehicle sold. The 

second step was to project the baselinethat 2008-based fleet sales volume into MYs 2017-2025. This is called the 

reference fleet, and it represents the fleet volumes (but, until later steps, not levels of technology) that the NHTSA 

and EPA expect would exist in MYs 2017-2025 absent any change due to regulation in 2017-2025. 

After determining the reference fleet, a third step is needed to account for technologies (and corresponding 

increases in cost and reductions in fuel consumption and CO2 emissions) that could be added to MY 2008-

technology vehicles in the future, taking into previously-promulgated standards, and assuming MY 2016 standards 

are extended through MY2025. NHTSA accomplished this by using the CAFE model to add technologies to that 

MY 2008-based market forecast such that each manufacturer’s car and truck CAFE and average CO2 levels reflect 

baseline standards. The model’s output, the reference case (or adjusted baseline, or no-action alternative), is the 

light-duty fleet estimated to exist in MYs 2017-2025 without new GHG/CAFE standards covering MYs 2017-2025. 
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technologies’ incremental cost and effectiveness, as well as for other relevant comparisons in the 

rule. 

b. What data did the agencies use to construct the baseline, and how did they do so? 

As explained in the draft joint TSD, both agencies used a baseline vehicle fleet 

constructed beginning with EPA fuel economy certification data for the 2008 model year, the 

most recent model year for which final data is currently available from manufacturers. These 

data were used as the source for MY 2008 production volumes and some vehicle engineering 

characteristics, such as fuel economy compliance ratings, engine sizes, numbers of cylinders, and 

transmission types. 

For this NPRM, NHTSA and EPA chose again to use MY 2008 vehicle data as the basis 

of the baseline fleet. MY 2008 is now the most recent model year for which the industry had 

what the agencies would consider to be “normal” sales. Complete MY 2009 data is now 

available for the industry, but the agencies believe that the model year was disrupted by the 

economic downturn and the bankruptcies of both General Motors and Chrysler. CAFE 

compliance data shows that there was a significant reduction in the number of vehicles sold by 

both companies and by the industry as a whole. These abnormalities led the agencies to 

conclude that MY 2009 data was likely not representative for projecting the future fleet for 

purposes of this analysis. While MY 2010 data is likely more representative for projecting the 

future fleet, it was not complete and available in time for it to be used for the NPRM analysis. 

Therefore, for purposes of the NPRM analysis, NHTSA and EPA chose to use MY 2008 CAFE 

compliance data for the baseline since it was the latest, most representative transparent data set 

that we had available. However, the agencies plan to use the MY 2010 data, if available, to 

develop an updated market forecast for use in the final rule. If and when the MY 2010 data 

becomes available, NHTSA will place a copy of this data into its rulemaking docket. 

Some information important for analyzing new CAFE standards is not contained in the 

EPA fuel economy certification data. EPA staff estimated vehicle wheelbase and track widths 

using data from Motortrend.com and Edmunds.com. This information is necessary for 

estimating vehicle footprint, which is required for the analysis of footprint-based standards. 

Considerable additional information regarding vehicle engineering characteristics is also 

important for estimating the potential to add new technologies in response to new CAFE 

standards. In general, such information helps to avoid “adding” technologies to vehicles that 

already have the same or a more advanced technology. Examples include valvetrain 

configuration (e.g., OHV, SOHC, DOHC), presence of cylinder deactivation, and fuel delivery 

(e.g., MPFI, SIDI). To the extent that such engineering characteristics were not available in 

certification data, EPA staff relied on data published by Ward’s Automotive, supplementing this 

with information from Internet sites such as Motortrend.com and Edmunds.com. NHTSA staff 

also added some more detailed engineering characteristics (e.g., type of variable valve timing) 
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using data available from ALLDATA® Online. Combined with the certification data, all of this 

information yielded the MY 2008 baseline vehicle fleet. NHTSA also reviewed information 

from manufacturers’ confidential product plans submitted to the agency, but did not rely on that 

information for developing the baseline or reference fleets. 

After the baseline was created the next step was to project the sales volumes for 2017–

2025 model years. EPA used projected car and truck volumes for this period from Energy 

Information Administration’s (EIA’s) 2011 Interim Annual Energy Outlook (AEO).42 However, 

AEO projects sales only at the car and truck level, not at the manufacturer and model-specific 

level, which are needed in order to estimate the effects new standards will have on individual 

manufacturers. Therefore, EPA purchased data from CSM–Worldwide and used their 

projections of the number of vehicles of each type predicted to be sold by manufacturers in 

2017–2025.43 This provided the year-by-year percentages of cars and trucks sold by each 

manufacturer as well as the percentages of each vehicle segment. Using these percentages 

normalized to the AEO projected volumes then provided the manufacturer-specific market share 

and model-specific sales for model years 2011–2016. 

The processes for constructing the MY 2008 baseline vehicle fleet and subsequently 

adjusting sales volumes to construct the MY 2017–2025 baseline vehicle fleet are presented in 

detail in Chapter 1 of the Joint Technical Support Document accompanying today’s proposed 

rule. 

The agencies assume that without adoption of the proposed rule, that during the 2017-

2025 period, manufacturers will not improve fuel economy levels beyond the levels required in 

the MY 2016 standards. However, it is possible that manufacturers may be driven by market 

forces to raise the fuel economy of their fleets. The recently-adopted fuel economy and 

environment labels (“window stickers”), for example, may make consumers more aware of the 

benefits of higher fuel economy, and may cause them to demand more fuel-efficient vehicles 

during that timeframe. Moreover, the agencies’ analysis indicates that some fuel-saving 

technologies may save money for manufacturers. In Chapter X of the PRIA, NHTSA examines 

the impact of an alternative “market-driven” baseline, which allows for some increases in fuel 

economy due to “voluntary overcompliance” beyond the MY 2016 levels. NHTSA seeks 

comment on what assumptions about fuel economy increases are most likely to accurately 

predict what would happen in the absence of the proposed rule. 

 42 Department of Energy, Energy Information Administration, Annual Energy Outlook (AEO) 2011, Early Release. 

Available at http://www.eia.gov/forecasts/aeo/. Both agencies regard AEO a credible source not only of such 

forecasts, but also of many underlying forecasts, including forecasts of the size of the future light vehicle market. 

The agencies used the early release version of AEO 2011 and confirmed later that changes reflected in the final 

version were insignificant with respect to the relative volumes of passenger cars and light trucks. 43 The agencies explain in Chapter I of the draft Joint TSD why data from CSM was chosen for creating the baseline 

for this rulemaking. 
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NHTSA invites comment on the process used to develop the market forecast, and on 

whether the agencies should consider alternative approaches to producing a forecast at the level 

of detail we need for modeling. If commenters wish to offer alternatives, we ask that they 

address how manufacturers’ future fleets would be defined in terms of specific products, and the 

sales volumes and technical characteristics (e.g., fuel economy, technology content, vehicle 

weight, and other engineering characteristics) of those products. The agency also invites 

comment regarding what sensitivity analyses—if any—we should do related to the market 

forecast. For example, should the agency evaluate the extent to which its analysis is sensitive to 

projections of the size of the market, manufacturers’ respective market shares, the relative 

growth of different market segments, and or the relative growth of the passenger car and light 

truck markets? If so, how would commenters suggest that we do that? 

c. How is the development of the baseline fleet for this rule different from the 

baseline fleet that NHTSA used for the MY 2012-2016 (May 2010) final rule? 

The development of the baseline fleet for this rulemaking utilizes the same procedures 

used in the development of the baseline fleet for the MY 2012-2016 rulemaking. Compared to 

that rulemaking, the change in the baseline is much less dramatic – the MY 2012-2016 

rulemaking was the first rulemaking in which NHTSA did not use manufacturer product plan 

data to develop the baseline fleet,44 so evaluating the difference between the baseline fleet used 

in the MY 2011 final rule and in the MY 2012-2016 rulemaking was informative at that time 

regarding some of the major impacts of that switch. In this proposal, we are using basically the 

same MY 2008 based file as the starting point in the MY 2012-2016 analysis, and simply using 

an updated AEO forecast and an updated CSM forecast. Of those, most differences are in input 

assumptions rather than the basic approach and methodology. These include changes in various 

macroeconomic assumptions underlying the AEO and CSM forecasts and the use of results 

obtained by using DOE’s National Energy Modeling System (NEMS) to repeat the AEO 2011 

analysis without forcing increased passenger car volumes, and without assuming post-MY 2016 

increases in the stringency of CAFE standards.45

 44 The agencies’ reasons for not relying on product plan data for the development of the baseline fleet were 

discussed in the Regulatory Impact Analysis for the MYs 2012-2016 rulemaking and at 74 Fed. Reg. 49487-89. 

While a baseline developed using publicly and commercially available sources has both advantages and 

disadvantages relative to a baseline developed using manufacturers’ product plans, NHTSA currently concludes, as 

it did in the course of that prior rulemaking, that the advantages outweigh the disadvantages. Commenters generally 

supported the more transparent approach employed in the MYs 2012-2016 rulemaking.

45 Similar to the analyses supporting the MYs 2012-2016 rulemaking, the agencies have used the Energy 

Information Administration’s (EIA’s) National Energy Modeling System (NEMS) to estimate the future relative 

market shares of passenger cars and light trucks. However, NEMS methodology includes shifting vehicle sales 

volume, starting after 2007, away from fleets with lower fuel economy (the light-truck fleet) towards vehicles with 

higher fuel economies (the passenger car fleet) in order to facilitate compliance with CAFE and GHG MYs 2012-

2016 standards. Because we use our market projection as a baseline relative to which we measure the effects of new 
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Another change in the baseline fleet from the last rulemaking involved our redefinition of 

the list of manufacturers to account for realignment and ownership changes taking place within 

the industry. The reported results supporting this rulemaking recognize that Volvo vehicles are 

no longer a part of Ford, but are reported as a separate company, Geely; that Saab vehicles are no 

longer part of GM, but are reported as part of Spyker which purchased Saab from GM in 2010; 

and that Chrysler, along with Ferrari and Maserati, are reported as Fiat. 

In addition, low volume specialty manufacturers omitted from the analysis supporting the 

MY 2012-2016 rulemaking have been included in the analysis supporting this rulemaking. 

These include Aston Martin, Lotus, and Tesla. 

d. How is this baseline different quantitatively from the baseline that NHTSA used 

for the MY 2012-2016 (May 2010) final rule? 

As discussed above, the current baseline was developed from adjusted MY 2008 

compliance data and covers MY 2017-2025. This section describes, for the reader’s comparison, 

some of the differences between the current baseline and the MY 2012-2016 CAFE rule 

baseline. This comparison provides a basis for understanding general characteristics and 

measures of the difference between the two baselines. The current baseline, while developed 

using the same methods as the baseline used for MY 2012-2016 rulemaking, reflects updates to 

the underlying commercially-available forecast of manufacturer and market segment shares of 

the future passenger car and light truck market. Again, the differences are in input assumptions 

rather than the basic approach and methodology. It also includes changes in various 

macroeconomic assumptions underlying the AEO forecasts and the use of the AEO Unforced 

Reference Case. Another change in the market input data from the last rulemaking involved our 

redefinition of the list of manufacturers to account for realignment taking place within the 

industry. 

Estimated vehicle sales: 

The sales forecasts, based on the Energy Information Administration’s (EIA’s) Early 

Annual Energy Outlook for 2011 (Interim AEO 2011), used in the current baseline indicate that 

the total number of light vehicles expected to be sold during MYs 2012-2016 is 79 million, or 

about 15.8 million vehicles annually. NHTSA’s MY 2012-2016 final rule forecast, based on 

 standards, and we attempt to estimate the industry’s ability to comply with new standards without changing product 

mix, the Interim AEO 2011-projected shift in passenger car market share as a result of required fuel economy 

improvements creates a circularity. Therefore, for the current analysis, the agencies developed a new projection of 

passenger car and light truck sales shares by running scenarios from the Interim AEO 2011 reference case that first 

deactivate the above-mentioned sales-volume shifting methodology and then hold post-2017 CAFE standards 

constant at MY 2016 levels. Incorporating these changes reduced the projected passenger car share of the light 

vehicle market by an average of about 5 percent during 2017-2025. NHTSA and EPA refer to this as the “Unforced 

Reference Case.”
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AEO 2010, of the total number of light vehicles likely to be sold during MY 2012 through MY 

2016 was 80 million, or about 16 million vehicles annually. Light trucks are expected to make 

up 37 percent of the MY 2016 baseline market forecast in the current baseline, compared to 34 

percent of the baseline market forecast in the MY 2012-2016 final rule. These changes in both 

the overall size of the light vehicle market and the relative market shares of passenger cars and 

light trucks reflect changes in the economic forecast underlying AEO, changes in AEO’s forecast 

of future fuel prices, and use of the Unforced Reference Case. 

Estimated manufacturer market shares: 

These changes are reflected below in Table IV-1, which shows the agency’s sales 

forecasts for passenger cars and light trucks under the current baseline and the MY 2012-2016 

final rule. There has been a general decrease in MY 2016 forecast overall sales (from AEO) and 

for all manufacturers (reflecting CSM’s forecast of manufacturers’ market shares), with the 

exception of Chrysler, when the current baseline is compared to that used in the MY 2012-2016 

rulemaking. There were no significant shifts in manufacturers’ market shares between the two 

baselines. The effect of including the low volume specialty manufacturers and accounting for 

known corporate realignments in the current baseline appear to be negligible. For individual 

manufacturers, there have been shifts in the shares of passenger car and light trucks, as would be 

expected given that the agency is relying on different underlying assumptions as discussed above 

and in Chapter 1 of the joint TSD. 

Table II-1. Sales Forecasts [Production for U.S. sale in MY 2016, thousand units]

 and MY 2025. Fleet-wide required CAFE levels for light trucks, in turn, are estimated to 

increase from 29.4 to 40.3 mpg. For the reader’s reference, Table II-2 also provides the 

estimated average fleet-wide required levels for the combined car and truck fleets, culminating in 

an estimated overall fleet average required CAFE level of 49.6 mpg in MY 2025. these 

combined car and truck increases, the proposed standards together represent approximately a 4.0 

percent annual rate of increase,46 on average, relative to the MY 2016 required CAFE levels. 

Table II-2 Estimated Average Required Fleet-Wide Fuel Economy (mpg) under Proposed 

Footprint-Based CAFE Standards 

 

MY 2012-2016 baseFinal 

Rule47 Current Baseline 

Manufacturer 

Passenger 

Cars 37.8Nonpassenger 40.0Passenger 41.4Nonpassenger

 46 This estimated average percentage increase includes the effect of changes in standard stringency and changes in 

the forecast fleet sales mix. 

47 Again, Aston Martin, Alfa Romeo, Ferrari, Maserati, Lotus and Tesla were not included in the baseline of the MY 

2012-2016 rulemaking; Volvo vehicles were reported under Ford and Saab vehicles were reported under GM; and 

Chrysler was reported as a separate company whereas now it is reported as part of Fiat and includes Alfa Romeo, 

Ferrari, and Maserati. 
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Aston Martin 1 

BMW 423 171 383 184

Daimler 271 126 245 136

Fiat/Chrysler 400 462 392 498

Ford 1,559 911 1,393 930

Geely/Volvo 94 50

General 

Motors 1,514 1,342 1,391 1,444

Honda 930 545 862 588

Hyundai 518 92 489 99

Kia 548 115 512 124

Light 

TrucksLotus 28.8 29.4 330.3 34.9 

Mazda 420 72 393 78

Mitsubishi 83 55 80 60

Nissan 946 381 869 410

Porsche 33 17 30 18

Spyker/Saab 18 2

Subaru 207 117 236 74

Suzuki 103 20 94 21

Tata 65 42 59 46

Tesla 27 

Combined Cars 

& 

TrucksToyota 2,226 34.1,077 35.32,043 36.41,159

Volkswagen 583 124 528 134

Total 10,832 5,669 10,139 6,055

The estimated average required mpg levels for cars and trucks under the proposed 

standards shown in Table II-2 above include the use of A/C efficiency improvements, as 

discussed above, but do not reflect a number of proposed flexibilities and credits that 

manufacturers could use for compliance that NHTSA cannot consider in establishing standards 

based on EPCA/EISA constraints. These flexibilities would cause the actual achieved fuel 

economy to be lower than the required levels in the table above. The flexibilities and credits that 

NHTSA cannot consider include the ability of manufacturers to pay civil penalties rather than 

achieving required CAFE levels, the ability to use FFV credits, the ability to count electric 

vehicles for compliance, the operation of plug-in hybrid electric vehicles on electricity for 

compliance prior to MY 2020, and the ability to transfer and carry-forward credits. When 

accounting for these flexibilities and credits, NHTSA estimates that the proposed CAFE 
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standards would lead to the following average achieved fuel economy levels, based on the 

projections of what each manufacturer’s fleet will comprise in each year of the program:48 

Table II-3 Estimated Average Achieved Fleet-Wide Fuel Economy (mpg) under Proposed 

Footprint-Based CAFE Standards 

Estimated achieved fuel economy levels: 

The current baseline market forecast shows industry-wide average fuel economy levels 

somewhat lower in MY 2016 than shown in the baseline market forecast for the MY 2012-2016 

rulemaking. Under the current baseline, average fuel economy for MY 2016 is 27.0 mpg, versus 

27.3 mpg under the baseline in the MY 2012-2016 rulemaking. The 0.3 mpg change relative to 

the MY 2012-2016 rulemaking’s baseline is the result of changes in the shares of passenger car 

and light trucks in the MY 2016 market as noted above – more light trucks generally equals 

lower average fuel economy – and not the result of changes in the capabilities of the car and 

truck fleets. 

These differences are shown in greater detail below in Table IV-2, which shows 

manufacturer-specific CAFE levels (not counting FFV credits that some manufacturers expect to 

earn) from the current baseline versus the MY 2012-2016 rulemaking baseline for passenger cars 

and light trucks. Table IV-3 shows the combined averages of these planned CAFE levels in the 

respective baseline fleets. These tables demonstrate that there are no significant differences in 

CAFE for either passenger cars or light trucks at the manufacturer level between the current 

baseline and the MY 2012-2016 rulemaking baseline. The differences become more significant 

at the manufacturer level when combined CAFE levels are considered. Here we see a general 

decline in CAFE at the manufacturer level due to the increased share of light trucks. Because the 

agencies have, as for the MY 2012-2016 rulemaking, based this market forecast on vehicles in 

the MY 2008 fleet, these changes in CAFE levels reflect changes in vehicle mix, not changes in 

the fuel economy achieved by individual vehicle models. 

Table II-4. Current Baseline CAFE Levels in MY 2016 versus MY 2012-2016 Rulemaking 

CAFE Levels 

 MY 2012-2016 baseFinal 

Rule 49

Current Baseline 

 48 The proposed CAFE program includes incentives for full size pick-up trucks that have mild HEV or strong HEV 

systems, and for full size pick-up trucks that have fuel economy performance that is better than the target curve by 

more than proposed levels. To receive these incentives, manufacturers must produce vehicles with these 

technologies or performance levels at volumes that meet or exceed proposed penetration levels (percentage of full 

size pick-up truck volume). This incentive is described in detail in Section IV.1. The NHTSA estimates in Table I2 do not account for the reduction in estimated average achieved fleet-wide CAFE fuel economy that would occur if 

manufacturers use this incentive. NHTSA has conducted a sensitivity study that estimates the effects for 

manufacturers’ potential use of this flexibility in Chapter X of the PRIA. 49 Again, Aston Martin, Alfa Romeo, Ferrari, Maserati, Lotus and Tesla were not included in the baseline of the MY 

2012-2016 rulemaking; Volvo vehicles were reported under Ford and Saab vehicles were reported under GM; and 
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Manufacturer Passenger 

Cars

37.5Non 

passenger

38.8Passenger 40.6Non passenger 

Aston Martin 18.83 

BMW 27.19 23.04 27.19 23.03

Daimler 25.25 21.12 25.50 21.13

Fiat/Chrysler 28.69 22.19 27.74 22.19

Ford 28.14 21.31 28.24 21.32

Geely/Volvo 25.89 21.08

General Motors 28.42 21.45 28.38 21.45

Light TrucksHonda 33.98 28.2.05 33.083 34.825.02 

Hyundai 32.02 24.30 31.74 24.29

Combined Cars & 

TrucksKia 

33.498 34.523.74 36.032.70 3923.7 

Lotus 29.66 

Mazda 30.94 26.41 30.77 26.40

Mitsubishi 28.94 23.59 28.86 23.57

Nissan 32.04 22.11 31.98 22.10

Porsche 26.22 19.98 26.22 19.98

Spyker/Saab 26.54 19.79

Subaru 29.44 26.91 29.59 27.37

Suzuki 30.84 23.29 30.77 23.29

Tata 24.58 19.74 24.58 19.71

Tesla 244.00 

Toyota 35.33 24.25 35.22 24.26

Volkswagen 28.99 20.23 28.90 20.24

Total/Average 30.73 22.59 30.65 22.56

NHTSA is also required by EISA to set a minimum fuel economy standard for 

domestically manufactured passenger cars in addition to the attribute-based passenger car 

standard. The minimum standard “shall be the greater of (A) 27.5 miles per gallon; or (B) 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...,” and applies to each manufacturer’s fleet of domestically 

manufactured passenger cars (i.e., like the other CAFE standards, it represents a fleet average 

requirement, not a requirement for each individual vehicle within the fleet). 

 Chrysler was reported as a separate company whereas now it is reported as part of Fiat and includes Alfa Romeo, 

Ferrari, and Maserati. 
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Based on NHTSA’s current market forecast, the agency’s estimates of these proposed 

minimum standards for domestic passenger cars for MYs -2025 are presented below in Table 

II-5. 

Table II-5. Current Baseline CAFE Levels in MY 2016 versus MY 2012-2016 Rulemaking 

CAFE Levels (Combined) 

Manufacturer

MY 2012-2016 Final 

Rule 50 Current Baseline 

Aston Martin 18.83

BMW 25.85 25.68

Daimler 23.77 23.75

Fiat/Chrysler 24.79 24.33

Ford 25.17 24.99

Geely/Volvo 23.99

General 

Motors 24.66 24.37

Honda 30.03 29.61

Hyundai 30.56 30.18

Kia 30.89 30.46

Lotus 29.66

Mazda 30.18 29.95

Mitsubishi 26.53 26.33

Nissan 28.38 27.97

Porsche 23.74 23.48

Spyker/Saab 25.70

Subaru 28.47 29.03

Suzuki 29.30 29.04

Tata 22.42 22.19

Tesla 244.00

Toyota 30.75 30.27

Volkswagen 26.94 26.60

Total/Average 27.34 27.03

 50 Again, Aston Martin, Alfa Romeo, Ferrari, Maserati, Lotus and Tesla were not included in the baseline of the MY 

2012-2016 rulemaking; Volvo vehicles were reported under Ford and Saab vehicles were reported under GM; and 

Chrysler was reported as a separate company whereas now it is reported as part of Fiat and includes Alfa Romeo, 

Ferrari, and Maserati. 
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EPA is proposing GHG emissions standards, and Table II-6 provides estimates of the 

projected overall fleet-wide CO2 emission compliance target levels. The values reflected in 

Table I-4 are those that correspond to the manufacturers’ projected CO2 compliance target levels 

from the car and truck footprint curves, but do not account for EPA’s projection of how 

manufactures will implement two of the proposed incentive programs (advanced technology 

vehicle multipliers, and hybrid and performance-based incentives for full-size pickup trucks). 

EPA’s projection of fleet-wide emissions levels that do reflect these incentives is shown in Table 

I-5 below. 

e. How does manufacturer product plan data factor into the baseline used in this 

rule? 

In December 2010, NHTSA requested that manufacturers provide information regarding 

future product plans, as well as information regarding the context for those plans (e.g., estimates 

of future fuel prices), and estimates of the future availability, cost, and efficacy of fuel-saving 

technologies.51 The purpose of this request was to acquire updated information regarding vehicle 

manufacturers’ future product plans to assist the agency in assessing what corporate CAFE 

standards should be established for passenger cars and light trucks manufactured in model years 

2017 and beyond. The request was being issued in preparation for today’s joint NPRM. 

NHTSA indicated that it requested information for MYs 2010-2025 primarily as a basis 

for subsequent discussions with individual manufacturers regarding their capabilities for the 

MYs 2017-2025 time frame as it worked to develop today’s NPRM. NHTSA indicated that the 

information received would supplement other information to be used by NHTSA to develop a 

realistic forecast of the vehicle market in MY 2017 and beyond, and to evaluate what 

technologies may feasibly be applied by manufacturers to achieve compliance with potential 

future standards. NHTSA further indicated that information regarding later model years could 

help the agency gain a better understanding of how manufacturers' plans through MY 2025 relate 

to their longer-term expectations regarding foreseeable regulatory requirements, market trends, 

and prospects for more advanced technologies. 

NHTSA also indicated that it would consider information regarding the model years 

requested when considering manufacturers' planned schedules for redesigning and freshening 

their products, in order to examine how manufacturers anticipate tying technology introduction 

to product design schedules. In addition, the agency requested information regarding 

manufacturers' estimates of the future vehicle population, and fuel economy improvements and 

incremental costs attributed to technologies reflected in those plans. 

 51 75 FR 80430 
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Given the importance that responses to this request for comment may have in informing 

NHTSA's proposed CAFE rulemaking, whether as part of the basis for the standards or as an 

independent check on them, NHTSA requested that commenters fully respond to each question, 

particularly by providing information regarding the basis for technology costs and effectiveness 

estimates. 

We have already noted that in past CAFE rulemakings, NHTSA used manufacturers’ 

product plans—and other information—to build market forecasts providing the foundation for 

the agency’s rulemaking analysis. This issue has been the subject of much debate over the past 

several rulemakings since NHTSA began actively working on CAFE again following the lifting 

of the appropriations riders in 2001. The agency continues to believe that these market forecasts 

reflected the most technically sound forecasts the agency could have then developed for this 

purpose. Because the agency could not disclose confidential business information in 

manufacturers’ product plans, NHTSA provided summarized information, such as planned 

CAFE levels and technology application rates, rather than the fuel economy levels and 

technology content of specific vehicle model types. 

In preparing the MY 2012-2016 rule jointly with EPA, however, NHTSA revisited this 

practice, and concluded that for that rulemaking, it was important that all reviewers have equal 

access to all details of NHTSA’s analysis. NHTSA provided this level of transparency by 

releasing not only the agency’s CAFE modeling system, but also by releasing all model inputs 

and outputs for the agency’s analysis, all of which are available on NHTSA’s website at 

http://www.nhtsa.gov/fuel-economy. Therefore, NHTSA worked with EPA, as it did in 

preparing for analysis supporting today’s proposal, to build a market forecast based on publiclyand commercially-available sources. NHTSA continues to believe that the potential technical 

benefits of relying on manufacturers’ plans for future products are outweighed by the 

transparency gained in building a market forecast that does not rely on confidential business 

information, but also continues to find product plan information to be an important point of 

reference for meetings with individual manufacturers. We seek comment on what value 

manufacturer product plan might have in the future, and whether it continues to be useful to 

request manufacturer product plans to inform rulemaking analyses, specifically the baseline 

forecast used in rulemaking analyses. 

f. What sensitivity analyses is NHTSA conducting on the baseline? 

 As discussed below in Section IV.G, when evaluating the potential impacts of new CAFE 

standards, NHTSA considered the potential that, depending on how the cost and effectiveness of 

available technologies compare to the price of fuel, manufacturers would add more fuel-saving 

technology than might be required solely for purposes of complying with CAFE standards. This 

reflects that agency’s consideration that there could, in the future, be at least some market for 

fuel economy improvements beyond the required MY 2016 CAFE levels. In this sensitivity 
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analysis, this causes some additional technology to be applied, more so under baseline standards 

than under the more stringent standards proposed today by the agency. Results of this sensitivity 

analysis are summarized in Section IV.G and in NHTSA’s PRIA accompanying today’s notice. 

g. How else is NHTSA considering looking at the baseline for the final rule? 

Beyond the sensitivity analysis discussed above, NHTSA is also considering developing 

and using a vehicle choice model to estimate the extent to which sales volumes would shift in 

response to changes in vehicle prices and fuel economy levels. As discussed IV.C.4, the agency 

is currently sponsoring research directed toward developing such a model. If that effort is 

successful, the agency will consider integrating the model into the CAFE modeling system and 

using the integrated system for future analysis of potential CAFE standards. If the agency does 

so, we expect that the vehicle choice model would impact estimated fleet composition not just 

under new CAFE standards, but also under baseline CAFE standards. 

2. How were the technology inputs developed? 

As discussed above in Section II.E, for developing the technology inputs for these 

proposed MYs 2017–2025 CAFE and GHG standards, the agencies primarily began with the 

technology inputs used in the MYs 2012-2016 CAFE final rule and in the 2010 TAR.. The 

agencies have also updated information based on newly completed FEV tear down studies and 

new vehicle simulation work conducted by Ricardo Engineering, both of which were contracted 

by EPA. Additionally, the agencies relied on a model developed by Argonne National 

Laboratory to estimate hybrid, plug-in hybrid and electric vehicle battery costs. More detail is 

available regarding how the agencies developed the technology inputs for this proposal above in 

Section II.E, in Chapter 3 of the Joint TSD, and in Section V of NHTSA’s PRIA. 

a. What technologies does NHTSA consider? 

Section II.E.1 above describes the fuel-saving technologies considered by the agencies 

that manufacturers could use to improve the fuel economy of their vehicles during MYs 2017-

2025. Many of the technologies described in this section are readily available, well known, and 

could be incorporated into vehicles once production decisions are made. Other technologies, 

added for this rulemaking analysis, are considered that are not currently in production, but are 

beyond the initial research phase, under development and are expected to be in production in the 

next 5-10 years. As discussed, the technologies considered fall into five broad categories: engine 

technologies, transmission technologies, vehicle technologies, electrification/accessory 

technologies, and hybrid technologies. Table IV-4 below lists all the technologies considered 
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and provides the abbreviations used for them in the CAFE model,52 as well as their year of 

availability, which for purposes of NHTSA’s analysis means the first model year in the 

rulemaking period that the CAFE model is allowed to apply a technology to a manufacturer’s 

fleet.53 “Year of availability” recognizes that technologies must achieve a level of technical 

viability before they can be implemented in the CAFE model, and are thus a means of 

constraining technology use until such time as it is considered to be technologically feasible. For 

a more detailed description of each technology and their costs and effectiveness, we refer the 

reader to Chapter 3 of the Joint TSD and Section V of NHTSA’s PRIA. 

Table II-6 Projected Fleet-Wide CO2 Compliance Targets under the Proposed FootprintBased CO2 Standards (g/mi). List of Technologies in NHTSA’s Analysis 

Technology Model abbreviation Year available 

Low Friction Lubricants - Level 1 LUB1 2007 

Engine Friction Reduction - Level 1 EFR1 2007 

Low Friction Lubricants and Engine Friction 

Reduction - Level 2 LUB2_EFR2 2017 

Variable Valve Timing (VVT) - Coupled Cam 

Phasing (CCP) on SOHC CCPS 2007 

Discrete Variable Valve Lift (DVVL) on 

SOHC DVVLS 2007 

Cylinder Deactivation on SOHC DEACS 2007 

Variable Valve Timing (VVT) - Intake Cam 

Phasing (ICP) ICP 2007 

Variable Valve Timing (VVT) - Dual Cam 

Phasing (DCP) DCP 2007 

Discrete Variable Valve Lift (DVVL) on 

DOHC DVVLD 2007 

Continuously Variable Valve Lift (CVVL) CVVL 2007 

Cylinder Deactivation on DOHC DEACD 2007 

Stoichiometric Gasoline Direct Injection 

(GDI) SGDI 2007 

Cylinder Deactivation on OHV DEACO 2007 

Variable Valve Actuation - CCP and DVVL 

on OHV VVA 2007 

Stoichiometric Gasoline Direct Injection 

(GDI) on OHV SGDIO 2007 

Turbocharging and Downsizing - Level 1 (18 

bar BMEP) - Small Displacement TRBDS1_SD 2007 

 52 The abbreviations are used in this section both for brevity and for the reader’s reference if they wish to refer to the 

expanded decision trees and the model input and output sheets, which are available in Docket No. NHTSA–2011–

[xxxx] and on NHTSA’s website.

53 A date of 2012 means the technology can be applied in all model years, while a date of 2020 means the 

technology can only be applied in model years 2020 through 2025.
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Turbocharging and Downsizing - Level 1 (18 

bar BMEP) - Medium Displacement TRBDS1_MD 2007 

Turbocharging and Downsizing - Level 1 (18 

bar BMEP) - Large Displacement TRBDS1_LD 2007 

Turbocharging and Downsizing - Level 2 (24 

bar BMEP) - Small Displacement TRBDS2_SD 2012 

Turbocharging and Downsizing - Level 2 (24 

bar BMEP) - Medium Displacement TRBDS2_MD 2012 

Turbocharging and Downsizing - Level 2 (24 

bar BMEP) - Large Displacement TRBDS2_LD 2012 

Cooled Exhaust Gas Recirculation (EGR) - 

Level 1 (24 bar BMEP) - Small Displacement CEGR1_SD 2012 

Cooled Exhaust Gas Recirculation (EGR) - 

Level 1 (24 bar BMEP) - Medium 

Displacement 

CEGR1_MD 2012 

Cooled Exhaust Gas Recirculation (EGR) - 

Level 1 (24 bar BMEP) - Large Displacement CEGR1_LD 2012 

Cooled Exhaust Gas Recirculation (EGR) - 

Level 2 (27 bar BMEP) - Small Displacement CEGR2_SD 2017 

Cooled Exhaust Gas Recirculation (EGR) - 

Level 2 (27 bar BMEP) - Medium 

Displacement 

CEGR2_MD 2017 

Cooled Exhaust Gas Recirculation (EGR) - 

Level 2 (27 bar BMEP) - Large Displacement CEGR2_LD 2017 

Advanced Diesel - Small Displacement ADSL_SD 2017 

Advanced Diesel - Medium Displacement ADSL_MD 2017 

Advanced Diesel - Large Displacement ADSL_LD 2017 

6-Speed Manual/Improved Internals 6MAN 2007 

High Efficiency Gearbox (Manual) HETRANSM 2017 

Improved Auto. Trans. Controls/Externals IATC 2007 

6-Speed Trans with Improved Internals 

(Auto) NAUTO 2007 

6-speed DCT DCT 2007 

8-Speed Trans (Auto or DCT) 8SPD 2014 

High Efficiency Gearbox (Auto or DCT) HETRANS 2017 

Shift Optimizer SHFTOPT 2017 

Electric Power Steering EPS 2007 

Improved Accessories - Level 1 IACC1 2007 

Improved Accessories - Level 2 (w/ 

Alternator Regen and 70% efficient 

alternator) 

IACC2 2014 

12V Micro-Hybrid (Stop-Start) MHEV 2007 

Strong Hybrid - Level 1 SHEV1 2012 

Strong Hybrid - Level 2 SHEV2 2017 

Plug-in Hybrid - 30 mi range PHEV1 2020 
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Electric Vehicle (Early Adopter) - 75 mile 

range 

EV1 2017 

Electric Vehicle (Broad Market) - 150 mile 

range 

EV4 2017 

Mass Reduction - Level 1 MR1 2007 

Mass Reduction - Level 2 MR2 2007 

Mass Reduction - Level 3 MR3 2007 

Mass Reduction - Level 4 MR4 2011 

Mass Reduction - Level 5 MR5 2016 

Low Rolling Resistance Tires - Level 1 ROLL1 2007 

Low Rolling Resistance Tires - Level 2 ROLL2 2017 

Low Drag Brakes LDB 2007 

Secondary Axle Disconnect SAX 2007 

Aero Drag Reduction, Level 1 AERO1 2007 

Aero Drag Reduction, Level 2 AERO2 2011 

by turbocharging, hybrids, etc.), As shown in Table II-6, projected fleet-wide CO2

emission compliance targets for cars increase in stringency from 213 to 144 g/mi between MY , 

the agencies have not explicitly incorporated maintenance costs (or potential savings) as a 

separate element in this analysis to 163 g/mi in MY 2025, which is equivalent to 54.5 mpg if all 

reductions were made with fuel economy improvements. 

EPA anticipates that manufacturers would take advantage of proposed program credits 

and incentives, such as car/truck credit transfers, air conditioning credits, off-cycle credits, 

advanced technology vehicle multipliers, and hybrid and performance-based incentives for full 

size pick-up trucks. Two of these flexibility provisions – advanced technology vehicle 

multipliers and the full size pick-up hybrid/performance incentives – are expected to have an 

impact on the fleet-wide emissions levels that manufacturers will actually achieve. Therefore, 

Table II-7 shows EPA’s projection of the achieved emission levels of the fleet for MY 2017 

through 2025. The differences between the emissions levels shown in Tables I-4 and I-5 reflect 

the impact on stringency due to the advanced technology vehicle multipliers and the full size 

pick-up hybrid/performance incentives, but do not reflect car-truck trading, air conditioning 

credits, or off-cycle credits, because, while those credit provisions should help reduce 

manufacturers’ costs of the program, EPA believes that they will result in real-world emission 

reductions that will not affect the achieved level of emission reductions. These estimates are 

more fully discussed in III.B 

Table II-7 Projected Fleet-Wide Achieved CO2-equivalent Emission Levels under the 

Proposed Footprint-Based CO2 Standards (g/mi)54

 Subcom Compac Midsize Large Perform Perform Perform Perform Minivan Small Midsize Large 

 54 Electric vehicles are assumed at 0 gram/mile in this analysis. 
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p. Car t Car Car Car . 

Subcom

p. Car 

. 

Compac

t Car 

. 

Midsize 

Car 

. Large 

Car 

LT LT LT LT 

VEHICLE TECHNOLOGY INCREMENTAL FUEL CONSUMPTION REDUCTION (-%) 

w friction 

icants 

el 1) 0.5 0.5 0.7 0.8 0.5 0.5 0.7 0.8 0.7 0.6 0.7 0.7

ine friction 

uction 

el 1) 2.0 2.0 2.6 2.7 2.0 2.0 2.6 2.7 2.6 2.0 2.6 2.4

T—Dual 

m phasing 

CP) 2.0 2.0 2.5 2.7 2.0 2.0 2.5 2.7 2.6 2.0 2.6 2.4

crete 

able valve 

(DVVL) on 

HC 2.8 2.8 3.6 3.9 2.8 2.8 3.6 3.9 3.5 2.8 3.5 3.4

inder 

ctivation on 

V 4.7 4.7 5.9 6.3 4.7 4.7 5.9 6.3 5.9 4.7 5.9 5.5

chiometric 

oline direct 

ction 1.6 1.6 1.5 1.5 1.6 1.6 1.5 1.5 1.5 1.6 1.5 1.5

bocharging 

wnsizing 

el 1) 7.2 7.2 8.3 7.8 7.2 6.7 7.5 7.8 7.9 7.1 7.9 7.3

bocharging 

wnsizing 

el 2) 2.9 2.9 3.5 3.7 2.9 2.9 3.5 3.7 3.4 2.9 3.4 3.4

led exhaust 

rculation 

GR) – (level 

3.6 3.6 3.5 3.5 3.6 3.6 3.5 3.5 3.6 3.6 3.6 3.6

led exhaust 

rculation 

GR) – (level 

1.0 1.0 1.4 1.4 1.0 1.0 1.4 1.4 1.1 1.0 1.1 1.2

vanced 

sel 5.5 5.5 2.8 2.9 5.5 5.5 2.8 2.9 3.4 5.3 3.4 3.5

peed auto. 

s. with 

roved 

rnals 1.9 1.9 2.0 2.0 1.9 1.9 2.0 2.0 2.0 2.0 2.0 2.1

peed DCT 4.0 4.0 4.1 3.8 4.0 3.4 4.1 3.8 n/a 3.8 n/a n/a

h

ciency 

rbox 2.2 2.2 2.7 2.6 2.2 2.2 2.7 2.6 3.1 2.5 3.1 3.7

ft 

imizer 3.3 3.3 4.1 4.3 3.3 3.3 4.1 4.3 4.1 3.3 4.1 3.9

ctric power 1.5 1.5 1.3 1.1 1.5 1.5 1.3 1.1 1.0 1.2 1.0 0.8
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ring 

V microrid 1.7 1.7 2.1 2.2 1.7 1.7 2.1 2.2 2.09 1.8 2.1 2.1

ng Hybrid 

el 2) 10.1 10.1 6.5 6.9 10.1 9.7 6.5 6.8 5.2 10.0 8.2 7.3

g-in Hybrid 40.7 40.7 40.7 40.7 40.7 40.7 40.7 40.7 40.7 40.7 40.7 40.7

ctric 

hicle (Early 

opter) 68.5 68.5 68.5 68.5 68.5 68.5 68.5 68.5 68.5 68.5 68.5 68.5

w Rolling 

istance 

s (level 1) 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9

w Rolling 

istance 

s (level 2) 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

o Drag 

uction 

el 1) 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3

o Drag 

uction 

el 2) 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5

As noted, NHTSA and EPA are proposing to continue attribute-based standards for 

passenger cars and light trucks, as required by EISA and as allowed by the CAA, and continue to 

use vehicle footprint as the attribute. Footprint is defined as a vehicle’s wheelbase multiplied by 

its track width -- in other words, the area enclosed by the points at which the wheels meet the 

ground. NHTSA and EPA adopted an attribute-based approach based on vehicle footprint for 

MYs 2012-2016 light-duty vehicle standards. 55 The agencies continue to believe that footprint 

is the most appropriate attribute on which to base the proposed standards, as discussed later in 

this notice and in Chapter 2 of the Joint TSD. 

Under the footprint-based standards, the curve defines a GHG or fuel economy 

performance target for each separate car or truck footprint. Using the curves, each manufacturer 

thus will have a GHG and CAFE average standard that is unique to each of its fleets, depending 

on the footprints and production volumes of the vehicle models produced by that manufacturer. 

A manufacturer will have separate footprint-based standards for cars and for trucks. The curves 

are mostly sloped, so that generally, larger vehicles (i.e., vehicles with larger footprints) will be 

subject to less stringent targets (i.e., higher CO2 grams/mile targets and lower CAFE mpg 

targets) than smaller vehicles. This is because, generally speaking, smaller vehicles are more 

capable of achieving lower levels of CO2 and higher levels of fuel economy than larger vehicles. 

Although a manufacturer’s fleet average standards could be estimated throughout the model year 

based on projected production volume of its vehicle fleet, the standards to which the 

 55 NHTSA also uses the footprint attribute in its Reformed CAFE program for light trucks for model years 2008-

2011 and passenger car CAFE standards for MY 2011. 
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manufacturer must comply will be based on 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.56

While the concept is the same, the proposed curve shapes for MYs Table-2025 compared 

to the light truck curves for MYs 2012-2016. The agencies are proposing changes to the lighttruck curve to increase the slope and to extend the large-footprint cutpoint over time to larger 

footprints, which we believe represent an appropriate balance of both technical and policy issues, 

as discussed in Section II.C below and Chapter 2 of the draft Joint TSD. 

NHTSA is proposing the attribute curves below for assigning a fuel economy target level 

to an individual car or truck’s footprint value, for model years through 2025. These mpg values 

will be production weighted to determine each manufacturer’s fleet average standard for cars and 

trucks. Although the general model of the target curve equation is the same for each vehicle 

category and each year, the parameters of the curve equation differ for cars and trucks. Each 

parameter also changes on a model year basis, resulting in the yearly increases in stringency. 

Figure II-1 below illustrates the passenger car CAFE standard curves for model years through 

2025 while II-2 below illustrates the light truck CAFE standard curves for model years through 

2025. 

EPA is proposing the attribute curves shown in II-3 and II-4 below for assigning a CO2

target level to an individual vehicle’s footprint value, for model years 2017 through 2025. These 

CO2 values would be production weighted to determine each manufacturer’s fleet average 

standard for cars and trucks. As with the CAFE curves, the general form of the equation is the 

same for each vehicle category and each year, but the parameters of the equation differ for cars 

and trucks. Again, each parameter also changes on a model year basis, resulting in the yearly 

increases in stringency. II-3 below illustrates the CO2 car standard curves for model years 2017 

through 2025 while II-4 shows the CO2 truck standard curves for model years 2017-2025. 

 56 As in the MYs 2012-2016 rule, a manufacturer may have some 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 sales weighted average of the target levels for each model) with fleet average performance (based on 

the sales weighted average of the performance for each model). 
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Figure II-1 CAFE Target Curves for Passenger Cars 
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8. II-2 CAFE Target Curves for Light Trucks 

20304050607080

30 35 40 45 50 55 60 65 70 75 80

Fuel Economy Target (mpg)

Footprint (sf)

20252024202320222021202020192018201720162015201420132012
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II-3 CO2 (g/mile) Car Standards Curves
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II-4 CO2 (g/mile) Truck Standard Curves
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NHTSA and EPA are proposing to use the same vehicle category definitions for determining 

which vehicles are subject to the car curve standards versus the truck curve standards as were 

used for MYs 2012-2016 standards. As in the MYs 2012-2016 rulemaking, a vehicle classified 

as a car under the NHTSA CAFE program will also be classified as a car under the EPA GHG 

program, and likewise for trucks.57 This approach of using CAFE definitions allows the CO2

standards and the CAFE standards to continue to be harmonized across all vehicles for the 

National Program. 

As just explained, generally speaking, a smaller footprint vehicle will tend to have higher 

fuel economy and lower CO2 emissions relative to a larger footprint vehicle when both have the 

same level of fuel efficiency improvement technology. Since the proposed standards apply to a 

manufacturer’s overall fleet, not to an individual vehicle, if a manufacturer’s fleet is dominated 

by small footprint vehicles, then that fleet will have a higher fuel economy requirement and a 

lower CO2 requirement than a manufacturer whose fleet is dominated by large footprint vehicles. 

Compared to the non-attribute based CAFE standards in place prior to MY 2011, the proposed 

standards more evenly distribute the compliance burdens of the standards among different 

manufacturers, based on their respective product offerings. With this footprint-based standard 

approach, EPA and NHTSA continue to believe that the rules will not create significant 

incentives to produce vehicles of particular sizes, and thus there should be no significant effect 

on the relative availability of different vehicle sizes in the fleet due to the proposed standards, 

which will help to maintain consumer choice during the rulemaking timeframe. Consumers 

should still be able to purchase the size of vehicle that meets their needs. Table II-9 helps to 

illustrate the varying CO2 emissions and fuel economy targets under the proposed standards that 

different vehicle sizes will have, although we emphasize again that these targets are not actual 

standards – the proposed standards are manufacturer-specific, rather than vehicle-specific. 

Table II-9 Model Year 2025 CO2 and Fuel Economy Targets for Various MY 2008 Vehicle 

Types 

Vehicle 

Type 

Example 

Models 

Example Model 

Footprint 

(sq. ft.) 

CO2 Emissions 

Target (g/mi)a

Fuel Economy 

Target (mpg)b

Example Passenger Cars 

Compact 

car Honda Fit 40 131 61.1 

Midsize 

car Ford Fusion 46 147 54.9 

Fullsize 

car Chrysler 300 53 170 48.0 

 57 See 49 CFR 523 for NHTSA’s definitions for passenger car and light truck under the CAFE program. 
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Example Light-duty Trucks 

Small SUV 4WD Ford 

Escape 44 170 47.5 

Midsize 

crossover 

Nissan Murano 49 188 43.4 

Minivan Toyota Sienna 55 209 39.2 

Large 

pickup 

truck 

Chevy 

Silverado 67 252 33.0 

a, b Real-world CO2 is typically 25 percent higher and real-world fuel economy is typically 20 percent lower 

than the CO2 and fuel economy target values presented here. 

3. Program Flexibilities for Achieving Compliance 

a. CO2/CAFE Credits Generated Based on Fleet Average Over-Compliance 

The MYs 2012-2016 rules contain several provisions which provide flexibility to 

manufacturers in meeting standards, many of which the agencies are not proposing to change for 

MYs 2017 and later. For example, the agencies are proposing to continue allowing 

manufacturers to generate credits for over-compliance with the CO2 and CAFE standards.58 

Under the agencies’ footprint-based approach to the standards, a manufacturer’s ultimate 

compliance obligations are determined at the end of each model year, when production of the 

model year is complete. Since the fleet average standards that apply to a manufacturer’s car and 

truck fleets are based on the applicable footprint-based curves, a production volume-weighted 

fleet average requirement will be calculated for each averaging set (cars and trucks) based on the 

mix and volumes of the models manufactured for sale by the manufacturer. If a manufacturer’s 

car and/or truck fleet achieves a fleet average CO2/CAFE level better than the car and/or truck 

standards, then the manufacturer generates credits. Conversely, if the fleet average CO2/CAFE 

level does not meet the standard, the fleet would incur debits (also referred to as a shortfall). As 

in the MY 2011 CAFE program under EPCA/EISA, and also in MYs 2012-2016 for the lightduty vehicle GHG and CAFE program, a manufacturer whose fleet generates credits in a given 

model year would have several options for using those credits, including credit carry-back, credit 

carry-forward, credit transfers, and credit trading. 

Credit “carry-back” means that manufacturers are able to use credits to offset a deficit 

that had accrued in a prior model year, while credit “carry-forward” means that manufacturers 

can bank credits and use them toward compliance in future model years. EPCA, as amended by 

EISA, requires NHTSA to allow manufacturers to carry-back credits for up to three model years, 

and to carry-forward credits for up to five model years. EPA’s MYs 2012-2016 light duty 

vehicle GHG program includes the same limitations and EPA is proposing to continue this 

 58 This credit flexibility is required by EPCA/EISA, see 49 U.S.C. 32903, and allowed by the CAA. 
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limitation in the MY 2017-2025 program. To facilitate the transition to the increasingly more 

stringent standards, EPA is proposing under its CAA authority a one-time CO2 carry-forward 

beyond 5 years, such that any credits generated from MY 2010 through 2016 will be able to be 

used any time through MY 2021. This provision would not apply to early credits generated in 

MY 2009. NHTSA’s program will continue the 5-year carry-forward and 3-year carry-back, as 

required by statute. 

Credit “transfer” means the ability of manufacturers to move credits from their passenger 

car fleet to their light truck fleet, or vice versa. EISA required NHTSA to establish by regulation 

a CAFE credits transferring program, now codified at 49 CFR Part 536, to allow a manufacturer 

to transfer credits between its car and truck fleets to achieve compliance with the standards. For 

example, credits earned by over-compliance with a manufacturer’s car fleet average standard 

could be used to offset debits incurred due to that manufacturer’s not meeting the truck fleet 

average standard in a given year. However, EISA imposed a cap on the amount by which a 

manufacturer could raise its CAFE through transferred credits: 1 mpg for MYs 2011-2013; 1.5 

mpg for MYs -2017; and 2 mpg for MYs 2018 and beyond.59 Under section 202 (a) of the CAA, 

in contrast, there is no statutory limitation on car-truck credit transfers, and EPA’s GHG 

program allows unlimited credit transfers across a manufacturer’s car-truck fleet to meet the 

GHG standard. This is based on the expectation that this flexibility will facilitate setting 

appropriate GHG standards that manufacturers' can comply with in the lead time provided, and 

will allow the required GHG emissions reductions to be achieved in the most cost effective way. 

Therefore, EPA did not constrain the magnitude of allowable car-truck credit transfers,60 as 

doing so would reduce the flexibility for lead time, and would increase costs with no 

corresponding environmental benefit. EISA also prohibits the use of transferred credits to meet 

the minimum domestic passenger car fleet CAFE standard.61 These statutory limits will 

necessarily continue to apply to the determination of compliance with the CAFE standards. 

Credit “trading” means the ability of manufacturers to sell credits to, or purchase credits 

from, one another. EISA allowed NHTSA to establish by regulation a CAFE credit trading 

program, also now codified at 49 CFR Part 536, to allow credits to be traded between vehicle 

manufacturers. EPA also allows credit trading in the light-duty vehicle GHG program. These 

sorts of exchanges between averaging sets are typically allowed under EPA’s current mobile 

source emission credit programs (as well as EPA’s and NHTSA’s recently promulgated GHG 

and fuel efficiency standards for heavy-duty vehicles and engines). EISA also prohibits 

 59 49 U.S.C. 32903(g)(3). 

60 EPA’s proposed program will continue to adjust car and truck credits by vehicle miles traveled (VMT), as in the 

MY2012-2016 program. 

61 49 U.S.C. 32903(g)(4). 
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manufacturers from using traded credits to meet the minimum domestic passenger car CAFE 

standard.62

b. Air Conditioning Improvement Credits/Fuel Economy Value Increases 

Air conditioning (A/C) systems contribute to GHG emissions in two ways. 

Hydrofluorocarbon (HFC) refrigerants, which are powerful GHGs, can leak from the A/C system 

(direct A/C emissions). In addition, operation of the A/C system places an additional load on the 

engine which increases fuel consumption and thus results in additional CO2 tailpipe emissions 

(indirect A/C related emissions). In the MYs 2012-2016 program, EPA allows manufacturers to 

generate credits by reducing either or both types of GHG emissions related to A/C systems. The 

expected generation of A/C credits is accounted for in setting the level of the overall CO2

standard. For the current proposal, as with the MYs 2012-2016 program, manufacturers will be 

able to generate CO2-equivalent credits to use in complying with the CO2 standards for 

improvements in air conditioning (A/C) systems, both for efficiency improvements (reduces 

tailpipe CO2 and improves fuel consumption) and for leakage reduction or alternative, lower 

GWP (global warming potential) refrigerant use (reduces hydrofluorocarbon (HFC) emissions). 

EPA is proposing that the maximum A/C credit available for cars is 18.8 grams/mile CO2 and for 

trucks is 24.4 grams/mile CO2. The proposed test methods used to calculate these direct and 

indirect A/C credits are very similar to those of the MYs 2012-2016 program, though EPA is 

seeking comment on a revised idle test as well as a new test procedure. 

For the first time in the current proposal, the agencies are proposing provisions that 

would account for improvements in air conditioner efficiency in the CAFE program. Improving 

A/C efficiency leads to real-world fuel economy benefits, because as explained above, A/C 

operation represents an additional load on the engine, so more efficient A/C operation imposes 

less of a load and allows the vehicle to go farther on a gallon of gas. Under EPCA, EPA has 

authority to adopt procedures to measure fuel economy and calculate CAFE. Under this 

authority EPA is proposing that manufacturers could generate fuel consumption improvement 

values for purposes of CAFE compliance based on air conditioning system efficiency 

improvements for cars and trucks. This increase in fuel economy would be allowed up to a 

maximum based on 0.000563 gallon/mile for cars and 0.000810 gallon/mile for trucks. This is 

equivalent to the A/C efficiency CO2 credit allowed by EPA under the GHG program. The same 

methods would be used in the CAFE program to calculate the values for air conditioning 

efficiency improvements for cars and trucks as are used in EPA’s GHG program. NHTSA is 

including in its proposed passenger car and light truck CAFE standards an increase in stringency 

in each model year from 2017-2025 by the amount industry is expected to improve air 

conditioning system efficiency in those years, in a manner consistent with EPA’s GHG 

standards. EPA is not proposing to allow generation of fuel consumption improvement values 

 62 49 U.S.C. 32903(f)(2). 
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for CAFE purposes, nor is NHTSA proposing to increase stringency of the CAFE standard, for 

the use of A/C systems that reduce leakage or employ alternative, lower GWP refrigerant, 

because those changes do not improve fuel economy. 

c. Off- cycle Credits/Fuel Economy Value Increases 

For MYs 2012-2016, EPA provided an option for manufacturers to generate credits for 

employing new and innovative technologies that achieve CO2 reductions that are not reflected on 

current test procedures. EPA noted in the MYs 2012-2016 rulemaking that examples of such 

“off-cycle” technologies might include solar panels on hybrids, adaptive cruise control, and 

active aerodynamics, among other technologies. See generally 75 FR at 25438-39. EPA’s 

current program allows off-cycle credits to be generated through MY 2016. 

EPA is proposing that manufacturers may continue to use off-cycle credits for MY 2017 

and later for the GHG program. As with A/C efficiency, improving efficiency through the use of 

off-cycle technologies leads to real-world fuel economy benefits and allows the vehicle to go 

farther on a gallon of gas. Thus, under its EPCA authority EPA is proposing to allow 

manufacturers to generate fuel consumption improvement values for purposes of CAFE 

compliance based on the use of off-cycle technologies. Increases in fuel economy under the 

CAFE program based on off-cycle technology will be equivalent to the off-cycle credit allowed 

by EPA under the GHG program, and these amounts will be determined using the same 

procedures and test methods as are used in EPA’s GHG program. For the reasons discussed in 

sections III and IV of this proposal, the ability to generate off-cycle credits and increases in fuel 

economy for use in compliance will not affect or change the level of the GHG or CAFE 

standards proposed by each agency. 

Many automakers indicated that they had a strong interest in pursuing off-cycle 

technologies, and encouraged the agencies to refine and simplify the evaluation process to 

provide more certainty as to the types of technologies the agencies would approve for credit 

generation. For 2017 and later, EPA is proposing to expand and streamline the MYs 2012-2016 

off-cycle credit provisions, including an approach by which the agencies would provide specified 

amounts of credit and fuel consumption improvement values for a subset of off-cycle 

technologies whose benefits are readily quantifiable. EPA is proposing a list of technologies and 

credit values, where sufficient data is available, that manufacturers could use without going 

through an advance approval process that would otherwise be required to generate credits. EPA 

believes that our assessment of off-cycle technologies and associated credit values on this 

proposed list is conservative, and automakers may apply for additional off-cycle credits beyond 

the minimum credit value if they have sufficient supporting data. Further, manufacturers may 

also apply for off-cycle technologies beyond those listed, again, if they have sufficient data. 

In addition, EPA is providing additional detail on the process and timing for the 

credit/fuel consumption improvement values application and approval process. EPA is 
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proposing a timeline for the approval process, including a 60-day EPA decision process from the 

time a manufacturer submits a complete application. EPA is also proposing a detailed, common, 

step-by-step process, including a specification of the data that manufacturers must submit. For 

off-cycle technologies that are both not covered by the pre-approved off-cycle credit/fuel 

consumption improvement values list and that are not quantifiable based on the 5-cycle test cycle 

option provided in the 2012-2016 rulemaking, EPA is proposing to retain the public comment 

process from the MYs 2012-2016 rule. 

d. Incentives for Electric Vehicles, Plug-in Hybrid Electric Vehicles, and Fuel Cell 

Vehicles 

To facilitate market penetration of the most advanced vehicle technologies as rapidly as 

possible, EPA is proposing an incentive multiplier for compliance purposes for all electric 

vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell vehicles (FCVs) sold in 

MYs 2017 through 2021. This multiplier approach means that each EV/PHEV/FCV would 

count as more than one vehicle in the manufacturer’s compliance calculation. EPA is proposing 

that EVs and FCVs start with a multiplier value of 2.0 in MY 2017, phasing down to a value of 

1.5 in MY 2021. PHEVs would start at a multiplier value of 1.6 in MY 2017 and phase down to 

a value of 1.3 in MY 2021.63 The multiplier would be 1.0 for MYs 2022-2025. 

NHTSA currently interprets EPCA and EISA as precluding the agency from offering 

additional incentives for EVs, FCVs and PHEVs, except as specified by statute,64 and thus is not 

proposing incentive multipliers comparable to the EPA incentive multipliers described above. 

For EVs, PHEVs and FCVs, EPA is proposing to set a value of 0 g/mile for the tailpipe 

compliance value for EVs, PHEVs (electricity usage) and FCVs for MY 2017-2021, with no 

limit on the quantity of vehicles eligible for 0 g/mi tailpipe emissions accounting. For MY 2022-

2025, EPA is proposing that 0 g/mi only be allowed up to a per-company cumulative sales cap, 

tiered as follows: 1) 600,000 vehicles for companies that sell 300,000 EV/PHEV/FCVs in MYs 

2019-2021; 2) 200,000 vehicles for all other manufacturers. EPA believes the industry-wide 

impact of such a tiered cap will be approximately 2 million vehicles. EPA proposes to phase-in 

the change in compliance value, from 0 grams per mile to net upstream accounting, for any 

manufacturer that exceeds its cumulative production cap for EV/PHEV/FCVs. EPA proposes 

 63 The multipliers for EV/FCV would be: 2017-2019 – 2.0, 2020 - 1.75, 2021 – 1.5; for PHEV: 2017-2019 – 1.6, 

2020 – 1.45, 2021 – 1.3. 

64 Because 49 U.S.C. 32904(a)(2)(B) expressly requires EPA to calculate the fuel economy of electric vehicles using 

the Petroleum Equivalency Factor developed by DOE, which contains an incentive for electric operation already, 

and because 49 U.S.C. 32905(a) expressly requires EPA to calculate the fuel economy of FCVs using a specified 

incentive, NHTSA believes that Congress’ having provided clear incentives for these technologies in the CAFE 

program suggests that additional incentives beyond those would not be consistent with Congress’ intent. Similarly, 

because the fuel economy of PHEVs’ electric operation must also be calculated using DOE’s PEF, the incentive for 

electric operation appears to already be inherent in the statutory structure. 
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that, starting with MY 2022, the compliance value for EVs, FCVs, and the electric portion of 

PHEVs in excess of individual automaker cumulative production caps would be one-half of the 

value based on net upstream accounting. EPA will revisit the issue of compliance treatment for 

MY 2026 and later EV/PHEV/FCVs in a future rulemaking. 

For EVs and other dedicated alternative fuel vehicles, EPA is proposing to calculate fuel 

economy for the CAFE program using the same methodology as in the MYs 2012-2016 

rulemaking, which aligns with EPCA/EISA statutory requirements. For liquid alternative fuels, 

this methodology generally counts 15 percent of the volume of fuel used in determine the mpgequivalent fuel economy. For gaseous alternative fuels, the methodology generally determines a 

gasoline equivalent mpg based on the energy content of the gaseous fuel consumed, and then 

adjusts the fuel consumption by effectively only counting 15 percent of the actual energy 

consumed. For electricity, the methodology generally determines a gasoline equivalent mpg by 

measuring the electrical energy consumed, and then using a petroleum equivalency factor (PEF) 

to convert to an mpg-equivalent value. The PEF for electricity includes an adjustment that 

effectively only counts 15 percent of the actual energy consumed. Counting 15 percent of the 

volume or energy provides an incentive for alternative fuels in the CAFE program. 

The methodology that EPA is proposing for dual fueled vehicles under the GHG program 

and to calculate fuel economy for the CAFE program is discussed below in subsection I.B.7.a. 

e. Incentives for “Game Changing” Technologies Performance for Full-Size Pickup 

Truck including Hybridization 

The agencies recognize that the standards under consideration for MYs 2017-2025 will 

be challenging for large trucks, including full size pickup trucks. In order to incentivize the 

penetration into the marketplace of “game changing” technologies for these pickups, including 

their hybridization, EPA is proposing a CO2 credit in the GHG program and an equivalent fuel 

consumption improvement value in the CAFE program for manufacturers that employ significant 

quantities of hybridization on full size pickup trucks, by including a per-vehicle CO2 credit and 

fuel consumption improvement value available for mild and strong hybrid electric vehicles 

(HEVs). EPA would provide the incentive for the GHG program under EPA’s CAA authority 

and the incentive for the CAFE program under EPA’s EPCA authority. EPA’s GHG and 

NHTSA’s CAFE proposed standards are set at levels that take into account this flexibility as an 

incentive for the introduction of advanced technology. This provides the opportunity to begin to 

transform the most challenging category of vehicles in terms of the penetration of advanced 

technologies, which, if successful at incentivizing these “game changing technologies,” should 

allow additional opportunities to successfully achieve the higher levels of truck stringencies in 

MYs 2022-2025. 

EPA is proposing that access to this credit and fuel consumption improvement value be 

conditioned on a minimum penetration of the technology in a manufacturer’s full size pickup 
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truck fleet, and is proposing criteria for a full size pickup truck (e.g., minimum bed size and 

minimum towing or payload capability). EPA is proposing that mild HEV pickup trucks would 

be eligible for a per vehicle credit of 10 g/mi65 during MYs 2017-2021 if the technology is used 

on a minimum percentage of a company’s full size pickups, beginning with at least 30% of a 

company’s full size pickup production in 2017 and ramping up to at least 80% in MY 2021. 

Strong HEV pickup trucks would be eligible for a 20 g/mi per66 vehicle credit during MYs 2017-

2025 if the technology is used on at least 10% of the company’s full size pickups. These volume 

thresholds are being proposed in order to encourage rapid penetration of these technologies in 

this vehicle segment. EPA and NHTSA are proposing specific definitions of mild and strong 

HEV pickup trucks. 

Because there are other technologies besides mild and strong hybrids which can 

significantly reduce GHG emissions and fuel consumption in pickup trucks, EPA is also 

proposing a performance-based incentive CO2 emissions credit and equivalent fuel consumption 

improvement value for full size pickup trucks that achieve a significant CO2 reduction 

below/fuel economy improvement above the applicable target. This would be available for 

vehicles achieving significant CO2 reductions/fuel economy improvements through the use of 

technologies other than hybrid drive systems. EPA is proposing that eligible pickup trucks 

achieving 15 percent below their applicable CO2 target would receive a 10 g/mi credit, and those 

achieving 20 percent below their target would receive a 20 g/mi credit. The 10 g/ mi 

performance-based credit would be available for MYs 2017 to 2021 and a vehicle meeting the 

requirements would receive the credit until MY 2021 unless its CO2 level increases. The 20 g/mi 

performance-based credit would be available for a maximum of 5 years within the model years 

of 2017 to 2025, provided the CO2 level does not increase for those vehicles earning the credit. 

The credits would begin in the model year of the eligible vehicle’s introduction, and could not 

extend past MY 2021 for the 10 g/mi credit and MY 2025 for the 20 g/mi credit. 

 To avoid double-counting, the same vehicle would not receive credit under both the 

HEV and the performance based approaches. 

4. Mid-Term Evaluation 

Given the long time frame at issue in setting standards for MYs 2022-2025, and given 

NHTSA’s obligation to conduct a separate rulemaking in order to establish final standards for 

vehicles for those model years, EPA and NHTSA are proposing a comprehensive mid-term 

evaluation and agency decision-making process. As part of this undertaking, both NHTSA and 

EPA will develop and compile up-to-date information for the evaluation, through a collaborative, 

robust and transparent process, including public notice and comment. The evaluation will be 

 65 0.001125 gallon/mile. 

66 0.00225 gallon/mile 
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based on (1) a holistic assessment of all of the factors considered by the agencies in setting 

standards, including those set forth in the rule and other relevant factors, and (2) the expected 

impact of those factors on the manufacturers’ ability to comply, without placing decisive weight 

on any particular factor or projection. The comprehensive evaluation process will lead to final 

agency action by both agencies. 

Consistent with the agencies’ commitment to maintaining a single national framework for 

regulation of vehicle emissions and fuel economy, the agencies fully expect to conduct the midterm evaluation in close coordination with the California Air Resources Board (CARB). 

Moreover, the agencies fully expect that any adjustments to the GHG standards will be made 

with the participation of CARB and in a manner that ensures continued harmonization of state 

and federal vehicle standards. 

Further discussion of the mid-term evaluation can be found in section III and IV of the 

proposal. 

5. Coordinated Compliance 

The MYs without additional action by the agencies and EPA and NHTSA are not 

proposing any significant modifications to them. In the MYs 2012-2016 final rule, NHTSA and 

EPA established a program that recognizes, and replicates as closely as possible, the compliance 

protocols associated with the existing CAA Tier 2 vehicle emission standards, and with earlier 

model year CAFE standards. The certification, testing, reporting, and associated compliance 

activities established for the GHG program closely track those in previously existing programs 

and are thus familiar to manufacturers. EPA already oversees testing, collects and processes test 

data, and performs calculations to determine compliance with both CAFE and CAA standards. 

Under this coordinated approach, the compliance mechanisms for both programs are consistent 

and non-duplicative. EPA also applies the CAA authorities applicable to its separate in-use 

requirements in this program. 

The compliance approach allows manufacturers to satisfy the GHG program 

requirements in the same general way they comply with previously existing applicable CAA and 

CAFE requirements. Manufacturers will demonstrate compliance on a fleet-average basis at the 

end of each model year, allowing model-level testing to continue throughout the year as is the 

current practice for CAFE determinations. The compliance program design includes a single set 

of manufacturer reporting requirements and relies on a single set of underlying data. This 

approach still allows each agency to assess compliance with its respective program under its 

respective statutory authority. The program also addresses EPA enforcement in cases of 

noncompliance. 
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6. Additional Program Elements 

a. Treatment of Compressed Natural Gas (CNG), Plug-in Hybrid Electric Vehicles 

(PHEVs), and Flexible Fuel Vehicles (FFVs) 

EPA is proposing that CO2 compliance values for plug-in hybrid electric vehicles 

(PHEVs) and bi-fuel compressed natural gas (CNG) vehicles will be based on estimated use of 

the alternative fuels, recognizing that, once a consumer has paid several thousand dollars to be 

able to use a fuel that is considerably cheaper than gasoline, it is very likely that the consumer 

will seek to use the cheaper fuel as much as possible. Accordingly, for CO2 emissions 

compliance, EPA is proposing to use the Society of Automotive Engineers “utility factor” 

methodology (based on vehicle range on the alternative fuel and typical daily travel mileage) to 

determine the assumed percentage of operation on gasoline and percentage of operation on the 

alternative fuel for both PHEVs and bi-fuel CNG vehicles, along with the CO2 emissions test 

values on the alternative fuel and gasoline. 

EPA is proposing to account for E85 use by flexible fueled vehicles (FFVs) as in the 

existing MY 2016 and later program, based on actual usage of E85 which represents a real-world 

reduction attributed to alternative fuels. Unlike PHEV and bi-fuel CNG vehicles, there is not a 

significant cost differential between an FFV and a conventional gasoline vehicle and historically 

consumers have only fueled these vehicles with E85 a very small percentage of the time. 

In the CAFE program for MYs 2017-2019, the fuel economy of dual fuel vehicles will be 

determined in the same manner as specified in the MY -2016 rule, and as defined by EISA. 

Beginning in MY 2020, EISA does not specify how to measure the fuel economy of dual fuel 

vehicles, and EPA is proposing under its EPCA authority to use the “utility factor” methodology 

for PHEV and CNG vehicles described above to determine how to proportion the fuel economy 

when operating on gasoline or diesel fuel and the fuel economy when operating on the 

alternative fuel. For FFVs, EPA is proposing to use the same methodology as it uses for the 

GHG program to determine how to proportion the fuel economy, which would be based on 

actual usage of E85. EPA is proposing to continue to use Petroleum Equivalency Factors and the 

0.15 divisor used in the MY 2012-2016 rule for the alternative fuels, however with no cap on the 

amount of fuel economy increase allowed. This issue is discussed further in Section III.B.10. 

b. Exclusion of Emergency and Police Vehicles 

Under EPCA, manufacturers are allowed to exclude emergency vehicles from their 

CAFE fleet67 and all manufacturers have historically done so. In the MYs 2012-2016 program, 

EPA’s GHG program applies to these vehicles. However, after further consideration of this 

 67 49 U.S.C. 32902(e) 
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issue, EPA is proposing the same type of exclusion provision for these vehicles for MY 2012 and 

later because of the unique features of vehicles designed specifically for law enforcement and 

emergency purposes, which have the effect of raising their GHG emissions and calling into 

question the ability of manufacturers to sufficiently reduce the emissions from these vehicles 

without compromising necessary vehicle features or dropping vehicles from their fleets. 

c. Small Businesses and Small Volume Manufacturers 

EPA is proposing provisions to address two categories of smaller manufacturers. The 

first category is small businesses as defined by the Small Business Administration (SBA). For 

vehicle manufacturers, SBA’s definition of small business is any firm with less than 1,000 

employees. As with the MYs 2012-2016 program, EPA is proposing to continue to exempt small 

businesses from the GHG standards, for any company that meets the SBA’s definition of a small 

business. EPA believes this exemption is appropriate given the unique challenges small 

businesses would face in meeting the GHG standards, and since these businesses make up less 

than 0.1% of total U.S. vehicle sales, and there is no significant impact on emission reductions. 

EPA’s proposal also addresses small volume manufacturers, with U.S. annual sales of 

less than 5,000 vehicles. Under the MYs 2012-2016 program, these small volume 

manufacturers are eligible for an exemption from the CO2 standards. EPA is proposing to bring 

small volume manufacturers into the CO2 program for the first time starting in MY 2017, and 

allow them to petition EPA for alternative standards. 

EPCA provides NHTSA with the authority to exempt from the generally applicable 

CAFE standards manufacturers that produce fewer than 10,000 passenger cars worldwide in the 

model year each of the two years prior to the year in which they seek an exemption.68 If NHTSA 

exempts a manufacturer, it must establish an alternate standard for that manufacturer for that 

model year, at the level that the agency decides is maximum feasible for that manufacturer. The 

exemption and alternative standard apply only if the exempted manufacturer also produces fewer 

than 10,000 passenger cars worldwide in the year for which the exemption was granted. 

Further, the Temporary Lead-time Allowance Alternative Standards (TLAAS) provisions 

included in EPA’s MYs 2012-2016 program for manufacturers with MY 2009 U.S. sales of less 

than 400,000 vehicles ends after MY 2015 for most eligible manufacturers.69 EPA is not 

proposing to extend or otherwise replace the TLAAS provisions for the proposed MYs 2017-

2025 program; however EPA is inviting comment on whether some form of additional flexibility 

is warranted for lower volume, limited line manufacturers, as further discussed in Section III.B.8. 

With the exception of the small businesses and small volume manufacturers discussed above, the 

proposed MYs 2017-2025 standards would apply to all manufacturers. 

 68 49 U.S.C. 32902(d). Implementing regulations may be found in 49 CFR Part 525. 

69 TLAAS ends after MY 2016 for manufacturers with MY 2009 U.S. sales of less than 50,000 vehicles. 
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E. Summary of Costs and Benefits for the Proposed National Program 

This section summarizes the projected costs and benefits of the proposed CAFE and 

GHG emissions standards. These projections helped inform the agencies’ choices among the 

alternatives considered and provide further confirmation that the proposed standards are 

appropriate under their respective statutory authorities. The costs and benefits projected by 

NHTSA to result from these CAFE standards are presented first, followed by those from EPA’s 

analysis of the GHG emissions standards. The agencies recognize that there are uncertainties 

regarding the benefit and cost values presented in this proposal. Some benefits and costs are not 

quantified. The value of other benefits and costs could be too low or too high. 

For several reasons, the estimates for costs and benefits presented by NHTSA and EPA, 

while consistent, are not directly comparable, and thus should not be expected to be identical. 

Most important, NHTSA and EPA’s standards would require slightly different fuel efficiency 

improvements. EPA’s proposed GHG standard is more stringent in part due to its assumptions 

about manufacturers’ use of air conditioning leakage credits, which result from reductions in air 

conditioning-related emissions of HFCs. NHTSA is proposing standards at levels of stringency 

that assume improvements in the efficiency of air conditioning systems, but that do not account 

for reductions in HFCs, which are not related to fuel economy or energy conservation. In 

addition, the CAFE and GHG standards offer somewhat different program flexibilities and 

provisions, and the agencies’ analyses differ in their accounting for these flexibilities (examples 

include the treatment of EVs, dual-fueled vehicles, and civil penalties), primarily because 

NHTSA is statutorily prohibited from considering some flexibilities when establishing CAFE 

standards,70 while EPA is not. These differences contribute to differences in the agencies’ 

respective estimates of costs and benefits resulting from the new standards. Nevertheless, it is 

important to note that NHTSA and EPA have harmonized the programs as much as possible, and 

this proposal to continue the National Program would result in significant cost and other 

advantages for the automobile industry by allowing them to manufacture one fleet of vehicles 

across the U.S., rather than comply with potentially multiple state standards that may occur in the 

absence of the National Program. 

 In summary, the projected costs and benefits presented by NHTSA and EPA are not 

directly comparable, because the levels being proposed by EPA include air conditioning-related 

improvements in HFC reductions, and because of the projection by EPA of complete compliance 

with the proposed GHG standards, whereas NHTSA projects some manufacturers will pay civil 

penalties as part of their compliance strategy, as allowed by EPCA. It should also be expected 

that overall EPA’s estimates of GHG reductions and fuel savings achieved by the proposed GHG 

standards will be slightly higher than those projected by NHTSA only for the CAFE standards 

because of the same reasons described above. For the same reasons, EPA’s estimates of 

 70 See 49 U.S.C. 32902(h). 
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manufacturers’ costs for complying with the proposed passenger car and light truck GHG 

standards are slightly higher than NHTSA’s estimates for complying with the proposed CAFE 

standards. 

1. Summary of Costs and Benefits for the Proposed NHTSA CAFE Standards 

In reading the following section, we note that tables are identified as reflecting “estimated 

required” values and “estimated achieved” values. When establishing standards, EPCA allows 

NHTSA to only consider the fuel economy of dual-fuel vehicles (for example, FFVs and 

PHEVs) when operating on gasoline, and prohibits NHTSA from considering the use of 

dedicated alternative fuel vehicle credits (including for example EVs), credit carry-forward and 

carry-back, and credit transfer and trading. NHTSA’s primary analysis of costs, fuel savings, and 

related benefits from imposing higher CAFE standards does not include them. However, EPCA 

does not prohibit NHTSA from considering the fact that manufacturers may pay civil penalties 

rather than comply with CAFE standards, and NHTSA’s primary analysis accounts for some 

manufacturers’ tendency to do so. The primary analysis is generally identified in tables 

throughout this document by the term “estimated required CAFE levels.” 

To illustrate the effects of the flexibilities and technologies that NHTSA is prohibited 

from including in its primary analysis, NHTSA performed a supplemental analysis of these 

effects on benefits and costs of the proposed CAFE standards that helps to demonstrate the realworld impacts. As an example of one of the effects, including the use of FFV credits reduces 

estimated per-vehicle compliance costs of the program, but does not significantly change the 

projected fuel savings and CO2 reductions, because FFV credits reduce the fuel economy levels 

that manufacturers achieve not only under the proposed standards, but also under the baseline 

MY 2016 CAFE standards. As another example, including the operation of PHEV vehicles on 

both electricity and gasoline, and the expected use of EVs for compliance may raise the fuel 

economy levels that manufacturers achieve under the proposed standards. The supplemental 

analysis is generally identified in tables throughout this document by the term “estimated 

achieved CAFE levels.” 

Thus, NHTSA’s primary analysis shows the estimates the agency considered for purposes 

of establishing new CAFE standards, and its supplemental analysis including manufacturer use 

of flexibilities and advanced technologies currently reflects the agency’s best estimate of the 

potential real-world effects of the proposed CAFE standards. 

Without accounting for the compliance flexibilities and advanced technologies that 

NHTSA is prohibited from considering when determining the maximum feasible level of new 

CAFE standards, since manufacturers’ decisions to use those flexibilities and technologies are 

voluntary, NHTSA estimates that the required fuel economy increases would lead to fuel savings 

totaling 173 billion gallons throughout the lives of vehicles sold in MYs 2017-2025. At a 3 

percent discount rate, the present value of the economic benefits resulting from those fuel 
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savings is $451 billion; at a 7 percent private discount rate, the present value of the economic 

benefits resulting from those fuel savings is $358 billion. 

The agency further estimates that these new CAFE standards would lead to 

corresponding reductions in CO2 emissions totaling 1.8 billion metric tons during the lives of 

vehicles sold in MYs 2017-2025. The present value of the economic benefits from avoiding 

those emissions is $49 billion, based on a global social cost of carbon value of $22 per metric ton 

(in 2010, and growing thereafter).71 It is important to note that NHTSA’s CAFE standards and 

EPA’s GHG standards will both be in effect, and each will lead to increases in average fuel 

economy and CO2 reductions. The two agencies standards together comprise the National 

Program, and this discussion of the costs and benefits of NHTSA’s CAFE standards does not 

change the fact that both the CAFE and GHG standards, jointly, are the source of the benefits 

and costs of the National Program. All costs are in 2009 dollars. 

Table II-10 NHTSA’s Estimated MYs 2017-2025 Costs, Benefits, and Net Benefits 

($Billion) under the CAFE Standards (Estimated Required) 

 3% discount rate 7% discount rate 

Lifetime 

present value 

Annualized 

value 

Lifetime 

present value 

Annualized 

value 

Costs 157 6.3 157 8.5

Benefits 515 31.8 419 36.3

Net benefits 358 25.5 262 27.8

 71 NHTSA also estimated the benefits associated with three more estimates of a one ton GHG reduction in 2009 ($5, 

$36, and $67), which will likewise grow thereafter. See Section II for a more detailed discussion of the social cost 

of carbon. 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

Page 72 of 426

Table II-11 NHTSA’s Estimated Fuel Saved (Billion Gallons and Barrels) and CO2 Emissions Avoided (mmt) under the 

CAFE Standards (Estimated Required) 

 Earlier72 2017 2018 2019 2020 2021 2022 2023 2024 2025 Total 

Passenger Cars 

Fuel (billion gallons) 3 2 5 7 9 11 13 15 17 19 104 

Fuel (billion barrels) 0.09 0.06 0.12 0.17 0.22 0.27 0.31 0.36 0.42 0.46 2.47 CO2 (mmt) 41 26 52 76 100 122 139 158 184 202 1100 

Light Trucks 

Fuel (billion gallons) 0 0 2 5 6 9 10 11 12 13 69 

Fuel (billion barrels) 0.01 0.01 0.05 0.11 0.15 0.21 0.24 0.27 0.29 0.32 1.65 CO2 (mmt) 4 5 22 49 65 93 108 118 129 141 734 

Combined 

Fuel (billion gallons) 4 3 7 12 16 20 23 26 30 33 173 

Fuel (billion barrels) 0.10 0.07 0.16 0.28 0.37 0.48 0.55 0.62 0.71 0.78 4.13 CO2 (mmt) 45 31 74 124 165 215 246 276 313 343 1834 

72 The “Earlier” column shows benefits that NHTSA forecasts manufacturers will implement in model years prior to 2017 that are in response to the proposed 

MY 2017-2025 standards. The CAFE model forecasts that manufactures will implement some technologies, and achieve benefits during vehicle redesigns that occur prior to MY 2017 in order to comply with MY 2017 and later standards in a cost effective manner. 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

Page 73 of 426

 Considering manufacturers’ ability to employ compliance flexibilities and advanced 

technologies for meeting the standards, NHTSA estimates the following for fuel savings and 

avoided CO2 emissions, assuming FFV credits would be used toward both the baseline and final 

standards: 

 
Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to

further policy review.

Page 74 of 426

Table II-12 NHTSA’s Estimated Fuel Saved (Billion Gallons and Barrels) and CO2 Emissions Avoided (mmt) under the 

CAFE Standards (Estimated Achieved) 

 Earlier 2017 2018 2019 2020 2021 2022 2023 2024 2025 Total 

Passenger Cars 

Fuel (billion gallons) 4 2 4 6 9 10 12 14 17 20 98 

Fuel (billion barrels) 0.09 0.05 0.10 0.15 0.21 0.25 0.29 0.34 0.40 0.47 2.34 CO2 (mmt) 41 23 43 69 93 111 128 151 177 204 1040 

Light Trucks 

Fuel (billion gallons) 0 1 2 4 6 8 9 10 11 13 65 

Fuel (billion barrels) 0.01 0.02 0.05 0.10 0.14 0.19 0.22 0.24 0.27 0.30 1.54 CO2 (mmt) 4 7 22 47 64 89 100 109 123 138 702 

Combined 

Fuel (billion gallons) 4 3 6 11 14 19 21 24 28 32 163 

Fuel (billion barrels) 0.10 0.07 0.14 0.25 0.34 0.44 0.50 0.58 0.67 0.77 3.88 CO2 (mmt) 45 31 65 116 157 200 227 260 300 341 1742 
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NHTSA estimates that the fuel economy increases resulting from the proposed standards would produce other benefits both to drivers 

(e.g., reduced time spent refueling) and to the U.S. as a whole (e.g., reductions in the costs of petroleum imports beyond the direct 

savings from reduced oil purchases),73 as well as some disbenefits (e.g., increased traffic congestion) caused by drivers’ tendency to 

travel more when the cost of driving declines (as it does when fuel economy increases). NHTSA has estimated the total monetary

value to society of these benefits and disbenefits, and estimates that the proposed standards will produce significant net benefits to 

society. Using a 3 percent discount rate, NHTSA estimates that the present value of these benefits would total more than $515 billion over the lives of the vehicles sold during MYs 2017-2025; using a 7 percent discount rate, more than $419 billion. More discussion 

regarding monetized benefits can be found in Section IV of this notice and in NHTSA’s PRIA. Note that the benefit calculation in the following tables includes the benefits of reducing CO2 emissions,74 but not the benefits of reducing other GHG emissions. 

73 We note, of course, that reducing the amount of fuel purchased also reduces tax revenue for the Federal and state/local governments. NHTSA discusses this 

issue in more detail in Chapter VIII of the PRIA. 

74 CO2 benefits for purposes of these tables are calculated using the $22/ton SCC values. Note that the net present value of reduced GHG emissions is calculated 

differently from other benefits. The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to calculate net present value of SCC for internal consistency. 
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Table II-13 NHTSA’s Discounted Benefits ($Billion) under the CAFE Standards Using a 3 and 7 Percent Discount Rate 

(Estimated Required) 

 Earlier 2017 2018 2019 2020 2021 2022 2023 2024 2025 Total 

3% discount rate 

Passenger cars 11 7 14 21 27 34 39 45 53 59 310 

Light trucks 1 1 6 13 18 26 30 33 37 40 206 

Combined 12 8 20 34 45 60 69 78 90 100 515 

7% discount rate 

Passenger cars 9 6 12 17 22 28 32 37 44 49 254 

Light trucks 1 1 5 10 14 21 24 27 30 33 165 

Combined 9 7 16 27 37 49 56 64 73 81 419 

 Considering manufacturers’ ability to employ compliance flexibilities and advanced technologies for meeting the standards, NHTSA estimates the present value of these benefits would be reduced as follows: 

Table II-14 NHTSA’s Discounted Benefits ($Billion) under the CAFE Standards Using a 3 and 7 Percent Discount Rate 

(Estimated Achieved) 

 Earlier 2017 2018 2019 2020 2021 2022 2023 2024 2025 Total 

3% discount rate 

Passenger cars 10 6 12 19 26 31 36 43 51 60 293 

Light trucks 1 2 6 12 17 24 28 30 35 39 195 

Combined 11 8 17 31 43 55 63 74 86 99 488 

7% discount rate 

Passenger cars 8 5 9 15 21 25 29 35 42 49 240 

Light trucks 1 2 5 10 14 20 22 24 28 32 157 

Combined 9 7 14 25 35 45 52 60 70 81 397 
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 NHTSA attributes most of these benefits (about $451 billion at a 3 percent discount rate, or about $358 billion at a 7 percentdiscount rate, excluding consideration of compliance flexibilities and advanced technologies for meeting the standards) to reductions in fuel consumption, valuing fuel (for societal purposes) at the future pre-tax prices projected in the Energy Information Administration’s (EIA) reference case forecast from the Annual Energy Outlook (AEO) . NHTSA’s PRIA accompanying this proposal presents a detailed analysis of specific benefits of the rule. 

Table II-15 Summary of NHTSA’s Fuel Savings and CO2 Emissions Reduction under the CAFE Standards (Estimated 

Required) 

 Amount 3% discount rate 7% discount rate 

Fuel savings 173 451 358CO2 emissions 

reductions 1,834 49 49

 NHTSA estimates that the increases in technology application necessary to achieve the projected improvements in fuel economy will entail considerable monetary outlays. The agency estimates that the incremental costs for achieving the proposed CAFE 

standards – that is, outlays by vehicle manufacturers over and above those required to comply with the MY 2016 CAFE standards –will total about $157 billion (i.e., during MYs 2017-2025). 

Table II-16 NHTSA’s Incremental Technology Cost Estimates Employed in the CAFE Model for Certain Technologies 

Subcom

p. Car 

Compac

t Car 

Midsize 

Car 

Large 

Car 

Perform

. 

Subcom

Perform

. 

Compac

Perform

. 

Midsize 

Perform

. Large 

Car 

Minivan 

LT 

Small 

LT 

Midsize 

LT 

Large 

LT 
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p. Car t Car Car 

VEHICLE TECHNOLOGY ICM COSTS PER VEHICLE ($) 

Nominal baseline engine (for cost purposes) Inline 4 Inline 4 Inline 4 V6 Inline 4 V6 V6 V8 V6 Inline 4 V6 V8Low friction lubricants (level 1) 16 16 16 24 16 24 24 32 24 16 24 32Engine friction reduction (level 1) 60 60 60 90 60 90 90 120 90 60 90 120VVT—Dual cam phasing (DCP) 44 44 44 88 44 88 88 88 88 44 88 88Discrete variable valve lift (DVVL) on DOHC 160 160 160 240 160 240 240 320 240 160 240 320Cylinder deactivation on OHV 204 204 204 204 204 204 204 204 204 204 204 204Stoichiometric gasoline direct injection 268 268 268 402 268 402 5402 536 402 268 402 536Turbocharging and downsizing (level 1) 490 490 490 20 490 20 20 639 20 490 20 639Turbocharging and downsizing (level 2) 26 26 26 260 26 260 260 439 260 26 260 439Cooled exhaust gas recirculation 303 303 303 303 303 303 303 303 303 303 303 303
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(EGR) – (level 1) Cooled exhaust gas recirculation (EGR) – (level 2) 520 520 520 (296) 520 (296) (296) (296) (296) 520 (296) (296)Advanced Diesel 873 873 873 844 873 844 844 1693 844 873 844 16936-speed auto. trans. with improved internals (39) (39) (39) (39) (39) (39) (39) (39) (39) (39) (39) (39)6-speed DCT (107) (107) (107) (73) (107) (73) (73) (73) n/a (107) n/a n/aHigh Efficiency Gearbox 248 248 248 248 248 248 248 248 248 248 248 248Shift Optimizer 2 2 2 2 2 2 2 2 2 2 2 2Electric power steering 108 108 108 108 108 108 108 108 108 108 108 10812V microhybrid 322 322 382 411 322 322 382 411 410 363 421 476Strong Hybrid (level 2) 3526 3526 3705 4261 3526 3526 3705 4261 4677 4057 4332 4464Plug-in Hybrid 10,686 10,686 11,864 15,514 10,686 10,686 11,864 15,514 n/a 14,055 n/a n/aElectric Vehicle (Early Adopter) 3,001 3,001 3.719 3,998 3,001 3,001 3.719 3,998 n/a 4,580 n/a n/aLow Rolling Resistance Tires (level 1) 7 7 7 7 7 7 7 7 7 7 7 7Low Rolling Resistance Tires (level 2) 72 72 72 72 72 72 72 72 72 72 72 72Aero Drag 48 48 48 48 48 48 48 48 48 48 48 48
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Reduction (level 1) Aero Drag Reduction (level 2) 162 162 162 162 162 162 162 162 162 162 162 162
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a. How does NHTSA use these assumptions in its modeling analysis? 

NHTSA relies on several inputs and data files to conduct the compliance analysis using 

the CAFE model, as discussed further below and in Chapter 5 of the PRIA. For the purposes of 

applying technologies, the CAFE model primarily uses three data files, one that contains data on 

the vehicles expected to be manufactured in the model years covered by the rulemaking and 

identifies the appropriate stage within the vehicle’s life-cycle for the technology to be applied, 

one that contains data/parameters regarding the available technologies the model can apply, and 

one that contains economic assumption inputs for calculating the costs and benefits of the 

standards. The inputs for the first two data files are discussed below. 

As discussed above, the CAFE model begins with an initial state of the domestic vehicle 

market, which in this case is the market for passenger cars and light trucks to be sold during the 

period covered by the proposed standards. The vehicle market is defined on a year-by-year, 

model-by-model, engine-by-engine, and transmission-by-transmission basis, such that each 

defined vehicle model refers to a separately defined engine and a separately defined 

transmission. Comparatively, EPA’s OMEGA model defines the vehicle market using 

representative vehicles at the vehicle platform level, which are binned into 5 year timeframes 

instead of year-by-year. 

For the current standards, which cover MYs 2017–2025, the light-duty vehicle (passenger 

car and light truck) market forecast was developed jointly by NHTSA and EPA staff using MY 

2008 CAFE compliance data. The MY 2008 compliance data includes about 1,100 vehicle 

models, about 400 specific engines, and about 200 specific transmissions, which is a somewhat 

lower level of detail in the representation of the vehicle market than that used by NHTSA in 

prior CAFE analyses—previous analyses would count a vehicle as “new” in any year when 

significant technology differences are made, such as at a redesign.75 However, within the 

limitations of information that can be made available to the public, it provides the foundation for 

a reasonable analysis of manufacturer-specific costs and the analysis of attribute-based CAFE 

standards, and is much greater than the level of detail used by many other models and analyses 

relevant to light-duty vehicle fuel economy.76 

In addition to containing data about each vehicle, engine, and transmission, this file 

contains information for each technology under consideration as it pertains to the specific vehicle 

 75 The market file for the MY 2011 final rule, which included data for MYs 2011–2015, had 5500 vehicles, about 5 

times what we are using in this analysis of the MY 2008 certification data.

76 Because CAFE standards apply to the average performance of each manufacturer’s fleet of cars and light trucks, 

the impact of potential standards on individual manufacturers cannot be credibly estimated without analysis of the 

fleets that manufacturers can be expected to produce in the future. Furthermore, because required CAFE levels 

under an attribute-based CAFE standard depend on manufacturers’ fleet composition, the stringency of an attributebased standard cannot be predicted without performing analysis at this level of detail. 
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(whether the vehicle is equipped with it or not), the estimated model year the vehicle is 

undergoing a refresh or redesign, and information about the vehicle’s subclass for purposes of 

technology application. In essence, the model considers whether it is appropriate to apply a 

technology to a vehicle. 

Is a vehicle already equipped, or can it not be equipped, with a particular technology? 

The market forecast file provides NHTSA the ability to identify, on a technology-bytechnology basis, which technologies may already be present (manufactured) on a particular 

vehicle, engine, or transmission, or which technologies are not applicable (due to technical 

considerations or engineering constraints) to a particular vehicle, engine, or transmission. These 

identifications are made on a model-by-model, engine-by-engine, and transmission-bytransmission basis. For example, if the market forecast file indicates that Manufacturer X’s 

Vehicle Y is manufactured with Technology Z, then for this vehicle Technology Z will be shown 

as used. Additionally, NHTSA has determined that some technologies are only suitable or 

unsuitable when certain vehicle, engine, or transmission conditions exist. For example, 

secondary axle disconnect is only suitable for 4WD vehicles and cylinder deactivation is 

unsuitable for any engine with fewer than 6 cylinders. Similarly, comments received to the 2008 

NPRM indicated that cylinder deactivation could not likely be applied to vehicles equipped with 

manual transmissions during the rulemaking timeframe, due primarily to the cylinder 

deactivation system not being able to anticipate gear shifts. The CAFE model employs 

“engineering constraints” to address issues like these, which are a programmatic method of 

controlling technology application that is independent of other constraints. Thus, the market 

forecast file would indicate that the technology in question should not be applied to the particular 

vehicle/engine/transmission (i.e., is unavailable). Since multiple vehicle models may be 

equipped with an engine or transmission, this may affect multiple models. In using this aspect of 

the market forecast file, NHTSA ensures the CAFE model only applies technologies in an 

appropriate manner, since before any application of a technology can occur, the model checks 

the market forecast to see if it is either already present or unavailable. NHTSA seeks comment 

on the continued appropriateness of the engineering constraints used by the model, and 

specifically whether many of the technical constraints will be resolved (and therefore the 

engineering constraints should be changed) given the increased focus of engineering resources 

that will be working to solve these technical challenges. 

Whether a vehicle can be equipped with a particular technology could also theoretically 

depend on certain technical considerations related to incorporating the technology into particular 

vehicles. For example, GM commented on the MY 2012-2016 NPRM that there are certain 

issues in implementing turbocharging and downsizing technologies on full-size trucks, like 

concerns related to engine knock, drivability, control of boost pressure, packaging complexity, 

enhanced cooling for vehicles that are designed for towing or hauling, and noise, vibration and 

harshness. NHTSA stated in response that we believed that such technical considerations are 

well recognized within the industry and it is standard industry practice to address each during the 
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design and development phases of applying turbocharging and downsizing technologies. The 

cost and effectiveness estimates used in the final rule for MYs 2012-2016, as well as the cost and 

effectiveness estimates employed in this NPRM, are based on analysis that assumes each of these 

factors is addressed prior to production implementation of the technologies. NHTSA continues 

to believe that these issues are accounted for by industry, but we seek comment on whether the 

engineering constraints should be used to address concerns like these (and if so, how), or 

alternatively, whether some of the things that the agency currently treats as engineering 

constraints should be (or actually are) accounted for in the cost and effectiveness estimates 

through assumptions like those described above, and whether the agency might be doubleconstraining the application of technology. 

Is a vehicle being redesigned or refreshed? 

Manufacturers typically plan vehicle changes to coincide with certain stages of a 

vehicle’s life cycle that are appropriate for the change, or in this case the technology being 

applied. In the automobile industry there are two terms that describe when technology changes 

to vehicles occur: Redesign and refresh (i.e., freshening). Vehicle redesign usually refers to 

significant changes to a vehicle’s appearance, shape, dimensions, and powertrain. Redesign is 

traditionally associated with the introduction of “new” vehicles into the market, often 

characterized as the “next generation” of a vehicle, or a new platform. Vehicle refresh usually 

refers to less extensive vehicle modifications, such as minor changes to a vehicle’s appearance, a 

moderate upgrade to a powertrain system, or small changes to the vehicle’s feature or safety 

equipment content. Refresh is traditionally associated with mid-cycle cosmetic changes to a 

vehicle, within its current generation, to make it appear “fresh.” Vehicle refresh generally occurs 

no earlier than two years after a vehicle redesign, or at least two years before a scheduled 

redesign. To be clear, this is a general description of how manufacturers manage their product 

lines and refresh and redesign cycles but in some cases the timeframes could be shorter and 

others longer depending on market factors, regulations, etc. For the majority of technologies 

discussed today, manufacturers will only be able to apply them at a refresh or redesign, because 

their application would be significant enough to involve some level of engineering, testing, and 

calibration work.77

Some technologies (e.g., those that require significant revision) are nearly always applied 

only when the vehicle is expected to be redesigned, like turbocharging and engine downsizing, or 

conversion to diesel or hybridization. Other technologies, like cylinder deactivation, electric 

power steering, and low rolling resistance tires can be applied either when the vehicle is expected 

 77 For example, applying material substitution through weight reduction, or even something as simple as low rollingresistance tires, to a vehicle will likely require some level of validation and testing to ensure that the vehicle may 

continue to be certified as compliant with NHTSA’s Federal Motor Vehicle Safety Standards (FMVSS). Weight 

reduction might affect a vehicle’s crashworthiness; low rolling-resistance tires might change a vehicle’s braking 

characteristics or how it performs in crash avoidance tests. 
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to be refreshed or when it is expected to be redesigned, while low friction lubricants, can be 

applied at any time, regardless of whether a refresh or redesign event is conducted. Accordingly, 

the model will only apply a technology at the particular point deemed suitable. These constraints 

are intended to produce results consistent with how we assume manufacturers will apply 

technologies in the future based on how they have historically implemented new technologies. 

For each technology under consideration, NHTSA specifies whether it can be applied any time, 

at refresh/redesign, or only at redesign. The data forms another input to the CAFE model. 

NHTSA develops redesign and refresh schedules for each of a manufacturer’s vehicles included 

in the analysis, essentially based on the last known redesign year for each vehicle and projected 

forward using a 5 to 8-year redesign and a 2–3 year refresh cycle, and this data is also stored in 

the market forecast file. While most vehicles are projected to follow a 5-year redesign a few of 

the niche market or small-volume manufacturer vehicles (i.e. luxury and performance vehicles) 

and large trucks are assumed to have 6- to 8-year redesigns based on historic redesign schedules 

and the agency’s understanding of manufacturers’ intentions moving forward. This approach is 

used because of the nature of the current baseline, which as a single year of data does not contain 

its own refresh and redesign cycle cues for future model years, and to ensure the complete 

transparency of the agency’s analysis. We note that this approach is different from what NHTSA 

has employed previously for determining redesign and refresh schedules, where NHTSA 

included the redesign and refresh dates in the market forecast file as provided by manufacturers 

in confidential product plans. Vehicle redesign/refresh assumptions are discussed in more detail 

in Chapter 5 of the PRIA and in Chapter 3 of the TSD. 

NHTSA has previously received comments stating that manufacturers do not necessarily 

adhere to strict five-year redesign cycles, and may add significant technologies by redesigning 

vehicles at more frequent intervals, albeit at higher costs. Conversely, other comments received 

stated that as compared to full-line manufacturers, small-volume manufacturers in fact may have 

7 to 8-year redesign cycles.78 The agency believes that manufacturers can and will accomplish 

 78 In the MY 2011 final rule, NHTSA noted that the CAR report submitted by the Alliance, prepared by the Center 

for Automotive Research and EDF, stated that “For a given vehicle line, the time from conception to first production 

may span two and one-half to five years,” but that “The time from first production (“Job#1”) to the last vehicle off 

the line (‘‘Balance Out’’) may span from four to five years to eight to ten years or more, depending on the dynamics 

of the market segment.” The CAR report then stated that “At the point of final production of the current vehicle 

line, a new model with the same badge and similar characteristics may be ready to take its place, continuing the 

cycle, or the old model may be dropped in favor of a different product.” See NHTSA–2008–0089–0170.1, 

Attachment 16, at 8 (393 of pdf). NHTSA explained that this description, which states that a vehicle model will be 

redesigned or dropped after 4–10 years, was consistent with other characterizations of the redesign and freshening 

process, and supported the 5-year redesign and 2–3 year refresh cycle assumptions used in the MY 2011 final rule. 

See id., at 9 (394 of pdf). Given that the situation faced by the auto industry today is not so wholly different from 

that in March 2009, when the MY 2011 final rule was published, and given that the commenters did not present 

information to suggest that these assumptions are unreasonable (but rather simply that different manufacturers may 

redesign their vehicles more or less frequently, as the range of cycles above indicates), NHTSA believes that the 

assumptions remain reasonable for purposes of this final rule analysis. See also “Car Wars 2009–2012, The U.S. 

automotive product pipeline,” John Murphy, Research Analyst, Merrill Lynch research paper, May 14, 2008 and 

“Car Wars 2010–2013, The U.S. automotive product pipeline,” John Murphy, Research Analyst, Bank of 
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much improvement in fuel economy and GHG reductions while applying technology consistent 

with their redesign schedules. 

Once the model indicates that a technology should be applied to a vehicle, the model 

must evaluate which technology should be applied. This will depend on the vehicle subclass to 

which the vehicle is assigned; what technologies have already been applied to the vehicle (i.e., 

where in the “decision tree” the vehicle is); when the technology is first available (i.e., year of 

availability); whether the technology is still available (i.e., “phase-in caps”); and the costs and 

effectiveness of the technologies being considered. Technology costs may be reduced, in turn, 

by learning effects and short- vs. long-term ICMs, while technology effectiveness may be 

increased or reduced by synergistic effects between technologies. In the technology input file, 

NHTSA has developed a separate set of technology data variables for each of the twelve vehicle 

subclasses. Each set of variables is referred to as an “input sheet,” so for example, the 

subcompact passenger car input sheet holds the technology data that is appropriate for the 

subcompact subclass. Each input sheet contains a list of technologies available for members of 

the particular vehicle subclass. The following items are provided for each technology: The name 

of the technology, its abbreviation, the decision tree with which it is associated, the (first) year in 

which it is available, the year-by-year cost estimates and effectiveness (fuel consumption 

reduction) estimates, its applicability and the consumer value loss. The phase-in values and the 

potential stranded capital costs are common for all vehicle subclasses and are thus listed in a 

separate input sheet that is referenced for all vehicle subclasses. 

To which vehicle subclass is the vehicle assigned? 

As part of its consideration of technological feasibility, the agency evaluates whether 

each technology could be implemented on all types and sizes of vehicles, and whether some 

differentiation is necessary in applying certain technologies to certain types and sizes of vehicles, 

and with respect to the cost incurred and fuel consumption and CO2 emissions reduction 

achieved when doing so. The 2010 NAS Report differentiated technology application using 

eight vehicle “classes” (4 car classes and 4 truck classes).79NAS’s purpose in separating vehicles 

into these classes was to create groups of “like” vehicles, i.e., vehicles similar in size, powertrain 

configuration, weight, and consumer use, and for which similar technologies are applicable. 

NAS also used these vehicle classes along with powertrain configurations (e.g..4 cylinder, 6 

cylinder or 8 cylinder engines) to determine unique cost and effectiveness estimates for each 

class of vehicles. 

 America/Merrill Lynch research paper, July 15, 2009. Available at 

http://www.autonews.com/assets/PDF/CA66116716.PDF (last accessed October 11, 2011). 79 The NAS classes included two-seater convertibles and coupes; small cars; intermediate and large cars; highperformance sedans; unit-body standard trucks; unit-body high-performance trucks; body-on-frame small and 

midsize trucks; and body 
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NHTSA similarly differentiates vehicles by “subclass” for the purpose of applying 

technologies to “like” vehicles and assessing their incremental costs and effectiveness. NHTSA 

assigns each vehicle manufactured in the rulemaking period to one of 12 subclasses: for 

passenger cars, Subcompact, Subcompact Performance, Compact, Compact Performance, 

Midsize, Midsize Performance, Large, and Large Performance; and for light trucks, Small 

SUV/Pickup/Van, Midsize SUV/Pickup/Van, Large SUV/Pickup/Van, and Minivan. The 

agency seeks comment on the appropriateness of these 12 subclasses for the MYs 2017-2025 

timeframe. The agency is also seeking comment on the continued appropriateness of 

maintaining separate “performance” vehicle classes or if as fuel economy stringency increases 

the market for performance vehicles will decrease. 

For this NPRM, NHTSA divides the vehicle fleet into subclasses based on model inputs, 

and applies subclass-specific estimates, also from model inputs, of the applicability, cost, and 

effectiveness of each fuel-saving technology. The model’s estimates of the cost to improve the 

fuel economy of each vehicle model thus depend upon the subclass to which the vehicle model is 

assigned. Each vehicle’s subclass is stored in the market forecast file. When conducting a 

compliance analysis, if the CAFE model seeks to apply technology to a particular vehicle, it 

checks the market forecast to see if the technology is available and if the refresh/redesign criteria 

are met. If these conditions are satisfied, the model determines the vehicle’s subclass from the 

market data file, which it then uses to reference another input called the technology input file. 

NHTSA reviewed its methodology for dividing vehicles into subclasses for purposes of 

technology application that it used in the MY 2011 final rule and for the MYs 2012-2016 

rulemaking, and concluded that the same methodology would be appropriate for this NPRM for 

MYs 2017–2025. Vehicle subclasses are discussed in more detail in Chapter 5 of the PRIA and 

in Chapter 3 of the TSD. 

For the reader’s reference, the subclasses and example vehicles from the market forecast 

file are provided in the tables below. 

Passenger Car Subclasses Example (MY 2008) Vehicles 

Class Example vehicles 

Subcompact Chevy Aveo, Hyundai Accent 

Subcompact performance Mazda MX-5, BMW Z4 

Compact Chevy Cobalt, Nissan Sentra and Altima 

Compact performance Audi S4, Mazda RX-8 

Mid-size Chevy Impala, Toyota Camry, Honda Accord, Hyundai Azera 

Mid-size performance Chevy Corvette, Ford Mustang (V8), Nissan G37 Coupe 

Large Audi A8, Cadillac CTS and DTS 

Large performance Bentley Arnage, Daimler CL600 

Light Truck Subclasses Example (MY 2008) Vehicles 

Class Example vehicles 
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Minivans Dodge Caravan, Toyota Sienna 

Small SUV/Pickup/Van Ford Escape and Ranger, Nissan Rogue 

Mid-size SUV/Pickup/Van Chevy Colorado, Jeep Wrangler, Toyota Tacoma 

Large SUV/Pickup/Van Chevy Silverado, Ford E-Series, Toyota Sequoia 

What Earlier 2017 2018 2019 2020 2021 2022 2023 2024 2025 Total 

Passenger 

cars 4 2 5 7 9 12 14 17 22 23 113 

Light trucks 0 0 1 2 4 5 6 7 8 9 44 

Combined 4 3 6 9 13 17 20 24 30 32 157 

 However, NHTSA estimates that manufacturers employing compliance flexibilities and 

advanced technologies to meet the standards could significantly reduce these outlays: 

technologies have already been applied to the vehicle (i.e., where in the “decision 

trees” is it)? 

NHTSA’s methodology for technology analysis evaluates the application of individual 

technologies and their incremental costs and effectiveness. Individual technologies are assessed 

relative to the prior technology state, which means that it is crucial to understand what 

technologies are already present on a vehicle in order to determine correct incremental cost and 

effectiveness values. The benefit of the incremental approach is transparency in accounting, 

insofar as when individual technologies are added incrementally to individual vehicles, it is clear 

and easy to determine how costs and effectiveness add up as technology levels increase and 

explicitly accounting for any synergies that exist between technologies which are already present 

on the vehicle and new technologies being applied. 

To keep track of incremental costs and effectiveness and to know which technology to 

apply and in which order, the CAFE model’s architecture uses a logical sequence, which 

NHTSA refers to as “decision trees,” for applying fuel economy-improving technologies to 

individual vehicles. For purposes of this proposal, NHTSA reviewed the MYs 2012-2016 final 

rule’s technology sequencing architecture, which was based on the MY 2011 final rule’s 

decision trees that were jointly developed by NHTSA and Ricardo, and, as appropriate, updated 

the decision trees to include new technologies that have been defined for the MYs 2017-2025 

timeframe. 

In general, and as described in great detail in Chapter 5 of the current PRIA,80 each 

technology is assigned to one of the five following categories based on the system it affects or 

impacts: engine, transmission, electrification/accessory, hybrid or vehicle. Each of these 

categories has its own decision tree that the CAFE model uses to apply technologies sequentially 

 80 Additional details about technologies are categorized can be found in the MY 2011 final rule. 
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during the compliance analysis. The decision trees were designed and configured to allow the 

CAFE model to apply technologies in a cost-effective, logical order that also considers ease of 

implementation. For example, software or control logic changes are implemented before 

replacing a component or system with a completely redesigned one, which is typically a much 

more expensive and integration intensive option. In some cases, and as appropriate, the model 

may combine the sequential technologies shown on a decision tree and apply them 

simultaneously, effectively developing dynamic technology packages on an as-needed basis. For 

example, if compliance demands indicate, the model may elect to apply LUB, EFR, and ICP on a 

dual overhead cam engine, if they are not already present, in one single step. An example 

simplified decision tree for engine technologies is provided below; the other simplified decision 

trees may be found in Chapter 5 of the PRIA. Expanded decision trees are available in the 

docket for this NPRM. 
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Simplified Engine Decision Tree Used in the CAFE Model 
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Each technology within the decision trees has an incremental cost and an incremental 

effectiveness estimate associated with it, and estimates are specific to a particular vehicle 

subclass (see the tables in Chapter 5 of the PRIA). Each technology’s incremental estimate takes 

into account its position in the decision tree path. If a technology is located further down the 

decision tree, the estimates for the costs and effectiveness values attributed to that technology are 

influenced by the incremental estimates of costs and effectiveness values for prior technology 

applications. In essence, this approach accounts for “in-path” effectiveness synergies, as well as 

cost effects that occur between the technologies in the same path. When comparing cost and 

effectiveness estimates from various sources and those provided by commenters in this and the 

previous CAFE rulemakings, it is important that the estimates evaluated are analyzed in the 

proper context, especially as concerns their likely position in the decision trees and other 

technologies that may be present or missing. Not all estimates available in the public domain or 

that have been (or will be) offered for the agencies’ consideration can be evaluated in an “applesto-apples” comparison with those used by the CAFE model, since in some cases the order of 

application, or included technology content, is inconsistent with that assumed in the decision 

tree. 

The MY 2011 final rule discussed in detail the revisions and improvements made to the 

CAFE model and decision trees during that rulemaking process, including the improved handling 

and accuracy of valve train technology application and the development and implementation of a 

method for accounting path-dependent correction factors in order to ensure that technologies are 

evaluated within the proper context. The reader should consult the MY 2011 final rule 

documents for further information on these modeling techniques, all of which continued to be 

utilized in developing this final rule.81 To the extent that the decision trees have changed for 

purposes of the MYs 2012-2016 final rule and this NPRM, it was due not to revisions in the 

order of technology application, but rather to redefinitions of technologies or addition or 

subtraction of technologies. 

Is the next technology available in this model year? 

Some of technologies considered are available on vehicles today, and thus will be 

available for application (albeit in varying degrees) in the model starting in MY 2017. Other 

technologies, however, will not become available for purposes of NHTSA’s analysis until later 

in the rulemaking time frame. When the model is considering whether to add a technology to a 

vehicle, it checks its year of availability—if the technology is available, it may be added; if it is 

not available, the model will consider whether to switch to a different decision tree to look for 

another technology, or will skip to the next vehicle in a manufacturer’s fleet. The year of 

availability for each technology is provided above in Table IV-4. 

 81 See, e.g., 74 FR 14238–46 (Mar. 30, 2009) for a full discussion of the decision trees in NHTSA’s MY 2011 final 

rule, and Docket No. NHTSA–2009–0062–0003.1 for an expanded decision tree used in that rulemaking. 
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The agency has received comments previously stating that if a technology is currently 

available or available prior to the rulemaking timeframe that it should be immediately made 

available in the model. In response, as discussed above, technology ‘‘availability’’ is not 

determined based simply on whether the technology exists, but depends also on whether the 

technology has achieved a level of technical viability that makes it appropriate for widespread 

application. This depends in turn on component supplier constraints, capital investment and 

engineering constraints, and manufacturer product cycles, among other things. Moreover, even if 

a technology is available for application, it may not be available for every vehicle. Some 

technologies may have considerable fuel economy benefits, but cannot be applied to some 

vehicles due to technological constraints—for example, cylinder deactivation cannot be applied 

to vehicles with current 4-cylinder engines (because not enough cylinders are present to 

deactivate some and continue moving the vehicle) or on vehicles with manual transmissions 

within the rulemaking timeframe. The agencies have provided for increases over time to reach 

the mpg level of the MY 2025 standards precisely because of these types of constraints, because 

they have a real effect on how quickly manufacturers can apply technology to vehicles in their 

fleets. NHTSA seeks comment on the appropriateness of the assumed years of availability. 

Has the technology reached the phase-in cap for this model year? 

Besides the refresh/redesign cycles used in the CAFE model, which constrain the rate of 

technology application at the vehicle level so as to ensure a period of stability following any 

modeled technology applications, the other constraint on technology application employed in 

NHTSA’s analysis is “phase-in caps.” Unlike vehicle-level cycle settings, phase-in caps 

constrain technology application at the vehicle manufacturer level.82 They are intended to reflect 

a manufacturer’s overall resource capacity available for implementing new technologies (such as 

engineering and development personnel and financial resources), thereby ensuring that resource 

capacity is accounted for in the modeling process. At a high level, phase-in caps and 

refresh/redesign cycles work in conjunction with one another to avoid the modeling process outpacing an OEM’s limited pool of available resources during the rulemaking time frame and the 

years leading up to the rulemaking time frame, especially in years where many models may be 

scheduled for refresh or redesign. Even though this rulemaking is being proposed 5 years before 

it takes effect, OEM’s will still be utilizing their limited resources to meet the MYs 2012-2016 

CAFE standards. This helps to ensure technological feasibility and economic practicability in 

determining the stringency of the standards. 

 82 While phase-in caps are expressed as specific percentages of a manufacturer’s fleet to which a technology may be 

applied in a given model year, phase-in caps cannot always be applied as precise limits, and the CAFE model in fact 

allows “override” of a cap in certain circumstances. When only a small portion of a phase-in cap limit remains, or 

when the cap is set to a very low value, or when a manufacturer has a very limited product line, the cap might 

prevent the technology from being applied at all since any application would cause the cap to be exceeded. 

Therefore, the CAFE model evaluates and enforces each phase-in cap constraint after it has been exceeded by the 

application of the technology (as opposed to evaluating it before application), which can result in the described 

overriding of the cap. 
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NHTSA has been developing the concept of phase-in caps for purposes of the agency’s 

modeling analysis over the course of the last several CAFE rulemakings, as discussed in greater 

detail in the MY 2011 final rule,83 in the MY 2012-2016 final rule and in Chapter 5 of the PRIA 

and Chapter 3 of the Joint TSD. The MYs 2012-2016 final rule like the MY 2011 final rule 

employed non-linear phase-in caps (that is, caps that varied from year to year) that were designed 

to respond to previously received comments on technology deployment. 

