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>, "''j ames.tamm@dot.gov' (james.tamm@dot.gov)'" <james.tamm@dot.gov>, "'rebecca.yoon@dot.gov'" <reb ecca.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 consideratio n 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.Woo d@dot.gov>; "'Neil.Eisner@dot.gov'" <Neil.Eisner@dot.gov>; "''james.tamm@dot.gov' (jame s.tamm@dot.gov)'" <james.tamm@dot.gov>; "'rebecca.yoon@dot.gov'" <rebecca.yoon@dot.gov>; Ro bin 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 consideratio n and are subject to further policy review.

Thanks. Chandana

Summary of working interagency comments received on draft rule under EO 12866 review.pdfSubject: 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>, "''j ames.tamm@dot.gov' (james.tamm@dot.gov)'" <james.tamm@dot.gov>, "'rebecca.yoon@dot.gov'" <reb ecca.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 consideratio n and are subject to further policy review.

Thanks. Chandana

Summary of working interagency comments received on draft rule under EO 12866 review.pdfSubject: Revised draft of EPA RIA Chapter 8 in response to r ecent 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 dra ft 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.Woo d@dot.gov>; "'Neil.Eisner@dot.gov'" <Neil.Eisner@dot.gov>; "''james.tamm@dot.gov' (jame s.tamm@dot.gov)'" <james.tamm@dot.gov>; "'rebecca.yoon@dot.gov'" <rebecca.yoon@dot.gov>; Ro bin 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 consideratio n and are subject to further policy review.

Thanks. Chandana

Summary of interagency working comments received on draft NPRM under EO 12866 reviewa.pdfSummary 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 bene fits of this rule that there is the possibility that car sales will decrease.

b. Interagency commenters also recommend th at 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 se tting the 1987-1989 passenger car standards, “NHTSA has always examined the safety consequences of the CAFE st andards 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 review. Subject to further policy review.

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. Histor ically, 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 CO 2 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 relations hip 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 fleet s, 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 ma ke important contributions to managing crash energy. Additionally, simply because footprint-based standards create no incentive to downsize
vehicles does not mean that ma nufacturers 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 cu rve, 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. A dditionally, 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 pri ce advantage. Because of these two reasons, the agencies believe that the incentive to in crease the sales of vehicles smaller than 41 sq uare feet due to this rulemaking, if any, is small. See Section II.C.1 above and Chapter 1 of the draf t 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 review. Subject to further policy review.

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 theassumption 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. Th e 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 ma de 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 compar tment 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 th e 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 review. Subject to further policy review.

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 standard s. 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 qu estion from several angles. First, we are using a statistical approach to study the effect of ve hicle 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 cons idered as the agencies’ best estimate of potential mass-safety effects. The agencies recognize that negative safety effects estimated based on the historical relationships could pot entially 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 ne w challenges these lighter vehicles might bring to vehicle safety and potential countermeasures available to manage those challenges effectively.

The sections below discuss more specifically th e 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, si ze 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 i ndependent 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 review. Subject to further policy review.

Page 5 of 33 First, NHTSA would contract wi th 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 histori cal data. This study will include a consideration of potential near multicollinearity in the historical da ta 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 fo r 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 vehi cles that are using material su bstitution and smart design, and to try to assess if there is sufficient crash data invo lving 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 vehicl es to vehicles of similar size and mass with more traditional designs.

Significant progress has been made on thes e tasks since the MY 2012-2016 final rule, as follows: The independent review of recent and upd ated statistical analyses of the relationship between vehicle mass, size, and crash fatality rates has been completed. NHTSA contracted with the University of Michigan Tran sportation Research Institute (U MTRI) 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 recen t CAFE rulemakings, including some vehicle manufacturers, suggested that the designs and mate rials of more recent model year vehicles may
have weakened the historical statistical rela tionships 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 be lieve, as UMTRI also found, that different statistical analyses may have had different resu lts because they each used slightly different

6 All three of the peer reviews are in docke t, 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.

Page 6 of 33 datasets for their analyses. In order to try to mitigate this pr oblem 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 n eed 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 awar e 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 tw o 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 examin e 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. Casual ty 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 ti me to inform this NPRM . The completed final peer reviewed-report on both assessments will be av ailable 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 co mments 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 review. Subject to further policy review.

Page 7 of 33 Finally, EPA and NHTSA with DOT’s Volpe Center, part of the Research and Innovative Technology Administration (RITA), attempted to in vestigate 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, selec ting analytical pairs of vehicles, and using theappropriate 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 in sufficient to produce consistent results that could be used to support conclusions regarding hi storical 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. De partment 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 an d 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 Tr ansportation 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), Sco tt Schmidt of th e 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 addresse d 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 review. Subject to further policy review.

Page 8 of 33 roles of mass and size on safety, and engineering r ealities – structural crashworthiness, occupant injury and advanced vehicle design.

The first session focused on previous and ongoi ng 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 pe r 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 usag e 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#VehicleSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

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 correlat ed with vehicle mass. Presenters had varying approaches for dealing with th e 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 “per haps the effect of we ight 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 subset s with similar footprint values) ma y 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 avoi d 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 presen ters noted the difficulties of projecting past
data into the future as new tec hnologies 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 50 th anniversary crash test of a 1959 Chevrolet Bel Air and 2009 Chevrolet Malibu graphically demo nstrated 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 cont emporary larger, heavier vehicles graphically showed the significance of vehicle mass and size.

Kahane presented results from his 2010 report 12 that found that a scenario which took
some mass out of heavier vehicles but little or no mass out of th e 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 ma y be more indicative of future crashes. There is apparent consistency with ot her presentations (e.g., Padmanab an, Nusholtz) th at 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.

Page 10 of 33 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, result ing in inflated variance with wrong signs and magnitudes in the results. Her re sults 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 review 13 of a set of statistical analyses of relationships between vehicle curb weight, the fo otprint 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 fa tality 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 conducte d by an outside third party without any interest in the reported outcome.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 11 of 33 questions considered in this study.

 In statistical analysis simpler models genera lly 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 report 14 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 comp licated than is necessary based on the data being analyzed, and summing regression coeffici ents 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. Includi ng 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 multicollin earity, other than to say th at 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 si ze. Kahane has comb ined 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 differen ces 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 w ith mass reduction whil e holding footprint constant, as reported by DRI.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 12 of 33 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 une quivocally 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 stabili ty 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 fa tality 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 anal ysis were that mass reduction in lighter cars, even while holding footprint constant, woul d significantly increase so cietal 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

.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 13 of 33 because it would reduce the fatality risk of occu pants 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 estimate s 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 relation ships 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 te chnologies, 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 proces sing 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 Ze llner, J. W. (2003). A Further Assessment of the Eff ects 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 1985-19 98 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/rulemak ing/pdf/MSS/MSSworks hop_VanAuken.pdfSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 14 of 33 databases available to the public 17 , 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 tr avel (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 Report 19 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 anal ysis 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 so me 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 pe riod 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. Th e annual target populatio n of fatalities or the annual fatality distribution baseline 21 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 Fatal ity 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 onl y 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 15 of 33 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 ESC 22

.
For the 2011 report, vehicles are now grouped into five classes rather than four: passenger cars (including both 2-door and 4-door car s) 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 mainta ining 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 16 of 33 It is interesting to compare the new result s to NHTSA’s 2010 analysis of MY 1991-1999 vehicles in CY 1995-2000, especially the new po int 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 b ecause 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 a nd 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 safe ty 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 compatibilit y-related self-protection improvem ents to cars, have made the heavier LTVs less aggressive in collisions with lighter vehicles (although the effect of massSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 17 of 33 disparity remains). This report’s analysis of CU Vs 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 th e 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 ve hicles of unequal mass collide, the delta V is higher in the lighter vehicle, in the same propor tion 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, redu ction 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 ha rmful and often beneficial, because the lighter vehicles respond more quickly to braking and st eering and are often more stable because their center of gravity is lower. Offsetting that benef it is the continuing historical tendency of lighter
and smaller vehicles to be driven less well – al though 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 somewh at 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 andSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 18 of 33 models have increased substantially in ma ss, 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 modelin g 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 an d 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 3,10 6 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 specificat ions 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 th e 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 fata lity 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%).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 19 of 33 The 1.44% increase in risk from reducing mass in the lighter cars was tested for sensitivity changes in the specification of, or th e data used in, the re gression 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 in cluding 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 i nduced 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 pe rcentage 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 st rongly correlated in the same regression model (referred to as multicollinearity) can lead to in accurate 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 consider ed 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 specificat ion, 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 wa s 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 heav er 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 20 of 33 Second, NHTSA conducted a stratification analysis of the effect of mass reduction on risk by dividing vehicles into deciles based on th eir 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 sim ilar footprint. The analysis indicates that mass reduction does not consistently increase risk acr oss 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, bu t 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 R 2 , 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 es timate 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 R 2 statistic in a linear regression model, does not exist. (SAS does generate a pseudo-R 2 value for logistic regression mo dels; 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 fa talities 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 pe r 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 R 2 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 increas es, 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 R 2 of 0.43, consistent with NHTSA’s basic regression results for the heavier LTVs (societal risk increases as mass increases). LBNL also examined the relationshi p 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 th e 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

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 fo otprint 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 wi th 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 7600Predicted US fatality risk (crash fdatalities per 1010VMT)

Curb weight (lbs) 2-dr cars
4-dr cars Small pickups Fullsize pickups
SUVs
CUVs
MinivansSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 22 of 33 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 importan t 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 attri bute-based standards will likely facilitate the design of vehicles such that manufacturers may reduce ma ss 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 st atistical 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 hist orical 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 pote ntial multicollinearity issues. The agencies will
continue to monitor issues with multicollinear ity in our analyses, and hope that outside researchers will do the same. And finally, base d 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 CA FE 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 ve hicle 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 recen t historical data shows a dampened effect in the relationship between mass reduction and safety is that all vehicles, including traditionallySummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

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 contin ued 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 cr ash 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 wh ich 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, manufactur ers will invest more and more resources in developing increasingly lightweight vehicle design s 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 co nsiderable 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 24 of 33 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 technol ogies – 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. A nother 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 Technolo gy (MIT) Materials Systems Laboratory’s research 26 to estimate the costs to OEMs for manufacturing parts. The cost will be broken

26 Frank Field, Randolph Kirchain and Richard Roth, Pr ocess cost modeling: Strategic engineering and economic evaluation of materials technologies, JOM Journal of th e Minerals, Metals and Materials Society, Volume 59, Number 10, 21-32. Available at http://msl.mit.edu/pubs/docs/Field_Kirch ainCM_StratEvalMatls.pdf (last accessed Aug. 22, 2011).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 25 of 33 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 processbetween 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 associat ed with it. NHTSA intends to have this study completed and peer reviewed before July 2012, in time for it to pl ay 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 feas ible 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 detaile d 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 Mode l Year Vehicle Program”. This study builds upon the low development (20% mass reduction) design in the 2010 Lotus Engineering study “An Assessment of Ma ss 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. Th e 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 wi ll provide an updated design with crash simulation, detailed costing and manufacturin g 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 Washin gton University (GWU) to build a fleet simulation model to study the impact and rela tionship 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 massSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

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 (I CCT). 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 feasibleamounts 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 engineer ing 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 Alli ance of Automobile Manufacturer s, Guy Nusholtz of Chrysler, and Frank Field of the Massachusetts Institute of Technology.

The second session explored what degree of we ight reduction and occupant protection are feasible from technical, economic, and manuf acturing perspectives. Field emphasized that technical feasibility alone does not constitute feasibility in the cont ext 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 mate rials and good designs will be necessary to reduce fatalities. For exampl e, 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 wi despread 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 27 of 33 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 estima te 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 wi th 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 isolatin g 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 influe nces, 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 we ight, and felt that effective design and driver attributes may start to dominate size and we ight 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 ma terial and component supply chains and manufacturing infrastructure. Schmidt described an industry perspective on the complicated decisions that must be made in the face of regul atory change, such as evaluating goals, gains, and timing.