For purposes of this NPRM for MYs 2017–2025, as in the MY 2011 and MYs 2012-2016 

final rules, NHTSA combines phase-in caps for some groups of similar technologies, such as 

valve phasing technologies that are applicable to different forms of engine design (SOHC, 

DOHC, OHV), since they are very similar from an engineering and implementation standpoint. 

When the phase-in caps for two technologies are combined, the maximum total application of 

either or both to any manufacturer’s fleet is limited to the value of the cap.84

In developing phase-in cap values for purposes of this NPRM, NHTSA reviewed the 

MYs 2012-2016 final rule’s phase-in caps, which for the majority of technologies were set to 

reach 85 or 100 percent by MY 2016, although more advanced technologies like diesels and 

strong hybrids reach only 15 percent by MY 2016. The phase-in caps used in the MYs 2012-

2016 final were developed to harmonize with EPA’s proposal and consider the fact that 

manufacturers, as part of the information shared during the discussions that occurred during 

summer 2011, appeared to be anticipating higher technology application rates than assumed in 

prior rules. NHTSA determined that these phase-in caps for MY 2016 were still reasonable and 

thus used those caps as the starting point for the MYs 2017-2025 phase-in caps. For many of the 

carryover technologies this means that for MYs 2017-2025 the phase-in caps are assumed to be 

100 percent. NHTSA along with EPA used confidential OEM submissions, trade press articles, 

company publications and press releases to estimate the phase-in caps for the newly defined 

technologies that will be entering the market just before or during the MYs 2017-2025 time 

frame. For example, advanced cooled EGR engines have a phase-in cap of 3 percent per year 

through MY 2021 and then 10 percent per year through 2025. The agency seeks comment on the 

appropriateness of both the carryover phase-in caps and the newly defined ones proposed in this 

NPRM. 

Is the technology less expensive due to learning effects? 

In the past two rulemakings NHTSA has explicitly accounted for the cost reductions a 

manufacturer might realize through learning achieved from experience in actually applying a 

technology. These cost reductions, due to learning effects, were taken into account through two 

kinds of mutually exclusive learning, “volume-based” and “time-based.” NHTSA and EPA 

 

84 See 74 FR at 14270 (Mar. 30, 2009) for further discussion and examples. 
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included a detailed description of the learning effect in the MYs 2012-2016 final rule and the 

more recent heavy-duty rule.85 

Most studies of the effect of experience or learning on production costs appear to assume 

that cost reductions begin only after some initial volume threshold has been reached, but not all 

of these studies specify this threshold volume. The rate at which costs decline beyond the initial 

threshold is usually expressed as the percent reduction in average unit cost that results from each 

successive doubling of cumulative production volume, sometimes referred to as the learning rate. 

Many estimates of experience curves do not specify a cumulative production volume beyond 

which cost reductions would no longer occur, instead depending on the asymptotic behavior of 

the effect for learning rates below 100 percent to establish a floor on costs. 

In past rulemaking analyses, as noted above, both agencies have used a learning curve 

algorithm that applied a learning factor of 20 percent for each doubling of production volume. 

NHTSA has used this approach in analyses supporting recent CAFE rules. In its analyses, EPA 

has simplified the approach by using an “every two years” based learning progression rather than 

a pure production volume progression (i.e., after two years of production it was assumed that 

production volumes would have doubled and, therefore, costs would be reduced by 20 percent).86 

In the MYs 2012-2016 light-duty rule, the agencies employed an additional learning 

algorithm to reflect the volume-based learning cost reductions that occur further along on the 

learning curve. This additional learning algorithm was termed “time-based” learning simply as a 

means of distinguishing this algorithm from the volume-based algorithm mentioned above, 

although both of the algorithms reflect the volume-based learning curve supported in the 

literature. To avoid confusion, we are now referring to this learning algorithm as the “flat 

portion” of the learning curve. This way, we maintain the clarity that all learning is, in fact, 

volume-based learning, and that the level of cost reductions depend only on where on the 

learning curve a technology’s learning progression is. We distinguish the flat portion of the 

curve from the “steep portion” of the curve to indicate the level of learning taking place in the 

years following implementation of the technology. The agencies have applied the steep portion 

 85 76 FR 57106, 57320 (Sept. 15, 2011). 

86 To clarify, EPA has simplified the steep portion of the volume learning curve by assuming that production 

volumes of a given technology will have doubled within two years time. This has been done largely to allow for a 

presentation of estimated costs during the years of implementation, without the need to conduct a feedback loop that 

ensures that production volumes have indeed doubled. If we were to attempt such a feedback loop, we would need 

to estimate first year costs, feed those into OMEGA, review the resultant technology penetration rate and volume 

increase, calculate the learned costs, feed those into OMEGA (since lower costs would result in higher penetration 

rates, review the resultant technology penetration rate and volume increase, etc., until an equilibrium was reached. 

To do this for the dozens of technologies considered in our analysis is simply not feasible. Instead, we have 

estimated the effects of learning on costs, fed those costs into OMEGA, and reviewed the resultant penetration rates. 

The assumption that volumes have doubled after two years is based solely on the assumption that year two sales are 

of equal or greater number than year one sales and, therefore, have resulted in a doubling of production. This could 

be done on a daily basis, a monthly basis, or a yearly basis as we have done. 
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learning algorithm for those technologies considered to be newer technologies likely to 

experience rapid cost reductions through manufacturer learning, and the flat portion learning 

algorithm for those technologies considered to be mature technologies likely to experience only 

minor cost reductions through manufacturer learning. As noted above, the steep portion learning 

algorithm results in 20 percent lower costs after two full years of implementation (i.e., the MY 

2016 costs are 20 percent lower than the MYs 2014 and 2015 costs). Once two steep portion 

learning steps have occurred (for technologies having the steep portion learning algorithm 

applied while flat portion learning would begin in year 2 for technologies having the flat portion 

learning algorithm applied), flat portion learning at 3 percent per year becomes effective for 5 

years. Beyond 5 years of learning at 3 percent per year, 5 years of learning at 2 percent per year, 

then 5 at 1 percent per year become effective. 

Technologies assumed to be on the steep portion of the learning curve are hybrids and 

electric vehicles, while no learning is applied to technologies likely to be affected by commodity 

costs (LUB, ROLL) or that have loosely-defined BOMs (EFR, LDB), as was the case in the MY 

2012-2016 final rule. Chapter 3 of the Joint TSD and the PRIA shows the specific learning 

factors that NHTSA has applied in this analysis for each technology, and discusses learning 

factors and each agency’s use of them further. Since the agencies had to project how learning 

will occur with new technologies over a long period of time, we request comments on the 

assumptions of learning costs and methodology. In particular, we are interested in input on the 

assumptions for certain advanced technologies not expected to be available until 2017, but for 

which we assume that learning (and the associated cost reductions) begin as early as 2012. We 

seek comment on the appropriateness of these pre-production applications of learning. 

Is the technology more or less effective due to synergistic effects? 

When two or more technologies are added to a particular vehicle model to improve its 

fuel efficiency and reduce CO2 emissions, the resultant fuel consumption reduction may 

sometimes be higher or lower than the product of the individual effectiveness values for those 

items.87 This may occur because one or more technologies applied to the same vehicle partially 

address the same source (or sources) of engine, drivetrain or vehicle losses. Alternately, this 

effect may be seen when one technology shifts the engine operating points, and therefore 

increases or reduces the fuel consumption reduction achieved by another technology or set of 

technologies. The difference between the observed fuel consumption reduction associated with a 

set of technologies and the product of the individual effectiveness values in that set is referred to 

 87 More specifically, the products of the differences between one and the technology-specific levels of effectiveness 

in reducing fuel consumption. For example, not accounting for interactions, if technologies A and B are estimated to 

reduce fuel consumption by 10 percent (i.e., 0.1) and 20 percent (i.e., 0.2) respectively, the “product of the 

individual effectiveness values” would be 1–0.1 times 1–0.2, or 0.9 times 0.8, which equals 0.72, corresponding to a 

combined effectiveness of 28 percent rather than the 30 percent obtained by adding 10 percent to 20 percent. The 

“synergy factors” discussed in this section further adjust these multiplicatively combined effectiveness values. 
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for purposes of this rulemaking as a “synergy.” Synergies may be positive (increased fuel 

consumption reduction compared to the product of the individual effects) or negative (decreased 

fuel consumption reduction). An example of a positive synergy might be a vehicle technology 

that reduces road loads at highway speeds (e.g., lower aerodynamic drag or low rolling resistance 

tires), that could extend the vehicle operating range over which cylinder deactivation may be 

employed. An example of a negative synergy might be a variable valvetrain system technology, 

which reduces pumping losses by altering the profile of the engine speed/load map, and a sixspeed automatic transmission, which shifts the engine operating points to a portion of the engine 

speed/load map where pumping losses are less significant. 

As the complexity of the technology combinations is increased, and the number of 

interacting technologies grows accordingly, it becomes increasingly important to account for 

these synergies. NHTSA and EPA determined synergistic impacts for this proposed rule using 

EPA’s “lumped parameter” analysis tool, which EPA describes at length in Chapter 3 of the 

TSD. The lumped parameter tool is a spreadsheet model that represents energy consumption in 

terms of average performance over the fuel economy test procedure, rather than explicitly 

analyzing specific drive cycles. The tool begins with an apportionment of fuel consumption 

across several loss mechanisms and accounts for the average extent to which different 

technologies affect these loss mechanisms using estimates of engine, drivetrain and vehicle 

characteristics that are averaged over the 2-cycle CAFE drive cycle. Results of this analysis 

were generally consistent with those of full-scale vehicle simulation modeling performed in 

2010-2011 for EPA by Ricardo, Inc. 

For the current rulemaking, NHTSA is using an updated version of lumped parameter 

tool that incorporates results from simulation modeling performed in 2010-2011 by Ricardo, Inc. 

NHTSA and EPA incorporate synergistic impacts in their analyses in slightly different manners. 

Because NHTSA applies technologies individually in its modeling analysis, NHTSA 

incorporates synergistic effects between pairings of individual technologies. The use of discrete 

technology pair incremental synergies is similar to that in DOE’s National Energy Modeling 

System (NEMS).88 Inputs to the CAFE model incorporate NEMS-identified pairs, as well as 

additional pairs from the set of technologies considered in the CAFE model. 

NHTSA notes that synergies that occur within a decision tree are already addressed 

within the incremental values assigned and therefore do not require a synergy pair to address. 

For example, all engine technologies take into account incremental synergy factors of preceding 

engine technologies, and all transmission technologies take into account incremental synergy 

factors of preceding transmission technologies. These factors are expressed in the fuel 

consumption improvement factors in the input files used by the CAFE model. 

 88 U.S. Department of Energy, Energy Information Administration, Transportation Sector Module of the National 

Energy Modeling System: Model Documentation 2007, May 2007, Washington, DC, DOE/EIAM070(2007), at 29–

30. Available at http://tonto.eia.doe.gov/ftproot/modeldoc/m070(2007).pdf (last accessed Sept. 25, 2011). 
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For applying incremental synergy factors in separate path technologies, the CAFE model 

uses an input table (see the tables in Chapter 3 of the TSD and in the PRIA) that lists technology 

pairings and incremental synergy factors associated with those pairings, most of which are 

between engine technologies and transmission/electrification/hybrid technologies. When a 

technology is applied to a vehicle by the CAFE model, all instances of that technology in the 

incremental synergy table which match technologies already applied to the vehicle (either preexisting or previously applied by the CAFE model) are summed and applied to the fuel 

consumption improvement factor of the technology being applied. Many of the synergies for the 

strong hybrid technology fuel consumption reductions are included in the incremental value for 

the specific hybrid technology block since the model applies all available electrification, engine 

and transmission technologies before applying strong hybrid technologies. 

The U.S. DOT Volpe Center has entered into a contract with Argonne National 

Laboratory (ANL) to provide full vehicle simulation modeling support for this MYs 2017-2025 

rulemaking. While this modeling was not completed in time for use in this NPRM, NHTSA 

intends to use this modeling to validate/update technology effectiveness estimates and synergy 

factors for the final rulemaking analysis. This simulation modeling will be accomplished using 

ANL’s full vehicle simulation tool called “Autonomie,” which is the successor to ANL’s 

Powertrain System Analysis Toolkit (PSAT) simulation tool, and ANL’s expertise with 

advanced vehicle technologies. 

b. Where can readers find more detailed information about NHTSA’s technology 

analysis? 

Much more detailed information is provided in Chapter 5 of the PRIA, and a discussion 

of how NHTSA and EPA jointly reviewed and updated technology assumptions for purposes of 

this NPRM is available in Chapter 3 of the TSD. Additionally, all of NHTSA’s model input and 

output files are now public and available for the reader’s review and consideration. The 

technology input files can be found in the docket for this NPRM, Docket No. NHTSA–2010–

0131, and on NHTSA’s website. And finally, because much of NHTSA’s technology analysis 

for purposes of this final rule builds on the work that was done for the MY 2011 and MYs 2012-

2016 final rules, we refer readers to those documents as well for background information 

concerning how NHTSA’s methodology for technology application analysis has evolved over 

the past several rulemakings, both in response to comments and as a result of the agency’s 

growing experience with this type of analysis.89

 89 74 FR 14233–308 (Mar. 30, 2009). 
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2. How did NHTSA develop its economic assumptions? 

NHTSA’s analysis of alternative CAFE standards for the model years covered by this 

rulemaking relies on a range of forecast variables, economic assumptions, and parameter values. 

This section describes the sources of these forecasts, the rationale underlying each assumption, 

and the agency’s choices of specific parameter values. These economic values play a significant 

role in determining the benefits of alternative CAFE standards, as they have for the last several 

CAFE rulemakings. Under those alternatives where standards would be established by reference 

to their costs and benefits, these economic values also affect the levels of the CAFE standards 

themselves. Some of these variables have more important effects on the level of CAFE 

standards and the benefits from requiring alternative increases in fuel economy than do others, 

and the following discussion places more emphasis on these inputs. 

In reviewing these variables and the agency’s estimates of their values for purposes of 

this proposed rule, NHTSA reconsidered comments it had previously received on the NPRM for 

MYs 2012-16 CAFE standards and to the NOI/Interim Joint TAR, and also reviewed newly 

available literature. The agency elected to revise some of its economic assumptions and 

parameter estimates for this rulemaking, while retaining others. For the reader’s reference, Table 

IV-7 below summarizes the values used to calculate the economic benefits from each alternative. 

Table II-17. Economic Values for Estimating Benefits (2009$) 

Fuel Economy Rebound Effect 10%

"Gap" between test and on-road MPG for liquid-fueled vehicles 20%

“Gap” between test and on-road wall electricity consumption for 

electric and plug-in hybrid electric vehicles 30%

Value of refueling time per ($ per vehicle-hour) $21.43

Average tank volume refilled during refueling stop 

65%

Annual annual growth in average vehicle use 2001-30: 

1.1%

2031-50: 0.5%

Fuel Prices (2017-50 average, $/gallon) 

Retail gasoline price $3.71

Pre-tax gasoline price $3.35

Economic Benefits from Reducing Oil Imports 

($/gallon) 

"Monopsony" Component $ 0.00

Price Shock Component $ 0.185 in 2025

Military Security Component $ 0.00

Total Economic Costs ($/gallon) $ 0.185 in 2025

Emission Damage Costs (2020, $/short ton) 

Carbon monoxide $ 0

Volatile organic compounds (VOC) $ 1,300

Nitrogen oxides (NOX) – vehicle use $ 5,500

Nitrogen oxides (NOX) – fuel production and $ 5,300
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distribution 

Particulate matter (PM2.5) – vehicle use $ 300,000

Particulate matter (PM2.5) – fuel production and 

distribution 

$ 250,000

Sulfur dioxide (SO2) $ 32,000

Annual CO2 Damage Cost (per metric ton)

variable 

depending on 

discount rate 

and year (see 

Table II-9 

above for 

2017 

estimates)

External Costs from Additional Automobile Use 

($/vehicle-mile) 

Congestion $ 0.056

Accidents $ 0.024

Noise $ 0.001

Total External Costs $ 0.080

External Costs from Additional Light Truck Use 

($/vehicle-mile) 

Congestion $0.049

Accidents $0.027

Noise $0.001

Total External Costs $0.077

Discount Rates Applied to Future Benefits 3%, 7%

a. Costs of Fuel Economy-Improving Technologies 

Building on cost estimates developed for the MYs 2012-2016 CAFE and GHG final rule 

and the 2010 TAR, the agencies incorporated new cost estimates for the new technologies being 

considered and some of the technologies carried over from the MYs 2012-2016 final rule and 

2010 TAR. This joint work is reflected in Chapter 3 of the Joint TSD and in Section II of this 

preamble, as summarized below. For more detailed information on cost of fuel-saving 

technologies, please refer to Chapter 3 of the Joint TSD and Chapter V of NHTSA’s PRIA. 

The technology cost estimates used in this analysis are intended to represent 

manufacturers’ direct costs for high-volume production of vehicles with these technologies. 

NHTSA explicitly accounts for the cost reductions a manufacturer might realize through learning 

achieved from experience in actually applying a technology, which means that technologies 

become cheaper over the rulemaking time frame; learning effects are described above and in 

Chapter 3 of the draft joint TSD and Chapters V and VII of NHTSA’s PRIA. NHTSA notes that, 

in developing technology cost estimates, the agencies have made every effort to hold constant 
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aspects of vehicle performance and utility typically valued by consumers, such as horsepower, 

carrying capacity, drivability, durability, noise, vibration and harshness (NVH) and towing and 

hauling capacity. For example, NHTSA includes in its analysis technology cost estimates that 

are specific to performance passenger cars (i.e., sports cars), as compared to nonperformance 

passenger cars. NHTSA seeks comment on the extent to which commenters believe that the 

agencies have been successful in holding constant these elements of vehicle performance and 

utility in developing the technology cost estimates. Additionally, the agency notes that the 

technology costs included in this proposal take into account only those associated with the initial 

build of the vehicle. Although comments were received to the MYs 2012-2016 rulemaking that 

suggested there could be additional maintenance required with some new technologies (e.g., 

turbocharging, hybrids, etc.), and that additional maintenance costs could occur as a result. The 

agency requests comments on this topic and will undertake a more detailed review of these 

potential costs for the final rule. 

Additionally, NHTSA recognizes that manufacturers’ actual costs for employing these 

technologies include additional outlays for accompanying design or engineering changes to 

models that use them, development and testing of prototype versions, recalibrating engine 

operating parameters, and integrating the technology with other attributes of the vehicle. 

Manufacturers’ indirect costs for employing these technologies also include expenses for product 

development and integration, modifying assembly processes and training assembly workers to 

install them, increased expenses for operation and maintaining assembly lines, higher initial 

warranty costs for new technologies, any added expenses for selling and distributing vehicles 

that use these technologies, and manufacturer and dealer profit. These indirect costs have been 

accounted for in this rulemaking through use of ICMs, which have been revised for this 

rulemaking as discussed above, in Chapter 3 of the draft joint TSD, and in Chapters V and VII of 

NHTSA’s PRIA. 

b. Potential Opportunity Costs of Improved Fuel Economy 

An important concern is whether achieving the fuel economy improvements required by 

the proposed CAFE standards will require manufacturers to modify the performance, carrying 

capacity, safety, or comfort of some vehicle models. To the extent that it does so, the resulting 

sacrifice in the value of those models represents an additional cost of achieving the required 

improvements in fuel economy. (This possibility is addressed in detail in Section IV.G.6.) 

Although exact dollar values that potential buyers attach to specific vehicle attributes are 

difficult to infer, differences in vehicle purchase prices and buyers’ choices among competing 
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models that feature varying combinations of these characteristics clearly demonstrate that 

changes in these attributes affect the utility and economic value they offer to potential buyers.90

NHTSA and EPA have approached this potential problem by developing cost estimates 

for fuel economy-improving technologies that include any additional manufacturing costs that 

would be necessary to maintain the originally planned levels of performance, comfort, carrying 

capacity, and safety of any light-duty vehicle model to which those technologies are applied. In 

doing so, the agencies followed the precedent established by the 2002 NAS Report, which 

estimated “constant performance and utility” costs for fuel economy technologies. NHTSA has 

followed this precedent in its efforts to refine the technology costs it uses to analyze alternative 

passenger car and light truck CAFE standards for MYs 2017–2025. Although the agency has 

reduced its estimates of manufacturers’ costs for most technologies for use in this rulemaking, 

these revised estimates are still intended to represent costs that would allow manufacturers to 

maintain the performance, carrying capacity, and utility of vehicle models while improving their 

fuel economy. 

While we believe that our cost estimates for fuel economy-improving technologies 

include adequate provisions for accompanying costs that are necessary to prevent any 

degradation in other vehicle attributes, it is possible that they do not include adequate allowance 

to prevent sacrifices in these attributes on all vehicle models. If this is the case, the true 

economic costs of achieving higher fuel economy should include the opportunity costs to vehicle 

owners of any accompanying reductions vehicles’ performance, carrying capacity, and utility, 

and omitting these will cause the agency’s estimated technology costs to underestimate the true 

economic costs of improving fuel economy. 

It would be desirable to estimate explicitly the changes in vehicle buyers’ welfare from 

the combination of higher prices for new vehicle models, increases in their fuel economy, and 

any accompanying changes in other vehicle attributes. The net change in buyer’s welfare that 

results from the combination of these changes would provide a more accurate estimate of the true 

economic costs for improving fuel economy. The agency is in the process of developing a model 

of potential vehicle buyers’ decisions about whether to purchase a new car or light truck and 

their choices from among the available models, which will allow it to conduct such an analysis. 

This process is expected to be completed for use in analyzing final CAFE standards for MY 

2017-25; in the meantime, Section IV.G.6 below includes a detailed analysis and discussion of 

how omitting possible changes in vehicle attributes other than their prices and fuel economy 

might affect its estimates of benefits and costs resulting from the standards proposed in this 

NPRM. 

 90 See, e.g., Kleit A.N., 1990. “The Effect of Annual Changes in Automobile Fuel Economy Standards.” Journal of 

Regulatory Economics 2: 151–172 (Docket EPA–HQ–OAR–2009–0472–0015); Berry, Steven, James Levinsohn, 

and Ariel Pakes, 1995. “Automobile Prices in Market Equilibrium,” Econometrica 63(4): 841–940 (Docket 

NHTSA–2009–0059–0031); McCarthy, Patrick S., 1996.
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c. The On-Road Fuel Economy “Gap” 

Actual fuel economy levels achieved by light-duty vehicles in on-road driving fall 

somewhat short of their levels measured under the laboratory-like test conditions used by EPA to 

establish its published fuel economy ratings for different models. In analyzing the fuel savings 

from alternative CAFE standards, NHTSA has previously adjusted the actual fuel economy 

performance of each light truck model downward from its rated value to reflect the expected size 

of this on-road fuel economy “gap.” On December 27, 2006, EPA adopted changes to its 

regulations on fuel economy labeling, which were intended to bring vehicles’ rated fuel economy 

levels closer to their actual on-road fuel economy levels.91 

In its Final Rule, however, EPA estimated that actual on-road fuel economy for light-duty 

vehicles averages approximately 20 percent lower than published fuel economy levels, somewhat 

larger than the 15 percent shortfall it had previously assumed. For example, if the overall EPA 

fuel economy rating of a light truck is 20 mpg, EPA estimated that the on-road fuel economy 

actually achieved by a typical driver of that vehicle is expected to be only 80 percent of that 

figure, or 16 mpg (20*.80). NHTSA employed EPA’s revised estimate of this on-road fuel 

economy gap in its analysis of the fuel savings resulting from alternative CAFE standards 

evaluated in the MY 2011 final rule. 

In the course of developing its CAFE standards for MY 2012-16, NHTSA conducted 

additional analysis of this issue. The agency used data on the number of passenger cars and light 

trucks of each model year that were registered for use during calendar years 2000 through 2006, 

average rated fuel economy for passenger cars and light trucks produced during each model year, 

and estimates of average miles driven per year by cars and light trucks of different ages. These 

data were combined to develop estimates of the average fuel economy that the U.S. passenger 

vehicle fleet would have achieved from 2000 through 2006 if cars and light trucks of each model 

year achieved the same fuel economy levels in actual on-road driving as they did under test 

conditions when new. 

Table IV-8 compares NHTSA’s estimates of fleet-wide average fuel economy under test 

conditions for 2000 through 2006 to the Federal Highway Administration’s (FHWA) published 

estimates of actual on-road fuel economy achieved by passenger cars and light trucks during 

each of those years.92 As it shows, FHWA’s estimates of actual fuel economy for passenger cars 

ranged from 21-23 percent lower than NHTSA’s estimates of its fleet-wide average value under 

test conditions over this period, and FHWA’s estimates of actual fuel economy for light trucks 

ranged from 16-18 percent lower than NHTSA’s estimates of its fleet-wide average value under 

test conditions. Thus, these results appear to confirm that the 20 percent on-road fuel economy 

 91 71 FR 77871 (Dec. 27, 2006). 

92 Federal Highway Administration, Highway Statistics, 2000 through 2006 editions, Table VM–1; See 

http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.cfm (last accessed March 1, 2010). 
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gap represents a reasonable estimate for use in evaluating the fuel savings likely to result from 

more stringent fuel economy and CO2 standards in MYs 2017-2025. 

Table II-18. Estimated Fleet-Wide Fuel Economy of Passenger Cars and Light Trucks 

Compared to Reported Fuel Economy 

YEAR 

PASSENGER CARS LIGHT-DUTY TRUCKS 

NHTSA 

Estimated 

Test MPG 

FHWA 

Reported 

Actual MPG 

Percent 

Difference 

NHTSA 

Estimated 

Test MPG 

FHWA 

Reported 

Actual MPG

Percent 

Difference 

2000 28.2 21.9 -22.2% 20.8 17.4 -16.3% 

2001 28.2 22.1 -21.7% 20.8 17.6 -15.5% 

2002 28.3 22.0 -22.3% 20.9 17.5 -16.2% 

2003 28.4 22.2 -21.9% 21.0 17.2 -18.0% 

2004 28.5 22.5 -21.1% 21.0 17.2 -18.3% 

2005 28.6 22.1 -22.8% 21.1 17.7 -16.3% 

2006 28.8 22.5 -21.8% 21.2 17.8 -16.2% 

Avg., 

2000-

2006 

28.4 22.2 -22.0% 21.0 17.5 -16.7% 

The comparisons reported in this table must be interpreted with some caution, however, 

because the estimates of annual car and truck use used to develop these estimates are submitted 

to FHWA by individual states, which use differing definitions of passenger cars and light trucks. 

(For example, some states classify minivans as cars, while others define them as light trucks.) 

At the same time, while total gasoline consumption can be reasonably estimated from excise tax 

receipts, separate estimates of gasoline consumption by cars and trucks are not available. For 

these reasons, NHTSA has chosen not to rely on its separate estimates of the on-road fuel 

economy gap for cars and light trucks. However, the agency does believe that these results 

confirm that the 20 percent on-road fuel economy discount represents a reasonable estimate for 

use in evaluating the fuel savings likely to result from CAFE standards for both cars and light 

trucks. NHTSA employs this value for vehicles operating on liquid fuels (gasoline, diesel, and 

gasoline/alcohol blends), and uses it to analyze the impacts of proposed CAFE standards for 

model years 2017-25 on the use of these fuels. 

 In the recent TAR, EPA and NHTSA assumed that the overall energy shortfall for the 

vehicles employing electric drivetrains, including plug-in hybrid and battery-powered electric 

vehicles, is 30 percent. This value was derived from the agencies’ engineering judgment based 

on the limited available information. During the stakeholder meetings conducted prior to the 

technical assessment, confidential business information (CBI) was supplied by several 

manufacturers which indicated that electrically powered vehicles had greater variability in their 

on-road energy consumption than vehicles powered by internal combustion engines, although 

other manufacturers suggested that the on-road/laboratory differential attributable to electric 
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operation should approach that of liquid fuel operation in the future. Second, data from EPA’s 

2006 analysis of the “five cycle” fuel economy label as part of the rulemaking discussed above 

supported a larger on-road shortfall for vehicles with hybrid-electric drivetrains, partly because 

real-world driving tends to have higher acceleration/deceleration rates than are employed on the 

2-cycle test. This diminishes the fuel economy benefits of regenerative braking, which can result 

in a higher test fuel economy for hybrids than is achieved under normal on-road conditions.93 

Finally, heavy accessory load, extremely high or low temperatures, and aggressive driving have 

deleterious impacts of unknown magnitudes on battery performance. Consequently, the agencies 

judged that 30 percent was a reasonable estimate for use in the TAR, and NHTSA believes that it 

continues to represent the most reliable estimate for use in the current analysis. 

One of the most significant factors responsible for the difference between test and onroad fuel economy is the use of air conditioning. While the air conditioner is turned off during 

the FTP and HFET tests, drivers often use air conditioning under warm, humid conditions. The 

air conditioning compressor can also be engaged during “defrost” operation of the heating 

system.94 In the MYs 2012-2016 rulemaking, EPA estimated the impact of an air conditioning 

system at approximately 14.3 grams CO2/mile for an average vehicle without any of the 

improved air conditioning technologies discussed in that rulemaking. For a 27 mpg (330 g 

CO2/mile) vehicle, this would account for is approximately 20 percent of the total estimated onroad gap (or about 4 percent of total fuel consumption). 

In the MY 2012-2016 rule, EPA estimated that 85 percent of MY 2016 vehicles would 

reduce their tailpipe CO2 emissions attributable to air conditioner efficiency by 40 percent 

through the use of advanced air conditioning technologies, and that incorporating this change 

would reduce the average on-road gap by about 2 percent.95 However, air conditioning-related 

fuel consumption does not decrease proportionally as engine efficiency improves, because the 

engine load due attributable to air conditioner operation is approximately constant across engine 

efficiency and technology. As a consequence, air conditioning operation represents an 

increasing percentage of vehicular fuel consumption as engine efficiency increases.96 Because 

these two effects are expected approximately to counterbalance each other, NHTSA has elected 

not to adjust its estimate of the on-road gap for use in this proposal. 

 93 EPA, Fuel Economy Labeling of Motor Vehicles: Revisions To Improve Calculation of Fuel Economy Estimates; 

Final Rule, 40 CFR Parts 86 and 600, 71 Fed. Reg. 77872, 77879 (Dec. 27, 2006). Available at

http://www.epa.gov/fedrgstr/EPA-AIR/2006/December/Day-27/a9749.pdf. 94 EPA, Final Technical Support Document: Fuel Economy Labeling of Motor Vehicle Revisions to Improve 

Calculation of Fuel Economy Estimates, at 70. Office of Transportation and Air Quality EPA420-R-06-017 

December 2006, Chapter II, http://www.epa.gov/fueleconomy/420r06017.pdf. 95 4% of the on-road gap x 40% reduction in air conditioning fuel consumption x 85% of the fleet = ~2%. 

96 As an example, the air conditioning load of 14.3 g/mile of CO2 is a smaller percentage (4.3%) of 330 g/mile than 

260 (5.4%). 
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d. Fuel Prices and the Value of Saving Fuel 

Future fuel prices are the single most important input into the economic analysis of the 

benefits of alternative CAFE standards because they determine the value of future fuel savings, 

which account for approximately 90% of total economic benefits from requiring higher fuel 

economy. NHTSA relies on the most recent fuel price projections from the U.S. Energy 

Information Administration’s (EIA) Annual Energy Outlook (AEO) 2011 Reference Case to 

estimate the economic value of fuel savings projected to result from alternative CAFE standards 

for MY 2017-25. The AEO 2011 Reference Case forecasts of gasoline and diesel fuel prices 

represents EIA’s most up-to-date estimate of the most likely course of future prices for 

petroleum products. EIA is widely recognized as an impartial and authoritative source of 

analysis and forecasts of U.S. energy production, consumption, and prices, and its forecasts are 

widely relied upon by federal agencies for use in regulatory analysis and for other purposes. Its 

forecasts are derived using EIA’s National Energy Modeling System (NEMS), which includes 

detailed representations of supply pathways, sources of demand, and their interaction to 

determine prices for different forms of energy. 

As compared to the gasoline prices used in NHTSA’s Final Rule establishing CAFE 

standards for MY 2012-2016 (which relied on forecasts from AEO 2010), the AEO 2011 

Reference Case fuel prices are slightly higher through the year 2020, but slightly lower for most 

years thereafter. Expressed in constant 2009 dollars, the AEO 2011 Reference Case forecast of 

retail gasoline prices (which include federal, state, and local taxes) during 2017 is $3.25 per 

gallon, rising gradually to $3.71 by the year 2035. However, valuing fuel savings over the full 

lifetimes of passenger cars and light trucks affected by the standards proposed for MYs 2017-25 

requires fuel price forecasts that extend through 2060, approximately the last year during which a 

significant number of MY 2025 vehicles will remain in service.97 To obtain fuel price forecasts 

for the years 2036 through 2060, the agency assumes that retail fuel prices will continue to 

increase after 2035 at the average annual rate (0.7%) projected for 2017-2035 in the AEO 2011 

Reference Case. This assumption results in a projected retail price of gasoline that reaches $4.16 

in 2050. Over the entire period from 2017-2050, retail gasoline prices are projected to average 

$3.67, as Table IV-7 reported previously. 

The value of fuel savings resulting from improved fuel economy to buyers of light-duty 

vehicles is determined by the retail price of fuel, which includes Federal, State, and any local 

taxes imposed on fuel sales. Because fuel taxes represent transfers of resources from fuel buyers 

to government agencies, however, rather than real resources that are consumed in the process of 

supplying or using fuel, NHTSA deducts their value from retail fuel prices to determine the value 

of fuel savings resulting from more stringent CAFE standards to the U.S. economy. 

 97 The agency defines the maximum lifetime of vehicles as the highest age at which more than 2 percent of those 

originally produced during a model year remain in service. In the case of light trucks, for example, this age has 

typically been 36 years for recent model years. 
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NHTSA follows the assumptions used by EIA in AEO 2011 that State and local gasoline 

taxes will keep pace with inflation in nominal terms, and thus remain constant when expressed in 

constant dollars. In contrast, EIA assumes that Federal gasoline taxes will remain unchanged in 

nominal terms, and thus decline throughout the forecast period when expressed in constant 

dollars. These differing assumptions about the likely future behavior of Federal and State/local 

fuel taxes are consistent with recent historical experience, which reflects the fact that Federal as 

well as most State motor fuel taxes are specified on a cents-per-gallon rather than an ad valorem 

basis, and typically require legislation to change. Subtracting fuel taxes from the retail prices 

forecast in AEO 2011 results in projected values for saving gasoline of $3.29 per gallon during 

2017, rising to $3.48 per gallon by the year 2035, and to $3.65 by the year 2050. Over this 

entire period, pre-tax gasoline prices are projected to average $3.32 per gallon. 

EIA also includes forecasts reflecting high and low global oil prices in each year’s 

complete AEO, which reflect uncertainties regarding OPEC behavior as well as future levels of 

oil production and demand. These alternative scenarios project retail gasoline prices that range 

from a low of $2.30 to a high of $4.85 per gallon during 2020, and from $2.12 to $5.36 per 

gallon during 2035 (all figures in 2009 dollars). In conjunction with our assumption that fuel 

taxes will remain constant in real or inflation-adjusted terms over this period, these forecasts 

imply pre-tax values of saving fuel ranging from $1.91 to $4.46 per gallon during 2020, and 

from $1.77 to $5.01 per gallon in 2035 (again, all figures are in constant 2009 dollars). In 

conducting the analysis of uncertainty in benefits and costs from alternative CAFE standards 

required by OMB, NHTSA evaluated the sensitivity of its benefits estimates to these alternative 

forecasts of future fuel prices; detailed results and discussion of this sensitivity analysis can be 

found in the agency’s PRIA. Generally, this analysis confirms that the primary economic benefit 

resulting from the rule—the value of fuel savings—is extremely sensitive to alternative forecasts 

of future fuel prices. 

e. Consumer Valuation of Fuel Economy and Payback Period 

The agency uses slightly different assumptions about the length of time over which 

potential vehicle buyers consider fuel savings from higher fuel economy, and about how they 

discount those future fuel savings, in different aspects of its analysis. For most purposes, the 

agency assumes that buyers value fuel savings over the first five years of a new vehicle’s 

lifetime; the five-year figure represents approximately the current average term of consumer 

loans to finance the purchase of new vehicles. 

To simulate manufacturers’ assessment of the net change in the value of an individual 

vehicle model to prospective buyers from improving its fuel economy, NHTSA discounts fuel 

savings over the first five years of its lifetime using a 7 percent rate. The resulting value is 

deducted from the technology costs that would be incurred by its manufacturer to improve that 

model’s fuel economy, in order to determine the change in its value to potential buyers. Since 

this is also the additional amount its manufacturer could expect to receive when selling the 
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106 

vehicle after improving its fuel economy, this can also be viewed as the “effective cost” of the 

improvement from its manufacturers’ perspective. The CAFE model uses these estimates of 

effective costs to identify the sequence in which manufacturers are likely to select individual 

models for improvements in fuel economy, as well as to identify the most cost-effective 

technologies for doing so. 

The average of effective cost to its manufacturer for increasing the fuel economy of a 

model also represents the change in its value from the perspective of potential buyers. Under the 

assumption that manufacturers change the selling price of each model by this amount, its average 

value also represents the average change in its net or effective price to would-be buyers. As part 

of our sensitivity case analyzing the potential for manufacturers to over-comply with CAFE 

standards – that is, to produce a lineup of vehicle models whose sales-weighted average fuel 

economy exceeds that required by prevailing standards – NHTSA used the extreme assumption 

that potential buyers value fuel savings only during the first year they expect to own a new 

vehicle. 

The agency notes that these varying assumptions about future time horizons and discount 

rates for valuing fuel savings are used only to analyze manufacturers’ responses to requiring 

higher fuel economy and buyers’ behavior in response to manufacturers’ compliance strategies. 

When estimating the aggregate value to the U.S. economy of fuel savings resulting from 

alternative increases in CAFE standards—or the “social” value of fuel savings—the agency 

includes fuel savings over the entire expected lifetimes of vehicles that would be subject to 

higher standards, rather than over the shorter periods we assume manufacturers employ to 

represent the preferences of vehicle buyers, or that buyers use to assess changes in the net price 

or new vehicles. 

Valuing fuel savings over vehicles’ entire lifetimes recognizes the savings in fuel costs 

that subsequent owners of vehicles will experience from higher fuel economy, even if their initial 

purchasers do not expect to recover the remaining value of fuel savings when they re-sell those 

vehicles, or for other reasons do not value fuel savings beyond the assumed five-year time 

horizon. The agency acknowledges that it has not accounted for any effects of increased costs 

for financing, insuring, or maintaining vehicles with higher fuel economy, over either this limited 

payback period or the full lifetimes of vehicles. 

The procedure the agency uses for calculating lifetime fuel savings is discussed in detail 

in the following section, while discussion about the time horizon over which potential buyers 

may consider fuel savings in their vehicle purchasing decisions is provided in more detail in 

Section IV.G.6 below. 
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f. Vehicle Survival and Use Assumptions 

NHTSA’s analysis of fuel savings and related benefits from adopting more stringent fuel 

economy standards for MYs 2017-2025 passenger cars and light trucks begins by estimating the 

resulting changes in fuel use over the entire lifetimes of the affected vehicles. The change in 

total fuel consumption by vehicles produced during each model year is calculated as the 

difference between their total fuel use over their lifetimes with a higher CAFE standard in effect, 

and their total lifetime fuel consumption under a baseline in which CAFE standards remained at 

their 2016 levels. The first step in estimating lifetime fuel consumption by vehicles produced 

during a model year is to calculate the number expected to remain in service during each year 

following their production and sale.98 This is calculated by multiplying the number of vehicles 

originally produced during a model year by the proportion typically expected to remain in 

service at their age during each later year, often referred to as a “survival rate.” 

As discussed in more detail in Section II.B.3 above and in Chapter 1 of the TSD, to 

estimate production volumes of passenger cars and light trucks for individual manufacturers, 

NHTSA relied on a baseline market forecast constructed by EPA staff beginning with MY 2008 

CAFE certification data. After constructing a MY 2008 baseline, EPA and NHTSA used 

projected car and truck volumes for this period from Energy Information Administration’s 

(EIA’s) Annual Energy Outlook (AEO) 2011 in the NPRM analysis.99 However, Annual Energy 

Outlook forecasts only total car and light truck sales, rather than sales at the manufacturer and 

model-specific level, which the agencies require in order to estimate the effects new standards 

will have on individual manufacturers.100

To estimate sales of individual car and light truck models produced by each 

manufacturer, EPA purchased data from CSM Worldwide and used its projections of the number 

of vehicles of each type (car or truck) that will be produced and sold by manufacturers in model 

 98 Vehicles are defined to be of age 1 during the calendar year corresponding to the model year in which they are 

produced; thus for example, model year 2000 vehicles are considered to be of age 1 during calendar year 2000, age 

2 during calendar year 2001, and to reach their maximum age of 26 years during calendar year 2025. NHTSA 

considers the maximum lifetime of vehicles to be the age after which less than 2 percent of the vehicles originally 

produced during a model year remain in service. Applying these conventions to vehicle registration data indicates 

that passenger cars have a maximum age of 26 years, while light trucks have a maximum lifetime of 36 years. See

Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, “Vehicle Survivability and Travel Mileage 

Schedules,” DOT HS 809 952, 8–11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/Pubs/809952.pdf 

(last accessed Sept. 26, 2011).

99 Available at http://www.eia.gov/forecasts/aeo/index.cfm (last accessed Sept. 26, 2011). NHTSA and EPA made 

the simplifying assumption that projected sales of cars and light trucks during each calendar year from 2012 through 

2016 represented the likely production volumes for the corresponding model year. The agency did not attempt to 

establish the exact correspondence between projected sales during individual calendar years and production volumes 

for specific model years.

100 Because AEO 2011’s “car” and “truck” classes did not reflect NHTSA’s recent reclassification (in March 2009 

for enforcement beginning MY 2011) of many two wheel drive SUVs from the non-passenger (i.e., light truck) fleet 

to the passenger car fleet, EPA staff made adjustments to account for such vehicles in the baseline.
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108 

years 2011 through 2015.101 This provided year-by-year estimates of the percentage of cars and 

trucks sold by each manufacturer, as well as the sales percentages accounted for by each vehicle 

market segment. (The distributions of car and truck sales by manufacturer and by market 

segment for the 2016 model year and beyond were assumed to be the same as CSM’s forecast for 

the 2015 calendar year.) Normalizing these percentages to the total car and light truck sales 

volumes projected for 2017 through 2025 in AEO 2011 provided manufacturer-specific market 

share and model-specific sales estimates for those model years. The volumes were then scaled to 

AEO 2011 total volume for each year. 

To estimate the number of passenger cars and light trucks originally produced during 

model years 2017 through 2025 that will remain in use during subsequent years, the agency 

applied age-specific survival rates for cars and light trucks to its forecasts of passenger car and 

light truck sales for each of those model years. In 2008, NHTSA updated its previous estimates 

of car and light truck survival rates using the most current registration data for vehicles produced 

during recent model years, in order to ensure that they reflected recent increases in the durability 

and expected life spans of cars and light trucks.102 However, the agency does not attempt to 

forecast changes in those survival rates over the future. 

The next step in estimating fuel use is to calculate the total number of miles that cars and 

light trucks remaining in use will be driven each year. To estimate the total number of miles 

driven by cars or light trucks produced in a model year during each subsequent year, the number 

projected to remain in use during that year is multiplied by the average number of miles those 

vehicles are expected to be driven at the age they will have reached in that year. The agency 

estimated annual usage of cars and light trucks of each age using data from the Federal Highway 

Administration’s 2001 National Household Travel Survey (NHTS).103 Because these estimates 

reflect the historically low gasoline prices that prevailed at the time the 2001 NHTS was 

conducted, however, NHTSA adjusted them to account for the effect on vehicle use of the higher 

fuel prices projected over the lifetimes of model year 2017-25 cars and light trucks. Details of 

this adjustment are provided in Chapter VIII of the PRIA and Chapter 4 of the draft Joint TSD. 

The estimates of annual miles driven at different vehicle ages derived from the 2001 

NHTS were also adjusted to reflect projected future growth in average use for vehicles at every 

age over their lifetimes. Increases in average annual use of cars and light trucks, which have 

averaged approximately 1 percent annually over the past two decades, have been an important 

 101 EPA also considered other sources of similar information, such as J.D. Powers, and concluded that CSM was 

better able to provide forecasts at the requisite level of detail for most of the model years of interest. 102 Lu, S., NHTSA, Regulatory Analysis and Evaluation Division, “Vehicle Survivability and Travel Mileage 

Schedules,” DOT HS 809 952, 8–11 (January 2006). Available at http://www-nrd.nhtsa.dot.gov/Pubs/809952.pdf 

(last accessed Sept. 26, 2011). These updated survival rates suggest that the expected lifetimes of recent-model 

passenger cars and light trucks are 13.8 and 14.5 years.

103 For a description of the Survey, see http://nhts.ornl.gov/introduction.shtml#2001 (last accessed September 26, 

2011). 
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source of historical growth in the total number of miles they are driven each year. To estimate 

future growth in their average annual use for purposes of this rulemaking, NHTSA calculated the 

rate of growth in the adjusted mileage schedules derived from the 2001 NHTS that would be 

necessary for total car and light truck travel to increase at the rate forecast in the AEO 2011 

Reference Case.104 This rate was calculated to be consistent with future changes in the overall 

size and age distributions of the U.S. passenger car and light truck fleets that result from the 

agency’s forecasts of total car and light truck sales and updated survival rates. The resulting 

growth rate in average annual car and light truck use is approximately 1.1 percent from 2017 

through 2030, and declines to 0.5 percent per year thereafter. 105 While the adjustment for future 

fuel prices reduces average annual mileage at each age from the values derived using the 2001 

NHTS, the adjustment for expected future growth in average vehicle use increases it. The net 

effect of these two adjustments is to increase expected lifetime mileage for MY 2017-25 

passenger cars and light trucks by about 22 percent from the estimates originally derived from 

the 2001 NHTS. 

Finally, the agency estimated total fuel consumption by passenger cars and light trucks 

remaining in use each year by dividing the total number of miles surviving vehicles are driven by 

the fuel economy they are expected to achieve under each alternative CAFE standard. Each 

model year’s total lifetime fuel consumption is the sum of fuel use by the cars or light trucks 

produced during that model year over its life span. In turn, the savings in lifetime fuel use by 

cars or light trucks produced during each model year affected by this proposed rule that will 

result from each alternative CAFE standard is the difference between its lifetime fuel use at the 

fuel economy level it attains under the Baseline alternative, and its lifetime fuel use at the higher 

fuel economy level it is projected to achieve under that alternative standard.106

 104 This approach differs from that used in the MY 2011 final rule, where it was assumed that future growth in the 

total number of cars and light trucks in use resulting from projected sales of new vehicles was adequate by itself to 

account for growth in total vehicle use, without assuming continuing growth in average vehicle use. 105 While the adjustment for future fuel prices reduces average mileage at each age from the values derived from the 

2001 NHTS, the adjustment for expected future growth in average vehicle use increases it. The net effect of these 

two adjustments is to increase expected lifetime mileage by about 18 percent significantly for both passenger cars 

and about 16 percent for light trucks.

106 To illustrate these calculations, the agency’s adjustment of the AEO 2009 Revised Reference Case forecast 

indicates that 9.26 million passenger cars will be produced during 2012, and the agency’s updated survival rates 

show that 83 percent of these vehicles, or 7.64 million, are projected to remain in service during the year 2022, when 

they will have reached an age of 10 years. At that age, passenger achieving the fuel economy level they are 

projected to achieve under the Baseline alternative are driven an average of about 800 miles, so surviving model 

year 2012 passenger cars will be driven a total of 82.5 billion miles (= 7.64 million surviving vehicles × 10,800 

miles per vehicle) during 2022. Summing the results of similar calculations for each year of their 26-year maximum 

lifetime, model year 2012 passenger cars will be driven a total of 1,395 billion miles under the Baseline alternative. 

Under that alternative, they are projected to achieve a test fuel economy level of 32.4 mpg, which corresponds to 

actual on-road fuel economy of 25.9 mpg (= 32.4 mpg × 80 percent). Thus their lifetime fuel use under the Baseline 

alternative is projected to be 53.9 billion gallons (= 1,395 billion miles divided by 25.9 miles per gallon). 
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g. Accounting for the Fuel Economy Rebound Effect 

The fuel economy rebound effect refers to the fact that some of the fuel savings expected 

to result from higher fuel economy, such as an increase in fuel economy required by the adoption 

of higher CAFE standards, may be offset by additional vehicle use. The increase in vehicle use 

occurs because higher fuel economy reduces the fuel cost of driving, which is typically the 

largest single component of the monetary cost of operating a vehicle, and vehicle owners 

respond to this reduction in operating costs by driving more. Even with their higher fuel 

economy, this additional driving consumes some fuel, so this effect reduces the fuel savings that 

result when raising CAFE standards requires manufacturers to improve fuel economy. The 

rebound effect refers to the fraction of fuel savings expected to result from increased fuel 

economy that is offset by additional driving.107 

The magnitude of the rebound effect is an important determinant of the actual fuel 

savings that are likely to result from adopting stricter CAFE standards. Research on the 

magnitude of the rebound effect in light-duty vehicle use dates to the early 1980s, and generally 

concludes that a significant rebound effect occurs when vehicle fuel efficiency improves.108 The 

most common approach to estimating its magnitude has been to analyze survey data on 

household vehicle use, fuel consumption, fuel prices, and other factors affecting household travel 

behavior to estimate the response of vehicle use to differences in the fuel efficiency of individual 

vehicles. Because this approach most closely matches the definition of the rebound effect, which 

is the response of vehicle use to differences in fuel economy, the agency regards these studies as 

likely to produce the most reliable estimates of the rebound effect. Other studies have relied on 

econometric analysis of annual U.S. data on vehicle use, fuel efficiency, fuel prices, and other 

variables to estimate the response of total or average vehicle use to changes in fleet-wide average 

fuel economy and its effect on fuel cost per mile driven. More recent studies have analyzed 

yearly variation in vehicle ownership and use, fuel prices, and fuel economy among states over 

an extended time period in order to measure the response of vehicle use to changing fuel costs 

per mile.109 

Another important distinction among studies of the rebound effect is whether they 

assume that the effect is constant, or allow it to vary in response to changes in fuel costs, 

personal income, or vehicle ownership. Most studies using aggregate annual data for the U.S. 

 107 Formally, the rebound effect is often expressed as the elasticity of vehicle use with respect to the cost per mile 

driven. Additionally, it is consistently expressed as a positive percentage (rather than as a negative decimal fraction, 

as this elasticity is normally expressed). 

108 Some studies estimate that the long-run rebound effect is significantly larger than the immediate response to 

increased fuel efficiency. Although their estimates of the adjustment period required for the rebound effect to reach 

its long-run magnitude vary, this long-run effect is probably more appropriate for evaluating the fuel savings and 

emissions reductions resulting from stricter standards that would apply to future model years. 109 In effect, these studies treat U.S. states as a data “panel” by applying appropriate estimation procedures to data 

consisting of each year’s average values of these variables for the separate states. 
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assume a constant rebound effect, although some of these studies test whether the effect varies as 

changes in retail fuel prices or average fuel efficiency alter fuel cost per mile driven. Studies 

using household survey data estimate significantly different rebound effects for households 

owning varying numbers of vehicles, with most concluding that the rebound effect is larger 

among households that own more vehicles. Finally, recent studies using state-level data 

conclude that the rebound effect varies directly in response to changes in personal income, the 

degree of urbanization of U.S. cities, and differences in traffic congestion levels, as well as fuel 

costs. Some studies conclude that the long-run rebound effect is significantly larger than the 

immediate response of vehicle use to increased fuel efficiency. Although their estimates of the 

time required for the rebound effect to reach its long-run magnitude vary, this long-run effect is 

probably more appropriate for evaluating the fuel savings likely to result from adopting stricter 

CAFE standards for future model years. 

In order to provide a more comprehensive overview of previous estimates of the rebound 

effect, NHTSA has updated its previous review of published studies of the rebound effect to 

include those conducted as recently as 2010. The agency performed a detailed analysis of 

several dozen separate estimates of the long-run rebound effect reported in these studies, which 

is summarized in Error! Reference source not found. below.110 As the table indicates, these 

estimates range from as low as 7 percent to as high as 75 percent, with a mean value of 23% 

percent. Both the type of data used and authors’ assumption about whether the rebound effect 

varies over time have important effects on its estimated magnitude. The 34 estimates derived 

from analysis of U.S. annual time-series data produce a mean estimate of 18 percent for the longrun rebound effect, while the mean of 23 estimates based on household survey data is 

considerably larger (31 percent), and the mean of 15 estimates based on pooled state data (23 

percent) is close to that for the entire sample. The 37 estimates assuming a constant rebound 

effect produce a mean of 23 percent, identical to the mean of the 29 estimates reported in studies 

that allowed the rebound effect to vary in response to fuel prices and fuel economy levels, 

vehicle ownership, or household income. Updated to reflect the most recent available 

information on these variables, the mean of these estimates is 19 percent, as Table IV-9 reports. 

Table II-19. Summary of Published Estimates of the Rebound Effect 

Category of 

Estimates 

Number 

of 

Studies 

Number 

of 

Estimates 

Range Distribution 

Low High Median Mean Std. 

Dev.

All Estimates 23 72 7% 75% 21% 23% 13% 

Published 

Estimates 17 50 7% 75% 22% 24% 14% 

 110 In some cases, NHTSA derived estimates of the overall rebound effect from more detailed results reported in the 

studies. For example, where studies estimated different rebound effects for households owning different numbers 

of vehicles but did not report an overall value, the agency computed a weighted average of the reported values using 

the distribution of households among vehicle ownership categories. 
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112 

Authors’ 

Preferred 

Estimates 

17 17 9% 75% 22% 22% 15% 

U.S. Time-Series 

Estimates 7 34 7% 45% 14% 18% 9% 

Household Survey 

Estimates 13 23 9% 75% 31% 31% 16% 

Pooled U.S. State 

Estimates 3 15 8% 58% 22% 23% 12% 

Constant Rebound 

Effect (1) 15 37 7% 75% 20% 23% 16% 

Variable Rebound 

Effect (1) 

Reported 

Estimates 

10 29 10% 45% 23% 23% 10% 

Updated to 2010 

(2) 11 33 6% 56% 15% 19% 13% 

Some recent studies provide evidence that the rebound effect has been declining over 

time. This result appears plausible for two reasons: first, the responsiveness of vehicle use to 

variation in fuel costs would be expected to decline as they account for a smaller proportion of 

the total monetary cost of driving, which has been the case until recently. Second, rising 

personal incomes would be expected to reduce the sensitivity of vehicle use to fuel costs as the 

time component of driving costs—which is likely to be related to income levels—accounts for a 

larger fraction the total cost of automobile travel. At the same time, however, rising incomes are 

strongly associated with higher auto ownership levels, which increase households’ opportunities 

to substitute among vehicles in response to varying fuel prices and differences in their fuel 

economy levels. This is likely to increase the sensitivity of households’ overall vehicle use to 

differences in the fuel economy levels of individual vehicles. 

Small and Van Dender combined time series data for states to estimate the rebound 

effect, allowing its magnitude to vary in response to fuel prices, fleet-wide average fuel 

economy, the degree of urbanization of U.S. cities, and personal income levels.111 The authors 

employ a model that allows the effect of fuel cost per mile on vehicle use to vary in response to 

changes in personal income levels and increasing urbanization of U.S. cities. For the time period 

1966-2001, their analysis implied a long-run rebound effect of 22 percent, which is consistent 

with previously published studies. Continued growth in personal incomes over this period 

reduces their estimate of the long-run rebound effect during its last five years (1997-2001) to 11 

 111 Small, K. and K. Van Dender, 2007a. “Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound 

Effect”, The Energy Journal, vol. 28, no. 1, pp. 25-51. 
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113 

percent, and an unpublished update through 2004 prepared by the authors reduced their estimate 

of the long-run rebound effect for the period 2000-2004 to 6 percent.112 

More recently, Hymel, Small and Van Dender extended the previous analysis to include 

traffic congestion levels in urbanized areas.113 Although controlling for the effect of congestion 

on vehicle use increased their estimates of the rebound effect, these authors also found that the 

rebound effect appeared to be declining over time. For the time period 1966-2004, their estimate 

of the long-run rebound effect was 24 percent, while for the last year of that period their estimate 

was 13 percent, significantly above the previous Small and Van Dender estimate of a 6 percent 

rebound effect for the period 2000-2004. 

Recent research by Greene (under contract to EPA) using U.S. national time-series data 

for the period 1966-2007 lends further support to the hypothesis that the rebound effect is 

declining over time.114 Greene found that fuel prices had a statistically significant impact on 

VMT, yet fuel efficiency did not, and statistical testing rejected the hypothesis of equal 

elasticities of vehicle use with respect to gasoline prices and fuel efficiency. Greene also tested 

model formulations that allowed the effect of fuel cost per mile on vehicle use to decline with 

rising per capita income; his preferred form of this model produced estimates of the rebound 

effect that declined to 12 percent in 2007. 

In light of findings from recent research, the agency’s judgment is that the apparent 

decline over time in the magnitude of the rebound effect justifies using a value for future analysis 

that is lower than many historical estimates, which average 15–25 percent. Because the lifetimes 

of vehicles affected by the alternative CAFE standards considered in this rulemaking will extend 

from 2017 until 2060, a value that is at the low end of historical estimates appears to be 

appropriate. Thus as it elected to do in its previous analysis of the effects of raising CAFE 

standards for MY 2012-16 cars and light trucks, NHTSA uses a 10 percent rebound effect in its 

analysis of fuel savings and other benefits from higher CAFE standards for MY 2017-25 

vehicles. Recognizing the wide range of uncertainty surrounding its correct value, however, the 

agency also employs estimates of the rebound effect ranging from 5 to 20 percent in its 

sensitivity testing. The 10 percent figure is at the low end of those reported in almost all 

previous research, and it is also below most estimates of the historical and current magnitude of 

the rebound effect developed by NHTSA. However, other recent research—particularly that 

 112 Small, K. and K. Van Dender, 2007b. "Long Run Trends in Transport Demand, Fuel Price Elasticities and 

Implications of the Oil Outlook for Transport Policy," OECD/ITF Joint Transport Research Centre Discussion 

Papers 2007/16, OECD, International Transport Forum. 

113 Hymel, Kent M., Kenneth A. Small, and Kurt Van Dender, “Induced demand and rebound effects in road 

transport,” Transportation Research Part B: Methodological, Volume 44, Issue 10, December 2010, Pages 1220-

1241, ISSN 0191-2615, DOI: 10.1016/j.trb.2010.02.007. 

114 Greene, David, “Rebound 2007: Analysis of National Light-Duty Vehicle Travel Statistics,” February 9, 2010. 

This paper has been accepted for an upcoming special issue of Energy Policy, although the publication date has not 

yet been determined. 
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conducted by Small and Van Dender and by Greene—suggests that the magnitude of the rebound 

effect has declined over time, and is likely to continue to do so. As a consequence, NHTSA 

concluded that a value at the low end of the historical estimates reported here is likely to provide 

a more reliable estimate of its magnitude during the future period spanned by NHTSA’s analysis 

of the impacts of this rule. The 10 percent estimate lies between the 10–30 percent range of 

estimates for the historical rebound effect reported in most previous research, and is at the upper 

end of the 5–10 percent range of estimates for the future rebound effect reported in recent 

studies. In summary, the 10 percent value was not derived from a single estimate or particular 

study, but instead represents a compromise between historical estimates and projected future 

estimates. Chapter 4.2.5 of the Joint TSD reviews the relevant literature and discusses in more 

depth the reasoning for the rebound value used here. 

h. Benefits From Increased Vehicle Use 

The 

increase in 

vehicle use 

from the 

rebound 

effect 

provides 

additional 

benefits to 

their users, 

who make 

more 

frequent 

trips or 

travel 

farther to 

reach more 

desirable 

destinations. 

This 

additional 

travel 

provides 

benefits to 

drivers and 

their 

passengers 

by 

improving 

their access 

to social and 

Earlier 2017 2018 2019 2020 2021 2022 2023 2024 2025 Total 
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

115 

economic 

opportunities 

away from 

home. 

Passenger 

cars 1 1 3 5 8 10 12 16 19 22 98 

Light trucks 0 0 1 2 3 4 5 6 6 8 35 

Combined 1 2 4 7 11 15 17 21 25 30 133 

 NHTSA projects that manufacturers will recover most or all of these additional costs 

through higher selling prices for new cars and light trucks. To allow manufacturers to recover 

these increased outlays (and, to a much less extent, the civil penalties that some manufacturers 

are expected to pay for non-compliance), the agency estimates that the standards would lead to 

increase in average new vehicle prices ranging from $161 per vehicle in MY 2017 to $1876 per 

vehicle in MY 2025: 

As evidenced by their decisions to make more frequent or longer trips when improved 

fuel economy reduces their costs for driving, the benefits from this additional travel exceed the 

costs drivers and passengers incur in traveling these additional distances. 

The agency’s analysis estimates the economic benefits from increased rebound-effect 

driving as the sum of fuel costs drivers incur plus the consumer surplus they receive from the 

additional accessibility it provides.115 NHTSA estimates the value of the consumer surplus 

provided by added travel as one-half of the product of the decline in fuel cost per mile and the 

resulting increase in the annual number of miles driven, a standard approximation for changes in 

consumer surplus resulting from small changes in prices. Because the increase in travel depends 

on the extent of improvement in fuel economy, the value of benefits it provides differs among 

model years and alternative CAFE standards. 

i. The Value of Increased Driving Range 

Improving vehicles’ fuel economy may also increase their driving range before they 

require refueling. By extending the upper limit of the range vehicles can travel before refueling 

is needed, the per-vehicle average number of refueling trips per year is expected to decline. This 

reduction in refueling frequency provides a time savings benefit to owners.116 

 115 The consumer surplus provided by added travel is estimated as one-half of the product of the decline in fuel cost 

per mile and the resulting increase in the annual number of miles driven.

116 If manufacturers respond to improved fuel economy by reducing the size of fuel tanks to maintain a constant 

driving range, the resulting cost saving will presumably be reflected in lower vehicle sales prices.
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

116 

NHTSA estimated a number of parameters regarding consumers’ refueling habits using 

newly-available observational and interview data from a 2010-2011 NASS study conducted at 

fueling stations throughout the nation. A (non-exhaustive) list of key parameters derived from 

this study is as follows: average number of gallons of fuel purchased, length of time to refuel and 

pay, length of time to drive to the fueling station, primary reason for refueling, and number of 

adult vehicle occupants. 

Using these and other parameters (detailed explanation of parameters and methodology 

provided in Chapter VIII of NHTSA’s PRIA), NHTSA estimated the decrease in number of 

refueling cycles for each model year’s fleet attributable to improvements in actual on-road MPG 

resulting from the proposed CAFE standards. NHTSA acknowledges – and adjusts for – the fact 

that many refueling trips occur for reasons other than a low reading on the gas gauge (for 

example, many consumers refuel on a fixed schedule). NHTSA separately estimated the value of 

vehicle-hour refueling time and applied this to the projected decrease in number of refueling 

cycles to estimate the aggregate fleet-wide value of refueling time savings for each year that a 

given model year’s vehicles are expected to remain in service. 

As noted in the PRIA, NHTSA assumed a constant fuel tank size in estimating the impact 

of higher CAFE requirements on the frequency of refueling. NHTSA seeks comment 

regarding this assumption. Specifically, NHTSA seeks comment from manufacturers regarding 

their intention to retain fuel tank size or driving range in their redesigned vehicles. Will fuel 

economy improvements translate into increased driving range, or will fuel tanks be reduced in 

size to maintain current driving range? 

j. Added Costs From Congestion, Crashes and Noise 

Increased vehicle use associated with the rebound effect also contributes to increased 

traffic congestion, motor vehicle accidents, and highway noise. To estimate the economic costs 

associated with these consequences of added driving, NHTSA applies estimates of per-mile 

congestion, accident, and noise costs caused by increased use of automobiles and light trucks 

developed previously by the Federal Highway Administration.117 These values are intended to 

measure the increased costs resulting from added congestion and the delays it causes to other 

drivers and passengers, property damages and injuries in traffic accidents, 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. After reviewing the procedures used by FHWA to 

develop them and considering other available estimates of these values, the agency continues to 

find them appropriate for use in this final rule. The agency multiplies FHWA’s estimates of permile costs by the annual increases in automobile and light truck use from the rebound effect to 

 117 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost Allocation Study; See 

http://www.fhwa.dot.gov/policy/hcas/final/index.htm (last accessed March 1, 2010).
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yield the estimated increases in congestion, accident, and noise externality costs during each 

future year. 

k. Petroleum Consumption and Import Externalities 

i. Changes in Petroleum Imports 

Based on a detailed analysis of differences in fuel consumption, petroleum imports, and 

imports of refined petroleum products among alternative scenarios presented in AEO 2011, 

NHTSA estimates that approximately 50 percent of the reduction in fuel consumption resulting 

from adopting higher CAFE standards is likely to be reflected in reduced U.S. imports of refined 

fuel, while the remaining 50 percent would reduce domestic fuel refining.118 Of this latter figure, 

90 percent is anticipated to reduce U.S. imports of crude petroleum for use as a refinery 

feedstock, while the remaining 10 percent is expected to reduce U.S. domestic production of 

crude petroleum.119 Thus on balance, each 100 gallons of fuel saved as a consequence of higher 

CAFE standards is anticipated to reduce total U.S. imports of crude petroleum or refined fuel by 

95 gallons.120

ii. Benefits from Reducing U.S. Petroleum Imports 

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 of petroleum products such as gasoline. These costs include (1) higher prices for 

petroleum products resulting from the effect of U.S. petroleum demand on the world oil price;(2) 

the risk of disruptions to the U.S. economy caused by sudden reductions in the supply of 

imported oil to the U.S.; and (3) expenses for maintaining a U.S. military presence to secure 

imported oil supplies from unstable regions, and for maintaining the strategic petroleum reserve 

(SPR) to cushion against resulting price increases.121 Reducing these costs by lowering U.S. 

petroleum imports represents another source of benefits from stricter CAFE standards and the 

savings in consumption of petroleum-based fuels that would result from higher fuel economy. 

 118 Differences in forecast annual U.S. imports of crude petroleum and refined products among the Reference, High 

Oil Price, and Low Oil Price scenarios analyzed in EIA’s Annual Energy Outlook 2011 range from 35-74percent of 

differences in projected annual gasoline and diesel fuel consumption in the U.S. These differences average 

53percent over the forecast period spanned by AEO 2011. 119 Differences in forecast annual U.S. imports of crude petroleum among the Reference, High Oil Price, and Low 

Oil Price scenarios analyzed in EIA’s Annual Energy Outlook 2011 ( range from 67-104 percent of differences in 

total U.S. refining of crude petroleum, and average 90 percent over the forecast period spanned by AEO 2011. 120 This figure is calculated as 50 gallons + 50 gallons*90% = 50 gallons + 45 gallons = 95 gallons. 121 See, e.g., Bohi, Douglas R. and W. David Montgomery (1982). Oil Prices, Energy Security, and Import Policy 

Washington, DC: Resources for the Future, Johns Hopkins University Press; Bohi, D.R., and M.A. Toman (1993). 

“Energy and Security: Externalities and Policies,” Energy Policy 21:1093– 1109 (Docket NHTSA–2009–0062–24); 

and Toman, M.A. (1993). “The Economics of Energy Security: Theory, Evidence, Policy,” in A.V. Kneese and J.L. 

Sweeney, eds. (1993) (Docket NHTSA–2009–0062–23). Handbook of Natural Resource and Energy Economics, 

Vol. III. Amsterdam: North-Holland, pp. 1167–1218. 
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Higher U.S. imports of crude oil or refined petroleum products increase the magnitude of these 

external economic costs, thus increasing the true economic cost of supplying transportation fuels 

above their market prices. Conversely, lowering U.S. imports of crude petroleum or refined 

fuels by reducing domestic fuel consumption can reduce these external costs, and any reduction 

in their total value that results from improved fuel economy represents an economic benefit of 

more stringent CAFE standards, in addition to the value of saving fuel itself. 

The first component of the external costs imposed by U.S. petroleum consumption and 

imports (often termed the “monopsony cost” of U.S. oil imports), measures the increase in 

payments from domestic oil consumers to foreign oil suppliers beyond the increased purchase 

price of petroleum itself that results when increased U.S. import demand raises the world price of 

petroleum.122 However, this monopsony cost or premium represents a financial transfer from 

consumers of petroleum products to oil producers, and does not entail the consumption of real 

economic resources. Thus the decline in its value that occurs when reduced U.S. demand for 

petroleum products causes a decline in global petroleum prices produces no savings in economic 

resources globally or domestically, although it does reduce the value of the financial transfer 

from U.S. consumers of petroleum products to foreign suppliers of petroleum. Accordingly, 

NHTSA’s analysis of the benefits from adopting proposed CAFE standards for MY 2017–2025 

cars and light trucks excludes the reduced value of monopsony payments by U.S. oil consumers 

that would result from lower fuel consumption. 

The second component of external costs imposed by U.S. petroleum consumption and 

imports reflects the potential costs to the U.S. economy from disruptions in the supply of 

imported petroleum. These costs arise because interruptions in the supply of petroleum products 

reduces U.S. economic output, as well as because firms are unable to adjust prices, output levels, 

and their use of energy, labor and other inputs smoothly and rapidly in response to the sudden 

changes in prices for petroleum products that are caused by interruptions in their supply. 

Reducing U.S. petroleum consumption and imports lowers these potential costs, and the amount 

by which it does so represents an economic benefit in addition to the savings in fuel costs that 

result from higher fuel economy. NHTSA estimates and includes this value in its analysis of the 

economic benefits from adopting higher CAFE standards for MY 2017–2025 cars and light 

trucks. 

The third component of external costs imposed by U.S. petroleum consumption and 

imports includes expenses for maintaining a U.S. military presence to secure imported oil 

supplies from unstable regions, and for maintaining the strategic petroleum reserve (SPR) to 

cushion against resulting price increases. NHTSA recognizes that potential national and energy 

security risks exist due to the possibility of tension over oil supplies. Much of the world’s oil 

 122 The reduction in payments from U.S. oil purchasers to domestic petroleum producers is not included as a benefit, 

since it represents a transfer that occurs entirely within the U.S. economy.
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and gas supplies are located in countries facing social, economic, and demographic challenges, 

thus making them even more vulnerable to potential local instability. Because of U.S. 

dependence on oil, the military could be called on to protect energy resources through such 

measures as securing shipping lanes from foreign oil fields. Thus, to the degree to which the 

proposed rules reduce reliance upon imported energy supplies or promotes the development of 

technologies that can be deployed by either consumers or the nation’s defense forces, the United 

States could expect benefits related to national security, reduced energy costs, and increased 

energy supply. Although NHTSA recognizes that there clearly is a benefit to the United States 

from reducing dependence on foreign oil, we have been unable to calculate the monetary benefit 

that the United States will receive from the improvements in national security expected to result 

from this program. We have therefore included only the macroeconomic disruption portion of 

the energy security benefits to estimate the monetary value of the total energy security benefits 

of this program. We have calculated energy security in very specific terms, as the reduction of 

both financial and strategic risks caused by potential sudden disruptions in the supply of 

imported petroleum to the U.S. Reducing the amount of oil imported reduces those risks, and 

thus increases the nation’s energy security. 

Similarly, while the costs for building and maintaining the SPR are more clearly 

attributable to U.S. petroleum consumption and imports, these costs have not varied historically 

in response to changes in U.S. oil import levels. Thus the agency has not attempted to estimate 

the potential reduction in the cost for maintaining the SPR that might result from lower U.S. 

petroleum imports, or to include an estimate of this value among the benefits of reducing 

petroleum consumption through higher CAFE standards. 

In analyzing benefits from its recent actions to increase light truck CAFE standards for 

model years 2005–07 and 2008–11, NHTSA relied on a 1997 study by Oak Ridge National 

Laboratory (ORNL) to estimate the value of reduced economic externalities from petroleum 

consumption and imports.123 More recently, ORNL updated its estimates of the value of these 

externalities, using the analytic framework developed in its original 1997 study in conjunction 

with recent estimates of the variables and parameters that determine their value.124 The updated 

ORNL study was subjected to a detailed peer review commissioned by EPA, and ORNL’s 

estimates of the value of oil import externalities were subsequently revised to reflect their 

 123 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits 

and Costs, ORNL–6851, Oak Ridge National Laboratory, November 1, 1997. Available at 

http://www.esd.ornl.gov/eess/energy_analysis/files/ORNL6851.pdf (last accessed October 11, 2011). 124 Leiby, Paul N. “Estimating the Energy Security Benefits of Reduced U.S. Oil Imports,” Oak Ridge National 

Laboratory, ORNL/TM–2007/028, Revised July 23, 2007. Available at 

http://www.esd.ornl.gov/eess/energy_analysis/files/Leiby2007%20Estimating%20the%20Energy%20Security%20B

enefits%20of%20Reduced%20U.S.%20Oil%20Imports%20ornl-tm-2007-028%20rev2007Jul25.pdf (last accessed 

October 11, 2011).
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comments and recommendations of the peer reviewers.125 Finally, at the request of EPA, ORNL 

has repeatedly revised its estimates of external costs from U.S. oil imports to reflect changes in 

the outlook for world petroleum prices, as well as continuing changes in the structure and 

characteristics of global petroleum supply and demand. 

As the preceding discussion indicates, NHTSA’s analysis of benefits from adopting 

higher CAFE standards includes only the reduction in economic disruption costs that is 

anticipated to result from reduced consumption of petroleum-based fuels and the associated 

decline in U.S. petroleum imports. ORNL’s updated analysis reports that this benefit, which is 

in addition to the savings in costs for producing fuel itself, is most likely to amount to $0.185 per 

gallon of fuel saved by requiring MY 2017-25 cars and light trucks to achieve higher fuel 

economy. However, considerable uncertainty surrounds this estimate, and ORNL’s updated 

analysis also indicates that a range of values extending from a low of $0.091 per gallon to a high 

of $0.293 per gallon should be used to reflect this uncertainty. 

We note that the calculation of energy security benefits does not include energy security 

costs associated with reliance on foreign sources of lithium and rare earth metals for HEVs and 

EVs. The agencies intend to attempt to quantify this impact for the final rule stage, and seek 

public input on information that would enable agencies to develop this analysis. NHTSA also 

seeks public input on the projections that energy security benefits will grow rapidly through 

2025. 

l. Air Pollutant Emissions 

i. Changes in Criteria Air Pollutant Emissions 

Criteria air pollutants include carbon monoxide (CO), hydrocarbon compounds (usually 

referred to as “volatile organic compounds,” or VOC), nitrogen oxides (NOX), fine particulate 

matter (PM2.5), and sulfur oxides (SOX). These pollutants are emitted during vehicle storage and 

use, as well as throughout the fuel production and distribution system. While reductions in 

domestic fuel refining, storage, and distribution that result from lower fuel consumption will 

reduce emissions of these pollutants, additional vehicle use associated with the fuel economy 

rebound effect will increase their emissions. The net effect of stricter CAFE standards on total 

emissions of each criteria pollutant depends on the relative magnitudes of reductions in its 

emissions during fuel refining and distribution, and increases 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 fuel savings from the proposed 

standards on total emissions of each pollutant is likely to differ. 

 125Peer Review Report Summary: Estimating the Energy Security Benefits of Reduced U.S. Oil Imports, ICF, Inc., 

September 2007. Available at Docket No. NHTSA–2009–0059–0160. 
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With the exception of SO2, NHTSA calculated annual emissions of each criteria pollutant 

resulting from vehicle use by multiplying its estimates of car and light truck use during each year 

over their expected lifetimes by per-mile emission rates for each vehicle class, fuel type, model 

year, and age. These emission rates were developed by U.S. EPA using its Motor Vehicle 

Emission Simulator (MOVES 2010a).126 Emission rates for SO2 were calculated by NHTSA 

using average fuel sulfur content estimates supplied by EPA, together with the assumption that 

the entire sulfur content of fuel is emitted in the form of SO2.

127 Total SO2 emissions under each 

alternative CAFE standard were calculated by applying the resulting emission rates directly to 

estimated annual gasoline and diesel fuel use by cars and light trucks. 

Changes in emissions of criteria air pollutants resulting from alternative increases in 

CAFE standards for MY 2017–2025 cars and light trucks are calculated from the differences 

between emissions under each alternative increase in CAFE standards, and emissions under the 

baseline alternative. 

Emissions of criteria air pollutants also occur during each phase of fuel production and 

distribution, including crude oil extraction and transportation, fuel refining, and fuel storage and 

transportation. NHTSA estimates the reductions in criteria pollutant emissions from producing 

and distributing fuel that would occur under alternative CAFE standards using emission rates 

obtained by EPA from Argonne National Laboratories’ Greenhouse Gases and Regulated 

Emissions in Transportation (GREET) model, which provides estimates of air pollutant 

emissions that occur in different phases of fuel production and distribution 128, 129 EPA modified 

the GREET model to change certain assumptions about emissions during crude petroleum 

extraction and transportation, as well as to update its emission rates to reflect adopted and 

pending EPA emission standards. 

The resulting emission rates were applied to the agency’s estimates of fuel consumption 

under alternative CAFE standards to develop estimates of total emissions of each criteria 

pollutant during fuel production and distribution. The agency then employed the estimates of the 

effects of changes in fuel consumption on domestic and imported sources of fuel supply 

 126 The MOVES model assumes that the per-mile rates at which these pollutants are emitted are determined by EPA 

regulations and the effectiveness of catalytic after-treatment of engine exhaust emissions, and are thus unaffected by 

changes in car and light truck fuel economy.

127 These are 30 and 15 parts per million (ppm, measured on a mass basis) for gasoline and diesel respectively, 

which produces emission rates of 0.17 grams of SO2 per gallon of gasoline and 0.10 grams per gallon of diesel. 128 Argonne National Laboratories, The Greenhouse Gas and Regulated Emissions from Transportation (GREET) 

Model, Version 1.8, June 2007, available at 

http://www.transportation.anl.gov/modeling_simulation/GREET/index.html (last accessed October 11, 2011). 129 Emissions that occur during vehicle refueling at retail gasoline stations (primarily evaporative emissions of 

volatile organic compounds, or VOCs) are already accounted for in the “tailpipe” emission factors used to estimate 

the emissions generated by increased light truck use. GREET estimates emissions in each phase of gasoline 

production and distribution in mass per unit of gasoline energy content; these factors are then converted to mass per 

gallon of gasoline using the average energy content of gasoline. 
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122 

discussed previously to calculate the effects of reductions in fuel use on changes in imports of 

refined fuel and domestic refining. NHTSA’s analysis assumes that reductions in imports of 

refined fuel would reduce criteria pollutant emissions during fuel storage and distribution only. 

Reductions in domestic fuel refining using imported crude oil as a feedstock are assumed to 

reduce emissions during fuel refining, storage, and distribution. Finally, reduced domestic fuel 

refining using domestically-produced crude oil is assumed to reduce emissions during all four 

phases of fuel production and distribution.130 

Finally, NHTSA calculated the net changes in domestic emissions of each criteria 

pollutant by summing the increases in emissions projected to result from increased vehicle use, 

and the reductions anticipated to result from lower domestic fuel refining and distribution.131 As 

indicated previously, the effect of adopting higher CAFE standards on total emissions of each 

criteria pollutant depends on the relative magnitudes of the resulting reduction in emissions from 

fuel refining and distribution, and the increase in emissions from additional vehicle use. 

Although these net changes vary significantly among individual criteria pollutants, the agency 

projects that on balance, adopting higher CAFE standards for MY 2017-25 cars and light trucks 

would reduce emissions of all criteria air pollutants except carbon monoxide (CO). 

The net changes in direct emissions of fine particulates (PM2.5) and other criteria 

pollutants that contribute to the formation of “secondary” fine particulates in the atmosphere 

(such as NOX, SOX, and VOCs) are converted to economic values using estimates of the 

reductions in health damage costs per ton of emissions of each pollutant that is avoided, which 

were developed by EPA. These savings represent the estimated reductions in the value of 

damages to human health resulting from lower atmospheric concentrations and population 

exposure to air pollution that occur when emissions of each pollutant that contributes to 

atmospheric PM2.5 concentrations are reduced. The value of reductions in the risk of premature 

death due to exposure to fine particulate pollution (PM2.5) account for a majority of EPA’s 

estimated values of reducing criteria pollutant emissions, although the value of avoiding other 

health impacts is also included in these estimates. 

These values do not include a number of unquantified benefits, such as reduction in the 

welfare and environmental impacts of PM2.5 pollution, or reductions in health and welfare 

impacts related to other criteria air pollutants (ozone, NO2, and SO2) and air toxics. EPA 

estimates different per-ton values for reducing emissions of PM and other criteria pollutants from 

 130 In effect, this assumes that the distances crude oil travels to U.S. refineries are approximately the same regardless 

of whether it travels from domestic oilfields or import terminals, and that the distances that gasoline travels from 

refineries to retail stations are approximately the same as those from import terminals to gasoline stations. We note 

that while assuming that all changes in upstream emissions result from a decrease in petroleum production and 

transport, our analysis of downstream criteria pollutant impacts assumes no change in the composition of the 

gasoline fuel supply.

131 All emissions from increased vehicle use are assumed to occur within the U.S., since CAFE standards would 

apply only to vehicles produced for sale in the U.S.
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vehicle use than for reductions in emissions of those same pollutants during fuel production and 

distribution.132 NHTSA applies these separate values to its estimates of changes in emissions 

from vehicle use and from fuel production and distribution to determine the net change in total 

economic damages from emissions of these pollutants. 

EPA projects that the per-ton values for reducing emissions of criteria pollutants from 

both mobile sources (including motor vehicles) and stationary sources such as fuel refineries and 

storage facilities will increase over time. These projected increases reflect rising income levels, 

which are assumed to increase affected individuals’ willingness to pay for reduced exposure to 

health threats from air pollution, as well as future population growth, which increases population 

exposure to future levels of air pollution. 

ii. Reductions in CO2 Emissions 

Emissions of carbon dioxide and other greenhouse gases (GHGs) occur throughout the 

process of producing and distributing transportation fuels, as well as from fuel combustion itself. 

Emissions of GHGs also occur in generating electricity, which NHTSA’s analysis anticipates 

will account for an increasing share of energy consumption by cars and light trucks produced in 

the model years that would be subject to their proposed rules. By reducing the volume of fuel 

consumed by passenger cars and light trucks, higher CAFE standards will reduce GHG 

emissions generated by fuel use, as well as throughout the fuel supply system. Lowering these 

emissions is likely to slow the projected pace and reduce the ultimate extent of future changes in 

the global climate, thus reducing future economic damages that changes in the global climate are 

expected to cause. By reducing the probability that climate changes with potentially catastrophic 

economic or environmental impacts will occur, lowering GHG emissions may also result in 

economic benefits that exceed the resulting reduction in the expected future economic costs 

caused by more gradual changes in the earth’s climatic systems. 

Quantifying and monetizing benefits from reducing GHG emissions is thus an important 

step in estimating the total economic benefits likely to result from establishing higher CAFE 

standards. Because carbon dioxide emissions account for nearly 95 percent of total GHG 

emissions that result from fuel combustion during vehicle use, NHTSA’s analysis of the effect of 

higher CAFE standards on GHG emissions focuses mainly on estimating changes in emissions of 

CO2. The agency estimates emissions of CO2 from passenger car and light truck use by 

multiplying the number of gallons of each type of fuel (gasoline and diesel) they are projected to 

consume under alternative CAFE standards by the quantity or mass of CO2 emissions released 

per gallon of fuel consumed. This calculation assumes that the entire carbon content of each fuel 

is converted to CO2 emissions during the combustion process. 

 132 These reflect differences in the typical geographic distributions of emissions of each pollutant, their contributions 

to ambient PM2.5 concentrations, pollution levels (predominantly those of PM2.5), and resulting changes in 

population exposure.
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NHTSA estimates emissions of CO2 that occur during fuel production and distribution 

using emission rates for each stage of this process (feedstock production and transportation, fuel 

refining and fuel storage and distribution) derived from Argonne National Laboratories’ 

Greenhouse Gases and Regulated Emissions in Transportation (GREET) model. For liquid fuels, 

NHTSA converts these rates to a per-gallon basis using the energy content of each fuel, and 

multiplies them by the number of gallons of each type of fuel produced and consumed under 

alternative standards to estimate total CO2 emissions from fuel production and distribution. 

GREET supplies emission rates for electricity generation that are expressed as grams of CO2 per 

unit of energy, so these rates are simply multiplied by the estimates of electrical energy used to 

charge the on-board storage batteries of plug-in hybrid and battery electric vehicles. As with all 

other effects of alternative CAFE standards, the reduction in CO2 emissions resulting from each 

alternative increase in standards is measured by the difference in total emissions from producing 

and consuming fuel energy used by MY 2017-25 cars and light trucks with those higher CAFE 

standards in effect, and total CO2 emissions from supplying and using fuel energy consumed 

under the baseline alternative. Unlike criteria pollutants, the agency’s estimates of CO2

emissions include those occurring in domestic fuel production and consumption, as well as in 

overseas production of petroleum and refined fuel for export to the U.S. Overseas emissions are 

included because GHG emissions throughout the world contribute equally to the potential for 

changes in the global climate. 

iii. Economic Value of Reductions in CO2 Emissions 

NHTSA takes the economic benefits from reducing CO2 emissions into account in 

developing and analyzing the alternative CAFE standards it has considered for MY 2017-25. 

Because research on the impacts of climate change does not produce direct estimates of the 

economic benefits from reducing CO2 or other GHG emissions, these benefits are assumed to be 

the “mirror image” of the estimated incremental costs resulting from increases in emissions. 

Thus the benefits from reducing CO2 emissions are usually measured by the savings in estimated 

economic damages that an equivalent increase in emissions would otherwise have caused. The 

agency does not include estimates of the economic benefits from reducing GHGs other than CO2

in its analysis of alternative CAFE standards. 

NHTSA estimates the value of the reductions in emissions of CO2 resulting from 

adopting alternative CAFE standards using a measure referred to as the “social cost of carbon,” 

abbreviated SCC. The SCC is intended to provide a monetary measure of the additional 

economic impacts likely to result from changes in the global climate that would result from an 

incremental increase in CO2 emissions. These potential effects include changes in agricultural 

productivity, the economic damages caused by adverse effects on human health, property losses 

and damages resulting from rising sea levels, and the value of ecosystem services. The SCC is 

expressed in constant dollars per additional metric ton of CO2 emissions occurring during a 
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specific year, and is higher for more distant future years because the damages caused by an 

additional ton of emissions increase with larger concentrations of CO2 in the earth’s atmosphere. 

Reductions in CO2 emissions that are projected to result from lower fuel production and 

consumption during each year over the lifetimes of MY 2017-25 cars and light trucks are 

multiplied by the estimated SCC appropriate for that year to determine the economic benefit 

from reducing emissions during that year. The net present value of these annual benefits is 

calculated using a discount rate that is consistent with that used to develop the estimate of each 

SCC estimate. This calculation is repeated for the reductions in CO2 emissions projected to 

result from each alternative increase in CAFE standards. 

NHTSA’s evaluates the economic benefits from reducing CO2 emissions using estimates 

of the SCC developed by an interagency working group convened for the specific purpose of 

developing new estimates for use by U.S. Federal agencies in regulatory evaluations. The 

group’s purpose in developing new estimates of the SCC was to allow Federal agencies to 

incorporate the social benefits of reducing CO2 emissions into cost-benefit analyses of regulatory 

actions that have relatively modest impacts on cumulative global emissions, as most Federal 

regulatory actions can be expected to have. NHTSA previously relied on the SCC estimates 

developed by this interagency group to analyze the alternative CAFE standards it considered for 

MY 2012-16 cars and light trucks, as well as the fuel efficiency standards it adopted for MY 

014-18 heavy-duty vehicles. 

The interagency group convened on a regular basis over the period from June 2009 

through February 2010, to explore technical literature in relevant fields and develop key inputs 

and assumptions necessary to generate estimates of the SCC. Agencies participating in the 

interagency process included the Environmental Protection Agency and the Departments of 

Agriculture, Commerce, Energy, Transportation, and Treasury. This process was convened by 

the Council of Economic Advisers and the Office of Management and Budget, with active 

participation and regular input from the Council on Environmental Quality, National Economic 

Council, Office of Energy and Climate Change, and Office of Science and Technology Policy. 

The interagency group’s main objective was to develop a range of SCC values using 

clearly articulated input assumptions grounded in the existing scientific and economic literatures, 

in conjunction with a range of models that employ different representations of climate change 

and its economic impacts. The group clearly acknowledged the many uncertainties that its 

process identified, and recommended that its estimates of the SCC should be updated 

periodically to incorporate developing knowledge of the science and economics of climate 

impacts. Specifically, it set a preliminary goal to revisit the SCC values within two years, or as 

substantial improvements in understanding of the science and economics of climate impacts and 

updated models for estimating and valuing these impacts become available. The group ultimately 

selected four SCC values for use in federal regulatory analyses. Three values were based on the 

average of SCC estimates developed using three different climate economic models (referred to 
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as integrated assessment models), using discount rates of 2.5, 3, and 5 percent. The fourth value, 

which represents the 95th percentile SCC estimate from the combined distribution of values 

generated by the three models at a 3 percent discount rate, represents the possibility of possibility 

of higher-than-expected impacts from the accumulation of GHGs in the earth’s atmosphere, and 

the consequently larger economic damages. 

Table IV-10 summarizes the interagency group’s estimates of the SCC during various 

future years, which the agency has updated to 2009 dollars to correspond to the other values it 

uses to estimate economic benefits from the alternative CAFE standards considered in this 

NPRM.133 

Table II-20 NHTSA’s Incremental Increases in Average New Vehicle Costs ($) under the 

CAFE Standards (Estimated Required) 

 2017 2018 2019 2020 2021 2022 2023 2024 2025 

Passenger 

cars 228 467 652 885 1,108 1,259 1,536 1,927 2,023 

Light 

trucks 44 187 427 688 965 1,102 1,284 1,428 1,578 

Combined 161 365 572 815 1,058 1,205 1,450 1,760 1,876 

 And as before, NHTSA estimates that manufacturers employing compliance 

flexibilities and advanced technologies to meet the standards could significantly reduce 

these increases.. Social Cost of CO2 Emissions for Selected Future Years (2009$ per metric 

ton) 

Discount Rate 5% 3% 2.5% 3% 

Source Average of estimates 95th

 percentile 

estimate 

2012 $5.28 $23.06 $37.53 $70.14

2015 $5.93 $24. 58 $39.57 $74.03

2020 $7.01 $27.10 $42.98 $83.17

2025 $8. 53 $30.43 $47.28 $93.11

2030 $10.05 $33.75 $51.58 $103.06

2035 $11.57 $37.08 $55.88 $113.00

2040 $13.09 $40.40 $60.19 $122.95

2045 $14.63 $43. 34 $63.59 $131.66

2050 $16.18 $46.27 $66.99 $140.37

 133 The SCC estimates reported in the table assume that the damages resulting from increased emissions are constant 

for small departures from the baseline emissions forecast incorporated in each estimate, an approximation that is 

reasonable for policies with projected effects on CO2 emissions that are small relative to cumulative global 

emissions. 
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As Table IV-10 shows, the four SCC estimates selected by the interagency group for use 

in regulatory analyses are $5, $23, $38, and $70 per metric ton (in 2009 dollars) for emissions 

occurring in the year 2012. The value that the interagency group centered its attention on is the 

average SCC estimate developed using different models and a 3 percent discount rate, or $23 per 

metric ton in 2012. To capture the uncertainties involved in regulatory impact analysis, 

however, the group emphasized the importance of considering the full range of estimated SCC 

values. As the table also shows, the SCC estimates also rise over time; for example, the average 

SCC at the 3 percent discount rate increases to $27 per metric ton of CO2 by 2020 and reaches 

$46 per metric ton of CO2 in 2050. 

Details of the process used by the interagency group to develop its SCC estimates, 

complete results including year-by-year estimates of each of the four values, and a thorough 

discussion of their intended use and limitations is provided in the document Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working 

Group on Social Cost of Carbon, United States Government, February 2010.134

m. Discounting Future Benefits and Costs 

Discounting future fuel savings and other benefits accounts for the reduction in their 

value when they are deferred until some future date, rather than received immediately. The 

value of benefits that are not expected to occur until the future is lower partly because people 

value current consumption more highly than equivalent consumption at some future date – stated 

simply, they are impatient – and partly because they expect their living standards to be higher in 

the future, so additional consumption will improve their well-being by more today than it will in 

the future. The discount rate expresses the percent decline in the value of these benefits—as 

viewed from today’s perspective—for each year they are deferred into the future. In evaluating 

the benefits from alternative increases in CAFE standards for MY 2017–2025 passenger cars and 

light trucks, NHTSA primarily employs a discount rate of 3 percent per year, but in accordance 

with OMB guidance, also presents these benefit and cost estimates using a 7 percent discount 

rate. 

While it presents results that reflect both discount rates, NHTSA believes that the 3 

percent rate is more appropriate for discounting future benefits from increased CAFE standards, 

because the agency expects that most or all of vehicle manufacturers’ costs for complying with 

higher CAFE standards will ultimately be reflected in higher selling prices for their new vehicle 

models. By increasing sales prices for new cars and light trucks, CAFE regulations will thus 

primarily affect vehicle purchases and other private consumption decisions. Both economic 

theory and OMB guidance on discounting indicate that the future benefits and costs of 

 134 This document is available in the docket for the 2012-2016 rulemaking (NHTSA–2009–0059). 
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regulations that mainly affect private consumption should be discounted at consumers’ rate of 

time preference.135

Current OMB guidance also indicates that savers appear to discount future consumption 

at an average real (that is, adjusted to remove the effect of inflation) rate of about 3 percent when 

they face little risk about the future. Since the real interest rate that savers require to persuade 

them to defer consumption into the future represents a reasonable estimate of consumers’ rate of 

time preference, NHTSA believes that the 3 percent rate is appropriate for discounting projected 

future benefits and costs resulting from higher CAFE standards. 

Because there is some uncertainty about whether vehicle manufacturers will completely 

recover their costs for complying with higher CAFE standards by increasing vehicle sales prices, 

however, NHTSA also presents benefit and cost estimates discounted using a higher rate. To the 

extent that manufacturers are unable to recover their costs for meeting higher CAFE standards by 

increasing new vehicle prices, these costs are likely to displace other investment opportunities 

available to them. OMB guidance indicates that the real economy-wide opportunity cost of 

capital is the appropriate discount rate to apply to future benefits and costs when the primary 

effect of a regulation is “...to displace or alter the use of capital in the private sector,” and OMB 

estimates that this rate currently averages about 7 percent.136 Thus the agency’s analysis of 

alternative increases in CAFE standards for MY 2017-25 cars and light trucks also reports 

benefits and costs discounted at a 7 percent rate. 

One important exception to the agency’s use of 3 percent and 7 percent discount rates is 

arises in discounting benefits from reducing CO2 emissions over the lifetimes of MY 2017–2025 

cars and light trucks to their present values. In order to ensure consistency in the derivation and 

use of the interagency group’s estimates of the unit values of reducing CO2 emissions (or SCC), 

the benefits from reducing CO2 emissions during each future year are discounted using the same 

“intergenerational” discount rates that were used to derive each of the alternative values. As 

indicated in Table IV-10 above, these rates are 2.5 percent, 3 percent, and 5 percent depending 

on which estimate of the SCC is being employed.137

n. Accounting for Uncertainty in Benefits and Costs 

In analyzing the uncertainty surrounding its estimates of benefits and costs from 

alternative CAFE standards, NHTSA considers alternative estimates of those assumptions and 

 135 Id. 136 Office of Management and Budget, Circular A–4, “Regulatory Analysis,” September 17, 2003, 33. Available at 

http://www.whitehouse.gov/omb/circulars/a004/a-4.pdf (last accessed Sept. 26, 2011). 137 The fact that the 3 percent discount rate used by the interagency group to derive its central estimate of the SCC is 

identical to the 3 percent short-term or “intra-generational” discount rate used by NHTSA to discount future benefits 

other than reductions in CO2 emissions is coincidental, and should not be interpreted as a required condition that 

must be satisfied in future rulemakings.
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parameters likely to have the largest effect. These include the projected costs of fuel economyimproving technologies and their anticipated effectiveness in reducing fuel consumption, 

forecasts of future fuel prices, the magnitude of the rebound effect, the reduction in external 

economic costs resulting from lower U.S. oil imports, and the discount rate applied to future 

benefits and costs. The range for each of these variables employed in the uncertainty analysis 

was previously identified in the sections of this notice discussing each variable. 

The uncertainty analysis was conducted by assuming either independent normal or beta 

probability distributions for each of these variables, using the low and high estimates for each 

variable as the values between which 90 percent of observed values are expected to fall. Each 

trial of the uncertainty analysis employed a set of values randomly drawn from these probability 

distributions, under the assumption that the value of each variable is independent from those of 

the others. In cases where the data on the possible distribution of parameters was relatively 

sparse, making a choice of distributions difficult, a beta distribution is commonly employed to 

give more weight to both tails than would be the case had a normal distribution been employed. 

Benefits and costs of each alternative standard were estimated using each combination of 

variables, and a total of nearly 40,000 trials were used to estimate the likely range of estimated 

benefits and costs for each alternative standard. 

o. Where can readers find more information about the economic assumptions? 

Much more detailed information is provided in Chapter VIII of the PRIA, and a 

discussion of how NHTSA and EPA jointly reviewed and updated economic assumptions for 

purposes of this final rule is available in Chapter 4 of the draft Joint TSD. In addition, all of 

NHTSA’s model input and output files are now public and available for the reader’s review and 

consideration. The economic input files can be found in the docket for this proposed rule, 

NHTSA-2010-0131, and on NHTSA’s website.138

Finally, because much of NHTSA’s economic analysis for purposes of this final rule 

builds on the work that was done for the final rule establishing CAFE standards for MYs 2012-

16, we refer readers to that document as well. It contains valuable background information 

concerning how NHTSA’s assumptions regarding economic inputs for CAFE analysis have 

evolved over the past several rulemakings, both in response to comments and as a result of the 

agency’s growing experience with this type of analysis.139

3. How does NHTSA use the assumptions in its modeling analysis? 

In developing today’s proposed CAFE standards, NHTSA has made significant use of 

results produced by the CAFE Compliance and Effects Model (commonly referred to as “the 

 138 See http://www.nhtsa.gov/fuel-economy 139 74 FR 14308–14358 (Mar. 30, 2009). 
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CAFE Model” or “the Volpe model”), which DOT’s Volpe National Transportation Systems 

Center developed specifically to support NHTSA’s CAFE rulemakings. The model, which has 

been constructed specifically for the purpose of analyzing potential CAFE standards, integrates 

the following core capabilities: 

(1) Estimating how manufacturers could apply technologies in response to new fuel 

economy standards, 

(2) Estimating the costs that would be incurred in applying these technologies, 

(3) Estimating the physical effects resulting from the application of these technologies, 

such as changes in travel demand, fuel consumption, and emissions of carbon dioxide and 

criteria pollutants, and 

(4) Estimating the monetized societal benefits of these physical effects. 

An overview of the model follows below. Separate model documentation provides a 

detailed explanation of the functions the model performs, the calculations it performs in doing 

so, and how to install the model, construct inputs to the model, and interpret the model’s outputs. 

Documentation of the model, along with model installation files, source code, and sample inputs 

are available at NHTSA’s Web site. The model documentation is also available in the docket for 

today’s proposed rule, as are inputs for and outputs from analysis of today’s proposed CAFE 

standards. 

a. How does the model operate? 

As discussed above, the agency uses the CAFE model to estimate how manufacturers 

could attempt to comply with a given CAFE standard by adding technology to fleets that the 

agency anticipates they will produce in future model years. This exercise constitutes a 

simulation of manufacturers’ decisions regarding compliance with CAFE standards. 

This compliance simulation begins with the following inputs: (a) the baseline and 

reference market forecast discussed above in Section IV.C.1 and Chapter 1 of the TSD, (b) 

technology-related estimates discussed above in Section IV.C.2 and Chapter 3 of the TSD, (c) 

economic inputs discussed above in Section IV.C.3 and Chapter 4 of the TSD, and (d) inputs 

defining baseline and potential new CAFE standards. For each manufacturer, the model applies 

technologies in a sequence that follows a defined engineering logic (“decision trees” discussed in 

the MY 2011 final rule and in the model documentation) and a cost-minimizing strategy in order 

to identify a set of technologies the manufacturer could apply in response to new CAFE 

standards.140 The model applies technologies to each of the projected individual vehicles in a 

 140 NHTSA does its best to remain scrupulously neutral in the application of technologies through the modeling 

analysis, to avoid picking technology “winners.” The technology application methodology has been reviewed by the 
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manufacturer’s fleet, considering the combined effect of regulatory and market incentives. 

Depending on how the model is exercised, it will apply technology until one of the following 

occurs: 

(1) The manufacturer’s fleet achieves compliance141 with the applicable standard, and 

continuing to add technology in the current model year would be attractive neither in 

terms of stand-alone (i.e., absent regulatory need) cost effectiveness nor in terms of 

facilitating compliance in future model years;142 

(2) The manufacturer “exhausts”143 available technologies; or 

(3) For manufacturers estimated to be willing to pay civil penalties, the manufacturer 

reaches the point at which doing so would be more cost-effective (from the 

manufacturer’s perspective) than adding further technology.144

As discussed below, the model has also been modified in order to—as an option—apply 

more technology than may be necessary to achieve compliance in a given model year, or to 

facilitate compliance in later model years. This ability to simulate “voluntary overcompliance” 

reflects the potential that manufacturers will apply some technologies to some vehicles if doing 

 agency over the course of several rulemakings, and commenters have been generally supportive of the agency’s 

approach. See, e.g., 74 FR 14238–14246 (Mar. 30, 2009). 141 The model has been modified to provide the ability—as an option—to account for credit mechanisms (i.e., carryforward, carry-back, transfers, and trades) when determining whether compliance has been achieved. For purposes 

of determining maximum feasible CAFE standards, NHTSA cannot consider these mechanisms, and exercises the 

CAFE model without enabling these options. 

142 In preparation for the MY2012-2016 rulemaking, the model was modified in order to apply additional technology 

in early model years if doing so will facilitate compliance in later model years. This is designed to simulate a 

manufacturer’s decision to plan for CAFE obligations several years in advance, which NHTSA believes better 

replicates manufacturers’ actual behavior as compared to the year-by-year evaluation which EPCA would otherwise 

require. 

143 In a given model year, the model makes additional technologies available to each vehicle model within several 

constraints, including (a) whether or not the technology is applicable to the vehicle model’s technology class, (b) 

whether the vehicle is undergoing a redesign or freshening in the given model year, (c) whether engineering aspects 

of the vehicle make the technology unavailable (e.g., secondary axle disconnect cannot be applied to two-wheel 

drive vehicles), and (d) whether technology application remains within “phase in caps” constraining the overall 

share of a manufacturer’s fleet to which the technology can be added in a given model year. Once enough 

technology is added to a given manufacturer’s fleet in a given model year that these constraints make further 

technology application unavailable, technologies are “exhausted” for that manufacturer in that model year. 

144 This possibility was added to the model to account for the fact that under EPCA/EISA, manufacturers must pay 

fines if they do not achieve compliance with applicable CAFE standards. 49 U.S.C. 32912(b). NHTSA recognizes 

that some manufacturers will find it more cost-effective to pay fines than to achieve compliance, and believes that to 

assume these manufacturers would exhaust available technologies before paying fines would cause unrealistically 

high estimates of market penetration of expensive technologies such as diesel engines and strong hybrid electric 

vehicles, as well as correspondingly inflated estimates of both the costs and benefits of any potential CAFE 

standards. NHTSA thus includes the possibility of manufacturers choosing to pay fines in its modeling analysis in 

order to achieve what the agency believes is a more realistic simulation of manufacturer decision-making. Unlike 

flex-fuel and other credits, NHTSA is not barred by statute from considering fine-payment in determining maximum 

feasible standards under EPCA/EISA. 49 U.S.C. 32902(h). 
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so would be sufficiently inexpensive compared to the expected reduction in owners’ outlays for 

fuel. 

The model accounts explicitly for each model year, applying most technologies when 

vehicles are scheduled to be redesigned or freshened, and carrying forward technologies between 

model years. The CAFE model accounts explicitly for each model year because EPCA 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.145 The 

multiyear planning capability and (optional) simulation of “voluntary overcompliance” and 

EPCA credit mechanisms increase the model’s ability to simulate manufacturers’ real-world 

behavior, accounting for the fact that manufacturers will seek out compliance paths for several 

model years at a time, while accommodating the year-by-year requirement. 

The model also calculates the costs, effects, and benefits of technologies that it estimates 

could be added in response to a given CAFE standard.146 It calculates costs by applying the cost 

estimation techniques discussed above in Section IV.C.2, and by accounting for the number of 

affected vehicles. It accounts for effects such as changes in vehicle travel, changes in fuel 

consumption, and changes in greenhouse gas and criteria pollutant emissions. It does so by 

applying the fuel consumption estimation techniques also discussed in Section IV.C.2, and the 

vehicle survival and mileage accumulation forecasts, the rebound effect estimate and the fuel 

properties and emission factors discussed in Section IV.C.3. Considering changes in travel 

demand and fuel consumption, the model estimates the monetized value of accompanying 

benefits to society, as discussed in Section IV.C.3. The model calculates both the undiscounted 

and discounted value of benefits that accrue over time in the future. 

The CAFE model has other capabilities that facilitate the development of a CAFE 

standard. The integration of (a) compliance simulation and (b) the calculation of costs, effects, 

and benefits facilitates analysis of sensitivity of results to model inputs. The model can also be 

used to evaluate many (e.g., 200 per model year) potential levels of stringency sequentially, and 

identify the stringency at which specific criteria are met. For example, it can identify the 

stringency at which net benefits to society are maximized, the stringency at which a specified 

total cost is reached, or the stringency at which a given average required fuel economy level is 

attained. This allows the agency to compare more easily the impacts in terms of fuel savings, 

 145 49 U.S.C. 32902(a) states that at least 18 months before the beginning of each model year, the Secretary of 

Transportation shall prescribe by regulation average fuel economy standards for automobiles manufactured by a 

manufacturer in that model year, and that each standard shall be the maximum feasible average fuel economy level 

that the Secretary decides the manufacturers can achieve in that year. NHTSA has long interpreted this statutory 

language to require year-by-year assessment of manufacturer capabilities. 49 U.S.C. 32902(b)(2)(C) also requires 

that standards increase ratably between MY 2011 and MY 2020. 

146 As for all of its other rulemakings, NHTSA is required by Executive Order 12866 (as amended by Executive 

Order 13563) and DOT regulations to analyze the costs and benefits of CAFE standards. Executive Order 12866, 58 

FR 51735 (Oct. 4, 1993); DOT Order 2100.5, “Regulatory Policies and Procedures,” 1979, available at 

http://regs.dot.gov/rulemakingrequirements.htm (last accessed February 21, 2010). 
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emissions reductions, and costs and benefits of achieving different levels of stringency according 

to different criteria. The model can also be used to perform uncertainty analysis (i.e., Monte 

Carlo simulation), in which input estimates are varied randomly according to specified 

probability distributions, such that the uncertainty of key measures (e.g., fuel consumption, costs, 

benefits) can be evaluated. 

b. Has NHTSA considered other models? 

As discussed in the most recent CAFE rulemaking, while nothing in EPCA requires 

NHTSA to use the CAFE model, and in principle, NHTSA could perform all of these tasks 

through other means, the model’s capabilities have greatly increased the agency’s ability to 

rapidly, systematically, and reproducibly conduct key analyses relevant to the formulation and 

evaluation of new CAFE standards.147

NHTSA notes that the CAFE model not only has been formally peer-reviewed and tested 

and reviewed through three rulemakings, but also has some features especially important for the 

analysis of CAFE standards under EPCA/EISA. Among these are the ability to perform year-byyear analysis, and the ability to account for engineering differences between specific vehicle 

models. 

EPCA requires that NHTSA set CAFE standards for each model year at the level that 

would be “maximum feasible” for that year.148 Doing so requires the ability to analyze each 

model year and, when developing regulations covering multiple model years, to account for the 

interdependency of model years in terms of the appropriate levels of stringency for each one. 

Also, as part of the evaluation of the economic practicability of the standards, as required by 

EPCA, NHTSA has traditionally assessed the annual costs and benefits of the standards. In 

response to comments regarding an early version of the CAFE model, DOT modified the CAFE 

model in order to account for dependencies between model years and to better represent 

manufacturers’ planning cycles, in a way that still allowed NHTSA to comply with the statutory 

requirement to determine the appropriate level of the standards for each model year. 

The CAFE model is also able to account for important engineering differences between 

specific vehicle models, and to thereby reduce the risk of applying technologies that may be 

incompatible with or already present on a given vehicle model. By combining technologies 

incrementally and on a model-by-model basis, the CAFE model is able to account for important 

engineering differences between vehicle models and avoid unlikely technology combinations 

The CAFE model also produces a single vehicle-level output file that, for each vehicle 

model, shows which technologies were present at the outset of modeling, which technologies 

 147 75 FR 25598-25599. 
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were superseded by other technologies, and which technologies were ultimately present at the 

conclusion of modeling. For each vehicle, the same file shows resultant changes in vehicle 

weight, fuel economy, and cost. This provides for efficient identification, analysis, and 

correction of errors, a task with which the public can now assist the agency, since all inputs and 

outputs are public. 

Such considerations, as well as those related to the efficiency with which the CAFE 

model is able to analyze attribute-based CAFE standards and changes in vehicle classification, 

and to perform higher-level analysis such as stringency estimation (to meet predetermined 

criteria), sensitivity analysis, and uncertainty analysis, lead the agency to conclude that the model 

remains the best available to the agency for the purposes of analyzing potential new CAFE 

standards. 

c. What changes has DOT made to the model? 

Between promulgation of the MY 2012-2016 CAFE standards and today’s proposal 

regarding MY 2017-2025 standards, the CAFE model has been revised to make some minor 

improvements, and to add some significant new capabilities: (1) accounting for electricity used 

to charge electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), (2) accounting 

for use of ethanol blends in flexible-fuel vehicles (FFVs), (3) accounting for costs (i.e., “stranded 

capital”) related to early replacement of technologies, (4) accounting for previously-applied 

technology when determining the extent to which a manufacturer could expand use of the 

technology, (5) applying technology-specific estimates of changes in consumer value, (6) 

simulating the extent to which manufacturers might utilize EPCA’s provisions regarding 

generation and use of CAFE credits, (7) applying estimates of fuel economy adjustments (and 

accompanying costs) reflecting increases in air conditioner efficiency, (8) reporting privatelyvalued benefits, (9) simulating the extent to which manufacturers might voluntarily apply 

technology beyond levels needed for compliance with CAFE standards, and (10) estimating 

changes in highway fatalities attributable to any applied reductions in vehicle mass. These 

capabilities are described below, and in greater detail in the CAFE model documentation.149

To support evaluation of the effects electric vehicles (EVs) and plug-in hybrid vehicles 

(PHEVs) could have on energy consumption and associated costs and environmental effects, 

DOT has expanded the CAFE model to estimate the amount of electricity that would be required 

to charge these vehicles (accounting for the potential that PHEVs can also run on gasoline). The 

model calculates the cost of this electricity, as well as the accompanying upstream criteria 

pollutant and greenhouse gas emissions. 

 Similar to this expansion to account for the potential the PHEVs can be refueled with 

gasoline or recharged with electricity, DOT has expanded the CAFE model to account for the 

 149 [Add reference to model documentation.] 
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potential that other flexible-fuel vehicles can be operated on multiple fuels. In particular, the 

model can account for ethanol FFVs consuming E85 or gasoline, and to report consumption of 

both fuels, as well as corresponding costs and upstream emissions. 

Among the concerns raised in the past regarding how technology costs are estimated has 

been one that stranded capital costs be considered. Capital becomes “stranded” when capital 

equipment is retired or its use is discontinued before the equipment has been fully depreciated 

and the equipment still retains some value or usefulness. DOT has modified the CAFE model to, 

if specified for a given technology, when that technology is replaced by a newly applied 

technology, apply a stream of costs representing the stranded capital cost of the replaced 

technology. This cost is in addition to the cost for producing the newly applied technology in the 

first year of production. 

As documented in prior CAFE rulemakings, the CAFE model applies “phase-in caps” to 

constrain technology application at the vehicle manufacturer level. They are intended to reflect 

a manufacturer’s overall resource capacity available for implementing new technologies (such as 

engineering and development personnel and financial resources), thereby ensuring that resource 

capacity is accounted for in the modeling process. This helps to ensure technological feasibility 

and economic practicability in determining the stringency of the standards. When the MY 2012-

2016 rulemaking analysis was completed, the model performed the relevant test by comparing a 

given phase-in cap to the amount (i.e., the share of the manufacturer’s fleet) to which the 

technology had been added by the model. DOT has since modified the CAFE model to take into 

account the extent to which a given manufacturer has already applied the technology (i.e., as 

reflected in the market forecast specified as a model inputs), and to apply the relevant test based 

on the total application of the technology. 

The CAFE model requires inputs defining the technology-specific cost and efficacy (i.e., 

percentage reduction of fuel consumption), and has, to date, effectively assumed that these input 

values reflect application of the technology in a manner that holds vehicle performance and 

utility constant. Considering that some technologies may, nonetheless, offer owners greater or 

lesser value (beyond that related to fuel outlays, which the model calculates internally based on 

vehicle fuel type and fuel economy), DOT has modified the CAFE model to accept and apply 

technology-specific estimates of any value gain realized or loss incurred by vehicle 

purchasers.150

For the MY 2012-2016 CAFE rulemaking analysis, DOT modified the CAFE model to 

accommodate specification and accounting for credits a manufacturer is assumed to earn by 

producing flexible fuel vehicles (FFVs). Although NHTSA cannot consider such credits when 

determining maximum feasible CAFE standards, the agency presented an analysis that included 

 150 For example, a value gain could be specified for a technology expected to improve ride quality, and a value loss 

could be specified for a technology expected to reduce vehicle range. 
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FFV credits, in order to communicate the extent to which use of such credits might cause actual 

costs, effects, and benefits to be lower than estimated in NHTSA’s formal analysis. As DOT 

explained at the time, it was unable to account for other EPCA credit mechanisms, because 

attempts to do so had been limited by complex interactions between those mechanisms and the 

multiyear planning aspects of the CAFE model. DOT has since modified the CAFE model to 

provide the ability to account for any or all of the following flexibilities provided by EPCA: 

FFV credits, credit carry-forward and carry-back (between model years), credit transfers 

(between passenger car and light truck fleets), and credit trades (between manufacturers). The 

model accounts for EPCA-specified limitations applicable to these flexibilities (e.g., limits on the 

amount of credit that can be transferred between passenger car and light truck fleets). These 

capabilities in the model provide a basis for more accurately estimating costs, effects, and 

benefits that may actually result from new CAFE standards. Insofar as some manufacturers 

actually do earn and use CAFE credits, this provides NHTSA with the ability to examine 

outcomes more realistically than EPCA allows for purposes of setting new CAFE standards. 

NHTSA is today proposing CAFE standards reflecting EPA’s proposal to change fuel 

economy calculation procedures such that a vehicle’s fuel consumption improvement will be 

accounted for if the vehicle has technologies that reduce the amount of energy needed to power 

the air conditioner. To facilitate analysis of these standards, DOT has modified the CAFE model 

to account for these adjustments, based on inputs specifying the average amount of improvement 

anticipated, and the estimated average cost to apply the underlying technology. 

Considering that past CAFE rulemakings indicate that most of the benefits of CAFE 

standards are realized by vehicle owners, DOT has modified the CAFE model to estimate not 

just social benefits, but also private benefits. The model accommodates separate discount rates 

for these two valuation methods (e.g., a 3% rate for social benefits with a 7% rate for private 

benefits). When calculating private benefits, the model includes changes in outlays for fuel taxes 

(which, as economic transfers, are excluded from social benefits) and excludes changes in 

economic externalities (e.g., monetized criteria pollutant and greenhouse gas emissions). 

Since 2003, the CAFE model (and its predecessors) have provided the ability to estimate 

the extent to which a manufacturer with a history of paying civil penalties allowed under EPCA 

might decide to add some fuel-saving technology, but not enough to comply with CAFE 

standards. In simulating this decision-making, the model considers the cost to add the 

technology, the calculated reduction in civil penalties, and the calculated present value (at the 

time of vehicle purchase) of the change in fuel outlays over a specified “payback period” (e.g., 5 

years). For a manufacturer assumed to be willing to pay civil penalties, the model stops adding 

technology once paying fines becomes more attractive than continuing to add technology, 

considering these three factors. As an extension of this simulation approach, DOT has modified 

the CAFE model to, if specified, simulate the potential that a manufacturer would add more 

technology than required for purposes of compliance with CAFE standards. When set to operate 

in this manner, the model will continue to apply technology to a manufacturer’s CAFE-
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compliant fleet until applying further technology will incur more in cost than it will yield in 

calculated fuel savings over a specified “payback period” that is set separately from the payback 

period applicable until compliance is achieved. In its analysis supporting MY 2012-2016 

standards adopted in 2010, NHTSA estimated the extent to which reductions in vehicle mass 

might lead to changes in the number of highway fatalities occurring over the useful life of the 

MY 2012-2016 fleet. NHTSA performed these calculations outside the CAFE model (using 

vehicle-specific mass reduction calculations from the model), based on agency analysis of 

relevant highway safety data. DOT has since modified the CAFE model to perform these 

calculations, using an analytical structure indicated by an update to the underlying safety 

analysis. The model also applies an input value indicating the economic value of a statistical 

life, and includes resultant benefits (or disbenefits) in the calculation of total social benefits. 

In comments on recent NHTSA rulemakings, some reviewers have suggested that the 

CAFE model should be modified to estimate the extent to which new CAFE standards would 

induce changes in the mix of vehicles in the new vehicle fleet. NHTSA agrees that a “market 

shift” model, also called a consumer vehicle choice model, could provide useful information 

regarding the possible effects of potential new CAFE standards. NHTSA has contracted with the 

Brookings Institution (which has subcontracted with researchers at U.C. Davis, U.C. Irvine) to 

develop a vehicle choice model estimated at the vehicle configuration level that can be 

implemented as part of DOT’s CAFE model. As discussed further in Section V of the PRIA, 

past efforts by DOT staff demonstrated that a vehicle could be added to the CAFE model, but did 

not yield credible coefficients specifying such a model. If a suitable and credibly calibrated 

vehicle choice model becomes available in time—whether through the Brookings-led research or 

from other sources, DOT may integrate a vehicle choice model into the CAFE model for the 

final rule. 

NHTSA anticipates this integration of a vehicle choice model would be structurally and 

operationally similar to the integration we implemented previously. As under the version applied 

in support of today’s announcement, the CAFE model would begin with an agency-estimated 

market forecast, estimate to what extent manufacturers might apply additional fuel-saving 

technology to each vehicle model in consideration of future fuel prices and baseline or 

alternative CAFE standards and fuel prices, and calculate resultant changes in the fuel economy 

(and possibly fuel type) and price of individual vehicle models. With an integrated market share 

model, the CAFE model would then estimate how the sales volumes of individual vehicle 

models would change in response to changes in fuel economy levels and prices throughout the 

light vehicle market, possibly taking into account interactions with the used vehicle 

market. Having done so, the model would replace the sales estimates in the original market 

forecast with those reflecting these model-estimated shifts, repeating the entire modeling cycle 

until converging on a stable solution. 

Based on past experience, we anticipate that this recursive simulation will be necessary to 

ensure consistency between sales volumes and modeled fuel economy standards, because 
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achieved CAFE levels depend on sales mix and, under attribute-based CAFE standards, required 

CAFE levels also depend on sales mix. NHTSA anticipates, therefore, that application of a 

market share model would impact estimates of all of the following for a given schedule of CAFE 

standards: overall market volume, manufacturer market shares and product mix, required and 

achieved CAFE levels, technology application rates and corresponding incurred costs, fuel 

consumption, greenhouse gas and criteria pollutant emissions, changes in highway fatalities, and 

economic benefits. 

Past testing by DOT/NHTSA staff did not indicate major shifts in broad measures (e.g., 

in total costs or total benefits), but that testing emphasized shorter modeling periods (e.g., 1-5 

model years) and less stringent standards than reflected in today’s proposal. Especially without 

knowing the characteristics of a future vehicle choice model, it is difficult to anticipate the 

potential degree to which its inclusion would impact analytical outcomes. 

NHTSA invites comment on the above changes to the CAFE model. The agency’s 

consideration of any alternative approaches will be facilitated by specific recommendations 

regarding implementation within the model’s overall structure. NHTSA also invites comment 

regarding above-mentioned prospects for inclusion of a vehicle choice model. The agency’s 

consideration will be facilitated by specific information demonstrating that inclusion of such a 

model would lead to more realistic estimates of costs, effects, and benefits, or that inclusion of 

such a model would lead to less realistic estimates. 

d. Does the model set the standards? 

Since NHTSA began using the CAFE model in CAFE analysis, some commenters have 21 

NHTSA’s Incremental Increases in Average New Vehicle Costs ($) under the CAFE 

Standards (Estimated Achieved) 

 2017 2018 2019 2020 2021 2022 2023 2024 2025 

Passenger 

cars 141 320 529 767 977 1,122 1,424 1,688 1,926 

Light 

trucks 57 178 359 524 755 863 976 1,141 1,348 

Combined 110 268 468 681 899 1,032 1,271 1,505 1,735 

 NHTSA estimates, therefore, that the total benefits of these proposed CAFE standards 

will be more than 2.5 times the magnitude of the corresponding costs. As a consequence, the 

proposed CAFE standards would produce net benefits of $358 billion at a 3 percent discount rate 

(with compliance flexibilities, $355 billion), or $262 billion at a 7 percent discount rate (with 

compliance flexibilities, $264 billion), over the useful lives of the vehicles sold during MYs 

2017-2025. 
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4. Summary of Costs and Benefits for the Proposed EPA GHG Standards 

EPA has analyzed in detail the costs and benefits of the proposed GHG standards. Table 

II-27 shows EPA’s estimated lifetime discounted cost, fuel savings, and benefits for all vehicles 

projected to be sold in model years 2017-2025. The benefits include impacts such as climaterelated economic benefits from reducing emissions of CO2 (but not other GHGs), reductions in 

energy security externalities caused by U.S. petroleum consumption and imports, the value of 

certain health benefits, the value of additional driving attributed to the rebound effect, the value 

of reduced refueling time needed to fill up a more fuel efficient vehicle. The analysis also 

includes economic impacts stemming from additional vehicle use, such as the economic damages 

caused by accidents, congestion and noise. Note that benefits depend on estimated values for the 

social cost of carbon (SCC), as described in Section III.H. 

Table II-22 EPA’s Estimated 2017-2025 Model Year Lifetime Discounted Costs, Benefits, 

and Net Benefits assuming the 3% discount rate SCC Valuea,b (Billions of 2009 dollars) 

Lifetime Present Valuec

 – 3% Discount Rate 

Program Costs $140 

Fuel Savings $444 

Benefits $117 

Net Benefitsd $421 

Annualized Valuee – 3% Discount Rate 

Annualized costs $6.43 

Annualized fuel savings $20.3 

Annualized benefits $5.36 

Net benefits $19.3 

Lifetime Present Valuec - 7% Discount Rate 

Program Costs $138 

Fuel Savings $347 

Benefits $101 

Net Benefitsd $311 

Annualized Valuee – 7% Discount Rate 

Annualized costs $10.64 

Annualized fuel savings $26.7 

Annualized benefits $6.35 

Net benefits $22.4 

Notes: a

 The agencies estimated the benefits associated with four different values of a one ton CO2

reduction (model average at 2.5% discount rate, 3%, and 5%; 95th percentile at 3%), which each 

increase over time. For the purposes of this overview presentation of estimated costs and benefits, 

however, we are showing the benefits associated with the marginal value deemed to be central by 
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140 

the interagency working group on this topic: the model average at 3% discount rate, in 2009 

dollars. Section III.H provides a complete list of values for the 4 estimates. b

 Note that net present value of reduced GHG emissions is calculated differently than other 

benefits. The same discount rate used to discount the value of damages from future emissions 

(SCC at 5, 3, and 2.5 percent) is used to calculate net present value of SCC for internal 

consistency. Refer to Section III.H for more detail 

c

Present value is the total, aggregated amount that a series of monetized costs or benefits that 

occur over time is worth in a given year. For this analysis, lifetime present values are calculated 

for the first year of each model year for MYs 2017-2025 (in year 2009 dollar terms). The lifetime 

present values shown here are the present values of each MY in its first year summed across MYs. d

 Net benefits reflect the fuel savings plus benefits minus costs. 

e The annualized value is the constant annual value through a given time period (the lifetime of 

each MY in this analysis) whose summed present value equals the present value from which it was 

derived. Annualized SCC values are calculated using the same rate as that used to determine the 

SCC value while all other costs and benefits are annualized at either 3% or 7%. 

Table II-23 shows EPA’s estimated lifetime fuel savings and CO2 equivalent emission 

reductions for all vehicles sold in the model years 2017-2025. The values in Table II-23 are 

projected lifetime totals for each model year and are not discounted. As documented in EPA’s 

draft RIA, the potential credit transfer between cars and trucks may change the distribution of the 

fuel savings and GHG emission impacts between cars and trucks. As discussed above with 

respect to NHTSA’s CAFE standards, it is important to note that NHTSA’s CAFE standards and 

EPA’s GHG standards will both be in effect, and each will lead to increases in average fuel 

economy and reductions in CO2 emissions. The two agencies’ standards together comprise the 

National Program, and this discussion of costs and benefits of EPA’s proposed GHG standards 

does not change the fact that both the proposed CAFE and GHG standards, jointly, are the source 

of the benefits and costs of the National Program. In general though, in addition to the added 

GHG benefit of HFC reductions from the EPA program, the fuel savings benefit are also 

somewhat higher than that from CAFE, primarily because of the possibility of paying civil 

penalties in lieu of applying technology in NHTSA’s program, which is required by EPCA. 
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Table II-23 EPA’s Estimated 2017-2025 Model Year Lifetime Fuel Saved and GHG Emissions Avoided 

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023 

MY 

2024 

MY 

2025 

MY Total 

Cars 

Fuel (billion gallons) 1 3 6 8 11 13 16 19 21 99 

Fuel (billion barrels) 0.0 0.1 0.1 0.2 0.3 0.3 0.4 0.4 0.5 2.4 

CO2 EQ (mmt) 17 44 71 101 131 160 186 213 241 1,163 

Light Trucks 

Fuel (billion gallons) 1 2 3 4 7 9 11 13 15 66 

Fuel (billion barrels) 0.0 0.0 0.1 0.1 0.2 0.2 0.3 0.3 0.4 1.6 

CO2 EQ (mmt) 12 26 38 51 88 113 136 159 181 805 

Combined 

Fuel (billion gallons) 2 6 9 12 18 23 27 32 37 165 

Fuel (billion barrels) 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.8 0.9 3.9 

CO2 EQ (mmt) 29 70 108 151 220 273 322 372 422 1,967 
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Table II-24 shows EPA’s estimated lifetime discounted benefits for all vehicles sold in 

model years 2017-2025. Although EPA estimated the benefits associated with four different 

values of a one ton GHG reduction ($5, $22 $36, $67 in CY 2010 and in 2009 dollars), for the 

purposes of this overview presentation of estimated benefits EPA is showing the benefits 

associated with one of these marginal values, $22 per ton of CO2, in 2009 dollars and 2010 

emissions. Table II-24 presents benefits based on the $22 value. Section III.H presents the four 

marginal values used to estimate monetized benefits of GHG reductions and Section III.H 

presents the program benefits using each of the four marginal values, which represent only a 

partial accounting of total benefits due to omitted climate change impacts and other factors that 

are not readily monetized. The values in the table are discounted values for each model year of 

vehicles throughout their projected lifetimes. The benefits include all benefits considered by 

EPA such as GHG reductions, PM benefits, energy security and other externalities such as 

reduced refueling time and accidents, congestion and noise. The lifetime discounted benefits are 

shown for one of four different social cost of carbon (SCC) values considered by EPA. The 

values in Table II-24 do not include costs associated with new technology required to meet the 

GHG standard and they do not include the fuel savings expected from that technology. 

Table II-24 EPA’s Estimated 2017-2025 Model Year Lifetime Discounted Benefits 

Assuming the $22/ton SCC Valuea,b,c,d (billions of 2009 dollars) 

Discount 

Rate 

Model Year 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

Sum of 

Present 

Values 

3% $1.62 $3.85 $6.02 $8.51 $12.7 $16.1 $19.3 $22.8 $26.2 $117 

7% $1.39 $3.31 $5.19 $7.34 $11.0 $14.0 $16.8 $19.8 $22.7 $101 a The benefits include all benefits considered by EPA savings in refueling time, climate-related economic 

benefits from reducing emissions of CO2 (but not other GHGs), economic benefits from reducing emissions of 

PM and other air pollutants that contribute to its formation, and reductions in energy security externalities caused 

by U.S. petroleum consumption and imports. The analysis also includes disbenefits stemming from additional 

vehicle use, such as the economic damages caused by accidents, congestion and noise. b

 Note that net present value of reduced GHG emissions is calculated differently than other benefits. The same 

discount rate used to discount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used 

to calculate net present value of SCC for internal consistency. Refer to Section III.H for more detail. c Monetized GHG benefits exclude the value of reductions in non-CO2 GHG emissions (HFC, CH4 and N2O) 

expected under this final rule. Although EPA has not monetized the benefits of reductions in these non-CO2 

emissions, the value of these reductions should not be interpreted as zero. Rather, the reductions in non-CO2 

GHGs will contribute to this rule’s climate benefits, as explained in Section III.F.2. The SCC TSD notes the 

difference between the social cost of non-CO2 emissions and CO2 emissions, and specifies a goal to develop 

methods to value non-CO2 emissions in future analyses. Also, as noted in Section III.H, SCC increases over 

time. The $22/ton (2009$) value applies to 2010 emissions and grows larger over time. d

 Model year values are discounted to the first year of each model year; the “Sum” represents those discounted 

values summed across model years. 
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143 

Table II-25 shows EPA’s estimated lifetime fuel savings, lifetime CO2 emission 

reductions, and the monetized net present values of those fuel savings and CO2 emission 

reductions. The fuel savings and CO2 emission reductions are projected lifetime values for all 

vehicles sold in the model years 2017-2025. The estimated fuel savings in billions of gallons 

and the GHG reductions in million metric tons of CO2 shown in Table II-25 are totals for the 

nine model years throughout their projected lifetime and are not discounted. The monetized 

values shown in Table II-25 are the summed values of the discounted monetized fuel savings and 

monetized CO2 reductions for the model years 2017-2025 vehicles throughout their lifetimes. 

The monetized values in Table II-25 reflect both a 3 percent and a 7 percent discount rate as 

noted. 

Table II-25 EPA’s Estimated 2017-2025 Model Year Lifetime Fuel Savings, CO2 Emission 

Reductions, and Discounted Monetized SCC Benefits using the $22/ton SCC Value 

(monetized values in 2009 dollars) 

Amount $ value 

(billions) 

Fuel savings (3% discount rate) 165 billion gallons 

(3.9 billion barrels) $444 

Fuel savings (7% discount rate) 165 billion gallons 

(3.9 billion barrels) $347 

CO2e emission reductions 

(CO2 portion valued assuming $22/ton 

CO2 in 2010) 

1,967 MMT CO2e $46.4a,b

a

 $46.4 billion for 1,743 MMT of reduced CO2 emissions. As noted in Section III.H, the $22/ton (2009$) value 

applies to 2010 emissions and grows larger over time. Monetized GHG benefits exclude the value of reductions 

in non-CO2 GHG emissions (HFC, CH4 and N2O) expected under this proposed rule. Although EPA has not 

monetized the benefits of reductions in these non-CO2 GHG emissions, the value of these reductions should not 

be interpreted the agency’s use of the model as the way by which the agency chooses the 

maximum feasible fuel economy standards. As the agency explained in Section III.F.2. The SCC 

TSD notes the difference between the social cost of non-CO2 emissions and CO2 emissions, and specifies a goal 

to develop methods to value non-CO2 emissions in future analyses. b

 Note that net present value of reduced CO2 emissions is calculated differently than other benefits. The same 

discount rate used to discount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used 

to calculate net present value of SCC for internal consistency. Refer to Section III.H for more detail. 

Table II-26 shows EPA’s estimated incremental and total technology outlays for cars and 

trucks for each of the model years 2017-2025. The technology outlays shown in Table II-26 are 

for the industry as a whole and do not account for fuel savings associated with the program. 

Table II-27 shows EPA’s estimated incremental cost increase of the average new vehicle for 

each model year 2017-2025. The values shown are incremental to a baseline vehicle and are not 

cumulative. In other words, the estimated increase for 2017 model year cars is $172 relative to a 

2017 model year car meeting the MY 2016 standards. The estimated increase for a 2018 model 

year car is $319 relative to a 2018 model year car meeting the MY 2016 standards (not $172 plus 

$319). 
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Table II-26 EPA’s Estimated Incremental Technology Outlays Associated with the 

Proposed Standards (billions of 2009 dollars) 

 2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023 

MY 

2024 

MY 

2025 

MY 

Sum of 

Present 

Values 

3% 

discount 

rate 

Cars $1.91 $3.45 $4.71 $6.04 $7.43 $12.3 $16.1 $20.0 $22.1 $94.1 

Trucks $0.32 $1.11 $1.68 $2.30 $4.28 $6.74 $8.55 $10.26 $11.0 $46.2 

Combined $2.27 $4.59 $6.41 $8.34 $11.7 $19.1 $24.7 $30.3 $33.1 $140 

7% 

discount 

rate 

Cars $1.88 $3.38 $4.63 $5.92 $7.29 $12.1 $15.8 $19.7 $21.7 $92.4 

Trucks $0.31 $1.09 $1.65 $2.26 $4.20 $6.62 $8.39 $10.07 $10.8 $45.4 

Combined $2.22 $4.50 $6.29 $8.19 $11.5 $18.7 $24.2 $29.7 $32.5 $138 

Model year values are discounted to the first year of each model year; the “Sum” represents those discounted values 

summed across model years

Table II-27 EPA’s Estimated Incremental Increase in Average New Vehicle Cost Relative 

to the Reference Casea

 (2009 dollars per unit) 

2017 

MY 

2018 

MY 

2019 

MY 

2020 

MY 

2021 

MY 

2022 

MY 

2023 

MY 

2024 

MY 

2025 

MY 

Cars $194 $353 $479 $595 $718 $1,165 $1,492 $1,806 $1,942 

Trucks $55 $198 $305 $417 $764 $1,200 $1,525 $1,834 $1,954 

Combined $146 $299 $418 $533 $734 $1,176 $1,503 $1,815 $1,946 

a

 The reference case assumes the 2016MY standards continue indefinitely. 

F. Background and Comparison of NHTSA and EPA Statutory Authority 

This section provides the agencies’ respective statutory authorities under which CAFE 

and GHG standards are established. 

1. NHTSA Statutory Authority 

NHTSA establishes CAFE standards for passenger cars and light trucks for each model 

year under EPCA, as amended by EISA. EPCA mandates a motor vehicle fuel economy 

regulatory program to meet the various facets of the need to conserve energy, including the 

environmental and foreign policy implications of petroleum use by motor vehicles. EPCA 

allocates the responsibility for implementing the program between NHTSA and EPA as follows: 

NHTSA sets CAFE standards for passenger cars and light trucks; EPA establishes the procedures 

for testing, tests vehicles, collects and analyzes manufacturers’ data, and calculates the individual 

and average fuel economy of each manufacturer’s passenger cars and light trucks; and NHTSA 

enforces the standards based on EPA’s calculations. 
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145 

a. Standard Setting 

We have summarized below the most important aspects of standard setting under its most 

recent CAFE rulemaking, this is incorrect.151 Although NHTSA currently uses the CAFE model 

as a tool to inform its consideration of potential CAFE standards, the CAFE model does not 

determine the CAFE standards that NHTSA proposes or promulgates as final regulations. The 

results it produces are completely dependent on inputs selected by NHTSA, based on the best 

available information and data available in the agency’s estimation at the time standards are set. 

Ultimately, NHTSA’s selection of appropriate CAFE standards is governed and guided by the 

statutory requirements of EPCA, as amended by EISA: NHTSA sets the standard at the 

maximum feasible average fuel economy level that it determines is achievable during a particular 

model year, considering technological feasibility, economic practicability, the effect of other 

standards of the Government on fuel economy, and the need of the nation to conserve energy. 

b. How does NHTSA make the model available and transparent? 

Model documentation, which is publicly available in the rulemaking docket and on 

NHTSA’s website, explains how the model is installed, how the model inputs (all of which are 

available to the public)152 and outputs are structured, and how the model is used. The model can 

be used on any Windows-based personal computer with Microsoft Office 2003 or 2007 and the 

Microsoft .NET framework installed (the latter available without charge from Microsoft). The 

executable version of the model and the underlying source code are also available at NHTSA’s 

Web site. The input files used to conduct the core analysis documented in this final rule are 

available in the public docket. With the model and these input files, anyone is capable of 

independently running the model to repeat, evaluate, and/or modify the agency’s analysis. 

Because the model is available on NHTSA’s web site, the agency has no way of knowing 

how widely the model has been used. The agency is, however, aware that the model has been 

used by other federal agencies, vehicle manufacturers, private consultants, academic researchers, 

and foreign governments. Some of these individuals have found the model complex and 

challenging to use. Insofar as the model’s sole purpose is to help DOT staff efficiently analyze 

potential CAFE standards, DOT has not expended significant resources trying to make the model 

as “user friendly” as commercial software intended for wide use. However, DOT wishes to 

facilitate informed comment on the proposed standards, and encourages reviewers to contact the 

agency promptly if any difficulties using the model are encountered. 

NHTSA arranged for a formal peer review of an older version of the model, has 

responded to reviewers’ comments, and has considered and responded to model-related 

 151 75 FR 25600. 152 We note, however, that files from any supplemental analysis conducted that relied in part on confidential 

manufacturer product plans cannot be made public, as prohibited under 49 CFR part 512. 
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comments received over the course of four CAFE rulemakings. In the agency’s view, this steady 

and expanding outside review over the course of nearly a decade of model development has 

helped DOT to significantly strengthen the model’s capabilities and technical quality, and has 

greatly increased transparency, such that all model code is publicly available, and all model 

inputs and outputs are publicly available in a form that should allow reviewers to reproduce the 

agency’s analysis. NHTSA is currently preparing arrangements for a formal peer review of the 

current CAFE model. Depending on the schedule for that review, DOT will consider possible 

model revisions and, as feasible, attempt to make any appropriate revisions before performing 

analysis supporting final CAFE standards for MY 2017 and beyond. 

G. Statutory Requirements 

1. EPCA, as Amended by EISA 

a. Standard Setting 

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 trucks153 for each model year,154 and each standard must be the maximum feasible that 

NHTSA believes the manufacturers can achieve in that model year.155 When determining the 

maximum feasible level achievable by the manufacturers, EPCA requires that the agency 

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.156 In addition, the agency has the authority to and traditionally does 

consider other relevant factors, such as the effect of the CAFE standards on motor vehicle safety. 

The ultimate determination of what standards can be considered maximum feasible involves a 

weighing and balancing of these factors, and the balance may shift depending on the information 

before the agency about the expected circumstances in the model years covered by the 

rulemaking. Always in conducting that balancing, however, the implication of the “maximum 

feasible” requirement is that it calls for setting a standard that exceeds what might be the 

minimum requirement if the agency determines that the manufacturers can achieve a higher 

level, and that the agency’s decision support the overarching purpose of EPCA, energy 

conservation.157 

 153 49 U.S.C. 32902(b)(1). 154 49 U.S.C. 32902(a). 155 Id. 156 49 U.S.C. 32902(f). 157 Center for Biological Diversity v. NHTSA, 538 F.3d 1172, 1197 (9th Cir. 2008) (“Whatever method it uses, 

NHTSA cannot set fuel economy standards that are contrary to Congress’ purpose in enacting the EPCA – energy 

conservation.”). 
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Besides the requirement that standards be maximum feasible for the fleet in question, 

EPCA/EISA also contains several other requirements. The standards must be attribute-based and 

expressed in the form of a mathematical function – NHTSA has thus far based standards on 

vehicle footprint, and for this rulemaking has expressed them in the form of a constrained linear 

function that generally sets higher (more stringent) mpg targets for smaller-footprint vehicles and 

lower (less stringent) mpg targets for larger-footprint vehicles. Second, the standards are subject 

to a minimum requirement regarding stringency: they 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 not later than MY 2020.158 Third, between MY 2011 and 

MY 2020, the standards must “increase ratably” in each model year.159 This requirement does 

not have a precise mathematical meaning, particularly because it must be interpreted in 

conjunction with the requirement to set the standards for each model year at the level determined 

to be the maximum feasible level for that model year. Generally speaking, the requirement for 

ratable increases means that the annual increases should not be disproportionately large or small 

in relation to each other. The second and third requirements no longer apply after MY 2020, at 

which point standards must simply be maximum feasible. And fourth, EISA requires NHTSA to 

issue CAFE standards for “at least 1, but not more than 5, model years.”160 This issue is 

discussed in section IV.B above. 

The following sections discuss the statutory factors behind “maximum feasible” in more 

detail. 

i. Statutory Factors Considered in Determining the Achievable Level of Average 

Fuel Economy 

As none of the four factors is defined in EPCA and each remains interpreted only to a 

limited degree by case law, NHTSA has considerable latitude in interpreting them. NHTSA 

interprets the four statutory factors as set forth below. 

(1) Technological Feasibility 

“Technological feasibility” refers to whether a particular technology for improving fuel 

economy is available or can become available for commercial application in the model year for 

which a standard is being established. Thus, the agency is not limited in determining the level of 

new standards to technology that is already being commercially applied at the time of the 

rulemaking. It can, instead, set technology-forcing standards, i.e., ones that make it necessary 

for manufacturers to engage in research and development in order to bring a new technology to 

market. There are certain technologies that the agency has considered for this rulemaking, for 

 158 49 U.S.C. 32902(b)(2)(A). 159 49 U.S.C. 32902(b)(2)(C). 160 49 U.S.C. 32902(b)(3)(B). 
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example, that we know to be in the research phase now but which we are fairly confident can be 

commercially applied by the rulemaking timeframe, and very confident by the end of the 

rulemaking timeframe. It is important to remember, however, that while the technological 

feasibility factor may encourage the agency to look toward more technology-forcing standards, 

and while this could certainly be appropriate given EPCA’s overarching purpose of energy 

conservation depending on the rulemaking, that factor must also be balanced with the other of 

the four statutory factors. Thus, while “technological feasibility” can drive standards higher by 

assuming the use of technologies that are not yet commercial, “maximum feasible” is still also 

defined in terms of economic practicability, for example, which might caution the agency against 

basing standards (even fairly distant future standards) entirely on such technologies. By setting 

standards at levels consistent with an analysis that assumes the use of these nascent technologies 

at levels that seem reasonable, the agency believes a more reasonable balance is ensured. 

Nevertheless, as the “maximum feasible” balancing may vary depending on the circumstances at 

hand for the model years in which the standards are set, the extent to which technological 

feasibility is simply met or plays a more dynamic role may also shift. 

(2) Economic Practicability 

“Economic practicability” refers 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 the unreasonable elimination of consumer choice.”161 The 

agency has explained in the past that this factor can be especially important during rulemakings 

in which the automobile industry is facing significantly adverse economic conditions (with 

corresponding risks to jobs). Consumer acceptability is also an element of economic 

practicability, one which is particularly difficult to gauge during times of uncertain fuel prices.162 

In a rulemaking such as the present one, looking out into the more distant future, economic 

practicability is a way to consider the uncertainty surrounding future market conditions and 

consumer demand for fuel economy in addition to other vehicle attributes. In an attempt to 

ensure the economic practicability of attribute-based standards, NHTSA considers a variety of 

factors, including the annual rate at which manufacturers can increase the percentage of their 

fleet that employ a particular type of fuel-saving technology, the specific fleet mixes of different 

manufacturers, and assumptions about the cost of the standards to consumers and consumers’ 

valuation of fuel economy, among other things. 

At the same time, however, the law does not preclude a CAFE standard that poses 

considerable challenges to any individual manufacturer. The Conference Report for EPCA, as 

 161 67 FR 77015, 77021 (Dec. 16, 2002). 162 See, e.g., Center for Auto Safety v. NHTSA (CAS), 793 F.2d 1322 (D.C. Cir. 1986) (Administrator’s consideration 

of market demand as component of economic practicability found to be reasonable); Public Citizen v. NHTSA, 848 

F.2d 256 (Congress established broad guidelines in the fuel economy statute; agency’s decision to set lower standard 

was a reasonable accommodation of conflicting policies). 
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enacted in 1975, makes clear, and the case law affirms, “(A) determination of maximum feasible 

average fuel economy should not be keyed to the single manufacturer which might have the most 

difficulty achieving a given level of average fuel economy.”163 Instead, the agency is compelled 

“to weigh the benefits to the nation of a higher fuel economy standard against the difficulties of 

individual automobile manufacturers.”164 The law permits CAFE standards exceeding the 

projected capability of any particular manufacturer as long as the standard is economically 

practicable for the industry as a whole. Thus, while a particular CAFE standard may pose 

difficulties for one manufacturer, it may also present opportunities for another. NHTSA has long 

held that the CAFE program is not necessarily intended to maintain the competitive positioning 

of each particular company. Rather, it is intended to enhance the fuel economy of the vehicle 

fleet on American roads, while protecting motor vehicle safety and being mindful of the risk to 

the overall United States economy. 

Consequently, “economic practicability” must be considered in the context of the 

competing concerns associated with different levels of standards. Prior to the MY 2005–2007 

rulemaking, the agency 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 vehicles are, on average, 

the heaviest and largest. In the first several rulemakings establishing attribute-based standards, 

the agency applied marginal cost benefit analysis. This ensured that the agency’s application of 

technologies was limited to those that would pay for themselves and thus should have significant 

appeal to consumers. We note that for this rulemaking, the agency can and has limited its 

application of technologies to those that are projected to be cost-effective within the rulemaking 

time frame, with or without the use of such analysis. 

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. In this case, the NHTSA analysis 

suggests that the maximum feasible balancing for this rulemaking points to another level besides 

the modeled net benefits maximum, and such a situation is well within the guidance provided by 

EO’s 12866 and 13563.165 While the agency’s consideration of economic practicability is 

informed by marginal cost benefit analysis, the fact that standards are cost-beneficial is not the 

only consideration for economic practicability. Expected availability of capital to make 

investments in new technologies matters; manufacturers’ expected ability to sell vehicles with 

 163 CEI–I, 793 F.2d 1322, 1352 (D.C.DC Cir. 1986). 164 Id. 165 See 70 Fed. Reg. at 51435 (Aug. 30, 2005); CBD v. NHTSA, 538 F.3d at 1197 (9th Cir. 2008). 
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new technologies matters; and so forth. The model NHTSA uses to estimate the impacts of this 

rulemaking does incorporate assumptions to capture aspects of consumer preferences, vehicle 

attributes, safety, and other factors relevant to an impact estimate; however, it is difficult to 

capture every such constraint. Therefore, NHTSA often uses the agency’s discretion to deviate 

from a modeled net benefits maximum in the face of evidence of economic impracticability. 

Economic practicability is a complex factor, 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 stringencies, NHTSA could well find that standards that maximize net benefits, or 

that are higher or lower, could be economically practicable, and thus maximum feasible. 

(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 an analysis of the effects of compliance with emission, safety, noise, or damageability 

standards on fuel economy capability and thus on average fuel economy. In previous CAFE 

rulemakings, the agency has said that pursuant to this provision, it considers the adverse effects 

of other motor vehicle standards on fuel economy. It said so because, from the CAFE program’s 

earliest years166 until present, the effects of such compliance on fuel economy capability over the 

history of the CAFE program have been negative ones. In those instances in which the effects 

are negative, NHTSA has said that it is called upon to “mak[e] a straightforward adjustment to 

the fuel economy improvement projections to account for the impacts of other Federal standards, 

principally those in the areas of emission control, occupant safety, vehicle damageability, and 

vehicle noise. However, only the unavoidable consequences should be accounted for. The 

automobile manufacturers must be expected to adopt those feasible methods of achieving 

compliance with other Federal standards which minimize any adverse fuel economy effects of 

those standards.”167 For example, safety standards that have the effect of increasing vehicle 

weight lower vehicle fuel economy capability and thus decrease the level of average fuel 

economy that the agency can determine to be feasible. 

The “other motor vehicle standards” consideration has thus in practice functioned in a 

fashion similar to the provision in EPCA, as originally enacted, for adjusting the statutorilyspecified CAFE standards for MY 1978–1980 passengers cars.168 EPCA did not permit NHTSA 

to amend those standards based on a finding that the maximum feasible level of average fuel 

economy for any of those three years was greater or less than the standard specified for that year. 

Instead, it provided that the agency could only reduce the standards and only on one basis: if the 

 166 42 FR 63184, 63188 (Dec. 15, 1977). See also 42 FR 33534, 33537 (Jun. 30, 1977). 167 42 FR 33534, 33537 (Jun. 30, 1977). 168 That provision was deleted as obsolete when EPCA was codified in 1994.
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agency found that there had been a Federal standards fuel economy reduction, i.e., a reduction in 

fuel economy due to changes in the Federal vehicle standards, e.g., emissions and safety, relative 

to the year of enactment, 1975. 

The “other motor vehicle standards” provision is broader than the Federal standards fuel 

economy reduction provision. Although the effects analyzed to date under the “other motor 

vehicle standards” provision have been negative, there could be circumstances in which the 

effects are positive. In the event that the agency encountered such circumstances, it would be 

required to consider those positive effects. For example, if changes in vehicle safety technology 

led to NHTSA’s amending a safety standard in a way that permits manufacturers to reduce the 

weight added in complying with that standard, that weight reduction would increase vehicle fuel 

economy capability and thus increase the level of average fuel economy that could be determined 

to be feasible. 

In the wake of Massachusetts v. EPA and of EPA’s endangerment finding, granting of a 

waiver to California for its motor vehicle GHG standards, and its own establishment of GHG 

standards, NHTSA is confronted with the issue of how to treat those standards under 

EPCA/EISA, such as in the context of the “other motor vehicle standards” provision. 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. 

Comment is requested on whether and in what way the effects of the California and EPA 

standards should be considered under EPCA/EISA, e.g., under the “other motor vehicle 

standards” provision, consistent with NHTSA’s independent obligation under EPCA/EISA to 

issue CAFE standards. Since this is a joint rulemaking that the agencies have concluded meet 

the obligations under both EPCA/EISA and the CAA, the agency has already considered EPA’s 

proposal and the harmonization benefits of the National Program in developing its own proposal. 

(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.”169 Environmental implications 

principally include those associated with reductions in emissions of criteria pollutants and CO2. 

A prime example of foreign policy implications are energy independence and energy security 

concerns. 

(a) Fuel Prices and the Value of Saving Fuel 

 169 42 FR 63184, 63188 (1977). 
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Projected future fuel prices are a critical input into the preliminary economic analysis of 

alternative CAFE standards, because they determine the value of fuel savings both to new 

vehicle buyers and to society, which is related to the consumer cost (or rather, benefit) of our 

need for large quantities of petroleum. In this rule, NHTSA relies on fuel price projections from 

the U.S. Energy Information Administration’s (EIA) most recent Annual Energy Outlook (AEO) 

for this analysis. Federal government agencies generally use EIA’s projections in their 

assessments of future energy-related policies. 

(b) Petroleum Consumption and Import Externalities 

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 of petroleum products such as gasoline. These costs include (1) higher prices for 

petroleum products resulting from the effect of U.S. oil import demand on the world oil price; (2) 

the risk of disruptions to the U.S. economy caused by sudden reductions in the supply of 

imported oil to the U.S.; and (3) expenses for maintaining a U.S. military presence to secure 

imported oil supplies from unstable regions, and 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 United States to meet part of its International Energy Agency 

obligation to maintain emergency oil stocks, and to provide a national defense fuel reserve. 

Higher U.S. imports of crude oil or refined petroleum products increase 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. imports of crude 

petroleum or refined fuels or reducing fuel consumption can reduce these external costs. 

(c) Air Pollutant Emissions 

While reductions in domestic fuel refining and distribution that result from lower fuel 

consumption will reduce U.S. emissions of various pollutants, additional vehicle use associated 

with the rebound effect170 from higher fuel economy will increase emissions of these pollutants. 

Thus, the net effect of stricter 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.171 Fuel savings from stricter CAFE standards also result in lower 

emissions of CO2, the main greenhouse gas emitted as a result of refining, distribution, and use 

of transportation fuels. Reducing fuel consumption reduces carbon dioxide emissions directly, 

because the primary source of transportation-related CO2 emissions is fuel combustion in internal 

combustion engines. 

 170 The “rebound effect” refers to the tendency of drivers to drive their vehicles more as the cost of doing so goes 

down, as when fuel economy improves.

171 See Section IV.G below for NHTSA’s evaluation of this effect. 
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NHTSA has considered environmental issues, both within the context of EPCA and the 

National Environmental Policy Act, in making decisions about the setting of standards from the 

earliest days of the CAFE program. As courts of appeal have noted in three decisions stretching 

over the last 20 years,172 NHTSA defined the “need of the Nation to conserve energy” in the late 

1970s as including “the consumer cost, national balance of payments, environmental, and foreign 

policy implications of our need for large quantities of petroleum, especially imported 

petroleum.”173 In 1988, NHTSA included climate change concepts in its CAFE notices and 

prepared its first environmental assessment addressing that subject.174 It cited concerns about 

climate change as one of its reasons for limiting the extent of its reduction of the CAFE standard 

for MY 1989 passenger cars.175 Since then, NHTSA has considered the benefits of reducing 

tailpipe carbon dioxide emissions in its fuel economy rulemakings pursuant to the statutory 

requirement to consider the nation’s need to conserve energy by reducing fuel consumption. 

ii. Other Factors Considered by NHTSA 

The agency historically has considered the potential for adverse safety consequences in 

setting CAFE standards. This practice is recognized approvingly in case law. As the 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 (DC 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 (DC 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 (DC 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 MY 2008–11 light truck CAFE rule). 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. 

Under the universal or “flat” CAFE standards that NHTSA was previously authorized to 

establish, manufacturers were encouraged to respond to higher standards by building smaller, 

less safe vehicles in order to “balance out” the larger, safer vehicles that the public generally 

 172 Center for Auto Safety v. NHTSA, 793 F.2d 1322, 1325 n. 12 (D.C. Cir. 1986); Public Citizen v. NHTSA, 848 

F.2d 256, 262–3 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”); and Center for Biological Diversity v. NHTSA, 538 

F.3d 1172 (9th Cir. 2007). 173 42 FR 63184, 63188 (Dec. 15, 1977) (emphasis added). 174 53 FR 33080, 33096 (Aug. 29, 1988). 175 53 FR 39275, 39302 (Oct. 6, 1988). 
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preferred to buy, which resulted in a higher mass differential between the smallest and the largest 

vehicles, with a correspondingly greater risk to safety. Under the attribute-based standards being 

proposed today, that risk is reduced because building smaller vehicles would tend to raise a 

manufacturer’s overall CAFE obligation, rather than only raising its fleet average CAFE, and 

because all vehicles are required to continue improving their fuel economy. In prior 

rulemakings, NHTSA limited the application of mass reduction in our modeling analysis to 

vehicles over 5,000 lbs GVWR,176 but for purposes of today’s proposed standards, NHTSA has 

revised its modeling analysis to allow some application of mass reduction for most types of 

vehicles, although it is concentrated in the largest and heaviest vehicles, because we believe that 

this is more consistent with how manufacturers will actually respond to the standards. However, 

as discussed above, NHTSA does not mandate the use of any particular technology by 

manufacturers in meeting the standards. More information on the approach to modeling 

manufacturer use of mass reduction is available in Chapter 3 of the draft Joint TSD and in 

Section V of the PRIA; and the estimated safety impacts that may be due to the proposed MY 

2017-2025 CAFE standards are described in section IV.G below. 

iii. 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 the CAFE standards and 

thereby reduce the costs of compliance.177 As discussed further below, manufacturers can earn 

compliance credits by exceeding the CAFE standards and then use those credits to achieve 

compliance in years in which their measured average fuel economy falls below the standards. 

Manufacturers can also increase their CAFE levels through MY 2019 by producing alternative 

fuel vehicles. EPCA provides an incentive for producing these vehicles by specifying that their 

fuel economy is to be determined using a special calculation procedure that results in those 

vehicles being assigned a high fuel economy level. 

The effect of the prohibitions against considering these statutory flexibilities in setting the 

CAFE standards is that the flexibilities remain voluntarily-employed measures. If the agency 

were instead to assume manufacturer use of those flexibilities in setting new standards, that 

assumption would result in higher standards and thus tend to require manufacturers to use those 

flexibilities. By keeping NHTSA from including them in our stringency determination, the 

provision ensures that the statutory credits remain described above remain true compliance 

flexibilities. 

On the other hand, NHTSA does not believe that flexibilities other than those expressly 

identified in EPCA are similarly prohibited from being included in the agency’s determination of 

 176 See 74 FR 14396–14407 (Mar. 30, 2009). 177 49 U.S.C. 32902(h). 
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what standards would be maximum feasible. In order to better meet EPCA’s overarching 

purpose of energy conservation, the agency is therefore considering manufacturers’ ability to 

increase the calculated fuel economy levels of their vehicles through A/C efficiency 

improvements, as proposed by EPA, in the proposed CAFE stringency levels for passenger cars 

and light trucks for MYs 2017-2025. NHTSA would similarly consider manufacturers’ ability to 

raise their fuel economy using off-cycle technologies as potentially relevant to our determination 

of maximum feasible CAFE standards, but because we and EPA do not believe that we can yet 

reasonably predict an average amount by which manufacturers will take advantage of this 

opportunity, it did not seem reasonable for the proposed standards to include it in our stringency 

determination at this time. We expect to re-evaluate whether and how to include off-cycle 

credits in determining maximum feasible standards as the off-cycle technologies and how 

manufacturers may be expected to employ them become better defined in the future. 

Additionally, because we interpret the prohibition against including the defined statutory 

credits in our determination of maximum feasible standards as applying only to the flexibilities 

expressly identified in 49 U.S.C. 32902(h), NHTSA must, for the first time in this rulemaking, 

determine how to consider the fuel economy of dual-fueled automobiles after the statutory credit 

sunsets in MY 2019. Once there is no statutory credit to protect as a compliance flexibility, it 

does not seem reasonable to NHTSA to continue to interpret the statute as prohibiting the agency 

from setting maximum feasible levels at a higher standard, if possible, by considering the fuel 

economy of dual-fueled automobiles as measured by EPA. The overarching purpose of EPCA is 

better served by interpreting 32902(h)(2) as moot once the statutory credits provided for in 49 

U.S.C. 32905 and 32906 have expired. 

49 U.S.C. 32905(b) and (d) states that the special fuel economy measurement prescribed 

by Congress for dual-fueled automobiles applies only “in model years 1993 through 2019.” 49 

U.S.C. 32906(a) also provides that the section 32905 calculation will sunset in 2019, as 

evidenced by the phase-out of the allowable increase due to that credit; it is clear that the phaseout of the allowable increase in a manufacturer’s CAFE levels due to use of dual-fueled 

automobiles relates only to the special statutory calculation (and not to other ways of 

incorporating the fuel economy of dual-fueled automobiles into the manufacturer’s fleet 

calculation) by virtue of language in section 32906(b), which states that “in applying subsection 

(a) [i.e., the phasing out maximum increase], the Administrator of the Environmental Protection 

Agency shall determine the increase in a manufacturer’s average fuel economy attributable to 

dual fueled automobiles by subtracting from the manufacturer’s average fuel economy calculated 

under section 32905(e) the number equal to what the manufacturer’s average fuel economy 

would be if it were calculated by the formula under section 32904(a)(1)....” By referring back to 

the special statutory calculation, Congress makes clear that the phase-out applies only to 

increases in fuel economy attributable to dual-fueled automobiles due to the special statutory 

calculation in sections 32905(b) and (d). Similarly, we interpret Congress’ statement in section 

32906(a)(7) that the maximum increase in fuel economy attributable to dual-fueled automobiles 
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is “0 miles per gallon for model years after 2019” within the context of the introductory language 

of section 32906(a) and the language of section 32906(b), which, again, refers clearly to the 

statutory credit, and not to dual-fueled automobiles generally. It would be an absurd result if the 

phase-out of the credit meant that manufacturers would be effectively penalized, in CAFE 

compliance, for building dual-fueled automobiles like plug-in hybrid electric vehicles, which 

may be important “bridge” vehicles in helping consumers move toward full electric vehicles. 

NHTSA has therefore considered the fuel economy of plug-in hybrid electric vehicles 

(the only dual-fueled automobiles that we predict in significant numbers in MY 2020 and 

beyond; E85-capable FFVs are not predicted in great numbers after the statutory credit sunsets, 

and we do not have sufficient information about potential dual-fueled CNG/gasoline vehicles to 

make reasonable estimates now of their numbers in that time frame in determining the maximum 

feasible level of the MY 2020-2025 CAFE standards for passenger cars and light trucks. 

iv. Determining the Level of the Standards by Balancing the Factors 

NHTSA has broad discretion in balancing the above factors in determining the 

appropriate levels of average fuel economy at which to set the CAFE standards for each model 

year. Congress “specifically delegated the process of setting * * * fuel economy standards with 

broad guidelines concerning the factors that the agency must consider.”178 The breadth of those 

guidelines, the absence of any statutorily prescribed formula for balancing the factors and other 

considerations, the fact that the relative weight to be given to the various factors may change 

from rulemaking to rulemaking as the underlying facts change, and the fact that the factors may 

often be conflicting with respect to whether they militate toward higher or lower standards give 

NHTSA broad discretion to decide what weight to give each of the competing policies and 

concerns and then determine how to balance them—“as long as. The exercise of that discretion 

is subject to the necessity of ensuring that NHTSA’s balancing does not undermine the 

fundamental purpose of EPCA:, energy conservation,”,179 and as long as that balancing 

reasonably accommodates “conflicting policies that were committed to the agency’s care by the 

statute.”180 The balancing of the factors in any given rulemaking is highly dependent on the 

factual and policy context of that rulemaking and the agency’s assumptions about the factual and 

policy context during the time frame covered by the standards at issue. Given the changes over 

time in facts bearing on assessment of the various factors, such as those relating to economic 

conditions, fuel prices, and the state of climate change science, the agency recognizes that what 

was a reasonable balancing of competing statutory priorities in one rulemaking may or may not 

 178 Center for Auto Safety v. NHTSA, 793 F.2d 1322, at 1341 (C.A.D.C. Cir. 1986). 179 CBDCenter for Biological Diversity v. NHTSA, 538 F.3d at1172, 1195 (9th Cir. 2008). 180 Id. CAS, 1338 (quoting Chevron U.S.A., Inc. v. Natural Resources Defense Council, Inc., 467 U.S. 837, 845). 
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be a reasonable balancing of those priorities in another rulemaking.181 Nevertheless, the agency 

retains substantial discretion under EPCA to choose among reasonable alternatives. 

EPCA neither requires nor precludes the use of any type of cost-benefit analysis as a tool 

to help inform the balancing process. As discussed above, while NHTSA used marginal costbenefit analysis in the first two rulemakings to establish attribute-based CAFE standards, it was 

not required to do so and is not required to continue to do so. Regardless of what type of 

analysis is or is not used, considerations relating to costs and benefits remain an important part of 

CAFE standard setting. 

Because the relevant considerations and factors can reasonably be balanced in a variety 

of ways under EPCA, and because of uncertainties associated with the many technological and 

cost inputs, NHTSA considers a wide variety of alternative sets of standards, each reflecting 

different balancing of those policies and concerns, to aid it in discerning reasonable outcomes. 

Among the alternatives providing for an increase in the standards in this rulemaking, the 

alternatives range in stringency from a set of standards that increase, on average, 2 percent 

annually to a set of standards that increase, on average, 7 percent annually. 

v. Other Standards 

(1) Minimum Domestic Passenger Car Standard 

The minimum domestic passenger car standard was added to the CAFE program through 

EISA, when Congress gave NHTSA explicit authority to set universal standards for 

domestically-manufactured passenger cars at the level of 27.5 mpg or 92 percent of the average 

fuel economy of the combined domestic and import passenger car fleets in that model year, 

whichever was greater.182 This minimum standard was intended to act as a “backstop,” ensuring 

that domestically-manufactured passenger cars reached a given mpg level even if the market 

shifted in ways likely to reduce overall fleet mpg. Congress was silent as to whether the agency 

could or should develop similar backstop standards for imported passenger cars and light trucks. 

NHTSA has struggled with this question since EISA was enacted. 

NHTSA has proposed minimum standards for domestically-manufactured passenger cars 

in Section IV.E below, but we also seek comment on whether to consider, for the final rule, the 

possibility of minimum standards for imported passenger cars and light trucks. Although we are 

not proposing such standards, we believe it may be prudent to explore this concept again given 

the considerable amount of time between now and 2017-2025 (particularly the later years), and 

the accompanying uncertainty in our market forecast and other assumptions, that might make 

such minimum standards relevant to help ensure that currently-expected fuel economy 

 181 CBD v. NHTSA, 538 F.3d 1172, 1198 (9th Cir. 2008). 182 49 U.S.C. 32902(b)(2)(C4). 
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improvements occur during that time frame. To help commenters’ consideration of this 

question, Section IV.E presents illustrative levels of minimum standards for those other fleets. 

The minimum domestic passenger car standard was added to the CAFE program through 

EISA, when Congress gave NHTSA explicit authority to set universal standards for 

domestically-manufactured passenger cars at the level explained above. This minimum standard 

was intended to act as a “backstop,” ensuring that domestically-manufactured passenger cars 

reached a given mpg level even if the market shifted in ways likely to reduce overall fleet mpg. 

Congress was silent as to whether the agency could or should develop similar backstop standards 

for imported passenger cars and light trucks. NHTSA has struggled with this question since 

EISA was enacted. 

In the MY 2011 final rule, facing comments split fairly evenly between support and 

opposition to additional backstop standards, NHTSA noted Congress’ silence with respect to 

minimum standards for imported passenger cars and light trucks and “accept[ed] at least the 

possibility that . . . [it] could be reasonably interpreted as permissive rather than restrictive,” but 

concluded based on the record for that rulemaking as a whole that additional minimum standards 

were not necessary for MY 2011, given the lack of leadtime for manufacturers to change their 

MY 2011 vehicles, the apparently-growing public preference for smaller vehicles, and the antibacksliding characteristics of the footprint-based curves.183

In the MYs 2012-2016 final rule where NHTSA declined to set minimum standards for 

imported passenger cars and light trucks, the agency did so not because we believed that we did 

not have authority to do so, but because we believed that our assumptions about the future fleet 

mix were reliable within the rulemaking time frame, and that backsliding was very unlikely and 

would not be sufficient to warrant the regulatory burden of additional minimum standards for 

those fleets.184 NHTSA also expressed concern about the possibility of additional minimum 

standards imposing inequitable regulatory burdens of the kind that attribute-based standards 

sought to avoid, stating that: 

Unless the backstop was at a very weak level, above the high end of this range, 

then some percentage of manufacturers would be above the backstop even if the 

performance of the entire industry remains fully consistent with the emissions and fuel 

economy levels projected for the final standards. For these manufacturers and any other 

manufacturers who were above the backstop, the objectives of an attribute-based standard 

would be compromised and unnecessary costs would be imposed. This could 

directionally impose increased costs for some manufacturers. It would be difficult if not 

impossible to establish the level of a backstop standard such that costs are likely to be 

imposed on manufacturers only when there is a failure to achieve the projected reductions 

 183 74 FR at 14412 (Mar. 30, 2009). 184 75 Fed. Reg. 25324, at 25368-70 (May 7, 2010). 
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across the industry as a whole. An example of this kind of industry-wide situation could 

be when there is a significant shift to larger vehicles across the industry as a whole, or if 

there is a general market shift from cars to trucks. The problem the agencies are 

concerned about in those circumstances is not with respect to any single manufacturer, 

but rather is based on concerns over shifts across the fleet as a whole, as compared to 

shifts in one manufacturer’s fleet that may be more than offset by shifts the other way in 

another manufacturer’s fleet. However, in this respect, a traditional backstop acts as a 

manufacturer-specific standard.185

NHTSA continues to believe that the risk of additional minimum standards imposing 

inequitable regulatory burdens on certain manufacturers is real, but at the same time, we 

recognize that given the time frame of the current rulemaking, the agency cannot be as certain 

about the unlikelihood of future market changes. Depending on the price of fuel and consumer 

preferences, the “kind of industry-wide situation” described in the MYs 2012-2016 rule is 

possible in the 2017-2025 time frame, particularly in the later years. 

Because the agency does not have sufficient information at this time regarding what 

tradeoffs might be associated with additional minimum standards, specifically, whether the risk 

of backsliding during MYs 2017-2025 sufficiently outweighs the possibility of imposing 

inequitable regulatory burdens on certain manufacturers, we are seeking comment in this NPRM 

on these issues but not proposing additional minimum standards at this time. We also seek 

comment on how to structure additional minimum standards (e.g., whether they should be flat or 

attribute-based, and if the latter, how that would work), and at what level additional minimum 

standards should potentially be set. The tables in Section IV.E provide an illustration of what 

levels the additional minimum standards would require if the agency followed the same 92 

percent guideline required by EISA for domestically-manufactured passenger cars. 

(2) Alternative standards for certain manufacturers 

Because EPCA states that standards must be set for “...automobiles manufactured by 

manufacturers,” and because Congress provided specific direction on how small-volume 

manufacturers could obtain exemptions from the passenger car standards, NHTSA has long 

interpreted its authority as pertaining to setting standards for the industry as a whole. Prior to 

this NPRM, some manufacturers raised with NHTSA the possibility of NHTSA and EPA setting 

alternate standards for part of the industry that met certain (relatively low) sales volume criteria – 

specifically, that separate standards be set so that “intermediate-size,” limited-line manufacturers 

do not have to meet the same levels of stringency that larger manufacturers have to meet until 

several years later. These manufacturers argued that the same level of standards would not be 

technologically feasible or economically practicable in the same time frame for them, due to 

 185 Id. at 25369. 
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their inability to spread compliance burden across a larger product lineup, and difficulty in 

obtaining fuel economy-improving technologies quickly from suppliers. NHTSA seeks 

comment on whether or how EPCA, as amended by EISA, could be interpreted to allow such 

alternate standards for certain parts of the industry. 

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 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 subjected to the two-step 

analysis of Chevron, U.S.A., Inc. v. Natural Resources Defense Council, 467 U.S. 837, 104 S.Ct. 

2778, 81 L.Ed.2d 694 (1984). Under step one, where a statute “has directly spoken to the precise 

question at issue,” id. at 842, 104 S.Ct. 2778, the court and the agency “must give effect to the 

unambiguously expressed intent of Congress,” id. at 843, 104 S.Ct. 2778. 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 recently in FCC v. Fox Television, 129 S.Ct. 1800 (2009). 

When an agency changes course from earlier regulations, “the requirement that an agency 

provide 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.”186 The APA also requires 

that agencies provide notice and comment to the public when proposing regulations,187 as we are 

doing here today. 

 186 Ibid., 1181. 187 5 U.S.C. 553. 
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161 

3. National Environmental Policy Act 

As discussed above, EPCA requires the agency to determine what level at which to set 

the 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. NEPA 

directs that environmental considerations be integrated into that process. To accomplish that 

purpose, NEPA requires an agency to compare the potential environmental impacts of its 

proposed action to those of a reasonable range of alternatives. 

To explore the environmental consequences in depth, NHTSA has prepared a draft 

environmental impact statement (“EIS”). 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.” 40 CFR 1502.1. 

NEPA is “a procedural statute that mandates a process rather than a particular result.” 

Stewart Park & Reserve Coal., Inc. v. Slater, 352 F.3d at 557. 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 Res. Def. Council, Inc., 462 U.S. 87, 97, 103 

S.Ct. 2246, 76 L.Ed.2d 437 (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, 109 S.Ct. 1835, 104 L.Ed.2d 351 (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 and 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. 4332(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 environmental 

consequences. 

H. What are the proposed CAFE standards? 

1. Form of the Standards 

Each of the CAFE standards that NHTSA is proposing today for passenger cars and light 

trucks is expressed as a mathematical function that defines a fuel economy target applicable to 

each vehicle model and, for each fleet, establishes a required CAFE level determined by 
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162 

computing the sales-weighted harmonic average of those targets.188

As discussed above in Section II.C, NHTSA has determined passenger car fuel economy 

targets using a constrained linear function defined according to the following formula: 

1

1 1

, ,

TARGET

MIN MAX c FOOTPRINT d

a b

              

Here, TARGET is the fuel economy target (in mpg) applicable to vehicles of a given 

footprint (FOOTPRINT, in square feet), b and a are the function’s lower and upper asymptotes 

(also in mpg), respectively, c is the slope (in gallons per mile per square foot) of the sloped 

portion of the function, and d is the intercept (in gallons per mile) of the sloped portion of the 

function (that is, the value the sloped portion would take if extended to a footprint of 0 square 

feet. The MIN and MAX functions take the minimum and maximum, respectively of the 

included values. 

NHTSA is proposing, consistent with the standards for MYs 2011-2016, that the CAFE 

level required of any given manufacturer be determined by calculating the production-weighted 

harmonic average of the fuel economy targets applicable to each vehicle model: 

௜ܱܰܫܶܥܷܦܴܱܲ ௜∑ ൌ ௗ௜௥௘௨௥௘௤ܧܨܣܥ

௜ܱܰܫܶܥܷܦܴܱܲ ∑

௜ܶܧܩܴܣܶ

௜

PRODUCTIONi is the number of units produced for sale in the United States of each i

th

unique footprint within each model type, produced for sale in the United States, and TARGETi is 

the corresponding fuel economy target (according to the equation shown above and based on the 

corresponding footprint), and the summations in the numerator and denominator are both 

performed over all unique footprint and model type combinations in the fleet in question. 

The proposed standards for passenger cars are, therefore, specified by the four 

coefficients defining fuel economy targets: 

 188 Required CAFE levels shown here are estimated required levels based on NHTSA’s current projection of 

manufacturers’ vehicle fleets in MYs 2017–2025. Actual required levels are not determined until the end of each 

model year, when all of the vehicles produced by a manufacturer in that model year are known and their compliance 

obligation can be determined with certainty. The target curves, as defined by the constrained linear function, and as 

embedded in the function for the sales-weighted harmonic average, are the real “standards” being proposed today. 
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163 

a = upper limit (mpg) 

b = lower limit (mpg) 

c = slope (gallon per mile per square foot) 

d = intercept (gallon per mile) 

For light trucks, NHTSA is proposing to define fuel economy targets in terms of a 

mathematical function under which the target is the maximum of values determined under each 

of two constrained linear functions. The second of these establishes a “floor” reflecting the MY 

2016 standard, after accounting for estimated adjustments reflecting increased air conditioner 

efficiency. This prevents the target at any footprint from declining between model years. The 

resultant mathematical function is as follows: 

ܶܧܩܴܣܶ

ൌ ܣܯܺ ൮ 1

1 ݀, ൅ ܰܶܫܱܱܴܶܲܨ ൈ ܿቀ ܺܣܯቂ ܰܫܯ

ܽቁ ,

1

ܾቃ

, 1

1 ݄, ൅ ܰܶܫܱܱܴܶܲܨ ൈ ݃ቀ ܺܣܯ൤ ܰܫܯ

݁ቁ ,

1

݂൨

൲

The proposed standards for light trucks are, therefore, specified by the eight coefficients 

defining fuel economy targets: 

a = upper limit (mpg) 

b = lower limit (mpg) 

c = slope (gallon per mile per square foot) 

d = intercept (gallon per mile) 

e = upper limit (mpg) of “floor” 

f = lower limit (mpg) of “floor” 

g = slope (gallon per mile per square foot) of “floor” 

h = intercept (gallon per mile) of “floor” 

2. Passenger Car Standards for MYs 2017–2025 

For passenger cars, NHTSA is proposing CAFE standards defined by the following 
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164 

coefficients during MYs 2017–2025: 

Table II-28. Coefficients Defining Proposed MYs 2017–2025 Fuel Economy Targets For 

Passenger Cars 

Coefficient 2017 2018 2019 2020 2021 2022 2023 2024 2025 

a (mpg) 42.50 42.94 43.39 43.84 44.29 44.75 45.21 45.68 46.16

b (mpg) 31.83 32.16 32.49 32.82 33.16 33.50 33.84 34.19 34.55

c (gpm/sf) 0.00053 0.00052 0.00052 0.00051 0.00051 0.00050 0.00050 0.00049 0.00049

d (gpm) 0.00196 0.00193 0.00191 0.00188 0.00186 0.00183 0.00181 0.00179 0.00176

For reference, the coefficients defining the MYs 2012-2016 passenger car standards are 

also provided below: 

Table II-29. Coefficients Defining Final MYs 2012–2016 Fuel Economy Targets For 

Passenger Cars 

Coefficient 2012 2013 2014 2015 2016 

a (mpg) 36.18 37.16 38.31 40.06 42.03

b (mpg) 28.09 28.67 29.35 30.37 31.49

c (gpm/sf) 0.00053 0.00053 0.00053 0.00053 0.00053

d (gpm) 0.00588 0.00515 0.00434 0.00320 0.00203

Also for reference, the following table presents the coefficients based on 2-cycle CAFE 

only for easier comparison to the MYs 2012-2016 coefficients presented above. We emphasize, 

again, that the coefficients in Table IV-11 define the proposed standards. 

Table II-30. Coefficients Based Only on 2-Cycle CAFE for MYs 2017–2025 Passenger Cars 

Coefficient 2017 2018 2019 2020 2021 2022 2023 2024 2025 

a (mpg) 42.57 44.09 45.66 47.44 49.42 51.66 54.01 56.47 59.04 

b (mpg) 32.06 33.21 34.39 35.73 37.22 38.92 40.69 42.54 44.47 

c (gpm/sf) 0.00051 0.00050 0.00048 0.00046 0.00044 0.00042 0.00040 0.00039 0.00037 

d (gpm) 0.00246 0.00237 0.00229 0.00221 0.00212 0.00203 0.00194 0.00185 0.00177 

Section II.C above and Chapter 2 of the draft Joint TSD discusses how the coefficients in 

Table IV-11 were developed for this proposed rule. The proposed coefficients result in the 

footprint-dependent targets shown graphically below for MYs 2017-2025. The MY 2012-2016 

final standards are also shown for comparison. 
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165 

Figure II-5. Fuel Economy Targets for MYs 2012–2016 and 2017–2025 Passenger Cars 

20253035404550556065

30 35 40 45 50 55 60 65 70 75 80

Fuel Economy Target (mpg)

Footprint (sf)

20252024202320222021202020192018201720162015201420132012
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166 

As discussed, the CAFE levels ultimately required of individual manufacturers will 

depend on the mix of vehicles they produce for sale in the United States. Based on the market 

forecast of future sales that NHTSA has used to examine today’s proposed CAFE standards, the 

agency currently estimates that the target curves shown above will result in the following 

average required fuel economy levels for individual manufacturers during MYs 2017–2025 (an 

updated estimate of the average required fuel economy level under the final MY 2016 standard is 

also shown for comparison):189

Table II-31. Estimated Average Fuel Economy Required Under Final MY 2016 and 

Proposed MYs 2017–2025 CAFE Standards For Passenger Cars 

Manufacturer 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 

Aston Martin 38.2 40.5 41.9 43.5 45.2 47.2 49.4 51.7 54.1 56.6 

BMW 37.3 39.4 40.9 42.4 44.1 46.0 48.1 50.4 52.7 55.2 

Daimler 35.9 37.8 39.1 40.5 42.2 44.0 46.1 48.2 50.4 52.8 

Chrysler/Fiat190 36.6 39.2 40.7 42.2 43.7 45.7 48.0 50.2 52.7 55.1 

Ford 37.1 39.1 40.6 42.1 43.7 45.6 47.7 49.9 52.3 54.7 

Geely (Volvo) 36.6 38.8 40.3 41.7 43.4 45.3 47.4 49.6 51.9 54.4 

General Motors 36.9 39.3 40.7 42.3 43.9 45.8 48.0 50.2 52.6 55.1 

Honda 38.3 40.5 42.0 43.6 45.3 47.3 49.5 51.7 54.2 56.7 

Hyundai 38.2 40.3 41.8 43.4 45.1 47.1 49.3 51.5 54.0 56.5 

Kia 37.9 40.0 41.5 43.1 44.8 46.7 48.9 51.2 53.6 56.1 

Lotus 41.1 43.6 45.2 46.9 48.7 50.8 53.2 55.7 58.3 61.1 

Mazda 38.3 40.5 41.9 43.5 45.2 47.1 49.2 51.5 54.0 56.6 

Mitsubishi 38.7 41.1 42.6 44.2 45.9 47.9 50.1 52.5 54.9 57.5 

Nissan 37.7 39.7 41.2 42.7 44.4 46.3 48.4 50.7 53.1 55.5 

Porsche 41.1 43.6 45.2 46.9 48.7 50.8 53.2 55.7 58.3 61.1 

Spyker (Saab) 38.3 40.6 42.1 43.6 45.3 47.3 49.5 51.8 54.2 56.8 

Subaru 39.3 41.7 43.2 44.8 46.6 48.6 50.9 53.3 55.8 58.4 

Suzuki 40.8 43.3 44.9 46.5 48.4 50.5 52.8 55.3 57.9 60.6 

Tata (Jaguar, Rover) 34.7 36.8 38.1 39.6 41.1 42.9 44.9 47.0 49.2 51.5 

Tesla 41.1 43.6 45.2 46.9 48.7 50.8 53.2 55.7 58.3 61.1 

Toyota 38.4 40.7 42.2 43.8 45.5 47.5 49.7 52.0 54.4 57.0 

VW191 38.9 41.2 42.7 44.2 46.0 48.0 50.2 52.6 55.0 57.6 

Average 37.8 40.0 41.4 43.0 44.7 46.6 48.8 51.0 53.5 56.0 

Because a manufacturer’s required average fuel economy level for a model year under 

 189 In the May 2010 final rule establishing MY 2016 standards for passenger cars and light trucks, 

NHTSA estimated that the required fuel economy levels for passenger cars would average 37.8 mpg under the MY 

2016 passenger car standard. Based on the agency’s current forecast of the MY 2016 passenger car market, NHTSA 

again estimates that the average required fuel economy level for passenger cars will be 37.8 mpg in MY 2016. 190 For purposes of CAFE compliance, “Chrysler/Fiat” is assumed to include Ferrari and Maserati in addition to the 

larger-volume Chrysler and Fiat brands. 

191 For purposes of CAFE compliance, VW is assumed to include Audi-Bentley, Bugatti, and Lamborghini, along 

with the larger-volume VW brand. 
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167 

the final standards will be based on its actual production numbers in that model year, its official 

required fuel economy level will not be known until the end of that model year. However, 

because the targets for each vehicle footprint will be established in advance of the model year, a 

manufacturer should be able to estimate its required level accurately. Readers should remember 

that the mpg levels describing the “estimated required standards” shown throughout this section 

are not necessarily the ultimate mpg level with which manufacturers will have to comply, for the 

reasons explained above, and that the mpg level designated as “estimated required” is exactly 

that, an estimate. 

Additionally, again for reference, the following table presents estimated mpg levels based 

on 2-cycle CAFE for easier comparison to the MYs 2012-2016 standards. 

Table II-32. Estimated Average Fuel Economy Required Under Final MY 2016 and Using 

2-Cycle CAFE for MYs 2017-2025 For Passenger Cars 

Manufacturer 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 

Aston Martin 38.2 39.6 41.0 42.4 44.1 45.9 48.0 50.2 52.5 54.9 

BMW 37.3 38.6 40.0 41.4 43.0 44.8 46.8 49.0 51.2 53.5 

Daimler 35.9 37.0 38.2 39.6 41.2 42.9 44.9 46.9 49.0 51.2 

Chrysler/Fiat192 36.6 38.3 39.7 41.2 42.7 44.6 46.7 48.8 51.2 53.5 

Ford 37.1 38.3 39.7 41.1 42.7 44.4 46.5 48.6 50.8 53.1 

Geely (Volvo) 36.6 38.0 39.4 40.8 42.4 44.1 46.1 48.2 50.5 52.8 

General Motors 36.9 38.4 39.8 41.3 42.9 44.7 46.7 48.8 51.1 53.4 

Honda 38.3 39.6 41.1 42.5 44.2 46.0 48.1 50.3 52.6 55.0 

Hyundai 38.2 39.5 40.9 42.3 44.0 45.8 47.9 50.1 52.4 54.8 

Kia 37.9 39.2 40.6 42.0 43.7 45.5 47.6 49.7 52.0 54.4 

Lotus 41.1 42.6 44.1 45.7 47.4 49.4 51.7 54.0 56.5 59.0 

Mazda 38.3 39.6 41.0 42.5 44.1 45.9 47.9 50.1 52.4 54.8 

Mitsubishi 38.7 40.1 41.6 43.1 44.8 46.6 48.8 51.0 53.3 55.7 

Nissan 37.7 38.9 40.3 41.7 43.3 45.1 47.2 49.3 51.5 53.9 

Porsche 41.1 42.6 44.1 45.7 47.4 49.4 51.7 54.0 56.5 59.0 

Spyker (Saab) 38.3 39.7 41.1 42.6 44.2 46.1 48.2 50.3 52.6 55.0 

Subaru 39.3 40.7 42.2 43.7 45.4 47.3 49.5 51.7 54.1 56.5 

Suzuki 40.8 42.2 43.8 45.3 47.1 49.1 51.3 53.6 56.1 58.6 

Tata (Jaguar, Rover) 34.7 36.0 37.3 38.6 40.1 41.8 43.7 45.7 47.7 49.9 

Tesla 41.1 42.6 44.1 45.7 47.4 49.4 51.7 54.0 56.5 59.0 

Toyota 38.4 39.8 41.2 42.7 44.4 46.2 48.3 50.5 52.8 55.2 

VW193 38.9 40.2 41.7 43.1 44.8 46.7 48.9 51.1 53.4 55.8 

Average 37.8 39.1 40.5 42.0 43.6 45.4 47.5 49.6 51.9 54.3 

 192 For purposes of CAFE compliance, “Chrysler/Fiat” is assumed to include Ferrari and Maserati in addition to the 

larger-volume Chrysler and Fiat brands. 

193 For purposes of CAFE compliance, VW is assumed to include Audi-Bentley, Bugatti, and Lamborghini, along 

with the larger-volume VW brand. 
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3. Minimum Domestic Passenger Car Standards 

EISA expressly requires each manufacturer to meet a minimum fuel economy standard 

for domestically manufactured passenger cars in addition to meeting the standards set by 

NHTSA. According to the statute (49 U.S.C. 32902(b)(4)), the minimum standard shall be the 

greater of (A) 27.5 miles per gallon; or (B) 92 percent of the average fuel economy projected by 

the Secretary for the combined domestic and nondomestic passenger automobile fleets 

manufactured for sale in the United States by all manufacturers in the model year. The agency 

must publish the projected minimum standards in the Federal Register when the passenger car 

standards for the model year in question are promulgated. As a practical matter, as standards for 

both cars and trucks continue to rise over time, 49 U.S.C. 32902(b)(4)(A) will likely eventually 

cease to be relevant. 

As discussed in the final rule establishing the MYs 2012-2016 CAFE standards, because 

49 U.S.C. 32902(b)(4)(B) states that the minimum domestic passenger car standard shall be 92 

percent of the projected average fuel economy for the passenger car fleet, “which projection shall 

be published in the Federal Register when the standard for that model year is promulgated in 

accordance with this section,” NHTSA interprets EISA as indicating that the minimum domestic 

passenger car standard should be based on the agency’s fleet assumptions when the passenger car 

standard for that year is promulgated. 

However, we note that we do not read this language to preclude any change, ever, in the 

minimum standard after it is first promulgated for a model year. As long as the 18-month leadtime requirement of 49 U.S.C. 32902(a) is respected, NHTSA believes that the language of the 

statute suggests that the 92 percent should be determined anew any time the passenger car 

standards are revised. This issue will be particularly relevant for the current rulemaking, given 

the considerable leadtime involved and the necessity of a mid-term review for the MYs 2022-

2025 standards. We seek comment on this interpretation, and on whether or not the agency 

should consider instead for MYs 2017-2025 designating the minimum domestic passenger car 

standards proposed here as “estimated,” just as the passenger car standards are “estimated,” and 

waiting until the end of each model year to finalize the 92 percent mpg value. 

We note also that in the MYs 2012-2016 final rule, we interpreted EISA as indicating that 

the 92 percent minimum standard should be based on the estimated required CAFE level rather 

than, as suggested by the Alliance, the estimated achieved CAFE level (which would likely be 

lower than the estimated required level if it reflected manufacturers’ use of dual-fuel vehicle 

credits under 49 U.S.C. 32905, at least in the context of the MYs 2012-2016 standards). NHTSA 

continues to believe that this interpretation is appropriate. 

Based on NHTSA’s current market forecast, the agency’s estimates of these minimum 

standards under the proposed MYs 2017-2025 CAFE standards (and, for comparison, the final 
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169 

MY 2016 minimum domestic passenger car standard) are summarized below in Table IV-16. 

Table II-33. Estimated Minimum Standard For Domestically Manufactured Passenger 

Cars Under Final MY 2016 and Proposed MYs 2017–2025 CAFE Standards For Passenger 

Cars 

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 

34.7 36.8 38.1 39.6 41.1 42.9 44.9 47.0 49.2 51.5

Again, for the reader’s reference, the following table the following table presents 

estimated mpg levels based on 2-cycle CAFE for easier comparison to the MYs 2012-2016 

standards. 

Table II-34. Estimated Minimum Standard For Domestically Manufactured Passenger 

Cars Under Final MY 2016 and Using 2-Cycle CAFE Only for MYs 2017–2025 Passenger 

Cars 

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 

34.7 35.9 37.2 38.6 40.1 41.7 43.7 45.6 47.7 49.9

As discussed in Section IV.D above, NHTSA is also seeking comment on whether to 

consider, for the final rule, the possibility of minimum standards for imported passenger cars and 

light trucks. Although we are not proposing such standards, we believe it may be prudent to 

explore this concept again given the considerable amount of time between now and 2017-2025 

(particularly the later years), and the accompanying uncertainty in our market forecast and other 

assumptions, that might make such minimum standards relevant to help ensure that currentlyexpected fuel economy improvements occur during that time frame. To help commenters’ 

consideration of this question, illustrative levels of minimum standards for those other fleets are 

presented below. 

Table II-35. Illustrative Estimated Minimum Standard For Imported Passenger Cars 

Under Proposed MYs 2017–2025 CAFE Standards For Passenger Cars 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

36.8 38.1 39.6 41.1 42.9 44.9 47.0 49.2 51.5

Table II-36. Illustrative Estimated Minimum Standard For Light Trucks Under Proposed 

MYs 2017–2025 CAFE Standards For Light Trucks 

2017 2018 2019 2020 2021 2022 2023 2024 2025 

27.1 27.6 28.1 28.7 30.7 32.1 33.7 35.4 37.1

NHTSA emphasizes again that we are not proposing additional minimum standards for 

imported passenger cars and light trucks at this time, but we may consider including them in the 

final rule if it seems reasonable and appropriate to do so based on the information provided by 

commenters and the agency’s analysis. NHTSA also may wait until we are able to observe 
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170 

potential market changes during the implementation of the MYs 2012-2016 standards and 

consider additional minimum standards in a future rulemaking action. Any additional minimum 

standards for MYs 2022-2025 that may be set in the future would, like the primary standards, be 

subject to the mid-term review discussed in Section IV.B above, and potentially revised at that 

time. 

4. Light Truck Standards 

For light trucks, NHTSA is proposing CAFE standards defined by the following 

coefficients during MYs 2017–2025: 

Table II-37. Coefficients Defining Proposed MYs 2017–2025 Fuel Economy Targets For 

Light Trucks 

Coefficient 2017 2018 2019 2020 2021 2022 2023 2024 2025 

a (mpg) 43.61 45.21 46.87 48.74 50.83 53.21 55.71 58.32 61.07

b (mpg) 32.65 33.84 35.07 36.47 38.02 39.79 41.64 43.58 45.61

c (gpm/sf) 0.00051 0.00050 0.00048 0.00046 0.00044 0.00042 0.00040 0.00039 0.00037

d (gpm) 0.00190 0.00181 0.00173 0.00164 0.00155 0.00146 0.00137 0.00129 0.00121

e (mpg) 42.06 42.06 42.06 42.06 42.06 42.06 42.06 42.06 42.06

f (mpg) 31.51 31.51 31.51 31.51 31.51 31.51 31.51 31.51 31.51

g (gpm/sf) 0.00053 0.00053 0.00053 0.00053 0.00053 0.00053 0.00053 0.00053 0.00053

h (gpm) 0.00201 0.00201 0.00201 0.00201 0.00201 0.00201 0.00201 0.00201 0.00201

For reference, the coefficients defining the MYs 2012-2016 light truck standards (which 

did not include a “floor” term defined by coefficients e, f, g, and h) are also provided below: 

Table II-38. Coefficients Defining Final MYs 2012–2016 Fuel Economy Targets For Light 

Trucks 

Coefficient 2012 2013 2014 2015 2016 

a (mpg) 36.18 37.16 38.31 40.06 42.03

b (mpg) 28.09 28.67 29.35 30.37 31.49

c (gpm/sf) 0.00053 0.00053 0.00053 0.00053 0.00053

d (gpm) 0.00588 0.00515 0.00434 0.00320 0.00203

The proposed coefficients result in the footprint-dependent targets shown graphically 

below for MYs 2017-2025. MYs 2012-2016 final standards are shown for comparison. 
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171 

Figure II-6. Fuel Economy Targets for MYs 2012–2016 and 2017–2025 Light Trucks 

20253035404550556065

30 35 40 45 50 55 60 65 70 75 80

Fuel Economy Target (mpg)

Footprint (sf)

20252024202320222021202020192018201720162015201420132012
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Also for reference, the following table presents the coefficients based on 2-cycle CAFE 

only for easier comparison to the MYs 2012-2016 coefficients presented above. We emphasize, 

again, that the coefficients in Table IV-20 define the proposed standards. 

Table II-39. Coefficients Based Only on 2-Cycle CAFE for MYs 2017–2025 Light Trucks 

Coefficient 2017 2018 2019 2020 2021 2022 2023 2024 2025 

a (mpg) 35.53 36.37 37.01 37.91 40.43 42.29 44.24 46.28 48.42

b (mpg) 24.74 24.74 24.74 24.74 24.74 25.74 26.93 28.17 29.47

c (gpm/sf) 0.00055 0.00054 0.00053 0.00051 0.00048 0.00046 0.00044 0.00042 0.00040

d (gpm) 0.00566 0.00553 0.00543 0.00530 0.00497 0.00475 0.00454 0.00434 0.00415

Again, given these targets, the CAFE levels required of individual manufacturers will 

depend on the mix of vehicles they produce for sale in the United States. Based on the market 

forecast NHTSA has used to examine today’s proposed CAFE standards, the agency currently 

estimates that the targets shown above will result in the following average required fuel economy 

levels for individual manufacturers during MYs 2017–2025 (an updated estimate of the average 

required fuel economy level under the final MY 2016 standard is shown for comparison):194

Table II-40. Estimated Average Fuel Economy Required Under Final MY 2016 and 

Proposed MYs 2017–2025 CAFE Standards For Light Trucks 

Manufacture

r 

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Aston Martin - - - - - - - - - -

BMW 30.2 30.6 31.4 32.1 32.9 35.1 36.7 38.4 40.2 42.1

Daimler 29.1 29.1 29.6 30.2 30.9 32.9 34.5 36.1 37.8 39.5

Chrysler/Fiat

195 29.0 29.6 30.2 30.8 31.6 33.7 35.3 37.0 38.8 40.7

Ford 28.0 28.4 29.0 29.4 29.9 31.8 33.3 35.0 36.8 38.6

Geely (Volvo) 30.5 31.1 32.1 32.7 33.5 35.8 37.5 39.3 41.2 43.1

General 

Motors 27.4 28.1 28.7 29.2 29.8 31.9 33.4 35.1 36.8 38.6

Honda 30.4 31.0 31.8 32.4 33.2 35.5 37.1 38.9 40.8 42.7

Hyundai 30.7 31.3 32.1 32.8 33.6 35.9 37.6 39.4 41.3 43.2

 194 In the May 2010 final rule establishing MYs 2012-2016 standards for passenger cars and light trucks, 

NHTSA estimated that the required fuel economy levels for light trucks would average 28.8 mpg under the MY 

2016 light truck standard. Based on the agency’s current forecast of the MY 2016 light truck market, NHTSA again 

estimates that the required fuel economy levels will average 28.8 mpg in MY 2016. However, the agency’s market 

forecast reflects less of a future market shift away from light trucks than reflected in the agency’s prior market 

forecast; as a result, NHTSA currently estimates that the combined (i.e., passenger car and light truck) average 

required fuel economy in MY 2016 will be 33.8 mpg, 0.3 mpg lower than the agency’s earlier estimate of 34.1 mpg. 

The agency has made no changes to MY 2016 standards and projects no changes in fleet-specific average 

requirements (although within-fleet market shifts could, under an attribute-based standard, produced such changes). 

195 For purposes of CAFE compliance, “Chrysler/Fiat” is assumed to include Ferrari and Maserati in addition to the 

larger-volume Chrysler and Fiat brands. 
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Kia 29.5 30.0 30.6 31.2 32.0 34.2 35.8 37.5 39.3 41.1

Lotus - - - - - - - - - -

Mazda 31.5 31.9 32.9 33.5 34.3 36.5 38.1 39.8 41.8 43.8

Mitsubishi 31.7 32.6 33.5 34.2 35.1 37.5 39.3 41.1 43.1 45.2

Nissan 29.1 29.6 30.3 30.9 31.6 33.5 35.1 36.9 38.7 40.6

Porsche 29.8 30.3 31.2 31.8 32.6 34.8 36.5 38.2 40.0 41.9

Spyker (Saab) 30.5 31.2 32.1 32.8 33.6 35.9 37.6 39.4 41.3 43.3

Subaru 31.9 33.0 34.0 34.7 35.5 38.0 39.8 41.7 43.6 45.7

Suzuki 31.4 32.2 33.2 33.9 34.7 37.1 38.9 40.7 42.7 44.7

Tata (Jaguar, 

Rover) 31.3 32.1 33.1 33.8 34.6 37.0 38.8 40.6 42.6 44.6

Tesla - - - - - - - - - -

Toyota 29.1 29.7 30.4 31.0 31.6 33.8 35.4 37.1 39.0 40.9

VW196 29.2 29.5 30.1 30.8 31.5 33.5 35.1 36.7 38.5 40.3

Average 28.8 29.4 30.0 30.6 31.2 33.3 34.9 36.6 38.5 40.3

As discussed above with respect to the proposed passenger cars standards, we note that a 

manufacturer’s required light truck fuel economy level for a model year under the ultimate final 

standards will be based on its actual production numbers in that model year. 

Additionally, again for reference, the following table presents estimated mpg levels based 

on 2-cycle CAFE for easier comparison to the MYs 2012-2016 standards. 

Table II-41. Estimated Average Fuel Economy Required Under Final MY 2016 and Using 

2-Cycle CAFE for MYs 2017-2025 For Light Trucks 

Manufacturer 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 

Aston Martin - - - - - - - - - - 

BMW 30.2 30.1 30.7 31.3 32.0 34.1 35.7 37.3 39.0 40.8 

Daimler 29.1 28.6 29.0 29.5 30.2 32.1 33.5 35.0 36.6 38.3 

Chrysler/Fiat197 29.0 29.1 29.6 30.0 30.8 32.8 34.3 35.9 37.6 39.4 

Ford 28.0 28.0 28.4 28.8 29.2 31.0 32.4 34.0 35.7 37.4 

Geely (Volvo) 30.5 30.6 31.3 31.9 32.6 34.8 36.4 38.1 39.9 41.7 

General Motors 27.4 27.7 28.1 28.5 29.1 31.1 32.5 34.1 35.7 37.4 

Honda 30.4 30.5 31.0 31.6 32.3 34.5 36.1 37.7 39.5 41.3 

Hyundai 30.7 30.8 31.4 31.9 32.7 34.9 36.5 38.1 39.9 41.8 

Kia 29.5 29.5 29.9 30.4 31.2 33.3 34.8 36.4 38.1 39.8 

Lotus - - - - - - - - - - 

Mazda 31.5 31.3 32.1 32.7 33.4 35.4 37.0 38.6 40.4 42.3 

Mitsubishi 31.7 32.0 32.7 33.3 34.1 36.4 38.1 39.8 41.6 43.6 

 196 For purposes of CAFE compliance, VW is assumed to include Audi-Bentley, Bugatti, and Lamborghini, along 

with the larger-volume VW brand. 

197 For purposes of CAFE compliance, “Chrysler/Fiat” is assumed to include Ferrari and Maserati in addition to the 

larger-volume Chrysler and Fiat brands. 
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Nissan 29.1 29.2 29.7 30.2 30.8 32.7 34.1 35.8 37.6 39.3 

Porsche 29.8 29.8 30.5 31.0 31.8 33.9 35.4 37.1 38.8 40.6 

Spyker (Saab) 30.5 30.7 31.4 32.0 32.7 34.9 36.5 38.2 40.0 41.8 

Subaru 31.9 32.4 33.1 33.7 34.5 36.8 38.5 40.3 42.2 44.1 

Suzuki 31.4 31.7 32.4 33.0 33.8 36.0 37.7 39.4 41.3 43.2 

Tata (Jaguar, Rover) 31.3 31.5 32.3 32.9 33.7 35.9 37.6 39.3 41.2 43.1 

Tesla - - - - - - - - - - 

Toyota 29.1 29.2 29.8 30.3 30.8 32.9 34.4 36.0 37.8 39.5 

VW198 29.2 29.0 29.5 30.0 30.7 32.7 34.1 35.7 37.3 39.1 

Average 28.8 29.0 29.4 29.9 30.4 32.5 33.9 35.6 37.3 39.0 

I. How do the proposed standards fulfill NHTSA’s statutory obligations? 

The discussion that follows is necessarily complex, but the central points are 

straightforward. NHTSA has tentatively concluded that the standards presented above in Section 

IV.E are the maximum feasible standards for passenger cars and light trucks in MYs 2017-2025. 

EPCA/EISA requires NHTSA to consider four statutory factors in determining the maximum 

feasible CAFE standards in a rulemaking: specifically, technological feasibility, economic 

practicability, the effect of other motor vehicle standards of the Government on fuel economy, 

and the need of the nation to conserve energy. The agency considered a number of regulatory 

alternatives in its analysis of potential CAFE standards for those model years, including several 

that increase stringency on average at set percentages each year, one that approximates the point 

at which the modeled net benefits are maximized in each model year, and one that approximates 

the point at which the modeled total costs equal total benefits in each model year. Some of those 

alternatives represent standards that would be more stringent than the proposed standards,199 and 

some are less stringent.200 The less stringent alternatives have lower net benefits and are below 

the point that would be the maximum feasible. The more stringent alternatives would raise 

serious questions about economic practicability. As the discussion below explains, we tentatively 

 198 For purposes of CAFE compliance, VW is assumed to include Audi-Bentley, Bugatti, and Lamborghini, along 

with the larger-volume VW brand. 

199 We recognize that higher standards would help the need of the nation to conserve more energy and might 

potentially be technologically feasible (in the narrowest sense) during those model years, but based on our analysis 

and the evidence presented by the industry, we tentatively conclude that higher standards would not represent the 

proper balancing for MYs 2017-2025 cars and trucks, because they would raise serious questions about economic 

practicability. As explained above, NHTSA’s modeled estimates necessarily do not perfectly capture all of the 

factors of economic practicability, and this conclusion regarding net benefits versus economic practicability is 

similar to the conclusion reached in the 2012-2016 analysis. 200 We also recognize that lower standards might be less burdensome on the industry, but considering the 

environmental impacts of the different regulatory alternatives as required under NEPA and the need of the nation to 

conserve energy, we do not believe they would have represented the appropriate balancing of the relevant factors, 

because they would have left technology, fuel savings, and emissions reductions on the table unnecessarily, and not 

contributed as much as possible to reducing our nation’s energy security and climate change concerns. They would 

also have lower net benefits than the Preferred Alternative. 
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conclude that the correct balancing of the relevant factors that the agency must consider in 

determining the maximum feasible standards recognizes economic practicability concerns as 

discussed below, and sets standards accordingly. Additionally, consistent with Executive Order 

13563, the agency believes that the benefits of the preferred alternative amply justify the costs; 

indeed, the monetized benefits exceed the monetized costs by $358 billion over the lifetime of 

the vehicles covered by the proposed standards. In full consideration of all of the information 

currently before the agency, we have weighed the statutory factors carefully and selected 

proposed passenger car and light truck standards that we believe are the maximum feasible for 

MYs 2017-2025. 

1. What are NHTSA’s statutory obligations? 

As discussed above in Section IV.D, NHTSA sets CAFE standards under EPCA, as 

amended by EISA, and is also subject to the APA and NEPA in developing and promulgating 

CAFE standards. 

NEPA requires the agency to develop and consider the findings of an Environmental 

Impact Statement (EIS) for “major Federal actions significantly affecting the quality of the 

human environment.” NHTSA has determined that this action is such an action and therefore 

that an EIS is necessary, and has accordingly prepared a Draft EIS to inform its development and 

consideration of the proposed standards. The agency has evaluated the environmental impacts of 

a range of regulatory alternatives in our proposal, and integrated the results of that consideration 

into our balancing of the EPCA/EISA factors, as discussed below. 

The APA and relevant case law requires our rulemaking decision to be rational, based on 

consideration of the relevant factors, and within the scope of the authority delegated to the 

agency by EPCA/EISA. The relevant factors are those required by EPCA/EISA and the 

additional factors approved in case law as ones historically considered by the agency in 

determining the maximum feasible CAFE standards, such as safety. The statute requires us to 

set standards at the maximum feasible level for passenger cars and light trucks for each model 

year, and the agency tentatively concludes that the standards, if adopted as proposed, would 

satisfy this requirement. NHTSA has carefully examined the relevant data and other 

considerations, as discussed below in our explanation of our tentative conclusion that the 

proposed standards are the maximum feasible levels for those model years based on our 

evaluation of the information before us for this NPRM. 

As discussed in Section IV.D, EPCA/EISA requires that NHTSA establish separate 

passenger car and light truck standards at “the maximum feasible average fuel economy level 

that it decides the manufacturers can achieve in that model year,” based on the agency’s 

consideration of four statutory factors: technological feasibility, economic practicability, the 
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effect of other standards of the Government on fuel economy, and the need of the nation to 

conserve energy.201 NHTSA has developed definitions for these terms over the course of 

multiple CAFE rulemakings202 and determines the appropriate weight and balancing of the terms 

given the circumstances in each CAFE rulemaking. For MYs 2011–2020, EPCA further requires 

that separate standards for passenger cars and for light trucks be set at levels high enough to 

ensure that the CAFE of the industry-wide combined fleet of new passenger cars and light trucks 

reaches at least 35 mpg not later than MY 2020. For model years after 2020, standards need 

simply be set at the maximum feasible level. 

The agency thus balances the relevant factors to determine the maximum feasible level of 

the CAFE standards for each fleet, in each model year. The next section discusses how the 

agency balanced the factors for this proposal, and why we believe the proposed standards are the 

maximum feasible. 

2. How did the agency balance the factors for this NPRM? 

There are numerous ways that the relevant factors can be balanced (and thus weight given 

to each factor) depending on the agency’s policy priorities and on the information before the 

agency regarding any given model year, and the agency therefore considered a range of 

alternatives that represent different regulatory options that we thought were potentially 

reasonable for purposes of this rulemaking. For this proposal, the regulatory alternatives 

considered in the agency’s analysis include several alternatives for fuel economy levels that 

increase annually, on average, at set rates – specifically, 2 %/year, 3 %/year, 4 %/year, 5 %/year, 

6 %/year, and 7 %/year.203 Analysis of these various rates of increase effectively encompasses 

the entire range of fuel economy improvements that, based on information currently available to 

the agency, could conceivably fall within the statutory boundary of “maximum feasible” 

standards. The regulatory alternatives also include two alternatives based on benefit-cost 

 201 As explained in Section IV.D, 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 statutory provisions that facilitate compliance with the CAFE standards and thereby reduce the costs of 

compliance. Specifically, in determining the maximum feasible level of fuel economy for passenger cars and light 

trucks, NHTSA cannot consider the fuel economy benefits of “dedicated” alternative fuel vehicles (like battery 

electric vehicles or natural gas vehicles), must consider dual-fueled automobiles to be operated only on gasoline or 

diesel fuel (at least through MY 2019), and may not consider the ability of manufacturers to use, trade, or transfer 

credits. This provision limits, to some extent, the fuel economy levels that NHTSA can find to be “maximum 

feasible” – if NHTSA cannot consider the fuel economy of electric vehicles, for example, NHTSA cannot set 

standards predicated on manufacturers’ usage of electric vehicles to meet the standards. 202 These factors are defined in Section IV.D; for brevity, we do not repeat those definitions here. 203 This is an approach similar to that used by the agency in the MY 2012-2016 rulemaking, in which we also 

considered several alternatives that increased annually, on average, at 3%, 4 %, 5 %, 6 % and 7 %/year. The 

“percent-per-year” alternatives in this proposal are somewhat different from those considered in the MY 2012-2016 

rulemaking, however, in terms of how the annual rate of increase is applied. For this proposal, the stringency curves 

are themselves advanced directly by the annual increase amount, without reference to any yearly changes in the fleet 

mix. In the 2012-2016 rule, the annual increases for the stringency alternatives reflected the estimated required fuel 

economy of the fleet which accounted for both the changes in the target curves and changes in the fleet mix. 
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criteria, one in which standards would be set at the point where the modeled net benefits would 

be maximized for each fleet in each year (MNB), and another in which standards would be set at 

the point at which total costs would be most nearly equal to total benefits for each fleet in each 

year (TC=TB),204 as well as the preferred alternative, which is within the range of the other 

alternatives. These alternatives are discussed in more detail in Chapter III of the PRIA 

accompanying this NPRM, which also contains an extensive analysis of the relative impacts of 

the alternatives in terms of fuel savings, costs (both per-vehicle and aggregate), carbon dioxide 

emissions avoided, and many other metrics. Because the agency could conceivably select any of 

the regulatory alternatives above, all of which fall between 2 %/year and 7 %/year, inclusive, the 

Draft EIS that accompanies this proposal analyzes these lower and upper bounds as well as the 

preferred alternative. Additionally, the Draft EIS analyzes a “No Action Alternative,” which 

assumes that, for MYs 2017 and beyond, NHTSA would set standards at the same level as MY 

2016. The No Action Alternative provides a baseline for comparing the environmental impacts 

of the other alternatives. 

NHTSA believes that this approach clearly communicates the level of stringency of each 

alternative and allows us to identify alternatives that represent different ways to balance 

NHTSA’s statutory factors under EPCA/EISA. Each of the listed alternatives represents, in part, 

a different way in which NHTSA could conceivably balance different policies and considerations 

in setting the standards that achieve the maximum feasible levels. For example, the 2% 

Alternative, the least stringent alternative, would represent a balancing in which economic 

practicability – which include concerns about availability of technology, capital, and consumer 

preferences for vehicles built to meet the future standards – weighs more heavily in the agency’s 

consideration, and the need of the nation to conserve energy would weigh less heavily. In 

contrast, under the 7% Alternative, one of the most stringent, the need of the nation to conserve 

energy – which includes energy conservation and climate change considerations – would weigh 

more heavily in the agency’s consideration, and other factors would weigh less heavily. 

Balancing and assessing the feasibility of different alternative can also be influenced by 

differences and uncertainties in the way in which key economic factors (e.g., the price of fuel 

and the social cost of carbon) and technological inputs could be assessed and estimated or 

valued. While NHTSA believes that our analysis conducted in support of this NPRM uses the 

best and most transparent technology-related inputs and economic assumption inputs that the 

agencies could derive for MYs 2017-2025, we recognize that there is uncertainty in these inputs, 

 204 We included the MNB and TC=TB alternatives in part for the reference of commenters familiar with NHTSA’s 

past several CAFE rulemakings – these alternatives represent balancings carefully considered by the agency in past 

rulemaking actions as potentially maximum feasible – and because Executive Orders 12866 and 13563 focus 

attention on an approach that maximizes net benefits. The assessment of maximum net benefits is challenging in the 

context of setting CAFE standards, in part because standards which maximize net benefits for each fleet, for each 

model year, would not necessarily be the standards that lead to the greatest net benefits over the entire rulemaking 

period 
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and the balancing could be different if, for example, the inputs are adjusted in response to new 

information. 

This is the first CAFE rulemaking in which the agency has looked this far into the future, 

which makes our traditional approach to balancing more challenging than in past (even recent 

past) rulemakings. NHTSA does not presently believe, for example, that technological 

feasibility as the agency defines it is as constraining in this rulemaking as it has been in the past 

in light of the time frame of this rulemaking. “Technological feasibility” refers to whether a 

particular method of improving fuel economy can be available for commercial application in the 

model year for which a standard is being established. In previous CAFE rulemakings, it has 

been more difficult for the agency to say that the most advanced technologies would be available 

for commercial application in the model years for which standards were being established. For 

this rulemaking, which is longer term, NHTSA has considered all types of technologies that 

improve real-world fuel economy, including air-conditioner efficiency and other off-cycle 

technology, PHEVs, EVs, and highly-advanced internal combustion engines not yet in 

production, but all of which the agencies’ expect to be commercially applicable by the 

rulemaking time frame. On the one hand, we recognize that some technologies that currently 

have limited commercial use cannot be deployed on every vehicle model in MY 2017, but 

require a realistic schedule for widespread commercialization to be feasible. On the other hand, 

however, the agency expects, based on our analysis, that all of the alternatives could narrowly be 

considered as technologically feasible, in that they could be achieved based on the existence or 

projected future existence of technologies that could be incorporated on future vehicles, and 

promote any of the alternatives to be achieved on a technical basis alone if the level of resources 

that might be required to implement the technologies is not considered. If all alternatives are at 

least theoretically technologically feasible in the MY 2017-2025 timeframe, and the need of the 

nation is best served by pushing standards as stringent as possible, then the agency might be 

inclined to select the alternative that results in the very most stringent standards considered. 

However, the agency must also consider what is required to practically implement 

technologies, which is part of economic practicability, and to which the most stringent 

alternatives give little weight. “Economic practicability” refers 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 the unreasonable elimination of 

consumer choice.” Consumer acceptability is also an element of economic practicability, one 

that is particularly difficult to gauge during times of uncertain fuel prices.205 In a rulemaking 

such as the present one, determining economic practicability requires consideration of the 

uncertainty surrounding relatively distant future market conditions and consumer demand for 

 205 See, e.g., Center for Auto Safety v. NHTSA (CAS), 793 F.2d 1322 (D.C. Cir. 1986) (Administrator’s consideration 

of market demand as component of economic practicability found to be reasonable); Public Citizen v. NHTSA, 848 

F.2d 256 (Congress established broad guidelines in the fuel economy statute; agency’s decision to set lower standard 

was a reasonable accommodation of conflicting policies). 
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fuel economy in addition to other vehicle attributes. In an attempt to evaluate the economic 

practicability of attribute-based standards, NHTSA includes a variety of factors in its analysis, 

including the annual rate at which manufacturers can increase the percentage of their fleet that 

employ a particular type of fuel-saving technology, the specific fleet mixes of different 

manufacturers, and assumptions about the cost of the standards to consumers and consumers’ 

valuation of fuel economy, among other things. Ensuring that a reasonable amount of lead time 

exists to make capital investments and to devote the resources and time to design and prepare for 

commercial production of a more fuel efficient fleet is also relevant to the agency’s consideration 

of economic practicability. Yet there are some aspects of economic practicability that the 

agency’s analysis is not able to capture at this time – for example, the computer model that we 

use to analyze alternative standards does not account for all aspects of uncertainty, in part 

because the agency cannot know what we cannot know. The agency must thus account for 

uncertainty in the context of economic practicability as best as we can based on the entire record 

before us. 

Both technological feasibility and economic practicability enter into the agency’s 

determination of the maximum feasible levels of stringency, and economic practicability 

concerns may cause the agency to decide that standards that might be technologically feasible 

are, in fact, beyond maximum feasible. Standards that require aggressive application of and 

widespread deployment of advanced technologies could raise serious issues with the adequacy of 

time to coordinate such significant changes with manufacturers’ redesign cycles, as well as with 

the availability of engineering resources to develop and integrate the technologies into products, 

and the pace at which capital costs can be incurred to acquire and integrate the manufacturing 

and production equipment necessary to increase the production volume of the technologies. 

Moreover, the agency must consider whether consumers would be likely to accept a specific 

technological change under consideration, and how the cost to the consumer of making that 

change might affect their acceptance of it. The agency maintains, as it has in prior CAFE 

rulemakings, that there is an important distinction between considerations of technological 

feasibility and economic practicability. As explained above, a given level of performance may 

be technologically feasible (i.e., setting aside economic constraints) for a given vehicle model. 

However, it would not be economically practicable to require a level of fleet average 

performance that assumes every vehicle will in the first year of the standards perform at the 

highest technologically feasible level, because manufacturers do not have unlimited access to the 

financial resources or may not practically be able to hire enough engineers, build enough 

facilities, and install enough tooling. 

NHTSA therefore believes, based on the information currently before us, that economic 

practicability concerns render certain standards that might otherwise be technologically feasible 

to be beyond maximum feasible within the meaning of the statute for the 2017-2025 standards. 

Our analysis indicated that technologies seem to exist to meet the stringency levels required by 

future standards under nearly all of the regulatory alternatives; but it also indicated that 
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manufacturers would not be able to apply those technologies quickly enough, given their 

redesign cycles, and the level of the resources that would be required to implement those 

technologies widely across their products, to meet all applicable standards in every model year 

under some of the alternatives. 

Another consideration for economic practicability is incremental per-vehicle increases in 

technology cost. In looking at the incremental technology cost results from our modeling 

analysis, the agency saw that in progressing from alternatives with lower stringencies to 

alternatives with higher stringencies, technology cost increases (perhaps predictably) at a 

progressively higher rate, until the model projects that manufacturers are unable to comply with 

the increasing standards and enter (or deepen) non-compliance. Table IV-25 and Table IV-26 

show estimated cumulative lifetime fuel savings and estimated average vehicle cost increase for 

passenger cars and light trucks. The results show that there is a significant increase in 

technology cost between the 4% alternatives and the 5% alternatives. 

Table II-42. Estimated Passenger Car Cumulative Lifetime Fuel Savings and Average 

Vehicle Cost Increase 

 

Cumulative 

Lifetime 

Fuel 

Savings 

2017-2021 

(billion 

gallons) 

Average 

Vehicle 

Cost 

Increase in 

2021 

(2009 $) 

Cumulative 

Lifetime 

Fuel 

Savings 

2017-2025 

(billion 

gallons) 

Average 

Vehicle 

Cost 

Increase in 

2025 

(2009 $) 

2% 22 $451 58 $684 

3% 32 $775 85 $1,367 

MNB 54 $1,060 108 $1,313 

Preferred 

Alternative 39 $1,108 104 $2,023 

4% 42 $1,252 110 $2,213 

TC=TB 62 $1,607 135 $2,515 

5% 51 $1,844 130 $3,040 

6% 57 $1,789 140 $3,229 

7% 61 $1,930 144 $3,304 

Table II-43. Estimated Light Truck Cumulative Lifetime Fuel Savings and Average 

Vehicle Cost Increase 

 Cumulative Average Cumulative Average 
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Lifetime 

Fuel 

Savings 

2017-2021 

(billion 

gallons) 

Vehicle 

Cost 

Increase in 

2021 

(2009 $) 

Lifetime 

Fuel 

Savings 

2017-2025 

(billion 

gallons) 

Vehicle 

Cost 

Increase in 

2025 

(2009 $) 

2% 22 $498 53 $706 

3% 33 $909 77 $1,308 

Preferred 

Alternative 22 $965 69 $1,578 

4% 41 $1,619 98 $2,423 

MNB 62 $2,262 126 $3,427 

TC=TB 62 $2,232 126 $3,416 

5% 50 $2,154 116 $3,444 

6% 56 $2,298 123 $3,611 

7% 59 $2,482 127 $3,692 

Thus, if technological feasibility and the need of the nation are not particularly limiting in 

a given rulemaking, then maximum feasible standards would be represented by the mpg levels 

that we could require of the industry to improve fuel economy before we reach a tipping point 

that presents risk of significantly adverse economic consequences. Standards that are lower than 

that point would likely not be maximum feasible, because such standards would leave fuelsaving technologies on the table unnecessarily; standards that are higher than that point would 

likely be beyond what the agency would consider economically practicable, and therefore 

beyond what we would consider maximum feasible, even if they might be technologically 

feasible or better meet the need of the nation to conserve energy. The agency does not believe 

that standards are balanced if they weight one or two factors so heavily as to ignore another. 

We explained above that part of the way that we try to evaluate economic practicability is 

through a variety of model inputs, such as phase-in caps (the annual rate at which manufacturers 

can increase the percentage of their fleet that employ a particular type of fuel-saving technology) 

and redesign schedules to account for needed lead time. These inputs limit how much 

technology can be applied to a manufacturer’s fleet in the agency’s analysis attempting to 

simulate a way for the manufacturer to comply with standards set under different regulatory 

alternatives. If the limits (and technology cost-effectiveness) prevent enough manufacturers 

from meeting the required levels of stringency, the agency may decide that the standards under 

consideration may not be economically practicable. The difference between the required fuel 

economy level that applies to a manufacturer’s fleet and the level of fuel economy that the 
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agency projects the manufacturer would achieve in that year, based on our analysis, is called a 

“compliance shortfall.”206 

We underscore again that the modeling analysis does not dictate the “answer,” it is 

merely one source of information among others that aids the agency’s balancing of the standards. 

These considerations, shortfalls and increases in incremental technology costs, do not entirely 

define economic practicability, but we believe they are symptomatic of it. In looking at the 

projected compliance shortfall results from our modeling analysis, the agency preliminarily 

concluded, based on the information before us at the time, that for both passenger car and for 

light trucks, the MNB and TC=TB alternatives, and the 5%, 6% and 7% alternatives did not 

appear to be economically practicable, and were thus likely beyond maximum feasible levels for 

MYs 2017-2025. In other words, despite the theoretical technological feasibility of achieving 

these levels, various manufacturers would likely lack the financial and engineering resources and 

sufficient lead time to do so. 

The analysis showed that for the passenger car 5% alternative, there were significant 

compliance shortfalls for Chrysler in MY 2025, Ford in MYs 2021 and 2023-2025, GM in MYs 

2022 and 2024-2025, Mazda in MYs 2021 and 2024-2025, and Nissan in MY 2025. For light 

trucks, the analysis showed the 5% alternative had significant compliance shortfalls for Chrysler 

in MYs 2022-2025, Ford in MY 2025, GM in MYs 2023-2025, Kia in MY 2025, Mazda in MYs 

2022 and 2025, and Nissan in MYs 2023-2025. However, the 4%, 3% and 2% alternatives did 

not appear, based on shortfalls, to be beyond the level of economic practicability, and thus 

appeared potentially to be within the range of alternatives that might yet be maximum feasible. 

Table II-44. Estimated Passenger Car Compliance Shortfall for the 5%/Year Alternative 

(MPG) 

Estimated Compliance Shortfall for Passenger Car (mpg) 

 2017 2018 2019 2020 2021 2022 2023 2024 2025 

Chrysler - - - - - 1.7 - - 2.3

Ford - - - 0.2 - - 1.5 2.9 5.2 

General Motors - - - - - 2.3 0.8 2.1 2.5 

Honda - - - - - - - - - 

Hyundai - - - - - - - - - 

Kia - - - - - - - - - 

Mazda - - - - 1.9 - - 1.6 1.9

Nissan - - - - - - - - 1.3 

 206 The agency’s modeling estimates how the application of technologies could increase vehicle costs, reduce fuel 

consumption, and reduce CO2 emissions, and affect other factors. As CAFE standards are performance-based, 

NHTSA does not mandate that specific technologies be used for compliance. CAFE modeling, therefore projects 

one way that manufacturers could comply. Manufacturers may choose a different mix of technologies based on 

their unique circumstances and products. 
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Toyota - - - - - - - - - 

Table II-45. Estimated Light Truck Compliance Shortfall for the 5%/Year Alternative 

(MPG) 

Estimated Compliance Shortfall for Light Truck (mpg) 

 2017 2018 2019 2020 2021 2022 2023 2024 2025

Chrysler - - - - - 1.8 0.9 3.2 0.9 

Ford - - - - - - - 0.1 1.8 

General Motors - - - - - 0.1 1.8 3.2 2.9 

Honda - - - - - - - - 0.6 

Hyundai - - - - - - 0.6 - - 

Kia - - - - - - - - 2.1 

Mazda - - - - - 1.0 - - 2.1 

Nissan - - - - - - 1.1 2.1 4.6 

Toyota - - - - - - - - - 

The preliminary analysis referred to above, in which the agency tentatively concluded 

that the 5%, 6%, 7%, MNB, and TC=TB alternatives were likely beyond the level of economic 

practicability based on the information available to the agency at the time, was conducted 

following the first SNOI and prior to the second SNOI – thus, between the end of 2010 and July 

2011. The agencies stated in the first SNOI that we had not conducted sufficient analysis at the 

time to narrow the range of potential stringencies that had been discussed in the initial NOI and 

in the Interim Joint TAR, and that we would be conducting more analyses and continuing 

extensive dialogue with stakeholders in the coming months to refine our proposal. Based on our 

initial consideration of how the factors might be balanced to determine the maximum feasible 

standards to propose for MYs 2017-2025 (i.e., where technological feasibility did not appear to 

be particularly limiting and the need of the nation would counsel for choosing more stringent 

alternatives, but economic practicability posed significant limitations), NHTSA’s preliminary 

analysis indicated that the alternatives including up to 4% per year for cars and 4% per year for 

trucks should reasonably remain under consideration. 

With that preliminary estimate of 4%/year for cars and trucks as the upper end of the 

range of alternatives that should reasonably remain under consideration for MYs 2017-2025, the 

agencies began meeting again intensely with stakeholders, including many individual 

manufacturers, between June 21, 2011 and July 27, 2011 to determine whether additional 

information would aid NHTSA in further consideration. Beginning in the June 21, 2011 

meeting, NHTSA and EPA presented the 4% alternative target curves as a potential concept 

along with preliminary program flexibilities and provisions, in order to get feedback from the 

manufacturer stakeholders. Manufacturer stakeholders provided comments, much of which was 

confidential business information, which included projections of how they might comply with 
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concept standards, the challenges that they expected, and their recommendations on program 

stringency and provisions.207 

Regarding passenger cars, several manufacturers shared projections that they would be 

capable of meeting stringency levels similar to NHTSA’s preliminary CAFE modeling 

projections for the 4% alternative in MY 2020 or in 2021, with some of those arguing that they 

faced challenges in the earlier years of that period with meeting a constant 4% rate throughout 

the entire period. Some manufacturers shared projections that they could comply with 

stringencies that ramped up, increasing more slowly in MY 2017 and then progressively 

increasing through MY 2021. Most manufacturers provided limited projections beyond MY 

2021, although some stated that they could meet the agency’s concept stringency targets in MY 

2025. Manufacturers generally suggested that the most significant challenges to meeting a 

constant 4% (or faster) year-over-year increase in the passenger car standards related to their 

ability to implement the new technologies quickly enough to achieve the required levels, given 

their need to implement fuel economy improvements in both the passenger car and light truck 

fleets concurrently; challenges related to the cadence of redesign and refresh schedules; the pace 

at which new technology can be implemented considering economic factors such as availability 

of engineering resources to develop and integrate the technologies into products; and the pace at 

which capital costs can be incurred to acquire and integrate the manufacturing and production 

equipment necessary to increase the production volume of the technologies. Manufacturers often 

expressed concern that the 4% levels could require greater numbers of advanced technology 

vehicles than they thought they would be able to sell in that time frame, given their belief that the 

cost of some technologies was much higher than the agencies had estimated and their 

observations of current consumer acceptance of and willingness to pay for advanced technology 

vehicles that are available now in the marketplace. A number of manufacturers argued that they 

did not believe that they could create a sustainable business case under passenger car standards 

that increased at the rate required by the 4% alternative. 

Regarding light trucks, most manufacturers expressed significantly greater concerns over 

the 4% alternative for light trucks than for passenger cars. Many manufacturers argued that 

increases in light truck standard stringency should be slower than increases in passenger car 

standard stringency, based on, among other things, the greater payload, cargo capacity and 

towing utility requirements of light trucks, and what they perceived to be lower consumer 

acceptance of certain (albeit not all) advanced technologies on light trucks. Many manufacturers 

also commented that redesign cycles are longer on trucks than they are on passenger cars, which 

reduces the frequency at which significant changes can be made cost-effectively to comply with 

increasing standards, and that the significant increases in stringency in the MY 2012-2016 

 207 Feedback from these stakeholder meetings is summarized in section IV.B and documents that are referenced in that section. 
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program208 in combination with redesign schedules would not make it possible to comply with 

the 4% alternative in the earliest years of the MY 2017-2025 program, such that only 

significantly lower stringencies in those years would be feasible in their estimation. As for cars, 

most manufacturers provided limited projections beyond MY 2021. Manufacturers generally 

stated that the most significant challenges to meeting a constant 4% (or faster) year-over-year 

increase in the light truck standards were similar to what they had described for passenger cars as 

enumerated in the paragraph above, but were compounded by concerns that applying 

technologies to meet the 4% alternative standards would result in trucks that were more 

expensive and provided less utility to consumers. As was the case for cars, manufacturers 

argued that their technology cost estimates were higher than the agencies’ and consumers are less 

willing to accept/pay for some advanced technologies in trucks, but manufacturers argued that 

these concerns were more significant for trucks than for cars, and that they were not optimistic 

that they could recoup the costs through higher prices for vehicles with the technologies that 

would be needed to comply with the 4% alternative. Given their concerns about having to 

reduce utility and raise truck prices, and about their ability to apply technologies quickly enough 

given the longer redesign periods for trucks, a number of manufacturers argued that they did not 

believe that they could create a sustainable business case under light truck standards that 

increased at the rate required by the 4% alternative. 

Other stakeholders, such as environmental and consumer groups, consistently stated that 

stringent standards are technically achievable and critical to important national interests, such as 

improving energy independence, reducing climate change, and enabling the domestic automobile 

industry to remain competitive in the global market. Labor interests stressed the need to 

carefully consider economic impacts and the opportunity to create and support new jobs, and 

consumer advocates emphasized the economic and practical benefits to consumers of improved 

fuel economy and the need to preserve consumer choice. In addition, a number of stakeholders 

stated that the standards under development should not have an adverse impact on safety. 

NHTSA, in collaboration with EPA and in coordination with CARB, carefully considered 

the inputs received from all stakeholders, conducted additional independent analyses, and 

deliberated over the feedback received on the agencies’ analyses. NHTSA considered individual 

manufacturers’ redesign cycles and, where available, the level of technologies planned for their 

future products that improve fuel economy, as well as some estimation of the resources that 

would likely be needed to support those plans and the potential future standards. The agency 

also considered whether we agreed that there could conceivably be compromises to vehicle 

utility depending on the technologies chosen to meet the potential new standards, and whether a 

change in the cadence of the rate at which standards increase could provide additional 

 208 Some manufacturers indicated that their light truck fleet fuel economy would be below what they anticipated 

their required fuel economy level would be in MY 2016, and that they currently expect that they will need to employ 

available flexibilities to comply with that standard. 
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opportunity for industry to develop and implement technologies that would not adversely affect 

utility. NHTSA considered feedback on consumer acceptance of some advanced technologies 

and consumers’ willingness to pay for improved fuel economy. In addition, the agency carefully 

considered whether manufacturer assertions about potential uncertainties in the agency’s 

technical, economic, and consumer acceptance assumptions and estimates were potentially valid, 

and if so, what the potential effects of these uncertainties might be on economic practicability. 

Regarding passenger cars, after considering this feedback from stakeholders, the agency 

considered further how it thought the factors should be balanced to determine the maximum 

feasible passenger car standards for MYs 2017-2025. Based on that reconsideration of the 

information before the agency and how it informs our balancing of the factors, NHTSA 

tentatively concludes that the points raised may indicate that the agency’s preliminary analysis 

supporting consideration of standards that increased up to 4%/year may not have captured fully 

the level of uncertainty that surrounds economic practicability in these future model years. 