Field and Schmidt noted that the introduction of technical innovations is generally an innate development process involv ing both tactical and strategic considerations that balance
desired vehicle attributes with economic and t echnical 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 andSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 28 of 33 supply chain modifications necessary for inclusi on 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 wide spread 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 implemen tations. Regulatory changes will affect how these tradeoffs are made and these risks are managed.

Koichi Kamiji presented data showing in in creased 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 applicat ion of certain optimized it designs can improve safety even when controlling for weight and size.

A philosophical theme developed that explor ed the ethics of consciously allowing the
total societal harm associated with mass reduc tion 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 real ity 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 estimat es 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 es timates 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 consumpt ion/emissions impacts of improved CAFE/GHG
standards. We note that the amount of mass reduction selected we 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 ultimatelySummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 29 of 33 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 airbag s, 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 th an 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 me thod, we estimate a 12.6 percen t 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 passenge r 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_St ratEvalMatls.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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 30 of 33 found. , the Kahane 2011 study shows that applying mass reduction to CUVs and light duty trucks will generally decrease societal fatalities, while applying mass re duction to passenger cars
will increase fatalities. The CAFE model uses co efficients 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 a void 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 numbe r means that fatalities are proj ected 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 th e 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. Abou t 1.4 million 2 wheel drive SUVs have been redefined as passenger cars instead of light trucks. The Kahane 2011 anal ysis 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 31 of 33 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 d ecreases in fatalities over the lifetime of the model year fleet. A positive numbe r means that fatalities are proj ected to increase; a negative number means that fatalities are projected to decr ease. 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 -201Summary 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 ru lemaking: lightweight vehicles could be designed to be both stronger and not more aggressive; rest raint 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 impr ove safety in rollovers. As also stated above, however, while we are confident that manufacturers w ill 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 light weighted vehicles become more prevalent. Going forward, W w e 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 vehicl e 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 ha ve 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 helpSummary 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 pr oposed standards will have no effe ct on the overall distribution of vehicle footprints. Notwithstanding the agen cies’ 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 repor t 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 di scussed will also be considered, along with any
other relevant information.

31 Kahane, C. J. (2011). “Relationships Between Fatali ty 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 Esta blish Light-Duty Vehicl e 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 agen cies incorporate the following language into Chapter 8 of the EPA RIA, and relevant secti ons of the Joint Preamble, NHTSA RIA and TSD chapters:

“For years, consumers have been learning a bout the benefits that accrue to them from owning and operating vehicles with greater fu el 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 purchas es 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 ru le, particularly given the new fuel economy labels that should reinforce that learning. Therefore, some increase in the demand for, and production of, more fuel effici ent 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 begi nning 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 e fficient 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 a nd across households. Most directly, the time and fuel savings associated with operating mo re fuel efficient vehicles will be more salient to individuals who ow n them, causing their subsequent purchase decisions to shift closer to minimizing the total cost of owners hip over the lifetime of the vehicle. Second, this appreciation may spread across househol ds through word of mouth and other forms of communications. Third, as more motorists va lue 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 in crease 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 i nduced learning takes plac e 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 c onsumer’s valuation of fuel efficiency in selecting vehicles.

The possibility that the rule will (after a lag for consumer learni ng) increase sales does not rest on the assumption that automobile ma nufacturers are failing to pursue profitable opportunities to supply the vehicles consumer s 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 profitab le to produce the more efficient vehicles mandated under the rule. In particular, no in dividual 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 e nhance 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 situa tion. In many industries companie s form trade associations to promote industry-wide public goods. For exam ple, 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 Esta blish Light-Duty Vehicl e 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 agen cies 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 accru e to them from owning and operating vehicles with greater fuel effici ency. 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. Th is 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 e ffects and should therefore reinforce that learning. Therefore, some increase in the dema nd for, and production of, more fuel efficient vehicles is incorporated in the [alternative] ba seline (i.e., without thes e rules) [developed by NHTSA]. The agency requests comment on the appr opriateness of using a fl at or rising baseline after 2016.

Today’s proposed rule, combined with the new and easier-to-unders tand fuel economy label required to be on all new vehicles beginning in 2012, may increase sales ab ove baseline levels by hastening this very type of c onsumer learning. As more consumers experience, as a result of the rule, the savings in time and expense from owni ng 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 to tal 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 effect s are strong, the rule could poten tially increase tota l vehicle sales over time. These increased sales would not occur in the model ye ars first affected by the rule, but they could occur once the induced learning ta kes 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 fu el 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 fo r consumer learning) increa se 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 to ward the more efficient vehicles mandated under the rule. In particular, no i ndividual company can fully intern alize 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 futu re, that company would capture only a fraction of the extra sales so generated, because the learni ng at issue is not specific to any one company's fleet. Many of the extra sales would accr ue to that company's competitors.

In the language of economics, consumer learning about the benefits of fuel efficient vehicles involves positive externalities (spi llovers) 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 associa tions 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 pos itive externalities may lead to benefits for manufacturers as a whole.

We emphasize that this discussion has been tentativ e and qualified. To be su re, social learning of related kinds has been identifie d in a number of contexts. [Foot note: See Hunt Alcott, Social Norms and Energy Conservation, Journal of Pub lic Economics (forthcoming 2011), available at http://web.mit.edu/allcott/ www/Allcott%202011%20JPubEc%20- %20Social%20Norms%20and%20Energy%20Conser vation.pdf; Christophe Chamley, Rational Herds: Economic Models of Social Learning (C ambridge, 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 ent ails comparing two competing effects. On the one hand, as a result of this rule, the vehi cles 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 cost s, which makes them more attractive to consumers. Which of these effects dominates for po tential 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 purch ase price. The empirical literature does not provide clear evidence on whether consumers ful ly consider the value of fuel savings at the time of purchase. It also generally does not s peak 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 commen t on the issues raised here and on methods for estimating the effect of this rule on vehicle s ales.

If consumers do not accurately compare the value of fuel savings with the increased cost of fuel economy technology in their vehicle pu rchase 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 veh icles and have provided the levels of improvement that consumers desire, then consumers s hould 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 c onsumer demand for fuel economy, or the rule motivates an increase in consumer deman d 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 increas ed sales if auto makers have missed some profit-making opportunities by not offering consume rs the actual future levels of fuel economy for which they would be willing to pay.

It is possible that previous greenhouse gas/fuel ec onomy rulemakings and the new fuel economy label, as well as this proposal, might incr ease 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 internali ze the future boost to demand resulting from consumer learning. If one company were to prov ide more information on the consumer benefits of improved vehicle efficiency, the compan y 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 unlikelyChapter 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 exper ience the savings in time and expense from owning more fuel efficient vehicles as a resul t of these rules, demand may shift yet further in the direction of the fuel economy/GHG im provements 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 o f 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 f uel efficiency of their vehicles. We cannot assess whether these improvements in fuel economy a re 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 e conomy in the absence of the rule. If so, the benefits and costs of that fuel economy would n ot be attributable to the rule, and both the benefits and costs estimated for this rule are over stated.

In addition to uncertainty over the expected offeri ngs of fuel economy in new vehicles in the absence of this rule, we note the uncertaint y 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 change s 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 deman d 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 fu el economy. We do not have data on the effect of the label on consumer vehicle purchas e decisions. If consumers put more emphasis on fuel economy in their purchase decision s, then automakers would be expected to respond to that interest independent of the standar ds 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 reason ing 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 occ urred otherwise, and introduce consumers to new vehicle attributes that impact veh icle demand in a positive way. Similarly, improvements in vehicle fuel economy might accompan y other changes in vehicle characteristics that are relevant for purchase deci sions. 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 ve hicle sales analyses by comparing the up-front costs of the vehicles with t he present value of five years’ worth of fuel savings. We assumed that the costs for the fuel-sa ving 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 cons umer costs: the increased sales tax that consumers would pay, the increase in insurance prem iums, 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 resu lted in an adjusted increase in costs to consumers. We then assumed that consumers consider ed the present value of five years of fuel savings in their vehicle purchase, which is co nsistent with the length of a typical new light-duty vehicle loan, and is similar to the aver age 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 qua ntity 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 savin gs outweighed the adjusted technology costs, then vehicle sales were predicted to increase; if t he fuel savings were smaller than the adjusted technology costs, sales would decrease, co mpared to a world without the standards.

We do not here present a vehicle sales analysis usi ng 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 mark et 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 co nditions – might change. Instead, we present a payback period analysis to estimate the n umber 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 buy ers. 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 s ales would be expected to increase as a result of the rule. On the other hand, if auto mak ers 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 tha n the break-even number of years in their buying decision s, 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 overal l sales in any given year

.

A In this proposal, the 5-year payback assumption co rresponds to an assumption that vehicle buyers take into account between 30 and 50 percent of the present va lue of lifetime vehicle fuel savings (with the vari ation 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 chan ges 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 tha t 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 pa yback 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 case s, the payback periods are less than the 5- year payback period that EPA and NHTSA have previou sly used in the vehicle sales analyses. Given the relatively short time frames for the payb ack periods, these results suggest that vehicle sales could increase as a result of improve d fuel economy. Alternatively, if consumers have shorter time frames for payback peri ods, then the rule may have a negative impact on vehicle sales. In either situation we wo uld 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 v ehicle purchase decisions.

Table 1 Estimated Payback Period for Model Years 20 21 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 discussi on, 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 so urces of uncertainty in estimating vehicle sales impacts. We welcome comments on the methodol ogy 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 assum ptions 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 t hat these costs are fully passed along to consumers. This assumption is appropriate for c ost increases in perfectly competitive markets. In less than perfectly competitive market s, 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 cos t increase for other effects on the consumer. The higher vehicle price is likely to le ad to an increase in sales tax, insurance, and vehicle financing costs.

The increase in insurance costs is estimated from t he 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 Institute 2 provides the average value of collision plus compr ehensive 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 p lus collision insurance as 1.85 percent of the price of a vehicle. If this same proportion ho lds 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 t he 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 start s 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 rat e for a 5 year new car loan was 5.51 percent. 6 We use this loan rate to calculate the increase i n vehicle costs due to financing a loan.

8.1.2 Consumer Vehicle Choice Modeling C

An alternative to the vehicle sales analysis discus sed 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 d escribe 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 c onsumer vehicle choice models depend critically on this value, we would consider any con sumer surplus estimates of the effect of our rule from such models to be unreliable. In additio n, 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 thes e 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 Respo nse to Fuel Economy: A Review of the Literature.” Int ernational 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 characteri stics – in particular, the fuel economy -- of the vehicles they produce. These cha nges will affect the cost of manufacturing the vehicle; as a result, the prices of the vehicle s 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 the ir 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 respon se to the changes in vehicle characteristics, consumers will reconsider their purchases. Increas es 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 purch ase. 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 in fluence consumers on multiple market scales: the total number of new vehicles sold; the mix of n ew 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 characteri stics 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 mi x 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, repre sented by the demand curve, and the amount they actually pay. For instance, if a consu mer were willing to pay $30,000 for a new vehicle, but ended up paying $25,000, the $5000 dif ference is consumer surplus.)

A number of consumer vehicle choice models have bee n developed. They vary in the methods used, the data sources, the factors include d in the models, the research questions they are designed to answer, and the results of the mode ls related to the effects of fuel economy on consumer decisions. This section will give some ba ckground 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 ex plain 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 va lue. 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) for2017 Regulatory Impact Analysis

8-7 each vehicle. Because the market share is, essenti ally, the probability that consumers will purchase a specific vehicle, these approaches are s imilar in process; they differ mostly in the kinds of data that they use.