Nevertheless, while we believe there may be some uncertainty, we do not agree that it is nearly 

as significant as a number of manufacturers maintained, especially for passenger cars. The most 

persuasive information received from stakeholders for passenger cars concerned practicability 

issues in the first phase of the MY 2017-2025 standards. We therefore tentatively conclude that 

the maximum feasible stringency levels for passenger cars are only slightly different from the 

4%/year levels suggested as the high end preliminarily considered by the agency; increasing on 

average 3.7%/year in MYs 2017-2021, and on average 4.5%/year in MYs 2022-2025. For the 

overall MY 2017-2025 period, the maximum feasible stringency curves increase on average at 

4.1%/year, and our analysis indicates that the costs and benefits attributable to the 4% alternative 

and the preferred alternative for passenger cars are very similar: the preferred alternative is 8.8 

percent less expensive for manufacturers than the 4% alternative (estimated total costs are $113 

billion for the preferred alternative and $124 billion for the 4% alternative), and achieves only 

$20 billion less in total benefits than the 4% alternative (estimated total benefits are $310 billion 

for the preferred alternative and $330 billion for the 4% alternative), a very small difference 

given that benefits are spread across the entire lifetimes of all vehicles subject to the standards. 

The analysis also shows that the lifetime cumulative fuel savings is only 5 percent higher for the 

4% alternative than the preferred alternative (the estimated fuel savings is 104 billion gallons for 

the preferred alternative, and 110 billion gallons for the 4% alternative). 

At the same time, the increase in average vehicle cost in MY 2025 is 9.4 percent higher 

for the 4% alternative (the estimated cost increase for the average vehicle is $2,023 for the 

preferred alternative, and $2,213 for the 4% alternative). The rates of increase in stringency for 

each model year are summarized in Table IV-29. NHTSA emphasizes that under 49 U.S.C. 

32902(b), the standards must be maximum feasible in each model year without reference to other 

model years, but we believe that the small amount of progressiveness in the proposed standards 

for MYs 2017-2021, which has very little effect on total benefits attributable to the proposed 

passenger car standards, will help to promote the continuation of, or increases in, research and 
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development into the more advanced technologies that will enable greater stringency increases in 

MYs 2022-2025, and help to capture the considerable fuel savings and environmental benefits 

similar to the 4% alternative beginning in MY 2025. 

We are concerned that requiring manufacturers to invest that capital to meet higher 

standards in MYs 2017-2021, rather than allowing them to increase fuel economy in those years 

slightly more slowly, would reduce the levels that would be feasible in the second phase of the 

program by diverting research and development resources to those earlier model years. Thus, 

after considerable deliberation with EPA and consultation with CARB, NHTSA selected the 

preferred alternative as the maximum feasible alternative for MYs 2017-2025 passenger cars 

based on consideration of inputs from manufacturers and the agency’s independent analysis, 

which reaches the stringency levels of the 4% alternative in MY 2025, but has a slightly slower 

ramp up rate in the earlier years. 

Table II-46. Annual Rate Of Increase in the Stringency of the Preferred Alternative for 

Each Model Year 

Model Year Passenger Car Light Truck 

2017 3.6% 0.6% 

2018 3.6% 2.1% 

2019 3.6% 1.7% 

2020 3.7% 2.0% 

2021 4.2% 6.4% 

2022 4.5% 4.5% 

2023 4.4% 4.7% 

2024 4.5% 4.7% 

2025 4.5% 4.6% 

Table II-47. Annual Rate Of Increase in the Stringency of the Preferred Alternative Over 

Various Periods 

Model Years Passenger Car Light Truck 

2017-2021 3.7% 2.6% 

2022-2025 4.5% 4.6% 

2017-2025 4.1% 3.5% 

Regarding light trucks, while NHTSA does not agree with the manufacturer’s overall cost 

assessments and believe that our technology cost and effectiveness assumptions should allow the 

most capable manufacturers to preserve all necessary vehicle utility, the agencies do believe 

there is merit to some of the concerns raised in stakeholder feedback. Specifically, concerns 

about longer redesign schedules for trucks, compounded by the need to invest simultaneously in 

raising passenger car fuel economy, may not have been fully captured in our preliminary 

analysis. This could lead manufacturers to implement technologies that do not maintain vehicle 
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utility, based on the cadence of the standards under the 4% alternative. A number of 

manufacturers repeatedly stated, in providing feedback, that the MYs 2012-2016 standards for 

trucks, while feasible, required significant investment to reach the required levels, and that given 

the redesign schedule for trucks, that level of investment throughout the entire MYs 2012-2025 

time period was not sustainable. Based on the confidential business information that 

manufacturers provided to us, we believe that this point may be valid. If the agency pushes 

CAFE increases that require considerable sustained investment at a faster rate than industry 

redesign cycles, adverse economic consequences could ensue. The best information that the 

agency has at this time, therefore, indicates that requiring light truck fuel economy 

improvements at the 4% annual rate could create potentially severe economic consequences. 

Thus, evaluating the inputs from stakeholders and the agency’s independent analysis, the 

agency also considered further how it thought the factors should be balanced to determine the 

maximum feasible light truck standards for MYs 2017-2025. Based on that consideration of the 

information before the agency and how it informs our balancing of the factors, NHTSA 

tentatively concludes that 4%/year CAFE stringency increases for light trucks in MYs 2017-2021 

are likely beyond maximum feasible, and in fact, in the earliest model years of the MY 2017-

2021 period, that the 3%/year and 2%/year alternatives for trucks are also likely beyond 

maximum feasible. NHTSA therefore tentatively concludes that the preferred alternative, which 

would in MYs 2017-2021 increase on average 2.6%/year, and in MYs 2022-2025 would increase 

on average 4.6%/year, is the maximum feasible level that the industry can reach in those model 

years. For the overall MY 2017-2025 period, the maximum feasible stringency curves would 

increase on average 3.5%/year. The rates of increase in stringency for each model year are 

summarized in Table IV-29 and Table IV-30. 

Our analysis indicates that the preferred alternative has 48 percent lower cost than the 4% 

alternative (estimated total costs are $44 billion for the preferred alternative and $83 billion for 

the 4% alternative), and the total benefits of the preferred alternative are 30 percent lower ($87 

billion lower) than the 4% alternative (estimated total benefits are $206 billion for the preferred 

alternative and $293 billion for the 4% alternative), spread across the entire lifetimes of all 

vehicles subject to the standards. The analysis also shows that the lifetime cumulative fuel 

savings is 42 percent higher for the 4% alternative than the preferred alternative (the estimated 

fuel savings is 69 billion gallons for the preferred alternative, and 98 billion gallons for the 4% 

alternative). At the same time, the increase in average vehicle cost in MY 2025 is 54 percent 

higher for the 4% alternative (the estimated cost increase for the average vehicle is $1,578 for the 

preferred alternative, and $2,423 for the 4% alternative). 

While these differences are larger than for passenger cars, NHTSA believes that 

standards set at these levels for these model years will help address concerns raised by 

manufacturer stakeholders and reduce the risk for adverse economic consequences, while at the 

same time ensuring most of the substantial improvements in fuel efficiency initially envisioned 

over the entire period and supported by other stakeholders. NHTSA believes that these 
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stringency levels, along with the provisions for incentives for advanced technologies to 

encourage their development and implementation, and the agencies’ expectation that some of the 

uncertainties surrounding consumer acceptance of new technologies in light trucks should have 

resolved themselves by that time frame based on consumers’ experience with the advanced 

technologies, will enable these increases in stringency over the entire MY 2017-2025 period. 

Although, as stated above, the light truck standards must be maximum feasible in each model 

year without reference to other model years, we believe that standards set at the stated levels for 

MYs 2017-2021 and the incentives for advanced technologies for pickup trucks will create the 

best opportunity to ensure that the MY 2022-2025 standards are economically practicable, and 

avoid adverse consequences. The first phase of light truck standards, in that respect, acts as a 

kind of bridge to the second phase, in which industry should be able to realize considerable 

additional improvements in fuel economy. 

The proposed standards also account for the effect of EPA’s standards, in light of the 

agencies’ close coordination and the fact that both sets of standards were developed together to 

harmonize as part of the National Program. Given the close relationship between fuel economy 

and CO2 emissions, and the efforts NHTSA and EPA have made to conduct joint analysis and 

jointly deliberate on information and tentative conclusions,209 the agencies have sought to 

harmonize and align their proposed standards to the greatest extent possible, consistent with their 

respective statutory authorities. In comparing the proposed standards, the agencies’ stringency 

curves are equivalent, except for the fact that the stringency of EPA’s proposed passenger car 

standards reflect the ability to improve GHG emissions through reductions in A/C system 

refrigerant leakage and the use of lower GWP refrigerants (direct A/C improvements),210 and that 

EPA provides incentives for PHEV, EV and FCV vehicles, which NHTSA does not provide 

because statutory incentives have already been defined for these technologies. The stringency of 

NHTSA’s proposed standards for passenger cars for MYs 2017-2025 align with the stringency of 

EPA’s equivalent standards when these differences are considered.211 NHTSA is proposing the 

preferred alternative based on the tentative determination of maximum feasibility as described 

earlier in the section, but, based on efforts NHTSA and EPA have made to conduct joint analysis 

 209 NHTSA and EPA conducted joint analysis and jointly deliberated on information and tentative conclusions 

related to technology cost, effectiveness, manufacturers’ capability to implement technologies, the cadence at which 

manufacturers might support the implementation of technologies, economic factors, and the assessment of 

comments from manufacturers. 

210 As these A/C system improvements do not influence fuel economy, the stringency of NHTSA’s preferred 

alternatives do not reflect the availability of these technologies. 211 We note, however, that the alignment is based on the assumption that manufacturers implement the same level of 

direct A/C system improvements as EPA currently forecasts for those model years, and on the assumption of PHEV, 

EV, and FCV penetration at specific levels. If a manufacturer implements a higher level of direct A/C improvement 

technology and/or a higher penetration of PHEVs, EVs and FCVs, then NHTSA’s proposed standards would 

effectively be more stringent than EPA’s. Conversely, if a manufacturer implements a lower level of direct A/C 

improvement technology and/or a lower penetration of PHEVs, EVs and FCVs, then EPA’s proposed standards 

would effectively be more stringent than NHTSA’s. 
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and jointly deliberate on information and tentative conclusions, NHTSA has also aligned the 

proposed CAFE standards with EPA’s proposed standards. 

Thus, consistent with President Obama’s announcement on July 29, 2011, and with the 

August 9, 2011 SNOI, NHTSA has tentatively concluded that the standards represented by the 

preferred alternative are the maximum feasible standards for passenger cars and light trucks in 

MYs 2017-2025. We recognize that higher standards would help the need of the nation to 

conserve more energy and might potentially be technologically feasible (in the narrowest sense) 

during those model years, but based on our analysis and the evidence presented by the industry, 

we tentatively conclude that higher standards would not represent the proper balancing for MYs 

2017-2025 cars and trucks.212 We tentatively conclude that the correct balancing recognizes 

economic practicability concerns as discussed above, and sets standards at the levels that the 

agency is proposing in this NPRM.213 In the same vein, lower standards might be less 

burdensome on the industry, but considering the environmental impacts of the different 

regulatory alternatives as required under NEPA and the need of the nation to conserve energy, 

we do not believe they would have represented the appropriate balancing of the relevant factors, 

because they would have left technology, fuel savings, and emissions reductions on the table 

unnecessarily, and not contributed as much as possible to reducing our nation’s energy security 

and climate change concerns. Standards set at the proposed levels for MYs 2017-2021 will 

provide the additional benefit of helping to promote further research and development into the 

more advanced fuel economy-improving technologies to provide a bridge to more stringent 

standards in MYs 2022-2025, and enable significant fuel savings and environmental benefits 

throughout the program, and particularly substantial benefits in the later years of the program 

and beyond. Additionally, consistent with Executive Order 13563, the agency believes that the 

benefits of the preferred alternative amply justify the costs; indeed, the monetized benefits 

exceed the monetized costs by $358 billion over the lifetime of the vehicles covered by the 

proposed standards. In full consideration of all of the information currently before the agency, 

we have weighed the statutory factors carefully and selected proposed passenger car and light 

truck standards that we believe are the maximum feasible for MYs 2017-2025. 

 212 We note, for example, that while Executive Orders 12866 and 13563 focus attention on an approach that 

maximizes net benefits, both Executive Orders recognize that this focus is subject to the requirements of the 

governing statute. In this rulemaking, the standards represented by the “MNB” alternative are more stringent than 

what NHTSA has tentatively concluded would be maximum feasible for MYs 2017-2025, and thus setting standards 

at that level would be inconsistent with the requirements of EPCA/EISA to set maximum feasible standards. 

However, we believe that the proposed standards can be seen as maximizing net benefits subject to the statutory and 

other considerations inherent in the determination of maximum feasible standards. 213 We underscore that the agency’s tentative decision regarding what standards would be maximum feasible for 

MYs 2017-2025 is made with reference to the rulemaking time frame and circumstances of this proposal. Each 

CAFE rulemaking (indeed, each stage of any given CAFE rulemaking) presents the agency with new information 

that may affect how we balance the relevant actors. 
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J. Impacts of the Proposed CAFE Standards 

1. How will these standards improve fuel economy and reduce GHG emissions for MY 

2017–2025 vehicles? 

As discussed above, the CAFE level required under an attribute-based standard depends 

on the mix of vehicles produced for sale in the U.S. Based on the market forecast that NHTSA 

and EPA have used to develop and analyze the proposed CAFE and CO2 emissions standards, 

NHTSA estimates that the proposed new CAFE standards would lead average required fuel 

consumption (fuel consumption is the inverse of fuel economy) levels to increase by an average 

of 4.0 percent annually through MY 2025, reaching a combined average fuel economy 

requirement of 49.6 mpg in that model year: 

Table II-48. Estimated Required Average Fuel Economy (mpg) under the Proposed 

Standards – MYs 2017-2021 

Model Year 2017 2018 2019 2020 2021 

Passenger cars 40.0 41.4 43.0 44.7 46.6 

Light trucks 29.4 30.0 30.6 31.2 33.3 

Combined 35.3 36.4 37.5 38.8 40.9 

Table II-49. Estimated Required Average Fuel Economy (mpg) under the Proposed 

Standards – MYs 2022-2025 

Model Year 2022 2023 2024 2025 

Passenger cars 48.8 51.0 53.5 56.0 

Light trucks 34.9 36.6 38.5 40.3 

Combined 42.9 45.0 47.3 49.6 

Accounting for differences between fuel economy levels under laboratory conditions, 

NHTSA estimates that these requirements would translate into the following required average 

levels under real-world operating conditions: 

Table II-50. Estimated Required Average Fuel Economy (real-world mpg) under the 

Proposed Standards – MYs 2017-2021 

Model Year 2017 2018 2019 2020 2021 

Passenger cars 32.0 33.1 34.4 35.8 37.3 

Light trucks 23.5 24.0 24.5 25.0 26.6 

Combined 28.2 29.1 30.0 31.0 32.7 

Table II-51. Estimated Required Average Fuel Economy (real-world mpg) under the 

Proposed Standards – MYs 2022-2025 

Model Year 2022 2023 2024 2025 

Passenger cars 39.0 40.8 42.8 44.8 
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Light trucks 27.9 29.3 30.8 32.2 

Combined 34.3 36.0 37.8 39.7 

If manufacturers apply technology only as far as necessary to comply with CAFE 

standards, NHTSA estimates that, setting aside factors the agency cannot consider for purposes 

of determining maximum feasible CAFE standards,214 average achieved fuel economy levels 

would correspondingly increase through MY 2025, but that manufacturers would, on average, 

under-comply215 in some model years and over-comply216 in others, reaching a combined 

average fuel economy of 47.4 mpg (taking into account estimated adjustments reflecting 

improved air conditioner efficiency) in MY 2025: 

Table II-52. Estimated Achieved Average Fuel Economy (mpg) under the Proposed 

Standards – MYs 2017-2021 

Model Year 2017 2018 2019 2020 2021 

Passenger cars 39.6 41.7 43.6 45.4 47.1 

Light trucks 29.5 30.6 32.4 33.6 35.6 

Combined 35.2 36.8 38.8 40.4 42.3 

Table II-53. Estimated Achieved Average Fuel Economy (mpg) under the Proposed 

Standards – MYs 2022-2025 

Model Year 2022 2023 2024 2025 

Passenger cars 48.2 49.5 51.3 52.7 

Light trucks 36.6 37.7 38.6 39.6 

Combined 43.5 44.7 46.2 47.5 

Accounting for differences between fuel economy levels under laboratory conditions, 

NHTSA estimates that these requirements would translate into the following required average 

levels under real-world operating conditions: 

Table II-54. Estimated Achieved Average Fuel Economy (real-world mpg) under the 

Proposed Standards – MYs 2017-2021 

 214 49 U.S.C. 32902(h) states that NHTSA may not consider the fuel economy of dedicated alternative fuel vehicles, 

the alternative-fuel portion of dual-fueled automobile fuel economy, or the ability of manufacturers to earn and use 

credits for over-compliance, in determining the maximum feasible stringency of CAFE standards. 215 “Under-compliance” with CAFE standards can be mitigated either through use of FFV credits, use of existing or 

“banked” credits, or through fine payment. Although, as mentioned above, NHTSA cannot consider availability of 

statutorily-provided credits in setting standards, NHTSA is not prohibited from considering fine payment. 

Therefore, the estimated achieved CAFE levels presented here include the assumption that Aston Martin, BMW, 

Daimler (i.e., Mercedes), Geely (i.e., Volvo), Lotus, Porsche, Spyker (i.e., Saab), and, Tata (i.e., Jaguar and Rover), 

and Volkswagen will only apply technology up to the point that it would be less expensive to pay civil penalties. 216 In NHTSA’s analysis, “over-compliance” occurs through multi-year planning: manufacturers apply some “extra” 

technology in early model years (e.g., MY 2014) in order to carry that technology forward and thereby facilitate 

compliance in later model years (e.g., MY 2016). 
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Model Year 2017 2018 2019 2020 2021 

Passenger cars 31.7 33.4 34.9 36.3 37.7 

Light trucks 23.6 24.5 25.9 26.9 28.5 

Combined 28.2 29.4 31.0 32.3 33.8 

Table II-55. Estimated Achieved Average Fuel Economy (real-world mpg) under the 

Proposed Standards – MYs 2022-2025 

Model Year 2022 2023 2024 2025 

Passenger cars 38.6 39.6 41.0 42.2 

Light trucks 29.4 30.2 30.9 31.7 

Combined 34.8 35.8 37.0 38.0 

Setting aside the potential to produce additional EVs (or, prior to MY 2020, PHEVs) or 

take advantage of EPCA’s provisions regarding CAFE credits, NHTSA estimates that today’s 

proposed standards could increase achieved fuel economy levels by average amounts of up to 0.5 

mpg during the few model years leading into MY 2017, as manufacturers apply technology 

during redesigns leading into model years covered by today’s new standards.217 As shown 

below, these “early” fuel economy increases yield corresponding reductions in fuel consumption 

and greenhouse gas emissions, and incur corresponding increases in technology outlays. 

Within the context EPCA requires NHTSA to apply for purposes of determining 

maximum feasible stringency of CAFE standards (i.e., setting aside EVs, pre-MY 2020 PHEVs, 

and all statutory CAFE credit provisions), NHTSA estimates that these fuel economy increases 

would lead to fuel savings totaling 173 billion gallons during the useful lives of vehicles 

manufactured in MYs 2017–2025 and the few MYs preceding MY 2017: 

Table II-56. Estimated Fuel Saved (billion gallons) under the Proposed Standards 

Model 

year 

Pre2017 2017 2018 2019 2020 2021 2022 2023 2024 2025 Total 

PC 4 2 5 7 9 11 13 15 17 19 104 

LT 0 0 2 5 6 9 10 11 12 13 69 

 217 This outcome is a direct result of revisions, made to DOT’s CAFE model in preparation for the MY 2012-2016 

rule, to simulate “multiyear planning” effects—that is, the potential that manufacturers will apply “extra” 

technology in one model year if doing so will be sufficiently advantageous with respect to the ability to comply with 

CAFE standards in later model years. For example, for today’s rulemaking analysis, NHTSA has estimated that 

Ford will redesign the F-150 pickup truck in MY 2015, and again in MY 2021. As explained in Chapter V of the 

PRIA, NHTSA’s expects that many technologies would be applied as part of a vehicle redesign. Therefore, in 

NHTSA’s analysis, if Ford does not anticipate ensuing standards when redesigning the MY 2015 F-150, Ford may 

find it more difficult to comply with light truck standard during MY 2016-2020. Through simulation of multiyear 

planning effects, NHTSA’s analysis indicates that Ford could apply more technology to the MY 2015 F-150 if 

standards continue to increase after MY 2016 than Ford need apply if standards remain unchanged after MY 2016, 

and that this additional technology would yield further fuel economy improvements of up to 1.3 mpg, depending on 

pickup configuration. 
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Combined 4 3 7 12 16 20 23 26 30 33 173 

 The agency also estimates that these new CAFE standards would lead to corresponding 

reductions of CO2 emissions totaling 1,834 million metric tons (mmt) during the useful lives of 

vehicles sold in MYs 2017–2025 and the few MYs preceding MY 2017: 

Table II-57. Carbon Dioxide Emissions Avoided (mmt) under the Proposed Standards 

Model 

year 

Pre2017 2017 2018 2019 2020 2021 2022 2023 2024 2025 Total 

PC 41 26 52 76 100 122 139 158 184 202 1,100 

LT 4 5 22 49 65 93 108 118 129 141 734 

Combined 45 31 74 124 165 215 246 276 313 343 1,834 

2. How will these standards improve fleet-wide fuel economy and reduce GHG 

emissions beyond MY 2025? 

Under the assumption that CAFE standards at least as stringent as those being proposed 

today for MY 2025 would be established for subsequent model years, the effects of the proposed 

standards on fuel consumption and GHG emissions will continue to increase for many years. 

This will occur because over time, a growing fraction of the U.S. light-duty vehicle fleet will be 

comprised of cars and light trucks that meet at least the MY 2025 standard. The impact of the 

new standards on fuel use and GHG emissions would therefore continue to grow through 

approximately 2060, when virtually all cars and light trucks in service will have met standards as 

stringent as those established for MY 2025. 

As Table IV-41 shows, NHTSA estimates that the fuel economy increases resulting from 

the proposed standards will lead to reductions in total fuel consumption by cars and light trucks 

of 3 billion gallons during 2020, increasing to 40 billion gallons by 2060. Over the period from 

2017, when the proposed standards would begin to take effect, through 2050, cumulative fuel 

savings would total 1,232 billion gallons, as Table IV-41 also indicates. 

Table II-58. Reduction in Fleet-Wide Fuel Use (billion gasoline gallon equivalents) under 

the Proposed Standards 

Calendar 

year 

2020 2030 2040 2050 2060 Total, 

2017-2060 

PC 1.6 10.9 16.5 19.1 21.6 596.6 

LT 1.4 11.2 17.3 20.7 23.7 635.2 

Combined 3.0 22.1 33.8 39.9 39.9 1,231.8 

The energy security analysis conducted for this rule estimates that the world price of oil 

will fall modestly in response to lower U.S. demand for refined fuel. One potential result of this 

decline in the world price of oil would be an increase in the consumption of petroleum products 
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outside the U.S., which would in turn lead to a modest increase in emissions of greenhouse 

gases, criteria air pollutants, and airborne toxics from their refining and use. While additional 

information would be needed to analyze this “leakage effect” in detail, NHTSA provides a 

sample estimate of its potential magnitude in its Draft EIS. This analysis indicates that the 

leakage effect is likely to offset only a very small fraction of the reductions in fuel use and 

emissions projected to result from the rule. 

As a consequence of these reductions in fleet-wide fuel consumption, the agency also 

estimates that the new CAFE standards for MYs 2017–2025 would lead to corresponding 

reductions in CO2 emissions from the U.S. light-duty vehicle fleet. Specifically, NHTSA 

estimates that total annual CO2 emissions associated with passenger car and light truck use in the 

U.S. use would decline by 32 million metric tons (mmt) in 2020 as a consequence of the new 

CAFE standards, as Table IV-42 reports. The table also shows that the this annual reduction is 

estimated to grow to nearly 488 million metric tons by the year 2060, and will total over 13 

billion metric tons over the period from 2017, when the proposed standards would take effect, 

through 2060. 

Table II-59. Reduction in Fleet-Wide Carbon Dioxide Emissions (mmt) from Passenger 

Car and Light Truck Use under the Proposed Standards 

Calendar 

year 

2020 2030 2040 2050 2060 Total, 

2017-2060 

PC 17.0 116.9 176.6 204.1 230.7 6,382.2 

LT 15.2 121.8 187.2 224.8 257.0 6,885.3 

Combined 32.2 238.7 363.8 428.9

487.7 

13,267.5 

These reductions in fleet-wide CO2 emissions, together with corresponding reductions in 

other GHG emissions from fuel production and use, would lead to small but significant 

reductions in projected changes in the future global climate. These changes, based on analysis 

documented in the draft Environmental Impact Statement (EIS) that informed the agency’s 

decisions regarding this proposal, are summarized in Table IV-43 below. 

Table II-60. Effects of Reduction in Fleet-Wide Carbon Dioxide Emissions (mmt) on 

Projected Changes in Global Climate [to be completed] 

Measure Units Date 

Projected change in measure 

No action 

With 

proposed 

standards 

Difference 

Atmospheric CO2 

concentration Ppm 2100 

Increase in global 

mean surface 

temperature 

°C 2100 
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Sea level rise Cm 2100 

Global mean 

precipitation 

% change from 

1980-1999 avg. 2090 

3. How will these proposed standards impact non-GHG emissions and their associated 

effects? 

Under the assumption that CAFE standards at least as stringent as those proposed for MY 

2025 would be established for subsequent model years, the effects of the new standards on air 

quality and its associated health effects will continue to be felt over the foreseeable future. This 

will occur because over time a growing fraction of the U.S. light-duty vehicle fleet will be 

comprised of cars and light trucks that meet the MY 2025 standard, and this growth will continue 

until approximately 2060. 

Increases in the fuel economy of light-duty vehicles required by the new CAFE standards 

will cause a slight increase in the number of miles they are driven, through the fuel economy 

“rebound effect.” In turn, this increase in vehicle use will lead to increases in emissions of 

criteria air pollutants and some airborne toxics, since these are products of the number of miles 

vehicles are driven. 

At the same time, however, the projected reductions in fuel production and use reported 

in Table IV-40 and IV-41 above will lead to corresponding reductions in emissions of these 

pollutants that occur during fuel production and distribution (“upstream” emissions). For most 

of these pollutants, the reduction in upstream emissions resulting from lower fuel production and 

distribution will outweigh the increase in emissions from vehicle use, resulting in a net decline in 

their total emissions.218

Tables IV-44 and IV-45 report estimated reductions in emissions of selected criteria air 

pollutants (or their chemical precursors) and airborne toxics expected to result from the proposed 

standards during calendar year 2030. By that date, cars and light trucks meeting the MY 2025 

CAFE standards will account for the majority of light-duty vehicle use, so these reductions 

provide a useful index of the long-term impact of the final standards on air pollution and its 

consequences for human health. In the tables below, positive values indicate increases in 

emissions, while negative values indicate reductions. 

Table II-61. Projected Changes in Emissions of Criteria Air Pollutants from Passenger 

Car and Light Truck Use (calendar year 2030; tons) 

 218 As stated elsewhere, while the agency’s analysis assumes that all changes in upstream emissions result from a 

decrease in petroleum production and transport, the analysis of non-GHG emissions in future calendar years also 

assumes that retail gasoline composition is unaffected by this rule; as a result, the impacts of this rule on 

downstream non-GHG emissions (more specifically, on air toxics) may be underestimated. See also Section III.G 

above for more information. 
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Vehicle 

class Source of emissions 

Criteria air pollutant 

Nitrogen 

oxides 

(NOX) 

Particulate 

matter 

(PM2.5) 

Sulfur 

oxides 

(SOX) 

Volatile 

organic 

compound

s (VOC) 

Passenger 

cars 

Vehicle use 

Fuel production and distribution 

All sources 

14,742.6 

-17,464.3 

-2,721.7 

-126.9 

-1,910.1 

-2,036.9 

-2,412.2 

-6,968.9 

-9,381.1 

334.8 

-39,230.5 

-38,895.7 

Light 

trucks 

Vehicle use 

Fuel production and distribution 

All sources 

6,097.1 

-18,978.4 

-12,881.3 

202.9 

-2,100.3 

-1,897.3 

-2,180.3 

-9,544.7 

-11,725.0 

2,014.8 

-32,679.8 

-30,665.0 

Total 

Vehicle use 

Fuel production and distribution 

All sources 

15,600 

-33,928 

-15,603.0 

-204 

-3,735 

-3,934.2 

-4,275 

-15,427 

-21,106.1 

-221 

-67,161 

-69,560.7 

Table II-62. Projected Changes in Emissions of Airborne Toxics from Passenger Car and 

Light Truck Use (calendar year 2030; tons) 

Vehicle class Source of emissions 

Toxic air pollutant 

Benzene 1,3-

Butadiene Formaldehyde 

Passenger 

cars 

Vehicle use

Fuel production and distribution

All sources

-114.6 

-172.0 

-286.6 

5.0 

-1.5 

3.6 

379.8 

-50.6 

329.2 

Light trucks 

Vehicle use

Fuel production and distribution

All sources

44.6 

-145.3 

-100.7 

11.9 

-1.5 

10.4 

92.2 

-51.4 

40.8 

Total 

Vehicle use

Fuel production and distribution

All sources

-147 

-296 

-387.3 

2.1 

-2.8 

14.0 

412 

370.0 

In turn, the reductions in emissions reported in Tables IV-44 and IV-45 are projected to 

result in significant declines in the adverse health effects that result from population exposure to 

these pollutants. Table IV-46 reports the estimated reductions in selected PM2.5-related human 

health impacts that are expected to result from reduced population exposure to unhealthful 

atmospheric concentrations of PM2.5. The estimates reported in Table IV-46, based on analysis 

documented in the draft Environmental Impact Statement (EIS) that informed the agency’s 

decisions regarding this proposed rule, are derived from PM2.5-related dollar-per-ton estimates 

that reflect the quantifiable reductions in health impacts likely to result from reduced population 

exposure to particular matter (PM2.5). They do not include all health impacts related to reduced 

exposure to PM, nor do they include any reductions in health impacts resulting from lower 

population exposure to other criteria air pollutants (particularly ozone) and air toxics. 
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However, emissions changes and dollar-per-ton estimates alone are not necessarily a 

good indication of local or regional air quality and health impacts, as there may be localized 

impacts associated with this rulemaking, because the atmospheric chemistry related to ambient 

concentrations of PM2.5, ozone, and air toxics is very complex. Full-scale photochemical 

modeling would provide the necessary spatial and temporal detail to more completely and 

accurately estimate the changes in ambient levels of these pollutants and their associated health 

and welfare impacts. NHTSA intends to conduct such modeling for purposes of the final rule, 

but it was not available in time to inform these proposed standards or to be included in the Draft 

EIS. 

Table II-63. Projected Reductions in Health Impacts from Exposure to Criteria Air 

Pollutants Due to Proposed Standards (calendar year 2030) [to be completed] 

Health impact Measure Projected 

reduction (2030) 

Mortality (ages 30 and older) premature deaths per year 

Chronic bronchitis cases per year 

Emergency room visits for asthma number per year 

Work loss workdays per year 

4. What are the estimated costs and benefits of these proposed standards? 

NHTSA estimates that the proposed standards could entail significant additional 

technology beyond the levels that could be applied under baseline CAFE standards (i.e., the 

application of MY 2016 CAFE standards to MYs 2017-2025). This additional technology will 

lead to increases in costs to manufacturers and vehicle buyers, as well as fuel savings to vehicle 

buyers. Also, as discussed above, NHTSA estimates that today’s proposed standards could 

induce manufacturers to apply technology during redesigns leading into model years covered by 

today’s new standards, and to incur corresponding increases in technology outlays. 

In order to pay for this additional technology (and, for some manufacturers, civil 

penalties), NHTSA estimates that the cost of an average passenger car and light truck will 

increase relative to levels resulting from compliance with baseline (MY 2016) standards by 

$228–$2,023 and $44–$1,578, respectively, during MYs 2017–2025. The following tables 

summarize the agency’s estimates of average cost increases for each manufacturer’s passenger 

car, light truck, and overall fleets (with corresponding averages for the industry): 

Table II-64. Average Passenger Car Incremental Cost Increases ($) under Proposed 

Standards – MYs 2017-2021 

Manufacturer MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Average 228 467 652 885 1,108 

Aston Martin 2 2 2 2 2 

BMW 2 3 1 1 (34) 
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Daimler (Mercedes) 2 2 1 11 11 

Fiat (Chrysler) 310 748 755 1,471 1,503 

Ford 434 784 954 1,213 1,448 

Geely (Volvo) 2 4 108 113 229 

General Motors 284 707 918 1,335 1,713 

Honda 155 469 518 549 1,160 

Hyundai 303 370 719 735 960 

Kia 179 243 508 1,048 1,418 

Lotus 2 2 2 2 2 

Mazda 631 905 871 1,411 1,387 

Mitsubishi 2 458 461 710 1,078 

Nissan 367 474 1,020 1,161 1,232 

Porsche (4) (4) (3) 10 16 

Spyker (Saab) 2 2 2 2 (5) 

Subaru 2 35 336 932 898 

Suzuki 624 612 1,920 1,924 1,880 

Tata (Jaguar, Rover) 44 43 40 39 106 

Tesla 2 2 2 2 2 

Toyota 114 375 552 780 969 

Volkswagen 4 4 4 6 9 

Table II-65. Average Passenger Car Incremental Cost Increases ($) under Proposed 

Standards – MYs 2022-2025 

Manufacturer MY 2022 MY 2023 MY 2024 MY 2025 

Average 1,259 1,536 1,927 2,023 

Aston Martin 2 2 2 2 

BMW (42) (41) 126 119 

Daimler (Mercedes) 11 243 243 645 

Fiat (Chrysler) 1,564 2,310 2,462 2,638 

Ford 1,658 2,349 3,261 2,897 

Geely (Volvo) 222 329 609 595 

General Motors 1,702 2,269 2,575 2,734 

Honda 1,271 1,531 1,615 1,754 

Hyundai 1,492 1,560 2,158 2,043 

Kia 1,522 1,704 1,688 1,836 

Lotus 2 2 2 2 

Mazda 1,848 2,089 2,480 3,474 

Mitsubishi 1,047 1,022 1,402 3,869 

Nissan 1,676 1,853 2,293 2,233 

Porsche 2 2 2 2 

Spyker (Saab) (6) 1 1 1 

Subaru 883 868 1,599 3,057 

Suzuki 1,869 1,839 3,831 3,347 

Tata (Jaguar, Rover) 338 550 543 547 
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Tesla 2 2 2 2 

Toyota 1,084 1,078 1,539 1,631 

Volkswagen 83 106 129 302 

Table II-66. Average Light Truck Incremental Cost Increases ($) under Proposed 

Standards – MYs 2017-2021 

Manufacturer MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Average 44 187 427 688 965 

Aston Martin - - - - - 

BMW 36 48 95 220 326 

Daimler (Mercedes) 48 59 63 178 168 

Fiat (Chrysler) 238 234 289 1,137 1,252 

Ford (6) 270 279 423 1,435 

Geely (Volvo) 1 68 69 68 66 

General Motors (5) 198 595 1,188 1,242 

Honda 154 163 340 373 757 

Hyundai 262 269 714 712 696 

Kia 60 73 192 321 907 

Lotus - - - - - 

Mazda (3) 472 450 480 550 

Mitsubishi 322 347 319 431 2,668 

Nissan 21 50 288 431 820 

Porsche 1 45 49 48 48 

Spyker (Saab) 1 13 18 18 446 

Subaru 176 505 1,366 1,337 1,334 

Suzuki 293 301 1,793 1,763 1,996 

Tata (Jaguar, Rover) 1 13 49 50 15 

Tesla - - - - - 

Toyota (13) 179 445 462 656 

Volkswagen 1 41 350 358 421 

Table II-67. Average Light Truck Incremental Cost Increases ($) under Proposed 

Standards – MYs 2022-2025 

Manufacturer MY 2022 MY 2023 MY 2024 MY 2025 

Average 1,102 1,284 1,428 1,578 

Aston Martin - - - - 

BMW 746 738 787 735 

Daimler (Mercedes) 1,134 1,130 1,096 1,050 

Fiat (Chrysler) 1,296 1,588 1,665 1,948 

Ford 1,482 1,981 2,005 2,064 

Geely (Volvo) 357 770 759 707 

General Motors 1,285 1,456 1,511 1,863 

Honda 1,279 1,263 1,296 1,370 
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Hyundai 1,207 1,191 1,693 1,687 

Kia 891 1,020 1,149 1,150 

Lotus - - - - 

Mazda 540 1,108 1,393 1,315 

Mitsubishi 2,606 2,564 2,526 2,356 

Nissan 1,169 1,213 1,469 1,677 

Porsche 75 640 629 568 

Spyker (Saab) 440 435 429 395 

Subaru 1,376 1,358 1,606 1,532 

Suzuki 2,000 1,965 1,935 2,252 

Tata (Jaguar, Rover) 15 16 16 15 

Tesla - - - - 

Toyota 663 858 1,175 1,253 

Volkswagen 426 555 959 884 

Table II-68. Average Incremental Cost Increases ($) by Manufacturer under Proposed 

Standards – MYs 2017-2021 

Manufacturer MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Average 161 365 572 815 1,058 

Aston Martin 2 2 2 2 2 

BMW 12 16 27 59 61 

Daimler (Mercedes) 13 15 16 51 50 

Fiat (Chrysler) 275 496 532 1,317 1,391 

Ford 271 597 718 943 1,444 

Geely (Volvo) 2 24 95 98 179 

General Motors 144 455 756 1,262 1,480 

Honda 155 370 462 494 1,035 

Hyundai 295 349 718 730 906 

Kia 151 203 431 881 1,304 

Lotus 2 2 2 2 2 

Mazda 523 827 796 1,246 1,239 

Mitsubishi 119 418 411 612 1,633 

Nissan 250 335 787 934 1,105 

Porsche (2) 9 9 19 23 

Spyker (Saab) 2 4 4 4 59 

Subaru 47 156 595 1,031 1,002 

Suzuki 559 552 1,882 1,895 1,901 

Tata (Jaguar, Rover) 22 28 44 44 61 

Tesla 2 2 2 2 2 

Toyota 61 297 511 659 847 

Volkswagen 4 12 78 79 92 

Table II-69. Average Incremental Cost Increases ($) by Manufacturer under Proposed 

Standards – MYs 2022-2025 
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Manufacturer MY 2022 MY 2023 MY 2024 MY 2025 

Average 1,205 1,450 1,760 1,876 

Aston Martin 2 2 2 2 

BMW 166 162 307 282 

Daimler (Mercedes) 290 467 451 738 

Fiat (Chrysler) 1,442 1,981 2,109 2,343 

Ford 1,599 2,231 2,866 2,640 

Geely (Volvo) 264 462 654 629 

General Motors 1,498 1,877 2,067 2,319 

Honda 1,274 1,451 1,522 1,641 

Hyundai 1,435 1,485 2,064 1,972 

Kia 1,384 1,555 1,572 1,690 

Lotus 2 2 2 2 

Mazda 1,617 1,919 2,294 3,115 

Mitsubishi 1,583 1,553 1,781 3,367 

Nissan 1,521 1,659 2,046 2,068 

Porsche 19 152 142 124 

Spyker (Saab) 56 59 57 52 

Subaru 998 981 1,601 2,714 

Suzuki 1,892 1,861 3,501 3,159 

Tata (Jaguar, Rover) 178 287 292 300 

Tesla 2 2 2 2 

Toyota 923 995 1,406 1,493 

Volkswagen 151 198 300 416 

These cost estimates reflect the potential that a given manufacturer’s efforts to minimize 

overall regulatory costs could focus technology where the most fuel can be saved at the least 

cost, and not necessarily, for example, where the cost to add technology would be smallest 

relative to baseline production costs. Therefore, if average incremental vehicle cost increases 

(including any civil penalties) are measured as increases relative to baseline prices (estimated by 

adding baseline costs to MY 2008 prices), the agency’s analysis shows relative cost increases 

declining as baseline vehicle price increases. Figure IV-3 shows the trend for MY 2025, for 

vehicles with estimated baseline prices up to $100,000: 
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Figure II-7. Estimated Incremental Cost Increases in MY 2025 

If manufacturers pass along these costs rather than reducing profits, and pass these costs 

along where they are incurred rather than “cross-subsidizing” among products, the quantity of 

vehicles produced at different price levels would change. Shifts in production may potentially 

occur, which could create marketing challenges for manufacturers that are active in certain 

segments. We recognize, however, that many manufacturers do in fact cross-subsidize to some 

extent, and take losses on some vehicles while continuing to make profits from others. NHTSA 

has no evidence to indicate that manufacturers will inevitably shift production plans in response 

to these proposed standards, but nevertheless believes that this issue is worth monitoring in the 

market going forward. NHTSA seeks comment on potential market effects related to this issue. 

As mentioned above, these estimated costs derive primarily from the additional 

application of technology under the proposed standards. The following three tables summarize 

the incremental extent to which the agency estimates technologies could be added to the 

passenger car, light truck, and overall fleets in each model year in response to the proposed 

standards. Percentages reflect the technology’s additional application in the market, relative to 

the estimated application under baseline standards (i.e., application of MY 2016 standards 

through MY 2025), and are negative in cases where one technology is superseded (i.e., 
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displaced) by another. For example, the agency estimates that manufacturers could apply many 

improvements to transmissions (e.g., dual clutch transmissions, denoted below by “DCT”) 

through MY 2025 under baseline standards. However, the agency also estimates that 

manufacturers could apply even more advanced high efficiency transmissions (denoted below by 

“HETRANS”) under the proposed standards, and that these transmissions would supersede 

DCTs and other transmission advances. Therefore, as shown in the following three tables, the 

incremental application of DCTs under the proposed standards is negative. 

Table II-70. Incremental Application of Technologies to Passenger Car Fleet under 

Proposed Standards – MYs 2017-2025 

Technology 2017 2018 2019 2020 2021 2022 2023 2024 2025 

LUB1 8% 16% 16% 16% 16% 16% 16% 16% 16% 

EFR1 20% 25% 28% 28% 27% 28% 28% 28% 28% 

LUB2_EFR2 2% 10% 21% 28% 38% 43% 46% 52% 53% 

CCPS 3% 4% 6% 6% 6% 6% 6% 7% 6% 

DVVLS 1% 1% 1% 1% 1% 1% 

DEACS (1%) (1%) (1%) 

ICP 

DCP 6% 11% 13% 15% 18% 19% 19% 18% 17% 

DVVLD 12% 16% 21% 25% 29% 34% 33% 32% 32% 

DEACD 

SGDI 11% 15% 22% 28% 36% 43% 46% 54% 55% 

DEACO (1%) (2%) (2%) (2%) (2%) (2%) (2%) 

VVA 

SGDIO 1% 2% 2% 2% 2% 2% 2% 

TRBDS1_SD 6% 8% 8% 9% 13% 9% 7% 4% 2% 

TRBDS1_MD 3% 3% 5% 6% 7% 6% 2% (4%) (7%) 

TRBDS1_LD 

TRBDS2_SD 3% 3% 3% 3% 4% 1% 

TRBDS2_MD (2%) (2%) (2%) (3%) (3%) (3%) (3%) (3%) (3%) 

TRBDS2_LD 

CEGR1_SD 1% 2% 2% 5% 9% 16% 18% 28% 32% 

CEGR1_MD 3% 5% 5% 7% 4% 6% 6% 10% 12% 

CEGR1_LD 

CEGR2_SD 1% 2% 2% 2% 5% 4% 

CEGR2_MD 3% 4% 6% 7% 6% 

CEGR2_LD 1% 1% 2% 2% 2% 2% 2% 1% 

ADSL_SD 1% 2% 2% 3% 3% 4% 5% 

ADSL_MD 1% 1% 1% 1% 1% 2% 2% 3% 

ADSL_LD 

6MAN 1% 1% (1%) (1%) (1%) (1%) (1%) (1%) 

HETRANSM 1% 1% 3% 4% 5% 5% 6% 7% 

IATC (1%) (1%) (1%) (1%) (1%) (1%) (1%) (1%) 

NAUTO (3%) (4%) (6%) (7%) (7%) (7%) (7%) (7%) (7%) 
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DCT (6%) (17%) (29%) (35%) (44%) (45%) (46%) (46%) (46%) 

8SPD 3% 3% 1% (4%) (7%) (8%) (9%) (10%) (14%) 

HETRANS 5% 20% 31% 43% 49% 53% 51% 48% 49% 

SHFTOPT 9% 27% 37% 49% 65% 68% 64% 60% 56% 

EPS 8% 10% 13% 17% 29% 32% 35% 35% 34% 

IACC1 13% 16% 18% 23% 41% 46% 49% 52% 52% 

IACC2 13% 21% 31% 40% 50% 55% 65% 70% 70% 

MHEV 3% 6% 11% 22% 29% 34% 36% 37% 33% 

SHEV1 

SHEV1_2 1% 

SHEV2 4% 6% 10% 

PHEV1 1% 3% 4% 

MR1 5% 11% 14% 14% 14% 14% 15% 14% 14% 

MR2 2% 15% 27% 30% 30% 30% 30% 30% 29% 

MR3 1% 4% 5% 7% 7% 7% 8% 9% 9% 

MR4 2% 4% 4% 6% 6% 7% 8% 10% 10% 

MR5 2% 2% 3% 4% 4% 5% 8% 9% 

ROLL1 2% 2% 3% 3% 3% 3% 3% 3% 

ROLL2 5% 23% 36% 45% 56% 66% 67% 68% 68% 

LDB 2% 2% 2% 2% 2% 3% 3% 3% 3% 

SAX 

AERO1 3% 3% 3% 3% 3% 3% 3% 3% 

AERO2 6% 15% 29% 35% 47% 51% 51% 51% 51% 

Table II-71. Incremental Application of Technologies to Light Truck Fleet under Proposed 

Standards – MYs 2017-2025 

Technology 2017 2018 2019 2020 2021 2022 2023 2024 2025 

LUB1 

EFR1 

LUB2_EFR2 2% 8% 21% 35% 49% 61% 77% 83% 84% 

CCPS 2% 1% 4% 4% 4% 4% 4% 

DVVLS 1% 1% 1% 1% 1% 

DEACS 2% 2% 2% 2% (2%) (5%) (5%) (5%) (5%) 

ICP 

DCP 4% 4% 4% 4% 4% 4% 5% 5% 3% 

DVVLD 1% 6% 2% 8% 9% 12% 14% 13% 

DEACD (1%) (1%) (2%) (2%) (2%) (2%) (2%) 

SGDI 8% 4% 19% 25% 29% 33% 33% 

DEACO (1%) (6%) (7%) (6%) (8%) (8%) (8%) 

VVA 4% 4% 3% 3% 3% 3% 3% 

SGDIO 2% 6% 7% 7% 8% 8% 9% 

TRBDS1_SD 

TRBDS1_MD (2%) 4% (2%) 4% 3% (3%) (14%) (18%)

TRBDS1_LD 3% 3% 5% 5% 5% 
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TRBDS2_SD 1% 1% 1% 3% 3% 6% 6% 

TRBDS2_MD 1% 1% 1% 2% 1% 

TRBDS2_LD 

CEGR1_SD 1% 1% 1% 2% 3% 

CEGR1_MD 

CEGR1_LD 

CEGR2_SD 

CEGR2_MD 

CEGR2_LD 1% 1% 3% 6% 7% 7% 7%

ADSL_SD 

ADSL_MD 5% 6% 6% 7% 6% 8% 

ADSL_LD 

6MAN 

HETRANSM 0% 1% 1% 1% 1% 1% 2% 2% 2% 

IATC (1%) (1%) (1%) (1%) (1%) (1%) (1%) 

NAUTO (2%) (7%) (16%) (17%) (26%) (26%) (25%) (25%) (25%)

DCT (1%) (7%) (7%) (8%) (9%) (12%) (12%) (12%) (12%)

8SPD 3% 3% (9%) (15%) (28%) (28%) (29%) (30%) (34%)

HETRANS 15% 34% 50% 61% 61% 61% 61% 66% 

SHFTOPT 4% 14% 27% 39% 61% 77% 78% 79% 79% 

EPS 4% 7% 17% 28% 35% 43% 49% 53% 54% 

IACC1 3% 5% 5% 12% 18% 21% 21% 25% 29% 

IACC2 3% 7% 17% 31% 42% 48% 54% 58% 62% 

MHEV 3% 8% 10% 10% 16% 20% 21% 

SHEV1_2 

SHEV2 

PHEV1 

MR1 10% 9% 10% 12% 14% 14% 14% 14% 14% 

MR2 1% 12% 33% 42% 50% 54% 56% 57% 58% 

MR3 (1%) 2% 6% 14% 24% 36% 48% 64% 77% 

MR4 1% 1% 1% 4% 10% 12% 16% 20% 39% 

MR5 1% 6% 8% 23% 

ROLL1 

ROLL2 2% 15% 32% 48% 59% 70% 74% 81% 82% 

ROLL3 

LDB 2% 3% 9% 15% 22% 28% 32% 38% 

SAX 4% 4% 5% 9% 11% 13% 15% 22% 

AERO1 

AERO2 6% 11% 27% 33% 33% 37% 37% 38% 38% 

Table II-72. Incremental Application of Technologies to Overall Fleet under Proposed 

Standards – MYs 2017-2025 

Technology 2017 2018 2019 2020 2021 2022 2023 2024 2025 

LUB1 5% 10% 10% 10% 10% 10% 11% 11% 10% 
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EFR1 12% 16% 18% 18% 18% 18% 18% 19% 19% 

LUB2_EFR2 2% 9% 21% 31% 42% 50% 57% 62% 64% 

CCPS 2% 3% 5% 5% 6% 6% 6% 6% 5% 

DVVLS 1% 1% 1% 1% 1% 

DEACS 1% 1% 1% (1%) (2%) (2%) (2%) (2%) 

ICP 

DCP 5% 8% 10% 11% 13% 14% 14% 13% 12% 

DVVLD 7% 10% 16% 17% 22% 25% 26% 26% 26% 

DEACD (1%) (1%) (1%) (1%) (1%)

SGDI 7% 10% 17% 19% 30% 37% 40% 47% 48% 

DEACO (1%) (3%) (4%) (4%) (4%) (4%) (4%) 

VVA 1% 1% 1% 1% 1% 1% 1% 

SGDIO 0% 0% 1% 4% 4% 4% 4% 4% 5% 

TRBDS1_SD 4% 5% 5% 6% 9% 6% 5% 3% 1% 

TRBDS1_MD 2% 1% 5% 4% 6% 5% (7%) (11%)

TRBDS1_LD 1% 1% 2% 2% 1% 

TRBDS2_SD 2% 2% 2% 3% 3% 3% 2% 

TRBDS2_MD (1%) (2%) (2%) (2%) (2%) (2%) (2%) (2%) (2%) 

CEGR1_SD 1% 1% 2% 4% 6% 11% 13% 19% 22% 

CEGR1_MD 2% 4% 5% 8% 8% 10% 12% 18% 21% 

CEGR1_LD 

CEGR2_SD 1% 1% 1% 2% 3% 2% 

CEGR2_MD 0% 2% 3% 4% 5% 5% 

CEGR2_LD 1% 2% 3% 3% 4% 4% 3% 

ADSL_SD 1% 1% 2% 2% 2% 3% 3% 

ADSL_MD 1% 1% 2% 3% 3% 3% 3% 5% 

ADSL_LD 

6MAN (1%) (1%) (1%) (1%) 

HETRANSM 1% 1% 2% 3% 4% 4% 5% 5% 

IATC (1%) (1%) (1%) (1%) (1%) (1%) (1%) (1%) 

NAUTO (3%) (5%) (10%) (10%) (13%) (13%) (13%) (13%) (13%)

DCT (4%) (14%) (21%) (26%) (32%) (34%) (34%) (35%) (35%)

8SPD 3% 3% (3%) (8%) (14%) (15%) (16%) (17%) (21%)

HETRANS 3% 18% 32% 46% 53% 56% 55% 53% 54% 

SHFTOPT 7% 22% 34% 46% 64% 71% 69% 67% 64% 

EPS 7% 9% 14% 21% 31% 36% 39% 41% 41% 

IACC1 9% 12% 13% 19% 33% 37% 39% 43% 45% 

IACC2 10% 16% 26% 37% 47% 52% 61% 66% 67% 

MHEV 2% 4% 8% 17% 22% 26% 29% 31% 29% 

SHEV1_2 

SHEV2 3% 4% 6% 

PHEV1 1% 2% 3% 

MR1 7% 11% 12% 14% 14% 14% 14% 14% 14% 

MR2 2% 14% 29% 34% 37% 38% 39% 39% 39% 

MR3 3% 5% 9% 13% 17% 22% 27% 32% 
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MR4 2% 3% 3% 5% 8% 9% 11% 14% 20% 

MR5 0% 1% 1% 2% 3% 3% 5% 8% 14% 

ROLL1 1% 1% 2% 2% 2% 2% 2% 2% 

ROLL2 4% 20% 34% 46% 57% 67% 70% 73% 73% 

LDB 1% 2% 2% 5% 7% 10% 11% 13% 15% 

SAX 2% 2% 2% 3% 4% 5% 5% 8% 

AERO1 2% 2% 2% 2% 2% 2% 2% 2% 

AERO2 6% 14% 28% 34% 42% 46% 46% 47% 47% 

Based on the agencies’ estimates of manufacturers’ future sales volumes, and taking into 

account early outlays attributable to multiyear planning effects (discussed above), the cost 

increases associated with this additional application of technology will lead to a total of nearly 

$157 billion in incremental outlays during MYs 2017–2025 (and model years leading up to MY 

2017) for additional technology attributable to the proposed standards: 

Table II-73. Incremental Technology Outlays ($M) under Proposed Standards – MYs 

2017-2021 

 Earlier MY2017 MY2018 MY2019 MY2020 MY2021 

Total 3,976 2,537 5,681 8,905 12,957 17,123 

Aston Martin - 0 0 0 0 0 

BMW - 6 7 13 29 30 

Daimler (Mercedes) - 5 5 6 20 20 

Fiat (Chrysler) 394 229 393 407 1,031 1,080 

Ford 734 559 1,230 1,472 1,976 3,055 

Geely (Volvo) - 0 3 13 13 24 

General Motors 1,029 408 1,327 2,266 3,881 4,580 

Honda 189 271 622 773 835 1,796 

Hyundai 231 220 255 530 549 698 

Kia 107 64 83 179 370 556 

Lotus - 0 0 0 0 0 

Mazda 360 160 265 258 409 414 

Mitsubishi 43 12 42 41 61 165 

Nissan 434 328 423 986 1,196 1,459 

Porsche - 0 0 0 1 1 

Spyker (Saab) - 0 0 0 0 1 

Subaru 42 14 45 172 305 304 

Suzuki 233 63 61 209 216 221 

Tata (Jaguar, Rover) 3 3 3 5 5 7 

Tesla - 0 0 0 0 0 

Toyota 177 194 907 1,521 2,003 2,643 

Volkswagen 0 2 8 54 56 68 

 

Passenger cars 3,641 2,280 4,621 6,521 9,103 11,636 

Light trucks 335 257 1,060 2,384 3,853 5,487 
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Combined 3,976 2,537 5,681 8,905 12,957 17,123 

Table II-74. Incremental Technology Outlays ($M) under Proposed Standards – MYs 

2022-2025 (and total through MY 2025) 

 MY2022 MY2023 MY2024 MY2025 Total 

Total 19,807 24,155 29,798 32,354 157,293 

Aston Martin 0 0 0 0 0 

BMW 81 79 164 155 564 

Daimler (Mercedes) 118 195 198 326 893 

Fiat (Chrysler) 1,145 1,569 1,641 1,818 9,708 

Ford 3,405 4,851 6,284 5,874 29,440 

Geely (Volvo) 35 64 93 90 336 

General Motors 4,623 5,824 6,471 7,415 37,823 

Honda 2,263 2,615 2,808 3,114 15,285 

Hyundai 1,127 1,181 1,701 1,667 8,160 

Kia 601 682 704 778 4,124 

Lotus 0 (0) (0) (0) (0) 

Mazda 552 689 832 1,147 5,085 

Mitsubishi 162 160 190 369 1,246 

Nissan 2,052 2,275 2,874 2,981 15,009 

Porsche (1) 7 7 6 25 

Spyker (Saab) 1 2 1 1 8 

Subaru 311 309 516 900 2,919 

Suzuki 224 223 426 393 2,272 

Tata (Jaguar, Rover) 21 34 35 37 153 

Tesla 0 0 0 0 0 

Toyota 2,972 3,247 4,622 4,954 23,239 

Volkswagen 112 149 229 327 1,004 

 

Passenger cars 11,636 13,519 16,852 21,695 113,218 

Light trucks 5,487 6,287 7,303 8,102 44,075 

Combined 19,807 24,155 29,798 32,354 157,293 

NHTSA notes that these estimates of the economic costs for meeting higher CAFE 

standards omit certain potentially important categories of costs, and may also reflect 

underestimation (or possibly overestimation) of some costs that are included. For example, 

although the agency’s analysis is intended—with very limited exceptions219—to hold vehicle 

performance, capacity, and utility constant when applying fuel-saving technologies to vehicles, 

the analysis imputes no cost to any actual reductions in vehicle performance, capacity, and utility 

 219 For example, the agencies have assumed no cost changes due to our assumption that HEV towing capability is 

not maintained; due to potential drivability issues with the P2 HEV; and due to potential drivability and NVH issues 

with the shift optimizer. 
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that may result from manufacturers’ efforts to comply with the proposed CAFE standards. 

Although these costs are difficult to estimate accurately, they nonetheless represent a notable 

category of omitted costs if they have not been adequately accounted for in the cost estimates. 

Similarly, the agency’s estimates of net benefits for meeting higher CAFE standards includes 

estimates of the economic value of potential changes in motor vehicle fatalities that could result 

from reductions in the size or weight of vehicles, but not of changes in non-fatal injuries that 

could result from reductions in vehicle size and/or weight. 

Finally, while NHTSA is confident that the cost estimates are the best available and 

appropriate for purposes of this proposed rule, it is possible that the agency may have 

underestimated or overestimated manufacturers’ direct costs for applying some fuel economy 

technologies, or the increases in manufacturer’s indirect costs associated with higher vehicle 

manufacturing costs. In either case, the technology outlays reported here will not correctly 

represent the costs of meeting higher CAFE standards. Similarly, NHTSA’s estimates of 

increased costs of congestion, accidents, and noise associated with added vehicle use are drawn 

from a 1997 study, and the correct magnitude of these values may have changed since they were 

developed. If this is the case, the costs of increased vehicle use associated with the fuel economy 

rebound effect will differ from the agency’s estimates in this analysis. Thus, like the agency’s 

estimates of economic benefits, estimates of total compliance costs reported here may 

underestimate or overestimate the true economic costs of the proposed standards. 

However, offsetting these costs, the achieved increases in fuel economy will also produce 

significant benefits to society. Most of these benefits are attributable to reductions in fuel 

consumption; fuel savings are valued using forecasts of pretax prices in EIA’s reference case 

forecast from AEO 2011. The total benefits also include other benefits and dis-benefits, 

examples of which include the social values of reductions in CO2 and criteria pollutant 

emissions, the value of additional travel (induced by the rebound effect), and the social costs of 

additional congestion, accidents, and noise attributable to that additional travel. The PRIA 

accompanying today’s proposed rule presents a detailed analysis of the rule’s specific benefits. 

As Tables IV-58 and 59 show, NHTSA estimates that at the discount rates of 3 percent 

prescribed in OMB guidance for regulatory analysis, the present value of total benefits from the 

proposed CAFE standards over the lifetimes of MY 2017–2025 (and, accounting for multiyear 

planning effects discussed above, model years leading up to MY 2017) passenger cars and light 

trucks will be $515 billion. 

Table II-75. Present Value of Benefits ($b) under Proposed Standards Using 3 Percent 

Discount Rate – MYs 2017-2021220

 220 Unless otherwise indicated, all tables in Section IV report benefits calculated using the Reference Case input 

assumptions, with future benefits resulting from reductions in carbon dioxide emissions discounted at the 3 percent 

rate prescribed in the interagency guidance on the social cost of carbon. 
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 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 11 7 14 21 27 34 

LT 1 1 6 13 18 26 

Combined 12 8 20 34 45 60 

Table II-76. Present Value of Benefits ($b) under Proposed Standards Using 3 Percent 

Discount Rate – MYs 2022-2025 (and total through MY 2025) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 39 45 53 59 310 

LT 30 33 37 40 206 

Combined 69 78 90 100 516 

Tables IV-60 and 61 report that the present value of total benefits from requiring cars and 

light trucks to achieve the fuel economy levels specified in the proposed CAFE standards for 

MYs 2017–25 will be $419 billion when discounted at the 7 percent rate also required by OMB 

guidance. Thus the present value of fuel savings and other benefits over the lifetimes of the 

vehicles covered by the proposed standards is $96 billion—or about 19 percent—lower when 

discounted at a 7 percent annual rate than when discounted using the 3 percent annual rate.221

Table II-77. Present Value of Benefits ($b) under Proposed Standards Using 7 Percent 

Discount Rate – MYs 2017-2021 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 9 6 12 17 22 28 

LT 1 1 5 10 14 21 

Combined 9 7 16 27 37 49 

Table II-78. Present Value of Benefits ($b) under Proposed Standards Using 7 Percent 

Discount Rate – MYs 2022-2025 (and total through MY2025) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 32 37 44 49 254 

LT 24 27 30 33 165 

Combined 56 64 73 81 419 

For both the passenger car and light truck fleets, NHTSA estimates that the benefits of 

today’s proposed standards will exceed the corresponding costs in every model year, so that the 

net social benefits from requiring higher fuel economy—the difference between the total benefits 

that result from higher fuel economy and the technology outlays required to achieve it—will be 

 221 For tables that report total or net benefits using a 7 percent discount rate, future benefits from reducing carbon 

dioxide emissions are discounted at 3 percent in order to maintain consistency with the discount rate used to develop 

the reference case estimate of the social cost of carbon. All other future benefits reported in these tables are 

discounted using the 7 percent rate. 
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substantial. Because the technology outlays required to achieve the fuel economy levels required 

by the proposed standards are incurred during the model years when the vehicles are produced 

and sold, however, they are not subject to discounting, so that their present value does not 

depend on the discount rate used. Thus the net benefits of the proposed standards differ 

depending on whether the 3 percent or 7 percent discount rate is used, but only because the 

choice of discount rates affects the present value of total benefits, and not that of technology 

costs. 

As Tables IV-62 and 63 show, over the lifetimes of the affected (MY 2017–2025, and 

MYs leading up to MY 2017) vehicles, the agency estimates that when the benefits of the 

proposed standards are discounted at a 3 percent rate, they will exceed the costs of the proposed 

standards by $358 billion: 

Table II-79. Present Value of Net Benefits ($b) under Proposed Standards Using 3 Percent 

Discount Rate – MYs 2017-2021 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 7 4 9 14 18 22 

LT 1 1 5 11 14 20 

Combined 8 6 14 25 32 43 

Table II-80. Present Value of Net Benefits ($b) under Proposed Standards Using 3 Percent 

Discount Rate – MYs 2022-2025 (and total through MY 2025) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 26 28 31 36 197 

LT 24 26 29 31 161 

Combined 49 54 60 67 358 

As indicated previously, when fuel savings and other future benefits resulting from the 

proposed standards are discounted at the 7 percent rate prescribed in OMB guidance, they are 

$96 billion lower than when the 3 percent discount rate is applied. Because technology costs are 

not subject to discounting, using the higher 7 percent discount rate reduces net benefits by 

exactly this same amount. Nevertheless, Tables IV-64 and 65 show that the net benefits from 

requiring passenger cars and light trucks to achieve higher fuel economy are still substantial even 

when future benefits are discounted at the higher rate, totaling $262 billion over MYs 2017–25. 

Net benefits are thus about 27 percent lower when future benefits are discounted at a 7 percent 

annual rate than at a 3 percent rate. 

Table II-81. Present Value of Net Benefits ($b) under Proposed Standards Using 7 Percent 

Discount Rate – MYs 2017-2021 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 5 3 7 10 13 16 

LT 1 1 4 8 10 15 
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Combined 5 4 11 18 24 31 

Table II-82. Present Value of Net Benefits ($b) under Proposed Standards Using 7 Percent 

Discount Rate – MYs 2022-2025 (and total through MY 2025) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 18 20 22 25 141 

LT 18 20 21 24 121 

Combined 36 40 43 49 262 

NHTSA’s estimates of economic benefits from establishing higher CAFE standards are 

subject to considerable uncertainty. Most important, the agency’s estimates of the fuel savings 

likely to result from adopting higher CAFE standards depend critically on the accuracy of the 

estimated fuel economy levels that will be achieved under both the baseline scenario, which 

assumes that manufacturers will continue to comply with the MY 2016 CAFE standards, and 

under alternative increases in the standards that apply to MYs 2017–25 passenger cars and light 

trucks. Specifically, if the agency has underestimated the fuel economy levels that 

manufacturers would have achieved under the baseline scenario—or is too optimistic about the 

fuel economy levels that manufacturers will actually achieve under the proposed standards—its 

estimates of fuel savings and the resulting economic benefits attributable to this rule will be too 

large. 

Another major source of potential overestimation in the agency’s estimates of benefits 

from requiring higher fuel economy stems from its reliance on the Reference Case fuel price 

forecasts reported in AEO 2011. Although NHTSA believes that these forecasts are the most 

reliable that are available, they are nevertheless significantly higher than the fuel price 

projections reported in most previous editions of EIA’s Annual Energy Outlook, and reflect 

projections of world oil prices that are well above forecasts issued by other firms and 

government agencies. If the future fuel prices projected in AEO 2011 prove to be too high, the 

agency’s estimates of the value of future fuel savings—the major component of benefits from 

this rule—will also be too high. 

However, it is also possible that NHTSA’s estimates of economic benefits from 

establishing higher CAFE standards underestimate the true economic benefits of the fuel savings 

those standards would produce. If the AEO 2011 forecast of fuel prices proves to be too low, for 

example, NHTSA will have underestimated the value of fuel savings that will result from 

adopting higher CAFE standards for MY 2017–25. As another example, the agency’s estimate 

of benefits from reducing the threat of economic damages from disruptions in the supply of 

imported petroleum to the U.S. applies to calendar year 2020. If the magnitude of this estimate 

would be expected to grow after 2015 in response to increases in U.S. petroleum imports, growth 

in the level of U.S. economic activity, or increases in the likelihood of disruptions in the supply 
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of imported petroleum, the agency may have underestimated the benefits from the reduction in 

petroleum imports expected to result from adopting higher CAFE standards. 

NHTSA’s benefit estimates could also be too low because they exclude or understate the 

economic value of certain potentially significant categories of benefits from reducing fuel 

consumption. As one example, EPA’s estimates of the economic value of reduced damages to 

human health resulting from lower exposure to criteria air pollutants includes only the effects of 

reducing population exposure to PM2.5 emissions. Although this is likely to be the most 

significant component of health benefits from reduced emissions of criteria air pollutants, it 

excludes the value of reduced damages to human health and other impacts resulting from lower 

emissions and reduced population exposure to other criteria air pollutants, including ozone and 

nitrous oxide (N2O), as well as to airborne toxics. EPA’s estimates exclude these benefits 

because no reliable dollar-per-ton estimates of the health impacts of criteria pollutants other than 

PM2.5 or of the health impacts of airborne toxics were available to use in developing estimates of 

these benefits. 

Similarly, the agency’s estimate of the value of reduced climate-related economic 

damages from lower emissions of GHGs excludes many sources of potential benefits from 

reducing the pace and extent of global climate change.222 For example, none of the three models 

used to value climate-related economic damages includes those resulting from ocean 

acidification or loss of species and wildlife. The models also may not adequately capture certain 

other impacts, including potentially abrupt changes in climate associated with thresholds that 

govern climate system responses, interregional interactions such as global security impacts of 

extreme warming, or limited near-term substitutability between damage to natural systems and 

increased consumption. Including monetized estimates of benefits from reducing the extent of 

climate change and these associated impacts would increase the agency’s estimates of benefits 

from adopting higher CAFE standards. 

The following tables present itemized costs and benefits for the combined passenger car 

and light truck fleets for each model year affected by the proposed standards and for all model 

years combined, using both discount rates prescribed by OMB regulatory guidance. Tables IV66 and 67 report technology outlays, each separate component of benefits (including costs 

associated with additional driving due to the rebound effect, labeled “dis-benefits”), the total 

value of benefits, and net benefits using the 3 percent discount rate. (Numbers in parentheses 

represent negative values.) 

Table II-83. Present Value of Net Benefits ($b) under Proposed Standards Using 3 Percent 

Discount Rate – MYs 2017-2021 

 222 Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working 

Group on Social Cost of Carbon, United States Government, February 2010. Available in Docket No. NHTSA–

2009–0059. 
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 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Costs 

Technology costs 4.0 3.0 6.0 9.0 13.0 17.0 

Benefits 

Savings in lifetime 

fuel expenditures 10.2 7.1 17.5 29.6 40.0 52.5 

Consumer surplus 

from additional 

driving 

0.6 0.4 0.7 1.1 1.2 1.7 

Value of savings in 

refueling time 0.1 0.1 0.2 0.5 0.7 1.0 

Reduction in 

petroleum market 

externalities 

0.6 0.4 0.9 1.6 2.1 2.7 

Reduction in 

climate-related 

damages from lower 

CO2 emissions223

1.1 0.7 1.8 3.1 4.2 5.6 

Reduction in 

highway fatalities 

from changes in 

vehicle mass 

(0.6) (0.0) 0.0 (0.1) (0.0) (0.0) 

Reduction in health 

damage costs from 

lower emissions of 

criteria air 

pollutants: 

 

CO - - - - - - 

VOC 0.0 0.0 0.0 0.0 0.1 0.1 

NOX 0.0 0.0 0.0 0.0 0.0 0.0 

PM 0.2 0.1 0.1 0.3 0.5 0.8 

SOX 0.2 0.1 0.3 0.4 0.6 0.8 

Dis-benefits from 

increased driving: 

Congestion costs (0.8) (0.6) (1.3) (2.2) (2.9) (3.8) 

Noise costs (0.0) (0.0) (0.0) (0.0) (0.1) (0.1) 

Crash costs (0.4) (0.3) (0.6) (1.0) (1.4) (1.8) 

Total benefits 12.0 8.0 20.0 34.0 45.0 60.0 

Net benefits 8.0 6.0 14.0 25.0 32.0 43.0 

 223 Using the central value of $22 per metric ton for the SCC, and discounting future benefits from reduced CO2

emissions at a 3 percent annual rate. Additionally, we note that the $22 per metric ton value for the SCC applies to 

calendar year 2010, and increases over time. See the interagency guidance on SCC for more information. 
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Table II-84. Present Value of Net Benefits ($b) under Proposed Standards Using 3 Percent 

Discount Rate – MYs 2022-2025 and Total for All MYs 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

Costs 

Technology costs 20.0 24.0 30.0 32.0 157 

Benefits 

Savings in lifetime fuel 

expenditures 60.6 68.6 78.4 87.0 452 

Consumer surplus from 

additional driving 2.0 2.2 2.5 2.4 14.8 

Value of savings in 

refueling time 1.3 1.6 2.0 2.4 9.9 

Reduction in petroleum 

market externalities 3.1 3.5 4.0 4.4 23.4 

Reduction in climaterelated damages from 

lower CO2 emissions 

6.6 7.5 8.7 9.7 49.1 

Reduction in highway 

fatalities from changes 

in vehicle mass 

(0.0) (0.0) (0.0) (0.0) (0.1) 

Reduction in health 

damage costs from 

lower emissions of 

criteria air pollutants: 

CO - - - - - 

VOC 0.1 0.1 0.1 0.1 0.7 

NOX 0.0 0.0 0.0 0.0 0.2 

PM 1.1 1.3 1.4 1.7 8.3 

SOX 0.9 1.0 1.0 1.1 6.3 

Dis-benefits from 

increased driving: 

Congestion costs (4.3) (4.9) (5.6) (6.2) (32.7) 

Noise costs (0.1) (0.1) (0.1) (0.1) (0.6) 

Crash costs (2.1) (2.3) (2.7) (3.0) (15.5) 

Total benefits 69 78 90 100 515 

Net benefits 49 54 60 67 358 

Similarly, Tables IV-68 and 69 below report technology outlays, the individual 

components of benefits (including “dis-benefits” resulting from additional driving) and their total 

and net benefits using the 7 percent discount rate. (Again, numbers in parentheses represent 

negative values.) 

Table II-85. Present Value of Net Benefits ($b) under Proposed Standards Using 7 Percent 

Discount Rate – MYs 2017-2021 
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 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Costs 

Technology costs 4.0 3 6 9 13 17 

Benefits 

Savings in lifetime 

fuel expenditures 8.1 5.7 13.9 23.4 31.7 41.6 

Consumer surplus 

from additional 

driving 

0.1 0.0 0.2 0.4 0.6 0.8 

Value of savings in 

refueling time 0.4 0.3 0.6 0.9 1.0 1.4 

Reduction in 

petroleum market 

externalities 

0.5 0.3 0.7 1.2 1.7 2.2 

Reduction in 

climate-related 

damages from lower 

CO2 emissions224

1.1 0.7 1.8 3.1 4.2 5.6 

Reduction in 

highway fatalities 

from changes in 

vehicle mass 

(0.0) (0.0) 0.0 (0.1) (0.0) (0.0) 

Reduction in health 

damage costs from 

lower emissions of 

criteria air 

pollutants: 

CO - - - - - - 

VOC 0.0 0.0 0.0 0.0 0.1 0.1 

NOX 0.0 0.0 0.0 0.0 0.0 0.0 

PM 0.1 0.1 0.2 0.4 0.6 0.8 

SOX 0.1 0.1 0.2 0.4 0.5 0.6 

Dis-benefits from 

increased driving: 

Congestion costs (0.7) (0.4) (1.1) (1.7) (2.3) (3.0) 

Noise costs (0.0) (0.0) (0.0) (0.0) (0.0) (0.1) 

Crash costs (0.3) (0.2) (0.5) (0.8) (1.1) (1.4) 

Total benefits 9.0 6.6 16.2 27.2 36.8 48.5 

Net benefits 5.0 4.0 10.5 18.3 23.8 31.4 

 224 Using the central value of $22 per metric ton for the SCC, and discounting future benefits from reduced CO2

emissions at a 3 percent annual rate. Additionally, we note that the $22 per metric ton value for the SCC applies to 

calendar year 2010, and increases over time. See the interagency guidance on SCC for more information. 
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Table II-86. Present Value of Net Benefits ($b) under Proposed Standards Using 7 Percent 

Discount Rate – MYs 2022-2025 and Total for All MYs 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

Costs 

Technology costs 20 24 30 32 157 

Benefits 

Savings in lifetime fuel 

expenditures 48.1 54.4 62.3 69.2 358.3 

Consumer surplus from 

additional driving 1.0 1.3 1.6 1.9 7.9 

Value of savings in 

refueling time 1.6 1.8 2.0 2.0 11.9 

Reduction in petroleum 

market externalities 2.5 2.8 3.2 3.6 18.7 

Reduction in climaterelated damages from 

lower CO2 emissions 

6.6 7.5 8.7 9.7 49.1 

Reduction in highway 

fatalities from changes 

in vehicle mass 

(0.0) (0.0) (0.0) (0.0) (0.1) 

Reduction in health 

damage costs from 

lower emissions of 

criteria air pollutants: 

 

CO - - - - - 

VOC 0.1 0.1 0.1 0.1 0.6 

NOX 0.0 0.0 0.0 0.0 0.2 

PM 0.9 1.0 1.1 1.3 6.6 

SOX 0.7 0.8 0.8 0.9 5.1 

Dis-benefits from 

increased driving: 

Congestion costs (3.5) (3.9) (4.5) (5.0) (26.2) 

Noise costs (0.1) (0.1) (0.1) (0.1) (0.5) 

Crash costs (1.6) (1.9) (2.1) (2.4) (12.4) 

Total benefits 56.2 63.8 73.2 81.2 419.2 

Net benefits 36.4 39.7 43.4 48.8 252.2 

These benefit and cost estimates do not reflect the availability and use of certain 

flexibility mechanisms, such as compliance credits and credit trading, because EPCA prohibits 

NHTSA from considering the effects of those mechanisms in setting CAFE standards. However, 

the agency notes that, in reality, manufacturers are likely to rely to some extent on flexibility 

mechanisms and would thereby reduce the cost of complying with the proposed standards to a 

meaningful extent. 
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As discussed in the PRIA, NHTSA has performed an analysis to estimate costs and 

benefits taking into account EPCA’s provisions regarding EVs, PHEVs produced before MY 

2020, FFV credits, and other CAFE credit provisions. Accounting for these provisions indicates 

that achieved fuel economies would be 0.5–1.6 mpg lower than when these provisions are not 

considered: 

Table II-87. Average Achieved Fuel Economy (mpg) under Proposed Standards – MYs 

2017-2021 (with EPCA AFV and credit provisions) 

 MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Passenger cars 38.8 40.6 42.7 44.6 46.1 

Light trucks 29.0 30.1 31.8 33.0 34.8 

Combined 34.5 36.0 38.0 39.7 41.4 

Table II-88. Average Achieved Fuel Economy (mpg) under Proposed Standards – MYs 

2022-2025 (with EPCA AFV and credit provisions) 

 MY 2022 MY 2023 MY 2024 MY 2025 

Passenger cars 47.2 48.8 50.5 52.7 

Light trucks 35.5 36.3 37.4 38.6 

Combined 42.4 43.7 45.2 47.0 

As a result, NHTSA estimates that, when EPCA AFV and credit provisions are taken into 

account, fuel savings will total 163 billion gallons—5.8 percent less than the 173 billion gallons 

estimated when these flexibilities are not considered: 

Table II-89. Fuel Saved (billion gallons) under Proposed Standards – MYs 2017-2021 (with 

EPCA AFV and credit provisions) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 4 2 4 6 9 10 

LT 0 1 2 4 6 8 

Combined 4 3 6 11 14 19 

Table II-90. Fuel Saved (billion gallons) under Proposed Standards – MYs 2022-2025 (with 

EPCA AFV and credit provisions) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 12 14 17 20 98 

LT 9 10 11 13 65 

Combined 21 24 28 32 153 
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The agency similarly estimates CO2 emissions reductions will total 1,742 million metric 

tons (mmt), 5.0 percent less than the 1,834 mmt estimated when these EPCA provisions are not 

considered:225

Table II-91. Avoided Carbon Dioxide Emissions (mmt) under Proposed Standards – MYs 

2017-2021 (with EPCA AFV and credit provisions) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 41 23 43 69 93 111 

LT 4 7 22 47 64 89 

Combined 45 31 65 116 157 200 

Table II-92. Avoided Carbon Dioxide Emissions (mmt) under Proposed Standards – MYs 

2022-2025 (with EPCA AFV and credit provisions) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 128 151 177 204 1,040 

LT 100 109 123 138 702 

Combined 227 260 300 341 1,742 

 

This analysis further indicates that significant reductions in outlays for additional 

technology will result when EPCA’s AFV and credit provisions are taken into account. Tables 

IV-76 and 77 below show that, total technology costs are estimated to decline to $133 billion as a 

result of manufacturers’ use of these provisions, or about 15 percent less than the $157 billion 

estimated when excluding these flexibilities: 

Table II-93. Incremental Technology Outlays ($ billion) under Proposed Standards – MYs 

2017-2021 (with EPCA AFV and credit provisions) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 1 1 3 5 8 10 

LT 0 0 1 2 3 4 

Combined 1 2 4 7 11 15 

Table II-94. Incremental Technology Outlays ($ billion) under Proposed Standards – MYs 

2022-2025 (with EPCA AFV and credit provisions) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 12 16 19 22 98 

LT 5 6 6 8 35 

Combined 17 21 25 30 133 

 225 Differences in the application of diesel engines and plug-in hybrid electric vehicles lead to differences in the 

percentage changes in fuel consumption and carbon dioxide emissions between the with- and without-credit cases. 
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Because NHTSA’s analysis indicated that these EPCA provisions will modestly reduce 

fuel savings and related benefits, the agency’s estimate of the present value of total benefits will 

be $488 billion when discounted at a 3 percent annual rate, as Tables IV-78 and 79 below report. 

This estimate of total benefits is $27 billion, or 5.2 percent, lower than the $515 billion reported 

previously for the analysis that excluded these provisions: 

Table II-95. Present Value of Benefits ($ billion) under Proposed Standards Using a 3 

Percent Discount Rate – MYs 2017-2021 (with EPCA AFV and credit provisions) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 10 6 12 19 26 31 

LT 1 2 6 12 17 24 

Combined 11 8 17 31 43 55 

Table II-96. Present Value of Benefits ($ billion) under Proposed Standards Using a 3 

Percent Discount Rate – MYs 2022-2025 (with EPCA AFV and credit provisions) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 36 43 51 60 293 

LT 28 30 35 39 195 

Combined 63 74 86 99 488 

Similarly, NHTSA estimates that the present value of total benefits will decline modestly 

from its previous estimate when future fuel savings and other benefits are discounted at the 

higher 7 percent rate. Tables IV-80 and 81 report that the present value of benefits from 

requiring higher fuel economy for MY 2017–25 cars and light trucks will total $397 billion when 

discounted using a 7 percent rate, about $22 billion (5.3 percent) below the previous $419.2 

billion estimate of total benefits when FFV credits were not permitted: 

Table II-97. Present Value of Benefits ($ billion) under Proposed Standards Using a 7 

Percent Discount Rate – MYs 2017-2021 (with EPCA AFV and credit provisions) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 8 5 9 15 21 25 

LT 1 2 5 10 14 20 

Combined 9 7 14 25 35 45 

Table II-98. Present Value of Benefits ($ billion) under Proposed Standards Using a 7 

Percent Discount Rate – MYs 2022-2025 (with EPCA AFV and credit provisions) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 29 35 42 49 240 

LT 22 24 28 32 157 

Combined 52 60 70 81 397 
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Although the discounted present value of total benefits will be modestly lower when 

EPCA AFV and credit provisions are taken into account, the agency estimates that these 

provisions will reduce net benefits by a smaller proportion. As Tables IV-82 and 83 show, the 

agency estimates that these will reduce net benefits from the proposed CAFE standards to $355 

billion from the previously-reported estimate of $358 billion without those credits, or by only 

about 1 percent. 

Table II-99. Present Value of Net Benefits ($ billion) under Proposed Standards Using a 3 

Percent Discount Rate – MYs 2017-2021 (with EPCA AFV and credit provisions) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 9 5 8 13 18 21 

LT 1 2 5 10 14 20 

Combined 10 6 13 24 32 41 

Table II-100. Present Value of Net Benefits ($ billion) under Proposed Standards Using a 3 

Percent Discount Rate – MYs 2022-2025 (with EPCA AFV and credit provisions) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 24 28 32 38 195 

LT 23 25 28 31 159 

Combined 47 52 60 69 354 

Similarly, Tables IV-84 and 85 immediately below show that NHTSA estimates 

manufacturers’ use of EPCA AFV and credit provisions will increase net benefits from requiring 

higher fuel economy for MY 2017–25 cars and light trucks, but very slightly—to $264 billion—

if a 7 percent discount rate is applied to future benefits. This estimate is $2 billion—or 0.8 

percent—higher than the previously-reported $262 billion estimate of net benefits without the 

availability of EPCA AFV and credit provisions using that same discount rate. 

Table II-101. Present Value of Net Benefits ($ billion) under Proposed Standards Using a 7 

Percent Discount Rate – MYs 2017-2021 (with EPCA AFV and credit provisions) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 7 4 6 10 13 15 

LT 1 1 4 8 11 15 

Combined 8 5 10 18 24 30 

Table II-102. Present Value of Net Benefits ($ billion) under Proposed Standards Using a 7 

Percent Discount Rate – MYs 2022-2025 (with EPCA AFV and credit provisions) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

PC 17 20 23 27 142 

LT 17 19 21 24 121 

Combined 35 39 44 51 263 
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The agency has performed several sensitivity analyses to examine important assumptions. 

All sensitivity analyses were based on the “standard setting” output of the CAFE model. We 

examine sensitivity with respect to the following economic parameters: 

1) The price of gasoline: The main analysis (i.e., the Reference Case) uses the AEO 2011 

Reference Case estimate for the price of gasoline. In this sensitivity analysis we examine the 

effect of using the AEO 2011 High Price Case or Low Price Case forecast estimates instead. 

2) The rebound effect: The main analysis uses a rebound effect of 10 percent to project 

increased miles traveled as the cost per mile driven decreases. In the sensitivity 

analysis, we examine the effect of using a 5, 15, or 20 percent rebound effect instead. 

3) The value of CO2 benefits: The main analysis uses $22 per ton discounted at a 3 

percent discount rate to quantify the benefits of reducing CO2 emissions and $0.174 per 

gallon to quantify the benefits of reducing fuel consumption. In the sensitivity analysis, 

we examine the following values and discount rates applied only to the social cost of 

carbon to value carbon benefits, considering low, high, and very high valuations of 

approximately $5, $36, and $67 per ton, respectively with regard to the benefits of 

reducing CO2 emissions.226 These are the 2010 values, which increase over time. 

These values can be translated into cents per gallon by multiplying by 0.0089,227 giving 

the following values: 

 ($4.86 per ton CO2) x 0.0089 = $0.043 per gallon discounted at 5% 

 ($22.00 per ton CO2) x 0.0089 = $0.196 per gallon discounted at 3% (used in the main 

analysis) 

 ($36.13 per ton CO2) x 0.0089 = $0.322 per gallon discounted at 2.5% 

 And a 95th percentile estimate of 

 ($66.88 per ton CO2) x 0.0089 = $0.595 per gallon discounted at 3% 

4) Military security: The main analysis does not assign a value to the military security 

benefits of reducing fuel consumption. In the sensitivity analysis, we examine the 

impact of using a value of 12 cents per gallon instead. 

5) Consumer Benefit: The main analysis assumes there is no loss in value to consumers 

resulting from vehicles that have an increase in price and higher fuel economy. This 

 226 The low, high, and very high valuations of $5, $36, and $67 are rounded for brevity; the exact values are $4.86, 

$36.13, and $66.88, respectively. While the model uses the unrounded values, the use of unrounded values is not 

intended to imply that the chosen values are precisely accurate to the nearest cent; rather, they are average levels 

resulting from the many published studies on the topic. 

227 The molecular weight of Carbon (C) is 12, the molecular weight of Oxygen (O) is 16, thus the molecular weight 

of CO2 is 44. 1 gallon of gas weighs 2,819 grams, of that 2,433 grams are carbon. One ton of CO2/One ton of C 

(44/12)* 2433grams C/gallon *1 ton/1000kg * 1 kg/1000g = (44 * 2433*1*1) / (12*1*1000 * 1000) = 

0.0089. Thus, one ton of CO2*0.0089 = 1 gallon of gasoline. 
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sensitivity analysis assumes that there is a 25, or 50 percent loss in value to consumers 

– equivalent to the assumption that consumers will only value the calculated benefits 

they will achieve at 75, or 50 percent, respectively, of the main analysis estimates. 

6) Battery cost: The agency conducted a sensitivity analysis of technology cost in relation 

to battery costs for HEV, PHEV, and EV batteries. The ranges are based on 

recommendations from technical experts in the field of battery energy storage 

technologies at the Department of Energy (DOE) and at Argonne National Laboratories 

(ANL), and were developed using the Battery Performance and Cost (BatPac) model 

developed by ANL and funded by DOE.228 The values for these ranges are shown in 

the table below and are calculated with 95 percent confidence intervals after analyzing 

the confidence bound using the BatPac model. 

Suggested Confidence Bounds as a Percentage of the Calculated Point Estimate for a 

Graphite-Based Li-ion Battery Using the Default Inputs in BatPac 

Battery Type Cathodes Confidence Interval

Lower Upper 

HEV LMO, LFP, NCA, NMC -10% 10% 

PHEV, EV NMC, NCA -10% 20% 

PHEV, EV LMO, LFP -20% 35% 

7) Mass reduction cost: Due to the wide range of mass reduction costs as discussed in 

Chapter 3 of the draft joint TSD, a sensitivity analysis was performed examining the 

impact of the cost of vehicle mass reduction to the total technology cost. The direct 

manufacturing cost (DMC) for mass reduction is represented as a linear function 

between the unit DMC versus percent of mass reduction, as shown in the figure below: 

 228 Section 3.4.3.9 in Chapter 3 of the draft Joint TSD has a detailed description of the history of the BatPac model 

and how the angecies used it in this NPRM analysis. 
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The slope of the line used in the central analysis for this NPRM is $4.32 per pound per 

percent of mass reduction. The slope of the line is varied + 40% as the upper and lower 

bound for this sensitivity study. The resultant values for the range of mass reduction cost are 

shown in the table below: 

Bounds for Mass Reduction Direct Manufacturing Cost 

Sensitivity Bound 

Slope of Mass 

Reduction Line 

[$/(lb / %MR)] 

Example Unit Direct 

Manufacturing Cost1

[$/lb]

Example Total 

Direct 

Manufacturing Cost2

[$/lb] 

Lower bound $2.59 $0.39 $233 

NPRM central 

analysis 

$4.32 $0.65 $389 

Upper bound $6.05 $0.91 $544 1

Example is based on 15% mass reduction 

2

Example is based on 15% mass reduction for a 4,000 lb vehicle 

8) Market-driven response: The baseline for the central analysis is based on the MY 2016 

CAFE standards and assumes that manufacturers will make no changes in the fuel 

economy from that level through MY 2025. A sensitivity analysis was performed to 

simulate potential increases in fuel economy over the compliance level required if MY 

2016 standards were to remain in place. The assumption is that the market would drive 

manufacturers to put technologies into their vehicles that they believe consumers would 

value and be willing to pay for. Using parameter values consistent with the central 

analysis, the agency simulated a market-driven response by applying a payback period 

$0.00

$0.20

$0.40

$0.60

$0.80

$1.00

$1.20

$1.40

0% 5% 10% 15% 20% 25%

Unit Cost [$/lb]

Percent of Mass Reduction

Mass Reduction Cost Sensitivity Bounds

Low Bound NPRM Central Analysis Upper Bound

Slope = 4.32

Slope = 2.59

Slope = 6.05
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of one year for purposes of calculating the value of future fuel savings when simulating 

whether manufacturers would apply additional technology to an already CAFEcompliant fleet. In other words we assumed that manufacturers that were above their 

MY 2016 CAFE level would compare the cost to consumers to the fuel savings in the 

first year of operation and decide to voluntarily apply those technologies to their 

vehicles when benefits for the first year exceeded costs for the consumer. For a 

manufacturer’s fleet that that has not yet achieved compliance with CAFE standards, 

the agency continued to apply a five-year payback period. In other words, for this 

sensitivity analysis the agency assumed that manufacturers that have not yet met CAFE 

standards for future model years will apply technology as if buyers were willing to pay 

for the technologies as long as the fuel savings throughout the first five years of vehicle 

ownership exceeded their costs. Once having complied with those standards, however, 

manufacturers are assumed to consider making further improvements in fuel economy 

as if buyers were only willing to pay for fuel savings to be realized during the first year 

of vehicle ownership. The ‘market-driven response’ assumes that manufacturers will 

overcomply if additional technology is sufficiently cost-effective. Because this 

assumption has a greater impact under the baseline standards, its application reduces 

the incremental costs, effects, and benefits attributable to the new standards. This does 

not mean that costs, effects, and benefits would actually be smaller with a marketdriven response; rather, it means that costs, effects, and benefits would be at least as 

great, but would be partially attributable not to the new standards, but instead to the 

market. 

Varying each of these eight parameters in isolation results in a variety of economic 

scenarios, in addition to the Reference case. These are listed in Table IV-86 below. 

Table II-103. Sensitivity Analyses Evaluated in NHTSA’s PRIA 

Name Fuel 

Price 

Discount 

Rate 

Rebound 

Effect SCC Military 

Security 

Reference Reference 3% 10% $22 0¢/gal 

High Fuel Price High 3% 10% $22 0¢/gal 

Low Fuel Price Low 3% 10% $22 0¢/gal 

5% Rebound Effect Reference 3% 5% $22 0¢/gal 

15% Rebound Effect Reference 3% 15% $22 0¢/gal 

20% Rebound Effect Reference 3% 20% $22 0¢/gal 

12¢/gal Military Security 

Value Reference 3% 10% $22 12¢/gal 

$5/ton CO2 Value Reference 3% 10% $5 0¢/gal 

$36/ton CO2 Value Reference 3% 10% $36 0¢/gal 

$67/ton CO2 Value Reference 3% 10% $67 0¢/gal 

50% Consumer Benefit Reference 3% 10% $22 0¢/gal 

75% Consumer Benefit Reference 3% 10% $22 0¢/gal 
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Low Battery Cost Reference 3% 10% $22 0¢/gal 

High Battery Cost Reference 3% 10% $22 0¢/gal 

Low Cost Mass 

Reduction Reference 3% 10% $22 0¢/gal 

High Cost Mass 

Reduction Reference 3% 10% $22 0¢/gal 

Market-Driven Response Reference 3% 10% $22 0¢/gal 

The basic results of this sensitivity analysis are contained in Chapter X of the PRIA, but 

several selected findings are as follows: 

1) Varying the economic assumptions has almost no impact on achieved mpg. The mass 

reduction cost sensitivities, battery cost reduction sensitivities, and the market-based 

baseline are the only cases in which achieved mpg differs from the Reference Case of the 

Preferred Alternative. None of these alter the outcome by more than 0.2 mpg for either 

fleet. 

2) Varying the economic assumptions has, at most, a small impact on per-vehicle costs, fuel 

saved, and CO2 emissions reductions, with none of the variations impacting the outcomes 

by more than 10 percent from their central analysis levels, save for several exceptions 

including alternate fuel price sensitivities and the sensitivity involving a 20 percent 

rebound effect. 

3) The category most affected by variations in the economic parameters considered in these 

sensitivity analyses is net benefits. The sensitivity analyses examining the AEO Low and 

High fuel price scenarios demonstrate the potential to negatively impact net benefits by 

up to 40.3 percent or to increase net benefits by 29.5 percent relative to those of the 

Preferred Alternative. Other large impacts on net benefits occurred with the 20 percent 

rebound effect (-38.4%), valuing benefits at 50 and 75 percent (-63.0% and -31.5%, 

respectively), and valuing the reduction in CO2 emissions at $67/ton (+28.1%). 

4) Even if consumers value the benefits achieved at 50% of the main analysis assumptions, 

total benefits still exceed costs. 

Regarding the lower fuel savings and CO2 emissions reductions predicted by the 

sensitivity analysis as fuel price increases, which initially may seem counterintuitive, we note 

that there are some counterbalancing factors occurring. As fuel price increases, people will drive 

less and so fuel savings and CO2 emissions reductions may decrease. 

The agency performed two additional sensitivity analyses presented in Tables IV-87 and 

IV-88. First, the agency analyzed the impact that having a retail price equivalent (RPE) factor of 

1.5 for all technologies would have on the various alternatives instead of using the indirect cost 

methodology (ICM). The ICM methodology in an overall markup factor of 1.2 to 1.25 compared 
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to the RPE markup factor from variable cost of 1.5. Next, the agency conducted a separate 

sensitivity analysis using values that were derived from the 2011 NAS Report. This analysis 

used an RPE markup factor of 1.5 for non-electrification technologies, which is consistent with 

the NAS estimation for technologies manufactured by suppliers, and an RPE markup factor of 

1.33 for electrification technologies (HEV, PHEV, and EV); three types of learning which 

include no learning for mature technologies, 1.25 percent annual learning for evolutionary 

technologies, and 2.5 percent annual learning for revolutionary technologies; technology cost 

estimates for 52 percent (33 out of 63) technologies; and technology effectiveness estimates for 

56 percent (35 out of 63) technologies. Cost learning was applied to technology costs in a 

manner similar to how cost learning is applied in the central analysis for many technologies 

which have base costs that are applicable to recent or near-term future model years. As noted 

above, the cost learning factors used for the sensitivity case are different from the values used in 

the central analysis. For the other inputs in the sensitivity case, where the NAS study has 

inconsistent information or lacks projections, NHTSA used the same input values that were used 

in the central analysis. 

Table II-104. Achieved mpg Level, MY 2025, Comparing Different Cost Mark-up 

Methodologies (3% Discount Rate) 

ICM Method 

(Main 

Analysis 

Costs)

RPE Method 

(Main 

Analysis 

Costs)

Difference 

(mpg)

Passenger Cars

Preferred Alternative 52.70 52.24 -0.46

Max Net Benefits 49.09 48.47 -0.61

Light trucks

Preferred Alternative 39.59 39.38 -0.21

Max Net Benefits 44.31 44.17 -0.14

Table II-105. Achieved mpg level, MY 2025, Comparing ICM Method with Main Analysis 

Costs vs. NAS Costs (3% Discount Rate) 

ICM Method 

(Main 

Analysis 

Costs)

ICM Method 

(NAS Cost 

Estimates)

Difference 

(mpg)

Passenger Cars

Preferred Alternative 52.70 52.11 -0.59

Max Net Benefits 49.09 48.28 -0.80

Light trucks

Preferred Alternative 39.59 39.08 -0.51
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Max Net Benefits 44.31 44.48 0.18

Table II-106. Sensitivity Analyses (Achieved mpg, Per-Vehicle Cost, Net Benefits, Fuel 

Saved, & CO2 Emissions Reduced) 

Cost Method and Set of 

Cost Estimates

MY 2025 

Achieved 

mpg

Average 

MY 2025 

Per-Vehicle 

Technology 

Cost

MY 2017-

2025 Net 

Benefits, 

Discounted 

3%, in 

Billions of 

$

MY 2017-

2025 Fuel 

Saved, in 

Billions 

of 

Gallons

MY 2017-

2025 CO2 

Emissions 

Reduced, 

in mmT

Passenger Cars

ICM w/Main Analysis 

Costs 52.70 $2,023 $190 100 1,059

RPE w/Main Analysis 

Costs 52.24 $2,509 $164 101 1,062

ICM w/NAS Costs 52.11 $2,811 $149 101 1,074

Light trucks

ICM w/Main Analysis 

Costs 39.59 $1,578 $161 69 729

RPE w/Main Analysis 

Costs 39.38 $2,038 $148 68 722

ICM w/NAS Costs 39.08 $2,405 $139 66 660

For today’s rulemaking analysis, the agency has also performed a sensitivity analysis 

where manufacturers are allowed to voluntarily apply more technology than would be required to 

comply with CAFE standards for each model year. Manufacturers are assumed to do so as long 

as applying each additional technology would increase vehicle production costs (including 

markup) by less than it would reduce buyers’ fuel costs during the first year they own the 

vehicle. This analysis makes use of the “voluntary overcompliance” simulation capability DOT 

has recently added to its CAFE model. This capability, which is discussed further above in 

section IV.C.4.c and in the CAFE model documentation, is a logical extension of the model’s 

simulation of some manufacturers’ decisions to respond to EPCA by paying civil penalties once 

additional technology becomes economically unattractive. It attempts to simulate 

manufacturers’ responses to buyers’ demands for higher fuel economy levels than prevailing 

CAFE standards would require when fuel costs are sufficiently high, and technologies that 

manufacturers have not yet fully utilized are available to improve fuel economy at relatively low 

costs. 

NHTSA performed this analysis because some stakeholders commenting on the recentlypromulgated standards for medium- and heavy-duty vehicles indicated that it would be 

unrealistic for the agency to assume that in the absence of new regulations, technology and fuel 
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economy would not improve at all in the future. In other words, these stakeholders argued that 

market forces are likely to result in some fuel economy improvements over time, as potential 

vehicle buyers and manufacturers respond to changes in fuel prices and in the availability and 

costs of technologies to increase fuel economy. NHTSA agrees that, in principle, its analysis 

should estimate a potential that manufacturers will apply technology as if buyers place some

value on fuel economy improvements. Considering current uncertainties discussed below 

regarding the degree to which manufacturers will do so, the agency currently judges it 

appropriate to conduct its central rulemaking analysis without attempting to simulate these 

effects. Nonetheless, the agency believes that voluntary overcompliance is sufficiently plausible 

that corresponding sensitivity analysis is warranted. 

NHTSA performed this analysis by simulating potential overcompliance under the noaction alternative, the preferred alternative, and other regulatory alternatives. In doing so, the 

agency used all the same parameter values as in the agency’s central analysis, but applied a 

payback period of one year for purposes of calculating the value of future fuel savings when 

simulating whether a manufacturer would apply additional technology to an already CAFEcompliant fleet. For technologies applied to a manufacturer’s fleet that has not yet achieved 

compliance with CAFE standards, the agency continued to apply a five-year payback period. 

In other words, for this sensitivity analysis the agency assumed that manufacturers that 

have not yet met CAFE standards for future model years will apply technology as if buyers were 

willing to pay for fuel savings throughout the first five years of vehicle ownership. Once having 

complied with those standards, however, manufacturers are assumed to consider making further 

improvements in fuel economy as if buyers were only willing to pay for fuel savings to be 

realized during the first year of vehicle ownership. This reflects the agency’s assumptions for 

this sensitivity analysis, that (1) civil penalties, though legally available, carry a stigma that 

manufacturers will strive to avoid, and that (2) having achieved compliance with CAFE 

standards, manufacturers will avoid competitive risks entailed in charging higher prices for 

vehicles that offer additional fuel economy, rather than offering additional performance or utility. 

Since CAFE standards were first introduced, some manufacturers have consistently 

exceeded those standards, and the industry as a whole has consistently overcomplied with both 

the passenger car and light truck standards. Although the combined average fuel economy of 

cars and light trucks declined in some years, this resulted from buyers shifting their purchases 

from passenger cars to light trucks, not from undercompliance with either standard. Even with 

those declines, the industry still overcomplied with both passenger car and light truck standards. 

In recent years, between MYs 1999 and 2009, fuel economy overcompliance has been increasing 

on average for both the passenger car and the light truck fleets. NHTSA considers it impossible 

to say with certainty why past fuel economy levels have followed their observed path . If the 

agency could say with certainty how fuel economy would have changed in the absence of CAFE 

standards, it might be able to answer this question; however, NHTSA regards this 

“counterfactual” case as simply unknowable. 
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NHTSA has, however, considered other relevant indications regarding manufacturers’ 

potential future decisions. Published research regarding how vehicle buyers have previously 

viewed fuel economy suggests that they have only a weak quantitative understanding of the 

relationship between fuel economy and future fuel outlays, and that potential buyers value fuel 

economy improvements by less than theoretical present-value calculations of lifetime fuel 

savings would suggest. These findings are generally consistent with manufacturers’ confidential 

and, in some cases, public statements. Manufacturers have tended to communicate not that 

buyers absolutely “don’t care” about fuel economy, but that buyers have, in the past, not been 

willing to pay the full cost of most fuel economy improvements. Manufacturers have also tended 

to indicate that sustained high fuel prices would provide a powerful incentive for increased fuel 

economy; this implies that manufacturers believe buyers are willing to pay for some fuel 

economy increases, but that buyers’ willingness to do so depends on their expectations for future 

fuel prices. In their confidential statements to the agency, manufacturers have also tended to 

indicate that in their past product planning processes, they have assumed buyers would only be 

willing to pay for technologies that "break even” within a relatively short time—generally the 

first two to four years of vehicle ownership. 

NHTSA considers it not only feasible but appropriate to simulate such effects by 

calculating the present value of fuel savings over some “payback period.” The agency also 

believes it is appropriate to assume that specific improvements in fuel economy will be 

implemented voluntarily if manufacturers’ costs for adding the technology necessary to 

implement them to specific models would be lower than potential buyers’ willingness to pay for 

the resulting fuel savings. This approach takes fuel costs directly into account, and is therefore 

responsive to manufacturers’ statements regarding the role that fuel prices play in influencing 

buyers’ demands and manufacturers’ planning processes. Under this approach, a short payback 

period can be employed if manufacturers are expected to act as if buyers place little value on fuel 

economy. Conversely, a longer payback period can be used if manufacturers are expected to act 

as if buyers will place comparatively greater value on fuel economy. 

NHTSA cannot be certain to what extent vehicle buyers will, in the future, be willing to 

pay for fuel economy improvements, or to what extent manufacturers would, in the future, 

voluntarily apply more technology than needed to comply with fuel economy standards. The 

agency is similarly hopeful that future vehicle buyers will be more willing to pay for fuel 

economy improvements than has historically been the case. In meetings preceding today’s 

proposed standards, two manufacturers stated they expected fuel economy to increase two 

percent to three percent per year after MY 2016, absent more stringent regulations. And in 

August 2010, one manufacturer stated its combined fleet would achieve 50 mpg by MY 2025, 

supporting that at a minimum some manufacturers believe that exceeding fuel economy 

standards will provide them a competitive advantage. The agency is hopeful that future vehicle 

buyers will be better-informed than has historically been the case, in part because recentlypromulgated requirements regarding vehicle labels will provide clearer information regarding 
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fuel economy and the dollar value of resulting fuel savings. The agency is similarly hopeful that 

future vehicle buyers will be more willing to pay for fuel economy improvements than past 

buyers. In meetings preceding today’s proposed standards, many manufacturers indicated 

significant shifts in their product plans—shifts consistent with expectations that compared to past 

buyers, future buyers will “care more” about fuel economy. 

Nevertheless, considering the uncertainties mentioned above, NHTSA continues to 

consider it appropriate to conduct its central rulemaking analysis in a manner that ignores the 

possibility that in the future, manufacturers will voluntarily apply more technology than the 

minimum necessary to comply with CAFE standards. Also, in conducting its sensitivity analysis 

to simulate voluntary overcompliance with the proposed standards, the agency has applied the 

extremely conservative assumption that when considering whether to employ “extra” 

technology, manufacturers will act as if buyers’ value the resulting savings in fuel costs only 

during their first year of ownership (i.e., as if a 1-year payback period applies). 

Results of the agency’s analysis simulating this potential for voluntary overcompliance 

are summarized below. Compared to results from the agencies’ central analysis presented above, 

differences are greatest for the baseline scenario (i.e., the No-Action Alternative), under which 

CAFE standards remain unchanged after MY 2016. These results also suggest, as the agency 

would expect, that because increasingly stringent standards require progressively more 

technology than the market will demand, the likelihood of voluntary overcompliance will decline 

with increasing stringency. Achieved fuel economy levels under baseline standards are as 

follows: 

Table II-107. Average Achieved Fuel Economy (mpg) under Baseline Standards – MYs 

2017-2021 (including voluntary overcompliance) 

 MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

PC 37.2 38.4 38.7 38.9 39.0 

LT 29.3 29.6 29.8 29.9 30.0 

Combined 34.3 34.7 35.0 35.1 35.3 

Table II-108. Average Achieved Fuel Economy (mpg) under Baseline Standards – MYs 

2022-2025 (including voluntary overcompliance) 

 MY 2022 MY 2023 MY 2024 MY 2025 

PC 39.1 39.1 39.2 39.3 

LT 30.0 30.2 30.4 30.8 

Combined 35.4 35.6 35.8 36.0 

With no change in standards after MY 2016, while combined average fuel economy is the 

same in MY 2017 both with and without simulated voluntary overcompliance, differences grow 

over time, reaching 0.8 mpg in MY 2025. In other words, without simulating voluntary 

overcompliance, the agency estimated that combined average achieved fuel economy would 
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reach 35.2 mpg in MY 2025, whereas the agency estimates that it would reach 36.0 mpg in that 

year if voluntary overcompliance occurred. 

In contrast, the effect on achieved fuel economy levels of allowing voluntary 

overcompliance with the proposed standards was minimal. Allowing manufacturers to 

overcomply with the proposed standards for MY 2025 led to combined average achieved fuel 

economy levels approximately equal to levels of values obtained without simulating voluntary 

overcompliance: 

Table II-109. Average Achieved Fuel Economy (mpg) under Proposed Standards – MYs 

2017-2021 (including voluntary overcompliance) 

 MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Passenger cars 39.5 41.6 43.6 45.3 47.0 

Light trucks 29.6 30.8 32.6 33.9 35.8 

Combined 35.2 36.9 38.9 40.5 42.3 

Table II-110. Average Achieved Fuel Economy (mpg) under Proposed Standards – MYs 

2022-2025 (including voluntary overcompliance) 

 MY 2022 MY 2023 MY 2024 MY 2025 

Passenger cars 48.2 49.4 51.2 52.6 

Light trucks 36.8 37.7 38.6 39.6 

Combined 43.5 44.7 46.2 47.5 

As a result, NHTSA estimates that, when the potential for voluntary overcompliance is 

taken into account, fuel savings attributable to more stringent standards will total 162 billion 

gallons—6.4 percent less than the 173 billion gallons estimated when potential voluntary 

overcompliance is not taken into account: 

Table II-111. Fuel Saved (billion gallons) under Proposed Standards – MYs 2017-2021 

(including voluntary overcompliance) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Passenger cars 4 2 5 7 9 11 

Light trucks 1 0 2 4 6 8 

Combined 5 3 7 11 15 19 

Table II-112. Fuel Saved (billion gallons) under Proposed Standards – MYs 2022-2025 

(including voluntary overcompliance) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

Passenger cars 12 14 17 18 99 

Light trucks 9 10 11 11 63 

Combined 22 24 27 30 152 
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The agency is not projecting, however, that fuel consumption will be greater when 

voluntary overcompliance is taken into account. Rather, under today’s proposed standards, the 

agency’s analysis shows virtually identical fuel consumption (0.2 percent less over the useful 

lives of MY 2017-2025 vehicles) when potential voluntary overcompliance is taken into account. 

Simulation of voluntary overcompliance, therefore, does not reduce the agency’s estimate of 

future fuel savings over the baseline scenario. Rather it changes the attribution of those fuel 

savings to the proposed standards, because voluntary overcompliance attributes some of the fuel 

savings to the market. The same holds for the attribution of costs, other effects, and monetized 

benefits—inclusion of voluntary overcompliance does not necessarily change their amounts, but 

it does attribute some of each cost, effect, or benefit to the workings of the market, rather than to 

the proposed standards. 

The agency similarly estimates CO2 emissions reductions attributable to today’s proposed 

standards will total 1,726 million metric tons (mmt), 5.8 percent less than the 1,834 mmt 

estimated when potential voluntary overcompliance is not taken into account:229

Table II-113. Avoided Carbon Dioxide Emissions (mmt) under Proposed Standards – MYs 

2017-2021 (including voluntary overcompliance) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Passenger cars 41 25 48 72 94 116 

Light trucks 6 5 22 45 63 88 

Combined 47 30 70 117 157 204 

Table II-114. Avoided Carbon Dioxide Emissions (mmt) under Proposed Standards – MYs 

2022-2025 (including voluntary overcompliance) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

Passenger cars 133 150 174 192 1,047 

Light trucks 101 110 118 122 679 

Combined 234 260 292 314 1,726 

Conversely, this analysis indicates slightly greater outlays for additional technology 

under the proposed standards when potential voluntary overcompliance is taken into account. 

This increase is attributable to slight increases in technology application when potential 

voluntary overcompliance is taken into account. Tables IV-98 and 99 below show that total 

technology costs attributable to today’s proposed standards are estimated to increase to $159 

billion, or 1.3 percent more than the $157 billion estimated when potential voluntary 

overcompliance was not taken into account: 

 229 Differences in the application of diesel engines and plug-in hybrid electric vehicles lead to differences in the 

incremental percentage changes in fuel consumption and carbon dioxide emissions.
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Table II-115. Incremental Technology Outlays ($ billion) under Proposed Standards – 

MYs 2017-2021 (including voluntary overcompliance) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Passenger cars 3.5 2 5 7 9 12 

Light trucks 0 0 1 2 4 5 

Combined 4 3 6 9 13 18 

Table II-116. Incremental Technology Outlays ($ billion) under Proposed Standards – 

MYs 2022-2025 (including voluntary overcompliance) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

Passenger cars 14 18 22 24 116 

Light trucks 6 7 8 9 43 

Combined 21 25 30 33 159 

Because NHTSA’s analysis indicated that voluntary overcompliance with baseline 

standards will slightly reduce the share of fuel savings attributable to today’s standards, the 

agency’s estimate of the present value of total benefits will be $484 billion when discounted at a 

3 percent annual rate, as Tables IV-100 and 101 following report. This estimate of total benefits 

is $31 billion, or about 6 percent, lower than the $515 billion reported previously for the analysis 

in which potential voluntary overcompliance was not taken into account: 

Table II-117. Present Value of Benefits ($ billion) under Proposed Standards Using a 3 

Percent Discount Rate – MYs 2017-2021 (including voluntary overcompliance) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Passenger cars 10 7 13 20 26 32 

Light trucks 2 1 6 12 17 24 

Combined 12 8 19 32 43 56 

Table II-118. Present Value of Benefits ($ billion) under Proposed Standards Using a 3 

Percent Discount Rate – MYs 2022-2025 (including voluntary overcompliance) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

Passenger cars 38 43 50 56 295 

Light trucks 28 31 33 35 189 

Combined 66 74 84 91 484 

Similarly, when accounting for potential voluntary overcompliance, NHTSA estimates 

that the present value of total benefits will decline from its previous estimate when future fuel 

savings and other benefits are discounted at the higher 7 percent rate. Tables IV-102 and 103 

report that the present value of benefits from requiring higher fuel economy for MY 2017–25 

cars and light trucks will total $394 billion when discounted using a 7 percent rate, about $25 

billion (or 6 percent) below the previous $419 billion estimate of total benefits when potential 

voluntary overcompliance is not taken into account: 
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Table II-119. Present Value of Benefits ($ billion) under Proposed Standards Using a 7 

Percent Discount Rate – MYs 2017-2021 (including voluntary overcompliance) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Passenger cars 8 5 11 16 21 26 

Light trucks 1 1 5 10 14 19 

Combined 10 6 15 26 35 46 

Table II-120. Present Value of Benefits ($ billion) under Proposed Standards Using a 7 

Percent Discount Rate – MYs 2022-2025 (including voluntary overcompliance) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

Passenger cars 31 35 41 46 242 

Light trucks 23 25 27 28 152 

Combined 53 60 68 74 397 

Based primarily on the reduction of benefits attributable to the proposed standards when 

voluntary overcompliance is taken into account, the agency estimates, as shown in Tables IV104 and 105, that net benefits from the proposed CAFE standards will be $325 billion—or 9.2 

percent -- less than the previously-reported estimate of $358 billion, which did not incorporate 

the potential for voluntary overcompliance. 

Table II-121. Present Value of Net Benefits ($ billion) under Proposed Standards Using a 3 

Percent Discount Rate – MYs 2017-2021 (including voluntary overcompliance) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Passenger cars 7 4 8 13 17 20 

Light trucks 1 1 5 10 13 19 

Combined 8 5 13 23 30 39 

Table II-122. Present Value of Net Benefits ($ billion) under Proposed Standards Using a 3 

Percent Discount Rate – MYs 2022-2025 (including voluntary overcompliance) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

Passenger cars 23 26 29 33 180 

Light trucks 22 23 25 26 145 

Combined 45 49 54 59 325 

Similarly, Tables IV-106 and 107 immediately below show that NHTSA estimates 

voluntary overcompliance could reduce net benefits attributable to today’s proposed standards to 

$235 billion if a 7 percent discount rate is applied to future benefits. This estimate is $24 

billion—or 10.3 percent—lower than the previously-reported $262 billion estimate of net 

benefits when potential voluntary overcompliance is not taken into account, using that same 

discount rate. 
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Table II-123. Present Value of Net Benefits ($ billion) under Proposed Standards Using a 7 

Percent Discount Rate – MYs 2017-2021 (including voluntary overcompliance) 

 Earlier MY 2017 MY 2018 MY 2019 MY 2020 MY 2021 

Passenger cars 5 3 6 9 12 14 

Light trucks 1 1 4 7 10 14 

Combined 6 4 9 17 22 28 

Table II-124. Present Value of Net Benefits ($ billion) under Proposed Standards Using a 7 

Percent Discount Rate – MYs 2022-2025 (including voluntary overcompliance) 

 MY 2022 MY 2023 MY 2024 MY 2025 Total 

Passenger cars 17 18 20 23 126 

Light trucks 16 18 19 19 108 

Combined 33 35 39 42 234 

As discussed above, these reductions in fuel savings and avoided CO2 emissions (and 

correspondingly, in total and net benefits) attributable to today’s proposed standards, do not 

indicate that fuel consumption and CO2 emissions will be higher when potential voluntary 

overcompliance with standards is taken into account than when it is set aside. Rather, these 

reductions reflect differences in attribution; when potential voluntary overcompliance is taken 

into account, portions of the avoided fuel consumption and CO2 emissions (and, 

correspondingly, in total and net benefits) are effectively attributed to the actions of the market, 

rather than to the proposed CAFE standards. 

NHTSA invites comment on this sensitivity analysis, in particular regarding the 

following questions: 

 Is it reasonable to assume that, having achieved compliance with CAFE standards, a 

manufacturer might consider further fuel economy improvements, depending on 

technology costs and fuel prices? 

 If so, does the agency’s approach—comparing technology costs to the present value of 

fuel savings over some payback period—provide a reasonable means to simulate 

manufacturers’ decisions? DOT’s consideration of any alternative methods will be 

facilitated by specific suggestions regarding their integration into DOT’s CAFE model. 

 Is it appropriate to assume different effective payback periods before and after 

compliance has been achieved? Why, or why not? 

 What payback period is (or, if more than one, are) most likely to reflect manufacturers’ 

decisions regarding technology application through MY2025? 

For more detailed information regarding NHTSA’s sensitivity analyses for this proposed 

rule, please see Chapter X of NHTSA’s PRIA. 
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5. Social Benefits, Private Benefits, and Potential Unquantified Consumer Welfare 

Impacts of the Proposed Standards 

There are two viewpoints for evaluating the costs and benefits of the increase in CAFE 

standards: the private perspective of vehicle buyers themselves on the higher fuel economy 

levels that the rule would require, and the economy-wide or “social” perspective on the costs and 

benefits of requiring higher fuel economy. In order to appreciate how these viewpoints may 

diverge, it is important to distinguish between costs and benefits that are “private” and costs and 

benefits that are “social,” The agency’s analysis of benefits and costs from requiring higher fuel 

efficiency, presented above, includes several categories of benefits (identified as “social 

benefits”) that are not limited to automobile purchasers, and that extend throughout the U.S. 

economy. Examples of these benefits include reductions in the energy security costs associated 

with U.S. petroleum imports, and in the economic damages expected to result from air pollution 

(including, but not limited to, climate change). In contrast, other categories of benefits—

principally future fuel savings projected to result from higher fuel economy, but also, for 

example, time savings—will be experienced exclusively by the initial purchasers and subsequent 

owners of vehicle models whose fuel economy manufacturers elect to improve (“private 

benefits”). 

The economy-wide or “social” benefits from requiring higher fuel economy represent an 

important share of the total economic benefits from raising CAFE standards. At the same time, 

NHTSA estimates that benefits to vehicle buyers themselves will significantly exceed vehicle 

manufacturers’ costs for complying with the stricter fuel economy standards this rule establishes. 

In short, consumers will benefit on net. Since the agency also assumes that the costs of new 

technologies manufacturers will employ to improve fuel economy will ultimately be borne by 

vehicle buyers in the form of higher purchase prices, NHTSA concludes that the benefits to 

potential vehicle buyers from requiring higher fuel efficiency will far outweigh the costs they 

will be required to pay to obtain it. NHTSA also recognizes that this conclusion raises certain 

issues, addressed directly below; NHTSA also seeks public comment on its discussion here. 

As an illustration, Tables IV-108 and 109 report the agency’s estimates of the average 

lifetime values of fuel savings for MY 2017–2025 passenger cars and light trucks calculated 

using projected future retail fuel prices. The table compares NHTSA’s estimates of the average 

lifetime value of fuel savings for cars and light trucks to the price increases it expects to occur as 

manufacturers attempt to recover their costs for complying with increased CAFE standards. As 

the table shows, the agency’s estimates of the present value of lifetime fuel savings (discounted 

using the OMB-recommended 3% rate) substantially outweigh projected vehicle price increases 

for both cars and light trucks in every model year, even under the assumption that all of 

manufacturers’ technology outlays are passed on to buyers in the form of higher selling prices 

for new cars and light trucks. By model year 2025, NHTSA projects that average lifetime fuel 
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savings will exceed the average price increase by more than $2,900 for cars, and by more than 

$5,200 for light trucks. 

Table II-125. Value of Lifetime Fuel Savings vs. Vehicle Price Increases – MYs 2017-2021 

Fleet Measure Model year 

2017 2018 2019 2020 2021 

Passenger cars 

Value of fuel savings $668 $1,409 $2,035 $2,643 $3,186 

Average price increase $228 $467 $652 $885 $1,108 

Difference $440 $942 $1,383 $1,758 $2,078 

Light trucks 

Value of fuel savings $228 $999 $2,278 $3,104 $4,400 

Average price increase $44 $187 $427 $688 $965 

Difference $184 $812 $1,851 $2,416 $3,435 

Table II-126. Value of Lifetime Fuel Savings vs. Vehicle Price Increases – MYs 2022-2025 

Fleet Measure Model year 

2022 2023 2024 2025 

Passenger cars 

Value of fuel savings $3,568 $4,010 $4,600 $4,999 

Average price increase $1,259 $1,536 $1,927 $2,023 

Difference $2,309 $2,474 $2,673 $2,976 

Light trucks 

Value of fuel savings $5,114 $5,675 $6,215 $6,804 

Average price increase $1,102 $1,284 $1,428 $1,578 

Difference $4,012 $4,391 $4,787 $5,226 

The comparisons above immediately raise the question of why current vehicle purchasing 

patterns do not already result in average fuel economy levels approaching those that this rule 

would require, and why raising CAFE standards should be necessary to increase the fuel 

economy of new cars and light trucks. They also raise the question of whether it is appropriate 

to assume that manufacturers would not elect to provide higher fuel economy even in the 

absence of increases in CAFE standards, since the comparisons in Tables IV-108 and 109 

suggest that doing so would increase the market value (and thus the selling prices) of many new 

vehicle models by far more than it would raise the cost of producing them. Thus, increasing fuel 

economy would be expected to increase sales of new vehicles and manufacturers’ profits. More 

specifically, why would potential buyers of new vehicles hesitate to purchase models offering 

higher fuel economy, when doing so would produce the substantial economic returns illustrated 

by the comparisons presented in Tables IV-108 and 109? And why would manufacturers 

voluntarily forego opportunities to increase the attractiveness, value, and competitive positioning 

of their car and light truck models—and thus their own profits—by improving their fuel 

economy? 

One explanation for why this situation might persist is that the market for vehicle fuel 

economy does not appear to work perfectly, in which case properly designed CAFE standards 
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would be expected to increase consumer welfare. Some of these imperfections might stem from 

standard market failures, such as limited availability of information to consumers about the value 

of higher fuel economy. It is true, of course, that such information is technically available and 

that new fuel economy and environment vehicle labels, emphasizing economic effects, will 

provide a wide range of relevant information. Other explanations would point to phenomena 

observed elsewhere in the field of behavioral economics, including loss aversion, inadequate 

consumer attention to long-term savings, or a lack of salience of relevant benefits (such as fuel 

savings, or time savings associated with refueling) to consumers at the time they make 

purchasing decisions. Both theoretical and empirical research suggests that many consumers are 

unwilling to make energy-efficient investments even when those investments appear to pay off in 

the relatively short-term.230 This research is in line with related findings that consumers may 

undervalue benefits or costs that are less salient, or that they will realize only in the future.231

Previous research provides some support for the agency’s conclusion that the benefits 

buyers will receive from requiring manufacturers to increase fuel economy outweigh the costs 

they will pay to acquire those benefits, even if private markets have not provided that amount of 

fuel economy. This research identifies aspects of normal behavior that may explain the market 

not providing vehicles whose higher fuel economy appears to offer an attractive economic return. 

For example, consumers’ aversion to the prospect of losses (“loss aversion”) and especially 

immediate, certain losses, may affect their decisions when they also have a sense of uncertainty 

about the value of future fuel savings. Loss aversion, accompanied with a sense of uncertainty 

about gains, may make purchasing a more fuel-efficient vehicle seem unattractive to some 

potential buyers, even when doing so is likely to be a sound economic decision. As an 

illustration, Greene et al. (2009) calculate that the expected net present value of increasing the 

fuel economy of a passenger car from 28 to 35 miles per gallon falls from $405 when calculated 

using standard net present value calculations, to nearly zero when uncertainty regarding future 

cost savings and buyers’ reluctance to accept the risk of losses are taken into account.232

 230 Jaffe, A. B., and Stavins, R. N. (1994). The Energy Paradox and the Diffusion of Conservation Technology. 

Resource and Energy Economics, 16(2); see Hunt Alcott and Nathan Wozny, Gasoline Prices, Fuel Economy, and 

the Energy Paradox (2009), available at http://web.mit.edu/allcott/www/Allcott%20and%20Wozny%202010%20- 

%20Gasoline%20Prices,%20Fuel%20Economy,%20and%20the%20Energy%20Paradox.pdf (last accessed Sept. 26, 

2011). For relevant background, with an emphasis on the importance of salience and attention, see Kahneman, D. 

Thinking, Fast and Slow (2011). 

231 Mutulinggan, S., C.Corbett, S.Benzarti, and B. Oppenheim. “Investment in Energy Efficiency by Small and 

Medium-Size Firms: An Empirical Analysis of the Adoption of Process Improvement Recommendations” (2011), 

available at http://papers.ssrn.com/sol3/papers/cfm?abstract_id=1947330. Hossain, Janjim, and John Morgan 

(2009). “ * * * Plus Shipping and Handling: Revenue (Non)Equivalence in Field Experiments on eBay,” Advances 

in Economic Analysis and Policy vol. 6; Barber, Brad, Terrence Odean, and Lu Zheng (2005). “Out of Sight, Out of 

Mind: The Effects of Expenses on Mutual Fund Flows,” Journal of Business vol. 78, no. 6, pp. 2095–2020. 

232 Greene, D., J. German, and M. Delucchi (2009). “Fuel Economy: The Case for Market Failure” in Reducing 

Climate Impacts in the Transportation Sector, Sperling, D., and J. Cannon, eds. Springer Science. Surprisingly, the 

authors find that uncertainty regarding the future price of gasoline appears to be less important than uncertainty 
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The well-known finding that as gas prices rise, consumers show more willingness to pay 

for fuel-efficient vehicles is not necessarily inconsistent with the possibility that many consumers 

undervalue potential savings in gasoline costs and fuel economy when purchasing new vehicles. 

In ordinary circumstances, such costs may be a relatively “shrouded” attribute in consumers’ 

decisions, in part because the savings from purchasing a more fuel efficient vehicle are 

cumulative and extend over a significant period of time. At the same time, it may be difficult for 

potential buyers to disentangle the cost of purchasing a more fuel-efficient vehicle from its 

overall purchase price, or to isolate the value of higher fuel economy form accompanying 

differences in other vehicle attributes. This possibility is consistent with recent evidence to the 

effect that many consumers are willing to pay less than $1 upfront to obtain a $1 reduction in the 

discounted present value of future gasoline costs.233

Some research suggests that the market’s apparent unwillingness to provide more fuel 

efficient vehicles stems from consumers’ inability to value future fuel savings correctly. For 

example, Larrick and Soll (2008) find evidence that consumers do not understand how to 

translate changes in fuel economy, which is denominated in miles per gallon (MPG), into 

resulting changes in fuel consumption, measured for example in gallons 100 miles traveled or 

per month or year.234 It is true that the recently redesigned fuel economy and environment label 

should help overcome this difficulty, because it draws attention to purely economic effects of 

fuel economy, but MPG remains a prominent measure. Sanstad and Howarth (1994) argue that 

consumers often resort to imprecise but convenient rules of thumb to compare vehicles that offer 

different fuel economy ratings, and that this can cause many buyers to underestimate the value of 

fuel savings, particularly from significant increases in fuel economy.235 If the behavior identified 

in these studies is widespread, then the agency’s estimates suggesting that the benefits to vehicle 

owners from requiring higher fuel economy significantly exceed the costs of providing it may be 

consistent with private markets not providing that fuel economy level. 

The agency projects that the typical vehicle buyer will experience net savings from the 

proposed standards, yet it is not simple to reconcile this projection with the fact that the average 

fuel economy of new vehicles sold currently falls well short of the level those standards would 

require. The foregoing discussion offers several possible explanations. Another possible 

explanation for this apparent inconsistency is that many of the technologies projected by the 

agency to be available through MY 2025 offer significantly improved efficiency per unit of cost, 

 surrounding the expected lifetimes of new vehicles. (Docket NHTSA–2009–0059–0154). On loss aversion in 

general, and its relationship to prospect theory (which predicts that certain losses will loom larger than probabilistic 

gains of higher expected value), see Kahneman. 233 See, e.g., Alcott and Wozny. On shrouded attributes and their importance, see Gabaix, Xavier, and David 

Laibson, 2006. “Shrouded Attributes, Consumer Myopia, and Information Suppression in Competitive Markets.” 

Quarterly Journal of Economics121(2): 505-540. 234 Larrick, R. P., and J.B. Soll (2008). “The MPG illusion” Science 320: 1593–1594. 235 Sanstad, A., and R. Howarth (1994). “ ‘Normal’ Markets, Market Imperfections, and Energy Efficiency.” 

Energy Policy 22(10): 811–818.
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but were not available for application to new vehicles sold currently. Another is that the 

perceived and real values of future savings resulting form the proposed standards will vary 

widely among potential vehicle buyers. When they purchase a new vehicle, some buyers value 

fuel economy very highly, and other svalue fuel economy very little, if at all. These differences 

undoubtedly reflect variation in the amount they drive, differences in their driving styles affect 

the fuel economy they expect to achieve, and varying expectations about future fuel prices, but 

they may also partly reflect differences in buyers’ understanding of what increased fuel economy 

is likely to mean to them financially, or in buyers’ preferences for paying lower prices today 

versus anticipated savings over the future. 

Unless the agency has overestimated their average value, however, the fact that the value 

of fuel savings varies among potential buyers cannot explain why typical buyers do not currently 

purchase what appear to be cost- saving increases in fuel economy. A possible explanation for 

this situation is that the effects of differing fuel economy levels are relatively modest when 

compared to those provided by other, more prominent features of new vehicles, such as 

passenger and cargo-carrying capacity, performance, or safety. In this situation, it may simply 

not be in many shoppers’ interest to spend the time and effort necessary to determine the 

economic value of higher fuel economy, to isolate the component of a new vehicle’s selling price 

that is related to its fuel economy, and compare these two. (This possibility is consistent with the 

view that fuel economy is a relatively “shrouded” attribute) In this case, the agency’s estimates 

of the average value of fuel savings that will result from requiring cars and light trucks to 

achieve higher fuel economy may be correct, yet those savings may not be large enough to lead a 

sufficient number of buyers to purchase vehicles with higher fuel economy to raise average fuel 

economy above its current levels. 

Defects in the market for cars and light trucks could also lead manufacturers to 

undersupply fuel economy, even in cases where many buyers were willing to pay the increased 

prices necessary to compensate manufacturers for providing it. To be sure, the market for new 

automobiles as a whole exhibits a great deal of competition. But this apparently vigorous 

competition among manufacturers may not extend to the provision of some individual vehicle 

attributes. Incomplete or “asymmetric” access to information about vehicle attributes such as 

fuel economy—whereby manufacturers of new cars and light trucks or sellers of used models 

have more complete knowledge about vehicles’ actual fuel economy performance than is 

available to their potential buyers—may also prevent sellers of new or used vehicles from being 

able to capture its full value. In this situation, the level of fuel efficiency provided in the markets 

for new or used vehicles might remain persistently lower than that demanded by well-informed 

potential buyers. 

Constraints on the combinations of fuel economy, carrying capacity, and performance 

that manufacturers can offer in individual vehicle models using current technologies undoubtedly 

limit the range of fuel economy available within certain vehicle classes, particularly those 

including larger vehicles. However, it is also possible that deliberate decisions by manufacturers 
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of cars and light trucks further limit the range of fuel economy available to buyers within 

individual vehicle market segments, such as large automobiles, SUVs, or minivans. 

Manufacturers may deliberately limit the range of fuel economy levels they offer in those market 

segments (by choosing not to invest in fuel economy and investing instead in providing a range 

of other vehicle attributes) because they underestimate the premiums that prospective buyers of 

those models are willing to pay for improved fuel economy, and thus mistakenly believe it will 

be unprofitable for them to offer more fuel-efficient models within those segments. Of course, 

this possibility is most realistic if it is also assumed that buyers are imperfectly informed, or if 

fuel economy savings are not sufficiently salient to shoppers in those particular market segments. 

As an illustration, once a potential buyer has decided to purchase a minivan, the range of 

highway fuel economy ratings among current models extends from 22 to 28 mpg, while their 

combined city and highway ratings extend only from 18 to 20 mpg.236 If this phenomenon is 

widespread, the average fuel efficiency of their entire new vehicle fleet could remain below the 

levels that potential buyers demand and are willing to pay for. 

Another possible explanation for the paradox posed by buyers’ apparent unwillingness to 

invest in higher fuel economy when it appears to offer such large financial returns is that 

NHTSA’s estimates of benefits and costs from requiring manufacturers to improve fuel 

efficiency do not match potential buyers’ assessment of the likely benefits and costs from 

purchasing models with higher fuel economy ratings. This could occur because the agency’s 

underlying assumptions about some of the factors that affect the value of fuel savings differ from 

those made by potential buyers, because NHTSA has used different estimates for some 

components of the benefits from saving fuel from those of buyers, or simply because the agency 

has failed to account for some potential costs of achieving higher fuel economy. 

For example, buyers may not value increased fuel economy as highly as the agency’s 

calculations suggest, because they have shorter time horizons than the full vehicle lifetimes 

NHTSA uses in these calculations, or because they discount future fuel savings using higher 

rates than those prescribed by OMB for evaluating Federal regulations. Potential buyers may 

also anticipate lower fuel prices in the future than those forecast by the Energy Information 

Administration, or may expect larger differences between vehicles’ MPG ratings and their own 

actual on-road fuel economy than the 20 percent gap (30 percent for HEVs) the agency 

estimates. 

To illustrate the first of these possibilities, Table IV-110 shows the effect of differing 

assumptions about vehicle buyers’ time horizons on their assessment of the value of future fuel 

savings. Specifically, the table reports the value of fuel savings consumers might consider when 

purchasing a MY 2025 car or light truck that features the higher fuel economy levels required by 

 236 This is the range of combined city and highway fuel economy levels from lowest (Toyota Sienna AWD) to 

highest (Honda Odyssey) available for model year 2010; http://www.fueleconomy.gov/feg/bestworstEPAtrucks.htm

(last accessed September 26, 2011).
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the proposed rule, when those fuel savings are evaluated over different time horizons. The table 

then compares these values to the agency’s estimates of the increases in these vehicles’ prices 

that are likely to result from the standards proposed for MY 2025. This table shows that when 

fuel savings are evaluated over the average lifetime of a MY 2025 car (approximately 14 years) 

or light truck (about 16 years), their present value (discounted at 3 percent) exceeds the estimated 

average price increase by more than $2,500 for cars and by over $4,500 for light trucks. 

If buyers are instead assumed to consider fuel savings over only a 10-year time horizon, 

Table IV-110 shows that this reduces the difference between the present value of fuel savings 

and the projected price increase for a MY 2025 car to about $1,800, and to about $3,350 for a 

MY 2025 light truck. Finally, Table IV-110 shows that if buyers consider fuel savings only over 

the length of time for which they typically finance new car purchases (slightly more than 5 years 

during 2011), the value of fuel savings exceeds the estimated increase in the price of a MY 2025 

car by only about $200, while the corresponding difference is reduced to slightly more than 

$1,200 for a MY 2025 light truck. 

Table II-127. Value of Fuel Savings Considered by Buyers vs. Vehicle Price Increases with 

Alternative Assumptions about Vehicle Buyer Time Horizons 

Vehicle Measure 

Value Over Alternative Time Horizons

(3% Discount Rate) 

Average 

Lifetime 10 Years Average Loan 

Term 

MY 2025 

Passenger 

Car 

Fuel Savings $4,562 $3,823 $2,227 

Price Increase $2,023 $2,023 $2,023 

Difference $2,539 $1,800 $204 

MY 2025 

Light 

Truck 

Fuel Savings $6,126 $4,924 $2,936 

Price Increase $1,578 $1,578 $1,578 

Difference $4,548 $3,346 $1,358 

Potential vehicle buyers may also discount future fuel savings using higher rates than 

those typically used to evaluate Federal regulations. OMB guidance prescribes that future 

benefits and costs of regulations that mainly affect private consumption decisions, as will be the 

case if manufacturers’ costs for complying with higher fuel economy standards are passed on to 

vehicle buyers, should be discounted using a consumption rate of time preference.237 OMB 

estimates that savers currently discount future consumption at an average real or inflationadjusted rate of about 3 percent when they face little risk about its likely level, which makes it a 

reasonable estimate of the consumption rate of time preference. 

 237 Office of Management and Budget, Circular A–4, “Regulatory Analysis,” September 17, 2003, 33. Available at 

http://www.whitehouse.gov/omb/assets/regulatory_matters_pdf/a-4.pdf (last accessed Sept. 26, 2010).
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However, vehicle buyers may view the value of future fuel savings that results from 

purchasing a vehicle with higher fuel economy as risky or uncertain, or they may instead 

discount future consumption at rates reflecting their costs for financing the higher capital outlays 

required to purchase more fuel-efficient models. In either case, buyers comparing models with 

different fuel economy ratings are likely to discount the future fuel savings from purchasing one 

that offers higher fuel economy at rates well above the 3% assumed in NHTSA’s evaluation. 

Table IV-111 shows the effects of higher discount rates on vehicle buyers’ evaluation of 

the fuel savings projected to result from the CAFE standards proposed in this NPRM, again 

using MY 2025 passenger cars and light trucks as an example. As Table IV-110 showed 

previously, average future fuel savings discounted at the OMB 3 percent consumer rate exceed 

the agency’s estimated price increases by more than $2,500 for MY 2025 passenger cars and by 

about $4,500 for MY 2025 light trucks. If vehicle buyers instead discount future fuel savings at 

the typical new-car loan rate prevailing during 2010 (approximately 5.2 percent), however, these 

differences decline to slightly more than $2,000 for cars and $3,900 for light trucks, as Table 

IV.G.6–3 illustrates. This is a plausible alternative assumption, because buyers are likely to 

finance the increases in purchase prices resulting from compliance with higher CAFE standards 

as part of the process of financing the vehicle purchase itself. 

Finally, as the table also shows, discounting future fuel savings using a consumer credit 

card rate (which averaged almost 14 percent during 2010) reduces these differences to less than 

$900 for a MY 2025 passenger car and about $2,250 for the typical MY 2025 light truck. Even 

at these significantly higher discount rates, however, the table shows that the private net benefits 

from purchasing new vehicles with the levels of fuel economy this rule would require—rather 

than those that would result from simply extending the MY 2016 CAFE standards to apply to 

future model years—remain large. 

Table II-128. Value of Fuel Savings Considered by Buyers vs. Vehicle Price Increases with 

Alternative Assumptions about Consumer Discount Rates 

Vehicle Measure 

Value at Alternative Discount Rates 

OMB 

consumer 

rate (3%) 

New car 

loan rate 

(5.2%)238

OMB 

investment 

rate (7%) 

Consumer 

credit 

card rate 

(13.8%)239

MY 2025 Fuel savings $4,999 $4,104 $3,787 $2,916

 238 Interest rates on 48-month new vehicle loans made by commercial banks during 2010 averaged 6.21%, while 

new car loan rates at auto finance companies averaged 4,26%; See Board of Governors of the Federal Reserve 

System, Federal Reserve Statistical Release G.19, Consumer Credit. Available at 

http://www.federalreserve.gov/releases/g19/Current (last accessed September 27, 2011). 239 The average rate on consumer credit card accounts at commercial banks during 2010 was 13.78%; See Board of 

Governors of the Federal Reserve System, Federal Reserve Statistical Release G.19, Consumer Credit. Available at 

http://www.federalreserve.gov/releases/g19/Current (last accessed September 27, 2011).
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passenger car Price increase $2,023 $2,023 $2,023 $2,023

Difference $2,976 $2,081 $1,764 $893

MY 2025 light 

truck 

Fuel savings $6,126 $5,477 $5,033 $3,840

Price increase $1,578 $1,578 $1,578 $1,578

Difference $4,548 $3,899 $3,455 $2,262

Some evidence also suggests that vehicle buyers may employ combinations of high 

discount rates and short time horizons in their purchase decisions. For example, consumers 

surveyed by Kubik (2006) reported that fuel savings would have to be adequate to pay back the 

additional purchase price of a more fuel-efficient vehicle in less than 3 years to persuade them to 

purchase it, and that even over this short time horizon they were likely to discount fuel savings 

using credit card-like rates.240 Combinations of a shorter time horizon and a higher discount rate 

could further reduce—or potentially even eliminate—the difference between the value of fuel 

savings and the agency’s estimates of increases in vehicle prices. One plausible combination 

would be for buyers to discount fuel savings over the term of a new car loan, using the interest 

rate on that loan as a discount rate. Doing so would reduce the amount by which future fuel 

savings exceed the estimated increase in the prices of MY 2025 vehicles considerably further, to 

about $117 for passenger cars and $1,250 for light trucks. 

As these comparisons illustrate, reasonable alternative assumptions about how consumers 

might evaluate future fuel savings, the major private benefit from requiring higher fuel economy, 

can significantly affect the benefits they consider when deciding whether to purchase more fuelefficient vehicles. Readily imaginable combinations of shorter time horizons, higher discount 

rates, and lower expectations about future fuel prices or annual vehicle use and fuel savings 

could make potential buyers hesitant—or perhaps even unwilling—to purchase vehicles offering 

the increased fuel economy levels this proposed rule would require manufacturers to provide in 

future model years. Thus, vehicle buyers’ assessment of the benefits and costs of this proposal in 

their purchase decisions may differ markedly from NHTSA’s estimates. 

If consumers’ views about critical variables such as future fuel prices or the appropriate 

discount rate differ sufficiently from the assumptions used by the agency, some or perhaps many 

potential vehicle buyers might conclude that the value of fuel savings and other benefits from 

higher fuel economy they are considering are not sufficient to justify the increase in purchase 

prices they expect to pay. In conjunction with the possibility that manufacturers misinterpret 

potential buyers’ willingness to pay for improved fuel economy, this might explain why the 

current choices among available models do not result in average fuel economy levels 

approaching those this rule would require. 

 240 Kubik, M. (2006). Consumer Views on Transportation and Energy. Second Edition. Technical Report: National 

Renewable Energy Laboratory. Available at Docket No. NHTSA–2009–0059–0038.
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Another possibility is that achieving the fuel economy improvements required by stricter 

fuel economy standards might lead manufacturers to forego planned future improvements in 

performance, carrying capacity, safety, or other features of their vehicle models that provide 

important sources of utility to their owners, even if it is technologically feasible to have both 

improvements in those other features and improved fuel economy. Although the specific 

economic values that vehicle buyers attach to individual vehicle attributes such as fuel economy, 

performance, passenger- and cargo-carrying capacity, or other features are difficult to infer from 

vehicle prices or buyers’ choices among competing models, changes in vehicle attributes can 

significantly affect the overall utility that vehicles offer to potential buyers. Thus if requiring 

manufacturers to provide higher fuel economy leads them to sacrifice improvements in these or 

other highly-valued attributes, potential buyers are likely to view these sacrifices as an additional 

cost of improving fuel economy. If those attributes are of sufficient value, or if the range of 

vehicles offered ensures that vehicles with those attributes will continue to be offered, then 

vehicle buyers will still have the opportunity to choose those attributes, though at increased cost 

compared to models without the fuel economy improvements. 

As indicated in its previous discussion of technology costs, NHTSA has approached this 

potential problem by attempting to develop cost estimates for fuel economy-improving 

technologies that include allowances for any additional costs that would be necessary to maintain 

the reference fleet (or baseline) levels of performance, comfort, capacity, or safety of light-duty 

vehicle models to which those technologies are applied. In doing so, the agency followed the 

precedent established by the 2002 NAS Report on improving fuel economy, which estimated 

“constant performance and utility” costs for technologies that manufacturers could employ to 

increase the fuel efficiency of cars or light trucks. Although NHTSA has revised its estimates of 

manufacturers’ costs for some technologies significantly for use in this rulemaking, these revised 

estimates are still intended to represent costs that would allow manufacturers to maintain the 

performance, safety, carrying capacity, and utility of vehicle models while improving their fuel 

economy, in the majority of cases. The agency’s continued specification of footprint-based 

CAFE standards also addresses this concern, by establishing less demanding fuel economy 

targets for larger cars and light trucks. 

Finally, vehicle buyers may simply prefer the choices of vehicle models they now have 

available to the combinations of price, fuel economy, and other attributes that manufacturers are 

likely to offer when required to achieve the higher overall fuel economy levels proposed in this 

NPRM. This explanation assumes that auto makers decide to change vehicle attributes other 

than price and fuel economy in response to this rule. If this is the case, their choices among 

models—and even some buyers’ decisions about whether to purchase a new vehicle—will 

respond accordingly, and their responses to these new choices will reduce their overall welfare. 

Some may buy models with combinations of price, fuel efficiency, and other attributes that they 

consider less desirable than those they would otherwise have purchased, while others may simply 

postpone buying a new vehicle. It leaves open the question, though, why auto makers would 
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change those other vehicle characteristics if consumers liked them as they were; as noted, the 

assumption of “constant performance and utility” built into the cost estimates means that these 

changes are not necessary. 

As the foregoing discussion makes clear, the agency cannot offer a complete answer to 

the question of why the apparently large differences between its estimates of private benefits 

from requiring higher fuel economy and the costs of supplying it would not result in higher fuel 

economy for new cars and light trucks in the absence of this rule. One explanation is that these 

estimates are reasonable, but that for the reasons outlined above, the market for fuel economy is 

not operating efficiently. NHTSA believes the existing literature offers some support for the 

view that various failures in the market for fuel economy prevent it from providing an 

economically desirable outcome, which implies that on balance there are likely to be substantial 

private gains from the proposed rule. The agency will continue to investigate new empirical 

literature addressing this question as it becomes available, and seeks comment on all of the 

relevant questions. 

NHTSA acknowledges the possibility that it has incorrectly characterized the impact on 

the market of the CAFE standards this rule proposes, and that this could cause its estimates of 

benefits and costs to misrepresent the effects of the proposed rule. To recognize this possibility, 

this section presents an alternative accounting of the benefits and costs of CAFE standards for 

MYs 2017–2025 passenger cars and light trucks and discusses its implications. Table IV-112 

displays the economic impacts of the rule as viewed from the perspective of potential buyers. 

As the table shows, the proposed rule’s total benefits to vehicle buyers (line 4) consist of 

the value of fuel savings over vehicles’ full lifetimes at retail fuel prices (line 1), the economic 

value of vehicle occupants’ savings in refueling time (line 2), and the economic benefits from 

added rebound-effect driving (line 3). As the zero entries in line 5 of the table suggest, no losses 

in consumer welfare from changes in vehicle attributes (other than those from increases in 

vehicle prices) are assumed to occur. Thus there is no reduction in the total private benefits to 

vehicle owners, so that net private benefits to vehicle buyers (line 6) are equal to total private 

benefits (reported previously in line 4). 

As Table IV-112 also shows, the decline in fuel tax revenues (line 7) that results from 

reduced fuel purchases is a transfer of funds between consumers and government and is thus not 

a social cost.241 (Thus the sum of lines 1 and 7 equals the savings in fuel production costs that 

were reported previously as the value of fuel savings at pre-tax prices in the agency’s previous 

 241 Strictly speaking, fuel taxes represent a transfer of resources from consumers of fuel to government agencies and 

not a use of economic resources. Reducing the volume of fuel purchases simply reduces the value of this transfer, 

and thus cannot produce a real economic cost or benefit. Representing the change in fuel tax revenues in effect as 

an economy-wide cost is necessary to offset the portion of fuel savings included in line 1 that represents savings in 

fuel tax payments by consumers. This prevents the savings in tax revenues from being counted as a benefit from the 

economy-wide perspective. 
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accounting of benefits and costs.) Lines 8 and 9 of Table IV-112 report the value of reductions 

in air pollution and climate-related externalities resulting from lower emissions of criteria air 

pollutants and GHGs during fuel production and consumption, while line 10 reports the savings 

in energy security externalities to the U.S. economy from reduced consumption and imports of 

petroleum and refined fuel. Line 12 reports the costs of increased congestion delays, accidents, 

and noise that result from additional driving due to the fuel economy rebound effect. Net 

external benefits from the proposed CAFE standards (line 13) are thus the sum of the change in 

fuel tax revenues, the reduction in environmental and energy security externalities, and increased 

external costs from added driving. 

Line 14 of Table IV-112 shows manufacturers’ technology outlays for meeting higher 

CAFE standards for passenger cars and light trucks, which represent the principal private and 

social cost of requiring higher fuel economy. The net social benefits (line 15 of the table) 

resulting from the proposed rule consist of the sum of private (line 6) and external (line 13) 

benefits, minus technology costs (line 14). As expected, the figures reported in line 15 of the 

table are identical to those reported previously in Table IV.G.4-10. 

Table IV-112 highlights several important features of this rule’s economic impacts. First, 

comparing the rule’s net private (line 6) and external (line 13) benefits makes it clear that a very 

large proportion of the proposed rule’s benefits would be experienced by vehicle buyers, while 

the small remaining fraction would be experienced throughout the remainder of the U.S. 

economy. In turn, the vast majority of private benefits resulting from the higher fuel economy 

levels the proposed rule would require stem from fuel savings to vehicle buyers. Net external 

benefits from the proposed rule are expected to be small, because the value of reductions in 

environmental and energy security externalities is likely almost exactly offset by the increased 

costs associated with added vehicle use. As a consequence, the net social benefits of the rule 

mirror almost exactly its net private benefits to vehicle buyers, under the assumption that 

manufacturers will recover their technology outlays for achieving higher fuel economy by raising 

new car and light truck prices. Once again, this result highlights the extreme importance of 

accounting for any other effects of the rule on the economic welfare of vehicle buyers. 

Table II-129. Private, Social, and Total Benefits and Costs of MYs 2017-2021 CAFE 

Standards – Passenger Cars Plus Light Trucks (3% discount rate) 

Entry Model Year 

2017 2018 2019 2020 2021 

1. Value of fuel savings (at retail prices) $8.0 $19.6 $33.1 $44.6 $58.5 

2. Savings in refueling time $0.4 $0.7 $1.1 $1.2 $1.7 

3. Consumer surplus from added driving $0.1 $0.2 $0.5 $0.7 $1.0 

4. Total private benefits (= 1 + 2 + 3) $8.50 $20.50 $34.70 $46.50 $61.20 

5. Reduction in private benefits from 

changes in other vehicle attributes $0.0 $0.0 $0.0 $0.0 $0.0 

6. Net private benefits (= 4 + 5) $8.50 $20.50 $34.70 $46.50 $61.20 
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7. Change in fuel tax revenues ($0.9) ($2.1) ($3.5) ($4.6) ($6.0) 

8. Reduced health damages from criteria 

emissions $0.2 $0.4 $0.8 $1.2 $1.5 

9. Reduced climate damages from CO2

emissions $0.7 $1.8 $3.1 $4.2 $5.6 

10. Reduced energy security externalities $0.4 $0.9 $1.6 $2.1 $2.7 

11. Reduction in externalities (= 8 + 9 + 10) $1.3 $3.1 $5.5 $7.5 $9.8 

12. Increased costs of congestion, etc. ($0.9) ($1.9) ($3.2) ($4.4) ($5.7) 

13. Net external benefits (= 7 + 11 + 12) ($0.50) ($0.90) ($1.20) ($1.50) ($1.90) 

14. Technology costs ($2.0) ($4.4) ($7.7) ($11.3) ($15.2) 

15. Net social benefits (= 6 + 13 – 14) $5.50 $13.90 $24.60 $32.00 $42.20 

Table II-130. Private, Social, and Total Benefits and Costs of MYs 2022-2025 and Total 

MYs 2017-2025 CAFE Standards – Passenger Cars Plus Light Trucks 

Entry 

Model Year 

2022 2023 2024 2025 Total,2017-

2025 

1. Value of fuel savings (at retail fuel 

prices) $67.5 $76.3 $87.1 $96.5 $491.1 

2. Savings in refueling time $2.0 $2.2 $2.5 $2.4 $14.3 

3. Consumer surplus from added driving $1.3 $1.6 $2.0 $2.4 $9.9 

4. Total private benefits (= 1 + 2 + 3) $70.80 $80.10 $91.60 $101.30 $515.30 

5. Reduction in private benefits from 

changes in other vehicle attributes $0.0 $0.0 $0.0 $0.0 $0.0 

6. Net private benefits (= 4 +52) $70.80 $80.10 $91.60 $101.30 $515.30 

7. Change in fuel tax revenues ($6.8) ($7.7) ($8.7) ($9.5) ($49.9)

8. Reduced health damages from criteria 

emissions $1.7 $1.9 $2 $2.3 $12.5 

9. Reduced climate damages from CO2

emissions $6.6 $7.5 $8.7 $9.7 $48.0 

10. Reduced energy security externalities $3.1 $3.5 $4.0 $4.4 $22.8 

11. Reduction in externalities (= 8 + 9 + 

10) $11.4 $12.9 $14.7 $16.4 $83.3 

12. Increased costs of congestion, etc. ($6.5) ($7.3) ($8.4) ($9.3) ($48.8) 

13. Net external benefits (= 7 + 11 + 12) ($1.90) ($2.10) ($2.40) ($2.40) ($15.40) 

14. Technology costs ($19.8) ($24.2) ($29.8) ($32.4) ($153.3) 

15. Net social benefits (= 6 + 13 – 14) $49.10 $53.80 $59.40 $66.50 $346.60 

As discussed in detail previously, NHTSA believes that the aggregate benefits from this 

proposed rule amply justify its total costs, but it remains possible that the agency has 

overestimated the role of fuel savings to buyers and subsequent owners of the cars and light 

trucks to which the higher CAFE standards it proposes would apply. It is also possible that the 

agency has failed to develop cost estimates that do not require manufacturers to make changes in 
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vehicle attributes as part of their efforts to achieve higher fuel economy. To acknowledge these 

possibilities, NHTSA has examined their potential impact on its estimates of the proposed rule’s 

benefits and costs. This analysis, which appears in Chapter VIII of the Preliminary RIA 

accompanying this proposed rule, shows the rule’s economic impacts under alternative 

assumptions about the private benefits from higher fuel economy, and the value of potential 

changes in other vehicle attributes. One conclusion is that even if the private savings are 

significantly overstated, the benefits of the proposed standards continue to exceed the costs. We 

seek comment on that analysis and the discussion above. 

6. What other impacts (quantitative and unquantifiable) will these proposed standards 

have? 

In addition to the quantified benefits and costs of fuel economy standards, the final 

standards will have other impacts that we have not quantified in monetary terms. The decision on 

whether or not to quantify a particular impact depends on several considerations: 

• How likely is it to occur, and can the magnitude of the impact reasonably be attributed 

to the outcome of this rulemaking? 

• Would quantification of its physical magnitude or economic value help NHTSA and the 

public evaluate the CAFE standards that may be set in rulemaking? 

• Is the impact readily quantifiable in physical terms? 

• If so, can it readily be translated into an economic value? 

• Is this economic value likely to be material? 

• Can the impact be quantified with a sufficiently narrow range of uncertainty so that the 

estimate is useful? 

NHTSA expects that this rulemaking will have a number of genuine, material impacts 

that have not been quantified due to one or more of these considerations. In some cases, further 

research may yield estimates that are useful for future rulemakings. 

Technology Forcing 

The proposed rule will improve the fuel economy of the U.S. new vehicle fleet, but it will 

also increase the cost (and presumably, the price) of new passenger cars and light trucks built 

during MYs 2017–2025. We anticipate that the cost, scope, and duration of this rule, as well as 

the steadily rising standards it requires, will cause automakers and suppliers to devote increased 

attention to methods of improving vehicle fuel economy. 

This increased attention will stimulate additional research and engineering, and we 

anticipate that, over time, innovative approaches to reducing the fuel consumption of light duty 
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vehicles will emerge. These innovative approaches may reduce the cost of the proposed rule in 

its later years, and also increase the set of feasible technologies in future years. We have 

attempted to estimate the effect of learning effects on the costs of producing known technologies 

within the period of the rulemaking, which is one way that technologies become cheaper over 

time, and may reflect innovations in application and use of existing technologies to meet the 

proposed future. However, we have not attempted to estimate the extent to which not-yetinvented technologies will appear, either within the time period of the current rulemaking or that 

might be available after MY 2016. NHTSA seeks comment on whether there are quantifiable 

costs and benefits associated with the potential technology forcing effects of the proposed 

standards, and if so, how the agency should consider attempting to account for them in the final 

rule analysis. 

Effects on Vehicle Costs 

Any action that increases the cost of new vehicles will subsequently make such vehicles 

more costly to maintain, repair, and insure. In general, this effect can be expected to be a 

positive linear function of vehicle costs. The proposed rule is estimated to raise vehicle costs by 

over $900 by 2025, and for some manufacturers costs will increase by as much as $1,000–

$1,800. Depending on the retail price of the vehicle, this could represent a significant increase in 

the overall vehicle cost and subsequently increase insurance rates, operation costs, and 

maintenance costs. Comprehensive insurance costs are likely to be directly related to price 

increases, but liability premiums will go up by a smaller proportion because the bulk of liability 

coverage reflects the cost of personal injury. The impact on operation and maintenance costs is 

less clear, because the maintenance burden and useful life of each technology are not known. 

However, one of the common consequences of using more complex or innovative technologies is 

a decline in vehicle reliability and an increase in maintenance costs. These costs are borne in 

part by vehicle manufacturers (through warranty costs, which are included in the indirect costs of 

production), and in part by vehicle owners. NHTSA believes that this effect is difficult to 

quantify for purposes of this proposed rule, but we seek comment on how we might attempt to do 

so for the final rule. 

Related, to the extent that the proposed standards require manufacturers to build and sell 

more PHEVs and EVs, vehicle manufacturers and owners may face additional costs for charging 

infrastructure and battery disposal. While Chapter 3 of the draft Joint TSD discusses the costs of 

charging infrastructure, neither of these costs have been incorporated into the rulemaking 

analysis due to time constraints. 

And finally on the subject of vehicle operation, NHTSA has received comments in the 

past that premium (higher octane) fuel may be necessary if certain advanced fuel economyimproving technologies are required by stringent CAFE standards. The agencies have not 

assumed in our development of technology costs that premium fuel would be required. 
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We intend to attempt to quantify these additional costs for the final rule stage, but we 

believe that doing so will be difficult and we seek comment on how we might go about it. We 

also seek comment on other costs or cost savings that are not accounted for in this analysis and 

how we might go about quantifying them for the final rule. 

Effects on Vehicle Miles Traveled (VMT) 

While NHTSA has estimated the impact of the rebound effect on the use of MY 2017-25 

vehicles, we have not estimated how a change in new vehicle sales would impact aggregate 

vehicle use. Changes in new vehicle sales may be accompanied by complex but difficult-toquantify effects on overall vehicle use and its composition by vehicle type and age, because the 

same factors affecting sales of new vehicles are also likely to influence their use, as well as how 

intensively older vehicles are used and when they are retired from service. These changes may 

have important consequences for total fleet-wide fuel consumption. NHTSA believes that this 

effect is difficult to quantify for purposes of this proposed rule, but we seek comment on how we 

might attempt to do so for the final rule, if commenters agree that attempting quantification of 

this effect could be informative. 

Effect on Composition of Passenger Car and Light Truck Sales 

To the extent that manufacturers pass on costs to buyers by raising prices for new vehicle 

models, they may distribute these price increases across their model lineups in ways that affect 

the composition of their total sales. . To the extent that changes in the composition of sales occur, 

this could affect fuel savings to some degree. However, NHTSA’s view is that the scope for 

such effects is relatively small, since most vehicles will to some extent be impacted by the 

standards. Compositional effects might be important with respect to compliance costs for 

individual manufacturers, but are unlikely to be material for the rule as a whole. 

NHTSA is continuing to develop methods of estimating the effects of these proposed 

standards on the sales of individual vehicle models, and plans to apply these methods in 

analyzing the impacts of its final CAFE standards for MY 2017-25. In the meantime, the agency 

seeks comment on the possibility that significant shifts in the composition of new vehicle sales 

by type or model could occur, the potential effects of such shifts on fuel consumption and fuel 

savings from the proposed standards, and methods for analyzing the potential extent and patterns 

of shifts in sales. 

Effects on the Used Vehicle Market 

The effect of this rule on the lifetimes, use, and retirement dates (“scrappage”) of older 

vehicles will be related to its effects on new vehicle prices, the fuel efficiency of new vehicle 

models, and total sales of new vehicles. If the value of fuel savings resulting from improved fuel 

efficiency to the typical potential buyer of a new vehicle outweighs the average increase in new 

models’ prices, sales of new vehicles will rise, while scrappage rates of used vehicles will 
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increase slightly. This will cause the “turnover” of the vehicle fleet – that is, the retirement of 

used vehicles and their replacement by new models – to accelerate slightly, thus accentuating the 

anticipated effect of the rule on fleet-wide fuel consumption and CO2 emissions. However, if 

potential buyers value future fuel savings resulting from the increased fuel efficiency of new 

models at less than the increase in their average selling price, sales of new vehicles will decline, 

as will the rate at which used vehicles are retired from service. This effect will slow the 

replacement of used vehicles by new models, and thus partly offset the anticipated effects of the 

final rules on fuel use and emissions. 

Because the agencies are uncertain about how the value of projected fuel savings from 

the final rules to potential buyers will compare to their estimates of increases in new vehicle 

prices, we have not attempted to estimate explicitly the effects of the rule on scrappage of older 

vehicles and the turnover of the vehicle fleet. 

Impacts of Changing Fuel Composition on Costs, Benefits, and Emissions 

EPAct, as amended by EISA, creates a Renewable Fuels Standard that sets targets for 

greatly increased usage of renewable fuels over the next decade. The law requires fixed volumes 

of renewable fuels to be used—volumes that are not linked to actual usage of transportation 

fuels. 

Ethanol and biodiesel (in the required volumes) may increase or decrease the cost of 

blended gasoline and diesel, depending on crude oil prices and tax subsidies offered for 

renewable fuels. The potential extra cost of renewable fuels would be borne through a crosssubsidy: the price of every gallon of blended gasoline could rise sufficiently to pay for any extra 

cost of using renewable fuels in these blends. However, if the price of gasoline or diesel 

increases enough, the consumer could actually realize a savings through the increased usage of 

renewable fuels. By reducing total fuel consumption, the CAFE standards proposed in this rule 

could tend to increase any necessary cross-subsidy per gallon of fuel, and hence raise the market 

price of transportation fuels, while there would be no change in the volume or cost of renewable 

fuels used. 

These effects are indirectly incorporated in NHTSA’s analysis of the proposed CAFE 

rule because they are reflected in EIA’s projections of future gasoline and diesel prices in the 

Annual Energy Outlook, which incorporates in its baseline both a Renewable Fuel Standard and 

an CAFE standards. 

The net effect of incorporating an RFS then might be to slightly reduce the benefits of the 

rule because affected vehicles might be driven slightly less if the RFS makes blended gasoline 

relatively more expensive, and because fuels blended with more ethanol emit slightly fewer 

greenhouse gas emissions per gallon. In addition, there might be corresponding benefit losses 
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from the induced reduction in VMT. All of these effects are difficult to estimate, because of 

uncertainty in future crude oil prices, uncertainty in future tax policy, and uncertainty about how 

petroleum marketers will actually comply with the RFS, but they are likely to be small, because 

the cumulative deviation from baseline fuel consumption induced by the final rule will itself be 

small. 

Distributional Effects 

The agency’s analysis of the proposed rule reports impacts only as nationwide aggregate 

or per-vehicle average values. NHTSA also shows the effects of the EIA high and low fuel price 

forecasts on the aggregate benefits in its sensitivity analysis. Generally, this proposed rule would 

have its largest effects on individuals who purchase new vehicles produced during the model 

years it would affect (2017-25). New vehicle buyers who drive more than the agency’s estimates 

of average vehicle use will experience larger fuel savings and economic benefits than the average 

values reported in this NPRM, while those who drive less than our average estimates will 

experience smaller fuel savings and benefits. NHTSA believes that this effect is difficult to 

quantify for purposes of this proposed rule, but we seek comment on how we might attempt to do 

so for the final rule, if commenters agree that attempting quantification of this effect could be 

informative. 

K. Vehicle Classification 

Vehicle classification, for purposes of the CAFE program, refers to whether NHTSA 

considers a vehicle to be a passenger car or a light truck, and thus subject to either the passenger 

car or the light truck standards.242 As NHTSA explained in the MY 2011 rulemaking and in the 

MYs 2012-2016 rulemaking, vehicle classification is based in part on EPCA/EISA, and in part 

on NHTSA’s regulations. EPCA categorizes some light 4-wheeled vehicles as “passenger 

automobiles” (cars) and the balance as “non-passenger automobiles” (light trucks). EPCA 

defines passenger automobiles as any automobile (other than an automobile capable of offhighway operation) which NHTSA decides by rule is manufactured primarily for use in the 

transportation of not more than 10 individuals.243 NHTSA created regulatory definitions for 

passenger automobiles and light trucks, found at 49 CFR Part 523, to guide the agency and 

manufacturers in classifying vehicles. 

Under EPCA, there are two general groups of automobiles that qualify as non-passenger 

automobiles or light trucks: (1) those defined by NHTSA in its regulations as other than 

passenger automobiles due to their having design features that indicate they were not 

 242 For the purpose of the MYs 2012-2016 standards and this NPRM for the MYs 2017–2025 standards, EPA has 

agreed to use NHTSA’s regulatory definitions for determining which vehicles would be subject to which CO2

standards. 

243 EPCA 501(2), 89 Stat. 901, codified at 49 U.S.C. 32901(a). 
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manufactured “primarily” for transporting up to ten individuals; and (2) those expressly excluded 

from the passenger category by statute due to their capability for off-highway operation, 

regardless of whether they might have been manufactured primarily for passenger 

transportation.244 49 CFR 523 directly tracks those two broad groups of non-passenger 

automobiles in subsections (a) and (b), respectively. We note that NHTSA tightened the 

definition of light truck in the MY 2011 rulemaking to ensure that only vehicles that actually 

have 4WD will be classified as off-highway vehicles by reason of having 4WD (to prevent 2WD 

SUVs that also come in a 4WD “version” from qualifying automatically as “off-road capable” 

simply by reason of the existence of the 4WD version), which resulted in the reclassification of 

over 1 million vehicles from the truck fleet to the car fleet. 

Since the original passage of EPCA, and consistently through the passage of EISA, 

Congress has expressed its intent that different vehicles with different characteristics and 

capabilities should be subject to different CAFE standards in two ways: first, through whether a 

vehicle is classified as a passenger car or as a light truck, and second, by requiring NHTSA to set 

separate standards for passenger cars and for light trucks.245 Creating two categories of vehicles 

and requiring separate standards for each, however, can lead to two issues which may either 

detract from the fuel savings that the program is able to achieve, or increase regulatory burden 

for manufacturers simply because they are trying to meet market demand. Specifically, 

1) If the stringency of the standards that NHTSA establishes seems to favor 

either cars or trucks, manufacturers may have incentive to change their 

vehicles’ characteristics in order to reclassify them and average them into the 

“easier” fleet; and 

2) “Like” vehicles, such as the 2WD and 4WD versions of the same CUV, may 

have generally similar fuel economy-achieving capabilities, but different 

targets due to differences in the car and truck curves. 

NHTSA recognizes that manufacturers may have an incentive to classify vehicles as light 

trucks if the fuel economy target for light trucks with a given footprint is less stringent than the 

target for passenger cars with the same footprint. This is often the case given the current fleet. 

Because of characteristics like 4WD and towing and hauling capacity (and correspondingly, 

although not necessarily, heavier weight), the vehicles in the current light truck fleet are 

 244 49 U.S.C. 32901(a)(18). The statute refers both to vehicles that are 4WD and to vehicles over 6,000 lbs GVWR 

as potential candidates for off-road capability, if they also meet the “significant feature . . . designed for off-highway 

operation” as defined by the Secretary. We note that we consider “AWD” vehicles as 4WD for purposes of this 

determination—they send power to all wheels of the vehicle all the time, while 4WD vehicles may only do so part of 

the time, which appears to make them equal candidates for off-road capability given other necessary characteristics. 

We also underscore, as we have in the past, that despite comments in prior rulemakings suggesting that any vehicle 

that appears to be manufactured “primarily” for transporting passengers must be classified as a passenger car, the 

statute as currently written clearly provides that vehicles that are off-highway capable are not passenger cars. 245 See, e.g., discussion of legislative history in 42 Fed. Reg. 38362, 38365-66 (Jul. 28, 1977). 
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generally less capable of achieving higher fuel economy levels as compared to the vehicles in the 

passenger car fleet. 2WD SUVs are the vehicles that could be most readily redesigned so that 

they can be “moved” from the passenger car to the light truck fleet. A manufacturer could do 

this by adding a third row of seats, for example, or boosting GVWR over 6,000 lbs for a 2WD 

SUV that already meets the ground clearance requirements for “off-road capability.” A change 

like this may only be possible during a vehicle redesign, but since vehicles are redesigned, on 

average, every 5 years, at least some manufacturers could possibly choose to make such changes 

before or during the model years covered by this rulemaking, either because of market demands 

or because of interest in changing the vehicle’s classification. 

NHTSA continues to believe that the definitions as they currently exist are consistent 

with the text of EISA and with Congress’ original intent. However, the time frame of this 

rulemaking is longer than any CAFE rulemaking that NHTSA has previously undertaken, and no 

one can predict with certainty how the market will change between now and 2025. The agency 

therefore has less assurance than in prior rulemakings that manufacturers will not have greater 

incentives and opportunities during that time frame to make more deliberate redesign efforts to 

move vehicles out of the car fleet and into the truck fleet in order to obtain the lower target, and 

potentially reducing overall fuel savings. Recognizing this possibility, we seek comment on how 

best to avoid it while still classifying vehicles appropriately based on their characteristics and 

capabilities. 

One of the potential options that we explored in the MYs 2012-2016 rulemaking for MYs 

2017 and beyond was changing the definition of light truck to remove paragraph (5) of 49 CFR 

523.5(a), which allows vehicles to be classified as light trucks if they have three or more rows of 

seats that can either be removed or folded flat to allow greater cargo-carrying capacity. NHTSA 

has received comments in the past arguing that vehicles with three or more rows of seats, unless 

they are capable of transporting more than 10 individuals, should be classified as passenger cars 

rather than as light trucks because they would not need to have so many seats if they were not 

intended primarily to carry passengers. 

NHTSA recognizes that there are arguments both for and against maintaining the 

definition as currently written for MYs 2017 and beyond. The agency continues to believe that 

three or more rows of seats that can be removed or folded flat is a reasonable proxy for a 

vehicle’s ability to provide expanded cargo space, consistent with the agency’s original intent in 

developing the light truck definitions that expanded cargo space is a fundamentally “truck-like” 

characteristic. Much of the public reaction to this definition, which is mixed, tends to be visceral 

and anecdotal – for example, for parents with minivans and multiple children, the ability of seats 

to fold flat to provide more room for child-related cargo may have been a paramount 

consideration in purchasing the vehicle, while for CUV owners with cramped and largely unused 

third rows, those extra seats may seem to have sprung up entirely in response to the regulation, 

rather than in response to the consumer’s need for utility. If we believe, for the sake of 

argument, that the agency’s decision might be reasonable from both a policy and a legal 
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perspective whether we decided to change the definition or to leave it alone, the most important 

questions in making the decision become (1) whether removing 523.5(a)(5), and thus causing 

vehicles with three or more rows to be classified as passenger cars in the future, will save more 

fuel, and (2) if more fuel will be saved, at what cost. 

In considering these questions in the MYs 2012-2016 rulemaking, NHTSA conducted an 

analysis in the final rule to attempt to consider the impact of moving these vehicles. We 

identified all of the 3-row vehicles in the baseline (MY 2008) fleet,246 and then considered 

whether any could be properly classified as a light truck under a different provision of 49 CFR 

523.5—about 40 vehicles were classifiable under § 523.5(b) as off-highway capable. We then 

transferred those remaining 3-row vehicles from the light truck to the passenger car input sheets 

for the CAFE model, re-estimated the relative stringency of the passenger car and light truck 

standards, shifted the curves to obtain the same overall average required fuel economy as under 

the final standards, and ran the model to evaluate potential impacts (in terms of costs, fuel 

savings, etc.) of moving these vehicles. The agency’s hypothesis had been that moving 3-row 

vehicles from the truck to the car fleet would tend to bring the achieved fuel economy levels 

down in both fleets—the car fleet achieved levels could theoretically fall due to the introduction 

of many more vehicles that are relatively heavy for their footprint and thus comparatively less 

fuel economy-capable, while the truck fleet achieved levels could theoretically fall due to the 

characteristics of the vehicles remaining in the fleet (4WDs and pickups, mainly) that are often 

comparatively less fuel economy-capable than 3-row vehicles, although more vehicles would be 

subject to the relatively more stringent passenger car standards, assuming the curves were not 

refit to the data. 

As the agency found, however, moving the vehicles reduced the stringency of the 

passenger car standards by approximately 0.8 mpg on average for the five years of the rule, and 

reduced the stringency of the light truck standards by approximately 0.2 mpg on average for the 

five years of the rule, but it also resulted in approximately 676 million fewer gallons of fuel 

consumed (equivalent to about 1 percent of the reduction in fuel consumption under the final 

standards) and 7.1 mmt fewer CO2 emissions (equivalent to about 1 percent of the reduction in 

CO2 emissions under the final standards) over the lifetime of the MYs 2012–2016 vehicles. This 

result was attributable to slight differences (due to rounding precision) in the overall average 

required fuel economy levels in MYs 2012–2014, and to the retention of the relatively high 

lifetime mileage accumulation (compared to “traditional” passenger cars) of the vehicles moved 

from the light truck fleet to the passenger car fleet. The net effect on technology costs was 

approximately $200 million additional spending on technology each year (equivalent to about 2 

percent of the average increase in annual technology outlays under the final standards). 

Assuming manufacturers would pass that cost forward to consumers by increasing vehicle costs, 

NHTSA estimated that vehicle prices would increase by an average of approximately $13 during 

 246 Of the 430 light trucks models in the fleet, 175 of these had 3 rows.
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MYs 2012–2016. With less fuel savings and higher costs, and a substantial disruption to the 

industry, removing 523.5(a)(5) did not seem advisable in the context of the MYs 2012-2016 

rulemaking. 

Looking forward, however, and given the considerable uncertainty regarding the 

incentive to reclassify vehicles in the MYs 2017 and beyond timeframe, the agency considered 

whether a fresh attempt at this analysis would be warranted, but did not believe that it would be 

informative given the uncertainty. One important point to note in the comparative analysis in the 

MYs 2012-2016 rulemaking is that, due to time constraints, the agency did not attempt to refit 

the respective fleet target curves or to change the intended required stringency in MY 2016 of 

34.1 mpg for the combined fleets. If we had refitted curves, considering the vehicles in question, 

we might have obtained a somewhat steeper passenger car curve, and a somewhat flatter light 

truck curve, which could have affected the agency’s findings. The same is true today. Without 

refitting the curves and changing the required levels of stringency for cars and trucks, simply 

moving vehicles from one fleet to another will not inform the agency in any substantive way as 

to the impacts of a change in classification. Moreover, even if we did attempt to make those 

changes, the results would be somewhat speculative; for example, the agencies continue to use 

the same MY 2008 baseline used in the MYs 2012-2016 rulemaking, which may have limited 

utility for predicting relatively small changes (moving only 40 vehicles, as noted above) in the 

fleet makeup during the rulemaking timeframe. As a result, NHTSA did not attempt to quantify 

the impact of such a reclassification of 3-row vehicles, but we seek comment on whether and 

how we should do so for the final rule. If commenters believe that we should attempt to quantify 

the impact, we specifically seek comment on how to refit the footprint curves and how the 

agency should consider stringency levels under such a scenario. 

Another potential option that we explored in the MYs 2012-2016 rulemaking for MYs 

2017 and beyond was classifying “like” vehicles together. Many commenters objected in the 

rulemaking for the MY 2011 standards to NHTSA’s regulatory separation of “like” vehicles. 

Industry commenters argued that it was technologically inappropriate for NHTSA to place 4WD 

and 2WD versions of the same SUV in separate classes. They argued that the vehicles are the 

same, except for their drivetrain features, thus giving them similar fuel economy improvement 

potential. They further argued that all SUVs should be classified as light trucks. Environmental 

and consumer group commenters, on the other hand, argued that 4WD SUVs and 2WD SUVs 

that are “off-highway capable” by virtue of a GVWR above 6,000 pounds should be classified as 

passenger cars, since they are primarily used to transport passengers. In the MY 2011 

rulemaking, NHTSA rejected both of these sets of arguments. NHTSA concluded that 2WD 

SUVs that were neither “off-highway capable” nor possessed “truck-like” functional 

characteristics were appropriately classified as passenger cars. At the same time, NHTSA also 

concluded that because Congress explicitly designated vehicles with GVWRs over 6,000 pounds 

as “off-highway capable” (if they meet the ground clearance requirements established by the 

agency), NHTSA did not have authority to move these vehicles to the passenger car fleet. 
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NHTSA continues to believe that this would not be an appropriate solution for addressing 

either the risk of gaming or perceived regulatory inequity going forward. As explained in the 

MYs 2012-2016 final rule, with regard to the first argument, that “like” vehicles should be 

classified similarly (i.e., that 2WD SUVs should be classified as light trucks because, besides 

their drivetrain, they are “like” the 4WD version that qualifies as a light truck), NHTSA 

continues to believe that 2WD SUVs that do not meet any part of the existing regulatory 

definition for light trucks should be classified as passenger cars. However, NHTSA recognizes 

the additional point raised by industry commenters in the MY 2011 rulemaking that 

manufacturers may respond to this tighter classification by ceasing to build 2WD versions of 

SUVs, which could reduce fuel savings. In response to that point, NHTSA stated in the MY 

2011 final rule that it expects that manufacturer decisions about whether to continue building 

2WD SUVs will be driven in much greater measure by consumer demand than by NHTSA’s 

regulatory definitions. If it appears, in the course of the next several model years, that 

manufacturers are indeed responding to the CAFE regulatory definitions in a way that reduces 

overall fuel savings from expected levels, it may be appropriate for NHTSA to review this 

question again. At this time, however, since so little time has passed since our last rulemaking 

action, we do not believe that we have enough information about changes in the fleet to ascertain 

whether this is yet ripe for consideration. We seek comment on how the agency might go about 

reviewing this question as more information about manufacturer behavior is accumulated over 

time. 

L. Compliance and Enforcement 

1. Overview 

NHTSA’s CAFE enforcement program is largely established by statute—unlike the 

CAA, EPCA, as amended by EISA, is very prescriptive with regard to enforcement. EPCA and 

EISA also clearly specify a number of flexibilities that are available to manufacturers to help 

them comply with the CAFE standards. Some of those flexibilities are constrained by statute – 

for example, while Congress required that NHTSA allow manufacturers to transfer credits 

earned for over-compliance from their car fleet to their truck fleet and vice versa, Congress also 

limited the amount by which manufacturers could increase their CAFE levels using those 

transfers.247 NHTSA believes Congress balanced the energy-saving purposes of the statute 

against the benefits of certain flexibilities and incentives and intentionally placed some limits on 

certain statutory flexibilities and incentives. With that goal in mind, of maximizing compliance 

flexibility while also implementing EPCA/EISA’s overarching purpose of energy conservation 

as fully as possible, NHTSA has done its best in crafting the credit transfer and trading 

regulations authorized by EISA to ensure that total fuel savings are preserved when 

manufacturers exercise their statutorily-provided compliance flexibilities. 

 247 See 49 U.S.C. 32903(g). 
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Furthermore, to achieve the level of standards described in this proposal for the 2017–

2025 program, NHTSA expects automakers to continue increasing the use of innovative and 

advanced technologies as they evolve. Additional incentive programs may encourage early 

adoption of these innovative and advanced technologies and help to maximize both compliance 

flexibility and energy conservation. These incentive programs for CAFE compliance would not 

be under NHTSA’s EPCA/EISA authority, but under EPA’s EPCA authority – as discussed in 

more detail below and in Section III of this preamble, EPA measures and calculates 

manufacturer compliance with the CAFE standards, and it would be in the calculation of fuel 

economy levels that additional incentives would most appropriately be applied, as a practical 

matter. Specifically, to be included in the CAFE program, EPA is proposing: 1) fuel economy 

performance adjustments due to improvements in air conditioning system efficiency; 2) 

utilization of “game changing” technologies installed on full size pick-up trucks including 

hybridization; and 3) installation of “off-cycle” technologies. In addition, for model years 2020 

and later, EPA is proposing calculation methods for dual-fueled vehicles, to fill the gap left in 

EPCA/EISA by the expiration of the dual-fuel incentive. A more thorough description of the 

basis for the new incentive programs can be found in Section III. 

The following sections explain how NHTSA determines whether manufacturers are in 

compliance with the CAFE standards for each model year, and how manufacturers may address 

potential non-compliance situations through the use of compliance flexibilities or fine payment. 

The following sections also explain, for the reader’s reference, the proposed new incentives and 

calculations, but we also refer readers to Section III.C for EPA’s explanation of its authority and 

more specific detail regarding these proposed changes to the CAFE program. 

2. How does NHTSA determine compliance? 

a. Manufacturer Submission of Data and CAFE Testing by EPA 

NHTSA begins to determine CAFE compliance by reviewing projected estimates in preand mid-model year reports submitted by manufacturers pursuant to 49 CFR part 537, 

Automotive Fuel Economy Reports.248 Those reports for each compliance model year are 

submitted to NHTSA by December of the calendar year prior to the corresponding subsequent 

model year (for the pre-model year report) and in July of the given model year (for the midmodel year report). NHTSA has already received pre-and mid-model year reports from 

manufacturers for MY 2011. NHTSA uses these reports for reference to help the agency, and the 

manufacturers who prepare them, anticipate potential compliance issues as early as possible, and 

help manufacturers plan compliance strategies. NHTSA also uses the reports for auditing and 

testing purposes, which helps manufacturers correct errors prior to the end of the model year and 

facilitates acceptance of their final CAFE report by EPA. In addition, NHTSA issues reports to 

 248 49 CFR part 537 is authorized by 49 U.S.C. 32907. 
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the public twice a year that provide a summary of manufacturers’ fleet fuel economy projected 

performances using pre- and mid model year data. Currently, NHTSA receives manufacturers’ 

CAFE reports in paper form. In order to facilitate submission by manufacturers, NHTSA 

amended part 537 to allow for electronic submission of the pre- and mid-model year CAFE 

reports in 2010 (see 75 FR 25324). Electronic reports are optional and must be submitted in a 

pdf format. NHTSA proposes to modify these provisions in this NPRM, as described below, in 

order to eliminate hardcopy submissions and help the agency more readily process and utilize the 

electronically-submitted data. 

Throughout the model year, NHTSA audits manufacturers’ reports and conducts vehicle 

testing to confirm the accuracy of track width and wheelbase measurements as a part of its 

footprint validation program,249 which helps the agency understand better how manufacturers 

may adjust vehicle characteristics to change a vehicle’s footprint measurement, and thus its fuel 

economy target. NHTSA resolve discrepancies with the manufacturer prior to the end of the 

calendar year corresponding to the respective model year with the primary goal of manufacturers 

submitting accurate final reports to EPA. NHTSA makes its ultimate determination of a 

manufacturer’s CAFE compliance obligation based on official reported and verified CAFE data 

received from EPA. Pursuant to 49 U.S.C. 32904(e), EPA is responsible for calculating 

manufacturers’ CAFE values so that NHTSA can determine compliance with its CAFE 

standards. The EPA-verified data is based on any considerations from NHTSA testing, its own 

vehicle testing, and final model year data submitted by manufacturers to EPA pursuant to 40 

CFR 600.512. A manufacturer’s final model year report must be submitted to EPA no later than 

90 days after December 31st of the model year. EPA test procedures including those used to 

establish the new incentive fuel economy performance values for model year 2017 to 2025 

vehicles are contained in sections 40 CFR Part 600 and 40 CFR Part 86. 

b. NHTSA Then Analyzes EPA-Certified CAFE Values for Compliance 

NHTSA’s determination of CAFE compliance is fairly straightforward: after testing, 

EPA verifies the data submitted by manufacturers and issues final CAFE reports sent to 

manufacturers and to NHTSA in a pdf format between April and October of each year (for the 

previous model year), and NHTSA then identifies the manufacturers’ compliance categories 

(fleets) that do not meet the applicable CAFE fleet standards. NHTSA plans to construct a new, 

more automated database system in the near future to store manufacturer data and the EPA 

data. The new database is expected to simplify data submissions to NHTSA, improve the quality 

of the agency’s data, expedite public reporting, improve audit verifications and testing, and 

enable more efficient tracking of manufacturers’ CAFE credits with greater transparency. 

 249 See http://www.nhtsa.gov/DOT/NHTSA/Vehicle%20Safety/Test%20Procedures/Associated%20Files/TP-537-

01.pdf 
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NHTSA uses the verified data from EPA to compare fleet average standards with 

performance. A manufacturer complies with NHTSA’s fuel economy standard if its fleet 

average performance is greater than or equal to its required standard, or if it is able to use 

available compliance flexibilities to resolve its non-compliance difference. NHTSA calculates a 

cumulative credit status for each of a manufacturer’s vehicle compliance categories according to 

49 U.S.C. 32903. If a manufacturer’s compliance category exceeds the applicable fuel economy 

standard, NHTSA adds credits to the account for that compliance category. The amount of 

credits earned in a given year are determined by multiplying the number of tenths of an mpg by 

which a manufacturer exceeds a standard for a particular category of automobiles by the total 

volume of automobiles of that category manufactured by the manufacturer for that model year. 

Credits may be used to offset shortfalls in other model years, subject to the three year “carryback” and five-year “carry-forward” limitations specified in 49 U.S.C. 32903(a); NHTSA does 

not have authority to allow credits to be carried forward or back for periods longer than that 

specified in the statute. A manufacturer may also transfer credits to another compliance 

category, subject to the limitations specified in 49 U.S.C. 32903(g)(3), or trade them to another 

manufacturer. The value of each credit received via trade or transfer, when used for compliance, 

is adjusted using the adjustment factor described in 49 CFR 536.4, pursuant to 49 U.S.C. 

32903(f)(1). 

If a manufacturer’s vehicles in a particular compliance category fall below the standard 

fuel economy value, NHTSA will provide written notification to the manufacturer that it has not 

met a particular fleet standard. The manufacturer will be required to confirm the shortfall and 

must either submit a plan indicating it will allocate existing credits, or if it does not have 

sufficient credits available in that fleet, how it will earn, transfer and/or acquire credits, or pay 

the appropriate civil penalty. The manufacturer must submit a plan or payment within 60 days of 

receiving agency notification. Credit allocation plans received from the manufacturer will be 

reviewed and approved by NHTSA. NHTSA will approve a credit allocation plan unless it finds 

the proposed credits are unavailable or that it is unlikely that the plan will result in the 

manufacturer earning sufficient credits to offset the subject credit shortfall. If a plan is 

approved, NHTSA will revise the manufacturer’s credit account accordingly. If a plan is 

rejected, NHTSA will notify the manufacturer and request a revised plan or payment of the 

appropriate fine. 

In the event that a manufacturer does not comply with a CAFE standard even after the 

consideration of credits, EPCA provides for the assessment of civil penalties. The Act specifies 

a precise formula for determining the amount of civil penalties for noncompliance.250 The 

penalty, as adjusted for inflation by law, is $5.50 for each tenth of a mpg that a manufacturer’s 

average fuel economy falls short of the standard for a given model year multiplied by the total 

volume of those vehicles in the affected fleet (i.e., import or domestic passenger car, or light 

 250 See 49 U.S.C. 32912.
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truck), manufactured for that model year. The amount of the penalty may not be reduced except 

under the unusual or extreme circumstances specified in the statute. All penalties are paid to the 

U.S. Treasury and not to NHTSA itself. 

Unlike the National Traffic and Motor Vehicle Safety Act, EPCA does not provide for 

recall and remedy in the event of a noncompliance. The presence of recall and remedy 

provisions251 in the Safety Act and their absence in EPCA is believed to arise from the difference 

in the application of the safety standards and CAFE standards. A safety standard applies to 

individual vehicles; that is, each vehicle must possess the requisite equipment or feature that 

must provide the requisite type and level of performance. If a vehicle does not, it is 

noncompliant. Typically, a vehicle does not entirely lack an item or equipment or feature. 

Instead, the equipment or features fails to perform adequately. Recalling the vehicle to repair or 

replace the noncompliant equipment or feature can usually be readily accomplished. 

In contrast, a CAFE standard applies to a manufacturer’s entire fleet for a model year. It 

does not require that a particular individual vehicle be equipped with any particular equipment or 

feature or meet a particular level of fuel economy. It does require that the manufacturer’s fleet, 

as a whole, comply. Further, although under the attribute-based approach to setting CAFE 

standards fuel economy targets are established for individual vehicles based on their footprints, 

the vehicles are not required to comply with those targets on a model-by-model or vehicle-byvehicle basis. However, as a practical matter, if a manufacturer chooses to design some vehicles 

so they fall below their target levels of fuel economy, it will need to design other vehicles so they 

exceed their targets if the manufacturer’s overall fleet average is to meet the applicable standard. 

Thus, under EPCA, there is no such thing as a noncompliant vehicle, only a 

noncompliant fleet. No particular vehicle in a noncompliant fleet is any more, or less, 

noncompliant than any other vehicle in the fleet. 

After enforcement letters are sent, NHTSA continues to monitor receipt of credit 

allocation plans or civil penalty payments that are due within 60 days from the date of receipt of 

the letter by the vehicle manufacturer, and takes further action if the manufacturer is delinquent 

in responding. If NHTSA receives and approves a manufacturer’s carryback plan to earn future 

credits within the following three years in order to comply with current regulatory obligations, 

NHTSA will defer levying fines for non-compliance until the date(s) when the manufacturer’s 

approved plan indicates that credits will be earned or acquired to achieve compliance, and upon 

receiving confirmed CAFE data from EPA. If the manufacturer fails to acquire or earn sufficient 

credits by the plan dates, NHTSA will initiate compliance proceedings. 49 CFR part 536 

contains the detailed regulations governing the use and application of CAFE credits authorized 

by 49 U.S.C. 32903. 

 251 49 U.S.C. 30120, Remedies for defects and noncompliance.
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3. What compliance flexibilities are available under the CAFE program and how do 

manufacturers use them? 

There are three basic flexibilities outlined by EPCA/EISA that manufacturers can 

currently use to achieve compliance with CAFE standards beyond applying fuel economyimproving technologies: (1) Building dual- and alternative-fueled vehicles; (2) banking (carryforward and carry-back), trading, and transferring credits earned for exceeding fuel economy 

standards; and (3) paying civil penalties. We note that while these flexibility mechanisms will 

reduce compliance costs to some degree for most manufacturers, 49 U.S.C. 32902(h) expressly 

prohibits NHTSA from considering the availability of statutorily-established credits (either for 

building dual- or alternative-fueled vehicles or from accumulated transfers or trades) in 

determining the level of the standards. Thus, NHTSA may not raise CAFE standards because 

manufacturers have enough of those credits to meet higher standards. This is an important 

difference from EPA’s authority under the CAA, which does not contain such a restriction, and 

which allows EPA to set higher standards as a result. 

a. Dual- and Alternative-Fueled Vehicles 

As discussed at length in prior rulemakings, EPCA encourages manufacturers to build 

alternative-fueled and dual- (or flexible-) fueled vehicles by providing special fuel economy 

calculations for “dedicated” (that is, 100 percent) alternative fueled vehicles and “dual-fueled” 

(that is, capable of running on either the alternative fuel or gasoline/diesel) vehicles. Consistent 

with the overarching purpose of EPCA/EISA, these statutory incentives help to reduce petroleum 

usage and thus improve our nation’s energy security. Per EPCA, the fuel economy of a 

dedicated alternative fuel vehicle is determined by dividing its fuel economy in equivalent miles 

per gallon of gasoline or diesel fuel by 0.15.252 Thus, a 15 mpg dedicated alternative fuel vehicle 

would be rated as 100 mpg. 

For dual-fueled vehicles, EPA measures the vehicle’s fuel economy rating by 

determining the average of the fuel economy on gasoline or diesel and the fuel economy on the 

alternative fuel vehicle divided by 0.15.253 This calculation procedure, provided in EPCA, turns 

a dual-fueled vehicle that averages 25 mpg on gasoline or diesel into a 40 mpg vehicle for CAFE 

purposes. This assumes that (1) the vehicle operates on gasoline or diesel 50 percent of the time 

and on alternative fuel 50 percent of the time; (2) fuel economy while operating on alternative 

fuel is 15 mpg (15/.15 = 100 mpg); and (3) fuel economy while operating on gas or diesel is 25 

mpg. Thus: 

CAFE FE = 1/{0.5/(mpg gas) + 0.5/(mpg alt fuel)} = 1/{0.5/25 + 0.5/100} = 40 mpg 

 252 49 U.S.C. 32905(a). 253 49 U.S.C. 32905(b). 
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In the case of natural gas, EPA’s calculation is performed in a similar manner. The fuel 

economy is the weighted average while operating on natural gas and operating on gas or diesel. 

The statute specifies that 100 cubic feet (ft3

) of natural gas is equivalent to 0.823 gallons of 

gasoline. The CAFE fuel economy while operating on the natural gas is determined by dividing 

its fuel economy in equivalent miles per gallon of gasoline by 0.15.254 Thus, if a vehicle 

averages 25 miles per 100 ft3

 of natural gas, then: 

CAFE FE = (25/100) * (100/.823)*(1/0.15) = 203 mpg 

Congress extended the dual-fueled vehicle incentive in EISA for dual-fueled automobiles 

through MY 2019, but provided for its phase-out between MYs 2015 and 2019.255 The 

maximum fleet fuel economy increase attributable to this statutory incentive is thus as follows: 

Model year mpg increase 

MYs 1993-2014..................................... 1.2 

MY 2015............................................. 1.0 

MY 2016............................................. 0.8 

MY 2017............................................. 0.6 

MY 2018............................................. 0.4 

MY 2019............................................. 0.2 

After MY 2019....................................... 0 

49 CFR part 538 codifies in regulation the statutory alternative-fueled and dual-fueled 

automobile manufacturing incentive. For purposes of consistency with the statute and to 

implement our intent in the MYs 2012-2016 rule, NHTSA is updating 49 CFR 538.9 to 

incorporate the 49 U.S.C. 32906(a) phase-out of the statutory incentive described above. 

Given that the statutory incentive for dual-fueled vehicles in 49 U.S.C. 32906 and the 

measurement methodology specified in 49 U.S.C. 32905(b) and (d) expire in MY 2019, the 

question becomes, how should the fuel economy of dual-fueled vehicles be determined for 

CAFE compliance in MYs 2020 and beyond? NHTSA and EPA believe that the expiration of 

the dual-fueled vehicle measurement methodology in the statute leaves a gap to be filled, to 

 254 49 U.S.C. 32905(c). 255 49 U.S.C. 32906(a). NHTSA notes that the incentive for dedicated alternative-fuel automobiles, automobiles that 

run exclusively on an alternative fuel, at 49 U.S.C. 32905(a), was not phased-out by EISA. 

 We note additionally and for the reader’s reference that EPA will be treating dual- and alternative-fueled 

vehicles under its GHG program similarly to the way EPCA/EISA provides for CAFE through MY 2015, but for 

MY 2016, EPA established CO2 emission levels for alternative fuel vehicles based on measurement of actual CO2 

emissions during testing, plus a manufacturer demonstration that the vehicles are actually being run on the 

alternative fuel. The manufacturer would then be allowed to weight the gasoline and alternative fuel test results 

based on the proportion of actual usage of both fuels. Because EPCA/EISA provides the explicit CAFE 

measurement methodology for EPA to use for dedicated vehicles and dual-fueled vehicles through MY 2019, we 

explained in the MYs 2012-2016 final rule that the CAFE program would not require that vehicles manufactured for 

the purpose of obtaining the credit actually be run on the alternative fuel.
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avoid the absurd result of dual-fueled vehicles’ fuel economy being measured like that of 

conventional gasoline vehicles. If the overarching purpose of the statute is energy conservation 

and reducing petroleum usage, the agencies believe that that goal is best met by continuing to 

reflect through CAFE calculations the reduced petroleum usage that dual-fueled vehicles 

achieve. 

As discussed in more detail in Section III.B.10, for MYs 2020 and beyond, to fill the gap 

left by the expiration of the statutory CAFE measurement methodology for dual-fueled vehicles, 

EPA is proposing to harmonize with the approach it uses under the GHG program to measure the 

emissions of dual-fueled vehicles, to reflect the real-world percentage of usage of alternative 

fuels by dual-fueled vehicles, but also to continue to incentivize the use of certain alternative 

fuels in dual-fueled vehicles as appropriate under EPCA/EISA to reduce petroleum usage. 

Specifically, for MYs 2020 and beyond, EPA will calculate the fuel economy test values for a 

plug-in hybrid electric vehicle (PHEV, that runs on both gasoline and electricity) and for CNGgasoline vehicles on both the alternative fuel and on gasoline, but rather than assuming that the 

dual-fueled vehicle runs on the alternative fuel 50 percent of the time as the current statutory 

measurement methodology requires, EPA will instead use the Society of Automotive Engineers 

(SAE) “utility factor” methodology (based on vehicle range on the alternative fuel and typical 

daily travel mileage) to determine the assumed percentage of operation on gasoline/diesel and 

percentage of operation on the alternative fuel for those vehicles. Using the utility factor, rather 

than making an a priori assumption about the amount of alternative fuel used by dual-fueled 

vehicles, recognizes that once a consumer has paid several thousand dollars to be able to use a 

fuel that is considerably cheaper than gasoline or diesel, it is very likely that the consumer will 

seek to use the cheaper fuel as much as possible. Consistent with this approach, however, EPA 

is not proposing to extend the utility factor method to flexible fueled vehicles (FFVs) that use E–

85 and gasoline, since there is not a significant cost differential between an FFV and 

conventional gasoline vehicle and historically consumers have only fueled these vehicles with 

E85 a very small percentage of the time. Therefore, EPA is proposing for CAFE compliance in 

MYs 2020 and beyond to continue treatment of E85 and other FFVs as finalized in the MY 2016 

GHG program, based on actual usage of the alternative fuel which represents a real-world 

reduction attributed to alternative fuels. 

Additionally, to avoid manufacturers building only dedicated alternative fuel vehicles 

(which may be harder to refuel in some instances) because of the continued statutory 0.15 CAFE 

divisor under 49 U.S.C. 32905(a) and the calculation for EV fuel economy under 49 U.S.C. 

32904, and declining to build dual-fueled vehicles which might not get a similar bonus, EPA is 

proposing to use the Petroleum Equivalency Factor (PEF) and a 0.15 divisor for calculating the 

fuel economy of PHEVs’ electrical operation and for natural gas operation of CNG-gasoline 

vehicles. This is consistent with the statutory approach for dedicated alternative fuel vehicles, 

and continues to incentivize the usage of alternative fuels and reduction of petroleum usage, but 

when combined with the utility factor approach described above, does not needlessly over-
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incentivize their usage – it gives credit for what is used, and does not give credit for what is not 

used. Because it does not give credit for what is not used, EPA would propose that 

manufacturers may increase their calculated fleet fuel economy for dual-fueled vehicles by an 

unlimited amount using these flexibilities. 

As an example, for MYs 2020 and beyond, the calculation procedure for a dual-fueled 

vehicle that uses both gasoline and CNG could result in a combined fuel economy value of 150 

mpg for CAFE purposes. This assumes that (1) the “utility factor” for the alternative fuel is 

found to be 95 percent, and so the vehicle operates on gasoline for the remaining 5 percent of the 

time; (2) fuel economy while operating on natural gas is 203 mpg [(25/100) * 

(100/.823)*(1/0.15)] as shown above utilizing the PEF and the .15 incentive factor; and (3) fuel 

economy while operating on gasoline is 25 mpg. Thus: 

CAFE FE = 1/{0.05/(mpg gas) + 0.95/(mpg CNG)} = 1/{0.05/25 + 0.95/203} = 150 mpg 

The agencies seek comment on this approach. 

b. Credit Trading and Transfer 

As part of the MY 2011 final rule, NHTSA created 49 CFR part 536 for credit trading 

and transfer. Part 536 implements the provisions in EISA authorizing NHTSA to establish by 

regulation a credit trading program and directing it to establish by regulation a credit transfer 

program.256 Since its enactment, EPCA has permitted manufacturers to earn credits for 

exceeding the standards and to carry those credits backward or forward. EISA extended the 

“carry-forward” period from three to five model years, and left the “carry-back” period at three 

model years. Under part 536, credit holders (including, but not limited to, manufacturers) will 

have credit accounts with NHTSA, and will be able to hold credits, use them to achieve 

compliance with CAFE standards, transfer them between compliance categories, or trade them. 

A credit may also be cancelled before its expiration date, if the credit holder so chooses. Traded 

and transferred credits are subject to an “adjustment factor” to ensure total oil savings are 

preserved, as required by EISA.257 EISA also prohibits credits earned before MY 2011 from 

being transferred, so NHTSA has developed several regulatory restrictions on trading and 

transferring to facilitate Congress’ intent in this regard. As discussed above, EISA establishes a 

“cap” for the maximum increase in any compliance category attributable to transferred credits: 

for MYs 2011–2013, transferred credits can only be used to increase a manufacturer’s CAFE 

 256 Congress required that DOT establish a credit “transferring” regulation, to allow individual manufacturers to 

move credits from one of their fleets to another (e.g., using a credit earned for exceeding the light truck standard for 

compliance with the domestic passenger car standard). Congress allowed DOT to establish a credit “trading” 

regulation, so that credits may be bought and sold between manufacturers and other parties.

257 NHTSA is proposing to revise the VMT estimates used in the credit adjustment factor for consistency with EPA 

starting in 2017.
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level in a given compliance category by 1.0 mpg; for MYs 2014–2017, by 1.5 mpg; and for MYs 

2018 and beyond, by 2.0 mpg. No changes are proposed for these Part 536 provisions. 

c. Payment of Civil Penalties 

If a manufacturer’s average miles per gallon for a given compliance category (domestic 

passenger car, imported passenger car, light truck) falls below the applicable standard, and the 

manufacturer cannot make up the difference by using credits earned or acquired, the 

manufacturer is subject to penalties. The penalty, as mentioned, is $5.50 for each tenth of a mpg 

that a manufacturer’s average fuel economy falls short of the standard for a given model year, 

multiplied by the total volume of those vehicles in the affected fleet, manufactured for that model 

year. NHTSA has collected $794,921,139.50 to date in CAFE penalties, the largest ever being 

paid by DaimlerChrysler for its MY 2006 import passenger car fleet, $30,257,920.00. For their 

MY 2009 fleets, six manufacturers paid CAFE fines for not meeting an applicable standard—

Fiat, which included Ferrari, Maserati, and Alfa Romeo; Daimler (Mercedes-Benz); Porsche; and 

Tata (Jaguar Land Rover)—for a total of $9,148,425.00. As mentioned above, civil penalties 

paid for CAFE non-compliance go to the U.S. Treasury, and not to DOT or NHTSA. 

NHTSA recognizes that some manufacturers may use the option to pay civil penalties as 

a CAFE compliance flexibility—presumably, when paying civil penalties is deemed more costeffective than applying additional fuel economy-improving technology, or when adding fuel 

economy-improving technology would fundamentally change the characteristics of the vehicle in 

ways that the manufacturer believes its target consumers would not accept. NHTSA has no 

authority under EPCA/EISA to prevent manufacturers from turning to payment of civil penalties 

if they choose to do so. This is another important difference from EPA’s authority under the 

CAA, which allows EPA to revoke a manufacturer’s certificate of conformity that permits it to 

sell vehicles if EPA determines that the manufacturer is in non-compliance, and does not permit 

manufacturers to pay fines in lieu of compliance with applicable standards. 

NHTSA has grappled repeatedly with the issue of whether civil penalties are motivational 

for manufacturers, and whether raising them would increase manufacturers’ compliance with the 

standards. EPCA authorizes increasing the civil penalty very slightly up to $10.00, exclusive of 

inflationary adjustments, if NHTSA decides that the increase in the penalty “will result in, or 

substantially further, substantial energy conservation for automobiles in the model years in 

which the increased penalty may be imposed; and will not have a substantial deleterious impact 

on the economy of the United States, a State, or a region of a State.” 49 U.S.C. 32912(c). 

To support a decision that increasing the penalty would result in “substantial energy 

conservation” without having “a substantial deleterious impact on the economy,” NHTSA would 

likely need to provide some reasonably certain quantitative estimates of the fuel that would be 

saved, and the impact on the economy, if the penalty were raised. Comments received on this 

issue in the past have not explained in clear quantitative terms what the benefits and drawbacks 
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to raising the penalty might be. Additionally, it may be that the range of possible increase that 

the statute provides, i.e., up to $10 per tenth of a mpg, is insufficient to result in substantial 

energy conservation, although changing this would require an amendment to the statute by 

Congress. NHTSA continues to seek to gain information on this issue and requests that 

commenters wishing to address this issue please provide, as specifically as possible, estimates of 

how raising or not raising the penalty amount will or will not substantially raise energy 

conservation and impact the economy. 

4. What new incentives are being added to the CAFE program for MYs 2017-2025? 

All of the CAFE compliance incentives discussed below are being proposed by EPA 

under its EPCA authority to calculate fuel economy levels for individual vehicles and for fleets. 

Because they are EPA proposals, we refer the reader to Section III for more details, as well as 

Chapter 5 of the draft Joint TSD for more information on the precise mechanics of the 

incentives, but we present them here in summary form so that the reader may understand more 

comprehensively what compliance options are proposed to be available for manufacturers for 

meeting the MYs 2017-2025 CAFE standards. 

a. “Game Changing” technologies for full size pick-up trucks 

EPA is proposing to adopt two new types of incentives for improving the fuel economy 

performance of full size pickup trucks. The first incentive would provide a credit to 

manufacturers that employ significant quantities of hybridization on full size pickup trucks. The 

second incentive would provide a performance-based incentive for full size pickup trucks that 

achieve a significant reduction in fuel consumption as compared to the applicable fuel economy 

target for the vehicle in question. These incentives are proposed due to the significant difficulty 

of large trucks, including full size pickup trucks, in meeting CAFE standards while still 

maintaining the levels of utility to which consumers have become accustomed, which require 

higher payload and towing capabilities and greater cargo volumes than other light-duty vehicles. 

Technologies that provide substantial fuel economy benefits are often not attractive to 

manufacturers of large trucks due to these tradeoffs in utility purposes, and therefore have not 

been taken advantage of to the same extent as they have in other vehicle classes. The goal of 

these incentives is to facilitate the application of these “game changing” technologies for large 

pickups, both to save more fuel and to help provide a bridge for industry to more stringent light 

truck standards in MYs 2022-2025 – as manufacturers gain experience with applying more fuelsaving technology for these vehicles and consumers become more accustomed to certain 

advanced technologies in pickup trucks, the agencies anticipate that higher CAFE levels will be 
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more feasible for the fleet as a whole.258 In the context of the CAFE program, these incentives 

would be used as an adjustment to a full size pickup truck’s fuel economy performance. The 

same vehicle would not be allowed to receive an adjustment to its calculated fuel economy for 

both the hybridization incentive and the performance-based incentive, to avoid double-counting. 

To accommodate the proposed changes to the CAFE program, NHTSA is proposing to 

adopt new definitions into regulation, 49 CFR 523, “Vehicle Classification.” Part 523 was 

established by NHTSA to include its regulatory definitions for passenger automobiles and trucks 

and to guide the agency and manufacturers in classifying vehicles. NHTSA proposes to add a 

definition in Part 523.2 defining the characteristics that identify full size pickup trucks. NHTSA 

believes that the definition is needed to help explain to readers which characteristics of full size 

pickup truck make them eligible to gain fuel economy improvement values allowed after a 

manufacturer meets either a minimum penetration of hybridized technologies or has other 

technologies that significantly reduce fuel consumption. The proposed improvement would be 

available on a per-vehicle basis for mild and strong HEVs, as well as for other technologies that 

significantly improve the efficiency of full sized pickup trucks. The proposed definition would 

specify that trucks meeting an overall bed width and length as well as a minimum towing or 

payload capacity could be qualified as full size pickup trucks. NHTSA is also proposing to 

modify Part 523 to include definitions for mild and strong hybrid electric full size pickup trucks. 

i. Pickup Truck Hybridization 

One proposed incentive would provide an adjustment to the fuel economy of a 

manufacturer’s full size pickup trucks if the manufacturer employs certain defined hybrid 

technologies on defined significant quantities of its full size pickup trucks. After meeting the 

minimum production percentages, manufacturers would gain an adjustment to the fuel economy 

performance for each “mild” or “strong” hybrid full size pickup truck it produces. 

Manufacturers producing mild hybrid pickup trucks, as defined in Chapter 5 of the draft Joint 

TSD, would gain the incentive by applying mild hybrid technology to at least 30 percent of the 

company’s full sized pickups produced in MY 2017, which would increase each year up to at 

least 80 percent of the company’s full size pickups produced in MY 2021, after which point the 

adjustment is no longer applicable. For strong hybrids, also defined in Chapter 5 of the draft 

Joint TSD, the strong hybrid technology must be applied to at least 10 percent of a company’s 

full sized pickup production in each year for model years 2017-2025. The fuel economy 

adjustment for each mild hybrid full size pickup would be a decrease in measured fuel 

consumption of 0.0011gal/mi; for each strong hybrid full size pickup, the decrease in measured 

fuel consumption would be 0.0023 gal/mi. These adjustments are consistent with the GHG 

 258 NHTSA is not prohibited from considering this availability of this incentive in determining the maximum 

feasible levels of stringency for the light truck standards, because it is not one of the statutory flexibilities 

enumerated in 49 U.S.C. 32902(h). 
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credits under EPA’s program of 10 g/mi CO2 for mild hybrid pickups and 20 g/mi CO2 for strong 

hybrid pickups. The specified adjustment in the fuel consumption performance would then be 

subtracted from the fuel consumption performance test result of each applicable hybrid vehicle, 

and the resultant then reciprocated to determine the improved adjusted fuel economy 

performance of each vehicle. EPA would use the adjusted fuel economy of each hybrid vehicle 

weighted to its production sales volume to determine the harmonic average fuel economy 

performance of the manufacturer’s fleet. 

ii. Performance-based incentive for full-size pickups 

Another proposed incentive for full size pickup trucks would provide an adjustment to 

the fuel economy of a manufacturer’s full sized pickup truck if it achieves a fuel economy 

performance level significantly above the CAFE target for that footprint. This incentive 

recognizes that not all manufacturers may wish to pursue hybridization for their pickup trucks, 

but still rewards them for applying fuel-saving technologies above and beyond what they might 

otherwise do. The fuel economy adjustment for each full size pickup that exceeds its applicable 

footprint curve target by 15 percent would be a decrease in measured fuel consumption of 

0.0011gal/mi; for each full size pickup that exceeds its applicable footprint curve target by 20 

percent, the decrease in measured fuel consumption would be 0.0023 gal/mi. These adjustments 

are consistent with the GHG credits under EPA’s program of 10 g/mi CO2 and 20 g/mi CO2, 

respectively, for beating the applicable CO2 targets by 15 and 20 percent, respectively. 

The 0.0011 gal/mi performance-based adjustment would be available for MYs 2017 to 

2021, and a vehicle meeting the requirement in a given model year would continue to receive the 

credit until MY 2021 – that is, the credit remains applicable to that vehicle model if the target is 

exceeded in only one model year – unless its fuel consumption increases. The 0.0023 gal/mi 

adjustment would be available for a maximum of 5 years within model years 2017-2025, 

provided the vehicle model’s fuel consumption does not increase. As explained above for the 

hybrid incentive, the decrease in each vehicle’s fuel consumption performance would be 

converted to an adjusted fuel economy performance and then averaged to determine the 

manufacturer’s CAFE fleet average value. 

b. A/C efficiency-improving technologies 

Air conditioning (A/C) use places excess load on an engine, which results in additional 

fuel consumption. A number of methods related to the A/C system components and their 

controls can be used to improve A/C system efficiencies. Starting in MY 2017, EPA is 

proposing to allow manufacturers to include fuel consumption reductions resulting from the use 

of improved A/C systems in their CAFE calculations. This will more accurately account for 

achieved real-world fuel economy improvements due to improved A/C technologies, and better 

fulfill EPCA’s overarching purpose of energy conservation. EPA is not proposing to allow 

manufacturers to claim CAFE-related benefits for reducing A/C leakage or switching to an A/C 
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refrigerant with a lower global warming potential, because while these improvements reduce 

GHGs consistent with the purpose of the CAA, they do not improve fuel economy and thus are 

not relevant to the CAFE program. 

The improvements that manufacturers would likely use to increase A/C efficiency would 

focus primarily, but not exclusively, on the compressor, electric motor controls, and system 

controls which reduce load on the A/C system (such as reduced “reheat” of the cooled air and 

increased of use re-circulated cabin air). 

Fuel consumption improvement values for CAFE resulting from A/C efficiency 

improvements would be quantified using a two-step process, the same as for the related CO2

credits for EPA’s GHG program. First, the vehicle with the improved A/C system would be 

tested in accordance with EPA testing guidelines, and compared with the baseline fuel 

consumption value for that vehicle. Second, the difference between the baseline fuel 

consumption value and the value for the vehicle with improved A/C technologies would be 

calculated, which would determine the fuel consumption improvement value. A maximum fuel 

consumption improvement value of 0.000563 gal/mi for cars and 0.000586 gal/mi for trucks 

would be available to manufacturers.259 Throughout this section, we discuss these values in 

terms of fuel consumption, even though ultimate CAFE compliance is determined in miles per 

gallon rather than in gallons per mile. Converting to miles per gallon might assist the reader 

accustomed to thinking in those terms, but would actually complicate the discussion since fuel 

economy, unlike CO2/fuel consumption, is not linear, and fuel economy values would be 

inversely related to CO2 reductions. 

EPA is proposing three different test approaches: (1) an idle test; (2) an idle test with 

displacement weighting; and (3) the “A/C 17 test.” Manufacturers would be allowed to use any 

of those three tests to measure A/C-related CO2 emissions from vehicles with improved A/C 

systems, which would be translated to fuel consumption to establish the ratio between the 

baseline vehicle and the improved-A/C vehicle to determine the value of the fuel consumption 

improvement. The three test procedures are described briefly below. 

i. What are the proposed testing approaches? 

The first test, the “idle test,” is the same as the test that EPA finalized for A/C efficiency 

improvements under the GHG program in the MYs 2012-2016 final rule. The idle test method 

measures CO2 in grams per minute (g/min) while the vehicle is stationary and idling. In the idle 

test method, baseline vehicle emissions are not tested. Emissions of the vehicle with improved 

 259 The maximum credit and fuel consumption improvement values represent the full 42 percent improvement in 

A/C emissions that the agencies estimate is feasible through the use of improved A/C technologies. The credits and 

fuel consumption improvement values in Table IV.I.1 add up to more than the maximum allowable credit and fuel 

consumption improvement value. This is because some technologies act on the same inefficiencies in the A/C 

system and thus using two such technologies together would not yield additive fuel consumption reductions. 
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A/C are compared to a pre-determined baseline emissions level.260 The fuel consumption 

performance is determined using the following equation: grams CO2/mi = 1/mpg * 8,887. 

The second test, the “modified idle test,” uses the same test method as is used in the idle 

test, but includes a correction factor for engine displacement. Engine displacement is not 

accounted for in the idle only test described above. This alternative is being proposed because a 

study conducted by EPA in which 29 vehicles were tested on the idle test showed that vehicles 

with smaller displacement engines have a higher idle test result (in CO2 g/min) than those with 

larger displacement engines. Having the option of taking displacement into account will help 

manufacturers account more accurately for improvements in A/C systems. 

The third test, the “A/C 17 test,” is a more extensive test than the idle test and has four 

elements. Unlike the idle test, it includes two drive cycles, US03 and the highway fuel economy 

cycle, which capture steady state and transient operating conditions. It also includes a solar soak 

period to measure the energy required to cool down a car that has been sitting in the sun, as well 

as a pre-conditioning cycle. The A/C 17 test cycle will be able to capture improvements in all 

areas related to efficient operation of a vehicle’s A/C system. Unlike the other tests, the AC 17 

test cycle measures CO2 emissions in grams per mile (g/mi), and requires that baseline emissions 

be measured in addition to emissions from vehicles with improved A/C systems, unlike the idle 

test where baseline emissions are pre-determined by EPA. 

Manufacturers then use the results of the testing approaches described above to determine 

the total available fuel consumption improvement value they can receive. The following section 

describes how that value is estimated. 

ii. How are fuel consumption improvement values then estimated? 

Using idle test results: 

Taking the measured test results in CO2 g/min, if the idle test result is 14.9 g/min or 

lower, manufactures may claim a pre-defined maximum fuel consumption improvement value. 

Manufacturers look up the technologies they have installed on their vehicles (shown in column 1 

of Table IV.I.1) and take the corresponding pre-defined improvement value (columns 3 and 4 for 

cars and trucks, respectively). If a manufacturer uses more than one technology for a given 

vehicle model, the A/C fuel consumption improvement value for each technology can be added, 

but the maximum improvement value possible is limited to 0.000563 gal/mi for cars, and 

0.000586-gal/mi for trucks.261

 260 EPA conducted idle tests on 29 vehicles to establish fleet average A/C related CO2 emissions. In testing, average 

car and truck A/C CO2 emissions equaled 21.3 grams per minute. 261 The maximum credit and fuel consumption improvement values represent the full 42% improvement in A/C 

emissions that the agencies estimate is feasible through the use of improved A/C technologies. The credits and fuel 
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For vehicles with an idle test result between 14.9 g/min and 21.3 g/min, manufacturers 

are eligible for a partial fuel consumption improvement value, using the following formula: 

Where: 

TCV = the total CO2 credit value that can be claimed by the manufacturer 

TCV1 = the maximum CO2 credit value available, equal to the sum of the reduction for 

the A/C technologies used, and 

ITP = the increase in CO2 emissions shown on the idle test. 

Finally, if the vehicle has a test result higher than 21.3 g/min, it would receive zero fuel 

consumption improvement value. 

Using results from the displacement-weighted idle test: 

For an air conditioning system to be eligible to generate fuel consumption improvement 

values, the increased CO2 emissions as a result of the operation of that air conditioning system 

determined according to the idle test procedure must be less than the value calculated by the 

following equation and rounded to the nearest tenth of gram per minute: 

ሻݐ݈ܽܿ݉݁݊݌ݏ݅ܦ ൈ 1.58ሺ െ 20.5 ൌ ݈݀݋݄ݏ݁ݎ݄ܶ	ݐݏܶ݁	݈݀݁ܫ

If the increased CO2 emissions determined from the idle test procedure is less than or 

equal to the Idle Test Threshold, the total fuel consumption improvement value that can be 

claimed by the manufacturer shall be determined by summing the maximum value in Table 

IV.I.1. If the increased CO2 emissions determined from the idle test procedure is greater than the 

Idle Test Threshold but less than the Idle Test Threshold plus 6.4, the total fuel consumption 

improvement value that can be claimed by the manufacturer shall be as determined according to 

the following formula: 

 consumption improvement values in Table IV.I.1 add up to more than the maximum allowable credit and fuel 

consumption improvement value. This is because some technologies act on the same inefficiencies in the A/C 

system and thus using two such technologies together would not yield additive C02 and fuel consumption 

reductions. 
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ܶܥܸ ൌ ܶܥܸଵ ൈ ൤1 െ ൬ܫܶܲ െ ܫܶܶ

6.4 ൰൨

Where: 

TCV = the total CO2 credit value that can be claimed; 

TCV1 = the sum of the CO2 credits for the technologies used in the A/C system; 

ITP = the increased CO2 emissions determined from the idle test procedure in 40 CFR 

86.165–14; and 

ITT = the Idle Test Threshold resulting from the use of the idle test threshold equation. 

Table II-131. Efficiency Improving A/C Technologies and Improvement Values [still to be 

finalized]

Technology Description 

A/C Fuel 

Consumption 

Reduction (% 

of total A/C 

improvement) 

Car A/C 

Fuel 

Efficiency 

Improvement 

Value 

(gals/mi) 

Truck A/C 

Fuel 

Efficiency 

Improvement 

Value 

(gals/mi) 

Reduced reheat, with externallycontrolled, variable-displacement 

compressor 

30% 0.000169 

(30% of 5.0 

g/mi impact) 

0.000248 

(30% of 7.2 

g/mi impact) 

Reduced reheat, with externallycontrolled, fixed-displacement or 

pneumatic variable displacement 

compressor 

20% 0.000113 0.000158 

Default to recirculated air with 

closed-loop control of the air supply 

(sensor feedback to control interior 

air quality) whenever the outside 

ambient temperature is 75 °F or 

higher262

30% 0.000169 0.000248 

Default to recirculated air with openloop control of the air supply (no 

sensor feedback) whenever the 

outside ambient temperature is 75 °F 

or higher263

20% 0.000113 0.000156 

Blower motor control which limit 15% 0.000090 0.000124 

 262 Deviations from this temperature are allowed if accompanied by an engineering analysis. 263 Deviations from this temperature are allowed if accompanied by an engineering analysis. 
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wasted electrical energy (e.g. 

pulsewidth modulated power 

controller) 

Internal heat exchanger (or suction 

line heat exchanger) 

20% 0.000113 0.000156 

Using results from the A/C 17 test: 

Manufacturers would take the A/C 17 test result on the new vehicle and compare that 

with the result from a baseline vehicle without A/C efficiency-improving technologies to 

demonstrate that less fuel is consumed from A/C operation with the improved A/C system. The 

fuel consumption improvement value is calculated in a same way as it is in the idle test. 

Fuel consumption improvement values due to the improved A/C technologies are derived 

using the approaches described above. Under the NHTSA CAFE program the fuel consumption 

improvement values are subtracted from the subject vehicle’s fuel consumption performance 

value determined from the required CAFE program 2-cycle test results. The resultant would 

then be reciprocated to determine the improved adjusted fuel economy performance value for the 

subject vehicle. EPA would use the adjusted fuel economy value of each vehicle weighted to its 

production sales volume to determine the harmonic average fuel economy performance, CAFE 

value, of the manufacturer’s fleet. Although the form of the NHTSA program standard is in 

miles per gallon, in Table IV.I.1 values are presented in terms of gallons per mile for 

transparency to the reader. 

The agencies seek comment on the proposal to allow manufacturers to estimate fuel 

consumption reductions from the use of A/C efficiency-improving technologies and to apply 

these reductions to their CAFE calculations. 

c. Off-cycle technologies and adjustments 

For MYs 2012-2016, EPA provided an optional credit for new and innovative “off-cycle” 

technologies that reduce vehicle CO2 emissions, but for which the CO2 reduction benefits are not 

recognized under the 2-cycle test procedure used to determine compliance with the fleet average 

standards. The off-cycle credit option was intended to encourage the introduction of off-cycle 

technologies that achieve real-world benefits. The off-cycle credits were to be determined using 

the 5-cycle methodology currently used to determine fuel economy label values, which EPA 

established to better represent real-world factors impacting fuel economy, including higher 

speeds and more aggressive driving, colder temperature operation, and the use of air 

conditioning. A manufacturer must determine whether the benefit of the technology could be 

captured using the 5-cycle test; if this determination is affirmative, the manufacture must follow 

the 5-cycle procedures to determine the CO2 reductions. If the manufacturer finds that the 

technology is such that the benefit is not adequately captured using the 5-cycle approach, then 

the manufacturer would have to develop a robust methodology, subject to EPA approval, to 
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demonstrate the benefit and determine the appropriate CO2 gram per mile credit. The 

demonstration program must be robust, verifiable, and capable of demonstrating the real-world 

emissions benefit of the technology with strong statistical significance. The non-5-cycle 

approach includes an opportunity for public comment as part of the approval process. 

EPA has been encouraged by automakers’ interest in off-cycle credits since the program 

was finalized and believes that extending the program to MY 2017 and beyond may continue to 

encourage automakers to invest in off-cycle technologies that could have the benefit of realizing 

additional reductions in the light-duty fleet over the longer-term. Therefore, EPA is proposing to 

extend the off-cycle credits program to 2017 and later model years. EPA is also proposing, 

under its EPCA authority, to make available a comparable off-cycle technology incentive under 

the CAFE program beginning in MY 2017. However, instead of manufacturers gaining credits 

as done under the GHG program, a direct adjustment would be made to the manufacturer’s fuel 

economy performance value. 

Starting with MY 2017, manufacturers may generate fuel economy improvements by 

applying technologies listed on the pre-defined and pre-approved technology list provided in 

Table IV-115. These credits would be verified and approved as part of certification, with no 

prior approval process needed. This new option should significantly simplify the program for 

manufacturers and provide certainty that improvement values may be generated through the use 

of pre-approved technologies. For improvements from technologies not on the pre-defined list, 

EPA is proposing to clarify the step-by-step application process for demonstration of fuel 

consumption reductions and approval. 

Table II-132. Off-cycle technologies and proposed improvement values for passenger cars 

and light trucks 

Technology Improvement value for 

passenger cars(gal/mi)

Improvement value 

for light trucks 

(gal/mi) 

High Efficiency Exterior Lighting 0.000124 0.000124 

Engine Heat Recovery 0.000778 0.000778 

Solar Roof Panels 0.000338 0.000338 

Active Grill Shutters 0.000113 0.000113 

Active Suspension Lowering 

Engine Start-Stop 0.000326 0.000506 

Electric Heater Circulation Pump 0.000123 0.000169 

Active Transmission Warm-Up 0.000203 0.000203 

Active Engine Warm-Up 0.000203 0.000203 

Solar Control 0.000338 0.000338 

High Efficiency Exterior Lighting 0.0001 0.0001 

Engine Heat Recovery 0.0001 0.0001 

Solar Roof Panels 0.0003 0.0003 
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Active Grill Shutters 0.0001 0.0001 

Active Suspension Lowering 

Engine Start-Stop 0.0003 0.0005 

Electric Heater Circulation Pump 0.0001 0.0002 

Active Transmission Warm-Up 0.0002 0.0002 

Active Engine Warm-Up 

Solar Control 0.0002 0.0002 

An example of technologies that could be used to generate off-cycle improvements are 

those that reduce electrical load and as a result, fuel consumption. The 2-cycle test does not 

require that all electrical components be turned on during testing. Headlights, for example, are 

always turned off during testing. Turning the headlights on during normal driving will add an 

additional load on the vehicle’s electrical system and will affect fuel economy. More efficient 

electrical systems or technologies that offset electrical loads will have a real-world impact on 

fuel economy but are not captured in the 2-cycle test. Therefore, technologies that reduce or 

offset electrical loads related to the operation or safety of the vehicle should merit consideration 

for off-cycle improvements. Reducing the electrical load on a vehicle by 100W will result in an 

average of 0.000337 gallons/mile reduction in fuel consumption over the course of a 2-cycle test, 

or 0.00042 gallons/mile over a 5-cycle test. To determine the off-cycle benefit of certain 100W 

electrical load reduction technologies, the benefit of the technology on the 2-cycle test is 

subtracted from the benefit of the technology on the 5-cycle test. This determines the actual 

benefit of the technology not realized in the 2-cycle test methodology, which in this case is 

0.000416 gal/mi minus 0.000337 gal/mi, or 0.000078 gal/mi. This method will avoid doublecounting the benefit of the electrical load reduction, which is already counted on the 2-cycle test. 

 Regardless of whether the off-cycle technology fuel consumption benefit is obtained 

from the table (columns 2 or 3) above or is based on an approved testing protocol as indicated in 

the preceding example, under the CAFE program the benefit or credit is treated as an adjustment 

and subtracted from the subject vehicle’s fuel consumption performance value determined from 

the required CAFE program 2-cycle test results. The resultant would then be reciprocated to 

determine the improved adjusted fuel economy performance value for the subject vehicle. EPA 

would use the adjusted fuel economy value of each vehicle weighted to its production sales 

volume to determine the harmonic average fuel economy performance, CAFE value, of the 

manufacturer’s fleet. 

Since one purpose of the off-cycle improvement incentive is to encourage market 

penetration of the technologies (see 75 FR at 25438), EPA is proposing to require minimum 

penetration rates for non-hybrid based listed technologies as a condition for generating 

improvements from the list as a way to further encourage their widespread adoption by MY 2017 

and later. At the end of the model year for which the off-cycle improvement is claimed, 

manufacturers would need to demonstrate that production of vehicles equipped with the 
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technologies for that model year exceeded the percentage thresholds in order to receive the listed 

improvement. EPA proposes to set the threshold at 10 percent of a manufacturer’s overall 

combined car and light truck production for all technologies not specific to HEVs. 10 percent 

would seem to be an appropriate threshold as it would encourage manufacturers to develop 

technologies for use on larger volume models and bring the technologies into the mainstream. 

For solar roof panels and electric heat circulation pumps, which are HEV-specific, EPA is not 

proposing a minimum penetration rate threshold for credit generation. Hybrids may be a small 

subset of a manufacturer’s fleet, less than 10 percent in some cases, and EPA does not believe 

that establishing a threshold for hybrid-based technologies would be useful and could 

unnecessarily complicate the introduction of these technologies. The agencies request comments 

on applying this type of threshold, the appropriateness of 10 percent as the threshold for listed 

technologies that are not HEV-specific, and the proposed treatment of hybrid-based 

technologies. 

Because the proposed improvements are based on limited data, however, and because 

some uncertainty is introduced when credits are provided based on a general assessment of offcycle performance as opposed to testing on the individual vehicle models, as part of the incentive 

EPA is proposing to cap the amount of improvement a manufacturer could generate using the 

above list to 0.001125 gal/mile per year on a combined car and truck fleet-wide average basis. 

The cap would not apply on a vehicle model basis, allowing manufacturers the flexibility to 

focus off-cycle technologies on certain vehicle models and generate improvements for that 

vehicle model in excess of 0.001125 gal/mile. If manufacturers wish to generate improvements 

in excess of the 0.001125 gal/mile limit using listed technologies, they could do so by generating 

necessary data and going through the approval process. 

For more details on the testing protocols used for determining off-cycle technology 

benefits and the step-by-step EPA review and approval process refer to Section III.C.5.b.iii and 

v.

5. Other CAFE enforcement issues 

a. Electronic Reporting 

Pursuant to 49 CFR part 537, manufacturers submit pre-model year fuel economy reports 

to NHTSA by December 31st prior to the model year, and mid-model year reports by July 31st of 

the model year. Manufacturers may also provide supplemental reports whenever changes are 

needed to a previously submitted CAFE report. NHTSA receives both non-confidential and 

confidential versions of reports, the basic difference being the inclusion of projected upcoming 

production sales volumes in reports seeking confidentiality. Manufacturers must include a 

request for confidentiality, in accordance with 49 CFR part 512, along with the report for which 
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confidential treatment is sought.264 Manufacturers may submit reports either in paper form or 

electronically to a secure e-mail address, cafe@dot.gov, that allows for the safe handling of 

confidential materials. All electronic submissions submitted to the CAFE email must be 

provided in a pdf format. NHTSA added electronic reporting to the 2012-2016 CAFE rule as an 

approach to simplify reporting for manufacturers and NHTSA alike. Currently, most 

manufacturers submit both electronic and paper reports.265 

NHTSA is proposing to modify its reporting requirements to receive all CAFE reports in 

electronic format, thereby eliminating the requirement for paper submissions. In the revised 

requirements, a manufacturer could either submit its reports on a CD-ROM or through the 

existing email procedures. Under the proposal, the contents of the CD must include the 

manufacturer’s request for confidentiality, the cover letter, and any other supporting documents 

in a pdf format. Any data included in the report must be provided in a Microsoft Excel 

spreadsheet format. The same approach is also proposed for submitting information by email. 

NHTSA emphasizes that submitting reports to the CAFE email address is completely voluntary, 

but if the option is selected, the manufacturer must follow the normal deadline dates as specified 

in 49 CFR 537.5. NHTSA believes that receiving CAFE data through electronic reports would 

be a significant improvement, improving the quality of its CAFE data, simplifying enforcement 

activities (e.g., auditing the data), and helping to expedite the tracking and reporting of CAFE 

credits. The agency also plans to eventually develop an XML schema for submitting CAFE 

reports electronically that will available through its website. Ultimately, the XML schema would 

be used as part of the new database system NHTSA plans to construct in the future to store its 

CAFE data. NHTSA seeks comments on the appropriateness of ending paper submissions, as 

well as information on any other electronic formats that should be considered for submissions. 

b. Reporting of how a vehicle is classified as a light truck 

As part of the reporting provisions in 49 CFR part 537, NHTSA requires manufacturers 

to provide information on some, but not all, of the functions and features that a manufacturer 

uses to classify an automobile as a light truck. The required data is distributed throughout the 

report, making it difficult for the agency to clearly and easily determine exactly what functions 

or features a manufacturer is actually using to make this determination. For example, related to 

the functions specified in 49 CFR 523.5(a) and discussed in Section IV.H above, manufacturers 

must provide the vehicles’ passenger and cargo carrying volumes,266 and identify whether their 

vehicles are equipped with three rows of seats that can be removed or folded flat for expanded 

 264 Pursuant to Part 537.12, NHTSA’s Office of Chief Counsel normally grants confidentiality to reports with 

projected production sales volumes until after the model year ends. 