The primary methods used to model vehicle choices a re nested logit and mixed logit. D In a nested logit, the model is structured in layer s. For instance, the first layer may be the choice of whether to buy a new or used vehicle. Gi ven that the person chooses a new vehicle, the second layer may be whether to buy a car or a t ruck. 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 consu mers 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 o f the effects of the distribution of consumer characteristics. As a result, mixed logit can be u sed 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 pr imary method for consumer choice modeling, others (such as Kleit 13 and Austin and Dinan 14 ) 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 Din an 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 a n increase in a person’s income of 10% would increase the probability of her purchasing ve hicle 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 othe r studies (calibrated models).

Estimated models use datasets on consumer purchase patterns, consumer characteristics, and vehicle characteristics to dev elop 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, t hey provide what is sometimes called micro-level data. Other studies, that estimate mar ket 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 cho ices (i.e., yes/no decisions or the choice among a countable nu mber of options).Chapter 8

8-8

Calibrated models rely on existing studies for thei r parameters. Researchers may draw on results from a number of estimated models, or ev en from research other than CCM, to choose the parameters of the models. The Fuel Econ omy Regulatory Analysis Model developed for the Energy Information Administration 17 and the New Vehicle Market Model developed by NERA Economic Consulting 18 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 an d 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 distribut ion of income in the population influencing choice; 23 others include varying numbers and kinds of vehicl e and consumer attributes.

Some models include only the consumer side of the v ehicle 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 pre ferences. Including producer behavior allows for vehicle characteristics such as price an d 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 re venue 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 vehic les sold. 26 Including auto maker response is a complex exercise. Auto makers are commonly co nsidered to have market power; they can influence the prices that consumers pay to incr ease 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 detai l, due to computational limits: it is unusual for models to allow both buyers and produce rs 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 analy ze 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 mult iple 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, researc h questions cover a wide gamut. These topics have included the effects of voluntary expor t restraints on Japanese vehicles compared to tariffs and quotas, 30 the market acceptability of alternative-fuel vehic les, 31 the effects of introduction and exit of vehicles from markets, 32 causes of the decline in market shares of2017 Regulatory Impact Analysis

8-9 U.S. automakers, 33 and the effects of gasoline taxes 34 and “feebates” 35 (subsidizing fuelefficient cars with revenue collected by taxing fue l-inefficient vehicles).

8.1.2.5 The Effect of Fuel Economy on Consumer Decisions

Consumer vehicle choice models typically consider t he effect of fuel economy on vehicle purchase decisions. It can appear in vario us forms.

Some models 36 incorporate fuel economy through its effects on th e cost of owning a vehicle. With assumptions on the number of miles t raveled 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 con sidered to reduce the cost of owning a vehicle: effectively, they reduce the purchase pri ce. This approach relies on the assumption that, when purchasing vehicles, consumers can estim ate the fuel savings that they expect to receive from a more fuel-efficient vehicle and cons ider the savings equivalent to a reduction in purchase price. Turrentine and Kurani 37 question this assumption; they find, in fact, that consumers do not make this calculation when they pu rchase a vehicle. The question remains, then, how or whether consumers take fuel economy in to account when they purchase their vehicles.

Most estimated consumer choice models, instead of m aking assumptions about how consumers incorporate fuel economy into their decis ions, use data on consumer behavior to identify that effect. In some models, miles per ga llon is one of the vehicle characteristics included to explain purchase decisions. Other mode ls use fuel consumption per mile, the inverse of miles per gallon, as a measure: 38 since consumers pay for gallons of fuel, then thi s 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 in crease in fuel economy as they do to a decrease in the price of fuel when each has the sam e 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 resear ch 42 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 u sing consumer vehicle choice models and estimated for each one ho w 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 decre ases are large shocks rather than small, gradual ch anges. The issue of potential asymmetric consumer response to increased fuel efficiency compared to other types o f changes to the cost of driving also arises and is discussed in the context of the VMT rebound effect (see Sect ion 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 r ange: 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. W hile this study is quite old, it suggests that, at least as of that time, consumer vehicle choice m odels produced widely varying estimates of the value of reduced vehicle operating costs.

A newer literature review from David Greene 44 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 expecte d value of fuel savings varied from highly positive to highly negative. Significant numbers o f 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 o ther 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 sa me time. Indeed, Gramlich 45 includes both fuel cost (dollars per mile) and miles per gallon i n his analysis, with the argument that miles per gallon measures other undesirable quality attri butes, while fuel cost picks up the consumer’s demand for improved fuel economy. Green e 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 res ult, further work needs to be conducted before it is possible to identify the role of fuel economy in consumer purchase decisions.

Some studies 46 argue that automakers could increase profits by in creasing fuel economy because the amount that consumers are willi ng to pay for increased fuel economy outweighs the costs of that improvement. Other stu dies 47 have found that increasing fuel economy standards imposes welfare losses on consume rs and producers, because consumers should already be buying as much fuel economy as th ey want. In the course of reaching this result, though, at least one of these studies 48 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-cos t” fuel economy improvements.

The models do not appear to yield very consistent r esults 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 – a t 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. Poten tial explanations are bounded by two scenarios. On the one hand, purely private benefit s of fuel economy (fuel savings, time savings, increases in driving time) must be accompa nied by private losses of the same magnitude. However, if there is no such private los s, or if it is small or insignificant, then there is a market or behavioral failure.2017 Regulatory Impact Analysis

8-11 On the consumer side, this disconnect between net p resent 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 co nsumer 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 th e most credible explanation. Possibilities include:

• Consumers put little weight on benefits from fuel e conomy in the future and show high discount rates;

• Consumers do not find the benefits from fuel econom y to be sufficiently salient at the time of purchase, even if it would be in consum ers’ 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 f uel savings;

• Consumers may use imprecise rules of thumb when dec iding 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 i mmediate;

• 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 un derlying 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 drive n and driving styles.

The producer side of this paradox is much less stud ied. We don’t know how producers incorporate information from consumers on their apparent willingness to pay for fuel economy when making production decisions. The re is a general expectation that producers maximize profits; some but not all possib le explanations are consistent with this expectation. Hypotheses for under provision of fue l economy by producers include:Chapter 8

8-12

• Producers put more effort into attributes that cons umers have regularly sought in the past, such as size and power, than into fuel an d 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 relate d to characteristics (such as numbers of doors or transmission types) that strong ly influence what vehicle a consumer will buy, and fuel economy and time saving s may not make that list;

• While consumer preferences for fuel economy may cha nge rapidly as fuel prices fluctuate, producers cannot change their design or production decisions as rapidly; as a result, vehicle designs may end up not satisfy ing consumer desires at a particular time;

• Producers may have underestimated the value that co nsumers place on fuel

economy.

How consumers buy, and producers provide, fuel econ omy 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 f rom fuel economy improvements. In addition, consumers trade off fuel economy with man y 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 do es not have data on how many of the new vehicles consumers buy. Nevertheless, it can be po ssible 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 a bove, 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 b e modeled by applying the parameters for those features to the new vehicle characteristics.

As discussed above, some models rely on vehicle pri ce 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 vehicl e. For instance, if the change is to create an electric vehicle (EV) version of an existing mod el, 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 ve hicle driving range and cost of fueling may be two such attributes. The next requirement is in formation 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 a n estimate of the benefit or cost associated2017 Regulatory Impact Analysis

8-13 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) fr om vehicle purchase price.

Incorporating new vehicles into a vehicle choice mo del, then, requires estimates of the changes in key attributes from conventional vehicle s, 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 chang es in vehicle characteristics from any baseline model. EVs are likely to have a small er driving range between refuelings than conventional vehicles, due to the large battery cap acity 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 th e street. If an infrastructure develops for recharging vehicles with the convenience approachin g 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 co nsumers. F They may have different performance or storage capacity. If sufficient dat a were available, the changes in these attributes, combined with WTP for each of the attri butes, 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 becaus e 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 m ajor factor in EV acceptability, it is starting to draw attention in the research communit y.

In several studies, researchers have used stated pr eference conjoint analysis to estimate the effect of vehicle range on consumer ve hicle choice. In a conjoint analysis, consumers are given a choice between several vehicl es 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 mor e range?) provide data that can be used to estimate the role of vehicle attributes in the cons umer’s choice. Stated preference analysis is sometimes considered less reliable than actual mark et behavior, because what people say they will do in hypothetical situations may not match wh at they would do in actual situations. On the other hand, stated preference methods can be us ed to study goods where market data do

F For instance, Hidrue et al. (Hidrue, Michael K., G eorge R. Parsons, Willett Kempton, and Meryl P. Gar dner. “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 pa y $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 undergoin g development (marketing studies often use stated preference methods), or environmental go ods. Because electric vehicles are not in widespread enough use for market studies, stated pr eference studies are, at this point, one of the few options to examine consumer behavior relati ng to these vehicles.

Table 8.1-2 summarizes results from several conjoint studies t hat include the effects of extending range (in the table, from 150 to 300 mile s, 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 chan ging (perhaps shrinking) in more recent studies.

Table 8.1- 2 Willingness 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 Commission c $2600 - $41,900 (1993$) $3700 - $58,700

Resource Systems Group (2009) for California Energy Commission d $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

a Values adjusted to 2009$ using the Bureau of Econom ic Analysis GDP deflator.

b Bunch, 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.

c Kavelek, Chris. “CALCARS: The California Conventi onal and Alternative Fuel Response Simulator.” Demand Analysis Office, California Energy Commissio n, April 1996. The variation in values is due to willingness to pay (WTP) varying by income levels a nd for one-car and two-car households. The coeffic ient 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 min ima and maxima presented here represent the values across both ownership and income categories.

d Resource Systems Groups, Inc. “Transportation Fuel Demand Forecast Household and Commercial Fleet Survey Task 8 Report: Logistic Regression Analysis and Results.” Prepared for California Energy Comm ission, June 2009. Deleted: Table 8.1-3

Deleted: 42017 Regulatory Impact Analysis

8-15 e Hess, S., T. Adler, M. Fowler and A. Bahreinian “Th e Use of Cross-nested Logit Models for Multi-Dimens ional Choice Processes: The Case of the Demand for Altern ative Fuel Vehicles,” Proceedings of the 2009 European Transport Conference , Leiden, Netherlands, 2009. This study uses the s ame data as the Resource Systems Group study. The coefficient on range was not stat istically significantly different from zero in thes e regressions: t-statistics varied from 1.29 to 1.52. The variati on in values is due to willingness to pay (WTP) var ying by income levels and statistical specification. The m inima and maxima presented here represent the value s across both income categories and specifications.

f Hidrue, Michael K., George R. Parsons, Willett Kemp ton, 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 int o “gasoline vehicle-oriented” and “electric vehicle -oriented” groups. The EV-oriented group has higher WTP for a dditional range.

Driving range may be a major factor in consumers’ d ecisions on EVs, but it is not the only attribute that may be important to potential b uyers (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 importa nt 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 o f 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 understa nding of the vehicles. Thus, challenges associated with predicting market shares for EVs ar e even more serious than those already serious challenges associated with predicting marke t 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, exce pt vehicle prices and fuel economy, stay the same. The model will predict changes in the vehicl e 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 c ommon 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 it s 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 passenge r vehicles, cargo vehicles, and ultraluxury vehicles. The next set of choices subdivide s each of these categories into vehicle type (e.g., standard car, minivan, SUV, etc.). Next, th e 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 ac ross nests, because the vehicles within a nest are more similar to each other than vehicles i n 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 8.1-3Chapter 8

8-16

and a pickup truck. In addition, substitution is g reater 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 elastici ties 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 th eoretical requirements, G assist in assigning values for the parameters. The model wil l 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 ec onomy and incorporated into the “effective price” of the vehicle. In practice, implementing t his calculation involves calculating the change in expenditures on fuel based on schedules o f VMT, vehicle survival, and fuel prices in the future consistent with those in OMEGA. As d iscussed in Chapter 8.1.2.5, there is no consensus value for consumers’ willingness to pay f or improved fuel economy: estimates vary tremendously. The model assumes that consumer s will use some years of discounted fuel savings, with the modeler able to input both t he number of years and the discount rate to be used in the analysis.

The vehicle choice model will take as inputs an ini tial fleet of vehicles (including the initial sales and fuel economy) in the absence of s tandards, the cost of technologies added to each vehicle to comply with standards, and the chan ge in fuel economy. With the initial sales mix, for each vehicle, the model calculates a vehic le-specific constant that summarizes the value of all attributes of the vehicle other than p rice and fuel economy. This constant ensures that the model will predict changes in consumer res ponse that would result only from changes in price and fuel economy. This constant substitut es for estimating the effects of changes in all other vehicle characteristics; the underlying a ssumption 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 i n vehicle price, and some years of fuel savings offset this price increase. It then calcul ates changes in total fleet size and in sales mix, at the individual-configuration level and at t he level of vehicle class, due to the changes in fuel economy and vehicle prices. It also calcul ates 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 pric e 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 th an at higher

ones. H As explained in Section III.D of the preamble, as part of the technology cost analysis for the propos ed rule, the agencies have estimated the cost of maintaining all vehicle utility, with minor exceptions.2017 Regulatory Impact Analysis

8-17 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 calcula tes costs and compliance) and get a new set of output, which could then be fed back int o the vehicle choice model. OMEGA would increase technologies, and thus costs, to imp rove compliance; those adjustments would then again affect vehicle demand. We expect that t his iterative process would converge to a fleet mix that would meet standards. Performing th is iteration requires development of an interface between the vehicle choice model and OMEG A 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 vehic le 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 method s 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 p rovide a great deal of useful information for regulatory analysis, helping to ans wer 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 pot ential to describe more accurately the impact of a policy, by identifying market shift s. 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 o n 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 va lues 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 fo r characteristics associated with advanced technology vehicles, such as EVs, the willingness-t o-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 complianc e costs of a rule. Consumers can either accept the new costs and vehicle charact eristics, or they can change which vehicles they buy. Using a vehicle choice model is likely t o reduce compliance costs: because the model allows consumers to choose among accepting th e new vehicle, buying a differentChapter 8

8-18

vehicle, or not buying a vehicle, consumers have ad ditional options, which improves their welfare relative to the assumption that consumers w ill not change their buying behavior. .

An additional complication associated with consumer choice modeling is accurate prediction of producers’ responses to the rule. Wh ile it is possible to include auto makers’ decisions (for instance, on setting prices) into ve hicle 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 u nderestimate the effect on the prices that consumers face.

Consumer choice models can be used to calculate con sumer surplus impacts on vehicle purchase decisions. Because these values a re based on the estimates of changes in vehicle sales and fleet mix, consumer surplus measu res 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 un derstanding 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 b uyers. With these assumptions, because welfare losses are monetary estimates of how much b uyers 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 passe d along to the buyer as an increase in price, the technology cost thus measure s 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 t he technology costs.

Given the current limitations in modeling the role of fuel economy in vehicle purchase decisions, and limitations in modeling market respo nses 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 vehicl e choice models, the estimates of

I This approach describes the economic concept of co mpensating variation, a payment of money after a ch ange that would make a consumer as well off after the ch ange as before it. A related concept, equivalent v ariation, estimates the income change that would be an altern ative to the change taking place. The difference b etween them is whether the consumer’s point of reference i s her welfare before the change (compensating varia tion) or after the change (equivalent variation). In practi ce, these two measures are typically very close tog ether.

J Indeed, it is likely to be an overestimate of the loss to the buyer, because the buyer has choices ot her than buying the same vehicle with a higher price; she co uld choose a different vehicle, or decide not to bu y a new vehicle. The buyer would choose one of those optio ns 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 co nsumer welfare, unless there are other changes to the vehi cle 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 ec onomy, 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 j ob creation in the United States.” 53,K The recently issued Executive Order 13563, “Improving R egulation and Regulatory Review” (January 18, 2011), states, “Our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, compe titiveness, and job creation ” (emphasis added). EPA is accordingly providing p artial estimates of the effects of this proposal on domestic employment in the auto ma nufacturing and parts sectors, while qualitatively discussing how it may affect employme nt in other sectors more generally.

This proposal is expected to affect employment in t he United States through the regulated sector – the auto manufacturing industry – and through several related sectors, specifically, industries that supply the auto manuf acturing 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 Aut omobile and Light Truck Manufacturing Sector (NAICS 33611) in December 2010; this is the directly regulated sector, since it encompasses the auto manufacturers that are respons ible 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 effect s of this rule are expected to expand beyond the regulated sector. Though some of the pa rts used to achieve the proposed standards are likely to be built by auto manufactur ers 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 vehicle s 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 exp enditures on fuel net of vehicle costs, consumers then will have additional discretionary i ncome that can be spent on other goods and services.

K The May 21, 2010 Presidential Memorandum also requ ested that EPA and NHTSA, in developing the technical assessment to inform the rulemaking proce ss (which was issued by the agencies and CARB on September 30, 2010), include, among other things, t he “impacts on jobs and the automotive manufacturin g base in the United States.”Chapter 8

8-20

When the economy is at full employment, an environm ental regulation is unlikely to have much impact on net overall U.S. employment; in stead, labor would primarily be shifted from one sector to another. These shifts in employ ment 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 wo rkers may need to be retrained or require time to search for new jobs, while shortages in som e sectors or regions could bid up wages to attract workers).

On the other hand, if a regulation comes into effec t during a period of high unemployment, a change in labor demand due to regul ation may affect net overall U.S. employment because the labor market is not in equil ibrium. Schmalansee and Stavins point out that net positive employment effects are possib le in the near term when the economy is at less than full employment due to the potential hiri ng of idle labor resources by the regulated sector to meet new requirements (e.g., to install n ew equipment) and new economic activity in sectors related to the regulated sector. 55 In the longer run, the net effect on employment i s more difficult to predict and will depend on the wa y in which the related industries respond to the regulatory requirements. As Schmalansee and St avins note, it is possible that the magnitude of the effect on employment could vary ov er time, region, and sector, and positive effects on employment in some regions or sectors co uld be offset by negative effects in other regions or sectors. For this reason, they urge cau tion in reporting partial employment effects since it can “paint an inaccurate picture of net em ployment impacts if not placed in the broader economic context.”

Given the time frame of this proposed rule, the sta te of the overall economy may have improved to the point where the only effect of chan ges in employment associated with the standards proposed here will be to shift workers fr om 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 econom y. In this situation, any effects on net employment are likely to be transitory as workers c hange jobs (e.g., some workers may need to be retrained or require time to search for new j obs, 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 o f 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, assumptio ns about the overall state of unemployment in the economy play a major role in me asured job impacts. The inputs to the models commonly are the changes in quantities or ex penditures 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. W hich sectors are included in the study can2017 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 ef fects, 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 redesigne d 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 Shih 56 provides a retrospective look at the impacts of regulation in employment in the regulate d sectors. The paper provides a theoretical framework that can be useful for examin ing 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 regul ated industry in response to a new (or more stringent) regulation.

• Demand effect : higher production costs due to the regulation wi ll 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 co nsumers as well as the demand elasticity of industry output.”

• Cost effect : as costs go up, plants add more capital and labo r (holding other factors constant), with potentially positive effect s on employment. In the authors’ words, as “production costs rise, more inputs, incl uding labor, are used to produce the same amount of output.”

• Factor shift effect : post-regulation production technologies may be m ore or less labor-intensive (i.e., more/less labor is required per dollar of output). In the authors’ words, “environmental activities may be mo re labor intensive than conventional production,” meaning that “the amount of labor per dollar of output will rise,” though it is also possible that “cleane r operations could involve automation and less employment, for example.”

According to the authors, the “demand effect” is ex pected to have a negative effect on employment, L

the “cost effect” to have a positive effect on emp loyment, 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 le vels in a regulated sector.

L As will be discussed below, the demand effect in t his proposal is potentially an exception to this ru le. While the vehicles become more expensive, they also produ ce reduced fuel expenditures; the reduced fuel cost s provide a countervailing impact on vehicle sales. As discu ssed in Preamble Section III.H.1, this possibility that vehicles may become more attractive to consumers after the p rogram poses a conundrum: why have interactions be tween vehicle buyers and producers not provided these ben efits without government intervention?Chapter 8

8-22

Morgenstern et al. estimated the effects on employm ent 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 be tween 1979 and 1991. The authors conclude that increased abatement expenditures gene rally have not caused a significant change in employment in those sectors. More specif ically, their results show that, on average across the industries studied, each additional $1 m illion spent on pollution abatement results in a (statistically insignificant) net increase of 1.5 jobs.

This approach to employment analysis has the advant age of carefully controlling for many possibly confounding effects in order to separ ate the effect of changes in regulatory costs on employment. It was, however, conducted fo r only four sectors. It could also be very difficult to update the study for other sectors, be cause 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 i ndustrial facilities that were considered in that study. In addition, it does not examine the e ffects of regulation on employment in sectors related to but outside of the regulated sector. Ne vertheless, the theory that Morgenstern et al. developed continues to be useful in this context.

The following discussion of additional methodologie s 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 oft en used to assess the impacts of policy. These models include a stylized representa tion 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 m arkets: “they allow for substitution among inputs in production and goods in consumption.” Th us, if one market experiences a change, such as a new regulation, then the effects can be o bserved in all other markets. As a result, they can measure the employment changes in the econ omy 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 model s 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 assumpti ons of equilibrium in all markets and forward-looking consumers and firms, they are desig ned for examining the long-run effects of a policy but may offer little insight into its shor t-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 cons uming sectors. In that sense, like CGE models, they describe the interconnections of the e conomy. These interconnections look at how changes in one sector ripple through the rest o f the economy. For instance, a requirement for additional technology for vehicles requires additional steel, which requires2017 Regulatory Impact Analysis

8-23 more workers in both the auto and steel sectors; th e additional workers in those sectors then have more money to spend, which leads to more emplo yment in retail sectors. These are known as “multiplier” effects, because an initial i mpact in one sector gets multiplied through the economy. Unlike CGE models, input-output model s 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 part icular, these models do not allow for price changes – an increase in the demand for labor or ca pital 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 ove r another. The multipliers take an initial impact and can increase it substantially.