265 For model year 2011, NHTSA received electronic mid-model year reports from 12 manufacturers. Each of the 

manufacturers also provided hardcopy reports.

266 49 CFR 537.7(c)(4)(xvi)(B). 
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cargo carrying purposes or if the vehicle includes temporary living quarters.267 Manufacturers 

are not required to identify whether the vehicles can transport more than 10 persons or if the 

vehicles are equipped with an open cargo bed. Related to the functions specified in Section 

523.5(b), for each model type classified as an automobile capable of off-highway operation, 

manufacturers are required to provide the five suspension parameter measurements and indicate 

the existence of 4-wheel drive,268 but they are not required to identify a vehicle’s GVWR, which 

is necessary for off-road determination when the vehicle is not equipped with 4-wheel drive. 

NHTSA proposes to eliminate the language requesting vehicle attribute information in Sections 

537.7(c)(4)(xvi)(A)(3) to (6) and (B)(3) to (6) and to relocate that language into a revised Section 

537.7(c)(5) to include identification of all the functions and features that can be used by a 

manufacturer for making a light truck classification determination. By incorporating all the 

requirements into one section, the agency believes the classification process will become 

significantly more accurate and efficient. NHTSA seeks comment on this proposed change. 

c. Base Tire Definition 

Beginning in model year 2011, manufacturers of light trucks and passenger cars are 

required to use vehicle footprint to determine the CAFE standards applicable to each of their 

vehicle fleets. To determine the appropriate footprint-based standards, a manufacturer must 

calculate each vehicle’s footprint value, which is the product of the vehicle track width and 

wheelbase dimensions. Vehicle track width dimensions are determined with a vehicle equipped 

with “base tires,”269 which NHTSA defines as the tire specified as standard equipment by a 

manufacturer on each vehicle configuration of a model type. 

NHTSA is concerned that the definition for “base tire” is insufficiently descriptive, and 

may lead to inconsistencies among manufacturers’ base tire selections. In meetings relating to 

CAFE enforcement, manufacturers have stated that various approaches in selecting base tires 

exist due to differences in the tires considered as standard equipment.270 Standard equipment is 

defined by EPA regulation as those features or equipment which are marketed on a vehicle over 

which the purchaser can exercise no choice, 271 but NHTSA regulations have no comparable 

definition. NHTSA considered whether adding a definition for “standard equipment” would 

clarify and strengthen the NHTSA regulations, but some manufacturers indicated that the 

definition of standard equipment provided by EPA does not effectively prevent differences in 

their interpretations. Some manufacturers, for example, view the base tire as the tire equipped as 

standard equipment for each trim level of a model type, as each trim level has standard 

 267 49 CFR 537.7(c)(4)(xvii) and (xviii). 268 49 CFR 537.7(c)(5). 269 See 49 CFR 523.2. 270 NHTSA has confirmed these differences in approach for the designating base tire exist through review of 

manufacturer-submitted CAFE reports. 

271 In the EPA regulation 40 CFR 600.002-08, standard equipment means those features or equipment which are 

marketed on a vehicle over which the purchaser can exercise no choice.
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equipment over which the purchaser cannot exercise a choice. This view can allow multiple base 

tires and footprint values within each model type: a manufacturer may have two vehicle 

configurations for a particular model type, with each configuration having three trim levels with 

different standard tires sizes. In that scenario, the model type could have 6 different trim level 

vehicle configurations, each having three or more unique footprint values with slightly different 

targets. The additional target fuel economy values could allow the manufacturer to reduce its 

required fleet standard despite a vehicle model type not having any inherent differences in 

physical feature between vehicle configurations other than the tire sizes. Other manufacturers, in 

contrast, avoid designating multiple base tires and choose the standard tire equipped on the most 

basic vehicle configuration of a model type, even if the most basic vehicle is rarely actually sold. 

In this scenario, the tires being used to derive a manufacturer fleet standard are not the same size 

tire equipped on the representative number of vehicles being sold. Yet others designate the base 

tire as the tire most commonly installed on a model type having the highest production volume. 

This approach most realistically reflects the manufacturer’s sales production fleet. 

To attempt to reconcile the varied approaches for designating base tires, NHTSA is 

proposing to modify its definition for base tire in 49 CFR 523.2. The proposed modification 

changes the definition of the base tire by dropping the reference to “standard equipment” and 

adding a reference to the “the tire installed by the vehicle manufacturer that is used on the 

highest production sales volume of vehicles within the configuration.” This modification should 

ensure that the tires installed on the vehicle most commonly sold within a vehicle configuration 

become the basis for setting a manufacturer’s fuel economy standards. It is NHTSA’s goal that a 

change to the definition of base tire for purposes of CAFE will help to reduce inconsistencies and 

confusion for both the agency and the manufacturers. NHTSA seeks comments on this 

approach, as well as other approaches that could be used for selecting the base tire(s). 

d. Confirming Target and Fleet Standards 

NHTSA requires manufacturers to provide reports containing fleet and model type CAFE 

standards and projections of expected performance results for each model year.272 The footprint, 

track width and wheelbase values are provided for each vehicle configuration within the model 

types making up the manufacturer’s fleets, along with other model type-specific information. 

Because this information is organized by vehicle configuration, instead of by each vehicle with a 

unique model type and footprint combination, it is not in the format needed to calculate 

performance standards. EPA, in contrast, requires manufacturers to provide all of the 

information necessary to calculate footprint values and CAFE standards. EPA provides an 

additional calculator (in the form of an Excel spreadsheet), which all manufacturers use and 

submit as part of their end-of-the-year reports, which includes the appropriate breakdown of 

footprint values for calculating standards. 

 272 49 CFR 537. 
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Since NHTSA only requires a breakdown of footprint values by vehicle configurations, 

instead of by each unique model type and footprint combination, NHTSA is currently unable to 

verify manufacturers’ reported target standards. By standardizing with EPA’s requirements for 

reported data, NHTSA would both simplify manufacturer reporting efforts and gain the 

necessary information for calculating attribute-based CAFE standards. Therefore, NHTSA is 

proposing to eliminate the language requesting information in Sections 537.7(c)(4)((xvi)(A)(3)-

(6) and (B)(3)-(6), and to relocate that language into a revised Section 537.7(b)(3). 

NHTSA requests comment on this proposed change. 

M. Regulatory notices and analyses 

1. Executive Order 12866, Executive Order 13563, and DOT Regulatory Policies and 

Procedures 

Executive Order 12866, “Regulatory Planning and Review” (58 FR 51735, Oct. 4, 1993), 

as amended by Executive Order 13563, “Improving Regulation and Regulatory Review” (76 FR 

3821, Jan. 21, 2011), provides for making determinations whether a regulatory action is 

“significant” and therefore subject to OMB review and to the requirements of the Executive 

Order. The Order defines a “significant regulatory action” as one that is likely to result in a rule 

that may: 

1) Have an annual effect on the economy of $100 million or more or adversely affect 

in a material way the economy, a sector of the economy, productivity, 

competition, jobs, the environment, public health or safety, or State, local, or 

Tribal governments or communities; 

2) Create a serious inconsistency or otherwise interfere with an action taken or 

planned by another agency; 

3) Materially alter the budgetary impact of entitlements, grants, user fees, or loan 

programs or the rights and obligations of recipients thereof; or 

4) Raise novel legal or policy issues arising out of legal mandates, the President’s 

priorities, or the principles set forth in the Executive Order. 

The rulemaking proposed in this NPRM will be economically significant if adopted. 

Accordingly, OMB reviewed it under Executive Order 12866. The rule, if adopted, would also 

be significant within the meaning of the Department of Transportation’s Regulatory Policies and 

Procedures. 

The benefits and costs of this proposal are described above. Because the proposed rule 

would, if adopted, be economically significant under both the Department of Transportation’s 

procedures and OMB guidelines, the agency has prepared a Preliminary Regulatory Impact 

Analysis (PRIA) and placed it in the docket and on the agency’s website. Further, pursuant to 

Circular A-4, we have prepared a formal probabilistic uncertainty analysis for this proposal. The 
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circular requires such an analysis for complex rules where there are large, multiple uncertainties 

whose analysis raises technical challenges or where effects cascade and where the impacts of the 

rule exceed $1 billion. This proposal meets these criteria on all counts. 

2. National Environmental Policy Act 

NHTSA has initiated the Environmental Impact Statement (EIS) process under the 

National Environmental Policy Act (NEPA), 42 U.S.C. 4321-4347, and implementing 

regulations issued by the Council on Environmental Quality (CEQ), 40 CFR part 1500, and 

NHTSA, 49 CFR part 520. On May 10, 2011, NHTSA published a notice of intent to prepare an 

EIS for this rulemaking and requested scoping comments. (76 FR 26996). The notice invited 

Federal, State, and local agencies, Indian tribes, and the public to participate in the scoping 

process and to help identify the environmental issues and reasonable alternatives to be examined 

in the EIS. The scoping notice also provided information about the proposed standards, the 

alternatives NHTSA expects to consider its NEPA analysis, and the scoping process. 

Concurrently with this NPRM, NHTSA is releasing a Draft Environmental Impact 

Statement (DEIS). NHTSA prepared the DEIS to analyze and disclose the potential 

environmental impacts of the proposed MY 2017-2025 CAFE standards for the total fleet of 

passenger cars and light trucks and reasonable alternative standards for the NHTSA CAFE 

program pursuant to the CEQ regulations implementing NEPA, DOT Order 5610.1C, and 

NHTSA regulations. The DEIS compares the potential environmental impacts of alternative 

mile per gallon (mpg) levels that will be considered by NHTSA for the final rule. It also 

analyzes direct, indirect, and cumulative impacts and analyzes impacts in proportion to their 

significance. 

The DEIS also describes potential environmental impacts to a variety of resources. 

Resources that may be affected by the proposed action and alternatives include water resources, 

biological resources, land use and development, safety, hazardous materials and regulated 

wastes, noise, socioeconomics, and environmental justice. These resource areas were assessed 

qualitatively in the DEIS. 

Throughout the DEIS, NHTSA has relied extensively on findings of the United Nations 

Intergovernmental Panel on Climate Change (IPCC), the U.S. Climate Change Science Program 

(CCSP), and EPA. Our discussion relies heavily on the most recent, thoroughly peer-reviewed, 

and credible assessments of global and U.S. climate change: the IPCC Fourth Assessment 

Report (Climate Change 2007), EPA’s Endangerment and Cause or Contribute Findings for 

Greenhouse Gases under Section 202(a) of the Clean Air Act and the accompanying Technical 

Support Document (TSD), and CCSP and National Science and Technology Council reports that 

include the Scientific Assessment of the Effects of Global Change on the United States and 

Synthesis and Assessment Products. The DEIS cites these sources and the studies they review 

frequently. 
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Because of the link between the transportation sector and GHG emissions, NHTSA 

recognizes the need to consider the possible impacts on climate and global climate change in the 

analysis of the effects of these fuel economy standards. NHTSA also recognizes the difficulties 

and uncertainties involved in such an impact analysis. Accordingly, consistent with CEQ 

regulations on addressing incomplete or unavailable information in environmental impact 

analyses, NHTSA has reviewed existing credible scientific evidence that is relevant to this 

analysis and summarized it in the DEIS. NHTSA has also employed and summarized the results 

of research models generally accepted in the scientific community. 

Although the alternatives have the potential to decrease GHG emissions substantially, 

they do not prevent climate change, but only result in reductions in the anticipated increases in 

CO2 concentrations, temperature, precipitation, and sea level. They would also, to a small 

degree, delay the point at which certain temperature increases and other physical effects 

stemming from increased GHG emissions would occur. As discussed below, NHTSA presumes 

that these reductions in climate effects will be reflected in reduced impacts on affected resources. 

The main direct and indirect effects resulting from the different alternatives analyzed in 

the DEIS are as follows: 

[to be filled in] 

3. Regulatory Flexibility Act 

Pursuant to the Regulatory Flexibility Act (5 U.S.C. 601 et seq., as amended by the Small 

Business Regulatory Enforcement Fairness Act (SBREFA) of 1996), whenever an agency is 

required to publish a notice of rulemaking for any proposed or final rule, it must prepare and 

make available for public comment a regulatory flexibility analysis that describes the effect of 

the rule on small entities (i.e., small businesses, small organizations, and small governmental 

jurisdictions). The Small Business Administration’s regulations at 13 CFR part 121 define a 

small business, in part, as a business entity “which operates primarily within the United States.” 

13 CFR 121.105(a). No regulatory flexibility analysis is required if the head of an agency 

certifies the rule will not have a significant economic impact of a substantial number of small 

entities. 

I certify that the proposed rule would not have a significant economic impact on a 

substantial number of small entities. The following is NHTSA’s statement providing the factual 

basis for the certification (5 U.S.C. 605(b)). 
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If adopted, the proposal would directly affect nineteen large single stage motor vehicle 

manufacturers.273 Based on our preliminary assessment, the proposal would also affect a total of 

about 21 entities that fir the Small Business Administration’s criteria for a small business. 

According to the Small Business Administration’s small business size standards (see 13 CFR 

121.201), a single stage automobile or light truck manufacturer (NAICS code 336111, 

Automobile Manufacturing; 336112, Light Truck and Utility Vehicle Manufacturing) must have 

1,000 or fewer employees to qualify as a small business. There are about 4 small manufacturers, 

including 3 electric vehicle manufacturers, 8 independent commercial importers, and 9 

alternative fuel vehicle converters in the passenger car and light truck market which are small 

businesses. We believe that the rulemaking would not have a significant economic impact on 

these small vehicle manufacturers because under 49 CFR part 525, passenger car manufacturers 

making fewer than 10,000 vehicles per year can petition NHTSA to have alternative standards 

set for those manufacturers. Manufacturers that produce only electric vehicles, or that modify 

vehicles to make them electric or some other kind of dedicated alternative fuel vehicle, will have 

average fuel economy values far beyond those proposed today, so we would not expect them to 

need a petition for relief. A number of other small vehicle manufacturers already petition the 

agency for relief under Part 525. If the standard is raised, it has no meaningful impact on those 

manufacturers, because they are expected to still go through the same process to petition for 

relief. Given that there is already a mechanism for handling small businesses, which is the 

purpose of the Regulatory Flexibility Act, a regulatory flexibility analysis was not prepared, but 

we welcome comments on this issue for the final rule. 

4. Executive Order 13132 (Federalism) 

Executive Order 13132 requires NHTSA to develop an accountable process to ensure 

“meaningful and timely input by State and local officials in the development of regulatory 

policies that have federalism implications.”274 The Order defines the term “Policies that have 

federalism implications” to include regulations that have “substantial direct effects on the States, 

on the relationship between the national government and the States, or on the distribution of 

power and responsibilities among the various levels of government.” Under the Order, NHTSA 

may not issue a regulation that has federalism implications, that imposes substantial direct 

compliance costs, and that is not required by statute, unless the Federal government provides the 

funds necessary to pay the direct compliance costs incurred by State and local governments, or 

NHTSA consults with State and local officials early in the process of developing the proposed 

regulation. 

 273 BMW, Daimler (Mercedes), Fiat/Chrysler (which also includes Ferrari and Maserati for CAFE compliance 

purposes), Ford, Geely (Volvo), General Motors, Honda, Hyundai, Kia, Lotus, Mazda, Mitsubishi, Nissan, Porsche, 

Subaru, Suzuki, Tata (Jaguar Land Rover), Toyota, and Volkswagen/Audi. 

274 64 FR 43255 (Aug. 10, 1999). 
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NHTSA solicits comment on this proposed action from State and local officials. The 

agency believes that it is unnecessary to address the question of preemption further at this time 

because of the consistent and coordinated Federal standards that would apply nationally under 

the proposed National Program. 

5. Executive Order 12988 (Civil Justice Reform) 

Pursuant to Executive Order 12988, “Civil Justice Reform,”275 NHTSA has considered 

whether this rulemaking would have any retroactive effect. This proposed rule does not have 

any retroactive effect. 

6. Unfunded Mandates Reform Act 

Section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA) requires Federal 

agencies to prepare a written assessment of the costs, benefits, and other effects of a proposed or 

final rule that includes a Federal mandate likely to result in the expenditure by State, local, or 

tribal governments, in the aggregate, or by the private sector, of more than $100 million in any 

one year (adjusted for inflation with base year of 1995). Adjusting this amount by the implicit 

gross domestic product price deflator for 2009 results in $134 million (109.729/81.606 = 1.34). 

Before promulgating a rule for which a written statement is needed, section 205 of UMRA 

generally requires NHTSA to identify and consider a reasonable number of regulatory 

alternatives and adopt the least costly, most cost-effective, or least burdensome alternative that 

achieves the objectives of the rule. The provisions of section 205 do not apply when they are 

inconsistent with applicable law. Moreover, section 205 allows NHTSA to adopt an alternative 

other than the least costly, most cost-effective, or least burdensome alternative if the agency 

publishes with the final rule an explanation of why that alternative was not adopted. 

This proposed rule will not result in the expenditure by State, local, or tribal 

governments, in the aggregate, of more than $134 million annually, but it will result in the 

expenditure of that magnitude by vehicle manufacturers and/or their suppliers. In developing 

this proposal, NHTSA considered a variety of alternative average fuel economy standards lower 

and higher than those proposed. NHTSA is statutorily required to set standards at the maximum 

feasible level achievable by manufacturers based on its consideration and balancing of relevant 

factors, and has tentatively concluded that the proposed fuel economy standards are the 

maximum feasible standards for the passenger car and light truck fleets for MYs 2017-2025 in 

light of the statutory considerations. 

 275 61 FR 4729 (Feb. 7, 1996). 
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7. Regulation Identifier Number 

The Department of Transportation assigns a regulation identifier number (RIN) to each 

regulatory action listed in the Unified Agenda of Federal Regulations. The Regulatory 

Information Service Center publishes the Unified Agenda in April and October of each year. 

You may use the RIN contained in the heading at the beginning of this document to find this 

action in the Unified Agenda. 

8. Executive Order 13045 

Executive Order 13045276 applies to any rule that: (1) is determined to be economically 

significant as defined under E.O. 12866, and (2) concerns an environmental, health, or safety risk 

that NHTSA has reason to believe may have a disproportionate effect on children. If the 

regulatory action meets both criteria, we must evaluate the environmental, health, or safety 

effects of the proposed rule on children, and explain why the proposed regulation is preferable to 

other potentially effective and reasonably foreseeable alternatives considered by us. 

Chapter 5 of NHTSA’s DEIS notes that breathing PM can cause respiratory ailments, 

heart attack, and arrhythmias (Dockery et al. 1993, Samet et al. 2000, Pope et al. 1995, 2002, 

2004, Pope and Dockery 2006, Dominici et al. 2006, Laden et al. 2006, all in Ebi et al. 2008). 

Populations at greatest risk could include children, the elderly, and those with heart and lung 

disease, diabetes (Ebi et al. 2008), and high blood pressure (Künzli et al. 2005, in Ebi et al. 

2008). Chronic exposure to PM could decrease lifespan by 1 to 3 years (Pope 2000, in American 

Lung Association 2008). Increasing PM concentrations are expected to have a measurable 

adverse impact on human health (Confalonieri et al. 2007). 

Additionally, the DEIS notes that substantial morbidity and childhood mortality has been 

linked to water- and food-borne diseases. Climate change is projected to alter temperature and 

the hydrologic cycle through changes in precipitation, evaporation, transpiration, and water 

storage. These changes, in turn, potentially affect water-borne and food-borne diseases, such as 

salmonellosis, campylobacter, leptospirosis, and pathogenic species of vibrio. They also have a 

direct impact on surface water availability and water quality. It has been estimated that more 

than 1 billion people in 2002 did not have access to adequate clean water (McMichael et al. 

2003, in Epstein et al. 2006). Increased temperatures, greater evaporation, and heavy rain events 

have been associated with adverse impacts on drinking water through increased waterborne 

diseases, algal blooms, and toxins (Chorus and Bartram 1999, Levin et al. 2002, Johnson and 

Murphy 2004, all in Epstein et al. 2006). A seasonal signature has been associated with waterborne disease outbreaks (EPA 2009b). In the United States, 68 percent of all water-borne 

 276 62 FR 19885 (Apr. 23, 1997). 
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diseases between 1948 and 1994 were observed after heavy rainfall events (Curriero et al. 2001a, 

in Epstein et al. 2006). 

Climate change could further impact a pathogen by directly affecting its lifecycle (Ebi et 

al. 2008). The global increase in the frequency, intensity, and duration of red tides could be 

linked to local impacts already associated with climate change (Harvell et al. 1999, in Epstein et 

al. 2006); toxins associated with red tide directly affect the nervous system (Epstein et al. 2006). 

Many people do not report or seek medical attention for their ailments of water-borne or 

food-borne diseases; hence, the number of actual cases with these diseases is greater than clinical 

records demonstrate (Mead et al. 1999, in Ebi et al. 2008). Many of the gastrointestinal diseases 

associated with water-borne and food-borne diseases can be self-limiting; however, vulnerable 

populations include young children, those with a compromised immune system, and the elderly. 

Thus, as detailed in the DEIS, NHTSA has evaluated the environmental, health, and 

safety effects of the proposed rule on children. The DEIS also explains why the proposed 

regulation is preferable to other potentially effective and reasonably foreseeable alternatives 

considered by the agency. 

9. National Technology Transfer and Advancement Act 

Section 12(d) of the National Technology Transfer and Advancement Act (NTTAA) 

requires NHTSA to evaluate and use existing voluntary consensus standards in its regulatory 

activities unless doing so would be inconsistent with applicable law (e.g., the statutory provisions 

regarding NHTSA’s vehicle safety authority) or otherwise impractical.277

Voluntary consensus standards are technical standards developed or adopted by voluntary 

consensus standards bodies. Technical standards are defined by the NTTAA as “performancebased or design-specific technical specification and related management systems practices.” 

They pertain to “products and processes, such as size, strength, or technical performance of a 

product, process or material.” 

Examples of organizations generally regarded as voluntary consensus standards bodies 

include the American Society for Testing and Materials (ASTM), the Society of Automotive 

Engineers (SAE), and the American National Standards Institute (ANSI). If NHTSA does not 

use available and potentially applicable voluntary consensus standards, we are required by the 

Act to provide Congress, through OMB, an explanation of the reasons for not using such 

standards. 

There are currently no voluntary consensus standards relevant to today’s proposed CAFE 

standards. 

 277 15 U.S.C. 272. 
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10. Executive Order 13211 

Executive Order 13211278 applies to any rule that: (1) is determined to be economically 

significant as defined under E.O. 12866, and is likely to have a significant adverse effect on the 

supply, distribution, or use of energy; or (2) that is designated by the Administrator of the Office 

of Information and Regulatory Affairs (OIRA) as a significant regulatory action. If the 

regulatory action meets either criterion, we must evaluate the adverse energy effects of the 

proposed rule and explain why the proposed regulation is preferable to other potentially effective 

and reasonably foreseeable alternatives considered by us. 

The proposed rule seeks to establish passenger car and light truck fuel economy standards 

that will reduce the consumption of petroleum and will not have any adverse energy effects. 

Accordingly, this proposed rulemaking action is not designated as a significant energy action. 

11. Department of Energy Review 

In accordance with 49 U.S.C. 32902(j)(1), we submitted this proposed rule to the 

Department of Energy for review. That Department did not make any comments that we have 

not addressed. [Pending conclusion of review, of course] 

12. Plain Language 

Executive Order 12866 requires each agency to write all rules in plain language. 

Application of the principles of plain language includes consideration of the following questions: 

 Have we organized the material to suit the public’s needs? 

 Are the requirements in the rule clearly stated? 

 Does the rule contain technical jargon that isn’t clear? 

 Would a different format (grouping and order of sections, use of headings, paragraphing) 

make the rule easier to understand? 

 Would more (but shorter) sections be better? 

 Could we improve clarity by adding tables, lists, or diagrams? 

 What else could we do to make the rule easier to understand? 

If you have any responses to these questions, please include them in your comments on 

this proposal. 

 278 66 FR 28355 (May 18, 2001). 
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13. Privacy Act 

Anyone is able to search the electronic form of all comments received into any of our 

dockets by the name of the individual submitting the comment (or signing the comment, if 

submitted on behalf of an organization, business, labor union, etc.). You may review DOT’s 

complete Privacy Act statement in the Federal Register (65 FR 19477-78, April 11, 2000) or you 

may visit http://www.dot.gov/privacy.html. 

 
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List of Subjects 

49 CFR Parts 523, 531, and 533 

Fuel Economy. 

49 CFR Parts 536 and 537 

Fuel Economy, Reporting and Recordkeeping Requirements. 

In consideration of the foregoing, under the authority of 49 U.S.C. 32901, 32902, and 

32903, and delegation of authority at 49 CFR 1.50, NHTSA proposes to amend 49 CFR Chapter 

V as follows: 

PART 523 – VEHICLE CLASSIFICATION

 1. The authority citation for part 523 continues to read as follows: 

Authority: 49 U.S.C 32901, delegation of authority at 49 CFR 1.50. 

 2. Revise § 523.2 to read as follows: 

§ 523.2 Definitions.

 Approach angle means the smallest angle, in a plane side view of an automobile, formed 

by the level surface on which the automobile is standing and a line tangent to the front tire static 

loaded radius arc and touching the underside of the automobile forward of the front tire. 

 Axle clearance means the vertical distance from the level surface on which an automobile 

is standing to the lowest point on the axle differential of the automobile. 

 Base tire (for passenger automobiles, light trucks, and medium duty passenger vehicles) 

means the tire that has the highest production sales volume that is installed by the vehicle 

manufacturer on each vehicle configuration of a model type. 

 Basic vehicle frontal area is used as defined in 40 CFR 86.1803. 

 Breakover angle means the supplement of the largest angle, in a plan side view of an 

automobile, that can be formed by two lines tangent to the front and rear static loaded radii arcs 

and intersecting at a point on the underside of the automobile. 

 Cab-complete vehicle means a vehicle that is first sold as an incomplete vehicle that 

substantially includes the vehicle cab section as defined in 40 CFR 1037.801. For example, 

vehicles known commercially as chassis-cabs, cab-chassis, box-deletes, bed-deletes, and cutaway vans are considered cab-complete vehicles. A cab includes a steering column and a 

passenger compartment. Note that a vehicle lacking some components of the cab is a cabcomplete vehicle if it substantially includes the cab. 
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 Cargo-carrying volume means the luggage capacity or cargo volume index, as 

appropriate, and as those terms are defined in 40 CFR 600.315-08, in the case of automobiles to 

which either of these terms apply. With respect to automobiles to which neither of these terms 

apply, “cargo-carrying volume” means the total volume in cubic feet, rounded to the nearest 0.1 

cubic feet, of either an automobile’s enclosed non-seating space that is intended primarily for 

carrying cargo and is not accessible from the passenger compartment, or the space intended 

primarily for carrying cargo bounded in the front by a vertical plane that is perpendicular to the 

longitudinal centerline of the automobile and passes through the rearmost point on the rearmost 

seat and elsewhere by the automobile’s interior surfaces. 

 Class 2b vehicles are vehicles with a gross vehicle weight rating (GVWR) ranging from 

8,501 to 10,000 pounds (lbs). 

 Class 3 through Class 8 vehicles are vehicles with a GVWR of 10,001 lbs or more, as 

defined in 49 CFR 565.15. 

 Commercial medium- and heavy-duty on-highway vehicle means an on-highway vehicle 

with a GVWR of 10,000 lbs or more, as defined in 49 U.S.C. 32901(a)(7). 

 Complete vehicle means a vehicle that requires no further manufacturing operations to 

perform its intended function and is a functioning vehicle that has the primary load-carrying 

device or container (or equivalent equipment) attached or is designed to pull a trailer. Examples 

of equivalent equipment include fifth wheel trailer hitches, firefighting equipment, and utility 

booms. 

 Curb weight is defined the same as vehicle curb weight in 40 CFR 86.1803-01. 

 Departure angle means the smallest angle, in a plane side view of an automobile, formed 

by the level surface on which the automobile is standing and a line tangent to the rear tire static 

loaded radius arc and touching the underside of the automobile rearward of the rear tire. 

 Final stage manufacturer has the meaning given in 49 CFR 567.3. 

 Footprint is defined as the product of track width (measured in inches, calculated as the 

average of front and rear track widths, and rounded to the nearest tenth of an inch) times 

wheelbase (measured in inches and rounded to the nearest tenth of an inch), divided by 144 and 

then rounded to the nearest tenth of a square foot. For purposes of this definition, “track width” 

is the lateral distance between the centerlines of the base tires at ground, including the camber 

angle. For purposes of this definition, “wheelbase” is the longitudinal distance between front 

and rear wheel centerlines. 

 Full-size pickup truck means a light truck or medium duty passenger vehicle that meets 

the requirements specified in 40 CFR 86.1866-12(e). 
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Gross combination weight rating (GCWR) means the value specified by the manufacturer as the 

maximum allowable loaded weight of a combination vehicle (e.g., tractor plus trailer). 

 Gross vehicle weight rating (GVWR) means the value specified by the manufacturer as 

the maximum design loaded weight of a single vehicle (e.g., vocational vehicle). 

 Heavy-duty engine means any engine used for (or which the engine manufacturer could 

reasonably expect to be used for) motive power in a heavy-duty vehicle. For purposes of this 

definition in this part, the term “engine” includes internal combustion engines and other devices 

that convert chemical fuel into motive power. For example, a fuel cell and motor used in a 

heavy-duty vehicle is a heavy-duty engine. 

Heavy-duty off-road vehicle means a heavy-duty vocational vehicle or vocational tractor 

that is intended for off-road use meeting either of the following criteria: 

(1) Vehicles with tires installed having a maximum speed rating at or below 55 mph. 

(2) Vehicles primarily designed to perform work off-road (such as in oil fields, forests, or 

construction sites), and meeting at least one of the criteria of paragraph (2)(i) of this 

definition and at least one of the criteria of paragraph (2)(ii) of this definition. 

(i) Vehicles must have affixed components designed to work in an off-road environment 

(for example, hazardous material equipment or drilling equipment) or be designed to 

operate at low speeds making them unsuitable for normal highway operation. 

(ii) Vehicles must: 

(A) Have an axle that has a gross axle weight rating (GAWR), as defined in 49 CFR § 5

 71.3, of 29,000 pounds or more; 

(B) Have a speed attainable in 2 miles of not more than 33 mph; or 

(C) Have a speed attainable in 2 miles of not more than 45 mph, an unloaded vehicle 

weight that is not less than 95 percent of its GVWR, and no capacity to carry occupants 

other than the driver and operating crew. 

Heavy-duty vehicle means a vehicle as defined in § 523.6. 

Incomplete vehicle means a vehicle which does not have the primary load carrying 

device or container attached when it is first sold as a vehicle or any vehicle that does not meet 

the definition of a complete vehicle. This may include vehicles sold to secondary vehicle 

manufacturers. Incomplete vehicles include cab-complete vehicles. 

Innovative technology means technology certified as such under 40 CFR 1037.610. 

Light truck means a non-passenger automobile as defined in § 523.5. 
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

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Medium duty passenger vehicle means a vehicle which would satisfy the criteria in § 

523.5 (relating to light trucks) but for its gross vehicle weight rating or its curb weight, which is 

rated at more than 8,500 lbs GVWR or has a vehicle curb weight of more than 6,000 lbs or has a 

basic vehicle frontal area in excess of 45 square feet, and which is designed primarily to 

transport passengers, but does not include a vehicle that: 

(1) Is an “incomplete vehicle”' as defined in this subpart; or 

(2) Has a seating capacity of more than 12 persons; or 

(3) Is designed for more than 9 persons in seating rearward of the driver's seat; or 

(4) Is equipped with an open cargo area (for example, a pick-up truck box or bed) of 72.0 

inches in interior length or more. A covered box not readily accessible from the 

passenger compartment will be considered an open cargo area for purposes of this 

definition. 

1.

Mild hybrid gasoline-electric vehicle means a vehicle as defined by EPA in 40 CFR 

86.1866–12(e). 

Motor home has the meaning given in 49 CFR 571.3. 

Motor vehicle has the meaning given in 40 CFR 85.1703. 

Passenger-carrying volume means the sum of the front seat volume and, if any, rear seat 

volume, as defined in 40 CFR 600.315-08, in the case of automobiles to which that term applies. 

With respect to automobiles to which that term does not apply, “passenger-carrying volume” 

means the sum in cubic feet, rounded to the nearest 0.1 cubic feet, of the volume of a vehicle's 

front seat and seats to the rear of the front seat, as applicable, calculated as follows with the head 

room, shoulder room, and leg room dimensions determined in accordance with the procedures 

outlined in Society of Automotive Engineers Recommended Practice J1100a, Motor Vehicle 

Dimensions (Report of Human Factors Engineering Committee, Society of Automotive 

Engineers, approved September 1973 and last revised September 1975). 

(1) For front seat volume, divide 1,728 into the product of the following SAE 

dimensions, measured in inches to the nearest 0.1 inches, and round the quotient to the 

nearest 0.001 cubic feet. 

(i) H61–Effective head room—front. 

(ii) W3–Shoulder room—front. 

(iii) L34–Maximum effective leg room-accelerator. 
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

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(2) For the volume of seats to the rear of the front seat, divide 1,728 into the product of 

the following SAE dimensions, measured in inches to the nearest 0.1 inches, and rounded 

the quotient to the nearest 0.001 cubic feet. 

(i) H63–Effective head room—second. 

(ii) W4–Shoulder room—second. 

(iii) L51–Minimum effective leg room—second. 

Pickup truck means a non-passenger automobile which has a passenger compartment and 

an open cargo area (bed). 

Recreational vehicle or RV means a motor vehicle equipped with living space and 

amenities found in a motor home. 

Running clearance means the distance from the surface on which an automobile is 

standing to the lowest point on the automobile, excluding unsprung weight. 

Static loaded radius arc means a portion of a circle whose center is the center of a 

standard tire-rim combination of an automobile and whose radius is the distance from that center 

to the level surface on which the automobile is standing, measured with the automobile at curb 

weight, the wheel parallel to the vehicle's longitudinal centerline, and the tire inflated to the 

manufacturer's recommended pressure. 

Strong hybrid gasoline-electric vehicle means a vehicle as defined by EPA in 40 CFR 

86.1866–12(e). 

Temporary living quarters means a space in the interior of an automobile in which people 

may temporarily live and which includes sleeping surfaces, such as beds, and household 

conveniences, such as a sink, stove, refrigerator, or toilet. 

Van means a vehicle with a body that fully encloses the driver and a cargo carrying or 

work performing compartment. The distance from the leading edge of the windshield to the 

foremost body section of vans is typically shorter than that of pickup trucks and sport utility 

vehicles. 

Vocational tractor means a tractor that is classified as a vocational vehicle according to 

40 CFR 1037.630. 

Vocational vehicle means a vehicle that is equipped for a particular industry, trade or 

occupation such as construction, heavy hauling, mining, logging, oil fields, refuse and includes 

vehicles such as school buses, motorcoaches and RVs. 
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Work truck means a vehicle that is rated at more than 8,500 pounds and less than or equal 

to 10,000 pounds gross vehicle weight, and is not a medium-duty passenger vehicle as defined in 

40 CFR 86.1803 effective as of December 20, 2007. 

PART 531 – PASSENGER AUTOMOBILE AVERAGE FUEL ECONOMY STANDARDS

 3. The authority citation for part 531 continues to read as follows: 

Authority: 49 U.S.C. 32902; delegation of authority at 49 CFR 1.50 

 4. Amend § 531.5 by revising the introductory text of paragraph (a), revising paragraphs 

(b), (c), and (d) to read as follows, redesignating paragraph (e) as paragraph (f) and adding a new 

paragraph (e) to read as follows: 

§ 531.5 Fuel economy standards.

 (a) Except as provided in paragraph (e) of this section, each manufacturer of passenger 

automobiles shall comply with the fleet average fuel economy standards in Table I, expressed in 

miles per gallon, in the model year specified as applicable: 

* * * * * 

 (b) For model year 2011, a manufacturer’s passenger automobile fleet shall comply with 

the fleet average fuel economy level calculated for that model year according to Figure 1 and the 

appropriate values in Table II. 

Figure 1:

ൌ ݈݁ݒ݁ܮ_ݕ݉݋݊݋ܿܧ_݈݁ݑܨ_݁݀ݎ݅ݑݍܴ݁

ܰ

∑ ܰ௜

ܶ௜ ௜

 Where: 

 N is the total number (sum) of passenger automobiles produced by a manufacturer; 

 Ni is the number (sum) of the ith passenger automobile model produced by the 

manufacturer; and 

 Ti is the fuel economy target of the ith model passenger automobile, which is determined 

according to the following formula, rounded to the nearest hundredth: 

ܶ ൌ

1

1

ܽ ൅ ቀ1

ܾ െ	1

ܽቁ ݁ሺ௫ି௖ሻௗ

1൅݁ሺ௫ି௖ሻௗ
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

Page 299 of 426

 Where: 

 Parameters a, b, c, and d are defined in Table II; 

 e = 2.718; and 

 x = footprint (in square feet, rounded to the nearest tenth) of the vehicle model. 

TABLE II – PARAMETERS FOR THE PASSENGER AUTOMOBILE FUEL ECONOMY TARGETS

Model year Parameters 

a (mpg) b (mpg) c (gal/mi/ft2) d (gal/mi) 

2011............................. 31.20 24.00 51.41 1.91 

 (c) For model years 2012-2025, a manufacturer’s passenger automobile fleet shall 

comply with the fleet average fuel economy level calculated for that model year according to 

Figure 2 and the appropriate values in Table III. 

Figure 2:

௜ܱܰܫܶܥܷܦܴܱܲ ௜∑ ൌ ௗ௜௥௘௨௥௘௤ܧܨܣܥ

௜ܱܰܫܶܥܷܦܴܱܲ ∑

௜ ௜ܶܧܩܴܣܶ

Where: 

CAFErequired is the fleet average fuel economy standard for a given fleet (domestic 

passenger automobiles or import passenger automobiles); 

Subscript i is a designation of multiple groups of automobiles, where each group’s 

designation, i.e., i = 1, 2, 3, etc., represents automobiles that share a unique model type and 

footprint within the applicable fleet, either domestic passenger automobiles or import passenger 

automobiles; 

Productioni is the number of passenger automobiles produced for sale in the United States 

within each ith designation, i.e., which share the same model type and footprint; 

 TARGETi is the fuel economy target in miles per gallon (mpg) applicable to the footprint 

of passenger automobiles within each ith designation, i.e., which share the same model type and 

footprint, calculated according to Figure 3 and rounded to the nearest hundredth of a mpg, i.e., 

35.455 = 35.46 mpg, and the summations in the numerator and denominator are both performed 

over all models in the fleet in question. 

Figure 3:
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

Page 300 of 426

1

1 1

, ,

TARGET

MIN MAX c FOOTPRINT d

a b

              

Where: 

 TARGET is the fuel economy target (in mpg) applicable to vehicles of a given footprint 

(FOOTPRINT, in square feet); 

 Parameters a, b, c, and d are defined in Table III; and 

 The MIN and MAX functions take the minimum and maximum, respectively, of the 

included values. 

TABLE III – PARAMETERS FOR THE PASSENGER AUTOMOBILE FUEL ECONOMY TARGETS, MYS 

2012-2025 

Model year Parameters 

a (mpg) b (mpg) c (gal/mi/ft2) d (gal/mi) 

2012............................. 35.95 27.95 0.0005308 0.006057 

2013............................. 36.80 28.46 0.0005308 0.005410 

2014............................. 37.75 29.03 0.0005308 0.004725 

2015............................. 39.24 29.90 0.0005308 0.003719 

2016............................. 41.09 30.96 0.0005308 0.002573 

2017............................. 42.50 31.83 0.00053 0.00196 

2018.............................. 42.94 32.16 0.00052 0.00193 

2019.............................. 43.39 32.49 0.00052 0.00191 

2020.............................. 43.84 32.82 0.00051 0.00188 

2021.............................. 44.29 33.16 0.00051 0.00186 

2022............................. 44.75 33.50 0.0005 0.00183 

2023............................. 45.21 33.84 0.0005 0.00181 

2024............................. 45.68 34.19 0.00049 0.00179 

2025............................. 46.16 34.55 0.00049 0.00176 

 (d) In addition to the requirements of paragraphs (b) and (c) of this section, each 

manufacturer shall also meet the minimum fleet standard for domestically manufactured 

passenger automobiles expressed in Table IV: 

TABLE IV – MINIMUM FUEL ECONOMY STANDARDS FOR DOMESTICALLY MANUFACTURED 

PASSENGER AUTOMOBILES, MYS 2011-2021 

Model year Minimum standard 

2011............................................. 27.8

2012............................................. 30.7

2013............................................. 31.4

2014............................................. 32.1
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2015............................................. 33.3

2016............................................. 34.7

2017............................................. 36.8

2018............................................. 38.1

2019............................................. 39.6

2020............................................. 41.1

2021............................................. 42.9

2022............................................. 44.9

2023............................................. 47.0

2024............................................. 49.2

2025............................................. 51.5

 

(e) For model years 2022-2025, each manufacturer shall comply with the standards set 

forth in paragraphs (c) and (d) in this section, if NHTSA determines in a rulemaking, initiated 

after January 1, 2017, and conducted in accordance with 49 U.S.C. 32902, that the standards in 

paragraphs (c) and (d) are the maximum feasible standards for model years 2022-2025. If, for 

any of those model years, NHTSA determines that the maximum feasible standard for passenger 

cars and the corresponding minimum standard for domestically manufactured passenger cars 

should be set at a different level, manufacturers shall comply with those different standards in 

lieu of the standards set forth for those model years in paragraphs (c) and (d), and NHTSA will 

revise this section to reflect the different standards. 

 (f) The following manufacturers shall comply with the standards indicated below for the 

specified model years: 

* * * * * 

5. Amend § 531.6 by revising paragraph (a) to read as follows: 

§ 531.6 Measurement and calculation procedures.

 (a) The fleet average fuel economy performance of all passenger automobiles that are 

manufactured by a manufacturer in a model year shall be determined in accordance with 

procedures established by the Administrator of the Environmental Protection Agency under 49 

U.S.C. 32904 and set forth in 40 CFR part 600. For model years 2017 to 2025, a manufacturer is 

eligible to increase the fuel economy performance of passenger cars in accordance with 

procedures established by EPA set forth in 40 CFR part 600, including any adjustments to fuel 

economy EPA allows, such as for fuel consumption improvements related to air conditioning 

efficiency and off-cycle technologies. 

* * * * * 

 6. Amend Appendix A to part 531 to read as follows: 

APPENDIX TO PART 531 – EXAMPLE OF CALCULATING COMPLIANCE UNDER § 531.5(C) 
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

Page 302 of 426

 Assume a hypothetical manufacturer (Manufacturer X) produces a fleet of domestic 

passenger automobiles in MY 2012 as follows: 

APPENDIX TABLE I 

Model type 

Description 

Actual 

measured fuel economy 

(mpg) 

Volum

Group Carline name Basic engine e (L) 

Transmission 

class 

1 PC A FWD 1.8 A5 2-door sedan 34.0 1,500

2 PC A FWD 1.8 M6 2-door sedan 34.6 2,000

3 PC A FWD 2.5 A6 4-door wagon 33.8 2,000

4 PC A AWD 1.8 A6 4-door wagon 34.4 1,000

5 PC A AWD 2.5 M6 2-door hatchback 32.9 3,000

6 PC B RWD 2.5 A6 4-door wagon 32.2 8,000

7 PC B RWD 2.5 A7 4-door sedan 33.1 2,000

8 PC C AWD 3.2 A7 4-door sedan 30.6 5,000

9 PC C FWD 3.2 M6 2-door coupe 28.5 3,000

Total............................................................................................................

............. 

27,500 

 NOTE TO APPENDIX TABLE I: Manufacturer X’s required fleet average fuel economy 

standard level would first be calculated by determining the fuel economy targets applicable to 

each unique model type and footprint combination for model type groups 1-9 as illustrated in 

Appendix Table II: 

APPENDIX TABLE II 

 Manufacturer X calculates a fuel economy target standard for each unique model type 

and footprint combination. 

Model type 

Description Base tire 

size 

Wheelbase

(inches) 

Track 

width 

F&R 

average

(inches) 

Foot

print

(ft2

)

Volume

Fuel 

economy 

target 

standard

(mpg) 

Group Carline 

name 

Basic 

engine 

(L) 

Transmission 

class 

1 PC A 

FWD 

1.8 A5 2-door sedan 205/75R

14 

99.8 61.2 42.4 1,500 35.01 

2 PC A 

FWD 

1.8 M6 2-door sedan 215/70R

15 

99.8 60.9 42.2 2,000 35.14 

3 PC A 

FWD 

2.5 A6 4-door 

wagon 

215/70R

15 

100.0 60.9 42.3 2,000 35.08 

4 PC A 1.8 A6 4-door 235/60R 100.0 61.2 42.5 1,000 35.95 
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AWD wagon 15 

5 PC A 

AWD 

2.5 M6 2-door 

hatchback 

225/65R

16 

99.6 59.5 41.2 3,000 35.81 

6 PC B 

RWD 

2.5 A6 4-door 

wagon 

265/55R

18 

109.2 66.8 50.7 8,000 30.33 

7 PC B 

RWD 

2.5 A7 4-door sedan 235/65R

17 

109.2 67.8 51.4 2,000 29.99 

8 PC C 

AWD 

3.2 A7 4-door sedan 265/55R

18 

111.3 67.8 52.4 5,000 29.52 

9 PC C 

FWD 

3.2 M6 2-door 

coupe 

225/65R

16 

111.3 67.2 51.9 3,000 29.76 

Total.............................................................................................

......... 

27,500 

NOTE TO APPENDIX TABLE II: With the appropriate fuel economy targets determined for each 

unique model type and footprint combination, Manufacturer X’s required fleet average fuel 

economy standard would be calculated as illustrated in Appendix Figure 1: 

Appendix Figure 1 – Calculation of Manufacturer X’s fleet average fuel economy standard 

using Table II: 

Fleet average fuel economy standard = 

′ݎ݁ݎݑݐ݂ܽܿݑܽ݊ܯሺ ൌ

ሻݎܻ݁ܽ	݈݀݁݋ܯ	݈ܾ݈݅ܿܽ݁݌݌ܣ	ݎ݋݂	݊݋݅ݐܿݑ݀݋ݎܲ	ܾ݈݅݁݋݉݋ݐݑܣ	ݎ݁݊݃݁ݏݏܲܽ	݅ܿݐݏ݉݁݋ܦ	ݏ

݊݋݅ݐܿݑ݀݋ݎܲ	ଵ݌ݑ݋ݎܩ ሺ∑ ݊݋݅ݐܿݑ݀݋ݎܲ	ଶ݌ݑ݋ݎܩ ൅ ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽଵ݌ݑ݋ݎܩ݊݋݅ݐܿݑ݀݋ݎܲ	ଽ݌ݑ݋ݎܩ ⋯ ൅ ௜ ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽଶ݌ݑ݋ݎܩ

ሻ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽଽ݌ݑ݋ݎܩ

ൌ ሺ27,500ሻ

ሺ

1500

35.01 ൅ 2000

35.14 ൅ 200035.08 ൅ 100035.95 ൅ 300035.81 ൅ 8000

30.33 ൅ 200029.99 ൅ 5000

29.52 ൅ 3000

29.79ሻ

 

 

 = 31.6 mpg 

Appendix Figure 2 – Calculation of Manufacturer X’s actual fleet average fuel economy 

performance level using Table I:

Fleet average fuel economy performance = 

′ݎ݁ݎݑݐ݂ܽܿݑܽ݊ܯሺ ൌ

ሻݎܻ݁ܽ	݈݀݁݋ܯ	݈ܾ݈݅ܿܽ݁݌݌ܣ	ݎ݋݂	݊݋݅ݐܿݑ݀݋ݎܲ	ܾ݈݅݁݋݉݋ݐݑܣ	ݎ݁݊݃݁ݏݏܲܽ	݅ܿݐݏ݉݁݋ܦ	ݏ

݊݋݅ݐܿݑ݀݋ݎܲ	ଵ݌ݑ݋ݎܩ ሺ∑ ݊݋݅ݐܿݑ݀݋ݎܲ	ଶ݌ݑ݋ݎܩ ൅ ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ଵ݌ݑ݋ݎܩ݊݋݅ݐܿݑ݀݋ݎܲ	ଽ݌ݑ݋ݎܩ ⋯ ൅ ௜ ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ଶ݌ݑ݋ݎܩ

ሻ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ଽ݌ݑ݋ݎܩ
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

Page 304 of 426

ൌ ሺ27,500ሻ

ሺ

1500

34.0 ൅ 2000

34.6 ൅ 200033.8 ൅ 100034.4 ൅ 300032.9 ൅ 800032.2 ൅ 200033.1 ൅ 500030.6 ൅ 3000

28.5 ሻ

 = 32.0 mpg 

NOTE TO APPENDIX FIGURE 2: Since the actual fleet average fuel economy performance of 

Manufacturer X’s fleet is 32.0 mpg, as compared to its required fleet fuel economy standard of 

31.6 mpg, Manufacturer X complied with the CAFE standard for MY 2012 as set forth in § 

531.5(c). 

PART 533 – LIGHT TRUCK FUEL ECONOMY STANDARDS

 7. The authority citation for part 531 continues to read as follows: 

Authority: 49 U.S.C. 32902; delegation of authority at 49 CFR 1.50 

 8. Amend § 533.5 by revising paragraphs (a), (f), (g), (h), (i) to read as follows and 

adding paragraphs (j) and (k) to read as follows: 

§ 533.5 Requirements.

 (a) Each manufacturer of light trucks shall comply with the following fleet average fuel 

economy standards, expressed in miles per gallon, in the model year specified as applicable: 

TABLE I 

Model year 

2-wheel drive light trucks 4-wheel drive light trucks Limited product Captive line light trucks imports Other Captive 

imports Other 

1979........ ............. 17.2 .............. 15.8 ..................

1980........ 16.0 16.0 14.0 14.0 14.0

1981........ 16.7 16.7 15.0 15.0 14.5

TABLE II 

Model year 

Combined standard 2-wheel drive light trucks 4-wheel drive light trucks 

Captive 

imports Others Captive imports Others Captive imports Others 

1982......... 17.5 17.5 18.0 18.0 16.0 16.0 

1983......... 19.0 19.0 19.5 19.5 17.5 17.5 
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1984......... 20.0 20.0 20.3 20.3 18.5 18.5 

1985......... 19.5 19.5 19.7 19.7 18.9 18.9 

1986......... 20.0 20.0 20.5 20.5 19.5 19.5 

1987......... 20.5 20.5 21.0 21.0 19.5 19.5 

1988......... 20.5 20.5 21.0 21.0 19.5 19.5 

1989......... 20.5 20.5 21.5 21.5 19.0 19.0 

1990......... 20.0 20.0 20.5 20.5 19.0 19.0 

1991......... 20.2 20.2 20.7 20.7 19.1 19.1 

TABLE III 

Model Year 

Combined standard 

Captive imports Other 

1992.................................. 20.2 20.2 

1993.................................. 20.4 20.4 

1994.................................. 20.5 20.5 

1995.................................. 20.6 20.6 

TABLE IV 

Model year Standard 

2001........................................................................... 20.7 

2002........................................................................... 20.7 

2003........................................................................... 20.7 

2004........................................................................... 20.7 

2005........................................................................... 21.0 

2006........................................................................... 21.6 

2007........................................................................... 22.2 

2008........................................................................... 22.5 

2009........................................................................... 23.1 

2010........................................................................... 23.5 
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

Page 306 of 426

 

Figure 1: 

ൌ ݈݁ݒ݁ܮ_ݕ݉݋݊݋ܿܧ_݈݁ݑܨ_݁݀ݎ݅ݑݍܴ݁

ܰ

∑ ܰ௜

ܶ௜ ௜

 Where: 

 N is the total number (sum) of light trucks produced by a manufacturer; 

 Ni is the number (sum) of the ith light truck model type produced by a manufacturer; and 

 Ti is the fuel economy target of the ith light truck model type, which is determined 

according to the following formula, rounded to the nearest hundredth: 

ܶ ൌ

1

1

ܽ ൅ ቀ

1

ܾ െ	1

ܽቁ ݁ሺ௫ି௖ሻௗ

1൅݁ሺ௫ି௖ሻௗ

 Where: 

 Parameters a, b, c, and d are defined in Table V; 

 e = 2.718; and 

 x = footprint (in square feet, rounded to the nearest tenth) of the model type. 

TABLE V – PARAMETERS FOR THE LIGHT TRUCK FUEL ECONOMY TARGETS FOR MYS 2008-2011 

Model year Parameters 

a (mpg) b (mpg) c (gal/mi/ft2) d (gal/mi)

2008............................. 28.56 19.99 49.30 5.58 

2009............................. 30.07 20.87 48.00 5.81 

2010............................. 29.96 21.20 48.49 5.50 

2011............................. 27.10 21.10 56.41 4.28 

 Figure 2:

௜ܱܰܫܶܥܷܦܴܱܲ ௜∑ ൌ ௗ௜௥௘௨௥௘௤ܧܨܣܥ

௜ܱܰܫܶܥܷܦܴܱܲ ∑

௜ ௜ܶܧܩܴܣܶ

 Where: 

 CAFErequired is the fleet average fuel economy standard for a given light truck fleet; 
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

Page 307 of 426

 Subscript i is a designation of multiple groups of light trucks, where each group’s 

designation, i.e., i = 1, 2, 3, etc., represents light trucks that share a unique model type and 

footprint within the applicable fleet. 

Productioni is the number of light trucks produced for sale in the United States within each ith 

designation, i.e., which share the same model type and footprint; 

 TARGETi is the fuel economy target in miles per gallon (mpg) applicable to the footprint 

of light trucks within each ith designation, i.e., which share the same model type and footprint, 

calculated according to either Figure 3 or Figure 4, as appropriate, and rounded to the nearest 

hundredth of a mpg, i.e., 35.455 = 35.46 mpg, and the summations in the numerator and 

denominator are both performed over all models in the fleet in question. 

Figure 3:

1

1 1

, ,

TARGET

MIN MAX c FOOTPRINT d

a b

              

 Where: 

 TARGET is the fuel economy target (in mpg) applicable to vehicles of a given footprint 

(FOOTPRINT, in square feet); 

 Parameters a, b, c, and d are defined in Table VI; and 

 The MIN and MAX functions take the minimum and maximum, respectively, of the 

included values. 

TABLE VI – PARAMETERS FOR THE LIGHT TRUCK FUEL ECONOMY TARGETS FOR MYS 2012-2016 

Model year Parameters 

a (mpg) b (mpg) c (gal/mi/ft2) d (gal/mi) 

2012............................. 29.82 22.27 0.0004546 0.014900 

2013............................. 30.67 22.74 0.0004546 0.013968 

2014............................. 31.38 23.13 0.0004546 0.013225 

2015............................. 32.72 23.85 0.0004546 0.011920 

2016............................. 34.42 24.74 0.0004546 0.010413 

Figure 4:

1 ൮ ܺܣܯ ൌ ܶܧܩܴܣܶ

1 ݀, ൅ ܰܶܫܱܱܴܶܲܨ ൈ ܿቀ ܺܣܯቂ ܰܫܯ

ܽቁ , 1

ܾቃ

, 1

1 ݄ ൅ ܰܶܫܱܱܴܶܲܨ ൈ ݃ቀ ܺܣܯ൤ ܰܫܯ

݁ቁ , 1

݂൨

൲
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

Page 308 of 426

 Where: 

 TARGET is the fuel economy target (in mpg) applicable to vehicles of a given footprint 

(FOOTPRINT, in square feet); 

 Parameters a, b, c, d, e, f, g, and h are defined in Table VII; and 

 The MIN and MAX functions take the minimum and maximum, respectively, of the 

included values. 

TABLE VII – PARAMETERS FOR THE LIGHT TRUCK FUEL ECONOMY TARGETS FOR MYS 2017-2025 

Model 

year 

Parameters 

a

(mpg) 

b

(mpg) 

c 

(gal/mi/ft2

) 

d

(gal/mi) 

e

(mpg)

f

(mpg)

g 

(gal/mi/ft2) 

h

(gal/mi) 

2017 43.61 32.65 0.00051 0.0019 42.06 31.51 0.00053 0.00201 

2018 45.21 33.84 0.0005 0.00181 42.06 31.51 0.00053 0.00201 

2019 46.87 35.07 0.00048 0.00173 42.06 31.51 0.00053 0.00201 

2020 48.74 36.47 0.00046 0.00164 42.06 31.51 0.00053 0.00201 

2021 50.83 38.02 0.00044 0.00155 42.06 31.51 0.00053 0.00201 

2022 53.21 39.79 0.00042 0.00146 42.06 31.51 0.00053 0.00201 

2023 55.71 41.64 0.0004 0.00137 42.06 31.51 0.00053 0.00201 

2024 58.32 43.58 0.00039 0.00129 42.06 31.51 0.00053 0.00201 

2025 61.07 45.61 0.00037 0.00121 42.06 31.51 0.00053 0.00201 

* * * * * 

 (f) For each model year 1996 and thereafter, each manufacturer shall combine its captive 

imports with its other light trucks and comply with the fleet average fuel economy standard in 

paragraph (a) of this section. 

 (g) For model years 2008–2010, at a manufacturer's option, a manufacturer's light truck 

fleet may comply with the fuel economy standard calculated for each model year according to 

Figure 1 and the appropriate values in Table V, with said option being irrevocably chosen for 

that model year and reported as specified in § 537.8. 

 (h) For model year 2011, a manufacturer's light truck fleet shall comply with the fleet 

average fuel economy standard calculated for that model year according to Figure 1 and the 

appropriate values in Table V. 

 (i) For model years 2012–2016, a manufacturer's light truck fleet shall comply with the 

fleet average fuel economy standard calculated for that model year according to Figures 2 and 3 

and the appropriate values in Table VI. 
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 (j) For model years 2017–2025, a manufacturer's light truck fleet shall comply with the 

fleet average fuel economy standard calculated for that model year according to Figures 2 and 4 

and the appropriate values in Table VII. 

(k) For model years 2022-2025, each manufacturer shall comply with the standards set 

forth in paragraph (j) in this section, if NHTSA determines in a rulemaking, initiated after 

January 1, 2017, and conducted in accordance with 49 U.S.C. 32902, that the standards in 

paragraph (j) are the maximum feasible standards for model years 2022-2025. If, for any of 

those model years, NHTSA determines that the maximum feasible standard for light trucks 

should be set at a different level, manufacturers shall comply with those different standards in 

lieu of the standards set forth for those model years in paragraph (j), and NHTSA will revise this 

section to reflect the different standards. 

* * * * * 

9. Amend § 533.6 by revising paragraph (b) to read as follows: 

§ 533.6 Measurement and calculation procedures. 

* * * * * 

 (b) The fleet average fuel economy performance of all vehicles subject to Part 533 that 

are manufactured by a manufacturer in a model year shall be determined in accordance with 

procedures established by the Administrator of the Environmental Protection Agency under 49 

U.S.C. 32904 and set forth in 40 CFR part 600. For model years 2017 to 2025, a manufacturer is 

eligible to increase the fuel economy performance of light trucks in accordance with procedures 

established by EPA set forth in 40 CFR part 600, including any adjustments to fuel economy 

EPA allows, such as for fuel consumption improvements related to air conditioning efficiency, 

off-cycle technologies, and hybridization and other over-compliance for full-size pickup trucks. 

* * * * * 

 10. Amend Appendix A to Part 533 by revising it to read as follows: 

APPENDIX TO PART 533 – EXAMPLE OF CALCULATING COMPLIANCE UNDER § 533.5(I) 

 Assume a hypothetical manufacturer (Manufacturer X) produces a fleet of light trucks in 

MY 2012 as follows: 

APPENDIX TABLE I 

Model type 

Description 

Actual 

measured fuel economy

(mpg) 

Volume 

Group Carline 

name 

Basic engine 

(L) 

Transmission 

class 
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1 Pickup A 

2WD 

4 A5 Reg cab, MB 27.1 800 

2 Pickup B 

2WD 

4 M5 Reg cab, MB 27.6 200 

3 Pickup C 

2WD 

4.5 A5 Reg cab, LB 23.9 300 

4 Pickup C 

2WD 

4 M5 Ext cab, MB 23.7 400 

5 Pickup C 

4WD 

4.5 A5 Crew cab, SB 23.5 400 

6 Pickup D 

2WD 

4.5 A6 Crew cab, SB 23.6 400 

7 Pickup E 

2WD 

5 A6 Ext cab, LB 22.7 500 

8 Pickup E 

2WD 

5 A6 Crew cab, MB 22.5 500 

9 Pickup F 

2WD 

4.5 A5 Reg cab, LB 22.5 1,600 

10 Pickup F 

4WD 

4.5 A5 Ext cab, MB 22.3 800 

11 Pickup F 

4WD 

4.5 A5 Crew cab, SB 22.2 800 

Total..........................................................................................................

............... 

6,700 

 NOTE TO APPENDIX TABLE I: Manufacturer X’s required fleet average fuel economy 

standard level would first be calculated by determining the fuel economy targets applicable to 

each unique model type and footprint combination for model type groups 1-11 as illustrated in 

Appendix Table II: 

APPENDIX TABLE II 

 Manufacturer X calculates a fuel economy target standard for each unique model type 

and footprint combination. 

Model type 

Description

Base 

tire size

Wheelbase

(inches) 

Track 

width 

F&R 

average

(inches)

Footprint

(ft2

) Volume

Fuel 

economy 

target 

standard 

Group (mpg) 

Carline 

name 

Basic 

engine 

(L) 

Transmission 

class 

1 Pickup A 

2WD 

4 A5 Reg cab, MB 235/75R

15 

100.0 68.8 47.8 800 27.30 

2 Pickup B 

2WD 

4 M5 Reg cab, MB 235/75R

15 

100.0 68.2 47.4 200 27.44 

3 Pickup C 4.5 A5 Reg cab, LB 255/70R 125.0 68.8 59.7 300 23.79 
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2WD 17 

4 Pickup C 

2WD 

4 M5 Ext cab, MB 255/70R

17 

125.0 68.8 59.7 400 23.79 

5 Pickup C 

4WD 

4.5 A5 Crew cab, SB 275/70R

17 

150.0 69.0 71.9 400 22.27 

6 Pickup D 

2WD 

4.5 A6 Crew cab, SB 255/70R

17 

125.0 68.8 59.7 400 23.79 

7 Pickup E 

2WD 

5 A6 Ext cab, LB 255/70R

17 

125.0 68.8 59.7 500 23.79 

8 Pickup E 

2WD 

5 A6 Crew cab, 

MB 

285/70R

17 

125.0 69.2 60.1 500 23.68 

9 Pickup F 

2WD 

4.5 A5 Reg cab, LB 255/70R

17 

125.0 68.9 59.8 1,600 23.76 

10 Pickup F 

4WD 

4.5 A5 Ext cab, MB 275/70R

17 

150.0 69.0 71.9 800 22.27 

11 Pickup F 

4WD 

4.5 A5 Crew cab, SB 285/70R

17 

150.0 69.2 72.1 800 22.27 

Total......................................................................................................

............... 

6,700 

NOTE TO APPENDIX TABLE II: With the appropriate fuel economy targets determined for each 

unique model type and footprint combination, Manufacturer X’s required fleet average fuel 

economy standard would be calculated as illustrated in Appendix Figure 1: 

Appendix Figure 1 – Calculation of Manufacturer X’s fleet average fuel economy standard 

using Table II: 

Fleet average fuel economy standard = 

′ݎ݁ݎݑݐ݂ܽܿݑܽ݊ܯሺ ൌ

ሻݎܻ݁ܽ	݈݀݁݋ܯ	݈ܾ݈݅ܿܽ݁݌݌ܣ	ݎ݋݂	݊݋݅ݐܿݑ݀݋ݎܲ		ܿ݇ݑݎܶ	ݐ݅݃ܮ		ݏ

݊݋݅ݐܿݑ݀݋ݎܲ	ଵ݌ݑ݋ݎܩ ሺ∑ ݊݋݅ݐܿݑ݀݋ݎܲ	௔ଶ݌ݑ݋ݎܩ ൅ ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽଵ݌ݑ݋ݎܩ݊݋݅ݐܿݑ݀݋ݎܲ	ଵଵ݌ݑ݋ݎܩ ⋯ ൅ ௜ ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽଶ݌ݑ݋ݎܩ

ሻ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽଵଵ݌ݑ݋ݎܩ

ൌ ሺ6,700ሻ

ሺ 800

27.30 ൅ 200

27.44 ൅ 30023.79 ൅ 40023.79 ൅ 40022.27 ൅ 40023.79 ൅ 50023.79 ൅ 50023.68 ൅ 160023.76 ൅ 80022.27 ൅ 80022.27ሻ

 

 

 = 23.7 mpg 

Appendix Figure 2 – Calculation of Manufacturer X’s actual fleet average fuel economy 

performance level using Table I:

Fleet average fuel economy performance = 
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′ݎ݁ݎݑݐ݂ܽܿݑܽ݊ܯሺ ൌ

ሻݎܻ݁ܽ	݈݀݁݋ܯ	݈ܾ݈݅ܿܽ݁݌݌ܣ	ݎ݋݂	݊݋݅ݐܿݑ݀݋ݎܲ	ܿ݇ݑݎܶ	ݐ݅݃ܮ	ݏ

݊݋݅ݐܿݑ݀݋ݎܲ	ଵ݌ݑ݋ݎܩ ሺ∑ ݊݋݅ݐܿݑ݀݋ݎܲ	ଶ݌ݑ݋ݎܩ ൅ ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ଵ݌ݑ݋ݎܩ݊݋݅ݐܿݑ݀݋ݎܲ	ଵଵ݌ݑ݋ݎܩ ⋯ ൅ ௜ ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ଶ݌ݑ݋ݎܩ

ሻ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ଵଵ݌ݑ݋ݎܩ

ൌ ሺ27,500ሻ

ሺ

1500

34.0 ൅ 2000

34.6 ൅ 200033.8 ൅ 100034.4 ൅ 300032.9 ൅ 800032.2 ൅ 200033.1 ൅ 500030.6 ൅ 3000

28.5 ሻ

 = 23.3 mpg 

NOTE TO APPENDIX FIGURE 2: Since the actual fleet average fuel economy performance of 

Manufacturer X’s fleet is 23.3 mpg, as compared to its required fleet fuel economy standard of 

23.7 mpg, Manufacturer X did not comply with the CAFE standard for MY 2012 as set forth in § 

533.5(i). 

PART 536 – TRANSFER AND TRADING OF FUEL ECONOMY CREDITS

 11. Amend the authority citation for part 536 by revising it to read as follows: 

Authority: 49 U.S.C. 32903; delegation of authority at 49 CFR 1.50. 

12. Amend § 536.4 by revising paragraph (c) to read as follows: 

§ 536.4 Credits.

* * * * * 

 (c) Adjustment factor. When traded or transferred and used, fuel economy credits are 

adjusted to ensure fuel oil savings is preserved. For traded credits, the user (or buyer) must 

multiply the calculated adjustment factor by the number of its shortfall credits it plans to offset in 

order to determine the number of equivalent credits to acquire from the earner (or seller). For 

transferred credits, the user of credits must multiply the calculated adjustment factor by the 

number of its shortfall credits it plans to offset in order to determine the number of equivalent 

credits to transfer from the compliance category holding the available credits. The adjustment 

factor is calculated according to the following formula: 

A = VMTu * MPGae * MPGse

 VMTe * MPGau * MPGsu 

 Where: 

 A = Adjustment factor applied to traded and transferred credits; 
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 VMTe = Lifetime vehicle miles traveled as provided in the following table for the model 

year and compliance category in which the credit was earned; 

 VMTu = Lifetime vehicle miles traveled as provided in the following table for the model 

year and compliance category in which the credit is used for compliance; 

Model year 

Lifetime Vehicle Miles Traveled (VMT) 

2011 2012 2013 2014 2015 2016 2017-

2025 

Passenger Cars 152,000 177,238 177,366 178,652 180,497 182,134 195,264

Light Trucks 179,000 208,471 208,537 209,974 212,040 213,954 225,865

 MPGse = Required fuel economy standard for the originating (earning) manufacturer, 

compliance category, and model year in which the credit was earned; 

 MPGae = Actual fuel economy for the originating manufacturer, compliance category, 

and model year in which the credit was earned; 

 MPGsu = Required fuel economy standard for the user (buying) manufacturer, 

compliance category, and model year in which the credit is used for compliance; and 

 MPGau = Actual fuel economy for the user manufacturer, compliance category, and 

model year in which the credit is used for compliance. 

* * * * * 

 13. Amend § 536.9 by revising paragraph (c) to read as follows: 

§ 536.9 Use of credits with regard to the domestically manufactured passenger automobile 

minimum standard. 

* * * * * 

 (c) Transferred or traded credits may not be used, pursuant to 49 U.S.C. 32903(g)(4) and 

(f)(2), to meet the domestically manufactured passenger automobile minimum standard specified 

in 49 U.S.C. 32902(b)(4) and in 49 CFR 531.5(d). 

* * * * * 

 14. Amend § 536.10 by revising paragraphs (b) and (c) and adding paragraph (d) to read 

as follows: 

§ 536.10 Treatment of dual-fuel and alternative-fuel vehicles – consistency with 49 CFR 

part 538.
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* * * * * 

(b) If a manufacturer's calculated fuel economy for a particular compliance category, 

including any statutorily-required calculations for alternative fuel and dual fuel vehicles, is 

higher or lower than the applicable fuel economy standard, manufacturers will earn credits or 

must apply credits or pay civil penalties equal to the difference between the calculated fuel 

economy level in that compliance category and the applicable standard. Credits earned are the 

same as any other credits, and may be held, transferred, or traded by the manufacturer subject to 

the limitations of the statute and this regulation. 

(c) For model years up to and including MY 2019, if a manufacturer builds enough dual 

fuel vehicles (except plug-in electric vehicles) to improve the calculated fuel economy in a 

particular compliance category by more than the limits set forth in 49 U.S.C. 32906(a), the 

improvement in fuel economy for compliance purposes is restricted to the statutory limit. 

Manufacturers may not earn credits nor reduce the application of credits or fines for calculated 

improvements in fuel economy based on dual fuel vehicles beyond the statutory limit. 

(d) For model years 2020 and beyond, a manufacturer must calculate the fuel economy 

of dual fueled vehicles in accordance with 40 CFR 600.510-12(c)(2)(v) and (vii). 

* * * * * 

PART 537 – AUTOMOTIVE FUEL ECONOMY REPORTS

 15. The authority citation for part 537 continues to read as follows: 

Authority: 49 U.S.C. 32907, delegation of authority at 49 CFR 1.50. 

 16. Amend § 537.5 by revising subparagraph (c)(4) to read as follows: 

* * * * * 

 (c) * * * 

 (4) Be submitted on CD or by email with the contents in a pdf or MS Word format 

except the information required in 537.7 must be provided in a MS Excel format. Submit 2 

copies of the CD to: Administrator, National Highway Traffic Administration, 1200 New Jersey 

Avenue, SW, Washington, DC 20590, or submit reports electronically to the following secure email address: cafe@dot.gov; 

* * * * * 

 17. Amend § 537.7 by revising subparagraphs (b)(3), (c)(4), and (c)(5) to read as 

follows: 

§ 537.7 Pre-model year and mid-model year reports.
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* * * * * 

 (b) * * * 

 (3) State the projected required fuel economy for the manufacturer’s passenger 

automobiles and light trucks determined in accordance with 49 CFR 531.5(c) and 49 CFR 533.5 

and based upon the projected sales figures provided under paragraph (c)(2) of this section. For 

each unique model type and footprint combination of the manufacturer’s automobiles, provide 

the information specified in paragraph (b)(3)(i) and (ii) of this section in tabular form. List the 

model types in order of increasing average inertia weight from top to bottom down the left side 

of the table and list the information categories in the order specified in paragraphs (i) and (ii) of 

this section from left to right across the top of the table. Other formats, such as those accepted 

by EPA, which contain all of the information in a readily identifiable format are also acceptable. 

(i) In the case of passenger automobiles: 

(1) Beginning model year 2013, base tire as defined in 49 CFR 523.2, 

(2) Beginning model year 2013, front axle, rear axle and average track width as defined 

in 49 CFR 523.2, 

(3) Beginning model year 2013, wheelbase as defined in 49 CFR 523.2, and 

(4) Beginning model year 2013, footprint as defined in 49 CFR 523.2. 

(ii) In the case of light trucks: 

(1) Beginning model year 2013, base tire as defined in 49 CFR 523.2, 

(2) Beginning model year 2013, front axle, rear axle and average track width as defined 

in 49 CFR 523.2, 

(3) Beginning model year 2013, wheelbase as defined in 49 CFR 523.2, and 

(4) Beginning model year 2013, footprint as defined in 49 CFR 523.2. 

* * * * * 

 (c) * * * 

 (4) (i) Loaded vehicle weight; 

(ii) Equivalent test weight; 

(iii) Engine displacement, liters; 
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(iv) SAE net rated power, kilowatts; 

(v) SAE net horsepower; 

(vi) Engine code; 

(vii) Fuel system (number of carburetor barrels or, if fuel injection is used, so indicate); 

(viii) Emission control system; 

(ix) Transmission class; 

(x) Number of forward speeds; 

(xi) Existence of overdrive (indicate yes or no); 

(xii) Total drive ratio (N/V); 

(xiii) Axle ratio; 

(xiv) Combined fuel economy; 

(xv) Projected sales for the current model year; 

(xvi) Air conditioning efficiency improvement technologies used to acquire the incentive 

in 40 CFR 86.1866 and the amount of the incentive; 

(xvii) Full size pickup truck technologies used to acquire the incentive in 40 CFR 

86.1866 and the amount of the incentive; 

(xviii) Off-cycle technologies used to acquire the incentive in 40 CFR 86.1866 and the 

amount of the incentive; 

(xix) (A) In the case of passenger automobiles: 

( 1 ) Interior volume index, determined in accordance with subpart D of 40 CFR part 600; 

( 2 ) Body style; 

(B) In the case of light trucks: 

( 1 ) Passenger-carrying volume; 

( 2 ) Cargo-carrying volume; 
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 (xx) Frontal area; 

 (xxi) Road load power at 50 miles per hour, if determined by the manufacturer for 

purposes other than compliance with this part to differ from the road load setting prescribed in 40 

CFR 86.177–11(d); 

(xxii) Optional equipment that the manufacturer is required under 40 CFR parts 86 and 

600 to have actually installed on the vehicle configuration, or the weight of which must be 

included in the curb weight computation for the vehicle configuration, for fuel economy testing 

purposes. 

 (5) For each model type of automobile which is classified as a non-passenger vehicle 

(light truck) under part 523 of this chapter, provide the following data: 

(i) For an automobile designed to perform at least one of the following functions in 

accordance with 523.5 (a) indicate (by “yes” or “no”) whether the vehicle can: 

(A) Transport more than 10 persons (if yes, provide actual designated seating positions); 

(B) Provide temporary living quarters (if yes, provide applicable conveniences as defined 

in 523.2); 

(C) Transport property on an open bed (if yes, provide bed size width and length); 

(D) Provide, as sold to the first retail purchaser, greater cargo-carrying than passengercarrying volume, such as in a cargo van and quantify the value; if a vehicle is sold with a secondrow seat, its cargo-carrying volume is determined with that seat installed, regardless of whether 

the manufacturer has described that seat as optional; or 

(E) Permit expanded use of the automobile for cargo-carrying purposes or other non 

passenger-carrying purposes through: 

(1) For non-passenger automobiles manufactured prior to model year 2012, the removal 

of seats by means installed for that purpose by the automobile's manufacturer or with simple 

tools, such as screwdrivers and wrenches, so as to create a flat, floor level, surface extending 

from the forward-most point of installation of those seats to the rear of the automobile's interior; 

or 

(2) For non-passenger automobiles manufactured in model year 2008 and beyond, for 

vehicles equipped with at least 3 rows of designated seating positions as standard equipment, 

permit expanded use of the automobile for cargo-carrying purposes or other nonpassengercarrying purposes through the removal or stowing of foldable or pivoting seats so as to create a 

flat, leveled cargo surface extending from the forward-most point of installation of those seats to 

the rear of the automobile's interior. 
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(ii) For an automobile capable of off-highway operation, identify which of the features 

below qualify the vehicle as off-road in accordance with 523.5 (b) and quantify the values of 

each feature: 

(A) 4-wheel drive; or 

(B) A rating of more than 6,000 pounds gross vehicle weight; and 

(C) Has at least four of the following characteristics calculated when the automobile is at 

curb weight, on a level surface, with the front wheels parallel to the automobile's longitudinal 

centerline, and the tires inflated to the manufacturer's recommended pressure. The exact value of 

each feature should be quantified: 

(1) Approach angle of not less than 28 degrees. 

(2) Breakover angle of not less than 14 degrees. 

(3) Departure angle of not less than 20 degrees. 

(4) Running clearance of not less than 20 centimeters. 

(5) Front and rear axle clearances of not less than 18 centimeters each. 

* * * * * 

 18. Amend § 537.8 by revising subparagraph (a)(3) to read as follows: 

§ 537.8 Supplementary reports.

 (a) * * * 

 (3) Each manufacturer whose pre-model year report omits any of the information 

specified in §537.7 (b), (c)(1) and (2), or (c)(4) shall file a supplementary report containing the 

information specified in paragraph (b)(3) of this section. 

* * * * * 

FERC, 297 F. 3d 1071, 1084 (D.C. Cir. 2002) (same). 

a. EPA’s Testing Authority 

Under section 203 of the CAA, sales of vehicles are prohibited unless the vehicle is 

covered by a certificate of conformity. EPA issues certificates of conformity pursuant to section 

206 of the Act, based on (necessarily) pre-sale testing conducted either by EPA or by the 

manufacturer. The Federal Test Procedure (FTP or ‘‘city’’ test) and the Highway Fuel Economy 

Test (HFET or ‘‘highway’’ test) are used for this purpose. Compliance with standards is 

required not only at certification but throughout a vehicle’s useful life, so that testing 
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Page 319 of 426

requirements may continue post-certification. Useful life standards may apply an adjustment 

factor to account for vehicle emission control deterioration or variability in use (section 206(a)). 

Pursuant to EPCA, EPA is required to measure fuel economy for each model and to 

calculate each manufacturer’s average fuel economy.279 EPA uses the same tests – the FTP and 

HFET – for fuel economy testing. EPA established the FTP for emissions measurement in the 

early 1970s. In 1976, in response to the Energy Policy and Conservation Act (EPCA) statute, 

EPA extended the use of the FTP to fuel economy measurement and added the HFET.280 The 

provisions in the 1976 regulation, effective with the 1977 model year, established procedures to 

calculate fuel economy values both for labeling and for CAFE purposes. Under EPCA, EPA is 

required to use these procedures (or procedures which yield comparable results) for measuring 

fuel economy for cars for CAFE purposes, but not for labeling purposes.281 EPCA does not pose 

this restriction on CAFE test procedures for light trucks, but EPA does use the FTP and HFET 

for this purpose. EPA determines fuel economy by measuring the amount of CO2 and all other 

carbon compounds (e.g. total hydrocarbons (THC) and carbon monoxide (CO)), and then, by 

mass balance, calculating the amount of fuel consumed. EPA’s proposed changes to the 

procedures for measuring fuel economy and calculating average fuel economy are discussed in 

section III.B.10. 

b. EPA Enforcement Authority 

Section 207 of the CAA grants EPA broad authority to require manufacturers to remedy 

vehicles if EPA determines there are a substantial number of noncomplying vehicles. In 

addition, section 205 of the CAA authorizes EPA to assess penalties of up to $37,500 per vehicle 

for violations of various prohibited acts specified in the CAA. In determining the appropriate 

penalty, EPA must consider a variety of factors such as the gravity of the violation, the economic 

impact of the violation, the violator’s history of compliance, and “such other matters as justice 

may require.” Unlike EPCA, the CAA does not authorize vehicle manufacturers to pay fines in 

lieu of meeting emission standards. 

c. Compliance 

EPA oversees testing, collects and processes test data, and performs calculations to 

determine compliance with both CAA and CAFE standards. CAA standards apply not only at 

the time of certification but also throughout the vehicle’s useful life, and EPA is accordingly is 

proposing in-use standards as well as standards based on testing performed at time of production. 

See section III.E. Both the CAA and EPCA provide for penalties should manufacturers fail to 

comply with their fleet average standards, but, unlike EPCA, there is no option for manufacturers 

 279 See 49 U.S.C. 32904(c). 280 See 41 FR 38674 (Sept. 10, 1976), which is codified at 40 CFR Part 600. 281 See 49 U.S.C. 32904(c). 
DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL 

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to pay fines in lieu of compliance with the standards. Under the CAA, penalties are typically 

determined on a vehicle-specific basis by determining the number of a manufacturer’s highest 

emitting vehicles that cause the fleet average standard violation. Penalties under Title II of the 

CAA are capped at $25,000 per day of violation and apply on a per vehicle basis. CAA section 

205 (a). 

d. Test Procedures 

EPA establishes the test procedures under which compliance with both the CAA GHG 

standards and the EPCA fuel economy standards are measured. EPA’s testing authority under 

the CAA is flexible, but testing for fuel economy for passenger cars is by statute is limited to the 

Federal Test procedure (FTP) or test procedures which provide results which are equivalent to 

the FTP. 49 USC section 32904 and section III.B, below. EPA developed and established the 

FTP in the early 1970s and, after enactment of EPCA in 1976, added the Highway Fuel 

Economy Test to be used in conjunction with the FTP for fuel economy testing. EPA has also 

developed tests with additional cycles (the so-called 5-cycle test) which test is used for purposes 

of fuel economy labeling and is also used in the EPA program for extending off-cycle credits 

under both the light-duty and (along with NHTSA) heavy-duty vehicle GHG programs. See 75 

FR at 25439; 76 FR at 57252. In this rule, EPA is proposing to retain the FTP and HFET for 

purposes of testing the fleetwide average standards, and is further proposing modifications to the 

N2O measurement test procedures and the A/C CO2 efficiency test procedures EPA initially 

adopted in the 2012-2016 rule. 

14. Comparing the Agencies’ Authority 

As the above discussion makes clear, there are both important differences between the 

statutes under which each agency is acting as well as several important areas of similarity. One 

important difference is that EPA’s authority addresses various GHGs, while NHTSA’s authority 

addresses fuel economy as measured under specified test procedures and calculated by EPA. 

This difference is reflected in this rulemaking in the scope of the two standards: EPA’s proposal 

takes into account reductions of direct air conditioning emissions, as well as proposed standards 

for methane and N2O, but NHTSA’s does not, because these things do not relate to fuel 

economy. A second important difference is that EPA is proposing certain compliance 

flexibilities, such as the multiplier for advanced technology vehicles, and takes those flexibilities 

into account in its technical analysis and modeling supporting its proposal. EPCA specifies a 

number of particular compliance flexibilities for CAFE, and expressly prohibits NHTSA from 

considering the impacts of those statutory compliance flexibilities in setting the CAFE standard 

so that the manufacturers’ election to avail themselves of the permitted flexibilities remains 

strictly voluntary.282 The Clean Air Act, on the other hand, contains no such prohibition. These 

 
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considerations result in some differences in the technical analysis and modeling used to support 

EPA’s and NHTSA’s proposed standards. 

Another important area where the two agencies’ authorities are similar but not identical 

involves the transfer of credits between a single firm’s car and truck fleets. EISA revised EPCA 

to allow for such credit transfers, but placed a cap on the amount of CAFE credits which can be 

transferred between the car and truck fleets. 49 U.S.C. 32903(g)(3). Under CAA section 202(a), 

EPA is proposing to continue to allow CO2 credit transfers between a single manufacturer’s car 

and truck fleets, with no corresponding limits on such transfers. In general, the EISA limit on 

CAFE credit transfers is not expected to have the practical effect of limiting the amount of CO2

emission credits manufacturers may be able to transfer under the CAA program, recognizing that 

manufacturers must comply with both the proposed CAFE standards and the proposed EPA 

standards. However, it is possible that in some specific circumstances the EPCA limit on CAFE 

credit transfers could constrain the ability of a manufacturer to achieve cost savings through 

unlimited use of GHG emissions credit transfers under the CAA program. 

These differences, however, do not change the fact that in many critical ways the two 

agencies are charged with addressing the same basic issue of reducing GHG emissions and 

improving fuel economy. The agencies are looking at the same set of control technologies (with 

the exception of the air conditioning leakage-related technologies). The standards set by each 

agency will drive the kind and degree of penetration of this set of technologies across the vehicle 

fleet. As a result, each agency is trying to answer the same basic question – what kind and 

degree of technology penetration is necessary to achieve the agencies’ objectives in the 

rulemaking time frame, given the agencies’ respective statutory authorities? 

In making the determination of what standards are appropriate under the CAA and 

EPCA, each agency is to exercise its judgment and balance many similar factors. NHTSA’s 

factors are provided by EPCA: 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. EPA has the discretion under the CAA to consider many related 

factors, such as the availability of technologies, the appropriate lead time for introduction of 

technology, and based on this the feasibility and practicability of their standards; the impacts of 

their standards on emissions reductions (of both GHGs and non-GHGs); the impacts of their 

standards on oil conservation; the impacts of their standards on fuel savings by consumers; the 

impacts of their standards on the auto industry; as well as other relevant factors such as impacts 

on safety. Conceptually, therefore, each agency is considering and balancing many of the same 

concerns, and each agency is making a decision that at its core is answering the same basic 

question of what kind and degree of technology penetration is it appropriate to call for in light of 

all of the relevant factors in a given rulemaking, for the model years concerned. Finally, each 

agency has the authority to take into consideration impacts of the standards of the other agency. 

EPCA calls for NHTSA to take into consideration the effects of EPA’s emissions standards on 

fuel economy capability (see 49 U.S.C. 32902 (f)), and EPA has the discretion to take into 
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consideration NHTSA’s CAFE standards in determining appropriate action under section 202(a). 

This is consistent with the Supreme Court’s statement that EPA’s mandate to protect public 

health and welfare is wholly independent from NHTSA’s mandate to promote energy efficiency, 

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 (). 

In this context, it is in the Nation’s interest for the two agencies to continue to work 

together in developing their respective proposed standards, and they have done so. For example, 

the agencies have committed considerable effort to develop a joint Technical Support Document 

that provides a technical basis underlying each agency’s analyses. The agencies also have 

worked closely together in developing and reviewing their respective modeling, to develop the 

best analysis and to promote technical consistency. The agencies have developed a common set 

of attribute-based curves that each agency supports as appropriate both technically and from a 

policy perspective. The agencies have also worked closely to ensure that their respective 

programs will work in a coordinated fashion, and will provide regulatory compatibility that 

allows auto manufacturers to build a single national light-duty fleet that would comply with both 

the GHG and the CAFE standards. The resulting overall close coordination of the proposed 

GHG and CAFE standards should not be surprising, however, as each agency is using a jointly 

developed technical basis to address the closely intertwined challenges of energy security and 

climate change. 

As set out in detail in Sections III and IV of this notice, both EPA and NHTSA believe 

the agencies’ proposals are fully justified under their respective statutory criteria. The proposed 

standards are feasible in each model year within the lead time provided, based on the agencies’ 

projected increased use of various technologies which in most cases are already in commercial 

application in the fleet to varying degrees. Detailed modeling of the technologies that could be 

employed by each manufacturer supports this initial conclusion. The agencies also carefully 

assessed the costs of the proposed rules, both for the industry as a whole and per manufacturer, 

as well as the costs per vehicle, and consider these costs to be reasonable during the rulemaking 

time frame and recoverable (from fuel savings). The agencies recognize the significant increase 

in the application of technology that the proposed standards would require across a high 

percentage of vehicles, which will require the manufacturers to devote considerable engineering 

and development resources before 2017 laying the critical foundation for the widespread 

deployment of upgraded technology across a high percentage of the 2017-2025 fleet. This 

clearly will be challenging for automotive manufacturers and their suppliers, especially in the 

current economic climate, and given the stringency of the recently-established MYs 2012-2016 

standards. However, based on all of the analyses performed by the agencies, our judgment is 

that it is a challenge that can reasonably be met. 

The agencies also evaluated the impacts of these standards with respect to the expected 

reductions in GHGs and oil consumption and, found them to be very significant in magnitude. 

The agencies considered other factors such as the impacts on noise, energy, and vehicular 
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congestion. The impact on safety was also given careful consideration. Moreover, the agencies 

quantified the various costs and benefits of the proposed standards, to the extent practicable. The 

agencies’ analyses to date indicate that the overall quantified benefits of the proposed standards 

far outweigh the projected costs. All of these factors support the reasonableness of the proposed 

standards. See section III (proposed GHG standards) and section IV (proposed CAFE standards) 

for a detailed discussion of each agency’s basis for its selection of its proposed standards. 

The fact that the benefits are estimated to considerably exceed their costs supports the 

view that the proposed standards represent an appropriate balance of the relevant statutory 

factors. In drawing this conclusion, the agencies acknowledge the uncertainties and limitations 

of the analyses. For example, the analysis of the benefits is highly dependent on the estimated 

price of fuel projected out many years into the future. There is also significant uncertainty in the 

potential range of values that could be assigned to the social cost of carbon. There are a variety 

of impacts that the agencies are unable to quantify, such as non-market damages, extreme 

weather, socially contingent effects, or the potential for longer-term catastrophic events, or the 

impact on consumer choice. The cost-benefit analyses are one of the important things the 

agencies consider in making a judgment as to the appropriate standards to propose under their 

respective statutes. Consideration of the results of the cost-benefit analyses by the agencies, 

however, includes careful consideration of the limitations discussed above. 

III. Joint Technical Work Completed for This Proposal 

A. Introduction 

In this section, NHTSA and EPA discuss several aspects of their joint technical analyses. 

These analyses are common to the development of each agency’s standards. Specifically we 

discuss: the development of the vehicle market forecast used by each agency for assessing costs, 

benefits, and effects, the development of the attribute-based standard curve shapes, the 

technologies the agencies evaluated and their costs and effectiveness, the economic assumptions 

the agencies included in their analyses, a description of the air conditioning and off-cycle 

technology (credit) programs, as well as the effects of the proposed standards on vehicle safety. 

The Joint Technical Support Document (TSD) discusses the agencies’ joint technical work in 

more detail. 

The agencies have based today’s proposal on a very significant body of data and analysis that 

we believe is the best information currently available on the full range of technical and other 

inputs utilized in our respective analyses. As noted in various places throughout this preamble, 

the draft Joint TSD, the NHTSA preliminary RIA, and the EPA draft RIA, we expect new 

information will become available between the proposal and final rulemaking. This new 

information will come from a range of sources: some is based on work the agencies have 

underway (e.g., work on technology costs and effectiveness, potentially updating our baseline 

year from model year 2008 to model year 2010); other sources are those we expect to be released 
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by others (e.g., the Energy Information Agency’s Annual Energy Outlook, which is published 

each year, and the most recent available version of which we expect to use for the final rule); and 

other information that will likely come from the public comment process. The agencies intend 

to evaluate all such new information as it becomes available, and where appropriate to update 

their analysis based on such information for purposes of the final rule. In addition, the agencies 

may make new information and/or analyses available in the agencies’ respective public dockets 

for this rulemaking prior to the final rule, where that is appropriate, in order to facilitate public 

comment. We encourage all stakeholders to periodically check the two agencies’ dockets 

between the proposal and final rules for any potential new docket submissions from the agencies. 

B. Developing the Future Fleet for Assessing Costs, Benefits, and Effects 

1. Why Did the Agencies Establish a Baseline and Reference Vehicle Fleet? 

In order to calculate the impacts of the EPA and NHTSA regulations, it is necessary to 

estimate the composition of the future vehicle fleet absent these regulations, to provide a 

reference point relative to which costs, benefits, and effects of the regulations are assessed. As 

in the 2012-2016 light duty vehicle rulemaking, EPA and NHTSA have developed this 

comparison fleet in two parts. The first step was to develop a baseline fleet based on model year 

2008 data. This baseline includes vehicle sales volumes, GHG/fuel economy performance, and 

contains a listing of the base technologies on every 2008 vehicle sold. The second step was to 

project that baseline fleet volume into model years 2017-2025. The vehicle volumes projected 

out to MY 2025 is referred to as the reference fleet volumes. The third step was to modify that 

MY 2017-2025 reference fleet such that it reflects technology manufacturers could apply if MY 

2016 standards are extended without change through MY 2025.283 Each agency used its 

modeling system to develop a modified or final reference fleet, or adjusted baseline, for use in its 

analysis of regulatory alternatives, as discussed below in Chapter 1 of the EPA draft RIA and 

[insert reference to DOT section], and in the agencies’ respective preliminary regulatory impact 

analyses. All of the agencies’ estimates of emission reductions, fuel economy improvements, 

costs, and societal impacts are developed in relation to the respective reference fleets. This 

section discusses the first two steps, development of the baseline fleet and the reference fleet. 

EPA and NHTSA used a transparent approach to developing the baseline and reference 

fleets, largely working from publicly available data. Because both input and output sheets from 

 283 EPA’s MY 2016 GHG standards under the CAA continue into the future until they are changed. While NHTSA 

must actively promulgate standards in order for CAFE standards to extend past MY 2016, the agency has, as in all 

recent CAFE rulemakings, defined a no-action (i.e., baseline) regulatory alternative as an indefinite extension of the 

last-promulgated CAFE standards for purposes of the main analysis of the standards in this preamble. 
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our modeling are public, stakeholders can verify and check EPA’s and NHTSA’s modeling, and 

perform their own analyses with these datasets.284

2. How Did the Agencies Develop the Baseline Vehicle Fleet? 

NHTSA and EPA developed a baseline fleet comprised of model year 2008 data gathered 

from EPA’s emission and fuel economy database. This baseline fleet was originally developed 

by EPA and NHTSA for the 2012-2016 final rule, and was updated for this proposal.285 The new 

fleet has the model year 2008 vehicle’s volumes and attributes along with the addition of 

projected volumes from 2017 to 2025. It also has some expanded footprint data for pickup 

trucks that was needed for a more detailed analysis of the truck curve. 

In this proposed rulemaking, the agencies are again choosing to use model year 2008 

vehicle data to be the basis of the baseline fleet, but for different reasons than in the 2012-2016 

final rule. Model year 2008 is now the most recent model year for which the industry had 

normal sales. Model year 2009 data is available, but the agencies believe that model year was 

disrupted by the economic downturn and the bankruptcies of both General Motors and Chrysler 

resulting in a significant reduction in the number of vehicles sold by both companies and the 

industry as a whole. These abnormalities led the agencies to conclude that 2009 data was not 

representative for projecting the future fleet. Model Year 2010 data was not complete because 

not all manufacturers have yet submitted it to EPA, and was thus not available in time for it to be 

used for this proposal. Therefore, the agencies chose to use model year 2008 again as the 

baseline since it was the latest complete representative and transparent data set available. 

However, the agencies will consider using Model Year 2010 for the final rule, based on 

availability and an analysis of the data representativeness. To the extent the MY 2010 data 

becomes available during the comment period the agencies will place a copy of this data in our 

respective dockets. We request comments on the relative merits of using MY 2008 and MY 2010 

data, and whether one provides a better foundation than the other for purposes of using such data 

as the foundation for a market forecast extending through MY 2025. 

The baseline fleet reflects all fuel economy technologies in use on MY 2008 light duty 

vehicles. The 2008 emission and fuel economy database included data on vehicle production 

volume, fuel economy, engine size, number of engine cylinders, transmission type, fuel type, 

etc., however it did not contain complete information on technologies. Thus, the agencies relied 

on publicly available data like the more complete technology descriptions from Ward’s 

 284 EPA’s Omega Model and input sheets are available at http://www.epa.gov/oms/climate/models.htm; 

DOT/NHTSA’s CAFE Compliance and Effects Modeling System (commonly known as the “Volpe Model”) and 

input and output sheets are available at http://www.nhtsa.gov/fuel-economy [we’ll have a more specific cite once we 

get the website settled, making changes right now]. 

285 Further discussion of the development of the 2008 baseline fleet for the MY2012-2016 rule can be found at 75 

Fed. Reg. 25324, 25349 (May 7, 2010). 
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Automotive Group.286 In a few instances when required vehicle information (such as vehicle 

footprint) was not available from these two sources, the agencies obtained this information from 

publicly accessible internet sites such as Motortrend.com and Edmunds.com.287 A description of 

all of the technologies used in modeling the 2008 vehicle fleet and how it was constructed are 

available in Chapter 1 of the Joint Draft TSD. 

Footprint data for the baseline fleet came mainly from internet searches, though detailed 

information about the pickup truck footprints with volumes was not available online. Where this 

information was lacking, the agencies used manufacturer product plan data for 2008 model year 

to find out the correct number footprint and distribution of footprints. The footprint data for 

pickup trucks was expanded from the original data used in the previous rulemaking. The 

agencies obtained this footprint data from MY 2008 product plans submitted by the various 

manufacturers, which can be made public at this time because by now all MY 2008 vehicle 

models are already in production, which makes footprint data about them essentially public 

information. A description of exactly how the agencies obtained all the footprints is available in 

Chapter 1 of the TSD. 

3. How Did the Agencies Develop the Projected MY 2017-2025 Vehicle Reference 

Fleet? 

As in the 2012-2016 light duty vehicle rulemaking, EPA and NHTSA have based the 

projection of total car and total light truck sales for MYs 2017-2025 on projections made by the 

Department of Energy’s Energy Information Administration (EIA). See 75 FR at 25349. EIA 

publishes a mid-term projection of national energy use called the Annual Energy Outlook 

(AEO). This projection utilizes a number of technical and econometric models which are 

designed to reflect both economic and regulatory conditions expected to exist in the future. In 

support of its projection of fuel use by light-duty vehicles, EIA projects sales of new cars and 

light trucks. EIA published its Early Annual Energy Outlook for 2011 in December 2010. EIA 

released updated data to NHTSA in February (Interim AEO). The final release of AEO for 2011 

came out in May 2011, but by that time EPA/NHTSA had already prepared modeling runs for 

potential 2017-2025 standards using the interim data release to NHTSA. EPA and NHTSA are 

using the interim data release for this proposal, but intend to use the newest version of AEO 

available for the FRM. 

The agencies used the Energy Information Administration’s (EIA’s) National Energy 

Modeling System (NEMS) to estimate the future relative market shares of passenger cars and 

light trucks. However, NEMS methodology includes shifting vehicle sales volume, starting after 

, away from fleets with lower fuel economy (the light-truck fleet) towards vehicles with higher 

fuel economies (the passenger car fleet) in order to facilitate projected compliance with CAFE 

 286 Note that WardsAuto.com is a fee-based service, but all information is public to subscribers. 287 Motortrend.com and Edmunds.com are free, no-fee internet sites. 
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and GHG standards. Because we use our market projection as a baseline relative to which we 

measure the effects of new standards, and we attempt to estimate the industry’s ability to comply 

with new standards without changing product mix (i.e., we analyze the effects of the proposed 

rules assuming manufacturers will not change fleet composition as a compliance strategy, as 

opposed to changes that might happen due to market forces), the Interim AEO 2011-projected 

shift in passenger car market share as a result of required fuel economy improvements creates a 

circularity. Therefore, for the current analysis, the agencies developed a new projection of 

passenger car and light truck sales shares by running scenarios from the Interim AEO 2011 

reference case that first deactivate the above-mentioned sales-volume shifting methodology and 

then hold post-2017 CAFE standards constant at MY 2016 levels. As discussed in Chapter 1 of 

the agencies’ joint Technical Support Document, incorporating these changes reduced the 

NEMS-projected passenger car share of the light vehicle market by an average of about 5% 

during 2017-2025. 

In the AEO 2011 Interim data, EIA projects that total light-duty vehicle sales will 

gradually recover from their currently depressed levels by around 2013. In 2017, car sales are 

projected to be 8.4 million (53 percent) and truck sales are projected to be 7.3 million (47 

percent). Although the total level of sales of 15.8 million units is similar to pre-2008 levels, the 

fraction of car sales is projected to be higher than that existing in the 2000- timeframe. This 

projection reflects the impact of assumed higher fuel prices. Sales projections of cars and trucks 

for future model years can be found in Chapter 1 of the joint TSD. 

In addition to a shift towards more car sales, sales of segments within both the car and 

truck markets have been changing and are expected to continue to change. Manufacturers are 

introducing more crossover utility vehicles (CUVs), which offer much of the utility of sport 

utility vehicles (SUVs) but use more car-like designs. The AEO 2011 report does not, however, 

distinguish such changes within the car and truck classes. In order to reflect these changes in 

fleet makeup, EPA and NHTSA used CSM Worldwide (CSM) as they did in the 2012-2016 

rulemaking analysis. EPA and NHTSA believe that CSM is the best source available for a long 

range forecast for 2017-2025, though when EPA and NHTSA contacted several forecasting firms 

none of them offered comparably-detailed forecasting for that time frame. NHTSA and EPA 

decided to use the forecast from CSM for several reasons presented in the Joint TSD chapter I. 

The long range forecast from CSM Worldwide is a custom forecast covering the years 

2017-2025 which the agencies purchased from CSM in December of 2009. CSM provides 

quarterly sales forecasts for the automotive industry, and updates their data on the industry 

quarter. For the public’s reference, a copy of CSM’s long range forecast has been placed in the 

docket for this rulemaking.288 EPA and NHTSA hope to purchase and use an updated forecast, 

 288 The CSM Sales Forecast Excel file (“CSM North America Sales Forecasts 2017-2025 for the Docket”) is 

available in the docket (Docket EPA-HQ-OAR-2010-0799). 
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whether from CSM or other appropriate sources, before the final rulemaking. To the extent that 

such a forecast becomes available during the comment period the agencies will place a copy in 

our respective dockets. 

The next step was to project the CSM forecasts for relative sales of cars and trucks by 

manufacturer and by market segment onto the total sales estimates of AEO 2011. Table III-1 

and III-2 show the resulting projections for the reference 2025 model year and compare these to 

actual sales that occurred in the baseline 2008 model year. Both tables show sales using the 

traditional definition of cars and light trucks. 

Table III-1 Annual Sales of Light-Duty Vehicles by in 2008 and Estimated for 2025 

 Cars Light Trucks Total 

 2008 MY 2025 MY 2008 MY 2025 MY 2008 MY 2025 MY 

Aston 

Martin 1,370 1,182 0 0 1,370 1,182

BMW 291,796 405,256 61,324 145,409 353,120 550,665

Chrysler/Fiat 703,158 436,479 956,792 331,762 1,659,950 768,241

Daimler 208,195 340,719 79,135 101,067 287,330 441,786

Ferrari 1,450 7,658 0 0 1,450 7,658

Ford 956,699 1,540,109 814,194 684,476 1,770,893 2,224,586

Geely/Volvo 65,649 101,107 32,748 42,588 98,397 143,696

GM 1,587,391 1,673,936 1,507,797 1,524,008 3,095,188 3,197,943

Honda 1,006,639 1,340,321 505,140 557,697 1,511,779 1,898,018

Hyundai 337,869 677,250 53,158 168,136 391,027 845,386

Kia 221,980 362,783 59,472 97,653 281,452 460,436

Lotus 252 316 0 0 252 316

Mazda 246,661 306,804 55,885 61,368 302,546 368,172

Mitsubishi 85,358 73,305 15,371 36,387 100,729 109,692

Nissan 717,869 1,014,775 305,546 426,454 1,023,415 1,441,229

Porsche 18,909 40,696 18,797 11,219 37,706 51,915

Spyker/Saab 21,706 23,130 4,250 3,475 25,956 26,605

Subaru 116,035 256,970 82,546 74,722 198,581 331,692

Suzuki 79,339 103,154 35,319 21,374 114,658 124,528

Tata/JLR 9,596 65,418 55,584 56,805 65,180 122,223

Tesla 800 31,974 0 0 800 31,974

Toyota 1,260,364 2,108,053 951,136 1,210,016 2,211,500 3,318,069

Volkswagen 291,483 630,163 26,999 154,284 318,482 784,447

Total 8,230,568 11,541,560 5,621,193 5,708,899 13,851,761 17,250,459
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III-2 Annual Sales of Light-Duty Vehicles by Market Segment in 2008 and Estimated for 

2025 

Cars Light Trucks 

 2008 MY 2025 MY 2008 MY 2025 MY

Full-Size Car 829,896 245,355 Full-Size Pickup 1,332,335 1,002,806

Luxury Car 1,048,341 1,637,410 Mid-Size Pickup 452,013 431,272

Mid-Size Car 2,103,108 2,713,078 Full-Size Van 33,384 88,572

Mini Car 617,902 1,606,114 Mid-Size Van 719,529 839,452

Small Car 1,912,736 2,826,190 Mid-Size MAV* 110,353 548,457

Specialty Car 469,324 808,183 Small MAV 231,265 239,065

 Full-Size SUV* 559,160 46,978

 Mid-Size SUV 436,080 338,849

 Small SUV 196,424 71,827

 Full-Size CUV* 264,717 671,665

 Mid-Size CUV 923,165 1,259,483

 Small CUV 1,612,029 1,875,703

Total Sales** 6,981,307 9,836,330 6,870,454 7,414,129

* MAV – Multi-Activity Vehicle, or a vehicle with a tall roof and elevated seating positions such as a Mazda5SUV 

– Sport Utility Vehicle, CUV – Crossover Utility Vehicle 

**Total Sales are based on the classic Car/Truck definition previously, NHTSA has 

changed the definition of a truck for 2011 model year and beyond. The new definition has 

moved some 2 wheel drive SUVs and CUVs to the car category. Table III-3 shows the different 

volumes for car and trucks based on the new and old NHTSA definition. The table shows the 

difference in 2008, 2021, and 2025 to give a feel for how the change in definition changes the 

car/truck split. 

Table III-3 New and Old Car and Truck definition in 2008, 2016, 2021, and 2025 

Vehicle Type 2008 2016 2021 2025

Old Cars 

Definition 

 

6,981,307 8,576,717 8,911,173 9,836,330 

New Cars 

Definition 

 

8,230,568 7,618,459 10,505,165 11,541,560 

 

Old Truck 

Definition 

 

6,870,454 10,140,463 7,277,894 7,414,129 

New Truck 

Definition 

 

5,621,193 6,054,713 5,683,902 5,708,899 

The CSM forecast provides estimates of car and truck sales by segment and by 

manufacturer separately. The forecast was broken up into two tables. One table with 
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manufacturer volumes by year and the other with vehicle segments percentages by year. 

III-4 and Table III-5 are examples of the data received from CSM. The task of estimating 

future sales using these tables is complex. We used the same methodology as in the 

previous rulemaking. A detailed description of how the projection process was done is 

found in Chapter 1 of the TSD. III-4 CSM Manufacturer Volumes in 2016, 2021, and 2025 

2016 2021 2025

BMW 328,220 325,231 317,178

Chrysler/Fiat 391,165 346,960 316,043

Daimler 298,676 272,049 271,539

Ford* 971,617 893,528 858,215

Subaru 205,486 185,281 181,062

General Motors 1,309,246 1,192,641 1,135,305

Honda 1,088,449 993,318 984,401

Hyundai 429,926 389,368 377,500

Kia 234,246 213,252 205,473

Mazda 215,117 200,003 199,193

Mitsubishi 47,414 42,693 42,227

Spyker/Saab 66 6

Tesla 800 800 800

Aston Martin 1,370 1,370 1,370

Lotus 252 252 252

Porsche 12 12 12

Nissan 803,177 729,723 707,361

Suzuki 88,142 81,042 76,873

Tata/JLR 58,594 53,143 52,069

Toyota 1,751,661 1,576,499 1,564,975

Volkswagen 578,420 530,378 494,596

*Ford volumes include Volvo in this table. 

Table III-5 CSM Segment Percentages in 2016, 2021, and 2025 

2016 2021 2025

Full-Size CUV 3.66% 8.34% 9.06%

Full-Size Pickup 19.39% 15.42% 13.53%

Full-Size SUV 3.27% 0.90% 0.63%

Full-Size Van 0.92% 1.29% 1.19%

Mid-Size CUV 19.29% 16.88% 16.99%

Mid-Size MAV 1.63% 5.93% 7.40%

Mid-Size Pickup 4.67% 5.74% 5.82%

Mid-Size SUV 2.28% 4.73% 4.57%
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Mid-Size Van 11.80% 11.63% 11.32%

Small CUV 30.67% 25.06% 25.30%

Small MAV 0.88% 2.98% 3.22%

Small Pickup 0.00% 0.00% 0.00%

Small SUV 1.53% 1.12% 0.97%

The overall result was a projection of car and truck sales for model years 2017-2025--the 

reference fleet--which matched the total sales projections of the AEO forecast and the 

manufacturer and segment splits of the CSM forecast. These sales splits are shown in III-6 

below. III-6 Car and Truck Volumes and Split Based on NHTSA New Truck Definition 

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Car Volume* 10,140 9,988 9,905 9,996 10,292 10,505 10,736 10,968 11,258 11,542 

Truck Volume* 6,054 5,819 5,671 5,583 5,604 5,684 5,704 5,687 5,676 5,709 

Car Split 62.6% 63.2% 63.6% 64.2% 64.7% 64.9% 65.3% 65.9% 66.5% 66.9% 

Truck Split 37.4% 36.8% 36.4% 35.8% 35.3% 35.1% 34.7% 34.1% 33.5% 33.1% 

*in thousands 

Given publicly- and commercially-available sources that can be made equally transparent 

to all reviewers, the forecast described above represents the agencies’ best technical judgment 

regarding the likely composition direction of the fleet. EPA and NHTSA recognize that it is 

impossible to predict with certainty how manufacturers’ product offerings and sales volumes will 

evolve through MY 2025 under baseline conditions—that is, without further changes in 

standards after MY 2016. The agencies have not developed alternative market forecasts to 

examine corresponding sensitivity of analytical results discussed below, and have not varied the 

market forecast when conducting probabilistic uncertainty analysis discussed in NHTSA’s 

preliminary Regulatory Impact Analysis. The agencies invite comment regarding alternative 

methods or projections to inform forecasts of the future fleet at the level of specificity and 

technical completeness required by the agencies’ respective modeling systems. 

The final step in the construction of the final reference fleet involves applying additional 

technology to individual vehicle models—that is, technology beyond that already present in MY 

2008——reflecting already-promulgated standards through MY 2016, and reflecting the 

assumption that MY 2016 standards would apply through MY 2025. A description of the 

agencies’ modeling work to develop their respective final reference (or adjusted baseline) fleets 

appear below in Sections III and IV of this preamble. 

C. Development of Attribute-Based Curve Shapes 

1. Why are standards attribute-based and defined by a mathematical function? 

As in the MYs 2012-2016 CAFE/GHG rules, and as NHTSA did in the MY 2011 CAFE 

rule, NHTSA and EPA are proposing to set attribute-based CAFE and CO2 standards that are 
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defined by a mathematical function. 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.289 The CAA has no 

such requirement, although such an approach is permissible under section 202 (a) and EPA has 

used the attribute-based approach in issuing standards under analogous provisions of the CAA 

(e.g., criteria pollutant standards for non-road diesel engines using engine size as the attribute,290

in the recent GHG standards for heavy duty pickups and vans using a work factor attribute,291

and in the MYs 2012-2016 GHG rule itself which used vehicle footprint as the attribute). Public 

comments on the MYs 2012-2016 rulemaking widely supported attribute-based standards for 

both agencies’ standards. 

Under an attribute-based standard, every vehicle model has a performance target (fuel 

economy and CO2 emissions for CAFE and CO2 emissions standards, respectively), the level of 

which depends on the vehicle’s attribute (for this proposal, footprint, as discussed below). Each 

manufacturers’ fleet average standard is determined by the production-weighted292 average (for 

CAFE, harmonic average) of those targets. 

The agencies believe that an attribute-based standard is preferable to a single-industrywide average standard in the context of CAFE and CO2 standards for several reasons. First, if 

the shape is chosen properly, every manufacturer is more likely to be required to continue adding 

more fuel efficient technology each year across their fleet, because the stringency of the 

compliance obligation will depend on the particular product mix of each manufacturer. 

Therefore a maximum feasible attribute-based standard will tend to require greater fuel savings 

and CO2 emissions reductions overall than would a maximum feasible flat standard (that is, a 

single mpg or CO2 level applicable to every manufacturer). 

 Second, depending on the attribute, attribute-based standards reduce the incentive for 

manufacturers to respond to CAFE and CO2 standards in ways harmful to safety.

293 Because 

each vehicle model has its own target (based on the attribute chosen), properly fitted attributebased standards provide little, if any, incentive to build smaller vehicles simply to meet a fleet-

 289 49 U.S.C. 32902(a)(3)(A). 290 69 FR 38958 (June 29, 2004). 291 76 FR 57106, 57162-64, (Sept. 15, 2011). 292 Production for sale in the United States. 293 The 2002 NAS Report described at length and quantified the potential safety problem with average fuel economy 

standards that specify a single numerical requirement for the entire industry. See 2002 NAS Report at 5, finding 12. 

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.
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wide average, because the smaller vehicles will be subject to more stringent compliance 

targets.294

Third, attribute-based standards provide a more equitable regulatory framework for 

different vehicle manufacturers.295 A single industry-wide average standard imposes 

disproportionate cost burdens and compliance difficulties on the manufacturers that need to 

change their product plans to meet the standards, and puts no obligation on those manufacturers 

that have no need to change their plans. As discussed above, attribute-based standards help to 

spread the regulatory cost burden for fuel economy more broadly across all of the vehicle 

manufacturers within the industry. 

attribute-based standards better respect economic conditions and consumer choice, as 

compared to single-value standards. A flat, or single value standard, encourages a certain 

vehicle size fleet mix by creating incentives for manufacturers to use vehicle downsizing as a 

compliance strategy. Under a footprint-based standard, manufacturers are required to invest in 

technologies that improve the fuel economy of the vehicles they sell rather than shifting the 

product mix, because reducing the size of the vehicle is generally a less viable compliance 

strategy given that smaller vehicles have more stringent regulatory targets. 

2. What attribute are the agencies proposing to use, and why? 

As in the MYs 2012-2016 CAFE/GHG rules, and as NHTSA did in the MY 2011 CAFE 

rule, NHTSA and EPA are proposing to set CAFE and CO2 standards that are based on vehicle 

footprint, which has an observable correlation to fuel economy and emissions. There are several 

policy and technical reasons why NHTSA and EPA believe that footprint is the most appropriate 

attribute on which to base the standards, even though some other vehicle attributes (notably curb 

weight) are better correlated to fuel economy and emissions. 

First, in the agencies’ judgment, from the standpoint of vehicle safety, it is important that 

the CAFE and CO2 standards be set in a way that does not encourage manufacturers to respond 

by selling vehicles that are in any way less safe. While NHTSA’s research of historical crash 

data also indicates that reductions in vehicle mass that are accompanied by reductions in vehicle 

footprint tend to compromise vehicle safety, footprint-based standards provide an incentive to 

use advanced lightweight materials and structures that would be discouraged by weight-based 

standards, because manufacturers can use them to improve a vehicle’s fuel economy and CO2 

emissions without their use necessarily resulting in a change in the vehicle’s fuel economy and 

emissions targets. 

Further, although we recognize that weight is better correlated with fuel economy and 

CO2 emissions than is footprint, we continue to believe that there is less risk of “gaming” 

 294 Assuming that the attribute is related to vehicle size. 295 Id. at 4-5, finding 10. 
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(changing the attribute(s) to achieve a more favorable target) by increasing footprint under 

footprint-based standards than 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 footprint. We also 

continue to agree with concerns raised in 2008 by some commenters on the MY 2011 CAFE 

rulemaking 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. 

The agencies agree with the assessment first presented in NHTSA’s MY 2011 CAFE final rule296

that the possibility of gaming is lowest with footprint-based standards, as opposed to weightbased 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 average 

fuel economy and CO2 reduction levels projected by the agencies. 

The agencies recognize that based on economic and consumer demand factors that are 

external to this rule, the distribution of footprints in the future may be different (either smaller or 

larger) than what is projected in this rule. However, the agencies continue to believe that there 

will not be significant shifts in this distribution as a direct consequence of this proposed rule. 

The agencies also recognize that some international attribute-based standards use attributes other 

than footprint and that there could be benefits for a number of manufacturers if there was greater 

international harmonization of fuel economy and GHG standards for light-duty vehicles, but this 

is largely a question of how stringent standards are and how they are tested and enforced. It is 

entirely possible that footprint-based and weight-based systems can coexist internationally and 

not present an undue burden for manufacturers if they are carefully crafted. Different countries 

or regions may find different attributes appropriate for basing standards, depending on the 

particular challenges they face—from fuel prices, to family size and land use, to safety concerns, 

to fleet composition and consumer preference, to other environmental challenges besides climate 

change. The agencies anticipate working more closely with other countries and regions in the 

future to consider how to address these issues in a way that least burdens manufacturers while 

respecting each country’s need to meet its own particular challenges. 