IO models are commonly used for regional analysis o f projects. In a regional analysis, the markets are commonly considered small enough th at 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 nat ional-level employment analysis, the use of input-output models requires the assumption that wo rkers flow into or out of the labor market perfectly freely. Wages do not adjust; instead, pe ople 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 res ult, IO models provide an upper bound on employment impacts. As Berck and Hoffmann note, “F or 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 prod uction processes, introductions of new technologies, or learning over time due to new regu latory requirements are also generally not captured by IO models, as they are calibrated to al ready 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 s eparately discussed by Berck and Hoffmann, some hybrid models combine some of the fe atures 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, su ch as labor being at less than full employment. Hybrid models depend on assumptions ab out 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 d epends on their success in overcoming the limitations of each of these approaches. Hybrid mo dels could potentially be used to model labor market impacts of various vehicle policy opti ons although a number of judgments need to be made about the appropriate assumptions underl ying 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 t he partial effect of regulation on employment in a single sector by estimating the cha nge 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 proportio nal to output or expenditures. As Berck and Hoffmann note, though, “Compliance with regulat ions may create additional jobs that are not accounted for.” While such an analysis can app roximate 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 th e 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 lo nger-run employment effects. 58 While often a sophisticated treatment of the issues analyzed, t hese 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 r egulation have advantages and limitations. CGE models may be most appropriate fo r 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 appropria te for small-scale, short-term effects, because they assume fixed relationships across sect ors 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 bui lt into them. Single-sector models are simple and straightforward, but they are often base d 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 commo nly 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 aff ect employment in the regulated sector (auto manufacturing) and other sectors direc tly affected by the proposal: auto parts suppliers, auto dealers, the fuel supply market (wh ich will face reduced petroleum production due to reduced fuel demand but which may see additi onal 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 e ffects depend much more heavily on the state of the macroeconomy than do the direct effect s. 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, bot tom-up quantitative estimate of the effects of this proposal on the regulated sector (t he 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 qual itative discussion of the effects of the proposal on other sectors. Focusing quantification of emplo yment impacts on the regulated sector has some advantages over quantifying all impacts. Firs t, the analysis relies on data generated as part of the rulemaking process, which focuses on th e regulated sector; as a result, what is presented here is based on internally consistent as sumptions and estimates made in this proposal. Secondly, as discussed above, net effect s on employment in the economy as a whole depend heavily on the overall state of the ec onomy when this rule has its effects. Focusing on the regulated sector provides insight i nto 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 em ployment effects on other sectors to provide a broader perspective on the impacts of thi s rule.

As noted above, in a full-employment economy, any c hanges in employment will result from people changing jobs or voluntarily ent ering or exiting the workforce. In a fullemployment economy, employment impacts of this prop osal will change employment in specific sectors, but it will have small, if any, e ffect 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 a nalysis does not include multiplier effects, but instead focuses on employment impacts in the mo st directly affected industries. Those sectors are likely to face the most concentrated em ployment impacts. The agencies seek comment on other sectors that are likely to be sign ificantly 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 framewo rk 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 shif t effect. However, we are only able to offer quantitative estimates for the cost effect. We not e that these estimates, based on extrapolations from current data, become more uncer tain as time goes on.

8.2.3.1.1 The Demand Effect

The demand effect depends on the effects of this pr oposal on vehicle sales. If vehicle sales increase, then more people will be required t o assemble vehicles and their components. If vehicle sales decrease, employment associated wi th these activities will unambiguously decrease. Unlike in Morgenstern et al.’s study, wh ere the demand effect unambiguously decreased employment, there are countervailing effe cts 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 domina tes. 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 employ ment changes due to new vehicle sales only. The cost effect measures emplo yment impacts due to the new or additional technologies needed for vehicles to comp ly 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 mil lion 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 ins tance, it is possible to estimate the average number of workers in the light-duty vehicle manufac turing sector per $1 million spent in the sector, rather than use the ratio from another, mor e 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 estima tes are averages for the sectors, covering all the activities in those sectors; they may not be re presentative of the labor required when expenditures are required on specific activities, a s the factor shift effect (discussed below) indicates. For instance, the ratio for the motor v ehicle manufacturing sector represents the ratio for all vehicle manufacturing, not just for f uel efficiency improvements. In addition, these estimates do not include changes in sectors t hat supply these sectors, such as steel or electronics producers. They thus may best be viewe d as the effects on employment in the auto sector due to the changes in expenditures in t hat sector, rather than as an assessment of all employment changes due to these changes in expe nditures.

Some of the costs of this proposal will be spent di rectly 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 expend itures 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 technol ogies to the vehicles to comply with the regulation.

We use several public sources for estimates of empl oyment 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 adju sted to remove the employment effects of imports through use of the ratio of domestic produc tion to domestic sales, described above, of 0.667. The values reported are for Motor Vehicle M anufacturing (NAICS 3361) and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Annual Survey of Manufactures 60 (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 t han the BLS ERM: for instance, while the ERM includes the Motor Vehicle Manufacturing sector , the ASM has detail at the 6-digit2017 Regulatory Impact Analysis

8-27 NAICS code level (e.g., automobile manufacturing vs . light truck and utility vehicle manufacturing). While the ERM provides direct esti mates 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 t hose values. The data in the ASM are updated annually, except for years when the full Ec onomic 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 Vehic le Manufacturing (NAICS 33611), and Motor Vehicle Parts Manufacturing (NAICS 3363).

The Economic Census includes all large companies an d 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 annu al. The values presented here use data from 2007 (adjusted to 2009$), with the domestic pr oduction-to-sales adjustment. The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Automobile and Light Duty Motor Vehicle Manufacturing (NAICS 33611), and Moto r Vehicle Parts Manufacturing (NAICS 3363).

Table 8.2-1 provides the values, either given (BLS) or calcula ted (ASM, Economic Census) for employment per $1 million of expenditur es, all based on 2009 dollars, though the underlying data come from different years (which ma y account for some of the differences). The different data sources provide similar magnitud es for the estimates for the sectors. Parts manufacturing appears to be more labor-intensive th an vehicle manufacturing; light-duty vehicle manufacturing appears to be slightly less l abor-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 Vehi cle Parts Mfg 3.073 2.049 Deleted: Table 8.2-3

Deleted: 3Chapter 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 Statisti cs’ Employment Requirement Matrix. ASM refers to t he 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 i ndustry has changed: automation and improved production methods have led to signifi cant 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$). 61 Because the ERM is available annually for 1993-2008, we used these data to estim ate productivity improvements over time. We regressed logged ERM values on year (to estimate percent change per year) for both the Motor Vehicle Manufacturing and Motor Vehicle Parts Manufacturing sectors. The results suggest a 4.4 percent per year productivity improve ment in the Motor Vehicle Manufacturing Sector, and a 3.6 percent per year improvement in t he Motor Vehicle Parts Manufacturing Sector. We then used the regression relationship t o project the ERM through 2025. In the results presented below, these projected values (ad justed to 2009$) 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 fut ure to the projected value in 2007 (the base year for that estimate). As noted above, we adjust ed the estimate of workers per vehicle for the demand effect in Chapter 8.2.3.1.1, above, usin g the productivity improvement for the Motor Vehicle Manufacturing sector; because the est imate 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 product ion) 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: th at is, the range of employment impacts is between 0.4 and 2 additional jobs per $1 million ex penditures in the sector in 2009. The results in Table 8.2-2 include the productivity adjustment described abov e.

While we estimate employment impacts beginning with the first year of the standard (2017), some of these job gains may occur earlier a s auto manufacturers and parts suppliers hire staff in anticipation of compliance with the s tandard.

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: 42017 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 emp loyment due to changes in labor intensity associated with a regulation. As noted a bove, the estimates of the cost effect assume constant labor per $1 million in expenditures, thou gh 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 man ufacturers could use to comply with the proposed standards. For a subset of the technologi es, though, EPA-sponsored research (discussed in Chapter 3.2.1.1 of the Joint TSD) whi ch 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 t echnologies to their individual components and looking at the differences in materi als 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 transmis sion to those needed for a 6-speed automatic transmission.

Because labor cost was one of the sources of differ ences 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 com pliance with the proposed standards. An increase in labor cost for the new technology indic ates 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 th e labor cost by a wage per hour estimate provides an estimate of the additional hours (and t hus 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 n eeds for the new technologies. For this conversion, a weighted average wage rate (90 percen t 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 theChapter 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 ba seline to new fuelsaving technologies for technologies in the FEV stu dy. It indicates that, in switching from the baseline to the new technologies, labor use per tec hnology increased: the fuel-saving technologies use more labor than the baseline techn ologies. For a subset of the technologies likely to be used to meet the standards in this pro posal, then, the factor shift effect increases labor demand, at least in the short run; in the lon g run, as with all technologies, the cost structure is likely to change due to learning, econ omies of scale, etc. The technologies examined in this research are, however, only a subs et of the technologies that auto makers may use to comply with the standards proposed in th is program. As a result, these results cannot be considered definitive evidence that the f actor 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 fac tor shift effects, the cost effect results show that the employment effects of the inc reased spending in the regulated sector (and, possibly, the parts sector) are expected to b e positive and on the order of a few thousand in the initial years of the program. As noted abov e, 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 ligh t duty vehicle sales. In addition, auto dealers Deleted: Table 8.2-5

Deleted: 52017 Regulatory Impact Analysis

8-31 may be affected by changes in maintenance and servi ce costs. Increases in those costs are likely to increase labor demand in dealerships.

Although this proposal predicts very small penetrat ion 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 a bility to fuel with electricity rather than gasoline, attractive; they may find other character istics, 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 vehi cles. 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 availab le.

8.2.5 Effects on Employment in the Auto Parts Sector

As discussed in the context of employment in the au to industry, some vehicle parts are made in-house by auto manufacturers; others are mad e 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; t he breakdown in employment between the two sectors is difficult to predict. The effects o n 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 manu facturing sector.

8.2.6 Effects on Employment for Fuel Suppliers

In addition to the effects on the auto manufacturin g and parts sectors, these rules will result in changes in fuel use that lower GHG emissi ons. Fuel saving, principally reductions in liquid fuels such as gasoline and diesel, will affe ct employment in the fuel suppliers industry sectors throughout the supply chain, from refinerie s to gasoline stations. To the extent that the proposed standards result in increased use of e lectricity 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 fo r 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 supplier s 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 req uire additional infrastructure, such as electricity charging locations. Providing this inf rastructure will require some increased employment. In addition, the generation of electri city is likely to require some additional labor. We have insufficient information at this ti me to predict whether the increases in laborChapter 8

8-32

associated with increased infrastructure provision and generation for electricity will be greater or less than the employment reductions associated w ith reduced demand for petroleum fuels.

8.2.7 Effects on Employment due to Impacts on Consumer Ex penditures

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 the ir vehicles are expected to find that their fuel savings per month exceed the increase in the l oan cost (though this depends on the particular loan rate a consumer receives). As a re sult, consumers will have additional money to spend on other goods and services, though, for t hose 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 t heir savings.

These increased expenditures will occur in 2017 and beyond. If the economy returns to full employment by that time, any change in cons umer 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 provid e additional insight into the effects of this proposed rule on consumer expenditu res, 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 standar ds 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, 201 1, 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 standard s. N See Memorandum to Docket, “Economy-Wide Impacts of Proposed Light-Duty Vehicle Greenhouse Gas Standards for Model Years 2017-2025,” November, 201 1, 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 standard s.

O GDP is one widely-used measure of overall economic activity. It measures the market value of all good s 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 household s 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 Heav y-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 G reenhouse 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 deale rs, 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 f ull 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 discu ssed above responds to this program will be influenced by how consumers consider fuel saving s in their vehicle purchase decisions, changes in vehicle manufacturing technologies and l ocations, and other factors.

These proposed standards initially take effect in m odel year 2017, a time period sufficiently far in the future that the current sus tained 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 empl oyment 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 th ose 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 expect ed to increase employment by 600 – 3,600 workers in 2017, depending on the share of th at employment that is in the auto manufacturing sector compared to the auto parts man ufacturing sector. As mentioned above, some of these job gains may occur earlier as auto m anufacturers 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 potent ial compliance technologies, the evidence which we do have for some technologies suggests tha t many of the technologies will have increased labor needs.

Effects in other sectors that are predicated on veh icle sales are also ambiguous. Changes in vehicle sales are expected to affect lab or needs in auto dealerships and in parts manufacturing. Increased expenditures for auto par ts 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 compl iance technologies for which data are available show increased labor requirements. Reduc ed fuel production implies less employment in the petroleum sectors. Finally, cons umer spending is expected to affect employment through changes in expenditures in gener al retail sectors; net fuel savings by consumers are expected to increase demand (and ther efore 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. Automobile Market,” Review of Economics and Statist ics 82: 316-324 (Docket EPA-HQOAR-2010-0799).

2 Insurance Information Institute, 2008, “Average Ex penditures for Auto Insurance By State, 2007-2008,” http://www.iii.org/media/facts/statsbyi ssue/auto/, accessed 8/22/11 (Docket EPA-HQ-OAR-2010-0799).

3 U.S. Department of Energy, 2011. “Transportation and the Economy,” Chapter 10 in “Transportation Energy Data Book,” http://cta.ornl. gov/data/tedb30/Edition30_Chapter10.pdf, accessed 8/22/11, Table 11 (Docket EPA-HQ-OAR-2010- 0799).

4 Solheim, Mark, 2006 “State Car Tax Rankings,” http://www.kiplinger.com/features/archives/2006/04/ cartax.html, accessed April 23, 2009 (Docket EPA-HQ-OAR-2009-0472-0010) (Docket EPA-HQ-O AR-2010-0799).

5 U.S. Census Bureau, "Table 2. Resident Population of the 50 States, the District of Columbia, and Puerto Rico: 2010 Census" http://2010 .census.gov/news/presskits/apportionment/apport.html .

6 “National Auto Loan Rates for July 21, 2011,” http://www.bankrate.com/finance/auto/national-auto- loan-rates-for-july-21-2011.aspx, accessed 7/26/11 (Docket EPA-HQ-OAR-2010-0799).

7 Goldberg, Pinelopi Koujianou, “Product Differentia tion and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799); Goldberg, Pinel opi Koujianou, “The Effects of the Corporate Average Fuel Efficiency Standards in the US,” Journal of Industrial Economics 46(1) (March 1998): 1-33 (Docket EPA-HQ-OAR-2010 -0799).

8 Greene, David L., K.G. Duleep, Doug Elliott, and S anjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy In formation Administration,” prepared by the Oak Ridge National Laboratory for the U.S. D epartment of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-079 9); 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,” En ergy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799).

9 McManus, Walter M., “Can Proactive Fuel Economy St rategies Help Automakers Mitigate Fuel-Price Risks?” University of Michigan Transpor tation Research Institute, September 14, 2006 (Docket EPA-HQ-OAR-2010-0799).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-20 10- 0799); Berry, Steven, James Levinsohn, and Ariel Pa kes, “Differentiated Products Demand Systems from a Combination of Micro and Macro Data: The New Car Market,” Journal of Political Economy

112(1) (2004): 68-105 (Docket EPA-HQ-OAR-2010-07 99).

11 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, 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 ).

12 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

13 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-OAR-2 010- 0799).

14 Austin, David, and Terry Dinan, “Clearing the Air: The Costs and Consequences of Higher CAFE Standards and Increased Gasoline Taxes,” Journ al of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799)

.

15 E.g., Bento, Antonio M., Lawrence H. Goulder, Emer ic 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-2010-07 99); Train, Kenneth E., and Clifford Winston, “Vehicle Choice B ehavior and the Declining Market Share of U.S. Automakers,” International Economic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

16 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

17 Greene, David L., K.G. Duleep, Doug Elliott, and S anjana Ahmad, “A Fuel Economy Regulatory Analysis Model (FERAM) For the Energy In formation Administration,” prepared by the Oak Ridge National Laboratory for the U.S. D epartment of Energy under contract No. DE-AC0500OR22725, 2005 (Docket EPA-HQ-OAR-2010-079 9).

18 NERA Economic Consulting, “Evaluation of NHTSA’s B enefit-Cost Analysis of 2011- 2015 CAFE Standards,” 2008, available at http://www.heartland.org/policybot/results/23495/Ev aluation_of_NHTSAs_BenefitCost_Anal ysis_Of_20112015_CAFE_Standards.html (Docket EPA-H Q-OAR-2010-0799).

19 E.g., Train, Kenneth E., and Clifford Winston, “Ve hicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic 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 Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

21 E.g., Bento, Antonio M., Lawrence H. Goulder, Emer ic 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-2010-07 99).

22 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, 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-2010-079 9); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Poll ution 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).

23 NERA Economic Consulting, “Appendix B: New Vehicl e Market Model,” “Impacts of the California Greenhouse Gas Emission Standards on Mot or Vehicle Sales,” comments submitted to the U.S. Environmental Protection Agen cy at Regulations.gov, document number EPA-HQ-OAR-2006-0173-9053.1 (Docket EPA-HQ- OAR-2010-0799).

24 E.g., Train, Kenneth E., and Clifford Winston, “Ve hicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48(4) (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799)

25 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).

26 Greene, David L., Philip D. Patterson, Margaret Si ngh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Incre ased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799).

27 Rex, Emma, and Henrikke Baumann (2007). “Beyond e colabels: what green marketing can learn from conventional marketing.” Journal of Cleaner Production 15: 567-576 (Docket EPA-HQ-OAR-2010-0799).

28 Whitefoot, Kate, Meredith Fowlie, and Steven Skerl os, “Product Design Responses to Industrial Policy: Evaluating Fuel Economy Standar ds Using an Engineering Model of Endogenous Product Design,” Energy Institute at Haa s Working Paper 214, University of California Energy Institute, February 2011 (Docket EPA-HQ-OAR-2010-0799).

29 Berry, Steven, James Levinsohn, and Ariel Pakes, “ Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 0799).2017 Regulatory Impact Analysis

8-37 30 Goldberg, Pinelopi Koujianou, “Product Differentia tion and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799).

31 Brownstone, David, and Kenneth Train, “Forecasting New Product Penetration with Flexible Substitution Patterns,” Journal of Econome trics 89 (1999): 109-129 (Docket EPAHQ-OAR-2010-0799); Brownstone, David, David S. Bunc h, and Kenneth Train, “Joint Mixed Logit Models of Stated and Revealed Preferences for Alternative-Fuel Vehicles,” Transportation Research Part B 34 (2000): 315-338 (Docket EPA-HQ-OAR-2010-0799) ; Greene David L., “TAFV Alternative Fuels and Vehicl es Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for t he U.S. Department of Energy, July 2001 (Docket EPA-HQ-OAR-2010-0799); Greene, David L., K. G. Duleep, and Walter McManus, “Future Potential of Hybrid and Diesel Powertrains in the U.S. Light-Duty Vehicle Market,” prepared by the Oak Ridge National Laboratory for t he U.S. Department of Energy, August 2004 (Docket EPA-HQ-OAR-2010-0799).

32 Petrin, Amil, “Quantifying the Benefits of New Pro ducts: The Case of the Minivan,” Journal of Political Economy 110 (2002): 705-729 (Docket EPA-HQ-OAR-2010-0799 ); Berry, Steven, James Levinsohn, and Ariel Pakes, “D ifferentiated Products Demand Systems from a Combination of Micro and Macro Data: The Ne w Car Market,” Journal of Political Economy 112 (2004): 68-105 (Docket EPA-HQ-OAR-2010-0799).

33 Train, Kenneth E., and Clifford Winston, “Vehicle Choice Behavior and the Declining Market Share of U.S. Automakers,” International Eco nomic Review 48 (November 2007): 1469-1496 (Docket EPA-HQ-OAR-2010-0799).

34 Bento, Antonio M., Lawrence H. Goulder, Emeric Hen ry, 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-2010-079 9); Feng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Poll ution 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 Si ngh, and Jia Li, “Feebates, Rebates, and Gas-Guzzler Taxes: A Study of Incentives for Incre ased Fuel Economy,” Energy Policy 33 (2005): 757-775 (Docket EPA-HQ-OAR-2010-0799); Fe ng, Yi, Don Fullerton, and Li Gan, “Vehicle Choices, Miles Driven and Pollution Polici es,” National Bureau of Economic Analysis Working Paper 11553, available at http://e conweb.tamu.edu/gan/w11553.pdf , accessed 5/12/09 (Docket EPA-HQ-OAR-2010-0799); Gr eene, David L., “Feebates, Footprints and Highway Safety,” Transportation Rese arch Part D 14 (2009): 375-384.

36 E.g., Austin, David, and Terry Dinan, “Clearing th e 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)

.Chapter 8

8-38 37 Turrentine, Thomas S., and Kenneth S. Kurani, “Car Buyers and Fuel Economy?” Energy Policy 35 (2007): 1213-1223 (Docket EPA-HQ-OAR-2010-079 9).

38 E.g., Espey, Molly, and Santosh Nair, 2005. “Autom obile Fuel Economy: What Is It Worth?” Contemporary Economic Policy 23: 317-323 ( Docket EPA-HQ-OAR-2010-0799).

39 Larrick, Richard P., and Jack B. Soll, 2008. “The MPG Illusion,” Science 320(5883): 1593-1594 (Docket EPA-HQ-OAR-2010-0799).

40 E.g., Goldberg, Pinelopi Koujianou, “Product Diffe rentiation and Oligopoly in International Markets: The Case of the U.S. Automobile Industry, ” Econometrica 63(4) (July 1995): 891- 951 (Docket EPA-HQ-OAR-2010-0799).

41 E.g., Berry, Steven, James Levinsohn, and Ariel Pa kes, “Automobile Prices in Market Equilibrium,” Econometrica 63(4) (July 1995): 841-940 (Docket EPA-HQ-OAR-20 10- 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/gaspap er_latest.pdf (accessed 11/1/11) (Docket EPA-HQ-OAR-2010-0799); Li, Shanjun, Christopher Tim mins, and Roger H. von Haefen, 2009. “How Do Gasoline Prices Affect Fleet Fuel Ec onomy?” 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 Pa per. Macalester College, http://www.macalester.edu/~wests/SarahWestMEA2007.p df, accessed 1/25/10 (Docket EPAHQ-OAR-2010-0799).

43 Greene, David L., and Jin-Tan Liu, 1988. “Automot ive 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 Fue l 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 Produc t Selection in the U.S. Automobile Industry,” http://faculty.msb.edu/jpg72/Autos_Graml ichJacob.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: P reliminary Results.” Ann Arbor, MI: University of Michigan Transportation Research Inst itute, 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-R ange Increases in the Fuel Economy (CAFE) Standard,” Economic Inquiry42(2) (April 2004): 279-294 (Docket EPA-HQ-OAR2010-0799); Austin, David, and Terry Dinan, “Cleari ng 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,” Resou rces for the Future Paper RFF DP 10- 50 (December 2010), http://www.rff.org/RFF/Document s/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,” w orking paper, at http://econ.ucsd.edu/~m3jacobs/Jacobsen_CAFE.pdf (a ccessed 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,” Journ al of Environmental Economics and Management 50 (2005): 562-582 (Docket EPA-HQ-OAR-2010-0799).

49 IEA. 2007. “Mind the Gap: Quantifying Principal-Ag ent 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 C limate 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 Kev in A. Hassett (1999). "Measuring the Energy Savings From Home Improvement Investment s: 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 – Energ y Efficiency Policy: Pipe Dream or Pipeline to the Future?” Review of Environmental E conomics and Policy 3(2): 304-320 (Docket EPA-HQ-OAR-2010-0799).

50 Helfand, Gloria, and Ann Wolverton (2011). “Evalu ating the Consumer Response to Fuel Economy: A Review of the Literature.” Internation al 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&pag e=42 (Docket EPA-HQ-OAR2010-0799)

52 Greene David L., “TAFV Alternative Fuels and Vehic les Choice Model Documentation,” prepared by the Oak Ridge National Laboratory for t he 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 Se cretary, May 21, 2010. http://www.whitehouse.gov/the-press-office/presiden tial-memorandum-regarding-fuelefficiency-standardsChapter 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 G uide 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 Jhi h-Shyang Shih. “Jobs Versus the Environment: An Industry-Level Perspective.” Jour nal 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 Re source Economics 22 (2002): 133-156 (Docket EPA-HQ-OAR-2010-0799).