The agencies continue to find that footprint is the most appropriate attribute upon which 

to base the proposed standards, but recognizing strong public interest in this issue, we seek 

comment on whether the agencies should consider setting standards for the final rule based on 

another attribute or another combination of attributes. If commenters suggest that the agencies 

should consider another attribute or another combination of attributes, the agencies specifically 

request that the commenters address the concerns raised in the paragraphs above regarding the 

 296 See 74 FR at 14359 (Mar. 30, 2009). 
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use of other attributes, and explain how standards should be developed using the other 

attribute(s) in a way that contributes more to fuel savings and CO2 reductions than the footprintbased standards, without compromising safety. 

3. What mathematical functions have the agencies previously used, and why? 

a. NHTSA in MY 2008 and MY 2011 CAFE (constrained logistic) 

For the MY 2011 CAFE rule, NHTSA estimated fuel economy levels after normalization 

for differences in technology, but did not make adjustments to reflect other vehicle attributes 

(e.g., power-to-weight ratios).297 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 gpm 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.298

b. MYs 2012-2016 Light Duty GHG/CAFE (constrained/piecewise linear) 

For the MYs 2012-2016 rules, NHTSA and EPA re-evaluated potential methods for 

specifying mathematical functions to define fuel economy and GHG standards. The agencies 

concluded that 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.299 The agencies judged that a range of methods to fit the curves would 

be reasonable, and used a minimum absolute deviation (MAD) regression without sales 

weighting on a technology-adjusted car and light truck fleet to fit a linear equation. This 

equation was used as a starting point to develop mathematical functions defining the standards as 

discussed above. The agencies then identified footprints at which to apply minimum and 

maximum values (rather than letting the standards extend without limit) and transposed these 

constrained/piecewise linear functions vertically (i.e., on a gpm or CO2 basis, uniformly 

 297 See 74 FR 14196, 14363-14370 (Mar. 30, 2009) for NHTSA discussion of curve fitting in the MY 2011 CAFE 

final rule. 

298 See 71 FR 17556, 17609-17613 (Apr. 6, 2006) for NHTSA discussion of “kinks” in the MYs 2008-2011 light 

truck CAFE final rule (there described as “edge effects”). A “kink,” as used here, is a portion of the curve where a 

small change in footprint results in a disproportionally large change in stringency. 299 75 FR at 25362 
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downward) to produce the fleetwide fuel economy and CO2 emission levels for cars and light 

trucks described in the final rule.300 

4. How have the agencies changed the mathematical functions for the proposed MYs 

2017-2025 standards, and why?

By requiring NHTSA to set CAFE standards that are attribute-based and defined by a 

mathematical function, Congress appears to have wanted the post-EISA standards to be datadriven – a mathematical function defining the standards, in order to be “attribute-based,” should 

reflect the observed relationship in the data between the attribute chosen and fuel economy.301 

EPA is also proposing to set attribute-based CO2 standards defined by similar mathematical 

functions, for the reasonable technical and policy grounds discussed below and in section II of 

the preamble to the proposed rule, and which supports a harmonization with the CAFE standards. 

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.302 There is thus a range of legitimate 

options open to the agencies in developing curve shapes. The agencies may of course consider 

statutory objectives in choosing among the many reasonable alternatives. For example, curve 

shapes that might have some theoretical basis could lead to perverse outcomes contrary to the 

intent of the statutes to conserve energy and protect human health and the environment.303 Thus, 

the decision of how to set the target curves cannot always be just about most “clearly” using a 

mathematical function to define the relationship between fuel economy and the attribute; it often 

has to have a normative aspect, where the agencies adjust the function that would define the 

relationship in order to avoid perverse results, improve equity of burden across manufacturers, 

preserve consumer choice, etc. This is true both for the decisions that guide the mathematical 

function defining the sloped portion of the target curves, and for the separate decisions that guide 

the agencies’ choice of “cutpoints” (if any) that define the fuel economy/CO2 levels and 

 300 See generally 74 FR at 49491-96; 75 FR at 25357-62. 301 A mathematical function can be defined, of course, that has nothing to do with the relationship between fuel 

economy and the chosen attribute – the most basic example is an industry-wide standard defined as the mathematical 

function average required fuel economy = X, where X is the single mpg level set by the agency. Yet a standard that 

is simply defined as a mathematical function that is not tied to the attribute(s) would not meet the requirement of 

EISA. 

302 In fact, numerous manufacturers have confidentially shared with the agencies what they describe as “physics 

based” curves, with each OEM showing significantly different shapes, and footprint relationships. The sheer variety 

of curves shown to the agencies further confirm the lack of an underlying principle of “fundamental physics” driving 

the relationship between CO2 emission or fuel consumption and footprint, and the lack of an underlying principle to 

dictate any outcome of the agencies’ establishment of footprint-based standards. 

303 For example, if the agencies set weight-based standards defined by a steep function, the standards might 

encourage manufacturers to keep adding weight to their vehicles to obtain less stringent targets. 
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footprints at each end of the curves where the curves become flat. Data informs these decisions, 

but how the agencies define and interpret the relevant data, and then the choice of methodology 

for fitting a curve to the data, must include a consideration of both technical data and policy 

goals. 

5. The next sections examine the policy concerns that the agencies considered in 

developing the proposed target curves that define the proposed MYs 2017-2025 

CAFE and CO2 standards, new technical work (expanding on similar analyses 

performed by NHTSA when the agency proposed MY 2011-2015 standards, and by 

both agencies during consideration of options for MY 2012-2016 CAFE and GHG 

standards) that was completed in the process of reexamining potential mathematical 

functions, how the agencies have defined the data, and how the agencies explored 

statistical curve-fitting methodologies in order to arrive at proposed curves. are the 

agencies proposing for the MYs 2017-2025 curves? 

The proposed mathematical functions for the proposed MYs 2017-2025 standards are 

somewhat changed from the functions for the MYs 2012-2016 standards, in response to 

comments received from stakeholders and in order to address technical concerns and policy 

goals that the agencies judge more significant in this 9-year rulemaking than in the prior one, 

which only included 5 years. This section discusses the methodology the agencies selected as, at 

this time, best addressing those technical concerns and policy goals, given the various technical 

inputs to the agencies’ current analyses. Below the agencies discuss how the agencies 

determined the cutpoints and the flat portions of the MYs 2017-2025 target curves. We also note 

that both of these sections address only how the target curves were fit to fuel consumption and 

CO2 emission values determined using the city and highway test procedures, and that in 

determining respective regulatory alternatives, the agencies made further adjustments to the 

resultant curves in order to account for adjustments for improvements to mobile air conditioners. 

Thus, recognizing that there are many reasonable statistical methods for fitting curves to 

data points that define vehicles in terms of footprint and fuel economy, the agencies have chosen 

for this proposed rule to fit curves using an ordinary least-squares formulation, on sales-weighted 

data, using a fleet that has had technology applied, and after adjusting the data for the effects of 

weight-to-footprint, as described below. This represents a departure from the statistical approach 

for fitting the curves in MYs 2012-2016, as explained in the next section. The agencies 

considered a wide variety of reasonable statistical methods in order to better understand the 

range of uncertainty regarding the relationship between fuel consumption (the inverse of fuel 

economy), CO2 emission rates, and footprint, thereby providing a range within which decisions 

about standards would be potentially supportable. 

What concerns were the agencies looking to address that led them to change from the 

approach used for the MYs 2012-2016 curves?the year and a half since the MYs 2012-2016 final 
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rule was issued, NHTSA and EPA have received a number of comments from stakeholders on 

how curves should be fitted to the passenger car and light truck fleets. Some limited-line 

manufacturers have argued that curves should generally be flatter in order to avoid discouraging 

small vehicles, because steeper curves tend to result in more stringent targets for smaller 

vehicles. Most full-line manufacturers have argued that a passenger car curve similar in slope to 

the MY 2016 passenger car curve would be appropriate for future model years, but that the light 

truck curve should be revised to be less difficult for manufacturers selling the largest full-size 

pickup trucks. These manufacturers argued that the MY 2016 light truck curve was not 

“physics-based,” and that in order for future tightening of standards to be feasible for full-line 

manufacturers, the truck curve for later model years should be steeper and extended further (i.e., 

made less stringent) into the larger footprints. The agencies do not agree that the MY 2016 light 

truck curve was somehow deficient in lacking a “physics basis,” or that it was somehow overly 

stringent for manufacturers selling large pickups—manufacturers making these arguments 

presented no “physics-based” model to explain how fuel economy should depend on footprint.304 

The same manufacturers indicated that they believed that the light truck standard should be 

somewhat steeper after MY 2016, primarily because, after more than ten years of progressive 

increases in the stringency of applicable CAFE standards, large pickups would be less capable of 

achieving further improvements without compromising load carrying and towing capacity. 

In developing the curve shapes for this proposed rule, the agencies were aware of the 

current and prior technical concerns raised by OEMs concerning the effects of the stringency on 

individual manufacturers and their ability to meet the standards with available technologies, 

while producing vehicles at a cost that allowed them to recover the additional costs of the 

technologies being applied. Although we continue to believe that the methodology for fitting 

curves for the MY2012-2016 standards was technically sound, we recognize manufacturers’ 

technical concerns regarding their abilities to comply with a similarly shallow curve after 

MY2016 given the anticipated mix of light trucks in MYs 2017-2025. As in the MYs 2012-2016 

rules, the agencies considered these concerns in the analysis of potential curve shapes. The 

agencies also considered safety concerns which could be raised by curve shapes creating an 

incentive for vehicle downsizing, as well as the potential loss to consumer welfare should vehicle 

upsizing be unduly disincentivized. In addition, the agencies sought to improve the balance of 

compliance burdens among manufacturers. Among the technical concerns and resultant policy 

trade-offs the agencies considered were the following: 

 Flatter standards (i.e., curves) increase the risk that both the weight and size of vehicles 

will be reduced, compromising highway safety. 

 304 See footnote 302 
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 Flatter standards potentially impact the utility of vehicles by providing an incentive for 

vehicle downsizing. 

 Steeper footprint-based standards may incentivize vehicle upsizing, thus increasing the 

risk that fuel economy and greenhouse gas reduction benefits will be less than expected. 

 Given the same industry-wide average required fuel economy or CO2 standard, flatter 

standards tend to place greater compliance burdens on full-line manufacturers 

 Given the same industry-wide average required fuel economy or CO2 standard, steeper 

standards tend to place greater compliance burdens on limited-line manufacturers 

(depending of course, on which vehicles are being produced). 

 If cutpoints are adopted, given the same industry-wide average required fuel economy, 

moving small-vehicle cutpoints to the left (i.e., up in terms of fuel economy, down in 

terms of CO2 emissions) discourages the introduction of small vehicles, and reduces the 

incentive to downsize small vehicles in ways that would compromise highway safety. 

 If cutpoints are adopted, given the same industry-wide average required fuel economy, 

moving large-vehicle cutpoints to the right (i.e., down in terms of fuel economy, up in 

terms of CO2 emissions) better accommodates the unique design requirements of larger 

vehicles—especially large pickups—and extends the size range over which downsizing is 

discouraged. 

All of these were policy goals that required trade-offs, and in determining the curves they 

also required balance against the comments from the OEMs discussed in the introduction to this 

section. Ultimately, the agencies do not agree that the MY 2017 target curves for this proposal, 

on a relative basis, should be made significantly flatter than the MY 2016 curve,305 as we believe 

that this would undo some of the safety-related incentives and balancing of compliance burdens 

among manufacturers—effects that attribute-based standards are intended to provide. 

 Nonetheless, the agencies recognize full-line OEM concerns and have tentatively 

concluded that further increases in the stringency of the light truck standards will be more 

feasible if the light truck curve is made steeper than the MY 2016 truck curve and the right (large 

footprint) cut-point is extended over time to larger footprints. This conclusion is supported by 

the agencies’ technical analyses of regulatory alternatives defined using the curves developed in 

the manner described below. 

a. What methodologies and data did the agencies consider in developing the 2017-

2025 curves? 

In considering how to address the various policy concerns discussed in the previous 

sections, the agencies revisited the data and performed a number of analyses using different 

combinations of the various statistical methods, weighting schemes, adjustments to the data and 

the addition of technologies to make the fleets less technologically heterogeneous. As discussed 

 305 While “significantly” flatter is subjective, the year over year change in curve shapes is discussed in greater detail 

in Section III.C.7.a and Chapter 2 of the joint TSD. 
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above, in the agencies’ judgment, there is no single “correct” way to estimate the relationship 

between CO2 or fuel consumption and footprint – rather, each statistical result is based on the 

underlying assumptions about the particular functional form, weightings and error structures 

embodied in the representational approach. These assumptions are the subject of the following 

discussion. This process of performing many analyses using combinations of statistical methods 

generates many possible outcomes, each embodying different potentially reasonable 

combinations of assumptions and each thus reflective of the data as viewed through a particular 

lens. The choice of a standard developed by a given combination of these statistical methods is 

consequently a decision based upon the agencies’ determination of how, given the policy 

objectives for this rulemaking and the agencies’ MY 2008-based forecast of the market through 

MY 2025, to appropriately reflect the current understanding of the evolution of automotive 

technology and costs, the future prospects for the vehicle market, and thereby establish curves 

(i.e., standards) for cars and light trucks. 

b. What information did the agencies use to estimate a relationship between fuel 

economy, CO2 and footprint? 

a. For each fleet, the agencies began with the MY 2008-based market forecast 

developed to support this proposal (i.e., the baseline fleet), with vehicles’ fuel 

economy levels and technological characteristics at MY 2008 levels.306 The 

development, scope, and content of this market forecast is discussed in detail in 

Chapter 1 of the joint Technical Support Document supporting this 

rulemaking.adjustments did the agencies evaluate? 

The agencies believe one possible approach is to fit curves to the minimally adjusted data 

shown above (the approach still includes sales mix adjustments, which influence results of salesweighted regressions), much as DOT did when it first began evaluating potential attribute-based 

standards in 2003.307 However, the agencies have found, as in prior rulemakings, that the data 

are so widely spread (i.e., when graphed, they fall in a loose “cloud” rather than tightly around 

an obvious line) that they indicate a relationship between footprint and CO2 and fuel 

consumption that is real but not particularly strong. Therefore, as discussed below, the agencies 

also explored possible adjustments that could help to explain and/or reduce the ambiguity of this 

relationship, or could help to produce policy outcomes the agencies judged to be more desirable. 

i. Adjustment to reflect differences in technology 

As in prior rulemakings, the agencies consider technology differences between vehicle 

models to be a significant factor producing uncertainty regarding the relationship between 

 306 While the agencies jointly conducted this analysis, the coefficients ultimately used in the slope setting analysis 

are from the CAFE model.

307 68 FR 74920-74926. 
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CO2/fuel consumption and footprint. Noting that attribute-based standards are intended to 

encourage the application of additional technology to improve fuel efficiency and reduce CO2

emissions, the agencies, in addition to considering approaches based on the unadjusted 

engineering characteristics of MY 2008 vehicle models, therefore also considered approaches in 

which, as for previous rulemakings, technology is added to vehicles for purposes of the curve 

fitting analysis in order to produce fleets that are less varied in technology content. 

The agencies adjusted the baseline fleet for technology by adding all technologies 

considered, except for the most advanced high-BMEP (brake mean effective pressure) gasoline 

engines, diesel engines, strong HEVs, PHEVs, EVs, and FCVs. The agencies included 15 

percent mass reduction on all vehicles. 

ii. Adjustments reflecting differences in performance and “density” 

For the reasons discussed above regarding revisiting the shapes of the curves, the 

agencies considered adjustments for other differences between vehicle models (i.e., inflating or 

deflating the fuel economy of each vehicle model based on the extent to which one of the 

vehicle’s attributes, such as power, is higher or lower than average). Previously, NHTSA had 

rejected such adjustments because they imply that a multi-attribute standard may be necessary, 

and the agencies judged multi-attribute standard to be more subject to gaming than a footprintonly standard.308,309 Having considered this issue again for purposes of this rulemaking, NHTSA 

and EPA conclude the need to accommodate in the target curves the challenges faced by 

manufacturers of large pickups currently outweighs these prior concerns. Therefore, the 

agencies also evaluated curve fitting approaches through which fuel consumption and CO2 levels 

were adjusted with respect to weight-to-footprint alone, and in combination with power-toweight. While the agencies examined these adjustments for purposes of fitting curves, the 

agencies are not proposing a multi-attribute standard; the proposed fuel economy and CO2

targets for each vehicle are still functions of footprint alone. No adjustment would be used in the 

compliance process. 

The agencies also examined some differences between the technology-adjusted car and 

truck fleets in order to better understand the relationship between footprint and CO2/fuel 

consumption in the agencies’ MY 2008 based forecast. The agencies investigated the 

relationship between HP/WT and footprint in the agencies’ MY2008-based market forecast. On 

a sales weighted basis, cars tend to become proportionally more powerful as they get larger. In 

 308 For example, in comments on NHTSA’s 2008 NPRM regarding MY 2011-2015 CAFE standards, Porsche 

recommended that standards be defined in terms of a “Summed Weighted Attribute”, wherein the fuel economy 

target would calculated as follows: target = f(SWA), where target is the fuel economy target applicable to a given 

vehicle model and SWA = footprint + torque1/1.5 + weight1/2.5. (NHTSA-2008-0089-0174). While the standards the 

agencies are proposing for MY 2017-2025 are not multi-attributes, that is the target is only a function of footprint, 

we are proposing curve shapes that were developed considering more than one attribute. 

309 74 FR 14359. 
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contrast, there is a minimally positive relationship between HP/WT and footprint for light trucks, 

indicating that light trucks become only slightly more powerful as they get larger. 

This analysis, presented in chapter 2.4.1.2 of the agencies’ joint TSD, indicated that 

vehicle performance (power-to-weight ratio) and “density” (curb weight divided by footprint) are 

both correlated to fuel consumption (and CO2 emission rate), and that these vehicle attributes are 

also both related to vehicle footprint. Based on these relationships, the agencies explored 

adjusting the fuel economy and CO2 emission rates of individual vehicle models based on 

deviations from “expected” performance or weight/footprint at a given footprint; the agencies 

inflated fuel economy levels of vehicle models with higher performance and/or weight/footprint 

than the average of the fleet would indicate at that footprint, and deflated fuel economy levels 

with lower performance and/or weight. Previously, NHTSA had rejected such adjustments 

because they imply that a multi-attribute standard may be necessary, and the agency judged 

multi-attribute standard to be more subject to gaming than a footprint-only standard.310,311 

While the agencies considered this technique for purposes of fitting curves, the agencies are not 

proposing a multi-attribute standard, as the proposed fuel economy and CO2 targets for each 

vehicle are still functions of footprint alone. No adjustment would be used in the compliance 

process. 

The agencies seek comment on the appropriateness of the adjustments as described in 

Chapter 2 of the joint TSD, particularly regarding whether these adjustments suggest that 

standards should be defined in terms of other attributes in addition to footprint, and whether they 

may encourage changes other than encouraging the application of technology to improve fuel 

economy and reduce CO2 emissions. The agencies also seek comment regarding whether these 

adjustments effectively “lock in” through MY 2025 relationships that were observed in MY 

2008. 

b. What statistical methods did the agencies evaluate? 

The above approaches resulted in three data sets each for (a) vehicles without added 

technology and (b) vehicles with technology added to reduce technology differences, any of 

which may provide a reasonable basis for fitting mathematical functions upon which to base the 

slope of the standard curves: (1) vehicles without any further adjustments; (2) vehicles with 

adjustments reflecting differences in “density” (weight/footprint); and (3) vehicles with 

adjustments reflecting differences in “density,” and adjustments reflecting differences in 

 310 For example, in comments on NHTSA’s 2008 NPRM regarding MY 2011-2015 CAFE standards, Porsche 

recommended that standards be defined in terms of a “Summed Weighted Attribute”, wherein the fuel economy 

target would calculated as follows: target = f(SWA), where target is the fuel economy target applicable to a given 

vehicle model and SWA = footprint + torque1/1.5 + weight1/2.5. (NHTSA-2008-0089-0174). While the standards the 

agencies are proposing for MY 2017-2025 are not multi-attribute standards, that is the target is only a function of 

footprint, we are proposing curve shapes that were developed considering more than one attribute. 

311 74 FR 14359. 
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performance (power/weight). Using these data sets, the agencies tested a range of regression 

methodologies, each judged to be possibly reasonable for application to at least some of these 

data sets. 

i. Regression Approach 

In the MYs 2012-2016 final rules, the agencies employed a robust regression approach 

(minimum absolute deviation, or MAD), rather than an ordinary least squares (OLS) 

regression.312 MAD is generally applied to mitigate the effect of outliers in a dataset, and thus 

was employed in that rulemaking as part of our interest in attempting to best represent the 

underlying technology. NHTSA had used OLS in early development of attribute-based CAFE 

standards, but NHTSA (and then NHTSA and EPA) subsequently chose MAD instead of OLS 

for both the MY 2011 and the MYs 2012-2016 rulemakings. These decisions on regression 

technique were made both because OLS gives additional emphasis to outliers313 and because the 

MAD approach helped achieve the agencies’ policy goals with regard to curve slope in those 

rulemakings.314 In the interest of taking a fresh look at appropriate regression methodologies as 

promised in the 2012-2016 light duty rulemaking, in developing this proposal, the agencies gave 

full consideration to both OLS and MAD. The OLS representation, as described, uses squared 

errors, while MAD employs absolute errors and thus weights outliers less. 

As noted, one of the reasons stated for choosing MAD over least square regression in the 

MYs 2012-2016 rulemaking was that MAD reduced the weight placed on outliers in the data. 

However, the agencies have further considered whether it is appropriate to classify these vehicles 

as outliers. Unlike in traditional datasets, these vehicles’ performance is not mischaracterized due 

to errors in their measurement, a common reason for outlier classification. Being certification 

data, the chances of large measurement errors should be near zero, particularly towards high CO2

or fuel consumption. Thus, they can only be outliers in the sense that the vehicle designs are 

unlike those of other vehicles. These outlier vehicles may include performance vehicles, 

vehicles with high ground clearance, 4WD, or boxy designs. Given that these are equally 

legitimate on-road vehicle designs, the agencies concluded that it would appropriate to 

reconsider the treatment of these vehicles in the regression techniques. 

Based on these considerations as well as the adjustments discussed above, the agencies 

concluded it was not meaningful to run MAD regressions on gpm data that had already been 

adjusted in the manner described above. Normalizing already reduced the variation in the data, 

and brought outliers towards average values. This was the intended effect, so the agencies 

deemed it unnecessary to apply an additional remedy to resolve an issue that had already been 

 312 See 75 FR at 25359. 313 Id. at 25362-63. 314 Id. at 25363. 
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addressed, but we seek comment on the use of robust regression techniques under such 

circumstances. 

ii. Sales Weighting 

Likewise, the agencies reconsidered employing sales-weighting to represent the data. As 

explained below, the decision to sales weight or not is ultimately based upon a choice about how 

to represent the data, and not by an underlying statistical concern. Sales weighting is used if the 

decision is made to treat each (mass produced) unit sold as a unique physical observation. Doing 

so thereby changes the extent to which different vehicle model types are emphasized as 

compared to a non-sales weighted regression. For example, while total General Motors 

Silverado (332,000) and Ford F-150 (322,000) sales differ by less than 10,000 in MY 2021 

market forecast, 62 F-150s models and 38 Silverado models are reported in the agencies 

baselines. Without sales-weighting, the F-150 models, because there are more of them, are given 

63 percent more weight in the regression despite comprising a similar portion of the marketplace 

and a relatively homogenous set of vehicle technologies. 

 The agencies did not use sales weighting in the 2012-2016 rulemaking analysis of the 

curve shapes. A decision to not perform sales weighting reflects judgment that each vehicle 

model provides an equal amount of information concerning the underlying relationship between 

footprint and fuel economy. Sales-weighted regression gives the highest sales vehicle model 

types vastly more emphasis than the lowest-sales vehicle model types thus driving the regression 

toward the sales-weighted fleet norm. For unweighted regression, vehicle sales do not matter. 

The agencies note that the light truck market forecast shows MY 2025 sales of 218,000 units for 

Toyota’s 2WD Sienna, and shows 66 model configurations with MY 2025 sales of fewer than 

100 units. Similarly, the agencies’ market forecast shows MY 2025 sales of 267,000 for the 

Toyota Prius, and shows 40 model configurations with MY2025 sales of fewer than 100 units. 

Sales-weighted analysis would give the Toyota Sienna and Prius more than a thousand times the 

consideration of many vehicle model configurations. Sales-weighted analysis would, therefore, 

cause a large number of vehicle model configurations to be virtually ignored in the 

regressions.315 

However, the agencies did note in the MYs 2012-2016 final rules that, “sales weighted 

regression would allow the difference between other vehicle attributes to be reflected in the 

analysis, and also would reflect consumer demand.” 316 In reexamining the sales-weighting for 

this analysis, the agencies note that there are low-volume model types account for many of the 

passenger car model types (50 percent of passenger car model types account for 3.3 percent of 

sales), and it is unclear whether the engineering characteristics of these model types should 

equally determine the standard for the remainder of the market. 

 315 75 FR at 25362 and n. 64 316 75 FR at 25632/3. 
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In the interest of taking a fresh look at appropriate methodologies as promised in the last 

final rule, in developing this proposal, the agencies gave full consideration to both salesweighted and unweighted regressions. 

iii. Analyses Performed 

We performed regressions describing the relationship between a vehicle’s CO2/fuel 

consumption and its footprint, in terms of various combinations of factors: initial (raw) fleets 

with no technology, versus after technology is applied; sales-weighted versus non-sales 

weighted; and with and without two sets of normalizing factors applied to the observations. 

The agencies excluded diesels and dedicated AFVs because the agencies anticipate that advanced 

gasoline-fueled vehicles are likely to be dominant through MY 2025, based both on our own 

assessment of potential standards (see Sections III and IV below) as well as our discussions with 

large number of automotive companies and suppliers. 

Thus, the basic OLS regression on the initial data (with no technology applied) and no 

sales-weighting represents one perspective on the relation between footprint and fuel economy. 

Adding sales weighting changes the interpretation to include the influence of sales volumes, and 

thus steps away from representing vehicle technology alone. Likewise, MAD is an attempt to 

reduce the impact of outliers, but reducing the impact of outliers might perhaps be less 

representative of technical relationships between the variables, although that relationship may 

change over time in reality. Each combination of methods and data reflects a perspective, and 

the regression results simply reflect that perspective in a simple quantifiable manner, expressed 

as the coefficients determining the line through the average (for OLS) or the median (for MAD) 

of the data. It is left to policy makers to determine an appropriate perspective and to interpret the 

consequences of the various alternatives.

We invite comments on the application of the weights as described above, and the 

implications for interpreting the relationship between fuel efficiency (or CO2) and footprint. 

c. What results did the agencies obtain, which methodology did the agencies 

choose for this proposal, and why is it reasonable? 

Both agencies analyzed the same statistical approaches. For regressions against data 

including technology normalization, NHTSA used the CAFE modeling system, and EPA used 

EPA’s OMEGA model. The agencies obtained similar regression results, and have based 

today’s joint proposal on those obtained by NHTSA. The draft Joint TSD Chapter 2 contains a 

large set of illustrative of figures which show the range of curves determined by the possible 

combinations of regression technique, with and without sales weighting, with and without the 

application of technology, and with various adjustments to the gpm variable prior to running a 

regression. 
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The choice among the alternatives presented in the draft Joint TSD Chapter 2 was to use 

the OLS formulation, on sales-weighted data, using a fleet that has had technology applied, and 

after adjusting the data for the effect of weight-to-footprint, as described above. The agencies 

believe that this represents a technically reasonable approach for purposes of developing target 

curves to define the proposed standards, and that it represents a reasonable trade-off among 

various considerations balancing statistical, technical, and policy matters, which include the 

statistical representativeness of the curves considered and the steepness of the curve chosen. The 

agencies judge the application of technology prior to curve fitting to provide a reasonable 

means—one consistent with the rule’s objective of encouraging manufacturers to add technology 

in order to increase fuel economy—of reducing variation in the data and thereby helping to 

estimate a relationship between fuel consumption/CO2 and footprint. 

Similarly, for the agencies’ current MY 2008-based market-forecast and the agencies’ 

current estimates of future technology effectiveness, the inclusion of the weight-to-footprint data 

adjustment prior to running the regression also helps to improve the fit of the curves by reducing 

the variation in the data, and the agencies believe that the benefits of this adjustment for this 

proposed rule likely outweigh the potential that resultant curves might somehow encourage 

reduced load carrying capability or vehicle performance (note that the we are not suggesting that 

we believe these adjustments will reduce load carrying capability or vehicle performance). In 

addition to reducing the variability, the truck curve is also steepened, and the car curve flattened 

compared to curves fitted to sales weighted data that do not include these normalizations. The 

agencies agree with manufacturers of full-size pick-up trucks that in order to maintain towing 

and hauling utility, the engines on pick-up trucks must be more powerful, than their low 

“density” nature would statistically suggest based on the agencies’ current MY2008-based 

market forecast and the agencies’ current estimates of the effectiveness of different fuel-saving 

technologies. Therefore, it may be more equitable (i.e., in terms of relative compliance 

challenges faced by different light truck manufacturers) to adjust the slope of the curve defining 

fuel economy and CO2 targets. 

As described above, however, other approaches are also technically reasonable, and also 

represent a way of expressing the underlying relationships. The agencies plan to revisit the 

analysis for the final rule, after updating the underlying market forecast and estimates of 

technology effectiveness, and based on relevant public comments received. In addition, the 

agencies intend to update the technology cost estimates, which could alter the NPRM analysis 

results and consequently alter the balance of the trade-offs being weighed to determine the final 

curves. 

d. Implications of the proposed slope compared to MY 2012-2016 

The proposed slope has several implications relative to the MY 2016 curves, with the 

majority of changes on the truck curve. With the agencies’ current MY2008-based market 

forecast and the agencies’ current estimates of technology effectiveness, the combination of sales 
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weighting and WT/FP normalization produced a car curve slope similar to that finalized in the 

MY 2012-2016 final rulemaking (4.7 g/mile in MY 2016, vs. 4.5 g/mile proposed in MY 2017). 

By contrast, the truck curve is steeper in MY 2017 than in MY 2016 (4.0 g/mile in MY 2016 vs. 

4.9 g/mile in MY 2017). As discussed previously, a steeper slope relaxes the stringency of 

targets for larger vehicles relative to those for smaller vehicles, thereby shifting relative 

compliance burdens among manufacturers based on their respective product mix. 

6. Once the agencies determined the appropriate slope for the sloped part, how did the 

agencies determine the rest of the mathematical function?

The agencies continue to believe that without a limit at the smallest footprints, the 

function—whether logistic or linear—can reach values that would be unfairly burdensome for a 

manufacturer that elects to focus on the market for small vehicles; depending on the underlying 

data, an unconstrained form could result in stringency levels that are technologically infeasible 

and/or economically impracticable for those manufacturers that may elect to focus on the 

smallest vehicles. On the other side of the function, without a limit at the largest footprints, the 

function may provide no floor on required fuel economy. Also, the safety considerations that 

support the provision of a disincentive for downsizing as a compliance strategy apply weakly, if 

at all, to the very largest vehicles. Limiting the function’s value for the largest vehicles thus 

leads to a function with an inherent absolute minimum level of performance, while remaining 

consistent with safety considerations. 

Just as for slope, in determining the appropriate footprint and fuel economy values for the 

“cutpoints,” the places along the curve where the sloped portion becomes flat, the agencies took 

a fresh look for purposes of this proposal, taking into account the updated market forecast and 

new assumptions about the availability of technologies. The next two sections discuss the 

agencies’ approach to cutpoints for the passenger car and light truck curves separately, as the 

policy considerations for each vary somewhat. 

a. Cutpoints for PC curve 

The passenger car fleet upon which the agencies have based the target curves for MYs 

2017-2025 is derived from MY 2008 data, as discussed above. In MY 2008, passenger car 

footprints ranged from 36.7 square feet, the Lotus Exige 5, to 69.3 square feet, the Daimler 

Maybach 62. In that fleet, several manufacturers offer small, sporty coupes below 41 square 

feet, such as the BMW Z4 and Mini, Honda S2000, Mazda MX-5 Miata, Porsche Carrera and 

911, and Volkswagen New Beetle. Because such vehicles represent a small portion (less than 10 

percent) of the passenger car market, yet often have performance, utility, and/or structural 

characteristics that could make it technologically infeasible and/or economically impracticable 

for manufacturers focusing on such vehicles to achieve the very challenging average 

requirements that could apply in the absence of a constraint, EPA and NHTSA are again 
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proposing to cut off the sloped portion of the passenger car function at 41 square feet, consistent 

with the MYs 2012-2016 rulemaking. The agencies recognize that for manufacturers who make 

small vehicles in this size range, putting the cutpoint at 41 square feet creates some incentive to 

downsize (i.e., further reduce the size, and/or increase the production of models currently smaller 

than 41 square feet) to make it easier to meet the target. Putting the cutpoint here may also 

create the incentive for manufacturers who do not currently offer such models to do so in the 

future. However, at the same time, the agencies believe that there is a limit to the market for cars 

smaller than 41 square feet -- most consumers likely have some minimum expectation about 

interior volume, among other things. The agencies thus believe that the number of consumers 

who will want vehicles smaller than 41 square feet (regardless of how they are priced) is small, 

and that the incentive to downsize to less than 41 square feet in response to this proposal, if 

present, will be at best minimal. On the other hand, the agencies note that some manufacturers 

are introducing mini cars not reflected in the agencies MY 2008-based market forecast, such as 

the Fiat 500, to the U.S. market, and that the footprint at which the curve is limited may affect 

the incentive for manufacturers to do so. 

 

Above 56 square feet, the only passenger car models present in the MY 2008 fleet were 

four luxury vehicles with extremely low sales volumes—the Bentley Arnage and three versions 

of the Rolls Royce Phantom. As in the MYs 2012-2016 rulemaking, NHTSA and EPA therefore 

are proposing again to cut off the sloped portion of the passenger car function at 56 square feet. 

While meeting with manufacturers prior to issuing the proposal, the agencies received 

comments from some manufacturers that, combined with slope and overall stringency, using 41 

square feet as the footprint at which to cap the target for small cars would result in unduly 

challenging targets for small cars. The agencies do not agree. No specific vehicle need meet its 

target (because standards apply to fleet average performance), and maintaining a sloped function 

toward the smaller end of the passenger car market is important to discourage unsafe 

downsizing, the agencies are thus proposing to again “cut off” the passenger car curve at 41 

square feet, notwithstanding these comments. 

The agencies seek comment on setting cutpoints for the MYs 2017-2025 passenger car 

curves at 41 square feet and 56 square feet. 

b. Cutpoints for LT curve 

The light truck fleet upon which the agencies have based the target curves for MYs 2017-

2025, like the passenger car fleet, is derived from MY 2008 data, as discussed in Section 2.4 

above. In MY 2008, light truck footprints ranged from 41.0 square feet, the Jeep Wrangler, to 

77.5 square feet, the Toyota Tundra. For consistency with the curve for passenger cars, the 

agencies are proposing to cut off the sloped portion of the light truck function at the same 

footprint, 41 square feet, although we recognize that no light trucks are currently offered below 
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41 square feet. With regard to the upper cutpoint, the agencies heard from a number of 

manufacturers during the discussions leading up to this proposal that the location of the cutpoint 

in the MYs 2012-2016 rules, 66 square feet, meant that the same standard applied to all light 

trucks with footprints of 66 square feet or greater, and that in fact the targets for the largest light 

trucks in the later years of that rulemaking were extremely challenging. Those manufacturers 

requested that the agencies extend the cutpoint to a larger footprint, to reduce targets for the 

largest light trucks which represent a significant percentage of those manufacturers light truck 

sales. At the same time, in re-examining the light truck fleet data, the agencies concluded that 

aggregating pickup truck models in the MYs 2012-2016 rule had led the agencies to 

underestimate the impact of the different pickup truck model configurations above 66 square feet 

on manufacturers’ fleet average fuel economy and CO2 levels (as discussed immediately below). 

In disaggregating the pickup truck model data, the impact of setting the cutpoint at 66 square feet 

after model year 2016 became clearer to the agencies. 

In the agencies’ view, there is legitimate basis for these comments. The agencies’ market 

forecast includes about 24 vehicle configurations above 74 square feet with a total volume of 

about 50,000 vehicles or less during any MY in the 2017-2025 time frame. While a relatively 

small portion of the overall truck fleet, for some manufacturers, these vehicles are non-trivial 

portion of sales. As noted above, the very largest light trucks have significant load-carrying and 

towing capabilities that make it particularly challenging for manufacturers to add fuel economyimproving/CO2-reducing technologies in a way that maintains the full functionality of those 

capabilities. 

Considering manufacturer CBI and our estimates of the impact of the 66 square foot 

cutpoint for future model years, the agencies have initially determined to adopt curves that 

transition to a different cut point. While noting that no specific vehicle need meet its target 

(because standards apply to fleet average performance), we believe that the information provided 

to us by manufacturers and our own analysis supports the gradual extension of the cutpoint for 

large light trucks in this proposal from 66 square feet in MY 2016 out to a larger footprint square 

feet before MY 2025. 
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Figure III-1 Footprint Distribution by Car and Truck* 

*Proposed truck cutpoints for MY 2025 shown in red, car cutpoints shown in green 

The agencies are proposing to phase in the higher cutpoint for the truck curve in order to 

avoid any backsliding from the MY 2016 standard. A target that is feasible in one model year 

should never become less feasible in a subsequent model year—manufacturers should have no 

reason to remove fuel economy-improving/CO2-reducing technology from a vehicle once it has 

been applied. Put another way, the agencies are proposing to not allow “curve crossing” from 

one model year to the next. In proposing MYs 2011-2015 CAFE standards and promulgating 

MY 2011 standards, NHTSA proposed and requested comment on avoiding curve crossing, as an 

“anti-backsliding measure.”317 The MY 2016 2 cycle- test curves are therefore a floor for the 

MYs 2017-2025 curves. For passenger cars, which have minimal change in slope from the MY 

2012-2016 rulemakings and no change in cut points, there are no curve crossing issues in the 

proposed standards. 

The minimum stringency determination was done using the two cycle curves. Stringency 

adjustments for air conditioning and other credits were calculated after curves that did not cross 

 317 74 Fed. Reg. at 14370 (Mar. 30, 2009). 
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were determined in two cycle space. The year over year increase in these adjustments cause 

neither the GHG nor CAFE curves (with A/C) to contact the 2016 curves when charted. 

7. Once the agencies determined the complete mathematical function shape, how did 

the agencies adjust the curves to develop the proposed standards and regulatory 

alternatives? 

The curves discussed above all reflect the addition of technology to individual vehicle 

models to reduce technology differences between vehicle models before fitting curves. This 

application of technology was conducted not to directly determine the proposed standards, but 

rather for purposes of technology adjustments, and set aside considerations regarding potential 

rates of application (i.e., phase-in caps), and considerations regarding economic implications of 

applying specific technologies to specific vehicle models. The following sections describe 

further adjustments to the curves discussed above, that affect both the shape of the curve, and the 

location of the curve, that helped the agencies determine curves that defined the proposed 

standards. 

a. Adjusting for Year over Year Stringency 

As in the MYs 2012-2016 rules, the agencies developed curves defining regulatory 

alternatives for consideration by “shifting” these curves. For the MYs 2012-2016 rules, the 

agencies did so on an absolute basis, offsetting the fitted curve by the same value (in gpm or 

g/mi) at all footprints. In developing this proposal, the agencies have reconsidered the use of this 

approach, and have concluded that after MY 2016, curves should be offset on a relative basis—

that is, by adjusting the entire gpm-based curve (and, equivalently, the CO2 curve) by the same 

percentage rather than the same absolute value. The agencies’ estimates of the effectiveness of 

these technologies are all expressed in relative terms—that is, each technology (with the 

exception of A/C) is estimated to reduce fuel consumption (the inverse of fuel economy) and 

CO2 emissions by a specific percentage of fuel consumption without the technology. It is, 

therefore, more consistent with the agencies’ estimates of technology effectiveness to develop 

the proposed standards and regulatory alternatives by applying a proportional offset to curves 

expressing fuel consumption or emissions as a function of footprint. In addition, extended 

indefinitely (and without other compensating adjustments), an absolute offset would eventually 

(i.e., at very high average stringencies) produce negative (gpm or g/mi) targets. Relative offsets 

avoid this potential outcome. Relative offsets do cause curves to become, on a fuel consumption 

and CO2 basis, flatter at greater average stringencies; however, as discussed above, this outcome 

remains consistent with the agencies’ estimates of technology effectiveness. In other words, 

given a relative decrease in average required fuel consumption or CO2 emissions, a curve that is 

flatter by the same relative amount should be equally challenging in terms of the potential to 

achieve compliance through the addition of fuel-saving technology. 
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On this basis, and considering that the “flattening” occurs gradually for the regulatory 

alternatives the agencies have evaluated, the agencies tentatively conclude that this approach to 

offsetting the curves to develop year-by-year regulatory alternatives neither re-creates a situation 

in which manufacturers are likely to respond to standards in ways that compromise highway 

safety, nor undoes the attribute-based standard’s more equitable balancing of compliance 

burdens among disparate manufacturers. The agencies invite comment on these conclusions, and 

on any other means that might avoid the potential outcomes—in particular, negative fuel 

consumption and CO2 targets—discussed above. 

b. Adjusting for anticipated improvements to mobile air conditioning systems 

The fuel economy values in the agencies’ market forecast are based on the 2-cycle (i.e., 

city and highway) fuel economy test and calculation procedures that do not reflect potential 

improvements in air conditioning system efficiency, refrigerant leakage, or refrigerant Global 

Warming Potential (GWP). Recognizing that there are significant and cost effective potential air 

conditioning system improvements available in the rulemaking timeframe (discussed in detail in 

Chapter 5 of the draft joint TSD), the agencies are increasing the stringency of the target curves 

based on the agencies’ assessment of the capability of manufacturers to implement these 

changes. For the proposed CAFE standards and alternatives, an offset is included based on air 

conditioning system efficiency improvements, as these improvements are the only improvements 

that effect vehicle fuel economy. For the proposed GHG standards and alternatives, a stringency 

increase is included based on air conditioning system efficiency, leakage and refrigerant 

improvements. As discussed above in Chapter 5 of the join TSD, the air conditioning system 

improvements affect a vehicle’s fuel efficiency or CO2 emissions performance as an additive 

stringency increase, as compared to other fuel efficiency improving technologies which are 

multiplicative. Therefore, in adjusting target curves for improvements in the air conditioning 

system performance, the agencies are adjusting the target curves by additive stringency increases 

(or vertical shifts) in the curves. 

For the GHG target curves, the offset for air conditioning system performance is being 

handled in the same manner as for the MY 2012-2016 rules. For the CAFE target curves, 

NHTSA for the first time is proposing to account for potential improvements in air conditioning 

system performance. Using this methodology, the agencies first use a multiplicative stringency 

adjustment for the sloped portion of the curves to reflect the effectiveness on technologies other 

that air conditioning system technologies, creating a series of curve shapes that are “fanned” 

based on two-cycle performance. Then the curves are offset vertically by the air conditioning 

improvement by an equal amount at every point. 

D. Joint Vehicle Technology Assumptions 

For the past four to five years, the agencies have been working together closely to follow 

the development of fuel consumption and GHG reducing technologies. Two major analyses 
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have been published jointly by EPA and NHTSA: the Technical Support Document to support 

the MYs 2012-2016 final rule and the 2010 Technical Analysis Report (which supported the 

2010 Notice of Intent). The latter of these analyses was also done in conjunction with CARB. 

Both of these analyses have both been published within the past 18 months. As a result, much of 

the work is still relevant and we continue to rely heavily on these references. However, some 

technologies -- and what we know about them -- are changing so rapidly that the analysis 

supporting this proposal contains a considerable amount of new work on technologies included 

in this rule, some of which were included in prior rulemakings, and others that were not. 

Notably, we have updated our battery costing methodology significantly since the MYs 

2012-2016 final rule and even relative to the 2010 TAR. We are now using a peer reviewed 

model developed by Argonne National Laboratory for the Department of Energy which provides 

us with more rigorous estimates for battery costs and allows us to estimate future costs specific 

to hybrids, plug-in hybrids and electric vehicles all of which have different battery design 

characteristics. 

We also have new cost data from more recently completed tear down and other cost 

studies by FEV which were not available in either the MYs 2012-2016 final rule or the 2010 

TAR. These new studies analyzed a 8-speed automatic transmission replacing 6-speed automatic 

transmission, a 8-speed dual clutch transmission replacing 6-speed dual clutch transmission, a 

power-split hybrid powertrain with an I4 engine replacing a conventional engine powertrain with 

V6 engine, a mild hybrid with stop-start technology and an I4 engine replacing a conventional I4 

engine, and the Fiat Multi-Air engine technology. We discuss the new tear down studies in 

Section II.D.2 of this preamble. Based on this, we have updated some of the FEV-developed 

costs relative to what we used in the 2012-2016 final rule, although these costs are consistent 

with those used in the 2010 TAR. Furthermore, we have completely re-worked our estimated 

costs associated with mass reduction relative to both the MYs 2012-2016 final rule and the 2010 

TAR. 

As would be expected given that some of our cost estimates were developed several years 

ago, we have also updated all of our base direct manufacturing costs to put them in terms of 

more recent dollars (2009 dollars for this proposal). We have also updated our methodology for 

calculating indirect costs associated with new technologies since both the MYs 2012-2016 final 

rule and the TAR. We continue to use the indirect cost multiplier (ICM) approach used in those 

analyses, but have made important changes to the calculation methodology – changes done in 

response to ongoing staff evaluation and public input. 

Lastly, we have updated many of the technologies’ effectiveness estimates largely based 

on new vehicle simulation work conducted by Ricardo Engineering. This simulation work 

provides the effectiveness estimates for a number of the technologies most heavily relied on in 

the agencies’ analysis of potential standards for MYs 2017-2025. 
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The agencies have also reviewed the findings and recommendations in the updated NAS 

report “Assessment of Fuel Economy Technologies for Light-Duty Vehicles” that was completed 

after the MYs 2012-2016 final rule was issued,318 and NHTSA has performed a sensitivity 

analysis (contained in its PRIA) to examine the impact of using some of the NAS cost and 

effectiveness estimates on the proposed standards. 

Each of these changes is discussed briefly in the remainder of this section and in much 

greater detail in Chapter 3 of the draft joint TSD. First we provide a brief summary of the 

technologies we have considered in this proposal before highlighting the above-mentioned items 

that are new for this proposal. We request comment on all aspects of our analysis as discussed 

here and detailed in the draft joint TSD. 

1. What Technologies did the Agencies Consider? 

For this proposal, the agencies project that manufacturers can add a variety of 

technologies to each of their vehicle models and or platforms in order to improve the vehicles’ 

fuel economy and GHG performance. In order to analyze a variety of regulatory alternative 

scenarios, it is essential to have a thorough understanding of the technologies available to the 

manufacturers. This analysis includes an assessment of the cost, effectiveness, availability, 

development time, and manufacturability of various technologies within the normal redesign and 

refresh periods of a vehicle line (or in the design of a new vehicle). As we describe in the draft 

Joint TSD, when a technology can be applied can affect the cost as well as the technology 

penetration rates (or phase-in caps) that are projected in the analysis. 

The agencies considered dozens of vehicle technologies that manufacturers could use to 

improve the fuel economy and reduce CO2 emissions of their vehicles during the MYs 2017-

2025 timeframe. Many of the technologies considered are available today, are well known, and 

could be incorporated into vehicles once product development decisions are made. These are 

“near-term” technologies and are identical or very similar to those anticipated in the agencies’ 

analyses of compliance strategies for the MYs 2012-2016 final rule. For this rulemaking, given 

its time frame, other technologies are also considered that are not currently in production, but 

that are beyond the initial research phase, and are under development and expected to be in 

production in the next 5-10 years. Examples of these technologies are downsized and 

turbocharged engines operating at combustion pressures even higher than today’s turbocharged 

engines, and an emerging hybrid architecture combined with an 8 speed dual clutch transmission, 

a combination that is not available today. These are technologies which the agencies believe 

can, for the most part, be applied both to cars and trucks, and which are expected to achieve 

significant improvements in fuel economy and reductions in CO2 emissions at reasonable costs 

in the MYs 2017 to 2025 timeframe. The agencies did not consider technologies that are 

 318 “Assessment of Fuel Economy Technologies for Light-Duty Vehicles”, National Research Council of the 

National Academies, June 2010. 
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currently in an initial stage of research because of the uncertainty involved in the availability and 

feasibility of implementing these technologies with significant penetration rates for this analysis. 

The agencies recognize that due to the relatively long time frame between the date of this 

proposal and 2025, it is very possible that new and innovative technologies will make their way 

into the fleet, perhaps even in significant numbers, that we have not considered in this analysis. 

We expect to reconsider such technologies as part of the mid-term evaluation, as appropriate, and 

possibly could be used to generate credits under a number of the proposed flexibility and 

incentive programs provided in the proposed rules. 

 The technologies considered can be grouped into four broad categories: engine 

technologies; transmission technologies; vehicle technologies (such as mass reduction, tires and 

aerodynamic treatments); and electrification technologies (including hybridization and changing 

to full electric drive).319 The specific technologies within each broad group are discussed below. 

The list of technologies presented below is nearly identical to that presented in both the MYs 

2012-2016 final rule and the 2010 TAR, with the following new technologies added to the list 

since the last final rule: the P2 hybrid, a newly emerging hybridization technology that was also 

considered in the 2010 TAR; continued improvements in gasoline engines, with greater 

efficiencies and downsizing; continued significant efficiency improvements in transmissions; 

and ongoing levels of improvement to some of the seemingly more basic technologies such as 

lower rolling resistance tires and aerodynamic treatments, which are among the most cost 

effective technologies available for reducing fuel consumption and GHGs. Not included in the 

list below are technologies specific to air conditioning system improvements and off-cycle 

controls, which are presented in Section II.F of this NPRM and in Chapter 5 of the draft Joint 

TSD. 

a. Types of Engine Technologies Considered 

Low-friction lubricants including low viscosity and advanced low friction lubricant oils 

are now available with improved performance. 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 costs in our analysis consider these engine changes and 

testing requirements. This level of low friction lubricants is expected to exceed 85 

percent penetration by the 2017 MY. 

Reduction of engine friction losses 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 engine components 

and subsystems that improve efficient engine operation. This level of engine friction 

reduction is expected to exceed 85 percent penetration by the 2017 MY. 

 319 NHTSA’s analysis considers these technologies in five groups rather than four – hybridization is one category, 

and “electrification/accessories” is another. 
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Advanced Low Friction Lubricant and Second Level of Engine Friction Reduction are 

new for this analysis. As technologies advance between now and the rulemaking 

timeframe, there will be further development in low friction lubricants and engine friction 

reductions. The agencies grouped the development in these two areas into a single 

technology and applied them for MY 2017 and beyond. 

Cylinder deactivation disables the 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 substantially reduces pumping losses. 

Variable valve timing alters the timing of the intake valves, exhaust valves, or both, 

primarily to reduce pumping losses, increase specific power, and control residual gases. 

Discrete variable valve lift increases efficiency by optimizing air flow over a broader 

range of engine operation which reduces pumping losses. This is accomplished by 

controlled switching between two or more cam profile lobe heights. 

Continuous variable valve lift is an electromechanical or electrohydraulic system in 

which valve timing is changed as lift height is controlled. This yields a wide range of 

performance optimization and volumetric efficiency, including enabling the engine to be 

valve throttled. 

Stoichiometric gasoline direct-injection technology injects fuel at high pressure directly 

into the combustion chamber to improve cooling of the air/fuel charge as well as 

combustion quality within the cylinder, which allows for higher compression ratios and 

increased thermodynamic efficiency. 

Turbo charging and downsizing increases the available airflow and specific power level, 

allowing a reduced engine size while maintaining performance. Engines of this type use 

gasoline direct injection (GDI) and dual cam phasing. This reduces pumping losses at 

lighter loads in comparison to a larger engine. We continue to include an 18 bar brake 

mean effective pressure (BMEP) technology (as in the MYs 2012-2016 final rule) and are 

also including both 24 bar BMEP and 27 bar BMEP technologies. The 24 bar BMEP 

technology would use a single-stage, variable geometry turbocharger which would 

provide a higher intake boost pressure available across a broader range of engine 

operation than conventional 18 bar BMEP engines. The 27 bar BMEP technology 

requires additional boost and thus would use a two-stage turbocharger necessitating use 

of cooled exhaust gas recirculation (EGR) as described below. The18 bar BMEP 

technology is applied with 33 percent engine downsizing, 24 bar BMEP is applied with 

50 percent engine downsizing, and 27 bar BMEP is applied with 56 percent engine 

downsizing. 

Cooled exhaust-gas recirculation (EGR) reduces the incidence of knocking combustion 

with additional charge dilution and obviates the need for fuel enrichment at high engine 

power. This allows for higher boost pressure and/or compression ratio and further 

reduction in engine displacement and both pumping and friction losses while maintaining 

performance. Engines of this type use GDI and both dual cam phasing and discrete 

variable valve lift. The EGR systems considered in this assessment would use a dual-
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loop system with both high and low pressure EGR loops and dual EGR coolers. For this 

proposal, cooled EGR is considered to be a technology that can be added to a 24 bar 

BMEP engine and is an enabling technology for 27 bar BMEP 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 NOx adsorption catalyst system or a 

urea/ammonia selective catalytic reduction system for control of NOx emissions during 

lean (excess air) operation. 

b. Types of Transmission Technologies Considered 

Improved automatic transmission controls optimize the shift schedule to maximize fuel 

efficiency under wide ranging conditions and minimizes losses associated with torque 

converter slip through lock-up or modulation. The first level of controls is expected to 

exceed 85 percent penetration by the 2017 MY. 

Shift optimization is a strategy whereby the engine and/or transmission controller(s) 

emulates a CVT by continuously evaluating all possible gear options that would provide 

the necessary tractive power and select the best gear ratio that lets the engine run in the 

most efficient operating zone. 

Six-, seven-, and eight-speed automatic transmissions are optimized by changing the gear 

ratio span 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 speed 

transmissions are expected to be readily available and applied in the MYs 2017 through 

2025 timeframe. 

Dual clutch or automated shift manual transmissions are similar to manual transmissions, 

but the vehicle controls shifting and launch functions. A dual-clutch automated shift 

manual transmission (DCT) 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. 

For this proposal we have considered both 6-speed and 8-speed DCT transmissions. 

Continuously variable transmission 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 ratios that enable the engine 

to operate in a more efficient operating range over a broader range of vehicle operating 

conditions. The CVT is maintained for existing baseline vehicles and not considered for 

future vehicles in this proposal due to the availability of more cost effective transmission 

technologies. 
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Manual 6-speed transmission offers an additional gear ratio, often with a higher 

overdrive gear ratio, than a 5-speed manual transmission. 

High Efficiency Gearbox (automatic, DCT or manual) – continuous improvement in 

seals, bearings and clutches, super finishing of gearbox parts, and development in the 

area of lubrication, all aimed at reducing frictional and other parasitic load in the system 

for an automatic or DCT type transmission. 

c. Types of Vehicle Technologies Considered 

Lower-rolling-resistance tires have characteristics that reduce frictional losses associated 

with the energy dissipated mainly in the deformation of the tires under load, thereby 

improving fuel economy and reducing CO2 emissions. New for this proposal (and also 

marking an advance over low rolling resistance tires considered during the heavy duty 

greenhouse gas rulemaking, see 76 FR at 57207, 57229) is a second level of lower rolling 

resistance tires that reduce frictional losses even further. The first level of low rolling 

resistance tires will have 10 percent rolling resistance reduction while the 2nd level would 

have 20 percent rolling resistance reduction compared to 2008 baseline vehicle. The first 

level of lower rolling resistance tires is expected to exceed 85 percent penetration by the 

2017 MY. 

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. 

Front or secondary axle disconnect for four-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. 

Aerodynamic drag reduction can be achieved via two approaches, either 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 

addition to the standard aerodynamic treatments, the agencies have included a second 

level of aerodynamic technologies which could include active grill shutters, rear visors, 

and larger under body panels. The first level of aero dynamic drag improvement is 

estimated to reduce aerodynamic drag by 10 percent relative to the baseline 2008 vehicle 

while the second level would reduce aero dynamic drag by 20 percent relative to 2008 

baseline vehicles. The second level of aerodynamic technologies was not considered in 

the MYs 2012-2016 final rule. 

Mass Reduction can be achieved in many ways, such as material substitution, design 

optimization, part consolidation, improving manufacturing process, etc. The agencies 

applied mass reduction of up to 20 percent relative to MY 2008 levels in this NPRM 

compared to only 10 percent in 2012-2016 final rule. The agencies also determined 

effectiveness values for hybrid, plug-in and electric vehicles based on net mass reduction, 

or the delta between the applied mass reduction (capped at 20 percent) and the added 

mass of electrification components. In assessing compliance strategies and in structuring 

the standards, the agencies only considered amounts of vehicle mass reduction that would 

result in what we estimated to be no adverse effect on overall fleet safety. The agencies 
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have an extensive discussion of mass reduction technologies as well as the cost of mass 

reduction in chapter 3 of the draft joint TSD. 

d. Types of Electrification/Accessory and Hybrid Technologies Considered 

Electric power steering (EPS)/ Electro-hydraulic 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 light-duty vehicles, including large trucks. 

However, the agencies have applied the EHPS technology to large trucks and the EPS 

technology to all other light-duty vehicles. 

Improved accessories (IACC) may include high efficiency alternators, electrically driven 

(i.e., on-demand) water pumps and cooling fans. This excludes other electrical 

accessories such as electric oil pumps and electrically driven air conditioner compressors. 

New for this proposal is a second level of IACC () which consists of the IACC 

technologies and the addition of a mild regeneration strategy and a higher efficiency 

alternator. The first level of IACC improvements is expected to be at more than 85 

percent penetration by the 2017MY. 

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 and cooling pumps to maintain vehicle systems during idle-stop. 

Higher Voltage Stop-Start/Belt Integrated Starter Generator (BISG) sometimes referred 

to as a mild hybrid, 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 starteralternator, that is belt driven and that can recover braking energy while the vehicle slows 

down (regenerative braking). This mild hybrid technology is not included by either 

agency as an enabling technology in the analysis supporting this proposal, although some 

automakers have expressed interest in possibly using the technology during the 

rulemaking time frame. EPA and NHTSA are providing incentives to encourage this and 

similar hybrid technologies on pick-up trucks in particular, as described in Section II.F, 

and the agencies are in the process of including this technology for the final rule analysis 

as we expand our understanding of the associated costs and limitations. 

Integrated Motor Assist (IMA)/Crank integrated starter generator (CISG) provides idlestop 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). The IMA technology is not included by 
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either agency as an enabling technology in the analysis supporting this proposal, although 

it is included as a baseline technology because it exists in our 2008 baseline fleet. 

P2 Hybrid 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 which 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 allelectric 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. 

2-Mode Hybrid is a hybrid electric drive system that uses an adaptation of a conventional 

stepped-ratio automatic transmission by replacing some of the transmission clutches with 

two electric motors that control the ratio of engine speed to vehicle speed, while clutches 

allow the motors to be bypassed. This improves both the transmission torque capacity for 

heavy-duty applications and reduces fuel consumption and CO2 emissions at highway 

speeds relative to other types of hybrid electric drive systems. The 2-mode hybrid 

technology is not included by either agency as an enabling technology in the analysis 

supporting this proposal, although it is included as a baseline technology because it exists 

in our 2008 baseline fleet. 

Power-split Hybrid 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 not included by either agency as an 

enabling technology in the analysis supporting this proposal, (the agencies evaluate the 

P2 hybrid technology discussed above where power-split hybrids might otherwise have 

been appropriate) although it is included as a baseline technology because it exists in our 

2008 baseline fleet. 

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 MYs 2017-2025 analysis, PHEVs with several all-electric ranges—both 

a 20 mile and a 40 mile all-electric range—have been included as potential technologies. 
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Electric vehicles (EV) are equipped with all-electric drive and with systems powered by 

energy-optimized batteries charged primarily from grid electricity. EVs with several 

ranges—75 mile, 100 mile and 150 mile range—have been included as potential 

technologies. 

e. Technologies Considered but Deemed “Not Ready” in the MYs 2017-2025 

Timeframe 

Fuel cell electric vehicles (FCEVs) utilize a full electric drive platform but consume 

electricity generated by an on-board fuel cell and hydrogen fuel. Fuel cells are electro-chemical 

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 

that are currently under development. 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). While we 

expect there will be some limited introduction of FCEVs into the market place in the time frame 

of this rule, we expect this introduction to be relatively small, and thus FCEVs are not considered 

in the modeling analysis conducted for this proposal.are a number of other technologies that the 

agencies have not considered in their analysis, but may be considered for the final rule. These 

include HCCI, “multi-air”, and camless valve actuation, and other advanced engines currently 

under development. 

2. How did the Agencies Determine the Costs of Each of these Technologies? 

As noted in the introduction to this section, most of the direct cost estimates for 

technologies carried over from the MYs 2012-2016 final rule and subsequently used in this 

proposal are fundamentally unchanged since the MYs 2012-2016 final rule analysis and/or the 

2010 TAR. We say “fundamentally” unchanged since the basis of the direct manufacturing cost 

estimates have not changed; however, the costs have been updated to more recent dollars, the 

learning effects have resulted in further cost reductions for some technologies, the indirect costs 

are calculated using a modified methodology and the impact of long-term ICMs is now present 

during the rulemaking timeframe. Besides these changes, there are also some other notable 

changes to the costs used in previous analyses. We highlight these changes in Section II.D.2.a, 

below. We highlight the changes to the indirect cost methodology and adjustments to more 

recent dollars in Sections II.D.2.b and c. Lastly, we present some updated terminology used for 

our approach to estimating learning effects in an effort to eliminate confusion with our past 

terminology. This is discussed in Section II.D.2.d, below. 

The agencies noteand that additional maintenance costs could occur as a result, the 

agencies believe that it is equally possible that maintenance costs could decrease for some 

vehicles, especially when considering full electric vehicles (which lack routine engine 

maintenance) or the replacement of automatic transmissions with simpler dual-clutch 
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transmissions. The agencies request comment on the possible maintenance cost impacts 

associated with this proposal, reminding potential commenters that increased warranty costs are 

already considered as part of the ICMs. 

a. Direct Manufacturing Costs (DMC) 

For direct manufacturing costs (DMC) related to turbocharging, downsizing, gasoline 

direct injection, transmissions, as well as non-battery-related costs on hybrid, plug-in hybrid and 

electric vehicles, the agencies have relied on costs derived from teardown studies. For battery 

related DMC for HEVs, PHEVs and EVs, the agencies have relied on the BatPaC model 

developed by Argonne National Laboratory for the Department of Energy. For mass reduction 

DMC, the agencies have relied on several studies as described in detail in the draft Joint TSD. 

We discuss each of these briefly here and in more detail in the draft joint TSD. For the majority 

of the other technologies considered in this proposal and described above, the agencies have 

relied on the 2012-2016 final rule and sources described there for estimates of DMC. 

i. Costs from Tear-down Studies 

As a general matter, the agencies believe that the best method to derive technology cost 

estimates is to conduct studies involving tear-down and analysis of actual vehicle components. 

A “tear-down” involves breaking down a technology into its fundamental parts and 

manufacturing processes by completely disassembling actual vehicles and vehicle subsystems 

and precisely determining what is required for its production. The result of the tear-down is a 

“bill of materials” for each and every part of the relevant vehicle systems. This tear-down 

method of costing technologies is often used by manufacturers to benchmark their products 

against competitive products. Historically, vehicle and vehicle component tear-down has not 

been done on a large scale by researchers and regulators due to the expense required for such 

studies. While tear-down studies are highly accurate at costing technologies for the year in 

which the study is intended, their accuracy, like that of all cost projections, may diminish over 

time as costs are extrapolated further into the future because of uncertainties in predicting 

commodities (and raw material) prices, labor rates, and manufacturing practices. The projected 

costs may be higher or lower than predicted. 

Over the past several years, EPA has contracted with FEV, Inc. and its subcontractor 

Munro & Associates, to conduct tear-down cost studies for a number of key technologies 

evaluated by the agencies in assessing the feasibility of future GHG and CAFE standards. The 

analysis methodology included procedures to scale the tear-down results to smaller and larger 

vehicles, and also to different technology configurations. FEV’s methodology was documented 

in a report published as part of the MY 2012-2016 rulemaking, detailing the costing of the first 
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tear-down conducted in this work (#1 in the below list).320 This report was peer reviewed by 

experts in the industry and revised by FEV in response to the peer review comments.321 

Subsequent tear-down studies (#2-5 in the below list) were documented in follow-up FEV 

reports made available in the public docket for the MY 2012-2016 rulemaking.322

Since then, FEV’s work under this contract work assignment has continued. Additional 

cost studies have been completed and are available for public review.323 The most extensive 

study, performed after the MY 2012-2016 Final Rule, involved whole-vehicle tear-downs of a 

2010 Ford Fusion powersplit hybrid and a conventional 2010 Ford Fusion. (The latter served as 

a baseline vehicle for comparison.) In addition to providing powersplit HEV costs, the results 

for individual components in these vehicles were subsequently used by FEV/Munro to cost 

another hybrid technology, the P2 hybrid, which employs similar hardware. This approach to 

costing P2 hybrids was undertaken because P2 HEVs were not yet in volume production at the 

time of hardware procurement for tear-down. Finally, an automotive lithium-polymer battery 

was torn down and costed to provide supplemental battery costing information to that associated 

with the NiMH battery in the Fusion. This HEV cost work, including the extension of results to 

P2 HEVs, has been extensively documented in a new report prepared by FEV.324 Because of the 

complexity and comprehensive scope of this HEV analysis, EPA commissioned a separate peer 

review focused exclusively on it. Reviewer comments generally supported FEV’s methodology 

and results, while including a number of suggestions for improvement many of which were 

subsequently incorporated into FEV’s analysis and final report. The peer review comments and 

responses are available in the rulemaking docket.325, 326

Over the course of this work assignment, teardown-based studies have been performed 

thus far on the technologies listed below. These completed studies provide a thorough 

evaluation of the new technologies’ costs relative to their baseline (or replaced) technologies. 

 320 U.S. EPA, “Light-Duty Technology Cost Analysis Pilot Study,” Contract No. EP-C-07-069, Work Assignment 1-

3, December 2009, EPA-420-R-09-020, Docket EPA-HQ-OAR-2009-0472- 11282 321 FEV pilot study response to peer review document November 6, 2009, is at EPA-HQ-OAR-2009-0472-11285 322 U.S. EPA, “Light-duty Technology Cost Analysis – Report on Additional Case Studies,” EPA-HQ-OAR-2009-

0472-11604 

323 FEV, Inc., “Light-Duty Technology Cost Analysis, Report on Additional Transmission, Mild Hybrid, and 

Valvetrain Technology Case Studies”, November 2011. 

324 FEV, Inc., “Light-Duty Technology Cost Analysis, Power-Ssplit and P2 HEV Case Studies”, EPA-420-R-11-

015, November 2011. 

325 ICF, “Peer Review of FEV Inc. Report “Light Duty Technology Cost Analysis, Power-Split and P2 Hybrid 

Electric Vehicle Case Studies”, EPA-420-R-11-016, November 2011March 31, 2011. 

326 FEV and EPA, “FEV Inc. Report ‘Light Duty Technology Cost Analysis, Power-Split and P2 Hybrid Electric 

Vehicle Case Studies’, Peer Review Report – Response to Comments Document”Peer Review Report - Response to 

Comments Document for “Light Duty Technology Cost Analysis, Power-Split and P2 Hybrid Electric Vehicle Case 

Studies”, EPA-420-R-11-017, November 2011October 3, 2011. 
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2. Stoichiometric gasoline direct injection () and turbocharging with engine downsizing 

(T-DS) on a DOHC (dual overhead cam) I4 engine, replacing a conventional DOHC 

I4 engine. 

3. SGDI and T-DS on a SOHC (single overhead cam) on a V6 engine, replacing a 

conventional 3-valve/cylinder SOHC V8 engine. 

4. SGDI and T-DS on a DOHC I4 engine, replacing a DOHC V6 engine. 

5. 6-speed automatic transmission (AT), replacing a 5-speed AT. 

6. 6-speed wet dual clutch transmission (DCT) replacing a 6-speed AT. 

7. 8-speed AT replacing a 6-speed AT. 

8. 8-speed DCT replacing a 6-speed DCT. 

9. Power-split hybrid (Ford Fusion with I4 engine) compared to a conventional vehicle 

(Ford Fusion with V6). The results from this tear-down were extended to address P2 

hybrids. In addition, costs from individual components in this tear-down study were 

used by the agencies in developing cost estimates for PHEVs and EVs.with stop-start 

technology (Saturn Vue with I4 engine), replacing a conventional I4 engine. 

(Although results from this cost study are included in the rulemaking docket, they 

were not used by the agencies in this rulemaking’s technical analyses.) 

10. Fiat Multi-Air engine technology. (Although results from this cost study are included 

in the rulemaking docket, they were not used by the agencies in this rulemaking’s 

technical analyses.) 

Items 6 through 10 in the list above are new since the 2012-2016 final rule. 

In addition, FEV and EPA extrapolated the engine downsizing costs for the following 

scenarios that were based on the above study cases: 

1. Downsizing a SOHC 2 valve/cylinder V8 engine to a DOHC V6. 

2. Downsizing a DOHC V8 to a DOHC V6. 

3. Downsizing a SOHC V6 engine to a DOHC 4 cylinder engine. 

4. Downsizing a DOHC 4 cylinder engine to a DOHC 3 cylinder engine. 

The agencies have relied on the findings of FEV for estimating the cost of the 

technologies covered by the tear-down studies. 

ii. Costs of HEV, EV & PHEV 

The agencies have also reevaluated the costs for HEVs, PHEVs, and EVs since both the 

2012-2016 final rule and the 2010 TAR. First, electrified vehicle technologies are developing 

rapidly and the agencies sought to capture results from the most recent analysis. Second, the 

2012-2016 rule employed a single $/kWhr estimate and did not consider the specific vehicle and 

technology application for the battery when we estimated the cost of the battery. Specifically, 

batteries used in HEVs (high power density applications) versus EVs (high energy density 

applications) need to be considered appropriately to reflect the design differences, the chemical 

material usage differences and differences in $/kWhr as the power to energy ratio of the battery 

changes for different applications. 
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To address these issues for this proposal, the agencies have done two things. First, EPA 

has developed a spreadsheet tool that was used to size the motor and battery based on the 

different road load of various vehicle classes. Second, the agencies have used a battery cost 

model developed by Argonne National Laboratory (ANL) for the Vehicle Technologies Program 

of the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy.327 

The model developed by ANL allows users to estimate unique battery pack costs using user 

customized input sets for different hybridization applications, such as strong hybrid, PHEV and 

EV. The DOE has established long term industry goals and targets for advanced battery systems 

as it does for many energy efficient technologies. ANL was funded by DOE to provide an 

independent assessment of Li-ion battery costs because of ANL’s expertise in the field as one of 

the primary DOE National Laboratories responsible for basic and applied battery energy storage 

technologies for future HEV, PHEV and EV applications. Since publication of the 2010 TAR, 

ANL’s battery cost model has been peer-reviewed and ANL has updated the model and 

documentation to incorporate suggestions from peer-reviewers, such as including a battery 

management system, a battery disconnect unit, a thermal management system, etc.328 In this 

proposal, NHTSA and EPA have used the recently revised version of this updated model. 

The agencies are using the ANL model as the basis for estimating large-format lithiumion batteries for this assessment for the following reasons. The model was developed by 

scientists at ANL who have significant experience in this area. The model uses a bill of 

materials methodology for developing cost estimates. The ANL model appropriately considers 

the vehicle application’s power and energy requirements, which are two of the fundamental 

parameters when designing a lithium-ion battery for an HEV, PHEV, or EV. The ANL model 

can estimate production costs based on user defined inputs for a range of production volumes. 

The ANL model’s cost estimates, while generally lower than the estimates we received from the 

OEMs, are consistent with some of the supplier cost estimates that EPA received from largeformat lithium-ion battery pack manufacturers. This includes data which was received from onsite visits done by the EPA in the 2008-2011 time frame. Finally, the ANL model has been 

described and presented in the public domain and does not rely upon confidential business 

information (which could not be reviewed by the public). 

The potential for future reductions in battery cost and improvements in battery 

performance relative to current batteries will play a major role in determining the overall cost 

and performance of future PHEVs and EVs. The U.S. Department of Energy manages major 

battery-related R&D programs and partnerships, and has done so for many years, including the 

 327 Insert cite to ANL BatPac model, if we can Docket number. 328 Nelson, P.A., Santinit, D.J., Barnes, J. “Factors Determining the Manufacturing Costs of Lithium-Ion Batteries 

for PHEVs,” 24th World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exposition EVS-24, 

Stavenger, Norway, May 13-16, 2009 (www.evs24.org).Cite ANL BatPaC peer review report.
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ANL model utilized in this report. DOE has reviewed the battery cost projections underlying 

this proposal and supports the use of the ANL model for the purposes of this rulemaking.329 

We have also estimated cost associated with in-home chargers and installation of in-home 

chargers expected to be necessary for PHEVs and EVs. Charger costs are covered in more detail 

in chapter 3 of the draft Joint TSD. 

iii. Mass Reduction Costs 

The agencies have revised the costs for mass reduction from the MYs 2012-2016 rule and 

the 2010 Technical Assessment Report. For this proposal, the agencies are relying on a wide 

assortment of sources from the literature as well as data provided from a number of OEMs. 

Based on this review, the agencies have estimated a new cost curve such that the costs increase 

as the levels of mass reduction increase. For the final rule the agencies will consider any new 

studies that become available, including two studies that the agencies are sponsoring and expect 

will be completed in time to inform the final rule. These studies are discussed in TSD chapter 3. 

b. Indirect Costs (IC) 

i. Markup Factors to Estimate Indirect Costs 

For this analysis, indirect costs are estimated by applying indirect cost multipliers (ICM) 

to direct cost estimates. ICMs were derived by EPA as a basis for estimating the impact on 

indirect costs of individual vehicle technology changes that would result from regulatory actions. 

Separate ICMs were derived for low, medium, and high complexity technologies, thus enabling 

estimates of indirect costs that reflect the variation in research, overhead, and other indirect costs 

that can occur among different technologies. ICMs were also applied in the MYs 2012-2016 

rulemaking. 

Prior to developing the ICM methodology,330 EPA and NHTSA both applied a retail price 

equivalent (RPE) factor to estimate indirect costs. RPEs are estimated by dividing the total 

revenue of a manufacturer by the direct manufacturing costs. As such, it includes all forms of 

indirect costs for a manufacturer and assumes that the ratio applies equally for all technologies. 

ICMs are based on RPE estimates that are then modified to reflect only those elements of 

indirect costs that would be expected to change in response to a regulatory-induced technology 

change. For example, warranty costs would be reflected in both RPE and ICM estimates, while 

marketing costs might only be reflected in an RPE estimate but not an ICM estimate for a 

 329 Cite a DOE memo or email that documents this support? 330 The ICM methodology was developed by RTI International, under contract to EPA. The results of the RTI report 

were published in Alex Rogozhin, Michael Gallaher, Gloria Helfand, and Walter McManus, “Using Indirect Cost 

Multipliers to Estimate the Total Cost of Adding New Technology in the Automobile Industry.” International 

Journal of Production Economics 124 (2010): 360-368. [Docket item #] 
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particular technology, if the new regulatory-induced technology change is not one expected to be 

marketed to consumers. Because ICMs calculated by EPA are for individual technologies, many 

of which are small in scale, they often reflect a subset of RPE costs; as a result, for low 

complexity technologies, the RPE is typically higher than the ICM. This is not always the case, 

as ICM estimates for particularly complex technologies, specifically hybrid technologies (for 

near term ICMs), and plug-in hybrid battery and full electric vehicle technologies ( for near term 

and long term ICMs), reflect higher than average indirect costs, with the resulting ICMs for those 

technologies equaling or exceeding the averaged RPE for the industry. 

There is some level of uncertainty surrounding both the ICM and RPE markup factors. 

The ICM estimates used in this proposed action group all technologies into four broad categories 

and treat them as if individual technologies within each of the categories (“low”, “medium”, 

“high1” and “high2” complexity) will have the same ratio of indirect costs to direct costs. This 

simplification means it is likely that the direct cost for some technologies within a category will 

be higher and some lower than the estimate for the category in general. More importantly, the 

ICM estimates have not been validated through a direct accounting of actual indirect costs for 

individual technologies. Rather, the ICM estimates were developed using adjustment factors 

developed in two separate occasions: the first, a consensus process, was reported in the RTI 

report; the second, a modified Delphi method, was conducted separately and reported in an EPA 

memo.331 Both these panels were composed of EPA staff members with previous background in 

the automobile industry; the memberships of the two panels overlapped but were not identical.332 

The panels evaluated each element of the industry’s RPE estimates and estimated the degree to 

which those elements would be expected to change in proportion to changes in direct 

manufacturing costs. The method and estimates in the RTI report were peer reviewed by three 

industry experts and subsequently by reviewers for the International Journal of Production 

Economics. RPEs themselves are inherently difficult to estimate because the accounting 

statements of manufacturers do not neatly categorize all cost elements as either direct or indirect 

costs. Hence, each researcher developing an RPE estimate must apply a certain amount of 

judgment to the allocation of the costs. Since empirical estimates of ICMs are ultimately derived 

from the same data used to measure RPEs, this affects both measures. However, the value of 

RPE has not been measured for specific technologies, or for groups of specific technologies. 

Thus applying a single average RPE to any given technology by definition overstates costs for 

very simple technologies, or understates them for advanced technologies. 

In every recent GHG and fuel economy rulemaking proposal, we have requested 

comment on our ICM factors and whether it is most appropriate to use ICMs or RPEs. We have 

 331 Helfand, Gloria, and Sherwood, Todd. “Documentation of the Development of Indirect Cost Multipliers for 

Three Automotive Technologies.” Memorandum, Assessment and Standards Division, Office of Transportation and 

Air Quality, U.S. Environmental Protection Agency, August 2009. [Docket item #] 332 NHTSA staff participated in the development of the process for the second, modified Delphi panel, and reviewed 

the results as they were developed, but did not serve on the panel. 
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generally received little to no comment on the issue specifically, other than basic comments that 

the ICM values are too low. In addition, in the June 2010 NAS report, NAS noted that the under 

the initial ICMs, no technology would be assumed to have indirect costs as high as the average 

RPE. NRC found that “RPE factors certainly do vary depending on the complexity of the task of 

integrating a component into a vehicle system, the extent of the required changes to other 

components, the novelty of the technology, and other factors. However, until empirical data 

derived by means of rigorous estimation methods are available, the committee prefers to use 

average markup factors.”333 The committee also stated that “The EPA (Rogozhin et al., 2009), 

however, has taken the first steps in attempting to analyze this problem in a way that could lead 

to a practical method of estimating technology-specific markup factors” where “this problem” 

spoke to the issue of estimating technology-specific markup factors and indirect cost 

multipliers.334

The agencies note that, since the committee completed their work, EPA has published its 

work in the Journal of Production Economics335 and has also published a memorandum 

furthering the development of ICMs,336 neither of which the committee had at their disposal. 

Further, having published two final rulemakings—the 2012-2016 light-duty rule (see 75 FR 

25324) and the more recent heavy-duty GHG rule (see 76 FR 57106)—as well as the 2010 TAR 

where ICMs served as the basis for all or most of the indirect costs, EPA believes that ICMs are 

indeed fully developed for regulatory purposes. As thinking has matured, we have adjusted our 

ICM factors such that they are slightly higher and, importantly, we have changed the way in 

which the factors are applied. 

The first change—increased ICM factors—has been done as a result of further thought 

among EPA and NHTSA that the ICM factors presented in the original RTI report for low and 

medium complexity technologies should no longer be used and that we should rely solely on the 

modified-Delphi values for these complexity levels. For that reason, we have eliminated the 

averaging of original RTI values with modified-Delphi values and instead are relying solely on 

the modified-Delphi values for low and medium complexity technologies. The second change—

the way the factors are applied—results in the warranty portion of the indirect costs being 

applied as a multiplicative factor (thereby decreasing going forward as direct manufacturing 

costs decrease due to learning), and the remainder of the indirect costs being applied as an 

additive factor (thereby remaining constant year-over-year and not being reduced due to 

learning). This second change has a comparatively large impact on the resultant technology 

 333 NRC, Finding 3-2 at page 3-23. 334 NRC at page 3-19. 335 Alex Rogozhin, Michael Gallaher, Gloria Helfand, and Walter McManus, “Using Indirect Cost Multipliers to 

Estimate the Total Cost of Adding New Technology in the Automobile Industry.” International Journal of 

Production Economics 124 (2010): 360-368 (Docket item #XX). 336 Helfand, Gloria, and Sherwood, Todd. “Documentation of the Development of Indirect Cost Multipliers for 

Three Automotive Technologies.” Memorandum, Assessment and Standards Division, Office of Transportation and 

Air Quality, U.S. Environmental Protection Agency, August 2009 (Docket item #XX). 
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costs and, we believe, more appropriately estimates costs over time. In addition to these 

changes, a secondary-level change was also made as part of this ICM recalculation to ICMs. 

That change was to revise upward the RPE level reported in the original RTI report from an 

original value of 1.46 to 1.5, to reflect the long term average RPE. The original RTI study was 

based on 2008 data. However, an analysis of historical RPE data indicates that, although there is 

year to year variation, the average RPE has remained roughly constant at 1.5. ICMs will be 

applied to future years’ data and, therefore, NHTSA and EPA staffs believe that it would be 

appropriate to base ICMs on the historical average rather than a single year’s result. Therefore, 

ICMs have been adjusted to reflect this average level. These changes to the ICMs and the 

methodology are described in greater detail in Chapter 3 of the draft Joint TSD. 

ii. Stranded Capital 

Because the production of automotive components is capital-intensive, it is possible for 

substantial capital investments in manufacturing equipment and facilities to become “stranded” 

(where their value is lost, or diminished). This would occur when the capital is rendered useless 

(or less useful) by some factor that forces a major change in vehicle design, plant operations, or 

manufacturer’s product mix, such as a shift in consumer demand for certain vehicle types. It can 

also be caused by new standards that phase-in at a rate too rapid to accommodate planned 

replacement or redisposition of existing capital to other activities. The lost value of capital 

equipment is then amortized in some way over production of the new technology components. 

It is difficult to quantify accurately any capital stranding associated with new technology 

phase-ins under the proposed standards because of the iterative dynamic involved – that is, the 

new technology phase-in rate strongly affects the potential for additional cost due to stranded 

capital, but that additional cost in turn affects the degree and rate of phase-in for other individual 

competing technologies. In addition, such an analysis is very company-, factory-, and 

manufacturing process-specific, particularly in regard to finding alternative uses for equipment 

and facilities. Nevertheless, in order to account for the possibility of stranded capital costs, the 

agencies asked FEV to perform a separate bounding analysis of potential stranded capital costs 

associated with rapid phase-in of technologies due to new standards, using data from FEV’s 

primary teardown-based cost analyses.337 

The assumptions made in FEV’s stranded capital analysis with potential for major 

impacts on results are: 

 337 FEV, Inc., “Potential Stranded Capital Analysis on EPA Light-Duty Technology Cost Analysis”, Contract No. 

EP-C-07-069 Work Assignment 3-3. November 2011. FEV stranded capital report [update with actual title, etc] [xx 

Kopinski, Docket item #] 
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• All manufacturing equipment was bought brand new when the old technology started 

production (no carryover of equipment used to make the previous components that 

the old technology itself replaced). 

• 10-year normal production runs: Manufacturing equipment used to make old 

technology components is straight-line depreciated over a 10-year life. 

• Factory managers do not optimize capital equipment phase-outs (that is, they are 

assumed to routinely repair and replace equipment without regard to whether or not it 

will soon be scrapped due to adoption of new vehicle technology). 

• Estimated stranded capital is amortized over 5 years of annual production at 450,000 

units (of the new technology components). This annual production is identical to that 

assumed in FEV’s primary teardown-based cost analyses. The 5-year recovery period 

is chosen to help ensure a conservative analysis; the actual recovery would of course 

vary greatly with market conditions. 

The stranded capital analysis was performed for three transmission technology scenarios, 

two engine technology scenarios, and one hybrid technology scenario. The methodology used 

by EPA in applying the results to the technology costs is described in EPA’s draft RIA section in 

DRIA chapter 3 The methodology used by NHTSA in applying the results to the technology 

costs is described in NHTSA’s preliminary RIA section V. 

c. Cost Adjustment to 2009 Dollars 

This simple change is to update any costs presented in earlier analyses to 2009 dollars using 

the GDP price deflator as reported by the Bureau of Economic Analysis on January 27, 

2011. The factors used to update costs from and 2008 dollars to 2009 dollars are shown 

below. For the final rule, we are considering moving to 2010 dollars but, for this analysis, 

given the timing of conducting modeling runs and developing inputs to those runs, the 

factors for converting to 2010 dollars were not yet available. III-7 GDP Price Deflators 

Used in this Proposal 

 2007 2008 2009 

Price Index for Gross Domestic Product 106.3 108.6 109.6 

Factor applied to convert to 2009 dollars 1.031 1.009 1.00 

Source: Bureau of Economic Analysis, Table 1.1.4. Price Indexes for Gross Domestic Product, 

downloaded 1/27/2011, last revised 12/22/2010. 

d. Cost Effects Due to Learning 

For many of the technologies considered in this rulemaking, the agencies expect that the 

industry should be able to realize reductions in their costs over time as a result of “learning 

effects,” that is, the fact that as manufacturers gain experience in production, they are able to 

reduce the cost of production in a variety of ways. The agencies continue to apply learning 

effects in the same way as we did in both the MYs 2012-2016 final rule and in the 2010 TAR. 
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However, we have employed some new terminology in an effort to eliminate some confusion 

that existed with our old terminology. This new terminology was described in the recent heavyduty GHG final rule (see 76 FR 57320). Our old terminology suggested we were accounting for 

two completely different learning effects—one based on volume production and the other based 

on time. This was not the case since, in fact, we were actually relying on just one learning 

phenomenon, that being the learning-by-doing phenomenon that results from cumulative 

production volumes. 

As a result, the agencies have also considered the impacts of manufacturer learning on 

the technology cost estimates by reflecting the phenomenon of volume-based learning curve cost 

reductions in our modeling using two algorithms depending on where in the learning cycle (i.e., 

on what portion of the learning curve) we consider a technology to be – “steep” portion of the 

curve for newer technologies and “flat” portion of the curve for more mature technologies. The 

observed phenomenon in the economic literature which supports manufacturer learning cost 

reductions are based on reductions in costs as production volumes increase with the highest 

absolute cost reduction occurring with the first doubling of production. The agencies use the 

terminology “steep” and “flat” portion of the curve to distinguish among newer technologies and 

more mature technologies, respectively, and how learning cost reductions are applied in cost 

analyses. 

Learning impacts have been considered on most but not all of the technologies expected 

to be used because some of the expected technologies are already used rather widely in the 

industry and, presumably, quantifiable learning impacts have already occurred. The agencies 

have applied the steep learning algorithm for only a handful of technologies considered to be 

new or emerging technologies such as PHEV and EV batteries which are experiencing heavy 

development and, presumably, rapid cost declines in coming years. For most technologies, the 

agencies have considered them to be more established and, hence, the agencies have applied the 

lower flat learning algorithm. For more discussion of the learning approach and the technologies 

to which each type of learning has been applied the reader is directed to Chapter 3 of the draft 

Joint TSD. 

3. How Did the Agencies Determine the Effectiveness of Each of these Technologies? 

 In EPA conducted a detailed vehicle simulation project to quantify the effectiveness of a 

multitude of technologies for the MYs 2012-2016 rule (as well as the 2010 NOI). This technical 

work was conducted by the global engineering consulting firm, Ricardo, Inc. and was peer 

reviewed and then published in 2008. For this current rule, EPA has conducted another peer 

reviewed study with Ricardo to broaden the scope of the original project in order to expand the 

range of vehicle classes and technologies considered, consistent with a longer-term outlook 

through model years MYs 2017-2025. The extent of the project was vast, including hundreds of 

thousands of vehicle simulation runs. The results were, in turn, employed to calibrate and update 

EPA’s lumped parameter model, which is used to quantify the synergies and dis-synergies 
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associated with combining technologies together for the purposes of generating inputs for the 

agencies respective OMEGA and CAFE modeling. 

Additionally, there were a number of technologies that Ricardo did not model explicitly. 

For these, the agencies relied on a variety of sources in the literature. A few of the values are 

identical to those presented in the MYs 2012-2016 final rule, while others were updated based on 

the newer version of the lumped parameter model. More details on the Ricardo simulation, 

lumped parameter model, as well as the effectiveness for supplemental technologies are 

described in Chapter 3 of the draft Joint TSD. 

The agencies note that the effectiveness values estimated for the technologies considered 

in the modeling analyses may represent average values, and do not reflect the virtually unlimited 

spectrum of possible values that could result from adding the technology to different vehicles. 

For example, while the agencies have estimated an effectiveness of 0.6 to 0.8 percent, depending 

on the vehicle subclass for low friction lubricants, each vehicle could have a unique effectiveness 

estimate depending on the baseline vehicle’s oil viscosity rating. Similarly, the reduction in 

rolling resistance (and thus the improvement in fuel economy and the reduction in CO2

emissions) due to the application of low rolling resistance tires depends not only on the unique 

characteristics of the tires originally on the vehicle, but on the unique characteristics of the tires 

being applied, characteristics which must be balanced between fuel efficiency, safety, and 

performance. Aerodynamic drag reduction is much the same—it can improve fuel economy and 

reduce CO2 emissions, but it is also highly dependent on vehicle-specific functional objectives. 

For purposes of the proposal, NHTSA and EPA believe that employing average values for 

technology effectiveness estimates, as adjusted depending on vehicle subclass, is an appropriate 

way of recognizing the potential variation in the specific benefits that individual manufacturers 

(and individual vehicles) might obtain from adding a fuel-saving technology. 

E. Joint Economic and Other Assumptions 

The agencies’ analysis of CAFE and GHG standards for the model years covered by this 

proposed rulemaking rely on a range of forecast information, estimates of economic variables, 

and input parameters. This section briefly describes the agencies’ proposed estimates of each of 

these values. These values play a significant role in assessing the benefits of both CAFE and 

GHG standards. 

In reviewing these variables and the agencies’ estimates of their values for purposes of 

this NPRM, NHTSA and EPA reconsidered comments that the agencies previously received on 

both the Interim Joint TAR and during the MYs 2012-2016 light duty vehicle rulemaking and 

also reviewed newly available literature. As a consequence, for today’s proposal, the agencies 

are proposing to update some economic assumptions and parameter estimates, while retaining a 

majority of values consistent with the Interim Joint TAR and the MYs 2012-2016 final rule. To 

review the parameters and assumptions the agencies used in the 2012-2016 final rule, please 
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refer to 75 FR 25378 and Chapter 4 of the Joint Technical Support Document that accompanied 

the final rule.338 The proposed values summarized below are discussed in greater detail in 

Chapter 4 of the joint TSD that accompanies this proposal and elsewhere in the preamble and 

respective RIAs. The agencies seek comment on all of the assumptions discussed below. 

• Costs of fuel economy-improving technologies—These inputs are discussed in 

summary form above and in more detail in the agencies’ respective sections of this 

preamble, in Chapter 3 of the draft joint TSD, and in the agencies’ respective RIAs. 

The technology direct manufacturing cost estimates used in this analysis are intended 

to represent manufacturers’ direct costs for high-volume production of vehicles with 

these technologies in the year for which we state the cost is considered “valid.” 

Technology direct manufacturing cost estimates are fundamentally unchanged from 

those employed by the agencies in the 2012-2016 final rule, the heavy-duty truck rule 

(to the extent relevant), and TAR for most technologies, although revised costs are 

used for batteries, mass reduction, transmissions, and a few other technologies. 

Indirect costs are accounted for by applying near-term indirect cost multipliers 

ranging from 1.24 to 1.77 to the estimates of vehicle manufacturers’ direct costs for 

producing or acquiring each technology, depending on the complexity of the 

technology and the time frame over which costs are estimated. These values are 

reduced to 1.19 to 1.50 over the long run as some aspects of indirect costs decline. 

Indirect cost markup factors have been revised from previous rulemakings and the 

Interim Joint TAR to reflect the agencies current thinking regarding a number of 

issues. These changes are discussed in detail in Section II.D.2 of this preamble and in 

Chapter 3 of the draft joint TSD. Details of the agencies’ technology cost 

assumptions and how they were derived can be found in Chapter 3 of the draft joint 

TSD. 

• Potential opportunity costs of improved fuel economy—This issue addresses the 

possibility that achieving the fuel economy improvements required by alternative 

CAFE or GHG standards would require manufacturers to compromise the 

performance, carrying capacity, safety, or comfort of their vehicle models. If it did so, 

the resulting sacrifice in the value of these attributes to consumers would represent an 

additional cost of achieving the required improvements, and thus of manufacturers’ 

compliance with stricter standards. Currently the agencies project that these vehicle 

attributes will not change as a result of this rule. Section II.C above and Chapter 2 of 

the draft joint TSD describes how the agency carefully selected an attribute-based 

standard to minimize manufacturers’ incentive to reduce vehicle capabilities. While 

manufacturers may choose to do this for other reasons, the agencies continue to 

believe that the rule itself will not result in such changes. Additionally, EPA and 

NHTSA have sought to include the cost of maintaining these attributes as part of the 

cost estimates for technologies that are included in the cost analysis for the proposal. 

For example, downsized engines are assumed to be turbocharged, so that they provide 

 338 See http://www.epa.gov/otaq/climate/regulations/420r10901.pdf. 
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the same performance and utility even though they are smaller.339 Nonetheless, it is 

possible that in some cases, the technology cost estimates may not include adequate 

allowance for the necessary efforts by manufacturers to maintain vehicle acceleration 

performance, payload, or utility while improving fuel economy and reducing GHG 

emissions. As described in Section III.D.3 and Section IV.G, there are two possible 

exceptions in cases where some vehicle types are converted to hybrid or full electric 

vehicles (EVs), but, in such cases, we believe that sufficient options would exist for 

consumers concerned about the possible loss of utility (e.g., they would purchase the 

non-hybridized version of the vehicle or not buy an EV) that welfare loss should not 

necessarily be assumed. Although consumer vehicle demand models can measure 

these effects, past analyses using such models have not produced consistent estimates 

of buyers’ willingness-to-pay for higher fuel economy, and it is difficult to decide 

whether one data source, model specification, or estimation procedure is clearly 

preferred over another. Thus, the agencies seek comment on how to estimate 

explicitly the changes in vehicle buyers’ choices and welfare from the combination of 

higher prices for new vehicle models, increases in their fuel economy, and any 

accompanying changes in vehicle attributes such as performance, passenger- and 

cargo-carrying capacity, or other dimensions of utility. 

• The on-road fuel economy “gap” – Actual fuel economy levels achieved by lightduty vehicles in on-road driving fall somewhat short of their levels measured under 

the laboratory test conditions used by EPA to establish compliance with the proposed 

CAFE and GHG standards. The modeling approach in this proposal follows the 

2012-2016 final rule and the Interim Joint TAR. In calculating benefits of the 

program, the agencies estimate that actual on-road fuel economy attained by lightduty vehicles that operate on liquid fuels will be 20 percent lower than published fuel 

economy ratings for vehicles that operate on liquid fuels. For example, if the 

measured CAFE fuel economy value of a light truck is 20 mpg, the on-road fuel 

economy actually achieved by a typical driver of that vehicle is expected to be 16 

mpg (20*.80).340 Based on manufacturer confidential business information, as well as 

data derived from the 2006 EPA fuel economy label rule, the agencies use a 30 

percent gap for consumption of wall electricity for electric vehicles and plug-in 

hybrid electric vehicles.341

• Fuel prices and the value of saving fuel – Projected future fuel prices are a critical 

input into the preliminary economic analysis of alternative standards, because they 

 339 The agencies do not believe that adding fuel-saving technology should preclude future improvements in 

performance, safety, or other attributes, though it is possible that the costs of these additions may be affected by the 

presence of fuel-saving technology. 

340 U.S. Environmental Protection Agency, Final Technical Support Document, Fuel Economy Labeling of Motor 

Vehicle Revisions to Improve Calculation of Fuel Economy Estimates, EPA420-R-06-017, December 2006. 341 See 71 FR at 77887, and U.S. Environmental Protection Agency, Final Technical Support Document, Fuel 

Economy Labeling of Motor Vehicle Revisions to Improve Calculation of Fuel Economy Estimates, EPA420-R-06-

017, December 2006 for general background on the analysis. See also EPA’s Response to Comments (EPA-420-R11-005) to the 2011 labeling rule, page 189, first paragraph, specifically the discussion of the derived five cycle 

equation and the non-linear adjustment with increasing MPG. 
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determine the value of fuel savings both to new vehicle buyers and to society, and 

fuel savings account for the majority of the proposed rule’s estimated benefits. For 

this proposed rule, the agencies are using the most recent fuel price projections from 

the U.S. Energy Information Administration’s (EIA) Annual Energy Outlook (AEO) 

2011 reference case forecast. The forecasts of fuel prices reported in EIA’s AEO 

2011 extend through 2035. Fuel prices beyond the time frame of AEO’s forecast 

were estimated using an average growth rate for the years 2017-2035 to each year 

after 2035. This is the same methodology used by the agencies in the 2012-2016 

rulemaking, in the heavy duty truck and engine rule (76 FR 57106), and in the Interim 

Joint TAR. For example, these forecasts of gasoline fuel prices in 2009$ include 

$3.25 per gallon in 2017, $3.39 in 2021 and $3.71 in 2035. Extrapolating as 

described above, retail gasoline prices reach $4.16 per gallon in 2050 (measured in 

constant 2009 dollars). As discussed in Chapter 4 of the draft Joint TSD, while the 

agencies believe that EIA’s AEO reference case generally represents a reasonable 

forecast of future fuel prices for purposes of use in our analysis of the benefits of this 

rule, we recognize that there is a great deal of uncertainty in any such forecast that 

could affect our estimates. The agencies request comment on how best to account for 

uncertainty in future fuel prices. 

• Consumer valuation of fuel economy and payback period—In estimating the value of 

fuel economy improvements to potential vehicle buyers that would result from 

alternative CAFE and GHG standards, the agencies assume that buyers value the 

resulting fuel savings over only part of the expected lifetimes of the vehicles they 

purchase. Specifically, we assume that buyers value fuel savings over the first five 

years of a new vehicle’s lifetime, and that buyers discount the value of these future 

fuel savings. The five-year figure represents the current average term of consumer 

loans to finance the purchase of new vehicles. 

• Vehicle sales assumptions—The first step in estimating lifetime fuel consumption by 

vehicles produced during a model year is to calculate the number that are expected to 

be produced and sold. The agencies relied on the AEO 2011 Reference Case for 

forecasts of total vehicle sales, while the baseline market forecast developed by the 

agencies (discussed in Section II.B and in Chapter 1 of the TSD) divided total 

projected sales into sales of cars and light trucks. 

• Vehicle lifetimes and survival rates – As in the 2012-2016 final rule and Interim Joint 

TAR, we apply updated values of age-specific survival rates for cars and light trucks 

to adjusted forecasts of passenger car and light truck sales to determine the number of 

these vehicles expected to remain in use during each year of their lifetimes. These 

values remain unchanged from prior analyses. 

• Vehicle miles traveled – We calculated the total number of miles that cars and light 

trucks produced in each model year will be driven during each year of their lifetimes 

using estimates of annual vehicle use by age tabulated from the Federal Highway 
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Administration’s 2001 National Household Travel Survey (NHTS),342 adjusted to 

account for the effects on vehicle use of subsequent increases in fuel prices. In order 

to insure that the resulting mileage schedules imply reasonable estimates of future 

growth in total car and light truck use, we calculated the rate of future growth in 

annual mileage at each age that would be necessary for total car and light truck travel 

to increase at the rates forecast in the AEO 2011 Reference Case. The growth rate in 

average annual car and light truck use produced by this calculation is approximately 1 

percent per year through 2030 and 0.5 percent thereafter. We applied these growth 

rates applied to the mileage figures derived from the 2001 NHTS to estimate annual 

mileage by vehicle age during each year of the expected lifetimes of MY 2017-2025 

vehicles. A similar approach to estimating future vehicle use was used in the 2012-

2016 final rule and Interim Joint TAR, but the future growth rates in average vehicle 

use have been revised for this proposal. 

• Accounting for the rebound effect of higher fuel economy – The rebound effect refers 

to the increase in vehicle use that results if an increase in fuel efficiency lowers the 

cost of driving. For purposes of this NPRM, the agencies elected to continue to use a 

10 percent rebound effect in their analyses of fuel savings and other benefits from 

higher standards, consistent with the 2012-2016 light-duty vehicle rulemaking and the 

Interim Joint TAR. That is, we assume a 10 percent decrease in fuel cost per mile 

resulting from our proposed standards would result in a 1 percent increase in the 

annual number of miles driven at each age over a vehicle’s lifetime. In Chapter 4 of 

the joint TSD, we provide a detailed explanation of the basis for our rebound 

estimate, including a summary of new literature published since the 2012-2016 

rulemaking that lends further support to the 10 percent rebound estimate. We also 

refer the reader to Chapters X and XII of NHTSA’s PRIA and Chapter 4 of the EPA 

DRIA that accompanies this preamble for sensitivity and uncertainty analyses of 

alternative rebound assumptions. 

• Benefits from increased vehicle use – The increase in vehicle use from the rebound 

effect provides additional benefits to drivers, who may make more frequent trips or 

travel farther to reach more desirable destinations. The analysis estimates the 

economic benefits from increased rebound-effect driving as the sum of the fuel costs 

they incur in that additional travel plus the consumer surplus drivers receive from the 

improved accessibility their travel provides. As in the 2012-2016 final rule we 

estimate the economic value of this consumer surplus using the conventional 

approximation, which is one half of the product of the decline in vehicle operating 

costs per vehicle-mile and the resulting increase in the annual number of miles driven. 

• Added costs from congestion, accidents, and noise – Although it provides benefits to 

drivers as described above, increased vehicle use associated with the rebound effect 

also contributes to increased traffic congestion, motor vehicle accidents, and highway 

noise. Depending on how the additional travel is distributed over the day and where 

 342 For a description of the Survey, see http://www.bts.gov/programs/national_household_travel_survey/ (last 

accessed Sept. 9, 2011). 
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it takes place, additional vehicle use can contribute to traffic congestion and delays by 

increasing traffic volumes on facilities that are already heavily traveled. These added 

delays impose higher costs on drivers and other vehicle occupants in the form of 

increased travel time and operating expenses. At the same time, this travel also 

increases costs associated with traffic accidents, and increased traffic noise. The 

agencies rely on estimates of congestion, accident, and noise costs caused by 

automobiles and light trucks developed by the Federal Highway Administration to 

estimate these increased external costs caused by added driving.343 This method is 

consistent with the 2012-2016 final rule. 

• Petroleum consumption and import externalities – U.S. consumption of imported 

petroleum products also impose costs on the domestic economy that are not reflected 

in the market price for crude petroleum, or in the prices paid by consumers of 

petroleum products such as gasoline. These costs include (1) higher prices for 

petroleum products resulting from the effect of increased U.S. demand for imported 

oil on the world oil price (“monopsony costs”); (2) the expected costs associated with 

the risk of disruptions to the U.S. economy caused by sudden reductions in the supply 

of imported oil to the U.S.; and (3) expenses for maintaining a U.S. military presence 

to secure imported oil supplies from unstable regions, and for maintaining the 

strategic petroleum reserve (SPR) to cushion the U.S. economy against the effects of 

oil supply disruptions.344 Although the reduction in the global price of petroleum and 

refined products due to decreased demand for fuel in the U.S. resulting from this rule 

represents a benefit to the U.S. economy, it simultaneously represents an economic 

loss to other countries that produce and sell oil or petroleum products to the U.S. 

Recognizing the redistributive nature of this “monopsony effect” when viewed from a 

global perspective (which is consistent with the agencies’ use of a global estimate for 

the social cost of carbon to value reductions in CO2 emissions, the energy security 

benefits estimated to result from this program exclude the value of this monopsony 

effect. In contrast, the macroeconomic disruption and adjustment costs that arise 

from sudden reductions in the supply of imported oil to the U.S. do not have 

offsetting impacts outside of the U.S., so the estimated reduction in their expected 

value stemming from reduced U.S. petroleum imports is included in the energy 

security benefits estimated for this program. U.S. military costs are excluded from 

the analysis because their attribution to particular missions or activities is difficult. 

Also, historical variation in U.S. military costs have not been associated with changes 

in U.S. petroleum imports, although we recognize that more broadly, there may be 

significant (if unquantifiable) benefits in improving national security by reducing oil 

imports. Similarly, since the size or other factors affecting the cost of maintaining the 

SPR historically have not varied in response to changes in U.S. oil import levels, 

 343 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost Allocation Study; 

http://www.fhwa.dot.gov/policy/hcas/final/index.htm (last accessed Sept. 9, 2011). 344 See, e.g., Bohi, Douglas R. and W. David Montgomery (1982). Oil Prices, Energy Security, and Import Policy 

Washington, DC: Resources for the Future, Johns Hopkins University Press; Bohi, D. R., and M. A. Toman (1993). 

"Energy and Security: Externalities and Policies," Energy Policy 21:1093-1109; and Toman, M. A. (1993). "The 

Economics of Energy Security: Theory, Evidence, Policy," in A. V. Kneese and J. L. Sweeney, eds. (1993). 

Handbook of Natural Resource and Energy Economics, Vol. III. Amsterdam: North-Holland, pp. 1167-1218. 
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changes in the costs of the SPR are excluded from the estimates of the energy security 

benefits of the program. To summarize, the agencies have included only the 

macroeconomic disruption and adjustment costs portion of the energy security 

benefits to estimate the monetary value of the total energy security benefits of this 

program. Based on a recent update of an earlier peer-reviewed Oak Ridge National 

Laboratory study that was used in support of the both the 2012-2016 light duty 

vehicle and the -2018 medium- and heavy-duty vehicle rulemaking, we estimate that 

each gallon of fuel saved will reduce the expected macroeconomic disruption and 

adjustment costs of sudden reductions in the supply of imported oil to the U.S. 

economy by $0.185 (2009$) in 2025. Each gallon of fuel saved as a consequence of 

higher standards is anticipated to reduce total U.S. imports of crude petroleum or 

refined fuel by 0.95 gallons.345 The energy security analysis conducted for this 

proposal also estimates that the world price of oil will fall modestly in response to 

lower U.S. demand for refined fuel.346,347 The energy security methodology used in 

this proposal is the same as that used by the agencies in both the 2012-2016 light duty 

vehicle and 2014-2018 medium- and heavy-duty vehicle rulemakings. In those 

rulemakings, the agencies addressed comments about the magnitude of their energy 

security estimates and methodological issues such as whether to include the 

monopsony benefits in energy security calculations. 

• Air pollutant emissions – 

o Impacts on criteria air pollutant emissions – Criteria air pollutants emitted by 

vehicles and during fuel production and distribution include carbon monoxide 

(CO), hydrocarbon compounds (usually referred to as “volatile organic 

compounds,” or VOC), nitrogen oxides (NOX), fine particulate matter (PM2.5), 

and sulfur oxides (SOX). Although reductions in domestic fuel refining and 

distribution that result from lower fuel consumption will reduce U.S. emissions of 

these pollutants, additional vehicle use associated with the rebound effect, and 

additional electricity production will increase emissions. Thus the net effect of 

stricter standards on emissions of each criteria pollutant depends on the relative 

magnitudes of reduced emissions from fuel refining and distribution, and 

increases in emissions resulting from added vehicle use. The agencies’ analysis 

assumes that the per-mile emission rates for cars and light trucks produced during 

the model years affected by the proposed rule will remain constant at the levels 

resulting from EPA’s Tier 2 light duty vehicle emissions standards. The 

agencies’ approach to estimating criteria air pollutant emissions is consistent with 

the method used in the 2012-2016 final rule (where the agencies received no 

 345 Each gallon of fuel saved is assumed to reduce imports of refined fuel by 0.5 gallons, and the volume of fuel 

refined domestically by 0.5 gallons. Domestic fuel refining is assumed to utilize 90 percent imported crude 

petroleum and 10 percent domestically-produced crude petroleum as feedstocks. Together, these assumptions imply 

that each gallon of fuel saved will reduce imports of refined fuel and crude petroleum by 0.50 gallons + 0.50 

gallons*90 percent = 0.50 gallons + 0.45 gallons = 0.95 gallons. 

346Leiby, Paul. Oak Ridge National Laboratory. “Approach to Estimating the Oil Import Security Premium for the 

MY 2017 -2025 Light Duty Vehicle Proposal” 2011. 

347 Note that this change in world oil price is not reflected in the AEO projections described earlier in this section. 
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significant adverse comments), although the agencies employ a more recent 

version of the EPA’s MOVES (Motor Vehicle Emissions Simulator) model.

.o Economic value of reductions in criteria pollutant emissions - For the purpose of 

the joint technical analysis, EPA and NHTSA estimate the economic value of the 

human health benefits associated with reducing population exposure to PM2.5

using a “benefit-per-ton” method. These PM2.5-related benefit-per-ton estimates 

provide the total monetized benefits to human health (the sum of reductions in 

premature mortality and premature morbidity) that result from eliminating one ton 

of directly emitted PM2.5, or one ton of other pollutants that contribute to 

atmospheric levels of PM2.5 (such as NOX, SOX, and VOCs), from a specified 

source. These unit values remain unchanged from the 2012-2016 final rule, and 

the agencies received no significant adverse comment on the analysis. Note that 

the agencies’ analysis includes no estimates of the direct health or other benefits 

associated with reductions in emissions of criteria pollutants other than PM2.5. 

o Impacts on greenhouse gas (GHG) emissions - NHTSA estimates reductions in 

emissions of carbon dioxide (CO2) from passenger car and light truck use by 

multiplying the estimated reduction in consumption of fuel (gasoline and diesel) 

by the quantity or mass of CO2 emissions released per gallon of fuel consumed. 

EPA directly calculates reductions in total CO2 emissions from the projected 

reductions in CO2 emissions by each vehicle subject to the proposed rule.348 Both 

agencies also calculate the impact on CO2 emissions that occur during fuel 

production and distribution resulting from lower fuel consumption, as well as the 

emission impacts due to changes in electricity production. Although CO2 

emissions account for nearly 95 percent of total GHG emissions that result from 

fuel combustion during vehicle use, emissions of other GHGs are potentially 

significant as well because of their higher “potency” as GHGs than that of CO2

itself. EPA and NHTSA therefore also estimate the change in upstream and 

downstream emissions of non-CO2 GHGs that occur during the aforementioned 

processes due to their respective standards.349 The agencies approach to 

estimating GHG emissions is consistent with the method used in the 2012-2016 

final rule and the Interim Joint TAR. 

o Economic value of reductions in CO2 emissions – EPA and NHTSA assigned a 

dollar value to reductions in CO2 emissions using recent estimates of the “social 

cost of carbon” (SCC) developed by a federal interagency group that included the 

two agencies. As that group’s report observed, “The SCC is an estimate of the 

monetized damages associated with an incremental increase in carbon emissions 

in a given year. It is intended to include (but is not limited to) changes in net 

 348 The weighted average CO2 content of certification gasoline is estimated to be 8,887 grams per gallon, while that 

of diesel fuel is estimated to be approximately 10,200 grams per gallon. 349 There is, however, an exception. NHTSA does not and cannot claim benefit from reductions in downstream 

emissions of HFCs because they do not relate to fuel economy, while EPA does because all GHGs are relevant for 

purposes of EPA’s Clean Air Act standards. 
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agricultural productivity, human health, property damages from increased flood 

risk, and the value of ecosystem services due to climate change.” 350 Published 

estimates of the SCC vary widely as a result of uncertainties about future 

economic growth, climate sensitivity to GHG emissions, procedures used to 

model the economic impacts of climate change, and the choice of discount 

rates.351 The SCC estimates used in this analysis were developed through an 

interagency process that included EPA, DOT/NHTSA, and other executive branch 

entities, and concluded in February 2010. We first used these SCC estimates in 

the benefits analysis for the 2012-2016 light-duty vehicle rulemaking. We have 

continued to use these estimates in other rulemaking analyses, including the 

Greenhouse Gas Emission Standards and Fuel Efficiency Standards for Mediumand Heavy-Duty Engines and Vehicles (76 FR 57106, p. 57332) . The SCC 

Technical Support Document (SCC TSD) provides a complete discussion of the 

methods used to develop these SCC estimates. 

• The value of changes in driving range – The agencies assume all of the imcreases in 

fuel economy will translate directly into increased driving range. By reducing the 

frequency with which drivers typically refuel their vehicles, and by extending the 

upper limit of the range they can travel before requiring refueling, improving fuel 

economy and reducing GHG emissions provides additional benefits to their owners. 

The primary benefits from the reduction in the number of required refueling cycles 

are the value of time saved to drivers and other adult vehicle occupants, as well as the 

savings to owners in terms of the cost of the fuel that would have otherwise been 

consumed in transit during those (now no longer required) refueling trips. Using 

recent data on vehicle owners’ refueling patterns gathered from a survey conducted 

by the National Automotive Sampling System (NASS), NHTSA was able to better 

estimate parameters associated with refueling trips. NASS data provided NHTSA 

with the ability to estimate the average time required for a refueling trip, the average 

time and distance drivers typically travel out of their way to reach fueling stations, the 

average number of adult vehicle occupants, the average quantity of fuel purchased, 

and the distribution of reasons given by drivers for refueling. From these estimates, 

NHTSA constructed an updated set of economic assumptions to update those used in 

the 2012-2016 FRM in calculating refueling-related benefits. The 2012-2016 FRM 

discusses NHTSA’s intent to utilize the NASS data on refueling trip characteristics in 

future rulemakings. While the NASS data improve the precision of the inputs used in 

the analysis of the benefits resulting from fewer refueling cycles, the framework of 

the analysis remains essentially the same as in the 2012-2016 final rule. Note that 

this topic and associated benefits were not covered in the Interim Joint TAR. Detailed 

 350 SCC TSD, see page 2. Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost 

of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social 

Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, 

Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, 

Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of 

Management and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). 

Also available at http://epa.gov/otaq/climate/regulations.htm 

351 SCC TSD, see pages 6-7. 
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discussion and examples of the agencies’ approach are provided in Chapter VIII of 

NHTSA’s PRIA and Chapter 8 of EPA’s DRIA. 

• Discounting future benefits and costs – Discounting future fuel savings and other 

benefits is intended to account for the reduction in their value to society when they 

are deferred until some future date, rather than received immediately.352 The 

discount rate expresses the percent decline in the value of these future fuel-savings 

and other benefits—as viewed from today’s perspective—for each year they are 

deferred into the future. In evaluating the non-climate related benefits of the final 

standards, the agencies have employed discount rates of both 3 percent and 7 percent, 

consistent with the 2012-2016 final rule and OMB Circular A-4 guidance. 

For the reader’s reference, Table III-8 and Table III-9 below summarize the values used 

to calculate the impacts of each proposed standard. The values presented in this table are 

summaries of the inputs used for the models; specific values used in the agencies’ respective 

analyses may be aggregated, expanded, or have other relevant adjustments. See Joint TSD 4 and 

each agency’s respective RIA for details. The agencies seek comment on the economic 

assumptions presented in the table. 

In addition, the agencies analyzed the sensitivity of their estimates of the benefits and 

costs associated with this proposed rule to variation in the values of many of these economic 

assumptions and other inputs. The values used in these sensitivity analyses and their results are 

presented their agencies’ respective RIAs. A wide range of estimates is available for many of 

the primary inputs that are used in the agencies’ CAFE and GHG emissions models. The 

agencies recognize that each of these values has some degree of uncertainty, which the agencies 

further discuss in the draft Joint TSD. The agencies have tested the sensitivity of their estimates 

of costs and benefits to a range of assumptions about each of these inputs, and present these 

sensitivity analyses in their respective RIAs. For example, NHTSA conducted separate 

sensitivity analyses for, among other things, discount rates, fuel prices, the social cost of carbon, 

the rebound effect, consumers’ valuation of fuel economy benefits, battery costs, mass reduction 

costs, the value of a statistical life, and the indirect cost markup factor. This list is similar in 

scope to the list that was examined in the MY 2012-2016 final rule, but includes battery costs 

and mass reduction costs, while dropping military security and monopsony costs. NHTSA’s 

sensitivity analyses are contained in Chapter X of NHTSA’s PRIA. EPA conducted sensitivity 

analyses on the rebound effect, battery costs, mass reduction costs, the indirect cost markup 

factor and on the cost learning curves used in this analysis. These analyses are found in Chapters 

3 and 4 of the EPA DRIA. In addition, NHTSA performs a probabilistic uncertainty analysis 

examining simultaneous variation in the major model inputs including technology costs, 

 352 Because all costs associated with improving vehicles’ fuel economy and reducing CO2 emissions are assumed to 

be incurred at the time they are produced, these costs are already expressed in their present values as of each model 

year affected by the proposed rule, and require discounting only for the purpose of expressing them as present values 

as of a common year. 
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technology benefits, fuel prices, the rebound effect, and military security costs. This information 

is provided in Chapter XII of NHTSA’s PRIA. These uncertainty parameters are consistent with 

those used in the MY 2012-2016 final rule. The agencies will consider conducting additional 

sensitivity and uncertainty analyses for the final rule as appropriate. 