58 Berman, Eli, and Linda T. Bui, (2001) “Environment al 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.ht m

60 http://www.census.gov/manufacturing/asm/index.html

61 http://www.bls.gov/emp/ep_data_emp_requirements.ht m; this analysis used data for sectors 88 (Motor Vehicle Manufacturing) and 90 (Motor Vehi cle Parts Manufacturing) from “Chainweighted (2000 dollars) real domestic employment re quirements table. . . adjusted to remove imports.”

62 FEV, Inc. “Light-Duty Technology Cost Analysis: P ower-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 further policy review.

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 Esta blish 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 Allo wance 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 S ection 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 2 of 426 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 CO 2 Emissions iii. Economic Value of Reductions in CO 2 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?Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 3 of 426 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 thisjoint 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 requ est, 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 repr esent 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 chan ge 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 supplement al 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 instructi ons 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. InSummary 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 Informatio n 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 informatio n 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 whos e 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 do cket. 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 a nd 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: ot aqpublicweb@epa.gov. NHTSA: Rebecca Yoon, Office of the Chief Counsel, National Highway Tr affic 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 Codes A 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, bu t rather provides a guide regarding entities likely to be regulated by this action. To dete rmine 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 NHTS A’s Draft Environmenta l 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 “m edium-duty passenger vehicle” are defined in 40 CFR 86.1803-01. Generally, the term “light-duty vehicle” mean s 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 passen ger 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 ar e 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 identificatio n, 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 wit hout 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 subm it. If EPA cannot read your comment due to technical difficulties and cannot contact you for cl arification, EPA may not be able to consider your comment. Electronic files should avoid th e 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 co mments 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 us ing 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 8 of 426 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 burde ns, 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 recei ving your comments, Docket Management will return the postcard by mail.

How Do I Submit Confidential Business Information?

Any confidential business informa tion (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 agenci es’ 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 sp ecific 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 containingSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

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 con cerning how the agencies should consider that information for the final rule. Ho wever, 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 inform al 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 Ce nter 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 appr opriate 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 pref er 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 10 of 426 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 PROP OSED 2017-2025 NA TIONAL 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 fo r 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 AuthoritySummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 11 of 426 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 CO 2 Credits and Fuel Consumption Improvement Values, Off-cycle Reductions, and Full-size Pickup Trucks

1. Proposed Air Conditioning CO 2 Credits and Fuel Consumption Improvement Values

2. Off-Cycle CO 2 Credits

3. Advanced Technology Incentives for Full Sized Pickup Trucks

G. Safety Considerations in Esta blishing 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 st atistical analysis of historical crash data?

4. How do the agencies think technological solutions might affect the safety estimates indicated by the statistical analysis?Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 12 of 426 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 Standa rds for Light-duty Vehicles, Light-duty Trucks, and Medium duty Passenger Vehicles

1. What Fleet-wide Emissions Levels Correspond to the CO 2 Standards?

2. What Are the Proposed CO 2 Attribute-based Standards?

3. Mid-Term Technology Review

4. Averaging, Banking, and Trading Provisions for CO 2 Standards

5. Small Volume Manufacturer Standards

6. Nitrous Oxide, Methane, and CO 2 -equivalent Approaches

7. Small Entity Exemption

8. Additional Leadtime Issues

9. Police and Emergency Vehicle Exemption from CO 2 Standards

10. Test Procedures

C. Additional Manufacturer Compliance Flexibilities

1. Air Conditioning Related Credits

2. Incentive for Electric Vehicles, Plug-in Hybr id 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 Vehi cles, 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 CO 2 Standards

1. How did EPA develop a reference and c ontrol fleet for evaluating standards?

2. What are the Effectiveness and Costs of CO 2 -reducing Technologies?

3. How were Technologies combined into “Packages” and What is the Cost and Effectiveness of Packages?Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 13 of 426 4. How does EPA Project how a Manufacturer Would Decide Between Options to Improve CO 2 Performance to Meet a Fleet Average Standard?

5. Projected Compliance Costs and Technology Penetrations

6. How does the technical assessment support the proposed CO 2 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 CO 2 Footprint-based Targets with Current Powertrain Designs?

E. Certification, Compliance, and Enforcement

1. Compliance Program Overview

2. Compliance with Fleet-Average CO 2 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 Emis sions and Their Associated Effects?

1. Impact on GHG Emissions

2. Climate Change Impacts from GHG Emissions

3. Changes in Global Climate Indicators Asso ciated with the Proposal’s GHG Emissions Reductions

G. How Would the Proposal Impact Non-GHG Em issions 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 ReducedSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 14 of 426 4. Reduction in Fuel Consumption and its Impacts

5. CO 2 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 Impa cts [PLACEHOLDER]

I. Statutory and Executive Order Reviews

J. Statutory Provisions and Legal AuthoritySummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 15 of 426 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?Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 16 of 426 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 Admini stration (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 theseSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 17 of 426 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 (CO 2 ) tailpipe emissions, a large majority of the projected be nefits 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 rule s 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 “Refer ence Case” inputs, which are based on the assumptions that NHTSA has employed for its main analysis (as opposed to sensiti vity 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/metr ic 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, “Capturi ng 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/en ergy_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 Strate gic and International Studies (January 2009), pp. 21–22; available at: http://pdf.wri.org/secure_low_carbon_ener gy_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/ReducingtheCostofAddressingClimateChang e_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/file s/5301_Globalwarmingont heroad_0.pdf (last accessed Sept. 24, 2011).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 18 of 426 significance accorded to improving fuel economy re flects 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 fro m regions with uncertain conditions enhances our energy security. Additionally, the emission of CO 2 from the tailpipes of cars and light trucks due to the combustion of petroleum is one of the largest sources of U.S. CO 2 emissions. 8 Using vehicle technology to improve fuel economy, an d thereby reducing tailpipe emissions of CO 2 , is one of the three main measures of reducing those tailpipe emissions of CO 2

. 9 The two other measures for reducing the tailpipe emissions of CO 2 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 (hereaf ter light-duty vehicles or LDVs). Together, these vehicle categories, which include passenge r 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://w ww.fueleconomy.gov/feg/why.shtml (last accessed Sept. 24, 2011); Robert Socolow, Roberta Hotinski, Jeffery B. Gree nblatt, 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/ENVIRONM ENTDec2004issue.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/emissi ons/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 Envi ronmental Protection Agency at http:// www.fueleconomy.gov/feg/climate.shtml (last accessed Sept. 24, 2011).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 19 of 426 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-d uty 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 mo re 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 fi rst 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 Re sources 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 woul d 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 signi ficant 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 (CO 2 ) 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 conditional 11 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 CO 2 is typically 25 percent higher and real-world fu el economy is typically 20 percent lower than the CO2 and CAFE compliance values discussed here. The reference to CO 2 here refers to CO 2 equivalent reductions, as this included some degree of reductions in greenhouse gases other than CO 2 , 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 20 of 426 standards are projected to be achieved through a range of technologies, including improvements in air conditioning efficiency, which reduces bo th GHG emissions and fuel consumption; the EPA standards also are projected to be achieved w ith 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 “footp rint.” For the MYs 2022-2025 standards, EPA and NHTSA are proposing a comprehensive mid-te rm 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 bill ion metric tons of GHG emissions over the
lifetimes of those vehicles sold in MY 2017-2025. The agencies estimate th at 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 bi llion 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 signifi cant 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 consumer s 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 consumer s 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 stan dards 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 ar e 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 th e 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 .Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 21 of 426 The agencies’ believe there is a wide range of technologies available for manufacturers to consider in reducing GHG emissions and improv ing 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-reduc ing technologies. The agencies believe that advances in gasoline engines and transmissions w ill continue for the fores eeable future, and that there will be continual improvement in other tec hnologies, including vehi cle weight reduction, lower tire rolling resistance, improvements in ve hicle aerodynamics, diesel engines, and more efficient vehicle accessories. The agencies also e xpect 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 c onditioners 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 tech nologies, 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-e ffective 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 (hereaf ter light-duty vehicles or LDVs). Together, these vehicle categories, which include passenge r cars, sport utility vehicles, crossover utility vehicles, minivans, and pickup trucks, are presentl y 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 availabl e 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 No tice of Intent (NOI) outlining the agencies’Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 22 of 426 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 manuf acturing 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 vehi cles and the agencies’ understanding of the cost and manufacturers’ ability to apply these technol ogies 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 coordina ted and harmonized approach to implementing these two statutes, in accordance with all substa ntive and procedural requirements imposed by
law. 15

The proposed approach allows for long-term planning by manufacturers and suppliers forthe continued development and deployment across their fleets of fuel saving and emissionsreducing technologies. NHTSA’s and EPA’s tech nology assessment indicates there is a wide range of technologies available for manufacturer s 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/ua w-supports-administration- proposal-light-duty-vehicl e-cafe-and-greenhouse-gasemissions-r (last accessed September 19, 2011)

15 For NHTSA, this includes the requirements of the National Environmental Policy Act (NEPA).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 23 of 426 transmissions will continue for the foreseeable futu re, 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 te chnologies including re ductions in vehicle weight, lower tire rolling resistance, improvements in vehicle aerodynamics, diesel engines, and more efficient vehicle accessories. The agencies also expect to see incr eased electrification of the fleet through the expanded production of st op/start, hybrid, plug-in hybrid and electric
vehicles. 17 Finally, the agencies expect that vehicl e air conditioners will continue to improve by becoming more efficient and by increasing the us e 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 penetr ation of these and other technologies across the fleet. We project that these potential comp liance 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’ opp ortunity 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 shoul d 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 technolo gies, 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, th e agencies project that advanced transmissions (such as automatic and dual clutch transmissions with eight forwar d 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 furt her by 2016, we anticipate that by 2025 these will be the dominant transmissions in new vehicle sales.

17 For example, while today less than th ree 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?Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 24 of 426 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 fact ors 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 coordi nation 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 foun d 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 approxim ately 2 billion metric to ns and save 4 billion
barrels of oil over the lifetime of MYs 2017-2 025 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 woul d 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 con centrations 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 rela tive to a future in which the MY 2016 standards remain in place for MYs 2017-2025 and manufacturer s comply on average at those levels.

19 74 FR 66,496, - 66,518, December 18, 2009; “Technical Support Document for Endangerment and Cause orContribute 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 25 of 426 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 continue s 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 hist orically 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 quantit y 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, m easures 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

CO 2 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 CO 2 it emits in traveling that distance. 22 Since the amount of CO 2 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 CO 2 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_de pendence.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: Mitig ation, Adaptation, and the Science Base,” National Academ ies Press, 1992, at 287. Available at http://www.nap.edu/catalog.p hp?record_id=1605 (last accessed Sept. 25, 2011).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 26 of 426 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 do cumented in Section III of this notice, and in NHTSA’s draft Environmental Impact Statem ent (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 CO 2 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 be gin 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 em issions 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_a rchive.html (last accessed Sept. 25, 2011).

24 The Presidential Memorandum is found at: http ://www.whitehouse.gov/the-pre ss-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 und er 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 recentl y promulgated final GHG and fuel efficiency standards for heavy duty vehicles and engines for MYs 2014-2018. 76 FR 57106 (September 15, 2011).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 27 of 426 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 trans portation sector greenhouse gas emissions and fossil fuel consumption, consistent with my Admi nistration'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 contin ue 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 CO 2 tailpipe emissions is a very close one. In the real world, there is a single pool of technol ogies for reducing fuel consumption and CO 2 emissions. Using these technologies in the way that minimizes fuel consumption also minimizes CO 2 emissions. While there are emission control t echnologies 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 CO 2 . In fact, the only way at present to reduce tailpipe
emissions of CO 2 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 CO 2 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/regulat ionsproposedregs.htm#prez cl; and http://www.nhtsa.gov/Laws+&+Regulations/CAFE+-+Fuel +Economy/Stakeholder+Commitment+Letters (last accessed August 28, 2011).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 28 of 426 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, co st 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 co mply 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 benef its 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 de veloped and compiled for the evaluation, through a collaborative, robust, and transparent process, in cluding 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 st ate and Federal vehicle standards.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 29 of 426 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 c onsistent 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 provi de 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 truc ks built in MYs 2017-2025. The ag encies 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 Regard ing Future Passenger Vehicle Greenhouse Gas Emissions Standards, California Air Resources Board, May 21, 2010. Available at: http://www.epa.gov/otaq/climate/regulations.htm.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 30 of 426 (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 (PHE Vs), and electric vehicles (EVs).