Table III-8 Economic Values for Benefits Computations (2009$)

Fuel Economy Rebound Effect 10%

“Gap” between test and on-road MPG for liquidfueled vehicles 20%

“Gap” between test and on-road wall electricity 

consumption for electric and plug-in hybrid 

electric vehicles 30%

Value of refueling time per ($ per vehicle-hour) $21.27 Cars 

$21.62 Trucks 

Annual growth in average vehicle use 1.1% through 2030, 

0.5% thereafter

Annual CO2 Damage Cost (per metric ton) 

Variable, depending 

on discount rate and 

year (see Table III-9 

for 2017 estimate)

Table III-9 Social Cost of CO2 ($/metric ton), 2017 (2009$) 

Discount Rate 5% 3% 2.5% 3% 

Source of 

Estimate Mean of Estimated Values 95th

 percentile 

estimate 

2017 Estimate $6 $26 $41 $78 

F. Air Conditioning Efficiency CO2 Credits and Fuel Consumption Improvement 

Values, Off-cycle Reductions, and Full-size Pickup Trucks 

For MYs 2012-2016, EPA provided an option for manufacturers to generate credits for 

complying with GHG standards by incorporating efficiency improving vehicle technologies that 

would reduce CO2 and fuel consumption from air conditioning (A/C) operation or from other 

vehicle operation that is not captured by the Federal Test Procedure (FTP) and Highway Fuel 

Economy Test (HFET), also collectively known as the “two-cycle” test procedure. EPA referred 

to these credits as “off-cycle credits.” 

For this proposal, EPA, in coordination with NHTSA, is proposing under their EPCA 

authorities to allow manufacturers to generate fuel consumption improvement values for 

purposes of CAFE compliance based on the use of A/C efficiency and off-cycle technologies. 
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This proposed expansion is a change from the 2012-16 final rule where EPA only provided the 

A/C efficiency and off-cycle credits for the GHG program. EPA is not proposing to allow these 

increases for compliance with the CAFE program for MYs 2012-2016, nor to allow any 

compliance with the CAFE program as a result of reductions in direct A/C emissions resulting 

from leakage of HFCs from air conditioning systems, which remains a flexibility unique to the 

GHG program. 

The agencies believe that because of the significant amount of credits and fuel 

consumption improvement values offered under the A/C program (up to 5.0 g/mi for cars and 7.2 

g/mi for trucks which is equivalent to a fuel consumption improvement value of 0.000563 gal/mi 

for cars and 0.000586 gal/mi for trucks) that manufacturers will maximize the benefits these 

credits and fuel consumption improvement values afford. Consistent with the 2012-2016 final 

rule, EPA will continue to adjust the stringency of the two-cycle tailpipe CO2 standards in order 

to account for this projected widespread penetration of A/C credits (as described more fully in 

Section III.C), and NHTSA has also accounted for expected A/C efficiency improvements in 

determining the maximum feasible CAFE standards. The agencies discuss these proposed CO2

credits/fuel consumption improvement values below and in more detail in the Joint TSD 

(Chapter 5). EPA discusses additional proposed GHG A/C leakage credits that are unrelated to 

CO2 and fuel consumption (though they are part of EPA’s CO2 equivalent calculation) in Section 

III.C below. 

EPA, in coordination with NHTSA, is also proposing to add for MYs 2017-2025 a new 

incentive for Advanced Technology for Full Sized Pickup Trucks. Under its EPCA authority for 

CAFE and under its CAA authority for GHGs, EPA is proposing GHG credits and fuel economy 

improvement values for manufacturers that hybridize a significant quantity of their full size 

pickup trucks, or that use other technologies that significantly reduce CO2 emissions and fuel 

consumption. Further discussions of the A/C, off-cycle, and the advanced technology for pickup truck incentive programs are provided below. 

1. Proposed Air Conditioning CO2 Credits and Fuel Consumption Improvement 

Values 

The credits/fuel consumption improvement values for higher-efficiency air conditioning 

technologies are very similar to those EPA included in the 2012-2016 GHG final rule. The 

proposed credits/fuel consumption improvement values represent an improved understanding of 

the relationships between A/C technologies and CO2 emissions and fuel consumption. Much of 

this understanding results from a new vehicle simulation tool that EPA has developed and the 

agencies are using for this proposal. EPA designed this model to simulate in an integrated way 

the dynamic behavior of the several key systems that affect vehicle efficiency: the engine, 

electrical, transmission, and vehicle systems. The simulation model is supported by data from a 

wide range of sources; Chapter 2 of the Draft Regulatory Impact Analysis discusses its 

development in more detail. 
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The agencies have identified several technologies that are key to the amount of fuel a 

vehicle consumes and thus the amount of CO2 it emits. Most of these technologies already exist 

on current vehicles, but manufacturers can improve the energy efficiency of the technology 

designs and operation. For example, 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, the less load the compressor places on the engine resulting in less fuel 

consumption and CO2 emissions. Thus, optimizing compressor operation with cabin demand 

using more sophisticated sensors, controls and control strategies, is one path to improving the 

overall efficiency of the A/C system. Additional components or control strategies are available 

to manufacturers to reduce the air conditioning load on the engine which are discussed in more 

detail in Chapter 5 of the joint TSD. Overall, the agencies have concluded that these improved 

technologies could together reduce A/C-related CO2 and fuel consumption of today’s typical air 

conditioning systems by 42%. The agencies propose to use this level of improvement to 

represent the maximum efficiency credit available to a manufacturer. 

Demonstrating the degree of efficiency improvement that a manufacturer’s air 

conditioning systems achieve – thus quantifying the appropriate amount of GHG credit and 

CAFE fuel consumption improvement value the manufacturer is eligible for -- would ideally 

involve a performance test. That is, a test that would directly measure CO2 (and thus allow 

calculation of fuel consumption) before and after the incorporation of the improved technologies. 

Progress toward such a test continues. As mentioned in the introduction to this section, the 

primary vehicle emissions and fuel consumption test, the Federal Test Procedure (FTP) or “twocycle” testing, does not require or simulate air conditioning usage through the test cycle. The 

SC03 test is designed to identify any effect the air conditioning system has on other emissions 

when it is operating under extreme conditions, but is not designed to measure the small 

differences in CO2 due to different A/C technologies. 

At the time of the final rule for the 2012-2016 GHG program, EPA concluded that a 

practical, performance-based test procedure capable of quantifying efficiency credits was not yet 

available. However, EPA introduced a specialized new procedure that it believed would be 

appropriate for the more limited purpose of demonstrating that the design improvements a 

manufacturer was earning credits for produced actual efficiency improvements. EPA’s test is a 

fairly simple test, performed while the vehicle is at idle. Beginning with the 2014 model year, 

the A/C Idle Test was to be used to qualify a manufacturer to be able to use the “menu” approach 

to quantify credits. That is, a manufacturer would need to achieve a certain CO2 level in order to 

use the “menu” and generate efficiency credits. 

Since that final rule was published, several manufacturers have provided data that raises 

questions about the ability of the Idle Test to fulfill its intended purpose. Especially for small, 

lower-powered vehicles, the data also shows that it is difficult to achieve reasonable test-to-test 

repeatability. The manufacturers have also informed EPA (in meetings subsequent to the 2012-

2016 final rule) that the Idle Test does not accurately capture the improvements from many of 
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the technologies listed in the menu. EPA has been aware of all of these issues, and proposing to 

modify the Idle Test such that the threshold is now a function of engine displacement in contrast 

to the flat threshold from the previous rule. EPA continues to consider this Idle Test to be a 

reasonable measure of some A/C CO2 emissions as there is significant real-world driving activity 

at idle, and the Idle Test significantly exercises a number of the A/C technologies from the menu. 

Sec III.C.1.b.i and Chapter 5 (5.1.3.5) of the Joint TSD describes further the adjustments EPA is 

making to the Idle Test for manufacturers to qualify for MYs 2014-2016 A/C efficiency credits. 

EPA proposes that manufacturers continue to use a technology lookup table (“menu”) for MYs 

2014-2016 to determine credits and fuel consumption improvement values for the GHG and 

CAFE programs. This was also the approach that EPA used for efficiency credits in the 

MY2012-2016 GHG rule. However for MYs 2017-2025, EPA is proposing a new test procedure 

to quantify A/C efficiency credits as described below. EPA requests comment on substituting 

the Idle Test requirement with a reporting requirement from this new test procedure as described 

in Section III.C.1.b.i. 

In order to correct the shortcomings of the available tests, EPA has developed a four-part 

performance test, called the AC17. The test includes the SC03 driving cycle, the fuel economy 

highway cycle, in addition to a pre-conditioning cycle, and a solar soak period. EPA is 

proposing that manufacturers use this test to demonstrate that A/C technologies result in 

efficiency improvements. EPA is also proposing to discontinue the credit menu starting in MY 

2017 and for manufacturers to utilize this new test procedure to quantify credits. EPA is 

proposing the AC17 test for MY2017 and later as a prerequisite for generating Efficiency 

Credits and fuel consumption improvement values. To demonstrate that a vehicle’s A/C system 

is delivering the efficiency benefits of the new technologies, manufacturers would run the AC17 

test procedure on a vehicle that incorporates the new technologies, with the A/C system off and 

then on, and then compare that result to the result from a previous model year or baseline vehicle 

with similar vehicle characteristics, except that the comparison vehicle would not have the new 

technologies. If the test result with the new technology demonstrated an emission reduction that 

is greater than or equal to the maximum credit potential (5g/mi for cars and 7.2 g/mi for trucks), 

full credit will be generated. However if the test result is less than the maximum credit potential, 

partial credit can still be generated, in proportion to how far away the result is from the expected 

result. . 

EPA discusses the new test in more detail in Section III.C.1.b below and in Chapter 5 

(5.1.3.5) of the joint TSD. Due to the length of time to conduct the test procedure, EPA is also 

proposing that required testing on the new AC17 test procedure be limited to a subset of 

vehicles. The agencies request comment on this approach to establishing A/C efficiency credits 

and fuel consumption improvement values and the use of the new A/C test. 
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2. Off-Cycle CO2 Credits 

For MYs 2012-2016, EPA provided an option for manufacturers to generate adjustments 

(credits) for employing new and innovative technologies that achieve CO2 reductions which are 

not reflected on current 2-cycle test procedures. For this proposal, EPA, in coordination with 

NHTSA, is proposing to apply the off-cycle credits and equivalent fuel consumption 

improvement values to both the CAFE and GHG programs. This proposed expansion is a change 

from the 2012-16 final rule where only EPA provided the off-cycle credits for the GHG 

program. For MY 2017 and later, EPA is proposing that manufacturers may continue to use offcycle credits for GHG compliance and begin to use fuel consumption improvement values for 

CAFE compliance. In addition, EPA is proposing a set of defined (e.g. default) values for 

identified off-cycle technologies that would apply unless the manufacturer demonstrates to EPA 

that a different value for its technology is appropriate. 

Starting with MY2008, EPA started employing a “five-cycle” test methodology to 

measure fuel economy for the fuel economy label. However, for GHG and CAFE compliance, 

EPA continues to use the established “two-cycle” (city and highway test cycles, also known as 

the FTP and HFET) test methodology. As learned through development of the “five-cycle” 

methodology and researching this proposal, EPA and NHTSA recognize that there are 

technologies that provide real-world GHG emissions and fuel consumption improvements, but 

those improvements are not fully reflected on the “two-cycle” test. 

During meetings with vehicle manufacturers, EPA received comments that the approval 

process for generating off-cycle credits was complicated and did not provide sufficient certainty 

on the amount of credits that might be approved. Commenters also maintained that it is 

impractical to measure small incremental improvements on top of a large tailpipe measurement, 

similar to comments received related to quantifying air conditioner improvements. These same 

manufacturers believed that such a process could stifle innovation and fuel efficient technologies 

from penetrating into the vehicle fleet. 

 In response to these concerns, EPA is proposing a menu with a number of technologies 

that the agency believes will show real-world CO2 and fuel consumption benefits which can be 

reasonably quantified by the agencies at this time. This list of pre-approved technologies 

includes a quantified default value that would apply unless the manufacturer demonstrates to 

EPA that a different value for a technology is appropriate. This list is similar to the menu driven 

approach described in the previous section on A/C efficiency credits. The estimates of these 

credits were largely determined from research, analysis and simulations, rather from full vehicle 

testing, which would have been cost and time prohibitive. These predefined estimates are 

somewhat conservative to avoid the potential for windfall. If manufactures believe their specific 

off-cycle technology achieves larger improvement, they may apply for greater credits and fuel 

consumption improvement values with supporting data. For technologies not listed, EPA is 

proposing a case-by-case approach for approval of off-cycle credits and fuel consumption 
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improvement values, similar to the approach in the 2012-2016 rule but with important 

modifications to streamline the approval process. EPA will also consult with NHTSA during the 

review process. See section III.C below; technologies for which EPA is proposing default offcycle credit values and fuel consumption improvement values are shown in Table II -2 below. 

Fuel consumption improvement values under the CAFE program based on off-cycle technology 

would be equivalent to the off-cycle credit allowed by EPA under the GHG program, and these 

amounts would be determined using the same procedures and test methods as are proposed for 

use in EPA’s GHG program. 

EPA and NHTSA are not proposing to adjust the stringency of the standards based on the 

availability of off-cycle credits and fuel consumption improvement values. There are a number 

of reasons for this. First, the agencies have limited technical information on the cost, 

development time necessary, and manufacturability of many of these technologies. The analysis 

presented below (and in greater detail in Chapter 5 of the joint TSD) is limited to quantifying the 

effectiveness of the technology (for the purposes of quantifying credits and fuel consumption 

improvement values). It is based on a combination of data and engineering analysis for each 

technology. Second, for most of these technologies the agencies have no data on what the rates 

of penetration of these technologies would be during the rule timeframe. Thus, with the 

exception of active aerodynamic improvements and stop start technology, the agencies do not 

have adequate information available to consider the technologies on the list when determining 

the appropriate GHG emissions or CAFE standards. The agencies expect to continue to improve 

their understanding of these technologies over time. If further information is obtained during the 

comment period that supports consideration of these technologies in setting the standards, EPA 

and NHTSA will reevaluate their positions. However, given the current lack of detailed 

information about these technologies, the agencies do not expect that it will be able to do more 

for the final rule than estimate some general amount of reasonable projected cost savings from 

generation of off-cycle credits and fuel consumption improvement values. Therefore, effectively 

the off-cycle credits and fuel consumption improvement values allow manufacturers additional 

flexibility in selecting technologies that may be used to comply with GHG emission and CAFE 

standards. 

Two technologies on the list - active aerodynamic improvements and stop start – are in a 

different position than the other technologies on the list. Both of these technologies are included 

in the agencies’ modeling analysis of technologies projected to be available for use in achieving 

the reductions needed for the standards. We have information on their effectiveness, cost, and 

availability for purposes of considering them along with the various other technologies we 

consider in determining the appropriate CO2 emissions standard. These technologies are among 

those listed in Chapter 3 of the joint TSD and have measureable benefit on the 2-cycle test. 

However, in the context of off-cycle credits and fuel consumption improvement values, stop start 

is any technology which enables a vehicle to automatically turn off the engine when the vehicle 

comes to a rest and restart the engine when the driver applies pressure to the accelerator or 
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releases the brake. This includes HEVs and PHEVs (but not EVs). In addition, active grill 

shutters is just one of various technologies that can be used as part of aerodynamic design 

improvements (as part of the “aero2” technology). The modeling and other analysis developed 

for determining the appropriate emissions standard includes these technologies, using the 

effectiveness values on the 2-cycle test. This is consistent with our consideration of all of the 

other technologies included in these analyses. Including them on the list for off-cycle credit and 

fuel consumption improvement value generation, for purposes of compliance with the standards, 

would recognize that these technologies have a higher degree of effectiveness than reflected in 

their 2-cycle effectiveness. As discussed in Sections III.C and Chapter 5 of the joint TSD, the 

agencies have taken into account the generation of off-cycle credits and fuel consumption 

improvement values by these two technologies in determining the appropriateness of the 

proposed standards, considering the amount of credit and fuel consumption improvement value, 

the projected degree of penetration of these technologies, and other factors. The proposed 

standards are appropriate recognizing that these technologies would also generate off-cycle 

credits and fuel consumption improvement values. Section III.D has a more detailed discussion 

on the feasibility of the standards within the context of the flexibilities (such as off-cycle credits 

and fuel consumption improvement values) proposed in this rule. 

For these technologies that provide a benefit on five-cycle testing, but show less benefit 

on two cycle testing, in order to quantify the emissions impacts of these technologies, EPA will 

simply subtract the two-cycle benefit from the five-cycle benefit for the purposes of assigning 

credit and fuel consumption improvement values for this pre-approved list. Other technologies, 

such as more efficient lighting show no benefit over any test cycle. In these cases, EPA will 

estimate the average amount of usage using MOVES353 data if possible and use this to calculate a 

duty-cycle-weighted benefit (or credit and fuel consumption improvement value). In the 2012-

2016 rule, EPA stated a technology must have “real world GHG reductions not significantly 

captured on the current 2-cycle tests...” For this proposal, EPA is proposing to modify this 

requirement to allow technologies as long as the incremental benefit in the real-world is 

significantly better than on the 2-cycle test. There are environmental benefits to encouraging 

these kinds of technologies that might not otherwise be employed, beyond the level that the 2-

cycle standards already do, thus we are now allowing credits and fuel consumption improvement 

values to be generated where the technology achieves an incremental benefit that is significantly 

better than on the 2-cycle test, as is the case for the technologies on the list. 

EPA and NHTSA evaluated many more technologies for off-cycle credits and fuel 

consumption improvement values and decided that the following technologies should be 

eligible for off-cycle credits and fuel consumption improvement values. These eleven 

technologies eligible for credits and fuel consumption improvement values are shown in 

III-10 below. EPA is proposing that a CAFE improvement value for off-cycle 

 353 MOVES is EPA’s MOtor Vehicle Emissions Simulator. This model contains (in its database) a wide variety of 

fleet and activity data as well as national ambient temperature conditions. 
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improvements be determined at the fleet level by converting the CO2 credits determined 

under the EPA program (in metric tons of CO2) for each fleet (car and truck) to a fleet fuel 

consumption improvement value. This improvement value would then be used to adjust the 

fleet’s CAFE level upward. See the proposed regulations at 40 CFR 600.510-12. Note that 

while the table below presents fuel consumption values equivalent to a given CO2 credit 

value, these consumption values are presented for informational purposes and are not 

meant to imply that these values will be used to determine the fuel economy for individual 

vehicles.III-10 Off-cycle Technologies and Proposed Credits and Equivalent Fuel 

Consumption Improvement Values for Cars and Light Trucks 

Technology Cars Light Trucks 

g/mi gallons/mi g/mi gallons/mi 

High Efficiency Exterior Lighting 1.1 0.000124 1.1 0.000124 

Engine Heat Recovery 0.7 0.000778 0.7 0.000778 

Solar Roof Panels 3.0 0.000338 3.0 0.000338 

Active Aerodynamic Improvements 0.6 0.0000675 1.0 0.000113 

Engine Start-Stop 2.9 0.000326 4.5 0.000506 

Electric Heater Circulation Pump 1.0 0.000123 1.5 0.000169 

Active Transmission Warm-Up 1.8 0.000203 1.8 0.000203 

Active Engine Warm-Up 1.8 0.000203 1.8 0.000203 

Solar Control Up to 

3.0 Up to 0.000338 Up to 4.3 Up to 0.000484 

III-10 shows the proposed list of off-cycle technologies and credits and equivalent fuel 

consumption improvement values for cars and trucks. The credits and fuel consumption 

improvement values for engine heat recovery and solar roof panels are scalable, depending on 

the amount of energy these systems can generate for the vehicle. The Solar/Thermal control 

technologies are varied and are limited to 3 and 4.3 g/mi (car and truck respectively) total. 

To ensure that the off cycle technology used by manufacturers seeking these credits and 

fuel consumption improvement values corresponds with the technology used to derive the credit 

and fuel consumption improvement values, EPA is proposing very specific definitions of each of 

the technologies in the table of the list of technologies in Chapter 5 of the draft joint TSD. The 

agencies are requesting comment on all aspects of the off-cycle credit and fuel consumption 

improvement value program, and would welcome any data to support an adjustment to this table, 

whether it is to adjust the values or to add or remove technologies. 

Vehicle Simulation Tool 

Chapter 2 of the RIA provides a detailed description of the vehicle simulation tool that 

EPA has been developing. This tool is capable of simulating a wide range of conventional and 

advanced engines, transmissions, and vehicle technologies over various driving cycles. It 

evaluates technology package effectiveness while taking into account synergy (and dis-synergy) 
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effects among vehicle components and estimates GHG emissions for various combinations of 

technologies. For the 2017 to 2025 GHG proposal, this simulation tool was used to assist 

estimating the amount of GHG credits for improved A/C systems and off-cycle technologies. 

EPA seeks public comments on this approach of using the tool for directly generating and finetuning some of the credits in order to capture the amount of GHG reductions provided by 

primarily off-cycle technologies. 

There are a number of technologies that could bring additional GHG reductions over the 

5-cycle drive test (or in the real world) compared to the combined FTP/Highway (or two) cycle 

test. These are called off-cycle technologies and are described in chapter 5 of the Joint TSD in 

detail. Among them are technologies related to reducing vehicle’s electrical loads, such as High 

Efficiency Exterior Lights, Engine Heat Recovery, and Solar Roof Panels. In an effort to 

streamline the process for approving off-cycle credits, we have set a relatively conservative 

estimate of the credit based on our efficacy analysis. EPA seeks comment on utilizing the model 

in order to quantify the credits more accurately, if actual data of electrical load reduction and/or 

on-board electricity generation by one or more of these technologies is available through data 

submission from manufacturers. Similarly, there are technologies that would provide additional 

GHG reduction benefits in the 5-cycle test by actively reducing the vehicle’s aerodynamic drag 

forces. These are referred to as active aerodynamic technologies, which include but are not 

limited to active grill shutters and active suspension lowering. Like the electrical load reduction 

technologies, the vehicle simulation tool can be used to more accurately estimate the additional 

GHG reductions (therefore the credits) provided by these active aerodynamic technologies over 

the 5-cycle drive test. EPA seeks comment on using the simulation tool in order to quantify 

these credits. In order to do this properly, manufacturers would be expected to submit two sets 

of coast-down coefficients (with and without the active aerodynamic technologies). Or, they 

could submit two sets of aerodynamic drag coefficient (with and without the active aerodynamic 

technologies) as a function of vehicle speed. 

There are other technologies that would result in additional GHG reduction benefits that 

cannot be fully captured on the combined FTP/Highway cycle test. These technologies typically 

reduce engine loads by utilizing advanced engine controls, and they range from enabling the 

vehicle to turn off the engine at idle, to reducing cabin temperature and thus A/C compressor 

loading when the vehicle is restarted. Examples include Engine Start-Stop, Electric Heater 

Circulation Pump, Active Engine/Transmission Warm-Up, and Solar Control. For these types of 

technologies, the overall GHG reduction largely depends on the control and calibration strategies 

of individual manufacturers and vehicle types. Also, the current vehicle simulation tool does not 

have the capability to properly simulate the vehicle behaviors that depend on thermal conditions 

of the vehicle and its surroundings, such as Active Engine/Transmission Warm-Up and Solar 

Control. Therefore, the vehicle simulation may not provide full benefits of the technologies on 

the GHG reductions. For this reason, the agency is not proposing to use the simulation tool to 
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generate the GHG credits for these technologies at this time, though future versions of the model 

may be more capable of quantifying the efficacy of these off-cycle technologies as well. 

3. Advanced Technology Incentives for Full Sized Pickup Trucks 

The agencies recognize that the standards under consideration for MY 2017-2025 will be 

most challenging to large trucks, including full size pickup trucks that are often used for 

commercial purposes and have generally higher payload and towing capabilities, and cargo 

volumes than other light-duty vehicles. In Section II.C and Chapter 2 of the joint TSD, EPA and 

NHTSA describe the proposal to adjust the slope of the truck curve compared to the 2012-2016 rule. 

In Sections III.B and IV.x, EPA and NHTSA describe the progression of the truck standards. In this 

section, the agencies describe a credit and fuel consumption improvement value for full size pickup 

trucks to incentivize advanced technologies on this class of vehicles. 

The agencies’ goal is to incentivize the penetration into the marketplace of “game 

changing” technologies for these pickups, including their hybridization. For that reason, EPA , 

in coordination with NHTSA, is proposing credits and corresponding equivalent fuel 

consumption improvement values for manufacturers that hybridize a significant quantity of their 

full size pickup trucks, or use other technologies that significantly reduce CO2 emissions and fuel 

consumption. This proposed creditand corresponding equivalent fuel consumption improvement 

value would be available on a per-vehicle basis for mild and strong HEVs, as well as other 

technologies that significantly improve the efficiency of the full sized pickup class.354 The 

credits and fuel consumption improvement values would apply for purposes of compliance with 

both the GHG emissions standards and the CAFE standards. This provides the incentive to begin 

transforming this most challenging category of vehicles toward use of the most advanced 

technologies. 

Access to this credit and fuel consumption improvement value is conditioned on a 

minimum penetration of the technologies in a manufacturer’s full size pickup truck fleet. To 

ensure its use for only full sized pickup trucks, EPA is proposing a very specific definition for a 

full sized pickup truck based on minimum bed size and minimum towing capability. The 

specifics of this proposed definition can be found in Chapter 5 of the draft joint TSD (see Section 

5.3.1). This proposed definition is meant to ensure that smaller pickup trucks, which do not offer 

the same level of utility (e.g., bed size, towing capability and/or payload capability) and thus 

may not face the same technical challenges to improving fuel economy and reducing CO2

 354 Note that EPA’s proposed calculation methodology in 40 CFR 600.510-12 does not use vehicle-specific fuel 

consumption adjustments to determine the CAFE increase due to the various incentives allowed under the proposed 

program. Instead, EPA would convert the total CO2 credits due to each incentive program from metric tons of CO2

to a fleetwide CAFE improvement value. The fuel consumption values are presented to give the reader some context 

and explain the relationship between CO2 and fuel consumption improvements. 
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emissions as compared to full sized pickup trucks, do not qualify.355 For this proposal, a full 

sized pickup truck would be defined as meeting requirements 1 and 2, below, as well as either 

requirement 3 or 4, below: 

1. The vehicle must have an open cargo box with a minimum width between the 

wheelhouses of 48 inches measured as the minimum lateral distance between the 

limiting interferences (pass-through) of the wheelhouses. The measurement would 

exclude the transitional arc, local protrusions, and depressions or pockets, if 

present.356 An open cargo box means a vehicle where the cargo bed does not have a 

permanent roof or cover. Vehicles sold with detachable covers are considered “open” 

for the purposes of these criteria. 

2. Minimum open cargo box length of 60 inches defined by the lesser of the pickup bed 

length at the top of the body (defined as the longitudinal distance from the inside 

front of the pickup bed to the inside of the closed endgate; this would be measured at 

the height of the top of the open pickup bed along vehicle centerline and the pickup 

bed length at the floor) and the pickup bed length at the floor (defined as the 

longitudinal distance from the inside front of the pickup bed to the inside of the 

closed endgate; this would be measured at the cargo floor surface along vehicle 

centerline).357 

3. Minimum Towing Capability – the vehicle must have a GCWR (gross combined 

weight rating) minus GVWR (gross vehicle weight rating) value of at least 5,000 

pounds.358

4. Minimum Payload Capability – the vehicle must have a GVWR (gross vehicle weight 

rating) minus curb weight value of at least 1,700 pounds. 

The technical basis for these proposed definitions is found in Section III.C below and 

Chapter 5 of the joint TSD. EPA is proposing that mild HEV pickup trucks would be eligible for 

a per-truck 10 g/mi CO2 credit (equal to a 0.001125 gal/mi fuel consumption improvement 

value) during MYs 2017-2021 if the mild HEV technology is used on a minimum percentage of 

 355 As discussed in TSD Section 5.3.1, EPA is seeking comment on expanding the scope of this credit to somewhat 

smaller pickups, provided they have the towing and/or hauling capabilities of the larger full-size trucks. 356 This dimension is also known as dimension W202 as defined in Society of Automotive Engineers Procedure 

J1100. 

357 The pickup body length at the top of the body is also known as dimension L506 in Society of Automotive 

Engineers Procedure J1100. The pickup body length at the floor is also known as dimension L505 in Society of 

Automotive Engineers Procedure J1100. 

358 Gross combined weight rating means the value specified by the vehicle manufacturer as the maximum weight of 

a loaded vehicle and trailer, consistent with good engineering judgment. Gross vehicle weight rating means the 

value specified by the vehicle manufacturer as the maximum design loaded weight of a single vehicle, consistent 

with good engineering judgment. Curb weight is defined in 40 CFR 86.1803, consistent with the provisions of 40 

CFR 1037.140. 
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a company’s full sized pickups. That minimum percentage would be 30 percent of a company’s 

full sized pickup production in MY 2017 with a ramp up to at least 80 percent of production in 

MY 2021. 

EPA is also proposing that strong HEV pickup trucks would be eligible for a per-truck 20 

g/mi CO2 credit (equal to a 0.002250 gal/mi fuel consumption improvement value) during MYs 

2017-2025 if the strong HEV technology is used on a minimum percentage of a company’s full 

sized pickups. That minimum percentage would be 10 percent of a company’s full sized pickup 

production in each year over the model years 2017-2025. 

To ensure that the hybridization technology used by manufacturers seeking one of these 

credits and fuel consumption improvement values meets the intent behind the incentives, EPA is 

proposing very specific definitions of what qualifies as a mild and a strong HEV. These 

definitions are described in detail in Chapter 5 of the draft joint TSD (see section 5.3.3). 

 For similar reasons, EPA is also proposing a performance-based incentive credit 

andequivalent fuel consumption improvement value for full size pickup trucks that achieve an 

emission level significantly below the applicable target.359 EPA, in coordination with NHTSA, 

proposes this credit to be either 10 g/mi CO2 (equivalent to 0.001125 gal/mi for the CAFE 

program) or 20 g/mi CO2 (equivalent to 0.002250 gal/mi for the CAFE program) for pickups 

achieving 15 percent or 20 percent, respectively, better CO2 than their footprint based target in 

a given model year. Because the footprint target curve has been adjusted to account for A/C 

related credits, the CO2 level to be compared with the target would also include any A/C related 

credits generated by the vehicle. Further details on this performance-based incentive are in 

Section III.C below and in Chapter 5 of the draft joint TSD (see Section 5.3.4). The 10 g/mi 

(equivalent to 0.001125 gal/mi) performance-based credit and fuel consumption improvement 

value would be available for MYs 2017 to 2021 and a vehicle meeting the requirements would 

receive the credit and fuel consumption improvement value until MY 2021 unless its CO2 level 

increases or fuel economy decreases. The 20 g/mi CO2 (equivalent to 0.0023 gal/mi fuel 

consumption improvement value) performance-based credit would be available for a maximum 

of 5 years within the model years of 2017 to 2025, provided its CO2 level and fuel consumption 

does not increase. The rationale for these limits is because of the year over year progression of 

the stringency of the truck target curves. The credits and fuel consumption improvement values 

would begin in the model year of introduction, and could not extend past MY 2021 for the 10 

g/mi credit (equivalent to 0.001125 gal/mi) and MY 2025 for the 20 g/mi credit (equivalent to 

0.002250 gal/mi). 

 359 The 15 and 20 percent thresholds would be based on CO2 performance compared to the applicable CO2 vehicle 

target for both CO2 credits and corresponding CAFE fuel consumption improvement values. As with A/C and offcycle credits, EPA would convert the total CO2 credits due to the pick-up incentive program from metric tons of 

CO2 to a fleetwide equivalent CAFE improvement value. 
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As with the HEV-based credit and fuel consumption improvement value, the 

performance-based credit and fuel consumption improvement value requires that the technology 

be used on a minimum percentage of a manufacturer’s full-size pickup trucks. That minimum 

percentage for the 10 g/mi GHG credit (equivalent to 0.001125 gal/mi fuel consumption 

improvement value) would be 15 percent of a company’s full sized pickup production in MY 

2017 with a ramp up to at least 40 percent of production in MY 2021. The minimum percentage 

for the 20 g/mi credit (equivalent to 0.002250 gal/mi fuel consumption improvement value) 

would be 10 percent of a company’s full sized pickup production in each year over the model 

years 2017-2025. 

Importantly, the same vehicle could not receive credit and fuel consumption 

improvement under both the HEV and the performance-based approaches. EPA and NHTSA 

request comment on all aspects of this proposed pickup truck incentive credit and fuel 

consumption improvement value, including the proposed definitions for full sized pickup truck 

and mild and strong HEV. 

G. Safety Considerations in Establishing CAFE/GHG Standards 

1. Why do the agencies consider safety?

The primary goals of the proposed CAFE and GHG standards are to reduce fuel 

consumption and GHG emissions from the on-road light-duty vehicle fleet, but in addition to 

these intended effects, the agencies also consider the potential of the standards to affect vehicle 

safety.360 As a safety agency, NHTSA has long considered the potential for adverse safety 

consequences when establishing CAFE standards,361 and under the CAA, EPA considers factors 

related to public health and human welfare, and safety, in regulating emissions of air pollutants 

from mobile sources.362 Safety trade-offs associated with fuel economy increases have occurred 

in the past (particularly before NHTSA CAFE standards were attribute-based), and the agencies 

must be mindful of the possibility of future ones. These past safety trade-offs may have occurred 

because manufacturers chose, at the time, to build smaller and lighter vehicles—partly in 

response to CAFE standards—rather than adding more expensive fuel-saving technologies (and 

maintaining vehicle size and safety), and the smaller and lighter vehicles did not fare as well in 

crashes as larger and heavier vehicles. Historically, as shown in FARS data analyzed by 

 360 In this rulemaking document, “vehicle safety” is defined as societal fatality rates per vehicle miles traveled 

(VMT), which include fatalities to occupants of all the vehicles involved in the collisions, plus any pedestrians. 361 This practice is recognized approvingly in case law. As the United States Court of Appeals for the D.C. Circuit 

stated in upholding NHTSA’s exercise of judgment in setting the 1987-1989 passenger car standards, “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 (“CEI I”), 901 

F.2d 107, 120 at n. 11 (D.C. Cir. 1990). 

362 See NRDC v. EPA, 655 F. 2d 318, 332 n. 31 (D.C. Cir. 1981).(EPA may consider safety in developing standards 

under section 202 (a) and did so appropriately in the given instance). 
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NHTSA, the safest cars generally have been heavy and large, while the cars with the highest 

fatal-crash rates have been light and small. The question, then, is whether past is necessarily 

prologue when it comes to potential changes in vehicle size (both footprint and “overhang”) and 

mass in response to these proposed future CAFE and GHG standards. Manufacturers have stated 

that they will reduce vehicle mass as one of the cost-effective means of increasing fuel economy 

and reducing CO2 emissions in order to meet the proposed standards, and the agencies have 

incorporated this expectation into our modeling analysis supporting the proposed standards. 

Because the agencies discern a historical relationship between vehicle mass, size, and safety, it is 

reasonable to assume that these relationships will continue in the future. The question of 

whether vehicle design can mitigate the adverse effects of mass reduction is discussed below. 

Manufacturers are less likely than they were in the past to reduce vehicle footprint in 

order to reduce mass for increased fuel economy. The primary mechanism in this rulemaking for 

mitigating the potential negative effects on safety is the application of footprint-based standards, 

which create a disincentive for manufacturers to produce smaller-footprint vehicles. See section 

II. C.1, above. This is because, as footprint decreases, the corresponding fuel economy/GHG 

emission target becomes more stringent. We also believe that the shape of the footprint curves 

themselves is approximately “footprint-neutral,” that is, that it should neither encourage 

manufacturers to increase the footprint of their fleets, nor to decrease it. Upsizing footprint is 

also discouraged through the curve “cut-off” at larger footprints.363 However, the footprintbased standards do not discourage downsizing the portions of a vehicle in front of the front axle 

and to the rear of the rear axle, or of other areas of the vehicle outside the wheels. The crush 

space provided by those portions of a vehicle can make important contributions to managing 

crash energy. Additionally, simply because footprint-based standards create no incentive to 

downsize vehicles does not mean that manufacturers will not downsize if doing so makes it 

easier to meet the overall CAFE/GHG standard, as for example if the smaller vehicles are so 

much lighter that they exceed their targets by much greater amounts. On balance, however, we 

 363 The agencies recognize that at the other end of the curve, manufacturers who make small cars and trucks below 

41 square feet (the small footprint cut-off point) have some incentive to downsize their vehicles to make it easier to 

meet the constant target. That cut-off may also create some incentive for manufacturers who do not currently offer 

models that size to do so in the future. However, at the same time, the agencies believe that there is a limit to the 

market for cars and trucks smaller than 41 square feet: most consumers likely have some minimum expectation 

about interior volume, for example, among other things. Additionally, vehicles in this segment are the lowest price 

point for the light-duty automotive market, with several models in the $10,000-$15,000 range. Manufacturers who 

find themselves incentivized by the cut-off will also find themselves adding technology to the lowest price segment 

vehicles, which could make it challenging to retain the price advantage. Because of these two reasons, the agencies 

believe that the incentive to increase the sales of vehicles smaller than 41 square feet due to this rulemaking, if any, 

is small. See Section II.C.1 above and Chapter 1 of the draft Joint TSD for more information on the agencies’ 

choice of “cut-off” points for the footprint-based target curves. 
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believe the target curves and the incentives they provide generally will not encourage downsizing (or up-sizing) in terms of footprint reductions (or increases).364

Given that we expect manufacturers to reduce vehicle mass in response to the proposed 

standards, and do not expect manufacturers to reduce vehicle footprint in response to the 

proposed standards, the agencies must attempt to predict the safety effects, if any, of the 

proposed standards based on the best information currently available. This section explained 

why the agencies consider safety; the following section discusses how the agencies consider 

safety. 

2. How do the agencies consider safety? 

Assessing the effects of vehicle mass reduction and size on societal safety is a complex 

issue. One part of estimating potential safety effects involves trying to understand better the 

relationship between mass and vehicle design. The extent of mass reduction that manufacturers 

may be considering to meet more stringent fuel economy and GHG standards may raise different 

safety concerns from what the industry has previously faced. The principal difference between 

the heavier vehicles, especially truck-based LTVs, and the lighter vehicles, especially passenger 

cars, is that 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 higher in the lighter 

vehicle, similar to the mass ratio proportion. As a result of the higher change in velocity, the 

fatality risk may also increase. Removing more mass from the heavier vehicle than in the lighter 

vehicle by amounts that bring the mass ratio closer to 1.0 reduces the delta V in the lighter 

vehicle, possibly resulting in a net societal benefit. 

Another complexity is that if a vehicle is made lighter, adjustments must be made to the 

vehicle’s structure such that it will be able to manage the energy in a crash while limiting 

intrusion into the occupant compartment after adopting materials that may be stiffer. To maintain 

an acceptable occupant compartment deceleration, the effective front end stiffness has to be 

managed such that the crash pulse does not increase as stiffer yet lighter materials are utilized. If 

the energy is not well managed, the occupants may have to “ride down” a more severe crash 

pulse, putting more burdens on the restraint systems to protect the occupants. There may be 

technological and physical limitations to how much the restraint system may mitigate these 

effects. 

The agencies must attempt to estimate now, based on the best information currently 

available to us, how the assumed levels of mass reduction without additional changes (i.e. 

footprint, performance, functionality) might affect the safety of vehicles, and how lighter 

vehicles might affect the safety of drivers and passengers in the entire on-road fleet, as we are 

 364 This statement makes no prediction of how consumer choices of vehicle size will change in the future, 

independent of this proposal. 
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analyzing potential future CAFE and GHG standards. The agencies seek to ensure that the 

standards are designed to encourage manufacturers to pursue a path toward compliance that is 

both cost-effective and safe. 

To estimate the possible safety effects of the MY 2017-2025 standards, then, the agencies 

have undertaken research that approaches this question from several angles. First, we are using a 

statistical approach to study the effect of vehicle mass reduction on safety historically, as 

discussed in greater detail in section C below. Statistical analysis is performed using the most 

recent historical crash data available, and is considered as the agencies’ best estimate of potential 

mass-safety effects. The agencies recognize that negative safety effects estimated based on the 

historical relationships could potentially be tempered with safety technology advances in the 

future, and may not represent the current or future fleet. Second, we are using an engineering 

approach to investigate what amount of mass reduction is affordable and feasible while 

maintaining vehicle safety and other major functionalities such as NVH and acceleration 

performance. Third, we are also studying the new challenges these lighter vehicles might bring 

to vehicle safety and potential countermeasures available to manage those challenges effectively. 

The sections below discuss more specifically the state of the research on the mass-safety 

relationship, and how the agencies integrate that research into our assessment of the potential 

safety effects of the MY 2017-2025 CAFE and GHG standards. 

3. What is the current state of the research on statistical analysis of historical crash 

data? 

a. Background 

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 

over time. In the MY 2012-2016 final rule, the agencies stated that 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 help 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/GHG rulemaking in regards to statistical analysis of historical data. 

First, NHTSA would contract with an independent institution to review the statistical 

methods that NHTSA and DRI have used to analyze historical data related to mass, size and 

safety, and to provide recommendation on whether the existing methods or other methods should 

be used for future statistical analysis of historical data. This study will include a consideration of 
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potential near multicollinearity in the historical data and how best to address it in a regression 

analysis. The 2010 NHTSA report was also peer reviewed by two other experts in the safety 

field - Charles Farmer (Insurance Institute for Highway Safety) and Anders Lie (Swedish 

Transport Administration).365 

Second, NHTSA and EPA, in consultation with DOE, would update the MYs 1991–1999 

database on which the 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 help address 

concerns that differences in data were leading to different results in statistical analyses by 

different researchers. 

And third, in order to assess if the design of recent model year vehicles that incorporate 

various mass reduction methods affect the relationships among vehicle mass, size and safety, the 

agencies sought to identify vehicles that are using material substitution and smart design, and to 

try 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. 

Significant progress has been made on these tasks since the MY 2012-2016 final rule, as 

follows: The independent review of recent and updated statistical analyses of the relationship 

between vehicle mass, size, and crash fatality rates has been completed. NHTSA contracted with 

the University of Michigan Transportation Research Institute (UMTRI) to conduct this review, 

and the UMTRI team led by Paul Green evaluated over 20 papers, including studies done by 

NHTSA’s Charles Kahane, Tom Wenzel of the US Department of Energy’s Lawrence Berkeley 

National Laboratory, Dynamic Research, Inc., and others. UMTRI’s basic findings will be 

discussed below. Some commenters in recent CAFE rulemakings, including some vehicle 

manufacturers, suggested that the designs and materials of more recent model year vehicles may 

have weakened the historical statistical relationships between mass, size, and safety. The 

agencies agree that the statistical analysis would be improved by using an updated database that 

reflects more recent safety technologies, vehicle designs and materials, and reflects changes in 

the overall vehicle fleet. The agencies also believe, as UMTRI also found, that different 

statistical analyses may have had different results because they each used slightly different 

datasets for their analyses. In order to try to mitigate this problem and to support the current 

rulemaking, NHTSA has created a common, updated database for statistical analysis that consists 

of crash data of model years 2000- vehicles in calendar years 2002-2008, as compared to the 

database used in prior NHTSA analyses which was based on model years 1991–1999 vehicles in 

 365 All three of the peer reviews are in docket, NHTSA-2010-0152. You can access the docket at 

http://www.regulations.gov/#!home by typing ‘NHTSA-2010-0152’ where it says “enter keyword or ID” and then 

clicking on “Search.”
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calendar years 1995-2000. The new database is 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 has made the new databases available to the public,366 enabling 

other researchers to analyze the same data and hopefully minimizing discrepancies in the results 

that would have been due to inconsistencies across databases.367 The agencies recognize, 

however, that the updated database may not represent the future fleet, because vehicles have 

continued and will continue to change. 

The agencies are aware that several studies have been initiated using NHTSA’s 2011 

newly established safety database. In addition to a new Kahane study, which is discussed in 

section II.G.4, other on-going studies include two by Wenzel at Lawrence Berkeley National 

Laboratory (LBNL) under contract with the U.S. DOE, and one by Dynamic Research, Inc. 

(DRI) contracted by the International Council on Clean Transportation (ICCT). These studies 

may take somewhat different approaches to examine the statistical relationship between fatality 

risk, vehicle mass and size. In addition to a detailed assessment of the NHTSA 2011 report, 

Wenzel is expected to consider the effect of mass and footprint reduction on casualty risk per 

crash, using data from thirteen states. Casualty risk includes both fatalities and serious or 

incapacitating injuries. DRI is expected to use a two-stage approach to separate the effect of 

mass reduction on two components of fatality risk, crash avoidance and crashworthiness. The 

LBNL assessment of the NHTSA 2011 report is available in the docket for this NPRM.368 The 

casualty risk effect study was not available in time to inform this NPRM. The completed final 

peer reviewed-report on both assessments will be available prior to the final rule. DRI has also 

indicated that it expects its study to be publicly available prior to the final rule. The agencies will 

consider these studies and any others that become available, and the results may influence the 

safety analysis for the final rule. 

Other researchers are free to download the database from NHTSA’s website, and we 

expect to see additional papers in the coming months and as comments to the rulemaking that 

may also inform our consideration of these issues for the final rule. Kahane’s updated study for 

2011 is currently undergoing peer-review, and is available in the docket for this rulemaking for 

review by commenters. 

Finally, EPA and NHTSA with DOT’s Volpe Center, part of the Research and Innovative 

Technology Administration (RITA), attempted to investigate the implications of “Smart Design,” 

by identifying and describing the types of “Smart Design” and methods for using “Smart 

 366 The new databases are available at http://www.nhtsa.gov/fuel-economy (look for “Download Crash Databases for 

Statistical Analysis of Relationships Between Vehicles’ Fatality Risk, Mass, and Footprint.” 367 75 Fed. Reg. 25324 (May 7, 2010); the discussion of planned statistical analyses is on pp. 25395-25396. 368 –Wenzel, T.P. (2011b). Assessment of NHTSA’s Report “Relationships between Fatality Risk, Mass, and 

Footprint in Model Year 2000-2007 Passenger Cars and LTVs”, available at...368 Need reference to LBNL study -

EPA... 
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Design” to result in vehicle mass reduction, selecting analytical pairs of vehicles, and using the 

appropriate crash database to analyze vehicle crash data. The analysis identified several onevehicle and two-vehicle crash datasets with the potential to shed light on the issue, but the 

available data for specific crash scenarios was insufficient to produce consistent results that 

could be used to support conclusions regarding historical performance of “smart designs.” 

Undertaking these tasks has helped the agencies come closer to resolving some of the 

ongoing debates in statistical analysis research of historical crash data. We intend to apply these 

conclusions going forward, and we believe that the public discussion of the issues will be 

facilitated by the research conducted. The following sections discuss the findings from these 

studies and others in greater detail, to present a more nuanced picture of the current state of the 

statistical research. 

b. NHTSA workshop on vehicle mass, size and safety 

On February 25, 2011, NHTSA hosted a workshop on mass reduction, vehicle size, and 

fleet safety at the Headquarters of the U.S. Department of Transportation in Washington, DC.369 

The purpose of the workshop was to provide the agencies with a broad understanding of current 

research in the field and provide stakeholders and the public with an opportunity to weigh in on 

this issue. NHTSA also created a public docket to receive comments from interested parties that 

were unable to attend. 

The speakers included Charles Kahane of NHTSA, Tom Wenzel of Lawrence Berkeley 

National Laboratory, R. Michael Van Auken of Dynamic Research Inc. (DRI), Jeya Padmanaban 

of JP Research, Inc., Adrian Lund of the Insurance Institute for Highway Safety, Paul Green of 

the University of Michigan Transportation Research Institute (UMTRI), Stephen Summers of 

NHTSA, Gregg Peterson of Lotus Engineering, Koichi Kamiji of Honda, John German of the 

International Council on Clean Transportation (ICCT), Scott Schmidt of the Alliance of 

Automobile Manufacturers, Guy Nusholtz of Chrysler, and Frank Field of the Massachusetts 

Institute of Technology. 

The wide participation in the workshop allowed the agencies to hear from a broad range 

of experts and stakeholders. The contributions were particularly relevant to the agencies’ 

analysis of the effects of weight reduction for this proposed rule. The presentations were divided 

into two sessions that addressed the two expansive sets of issues – statistical evidence of the 

roles of mass and size on safety, and engineering realities – structural crashworthiness, occupant 

injury and advanced vehicle design. 

 369 A video recording, transcript, and the presentations from the NHTSA workshop on mass reduction, vehicle size 

and fleet safety is available at http://www.nhtsa.gov/fuel-economy (look for “NHTSA Workshop on Vehicle MassSize-Safety on Feb. 25”) 
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The first session focused on previous and ongoing statistical studies of crash data that 

attempt to identify the relative effects of vehicle mass and size on fleet safety. There was 

consensus that there is a complicated relationship with many confounding influences in the data. 

Wenzel summarized a recent study he conducted comparing four types of risk (fatality or 

casualty risk, per vehicle registration-years or per crash) using police-reported crash data from 

five states.370 He showed that the trends in risk for various classes of vehicles (e.g., non-sports 

car passenger cars, vans, SUVs, crossover SUVs, pickups) were similar regardless of what risk 

was being measured (fatality or casualty) or what exposure metric was used (e.g., registration 

years, police-reported crashes, etc.). In general, most trends showed a lower risk for drivers of 

larger, heavier vehicles. 

Although Wenzel’s analysis was focused on differences in the four types of risk on the 

relative risk by vehicle type, he cautioned that, when analyzing casualty risk per crash, analysts 

should control for driver age and gender, crash location (urban vs. rural), and the state in which 

the crash occurred (to account for crash reporting biases). 

Several participants pointed out that analyses must also control for individual 

technologies with significant safety effects (e.g., Electronic Stability Control, airbags).It was not 

always conclusive whether a specialty vehicle group (e.g., sports cars, two-door cars, early 

crossover SUVs) were outliers that confound the trend or unique datasets that isolate specific 

vehicle characteristics. Unfortunately, specialty vehicle groups are usually adopted by specific 

driver groups, often with outlying vehicle usage or driver behavior patterns. Green, who 

conducted an independent review of the previous statistical analyses, suggested that evaluating 

residuals will give an indication of whether or not a data subset can be legitimately removed 

without inappropriately affecting the analytical results. 

It was recognized that the physics of a two-vehicle crash require that the lighter vehicle 

experience a greater change in velocity, which often leads to disproportionately more injury risk. 

Lund noted persistent historical trends that, in any time period, occupants of the smallest and 

lightest vehicles had, on average, fatality rates approximately twice those of occupants of the 

largest and heaviest vehicles but predicted “the sky will not fall” as the fleet downsizes, we will 

not see an increase in absolute injury risk because smaller cars will become increasingly 

protective of their occupants. Padmanaban also noted in her research of the historical trends that 

mass ratio and vehicle stiffness are significant predictors with mass ratio consistently the 

dominant parameter when correlating harm. Reducing the mass of any vehicle may have 

competing societal effects as it increases the injury risk in the lightened vehicle and decreases 

them in the partner vehicle 

 370 Wenzel, T.P. (2011a). Analysis of Casualty Risk per Police-Reported Crash for Model Year 2000 to 2004 

Vehicles, using Crash Data from Five States, March 2011, LBNL-4897E, available at: 

http://eetd.lbl.gov/EA/teepa/pub.html#Vehicle 
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The separation of key parameters was also discussed as a challenge to the analyses, as 

vehicle size has historically been highly correlated with vehicle mass. Presenters had varying 

approaches for dealing with the potential multicollinearity between these two variables. Van 

Auken of DRI stated that there was latitude in the value of Variance Inflation Factor (VIF, a 

measure of multicollinearity) that would call results into question, and suggested that the large 

value of VIF for curb weight might imply “perhaps the effect of weight is too small in 

comparison to other factors.” Green, of UMTRI, stated that highly correlated variables may not 

be appropriate for use in a predictive model and that “match[ing] on footprint” (i.e., conducting 

multiple analyses for data subsets with similar footprint values) may be the most effective way to 

resolve the issue. 

There was no consensus on the overall effect of the maneuverability of smaller, lighter 

vehicles. German noted that lighter vehicles should have improved handling and braking 

characteristics and “may be more likely to avoid collisions”. Lund presented crash involvement 

data that implied that, among vehicles of similar function and use rates, crash risk does not go 

down for more “nimble” vehicles. Several presenters noted the difficulties of projecting past 

data into the future as new technologies will be used that were not available when the data were 

collected. The advances in technology through the decades have dramatically improved safety 

for all weight and size classes. A video of IIHS’s 50th anniversary crash test of a 1959 Chevrolet 

Bel Air and 2009 Chevrolet Malibu graphically demonstrated that stark differences in design and 

technology that can possibly mask the discrete mass effects, while videos of compatibility crash 

tests between smaller, lighter vehicles and contemporary larger, heavier vehicles graphically 

showed the significance of vehicle mass and size. 

Kahane presented results from his 2010 report371 that found that a scenario which took 

some mass out of heavier vehicles but little or no mass out of the lightest vehicles did not impact 

safety in absolute terms. Kahane noted that if the analyses were able to consider the mass of 

both vehicles in a two-vehicle crash, the results may be more indicative of future crashes. There 

is apparent consistency with other presentations (e.g., Padmanaban, Nusholtz) that reducing the 

overall ranges of masses and mass ratios seems to reduce overall societal harm. That is, the effect 

of mass reduction exclusively does not appear to be a “zero sum game” in which any increase in 

harm to occupants of the lightened vehicle is precisely offset by a decrease in harm to the 

occupants of the partner vehicle. If the mass of the heavier vehicle is reduced by a larger 

percentage, the changes in velocity from the collision are more nearly equal and the injuries 

suffered in the lighter vehicle are likely to be reduced more than the injuries in the heavier 

 371 Kahane, C. J. (2010). “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 1991-1999 and 

Other Passenger Cars and LTVs,” Final Regulatory Impact Analysis: Corporate Average Fuel Economy for MY 

2012-MY 2016 Passenger Cars and Light Trucks. Washington, DC: National Highway Traffic Safety 

Administration, pp. 464-542, available at http://www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/CAFE_2012-

2016_FRIA_04012010.pdf. 
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vehicle are increased. Alternatively, a fixed mass reduction (say, 100 lbs) in all vehicles could 

increase societal harm whereas a fixed percentage mass reduction is more likely to be neutral. 

Padmanaban described a series of studies conducted in recent years. She included 

numerous vehicle parameters including bumper height and several measures of vehicle size and 

stiffness and also commented on previous analyses that using weight and wheelbase together in a 

logistic model distorts the estimates, resulting in inflated variance with wrong signs and 

magnitudes in the results. Her results consistently showed that vehicle mass ratio was a more 

important parameter than those describing vehicle geometry or stiffness. Her ultimate 

conclusion was that removing mass (e.g., 100 lbs.) from all passenger cars would cause an 

overall increase in fatalities in truck-to-car crashes while removing the same amount from light 

trucks would cause an overall decrease in fatalities. 

c. Report by Green et al., UMTRI – “Independent Review: Statistical Analyses of 

Relationship Between Vehicle Curb Weight, Track Width, Wheelbase and 

Fatality Rates,” April 2011. 

As explained above, NHTSA contracted with the University of Michigan Transportation 

Research Institute (UMTRI) to conduct an independent review372 of a set of statistical analyses 

of relationships between vehicle curb weight, the footprint variables (track width, wheelbase) 

and fatality rates from vehicle crashes. The purpose of this review was to examine analysis 

methods, data sources, and assumptions of the statistical studies, with the objective of identifying 

the reasons for any differences in results. Another objective was to examine the suitability of the 

various methods for estimating the fatality risks of future vehicles. 

 UMTRI reviewed a set of papers, reports, and manuscripts provided by NHTSA 

(listed in Appendix A of UMTRI’s report, which is available in the docket to this rulemaking) 

that examined the statistical relationships between fatality or casualty rates and vehicle properties 

such as curb weight, track width, wheelbase and other variables. 

 It is difficult to summarize a study of that length and complexity for purposes of this 

discussion, but fundamentally, the UMTRI team concluded the following: 

 Differences in data may have complicated comparisons of earlier analyses, but if the 

methodology is robust, and the methods were applied in a similar way, small changes in 

data should not lead to different conclusions. The main conclusions and findings should 

be reproducible. The data base created by Kahane appears to be an impressive collection 

of files from appropriate sources and the best ones available for answering the research 

questions considered in this study. 

 In statistical analysis simpler models generally lead to improved inference, assuming the 

 372 The review is independent in the sense that it was conducted by an outside third party without any interest in the 

reported outcome. 
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data and model assumptions are appropriate. In that regard, the disaggregate logistic 

regression model used by NHTSA in the 2003 report373 seems to be the most appropriate 

model, and valid for the analysis in the context that it was used: finding general 

associations between fatality risk and mass – and the general directions of the reported 

associations are correct. 

 The two-stage logistic regression model in combination with the two-step aggregate 

regression used by DRI seems to be more complicated than is necessary based on the data 

being analyzed, and summing regression coefficients from two separate models to arrive 

at conclusions about the effects of reductions in weight or size on fatality risk seems to 

add unneeded complexity to the problem. 

 One of the biggest issues regarding this work is the historical correlation between curb 

weight, wheelbase, and track width. Including three variables that are highly correlated 

in the same model can have adverse effects on the fit of the model, especially with 

respect to the parameter estimates, as discussed by Kahane. UMTRI makes no 

conclusions about multicollinearity, other than to say that inferences made in the 

presence of multicollinearity should be judged with great caution. At the NHTSA 

workshop on size, safety and mass, Paul Green suggested that a matched analysis, in 

which regressions are run on the relationship between mass reduction and risk separately 

for vehicles of similar footprint, could be undertaken to investigate the effect of 

multicollinearity between vehicle mass and size. Kahane has combined wheelbase and 

track width into one variable (footprint) to compare with curb weight. NHTSA believes 

that the 2011 Kahane analysis has done all it can to lessen concerns about 

multicollinearity, but a concern still exists. In considering other studies provided by 

NHTSA for evaluation by the UMTRI team: 

o Papers by Wenzel, and Wenzel and Ross, addressing associations between fatality 

risk per vehicle registration-year, weight, and size by vehicle model contribute to 

understanding some of the relationships between risk, weight, and size. However, 

least squares linear regression models, without modification, are not exposurebased risk models and inference drawn from these models tends to be weak since 

they do not account for additional differences in vehicles, drivers, or crash 

conditions that could explain the variance in risk by vehicle model. 

o A 2009 J.P. Research paper focused on the difficulties associated with separating 

out the contributions of weight and size variables when analyzing fatality risk 

properly recognized the problem arising from multicollinearity and included a 

clear explanation of why fatality risk is expected to increase with increasing mass 

ratio. UMTRI concluded that the increases in fatality risk associated with a 100-

pound reduction in weight allowing footprint to vary with weight as estimated by 

Kahane and JP Research, are broadly more convincing than the 6.7 percent 

reduction in fatality risk associated with mass reduction while holding footprint 

constant, as reported by DRI. 

o A paper by Nusholtz et al. focused on the question of whether vehicle size can 

reasonably be the dominant vehicle factor for fatality risk, and finding that 

 
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changing the mean mass of the vehicle population (leaving variability unchanged) 

has a stronger influence on fatality risk than corresponding (feasible) changes in 

mean vehicle dimensions, concluded unequivocally that reducing vehicle mass 

while maintaining constant vehicle dimensions will increase fatality risk. UMTRI 

concluded that if one accepts the methodology, this conclusion is robust against 

realistic changes that may be made in the force vs. deflection characteristics of the 

impacting vehicles. 

o Two papers by Robertson, one a commentary paper and the other a peer-reviewed 

journal article, were reviewed. The commentary paper did not fit separate models 

according to crash type, and included passenger cars, vans, and SUVs in the same 

model. UMTRI concluded that some of the claims in the commentary paper 

appear to be overstated, and intermediate results and more documentation would 

help the reader determine if these claims are valid. The second paper focused 

largely on the effects of electronic stability control (ESC), but generally followed 

on from the first paper except that curb weight is not fit and fuel economy is used 

as a surrogate. 

 

The UMTRI study provided a number of useful suggestions that Kahane considered in 

updating his 2011 analysis, and that have been incorporated into the safety effects estimates for 

the current rulemaking. 

d. Report by Dr. Charles Kahane, NHTSA – “Relationships Between Fatality Risk, 

Mass, and Footprint in Model Year 2000- Passenger Cars and LTVs,” 2011 

The relationship between a vehicle’s mass, size, and fatality risk is complex, and it varies 

in different types of crashes. NHTSA, along with others, has been examining this relationship 

for over a decade. The safety chapter of NHTSA’s April 2010 final regulatory impact analysis 

(FRIA) of CAFE standards for MY 2012-2016 passenger cars and light trucks included a 

statistical analysis of relationships between fatality risk, mass, and footprint in MY 1991-1999 

passenger cars and LTVs (light trucks and vans), based on calendar year (CY) 1995-2000 crash 

and vehicle-registration data.374 The 2010 analysis used the same data as the 2003 analysis, but 

included vehicle mass and footprint in the same regression model. 

The principal findings of NHTSA’s 2010 analysis were that mass reduction in lighter 

cars, even while holding footprint constant, would significantly increase societal fatality risk, 

whereas mass reduction in the heavier LTVs would significantly reduce net societal fatality risk, 

because it would reduce the fatality risk of occupants in lighter vehicles which collide with the 

 374 Kahane, C. J. (2010). “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 1991-1999 and 

Other Passenger Cars and LTVs,” Final Regulatory Impact Analysis: Corporate Average Fuel Economy for MY 

2012-MY 2016 Passenger Cars and Light Trucks. Washington, DC: National Highway Traffic Safety 

Administration, pp. 464-542, available at http://www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/CAFE_2012-

2016_FRIA_04012010.pdf. 
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heavier LTVs. NHTSA concluded that, as a result, any reasonable combination of mass 

reductions while holding footprint constant in MY 2012-2016 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’s 2010 report partially agreed and partially disagreed with analyses published 

during 2003-2005 by Dynamic Research, Inc. (DRI). NHTSA and DRI both found a significant 

protective effect for footprint, and that reducing mass and footprint together (downsizing) on 

smaller vehicles was harmful. DRI’s analyses estimated a significant overall reduction in 

fatalities from mass reduction in all light-duty vehicles if wheelbase and track width were 

maintained, whereas NHTSA’s report showed overall fatality reductions only in the heavier 

LTVs, and benefits only in some types of crashes for other vehicle types. Much of NHTSA’s 

2010 report, as well as recent work by DRI, involved sensitivity tests on the databases and 

models, which generated a range of estimates somewhere between the initial DRI and NHTSA 

results.375 

Immediately after issuing the final rule for MYs 2012-2016 CAFE and GHG standards in 

May 2010, NHTSA and EPA began work on the next joint rulemaking to develop CAFE and 

GHG standards for MY 2017 to 2025 and beyond. The preamble to the 2012-2016 final rule 

stated that NHTSA, working closely with EPA and the Department of Energy (DOE), would 

perform a new statistical analysis of the relationships between fatality rates, mass and footprint, 

updating the crash and exposure databases to the latest available model years, refining the 

methodology in response to peer reviews of the 2010 report and taking into account changes in 

vehicle technologies. The previous databases of MY 1991-1999 vehicles in CY 1995-2000 

crashes has become outdated as new safety technologies, vehicle designs and materials were 

introduced. The new databases comprising MY 2000- vehicles in CY 2002-2008 crashes with 

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 has made the new 

 375 Van Auken, R. M., and Zellner, J. W. (2003). A Further Assessment of the Effects of Vehicle Weight and Size 

Parameters on Fatality Risk in Model Year 1985-98 Passenger Cars and 1986-97 Light Trucks. Report No. DRITR-03-01. Torrance, CA: Dynamic Research, Inc.; Van Auken, R. M., and Zellner, J. W. (2005a). An Assessment of 

the Effects of Vehicle Weight and Size on Fatality Risk in 1985 to 1998 Model Year Passenger Cars and 1985 to 

1997 Model Year Light Trucks and Vans. Paper No. 2005-01-1354. Warrendale, PA: Society of Automotive 

Engineers; Van Auken, R. M., and Zellner, J. W. (2005b). Supplemental Results on the Independent Effects of Curb 

Weight, Wheelbase, and Track on Fatality Risk in 1985-1998 Model Year Passenger Cars and 1986-97 Model Year 

LTVs. Report No. DRI-TR-05-01. Torrance, CA: Dynamic Research, Inc.; Van Auken, R.M., and Zellner, J. W. 

(2011). “Updated Analysis of the Effects of Passenger Vehicle Size and Weight on Safety,” NHTSA Workshop on 

Vehicle Mass-Size-Safety, Washington, February 25, 2011, 

http://www.nhtsa.gov/staticfiles/rulemaking/pdf/MSS/MSSworkshop_VanAuken.pdf 
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databases available to the public376, enabling other researchers to analyze the same data and 

hopefully minimizing discrepancies in the results due to inconsistencies across the data used.377

One way to estimate these effects is via statistical analyses of societal fatality rates per 

vehicle miles traveled (VMT), by vehicles’ mass and footprint, for the current on-road vehicle 

fleet. The basic analytical method used for the 2011 NHTSA report is the same as in NHTSA’s 

2010 report: cross-sectional analyses of the effect of mass and footprint reductions on the 

societal fatality rate per billion vehicle miles of travel (VMT), while controlling for driver age 

and gender, vehicle type, vehicle safety features, crash times and locations, and other factors. 

Separate logistic regression models are run for three types of vehicles and nine types of crashes. 

Societal fatality rates include occupants of all vehicles in the crash, as well as non-occupants, 

such as pedestrians and cyclists. NHTSA’s 2011 Report378 analyzes MY 2000- cars and LTVs in 

CY 2002-2008 crashes. Fatality rates were derived from FARS data, 13 State crash files, and 

registration and mileage data from R.L. Polk. 

The most noticeable change in MY 2000- vehicles from MY 1991-1999 has been the 

increase in crossover utility vehicles (CUV), which are SUVs of unibody construction, often but 

not always built upon a platform shared with passenger cars. CUVs have blurred the distinction 

between cars and trucks. The new analysis treats CUVs and minivans as a separate vehicle class, 

because they differ in some respects from pickup-truck-based LTVs and in other respects from 

passenger cars. In the 2010 report, the many different types of LTVs were combined into a 

single analysis and NHTSA believes that this may have made the analyses too complex and 

might have contributed to some of the uncertainty in the results. 

The new database has accurate VMT estimates, derived from a file of odometer readings 

by make, model, and model year recently developed by R.L. Polk and purchased by NHTSA.379 

For the 2011 report, the relative distribution of crash types has been changed to reflect the 

projected distribution of crashes during the period from 2017 to 2025, based on the estimated 

effectiveness of electronic stability control (ESC) in reduction the number of fatalities in rollover 

crashes and crashes with a stationary object. The annual target population of fatalities or the 

annual fatality distribution baseline380 was not decreased in the period between 2017 and 2025 

 376 http://www.nhtsa.gov/fuel-economy 377 75 Fed. Reg. 25324 (May 7, 2010); the discussion of planned statistical analyses is on pp. 25395-25396. 378 Kahane, C. J. (2011). “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2000-2007 

Passenger Cars and LTVs,”, July 2011. The report is available in the NHTSA docket, NHTSA-2010-0152. You can 

access the docket at http://www.regulations.gov/#!home by typing ‘NHTSA-2010-0152’ where it says “enter 

keyword or ID” and then clicking on “Search.” 

379 In the 1991-1999 data base, VMT was estimated only by vehicle class, based on NASS CDS data. 380 MY 2004 -2007 vehicles with fatal crashes occurred in CY 2004-2008 are selected as the annual fatality 

distribution baseline in the Kahane analysis. 
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for the safety statistics analysis, but is taken into account later in the Volpe model analysis, since 

all vehicles in the future will be equipped with ESC381. 

For the 2011 report, vehicles are now grouped into five classes rather than four: 

passenger cars (including both 2-door and 4-door cars) are split in half by median weight; CUVs 

and minivans; and truck-based LTVs, which are also split in half by median weight of the model 

year 2000- vehicles. Table III-11 presents the estimated percent increase in U.S. societal fatality 

risk per ten billion VMT for each 100-pound reduction in vehicle mass, while holding footprint 

constant, for each of the five classes of vehicles. 

Table III-11 Results of 2011 NHTSA report Fatality Increase (%) per 100-Pound Mass 

Reduction While Holding Footprint Constant 

MY 2000-2007 

CY 2002-2008 

Fatality Increase (%) Per 100-Pound Mass 

Reduction While Holding Footprint Constant 

Point Estimate 95% Confidence Bounds 

Cars < 3,106 pounds 1.44 + .29 to +2.59 

Cars > 3,106 pounds .47 - .58 to +1.52 

CUVs and minivans - .46 -1.75 to + .83 

Truck-based LTVs < 4,594 pounds .52 - .43 to +1.46 

Truck-based LTVs > 4,594 pounds - .39 -1.06 to + .27 

Only the 1.44 percent risk increase in the lighter cars is statistically significant. There are 

non-significant increases in the heavier cars and the lighter truck-based LTVs, and nonsignificant societal benefits for mass reduction in CUVs, minivans, and the heavier truck-based 

LTVs. Based on these results, potential combinations of mass reductions that maintain footprint 

and are proportionately somewhat higher for the heavier vehicles may be safety-neutral or better 

as point estimates and, in any case, unlikely to significantly increase fatalities. The primarily 

non-significant results are not due to a paucity of data, but because the societal effect of mass 

reduction while maintaining footprint, if any, is small. 

MY 2000- vehicles of all types are heavier and larger than their MY 1991-1999 

counterparts. The average mass of passenger cars increased by 5 percent from 2000 to and the 

average mass of pickup trucks increased by 19 percent. Other types of vehicles became heavier, 

on the average, by intermediate amounts. There are several reasons for these increases: during 

this time frame, some of the lighter make-models were discontinued; many models were 

redesigned to be heavier and larger; and consumers more often selected stretched versions such 

as crew cabs in their new-vehicle purchases. 

 381 In the Volpe model, NHTSA assumed that the safety trend would result in 12.6 percent reduction between 2007 

and 2020 due toin the combination of ESC, new safety standard, and behavior changes anticipated. 
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It is interesting to compare the new results to NHTSA’s 2010 analysis of MY 1991-1999 

vehicles in CY 1995-2000, especially the new point estimate to the “actual regression result 

scenario” in the 2010 report:III-12 2010 Report: MY 1991-1999, CY 1995-2000 Fatality 

Increase (%) per 100-Pound Mass Reduction While Holding Footprint Constant 

Actual Regression 

Result Scenario 

Upper-Estimate 

Scenario 

Lower-Estimate 

Scenario 

Cars < 2,950 pounds 2.21 2.21 1.02 

Cars > 2,950 pounds 0.90 0.90 0.44 

LTVs < 3,870 pounds 0.17 0.55 0.41 

LTVs > 3,870 pounds -1.90 -0.62 -0.73 

Table III-13 Fatality Increase (%) per 100-Pound Mass Reduction While Holding 

Footprint Constant 

NHTSA (2010) NHTSA (2011) 

Lighter cars 2.21% 1.43% 

Heavier cars 0.89% 0.48% 

Lighter LTVs 0.17%* 0.52% 

Heavier LTVs -1.90%* -0.40% 

CUV/ minivan -0.47% 

*Includes CUV/minivan 

The new results are directionally the same as in 2010: fatality increase in the lighter cars, 

safety benefit in the heavier LTVs, but the effects may have become weaker at both ends. (The 

agencies do not consider this conclusion to be definitive because of the relatively wide 

confidence bounds of the estimates.) The fatality increase in the lighter cars tapered off from 

2.21 percent to 1.44 percent while the societal benefit of mass reduction in the heaviest LTVs 

diminished from 1.90 percent to 0.39 percent and is no longer statistically significant. 

The agencies believe that the changes may be due to a combination of both changes in the 

characteristics of newer vehicles and revisions to the analysis. NHTSA believes, above all, that 

several light, small car models with poor safety performance were discontinued by 2000 or 

during 2000-. Also, the tendency of light, small vehicles to be driven poorly is not as strong as it 

used to be – perhaps in part because safety improvements in lighter and smaller vehicles have 

made some good drivers more willing to buy them. Both agencies believe that at the other end 

of the weight/size spectrum, blocker beams and other voluntary compatibility improvements in 

LTVs, as well as compatibility-related self-protection improvements to cars, have made the 

heavier LTVs less aggressive in collisions with lighter vehicles (although the effect of mass 

disparity remains). This report’s analysis of CUVs and minivans as a separate class of vehicles 

may have relieved some inaccuracies in the 2010 regression results for LTVs. Interestingly, the 

new actual-regression results are quite close to the previous report’s “lower-estimate scenario,” 
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which was an attempt to adjust for supposed inaccuracies in some regressions and for a 

seemingly excessive trend toward higher crash rates in smaller and lighter cars. 

The principal difference between the heavier vehicles, especially truck-based LTVs, and 

the lighter vehicles, especially passenger cars, is that mass reduction has a different effect in 

collisions with another car or LTV. When two vehicles of unequal mass collide, the delta V is 

higher in the lighter vehicle, in the same proportion as the mass ratio. As a result, the fatality 

risk is also higher. Removing some mass from the heavy vehicle reduces delta V in the lighter 

vehicle, where fatality risk is high, 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: fatality 

increase in the lighter cars, reduction in the heavier LTVs, and little effect in the intermediate 

groups. However, in some types of crashes, especially first event rollovers and impacts with 

fixed objects, mass reduction is usually not harmful and often beneficial, because the lighter 

vehicles respond more quickly to braking and steering and are often more stable because their 

center of gravity is lower. Offsetting that benefit is the continuing historical tendency of lighter 

and smaller vehicles to be driven less well – although it continues to be unknown why that is so, 

and to what extent, if any, the lightness or smallness of the vehicle contributes to people driving 

it less safely. 

The estimates of the model are formulated for each 100-pound reduction in mass; in other 

words, if risk increases by 1 percent for 100 pounds reduction in mass, it would increase by 2 

percent for a 200-pound reduction, and 3 percent for a 300-pound reduction (more exactly, 2.01 

percent and 3.03 percent, because the effects work like compound interest). Confidence bounds 

around the point estimates will grow wider by the same proportions. 

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, the model may become somewhat less accurate and it is difficult to assess 

the sensitivity to additional mass reduction greater than 100 pounds. The agencies recognize that 

the light-duty vehicle fleet in the 2017-2025 timeframe will be different than the 2000- fleet 

analyzed for this study. Nevertheless, one consideration provides some basis for confidence. 

This is NHTSA’s fourth evaluation of the effects of mass reduction and/or downsizing, 

comprising databases ranging from MY 1985 to . The results of the four studies are not 

identical, but they have been consistent up to a point. During this time period, many makes and 

models have increased substantially in mass, sometimes as much as 30-40 percent.382 If the 

 382 For example, one of the most popular models of small 4-door sedans increased in curb weight from 1,939 pounds 

in MY 1985 to 2,766 pounds in MY 2007, a 43 percent 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 2-door 

cab and rear-wheel drive; and a popular minivan from 2,940 to 3,862 pounds (31%). 
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statistical analysis has, over the past years, been able to accommodate mass increases of this 

magnitude, perhaps it will also succeed in modeling the effects of mass reductions on the order 

of 10-20 percent, if they occur in the future. 

e. Report by Tom Wenzel, LBNL, “An Assessment of NHTSA’s Report 

‘Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2000- 

Passenger Cars and LTVs’”, 2011 

DOE contracted with Tom Wenzel of Lawrence Berkeley National Laboratory to conduct 

an assessment of NHTSA’s updated 2011 study of the effect of mass and footprint reductions on 

US fatality risk per vehicle miles traveled, and to provide an analysis of the effect of mass and 

footprint reduction on casualty risk per police-reported crash, using independent data from 

thirteen states. The assessment has been completed and reviewed by NHTSA and EPA staff, and 

a draft final version is included in the docket of today’s rulemaking; the separate analysis of 

crash data from thirteen states will be completed and included in the docket shortly. Both reports 

will be peer reviewed by outside experts. 

The LBNL report replicates Kahane’s analysis for NHTSA, using the same data and 

methods, and in many cases using the same SAS programs. The Wenzel report finds that 

although mass reduction in lighter (less than 3,106 lbs) cars leads to a statistically significant 

1.44% increase in fatality risk per vehicle miles travelled (VMT), the increase is small. He tests 

this result for sensitivity to changes in specifications of the regression models and what data are 

used. In addition Wenzel shows that there is a wide range in fatality rates by vehicle model for 

models that have the same mass, even after accounting for differences in drivers’ age and gender, 

safety features installed, and crash times and locations. This section summarizes the results of 

the Wenzel assessment of the most recent NHTSA analysis. 

The LBNL report highlights the effect of the other driver, vehicle, and crash control 

variables, in addition to the effect of mass and footprint reduction, on risk. Some of the other 

variables NHTSA included in its regression models have much larger effects on fatality risk than 

mass or footprint reduction. For example, the models indicate that a 100-lb increase in the mass 

of a lighter car results in a 1.44% reduction in fatality risk; this is the largest estimated effect of 

changes in vehicle mass, and the only one that is statistically significant. For comparison this 

reduction in fatality risk could also be achieved by a 13% increase in 4-door sedans equipped 

with ESC. 

The 1.44% increase in risk from reducing mass in the lighter cars was tested for 

sensitivity changes in the specification of, or the data used in, the regression models. For 

example, using the current distribution of crashes, rather than adjusting the distribution to that 

expected after full adoption of ESC, reduces the effect to 1.18%; excluding the calendar year 

variables from the model, which may be weakening the modeled benefits of vehicle safety 

technologies, reduces the effect to 1.39%; and including vehicle make in the model increases the 
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effect to 1.81%. The results also are sensitive to the selection of data to include in the analysis: 

excluding bad drivers increases the effect to 2.03%, while excluding crashes involving alcohol or 

drugs increases the effect to 1.66%, and including sports, police, and all-wheel drive cars 

increases the effect to 1.64%. Finally, changing the definition of risk also affects the result for 

lighter cars: using the number of fatalities per induced exposure crash reduces the effect to -

0.24% (that is, a 0.24% reduction in risk), while using the number of fatal crashes (rather than 

total fatalities) per VMT increases the effect to 1.84%. These sensitivity tests, except one, 

changed the estimated coefficient by less than 1 percentage point, which is within its statistical 

confidence bounds of 0.29 to 2.59 percent and may be considered compatible with the baseline 

result. Using two or more variables that are strongly correlated in the same regression model 

(referred to as multicollinearity) can lead to inaccurate results. However, the correlation between 

vehicle mass and footprint may not be strong enough to cause serious concern. Experts suggest 

that a correlation of greater than 0.60 (or a variance inflation factor of 2.5) raises concern about 

multicollinearity.383 The correlation between vehicle mass and footprint ranges from over 0.80 

for four-door sedans, pickups, and SUVs, to about 0.65 for two-door cars and CUVs, to 0.26 for 

minivans; when pickups and SUVs are considered together, the correlation between mass and 

footprint is 0.65. Wenzel notes that the 2011 NHTSA report recognizes that the “near” 

multicollinearity between mass and footprint may not be strong enough to invalidate the results 

from a regression model that includes both variables. In addition, NHTSA included several 

analyses to address possible effects of the near-multicollinearity between mass and footprint. 

First, NHTSA ran a sensitivity model specification, where footprint is not held constant, 

but rather allowed to vary as mass varies (i.e. NHTSA ran a regression model which includes 

mass but not footprint). If the multicollinearity was so great that including both variables in the 

same model gave misleading results, removing footprint from the model could give mass 

coefficients five or more percentage points different than keeping it in the model. NHTSA’s 

sensitivity test indicates that when footprint is allowed to vary with mass, the effect of mass 

reduction on risk increases from 1.44% to 2.64% for lighter cars, and from a non-significant 

0.47% to a statistically-significant 1.94% for heaver cars (changes of less than two percentage 

points); however, the effect of mass reduction on light trucks is unchanged, and is still not 

statistically significant for CUVs/minivans. 

Second, NHTSA conducted a stratification analysis of the effect of mass reduction on 

risk by dividing vehicles into deciles based on their footprint, and running a separate regression 

model for each vehicle and crash type, for each footprint decile (3 vehicle types times 9 crash 

types times 10 deciles equals 270 regressions). This analysis estimates the effect of mass 

reduction on risk separately for vehicles with similar footprint. The analysis indicates that mass 

reduction does not consistently increase risk across all footprint deciles for any combination of 

 

383 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy 

Standards; Final Rule, April 1, 2010, Section II.G.3., page 139. 
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vehicle type and crash type. Mass reduction increases risk in a majority of footprint deciles for 

13 of the 27 crash and vehicle combinations, but few of these increases are statistically 

significant. On the other hand, mass reduction decreases risk in a majority of footprint deciles 

for 9 of the 27 crash and vehicle combinations; in some cases these risk reductions are large and 

statistically significant.384 If reducing vehicle mass while maintaining footprint inherently leads 

to an increase in risk, the coefficients on mass reduction should be more consistently positive, 

and with a larger R2, across the 27 vehicle/crash combinations, than shown in the analysis. 

These findings are consistent with the conclusion of the basic regression analyses, namely, that 

the effect of mass reduction while holding footprint constant, if any, is small.limitation of using 

logistic regression to estimate the effect of mass reduction on risk is that a standard statistic to 

measure the extent to which the variables in the model explain the range in risk, equivalent to the 

R2

 statistic in a linear regression model, does not exist. (SAS does generate a pseudo-R2 value 

for logistic regression models; in almost all of the NHTSA regression models this value is less 

than 0.10). For this reason LBNL conducted an analysis of risk versus mass by vehicle model. 

LBNL used the results of the NHTSA logistic regression model to predict the number of 

fatalities expected after accounting for all vehicle, driver, and crash variables included in the 

NHTSA regression model except for vehicle weight and footprint. LBNL then plotted expected 

fatality risk per VMT by vehicle model against the mass of each model, and analyzed the change 

in risk as mass increases, as well as how much of the change in risk was explained by all of the 

variables included in the model. 

The analysis indicates that, after accounting for all the variables, risk does decrease as 

mass increases; however, risk and mass are not strongly correlated, with the R2

 ranging from 

0.33 for CUVs to less than 0.15 for all other vehicle types (as shown in Figure x). This means 

that, on average, risk decreases as mass increases, but the variation in risk among individual 

vehicle models is stronger than the trend in risk from light to heavy vehicles. For fullsize (i.e. 3⁄4- 

and 1-ton) pickups, risk increases as mass increases, with an R2

 of 0.43, consistent with 

NHTSA’s basic regression results for the heavier LTVs (societal risk increases as mass 

increases). LBNL also examined the relationship between residual risk, that is the remaining 

unexplained risk after accounting for all vehicle, driver and crash variables, and mass, and found 

similarly poor correlations. This implies that the remaining factors not included in the regression 

model that account for the observed range in risk by vehicle model also are not correlated with 

mass. (LBNL found similar results when the analysis compared risk to vehicle footprint.) 

Figure III-2 indicates that some vehicles on the road today have the same, or lower, 

fatality rates than models that weigh substantially more, and are substantially larger in terms of 

footprint. After accounting for differences in driver age and gender, safety features installed, and 

crash times and locations, there are numerous examples of different models with similar weight 

 384 And in 5 of the 27 crash and vehicle combinations, mass reduction increased risk in 5 deciles and decreased risk 

in 5 deciles. 
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and footprint yet widely varying fatality rates. The variation of fatality rates among individual 

models may reflect differences in vehicle design, differences in the drivers who choose such 

vehicles (beyond what can be explained by demographic variables such as age and gender), and 

statistical variation of fatality rates based on limited data for individual models. Differences in 

vehicle design can, and already do, mitigate some safety penalties from reduced mass; this is 

consistent with NHTSA’s opinion that some of the changes in its regression results between the 

2003 study and the 2011 study are due to the redesign or removal of certain smaller and lighter 

models of poor design. 

Figure III-2 Predicted US fatality risk per VMT vs. curb weight by vehicle type, after 

accounting for all driver, crash, and vehicle variables except mass and footprint 

f. Based on this information, what do the agencies consider to be the current state of 

statistical research on vehicle mass and safety? 

The agencies believe that statistical analysis of historical crash data continues to be an 

informative and important tool in assessing the potential safety impacts of the proposed 

standards. The effect of mass reduction while maintaining footprint is a complicated topic and 

there are open questions whether future designs will reduce the historical correlation between 

weight and size. It is important to note that while the updated database represents more current 

vehicles with technologies more representative of vehicles on the road today, they still do not 

fully represent what vehicles will be on the road in the 2017-2025 timeframe. The vehicles 

manufactured in the 2000- timeframe were not subject to footprint-based fuel economy 

standards. The agencies expect that the attribute-based standards will likely facilitate the design 

of vehicles such that manufacturers may reduce mass while maintaining footprint. Therefore, it is 

R2 = 0.12 

R2 = 0.04 

R2 = 0.00 

R2 = 0.43 

R2 = 0.05 

R2 = 0.33 

R2 = 0.09 

0 

40 

80 

120 

160 

200 

240 

280 

320 

360 

400 

2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600 

Predicted US fatality risk (crash fdatalities per 1010 VMT) 

Curb weight (lbs) 

2-dr cars 

4-dr cars 

Small pickups 

Fullsize pickups 

SUVs 

CUVs 

Minivans 
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possible that the analysis for 2000- vehicles may not be fully representative of the vehicles that 

will be on the road in 2017 and beyond. 

While we recognize that statistical analysis of historical crash data may not be the only 

way to think about the future relationship between vehicle mass and safety, we also recognize 

that other assessment methods are also subject to uncertainties, which makes statistical analysis 

of historical data an important starting point if employed mindfully and recognized for how it 

can be useful and what its limitations may be. 

NHTSA undertook the independent review of statistical studies and held the mass-safety 

workshop in February 2011 in order to help the agencies sort through the ongoing debates over 

what statistical analysis of historical data is actually telling us. Previously, the agencies have 

assumed that differences in results were due in part to inconsistent databases; by creating the 

updated common database and making it publicly available, we are hopeful that that aspect of the 

problem has been resolved, and moreover, the UMTRI review suggested that differences in data 

were probably less significant than the agencies may have thought. Statistical analyses of 

historical crash data should be examined for potential multicollinearity issues. . The agencies 

will continue to monitor issues with multicollinearity in our analyses, and hope that outside 

researchers will do the same. And finally, based on the findings of the independent review, the 

agencies continue to be confident that Kahane’s analysis is one of the best for the purpose of 

analyzing potential safety effects of future CAFE and GHG standards. UMTRI concluded that 

Kahane’s approach is valid, and Kahane has continued and refined that approach for the current 

analysis. The NHTSA 2011 statistical fatality report finds directionally similar but less 

statistically significant relationships between vehicle mass, size, and footprint, as discussed 

above. Based on these findings, the agencies believe that in the future, fatalities due to mass 

reduction will be best reduced if mass reduction is concentrated in the heaviest vehicles. 

NHTSA considers part of the reason that more recent historical data shows a dampened effect in 

the relationship between mass reduction and safety is that all vehicles, including traditionally 

lighter ones, grew heavier during that timeframe (2000s). As lighter vehicles might become 

more prevalent in the fleet again over the next decade, it is possible that the trend could 

strengthen again. On the other hand, extensive use of new lightweight materials and optimized 

vehicle design may weaken the relationship. Future updated analyses will be necessary to 

determine how the effect of mass reduction on risk changes over time. 

Both agencies agree that there are several identifiable safety trends already in place or 

expected to occur in the foreseeable future that are not accounted for in the study, since they 

were not in effect at the time that the vehicles in question were manufactured. For example, 

there are two important new safety standards that have already been issued and will be phasing in 

after MY 2008. FMVSS No. 126 (49 CFR § 571.126) requires electronic stability control in all 

new vehicles by MY 2012, and the upgrade to FMVSS No. 214 (Side Impact Protection, 49 CFR 

§ 571.214) will likely result in all new vehicles being equipped with head-curtain air bags by 

MY 2014. Additionally, we anticipate continued improvements in driver (and passenger) 
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behavior, such as higher safety belt use rates. All of these may tend to reduce the absolute 

number of fatalities. On the other hand, as crash avoidance technology improves, future 

statistical analysis of historical data may be complicated by a lower number of crashes. In 

summary, the agencies have relied on the coefficients in the Kahane 2011 study for estimating 

the potential safety effects of the proposed CAFE and GHG standards for MYs 2017-2025, based 

on our assumptions regarding the amount of mass reduction that could be used to meet the 

standards in a cost-effective way without adversely affecting safety. Section E below discusses 

the methodology used by the agencies in more detail; while the results of the safety effects 

analysis are less significant than the results in the MY 2012-2016 final rule, the agencies still 

believe that any statistically significant results warrant careful consideration of the assumptions 

about appropriate levels of mass reduction on which to base future CAFE and GHG standards, 

and have acted accordingly in developing the proposed standards. 

4. How do the agencies think technological solutions might affect the safety estimates 

indicated by the statistical analysis? 

As mass reduction becomes a more important technology option for manufacturers in 

meeting future CAFE and GHG standards, manufacturers will invest more and more resources in 

developing increasingly lightweight vehicle designs that meet their needs for manufacturability 

and the public’s need for vehicles that are also safe, useful, affordable, and enjoyable to drive. 

There are many different ways to reduce mass, as discussed in Chapter 3 of this TSD and in 

Sections II, III, and IV of the preamble, and a considerable amount of information is available 

today on lightweight vehicle designs currently in production and that may be able to be put into 

production in the rulemaking timeframe. Discussion of lightweight material designs from 

NHTSA’s workshop is presented below. 

Besides “lightweighting” technologies themselves, though, there are a number of 

considerations when attempting to evaluate how future technological developments might affect 

the safety estimates indicated by the statistical analysis. As discussed in the first part of this 

chapter, for example, careful changes in design and/or materials used might mitigate some of the 

potential decrease in safety from mass reduction – through improved distribution of crash pulse 

energy, etc. – but these techniques can sometimes cause other problems, such as increased crash 

forces on vehicle occupants that have to be mitigated, or greater aggressivity against other 

vehicles in crashes. Manufacturers may develop new and better restraints – air bags, seat belts, 

etc. – to protect occupants in lighter vehicles in crashes, but NHTSA’s current safety standards 

for restraint systems are designed based on the current fleet, not the yet-unknown future fleet. 

The agency will need to monitor trends in the crash data to see whether changes to the safety 

standards (or new safety standards) become necessary. Manufacturers are also increasingly 

investigating a variety of crash avoidance technologies – ABS, electronic stability control (ESC), 

lane departure warnings, vehicle-to-vehicle (V2V) communications – that, as they become more 

prevalent in the fleet, are expected to reduce the number of overall crashes, and fatal, crashes. 
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Until these technologies are present in the fleet in greater numbers, however, it will be difficult 

to assess whether they can mitigate the observed relationship between vehicle mass and safety in 

the historical data. 

Along with the California Air Resources Board (CARB), the agencies have initiated 

several projects to estimate the maximum potential for advanced materials and improved designs 

to reduce mass in the MY 2017-2021 timeframe, while continuing to meeting safety regulations 

and maintaining functionality of vehicles. Another NHTSA-sponsored study will estimate the 

effects of these design changes on overall fleet safety. 

A. NHTSA has awarded a contract to Electricore, with EDAG and George Washington 

University (GWU) as subcontractors, to study the maximum feasible amount of mass 

reduction for a mid-size car – specifically, a Honda Accord. The study tore down a MY 

2011 Honda Accord, studied each component and sub-system, and then redesigned each 

component and sub-system trying to maximize the amount of mass reduction with 

technologies that are considered feasible for 200,000 units per year production volume 

during the time frame of this rulemaking. Electricore and its sub-contractors are 

consulting industry leaders and experts for each component and sub-system when 

deciding which technologies are feasible. Electricore and its sub-contractors are also 

building detailed CAD/CAE/powertrain models to validate vehicle safety, stiffness, NVH, 

durability, drivability and powertrain performance. For OEM-supplied parts, a detailed 

cost model is being built based on a Technical Cost Modeling (TCM) approach developed 

by the Massachusetts Institute of Technology (MIT) Materials Systems Laboratory’s 

research385 to estimate the costs to OEMs for manufacturing parts. The cost will be 

broken down into each of the operations involved in the manufacturing; for example, for a 

sheet metal part, production costs will be estimated from the blanking of the steel coil to 

the final operation to fabricate the component. Total costs are then categorized into fixed 

cost, such as tooling, equipment, and facilities; and variable costs such as labor, material, 

energy, and maintenance. These costs will be assessed through an interactive process 

between the product designer, manufacturing engineers, and cost analysts. For OEMpurchased parts, the cost will be estimated by consultation with experienced cost analysts 

and Tier 1 system suppliers. This study will help to inform the agencies about the feasible 

amount of mass reduction and the cost associated with it. NHTSA intends to have this 

study completed and peer reviewed before July 2012, in time for it to play an integral role 

in informing the final rule. 

B. EPA has awarded a similar contract to FEV, with EDAG and Monroe & Associates, Inc. 

as subcontractors, to study the maximum feasible amount of mass reduction for a mid-size 

CUV (cross over vehicle) specifically, a Toyota Venza. The study tears down a MY 2010 

vehicle, studies each component and sub-system, and then redesigns each component and 

 385 Frank Field, Randolph Kirchain and Richard Roth, Process cost modeling: Strategic engineering and economic 

evaluation of materials technologies, JOM Journal of the Minerals, Metals and Materials Society, Volume 59, 

Number 10, 21-32. Available at http://msl.mit.edu/pubs/docs/Field_KirchainCM_StratEvalMatls.pdf (last accessed 

Aug. 22, 2011). 
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sub-system trying to maximize the amount of mass reduction with technologies that are 

considered feasible for high volume production for a 2017 MY vehicle. FEV in 

coordination with EDAG is building detailed CAD/CAE/powertrain models to validate 

vehicle safety, stiffness, NVH, durability, drivability and powertrain performance to 

assess the safety of this new design. This study builds upon the low development (20% 

mass reduction) design in the 2010 Lotus Engineering study “An Assessment of Mass 

Reduction Opportunities for a 2017-2020 Model Year Vehicle Program”. This study 

builds upon the low development (20% mass reduction) design in the 2010 Lotus 

Engineering study “An Assessment of Mass Reduction Opportunities for a 2017-2020 

Model Year Vehicle Program”. This study will undergo a peer review. EPA intends to 

have this study completed and peer reviewed before July 2012, in time for it to play an 

integral role in informing the final rule. 

C. California Air Resources Board (CARB) has awarded a contract to Lotus Engineering, to 

study the maximum feasible amount of mass reduction for a mid-size CUV (cross over 

vehicle) specifically, a Toyota Venza. The study will concentrate on the Body-in-White 

and closures in the high development design (40% mass reduction) in the Lotus 

Engineering study cited above. The study will provide an updated design with crash 

simulation, detailed costing and manufacturing feasibility of these two systems for a 

MY2020 high volume production vehicle. This study will undergo a peer review. EPA 

intends to have this study completed and peer reviewed before July 2012, in time for it to 

play an integral role in informing the final rule. 

D. NHTSA has contracted with George Washington University (GWU) to build a fleet 

simulation model to study the impact and relationship of light-weight vehicle design and 

injuries and fatalities. This study will also include an evaluation of potential 

countermeasures to reduce any safety concerns associated with lightweight vehicles. 

NHTSA will include three light-weighted vehicle designs in this study: the one from 

Electricore/EDAG/GWU mentioned above, one from Lotus Engineering funded by 

California Air Resource Board for the second phase of the study, evaluating mass 

reduction levels around 35 percent of total vehicle mass, and two funded by EPA and the 

International Council on Clean Transportation (ICCT). This study will help to inform the 

agencies about the possible safety implications for light-weight vehicle designs and the 

appropriate counter-measures,386 if applicable, for these designs, as well as the feasible 

amounts of mass reduction. All of these analyses are expected to be finished and peerreviewed before July 2012, in time to inform the final rule. 

a. NHTSA workshop on vehicle mass, size and safety 

As stated above, in section C.2, on February 25, 2011, NHTSA hosted a workshop on 

mass reduction, vehicle size, and fleet safety at the Headquarters of the US Department of 

Transportation in Washington, DC. The purpose of the workshop was to provide the agencies 

with a broad understanding of current research in the field and provide stakeholders and the 

 386 Countermeasures could potentially involve improved front end structure, knee bags, seat ramps, buckle 

pretensioners, and others. 
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public with an opportunity to weigh in on this issue. The agencies also created a public docket 

to receive comments from interested parties that were unable to attend. The presentations were 

divided into two sessions that addressed the two expansive sets of issues. The first session 

explored statistical evidence of the roles of mass and size on safety, and is summarized in section 

C.2. The second session explored the engineering realities of structural crashworthiness, 

occupant injury and advanced vehicle design, and is summarized here. The speakers in the 

second session included Stephen Summers of NHTSA, Gregg Peterson of Lotus Engineering, 

Koichi Kamiji of Honda, John German of the International Council on Clean Transportation 

(ICCT), Scott Schmidt of the Alliance of Automobile Manufacturers, Guy Nusholtz of Chrysler, 

and Frank Field of the Massachusetts Institute of Technology. 

The second session explored what degree of weight reduction and occupant protection are 

feasible from technical, economic, and manufacturing perspectives. Field emphasized that 

technical feasibility alone does not constitute feasibility in the context of vehicle mass reduction. 

Sufficient material production capacity and viable manufacturing processes are essential to 

economic feasibility. Both Kamiji and German noted that both good materials and good designs 

will be necessary to reduce fatalities. For example, German cited the examples of hexagonally 

structured aluminum columns, such as used in the Honda Insight, that can improve crash 

absorption at lower mass, and of high-strength steel components that can both reduce weight and 

improve safety. Kamiji made the point that widespread mass reduction will reduce the kinetic 

energy of all crashes which should produce some beneficial effect. 

Summers described NHTSA’s plans for a model to estimate fleetwide safety effects 

based on an array of vehicle-to-vehicle computational crash simulations of current and 

anticipated vehicle designs. In particular, three computational models of lightweight vehicles are 

under development. They are based on current vehicles that have been modified to substantially 

reduce mass. The most ambitious was the “high development” derivative of a Toyota Venza 

developed by Lotus Engineering and discussed by Mr. Peterson. Its structure currently contains 

about 75% aluminum, 12% magnesium, 8% steel, and 5% advanced composites. Peterson 

expressed confidence that the design had the potential to meet federal safety standards. Nusholtz 

emphasized that computational crash simulations involving more advanced materials were less 

reliable than those involving traditional metals such as aluminum and steel. 

Nusholtz presented a revised data-based fleet safety model in which important vehicle 

parameters were modeled based on trends from current NCAP crash tests. For example, crash 

pulses and potential intrusion for a particular size vehicle were based on existing distributions. 

Average occupant deceleration was used to estimate injury risk. Through a range of simulations 

of modified vehicle fleets, he was able to estimate the net effects of various design strategies for 

lighter weight vehicles, such as various scaling approaches for vehicle stiffness or intrusion. The 

approaches were selected based on engineering requirements for modified vehicles. Transition 

from the current fleet was considered. He concluded that protocols resulting in safer transitions 

(e.g., removing more mass from heavier vehicles with appropriate stiffness scaling according to a 
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3/2 power law) were not generally consistent with those that provide the greatest reduction in 

GHG production. 

German discussed several important points on the future of mass reduction. Similar to 

Kahane’s discussion of the difficulties of isolating the impact of weight reduction, German stated 

that other important variables, such as vehicle design and compatibility factors, must be held 

constant in order for size or weight impacts to be quantified in statistical analyses. He presented 

results that, compared to driver, driving influences, and vehicle design influences, the safety 

impacts of size and weight are small and difficult to quantify. He noted that several scenarios, 

such as rollovers, greatly favored the occupants of smaller and lighter cars once a crash occurred. 

He pointed out that if size and design are maintained, lower weight should translate into a lower 

total crash force. He thought that advanced material designs have the potential to “decouple” the 

historical correlation between vehicle size and weight, and felt that effective design and driver 

attributes may start to dominate size and weight issues in future vehicle models. 

Other presenters noted industry’s perspective of the effect of incentivizing weight 

reduction. Field highlighted the complexity of institutional changes that may be necessitated by 

weight reduction, including redesign of material and component supply chains and 

manufacturing infrastructure. Schmidt described an industry perspective on the complicated 

decisions that must be made in the face of regulatory change, such as evaluating goals, gains, and 

timing. 

Field and Schmidt noted that the introduction of technical innovations is generally an 

innate development process involving both tactical and strategic considerations that balance 

desired vehicle attributes with economic and technical risk. In the absence of challenging 

regulatory requirements, a substantial technology change is often implemented in stages, starting 

with lower volume pilot production before a commitment is made to the infrastructure and 

supply chain modifications necessary for inclusion on a high-volume production model. Joining, 

damage characterization, durability, repair, and significant uncertainty in final component costs 

are also concerns. Thus, for example, the widespread implementation of high-volume composite 

or magnesium structures might be problematic in the short or medium term when compared to 

relatively transparent aluminum or high strength steel implementations. Regulatory changes will 

affect how these tradeoffs are made and these risks are managed. 

Koichi Kamiji presented data showing in increased use of high strength steel in their 

Honda product line to reduced vehicle mass and increase vehicle safety. He stated that mass 

reduction is clearly a benefit in 42% of all fatal crashes because absolute energy is reduced. He 

followed up with slides showing the application of certain optimized it designs can improve 

safety even when controlling for weight and size. 

A philosophical theme developed that explored the ethics of consciously allowing the 

total societal harm associated with mass reduction to approach the anticipated benefits of 
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enhanced safety technologies. Although some participants agreed that there may eventually be 

specific fatalities that would not have occurred without downsizing, many also agreed that safety 

strategies will have to be adapted to the reality created by consumer choices, and that “We will 

be ok if we let data on what works – not wishful thinking – guide our strategies.” 

5. How have the agencies estimated safety effects for the proposed standards? 

a. What was the agencies’ methodology for estimating safety effects for the 

proposed standards? 

As explained above, the agencies consider the 2011 statistical analysis of historical crash 

data by NHTSA to represent the best estimates of the potential relationship between mass 

reduction and fatality increases in the future fleet. This section discusses how the agencies used 

NHTSA’s 2011 analysis to calculate specific estimates of safety effects of the proposed 

standards, based on the analysis of how much mass reduction manufacturers might use to meet 

the proposed standards. 

Neither the proposed CAFE/GHG standards nor the agencies’ analysis mandates mass 

reduction, or mandates that mass reduction occur in any specific manner. However, mass 

reduction is one of the technology applications available to the manufacturers and a degree of 

mass reduction is used by both agencies’ models to determine the capabilities of manufacturers 

and to predict both cost and fuel consumption/emissions impacts of improved CAFE/GHG 

standards. We note that the amount of mass reduction selected for this rulemaking is based on 

our assumptions about how much is technologically feasible without compromising safety. 

While we are confident that manufacturers will build safe vehicles, we cannot predict with 

certainty that they will choose to reduce mass in exactly the ways or magnitudes that the 

agencies have analyzed in response to the standards. In the event that manufacturers ultimately 

choose to reduce mass and/or footprint in ways not analyzed or anticipated by the agencies, the 

safety effects of the rulemaking may likely differ from the agencies’ estimates. 

NHTSA utilized the 2011 Kahane study relationships between weight and safety, 

expressed as percent changes in fatalities per 100-pound weight reduction while holding 

footprint constant. However, as mentioned previously, there are several identifiable safety trends 

already occurring, or expected to occur in the foreseeable future, that are not accounted for in the 

study. For example, the two important new safety standards that were discussed above for 

electronic stability control and head curtain airbags, have already been issued and began phasing 

in after MY 2008. The recent shifts in market shares from pickups and SUVs to cars and CUVs 

may continue, or accelerate, if gasoline prices remain high, or rise further. The growth in vehicle 

miles travelled may continue to stagnate if the economy does not improve, or gasoline prices 

remain high. And improvements in driver (and passenger) behavior, such as higher safety belt 

use rates, may continue. All of these will tend to reduce the absolute number of fatalities in the 

future. The agency estimated the overall change in fatalities by calendar year after adjusting for 
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ESC, Side Impact Protection, and other Federal safety standards and behavioral changes 

projected through this time period. The smaller percent changes in risk from mass reduction 

(from the 2011 NHTSA analysis), coupled with the reduced number of baseline fatalities, results 

in smaller absolute increases in fatalities than those predicted in the 2010 rulemaking. 

NHTSA examined the impacts of identifiable safety trends over the lifetime of the 

vehicles produced in each model year. An estimate of these impacts was contained in a previous 

agency report.387 The impacts were estimated on a year-by-year basis, but could be examined in 

a combined fashion. Using this method, we estimate a 12.6 percent reduction in fatality levels 

between and 2020 for the combination of safety standards and behavioral changes anticipated 

(ESC, head-curtain air bags, and increased belt use). Since the same safety standards are taking 

effect in the same years, the estimates derived from applying NHTSA fatality percentages to a 

baseline of fatalities were thus multiplied by 0.874 to account for changes that NHTSA believes 

will take place in passenger car and light truck safety between the baseline on-road fleet used for 

this particular safety analysis and year 2025. 

To estimate the amount of mass reduction to apply in the rulemaking analysis, the 

agencies considered fleet safety effects for mass reduction. As previously discussed and shown 

in Table III-15, the Kahane 2011 study shows that applying mass reduction to CUVs and light 

duty trucks will generally decrease societal fatalities, while applying mass reduction to passenger 

cars will increase fatalities. The CAFE model uses coefficients from the Kahane study along 

with the mass reduction level applied to each vehicle model to project societal fatality effects in 

each model year. NHTSA used the CAFE model and conducted iterative modeling runs varying 

the maximum amount of mass reduction applied to each subclass in order to identify a 

combination that achieved a high level of overall fleet mass reduction while not adversely 

affecting overall fleet safety. These maximum levels of mass reduction for each subclass were 

then used in the CAFE model for the rulemaking analysis. The agencies believe that mass 

reduction of up to 20 percent is feasible on light trucks, CUVs and minivans, 388 but that less 

mass reduction should be implemented on other vehicle types to avoid increases in societal 

fatalities.. For this proposal, NHTSA used the mass reduction levels shown in Table III-14. 

 ls technologies, JOM Journal of the Minerals, Metals and Materials Society, Volume 59, Number 10, 21-32. 

Available at http://msl.mit.edu/pubs/docs/Field_KirchainCM_StratEvalMatls.pdf (last accessed Aug. 22, 2011). 387 Countermeasures could potentially involve improved front end structure, knee bags, seat ramps, buckle 

pretensioners, and others. 

387 Blincoe, L. and Shankar, U, “The Impact of Safety Standards and Behavioral Trends on Motor Vehicle Fatality 

Rates,” DOT HS 810 777, January 2007. See Table 4 comparing 2020 to 2007 (37,906/43,363 = 12.6% reduction 

(1-.126 = .874). Since 2008 was a recession year, it does not seem appropriate to use that as a baseline. We believe 

this same ratio should hold for this analysis which should compare 2025 to 2008. Thus, we are inclined to continue 

to use the same ratioration. 388 When applying mass reduction, NHSTA capped the maximum amount of mass reduction to 20 percent 

for any individual vehicle class. The 20 percent cap is the maximum amount of mass reduction the agencies believe 

to be feasible in MYs 2017-2025 time frame. 
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Table III-15 Mass Reduction Levels Applied in CAFE Model 

Absolute 

% 

Subcompact 

and 

Subcompact 

Perf. PC

Compact 

and 

Compact 

Perf. PC

Midsize PC 

and Midsize 

Perf. PC

Large PC 

and Large 

Perf. PC

Minivan LT

Small, 

Midsize and 

Large LT

MR1* 0.0% 2.0% 1.5% 1.5% 1.5% 1.5%

MR2 0.0% 0.0% 5.0% 7.5% 7.5% 7.5%

MR3 0.0% 0.0% 0.0% 10.0% 10.0% 10.0%

MR4 0.0% 0.0% 0.0% 0.0% 15.0% 15.0%

MR5 0.0% 0.0% 0.0% 0.0% 20.0% 20.0%

Notes: 

*MR1-MR5: different levels of mass reduction used in CAFE model 

After applying the mass reduction levels in the CAFE model, Table III-16 shows the 

results of NHTSA’s safety analysis separately for each model year.389 These 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 (indicated by parentheses) 

means that fatalities are projected to decrease. The results are significantly affected by the 

assumptions put into the Volpe model to take more weight out of the heavy LTVs, CUVs, and 

minivans than out of other vehicles. As the negative coefficients only appear for LTVs greater 

than 4,594 lbs., CUVs, and minivans, a statistically improvement in safety can only occur if 

more weight is taken out of these vehicles than passenger cars or smaller light trucks. Combining 

passenger car and light truck safety estimates for the proposed standards results in an increase in 

fatalities over the lifetime of the nine model years of MY 2017-2025 of 4 fatalities, broken up 

into an increase of 61 fatalities in passenger cars and 56 decrease in fatalities in light trucks. 

NHTSA also analyzed the results for different regulatory alternatives in Chapter IX of its PRIA; 

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 that the weight reduction applies to. 

Table III-16 NHTSA Calculated Mass-Safety-Related Fatality Impacts of the Proposed 

Standards over the Lifetime of the Vehicles Produced in each Model Year 

MY 

2017

MY 

2018

MY 

2019

MY 

2020

MY 

2021

MY 

2022

MY 

2023

MY 

2024

MY 

2025 Total

Passenger 

cars 

(2) (0) 11 16 15 13 12 1 (2) 63 

 389 NHTSA has changed the definitions of a passenger car and light truck for fuel economy purposes between the 

time of the Kahane 2003 analysis and this final rule. About 1.4 million 2 wheel drive SUVs have been redefined as 

passenger cars instead of light trucks. The Kahane 2011 analysis continues with the definitions used in the Kahane 

2003 analysis. Thus, there are different definitions between Tables IX-1 and IX-2 (which use the old definitions) 

and Table IX-3 (which uses the new definitions). 
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Light 

trucks 3 (7) 0 (13) (8) (15) (12) (8) 0 (56) 

Total 1 (8) 12 3 7 (2) 0 (8) (2) 4 

Using the same coefficients from the 2011 Kahane study, EPA used the OMEGA model 

to conduct a similar analysis. After applying these percentage increases to the estimated weight 

reductions per vehicle size by model year assumed in the Omega model, Table III-17 shows the 

results of EPA’s safety analysis separately for each model year. These 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 means that fatalities are projected to 

decrease. For details, see the EPA RIA Chapter 3. 

Table III-17 EPA Calculated Mass-Safety-Related Fatality Impacts of the Proposed 

Standards over the Lifetime of the Vehicles Produced in each Model Year 

MY 

2017

MY 

2018

MY 

2019

MY 

2020

MY 

2021

MY 

2022

MY 

2023

MY 

2024

MY 

2025 Total

Passenger 

cars -3 -5 -8 -11 -14 8 32 58 86 143 

Light 

trucks -1 -1 -2 -2 -4 -35 -67 -99 -133 -343 

Total -3 -7 -10 -13 -18 -27 -34 -41 -47 -201 

Why might the real-world effects be less than or greater than what the agencies have 

calculated? the ways in which future technological advances could potentially mitigate the 

safety effects estimated for this rulemaking: lightweight vehicles could be designed to be both 

stronger and not more aggressive; restraint systems could be improved to deal with higher crash 

pulses in lighter vehicles; crash avoidance technologies could reduce the number of overall 

crashes; roofs could be strengthened to improve safety in rollovers. As also stated above, 

however, while we are confident that manufacturers will strive to build safe vehicles, it will be 

difficult for both the agencies and the industry to know with certainty ahead of time how crash 

trends will change in the future fleet as lightweighted vehicles become more prevalent. We will 

have to continue to monitor the crash data going forward. 

Additionally, we note that the total amount of mass reduction used in the agencies’ 

analysis for this rulemaking were chosen based on our assumptions about how much is 

technologically feasible without compromising safety. Again, while we are confident that 

manufacturers are motivated to build safe vehicles, we cannot predict with certainty that they 

will choose to reduce mass in exactly the ways that the agencies have analyzed in response to the 

standards. In the event that manufacturers ultimately choose to reduce mass and/or footprint in 

ways not analyzed by the agencies, the safety effects of the rulemaking may likely differ from 

the agencies’ estimates. 
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The agencies acknowledge the proposal does not prohibit manufacturers from 

redesigning a vehicle to increase wheelbase and/or track width. However, as NHTSA explained 

in promulgating MY2008-2011 light truck CAFE standards and MY2011 passenger car and light 

truck CAFE standards, and as the agencies jointly explained in promulgating MY2012-2016 

CAFE and GHG standards, the agencies believes such engineering changes are significant 

enough to be unattractive as a measure to undertake solely to reduce compliance burdens. 

Similarly, the agencies acknowledge that a manufacturer could, without actually reengineering 

specific vehicles to increase footprint, shift production toward those that perform well compared 

to their respective footprint-based targets. However, NHTSA and, more recently NHTSA and 

EPA have previously explained, such production shifts would come at a cost to the manufacturer 

Based on this regulatory design, the analysis assumes this proposal will not have either of the 

effects described above. 

As discussed in Chapter 2 of the Draft Joint TSD, the agencies note that the standard is 

flat for vehicles smaller than 41 square feet and that downsizing in this category could help 

achieve overall compliance, if the vehicles are desirable to consumers. The agencies note that 

only xxxx percent of MY2008 passenger cars were below 41 square feet, and due to the overall 

lower level of utility of these vehicles, and the engineering challenges involved in ensuring that 

these vehicles meet all applicable federal motor vehicle safety standards (FMVSS), we expect a 

significant increase in this segment of the market in the future is unlikely. Please see Chapter 2 

of the Draft Joint TSD for additional discussion. 

We seek comment on the appropriateness of the overall analytic assumption that the 

attribute-based aspect of the proposed standards will have no effect on the overall distribution of 

vehicle footprints. Notwithstanding the agencies current judgment that such deliberate 

reengineering or production shift are unlikely as pure compliance strategies, both agencies are 

considering the potential future application of vehicle choice models, and anticipate that doing so 

could result in estimates that market shifts induced by changes in vehicle prices and fuel 

economy levels could lead to changes in fleet’s footprint distribution. However, neither agency 

is currently able to include vehicle choice modeling in our analysis. 

As discussed in Chapter 2 of the Draft Joint TSD, the agencies note that the standard is 

flat for vehicles smaller than 41 square feet and that downsizing in this category could help 

achieve overall compliance, if the vehicles are desirable to consumers. The agencies note that 

only xxxx percent of MY2008 passenger cars were below 41 square feet, and due to the overall 

lower level of utility of these vehicles, and the engineering challenges involved in ensuring that 

these vehicles meet all applicable federal motor vehicle safety standards (FMVSS), we expect a 

significant increase in this segment of the market in the future is unlikely. Please see Chapter 2 

of the Draft Joint TSD for additional discussion. 
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b. Do the agencies plan to make any changes in these estimates for the final rule? 

As discussed above, the agencies have based our estimates of safety effects due to the 

proposed standards on Kahane’s 2011 report. That report is currently undergoing peer review 

and is docketed for public review;390 the peer review comments and response to peer review 

comments, along with any revisions to the report in response to that review, will also be 

docketed there. Depending on the results of the peer review, our calculation of safety effects for 

the final rule will also be revised accordingly. The agencies will also consider any comments 

received on the proposed rule, and determine at that time whether and how our estimates should 

be changed in response to those comments. Additional studies published by the agencies or 

other independent researchers as previously discussed will also be considered, along with any 

other relevant information. 

 390 Kahane, C. J. (2011). “Relationships Between Fatality Risk, Mass, and Footprint in Model Year 2000-2007 

Passenger Cars and LTVs,”, July 2011. The report is available in the NHTSA docket, NHTSA-2010-0152. You can 

access the docket at http://www.regulations.gov/#!home by typing ‘NHTSA-2010-0152’ where it says “enter 

keyword or ID” and then clicking on “Search.”