Manufacturers and others commented extensivel y 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, an d 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 them es 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, ma nufacturer organizations , automotive suppliers, a labor union, environmental groups, consumer inte rest 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 valu es based on the CAFE test cycle. We note, however, that because these mpg values are translated from CO 2 e values that include reductions in hydrofluorocarbon (HFC) leakage due to use of advanced refrigerants and leak age improvements, therefore these numbers are not as representative of either CAFE te st cycle or real-world mpg.

33 75 FR 76337, December 8, 2010.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 31 of 426 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 CO 2 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 st andards 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 th e 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 releasi ng 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 sh all be deemed compliance with the California GHG emissions standards, as long as EPA’s fina l 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 Pro cedures 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/oa ltp.pdf (last accessed August 28, 2011).

36 See State of California July 28, 2011 letter availa ble at: http://www.epa.gov/otaq/climate/regulations.htm.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 32 of 426 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 T echnical 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 eff ectiveness 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 CO 2 and CAFE standards for passenger cars and light trucks would be footprint-based, simila r 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 CO 2 in model year 2025. This is projected to be achieved through improvements in fuel efficiency with some additional reductions achieved through reductions inSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 33 of 426 non-CO 2 GHG emissions from reduced AC system leakage and the use of lower global warming potential (GWP) refrigerants. The level of 163 grams/mile CO 2 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 CO 2 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 ofmanufacturers’ 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., to wing 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 CO 2 emissions reduction in model years 2017 through 2021 is 3.5 percent per year. EPA is also proposing to change the slopes of the CO 2 -footprint curves for light-duty trucks from those in the 2012-2016 rule, in a manner that effectively means that the annual rate of impr ovement 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 CO 2 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, wh ile 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
conditional 38 proposed standards that are projected to require, on an average industry fleet wide

37 Real-world CO 2 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 CO 2 here refers to CO 2 equivalent reductions, as this included some degree of reductions in greenhouse gases other than CO 2 , as one part of the AC related reductions.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 34 of 426 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 seve ral statutory constraints in EPCA/EISA. As a primary example, NHTSA’s proposed standards, unlik e EPA’s, do not reflect the inclusion of air conditioning system refrigerant and leakage impr ovements, 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 manufact urer’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-base d 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 review able 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 sta ndards for passenger cars and light trucks. 49 U.S.C. 32902(b)(3).

40 For CAFE calculations, a harmonic average is used.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 35 of 426 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 refine ment 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 CO 2 emissions. The agencies therefore established a “baseline” vehicle fleet representing those vehicles, based on the best available transparent information. The agencies then de veloped 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 propos ed 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 CO 2 -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, unt il 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 CO 2 emissions) that could be added to MY 2008- technology vehicles in the future, taking into previous ly-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 ma nufacturer’s car and truck CAFE and average CO 2 levels reflect baseline standards. The model’s output, the reference cas e (or adjusted baseline, or no-action alternative), is the light-duty fleet estimated to exist in MYs 2017-2025 wi thout new GHG/CAFE standard s covering MYs 2017-2025.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 36 of 426 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 ce rtification 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 informa tion 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)Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 37 of 426 using data available from ALLDATA® Online. Co mbined with the certification data, all of this information yielded the MY 2008 baseline vehicl e 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 leve l, 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 pr ovided 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 20 17–2025 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 th e 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 Administra tion, 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 rela tive volumes of passenger cars and light trucks.

43 The agencies explain in Chapter I of the draft Joint TSD why data from CS M was chosen for creating the baseline for this rulemaking.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 38 of 426 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 th e 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 foreca st. 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 da ta 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 commer cially available sources has both advantages and disadvantages relative to a baseline developed using manuf acturers’ product plans, NHTSA currently concludes, as it did in the course of that prior rulemaking, that the a dvantages outweigh the disadvantages. Commenters generally supported the more transparent approach employed in the MYs 2012-2016 rulemaking. 45 Similar to the analyses supporti ng the MYs 2012-2016 rulemaking, the agencies have used the Energy Information Administration’s (EIA’s) National Energy M odeling 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 orde r 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 newSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 39 of 426 Another change in the baseline fleet from the last rulemaking involved our redefinition of the list of manufacturers to account for realignm ent 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 quantitativ ely 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 In formation 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 abil ity 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 cu rrent 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 me thodology and then hold po st-2017 CAFE standards constant at MY 2016 levels. Incorporating these change s 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.”Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 40 of 426 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 fo recast 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 base line 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 th e 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 base Final Rule 47 Current Baseline

Manufacturer Passenger Cars 37.8 Nonpassenger 40.0 Passenger 41.4 Nonpassenger

46 This estimated average percentage increase includes the e ffect of changes in standard stringency and changes in the forecast fleet sales mix.

47 Again, Aston Martin, Alfa Romeo, Ferrari, Maserati, Lotu s 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 41 of 426 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 numbe r of proposed flexibilities and credits that manufacturers could use for compliance that NHTS A cannot consider in establishing standards
based on EPCA/EISA constraints. These flexib ilities would cause the actual achieved fuel economy to be lower than the required levels in th e 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 CAFESummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 42 of 426 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 be tter than the target curve by more than proposed levels. To receive these incenti ves, 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 estim ated average achieved fleet-wide CAFE fuel economy that would occur if manufacturers use this incentive. NHTSA has conducte d a sensitivity study that estimates the effects for manufacturers’ potential use of this fl exibility in Chapter X of the PRIA.

49 Again, Aston Martin, Alfa Romeo, Ferrari, Maserati, Lotu s 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; andSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 43 of 426 Manufacturer Passenger Cars 37.5 Non

passenger 38.8 Passenger 40.6 Non 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 44 of 426 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, Lotu s 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 45 of 426 EPA is proposing GHG emissions standards, and Table II-6 provides estimates of the projected overall fleet-wide CO 2 emission compliance target levels. The values reflected in Table I-4 are those that correspond to the manufacturers’ projected CO 2 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 pr ograms (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 informa tion regarding the context for those plans ( e.g. , estimates of future fuel prices), and estimates of the futu re 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 th e 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 manufa cturers 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 tech nologies reflected in those plans.

51 75 FR 80430Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 46 of 426 Given the importance that responses to this request for comment may have in informing NHTSA's proposed CAFE rulemaking, whether as pa rt 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 ap plication 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 sy stem, 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. Therefor e, NHTSA worked with EPA, as it did inpreparing for analysis supporting today’s proposal, to build a market forecast based on publiclyand commercially-available sources. NHTSA conti nues 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 NH TSA 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 purp oses 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 sensitivitySummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 47 of 426 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 a bove, 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 pro duction 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, ve hicle technologies, electrification/accessory technologies, and hybrid technologies. Table IV -4 below lists all the technologies consideredSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 48 of 426 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 allowe d to apply a technology to a manufacturer’s
fleet. 53 “Year of availability” recognizes that tech nologies 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 CO 2 Compliance Targets under the Proposed FootprintBased CO 2 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 technolo gy 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 49 of 426 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 (A uto 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 2020Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 50 of 426 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 CO 2 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 flexibi lity 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 achie ved 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 trad ing, air conditioning credits, or off-cycle credits, because, while those credit provisions should help reduce manufacturers’ costs of the program, EPA believe s 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 CO 2 -equivalent Emission Levels under the Proposed Footprint-Based CO 2 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 51 of 426 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 i cants

e l 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
i ne friction

u ction

e l 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 C P) 2.0 2.0 2.5 2.7 2.0 2.0 2.5 2.7 2.6 2.0 2.6 2.4

c rete

a ble valve ( DVVL) on
H C 2.8 2.8 3.6 3.9 2.8 2.8 3.6 3.9 3.5 2.8 3.5 3.4
i nder

c tivation 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

o line direct

c tio n 1.6 1.6 1.5 1.5 1.6 1.6 1.5 1.5 1.5 1 .6 1.5 1.5
b ocharging

w nsizing

e l 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 b ocharging

w nsizing

e l 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

r culation G R) – (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

r culation G R) – (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

v anced

s el 5.5 5.5 2.8 2.9 5.5 5.5 2.8 2.9 3.4 5.3 3.4 3.5

p eed auto.

s . with

r oved

r nals 1.9 1.9 2.0 2.0 1.9 1.9 2.0 2.0 2.0 2.0 2.0 2.1

p eed 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

c iency

r box 2.2 2.2 2.7 2.6 2.2 2.2 2.7 2.6 3.1 2.5 3.1 3.7
f t i mize r 3.3 3.3 4.1 4.3 3.3 3.3 4.1 4.3 4.1 3.3 4.1 3.9

c tric power 1.5 1.5 1.3 1.1 1.5 1.5 1.3 1.1 1.0 1.2 1.0 0.8Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 52 of 426 r ing

V micror i d 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

e l 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

c tric h icle (Early

o pter) 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 i stance 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 i stance 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

u ction

e l 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

u ction

e l 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 attrib ute. 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 ve hicle 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 CO 2 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 CO 2 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 Re formed CAFE program for light trucks for model years 2008- 2011 and passenger car CAFE standards for MY 2011.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 53 of 426 manufacturer must comply will be based on its final model year production figures. A manufacturer’s calculation of its fleet averag e 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 comparedto the light truck curves for MYs 2012-2016. The agencies are proposing changes to the lighttruck curve to increase the slope and to extend th e 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 manu facturer’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 fo r model years through 2025 while II-2 below illustrates the light truck CA FE standard curves for model years through
2025.

EPA is proposing the attribute curves show n in II-3 and II-4 below for assigning a CO 2 target level to an individual vehicle’s footprint value, for model years 2017 through 2025. These
CO 2 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 CO 2 car standard curves for model years 2017 through 2025 while II-4 shows the CO 2 truck standard curves for model years 2017-2025.

56 As in the MYs 2012-2016 rule, a manufacturer may have so me models that exceed their target, and some that are below their target. Compliance with a fl eet 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 th e performance for each model).SummaryofInteragencyWorkingCommentsReceivedonDraftruleunderEO12866review.Subjecttofurtherpolicyreview.Page54of426Figure II-1 CAFE Target Curves for Passenger CarsSummaryofInteragencyWorkingCommentsReceivedonDraftruleunderEO12866review.Subjecttofurtherpolicyreview.Page55of4268. II-2 CAFE Target Curves for Light Trucks2030405060708030 35 40 45 50 55 60 65 70 75 80

Fuel Economy Target (mpg)Footprint(sf)20252024202320222021202020192018201720162015201420132012SummaryofInteragencyWorkingCommentsReceivedonDraftruleunderEO12866review.Subjecttofurtherpolicyreview.Page56of426II-3 CO2 (g/mile) Car Standards CurvesSummaryofInteragencyWorkingCommentsReceivedonDraftruleunderEO12866review.Subjecttofurtherpolicyreview.Page57of426II-4 CO2 (g/mile) Truck Standard CurvesSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 58 of 426 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 al so be classified as a car under the EPA GHG program, and likewise for trucks. 57 This approach of using CAFE definitions allows the CO 2 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 CO 2 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 ve hicle, if a manufacturer’s fleet is dominated by small footprint vehicles, then that fleet w ill have a higher fuel economy requirement and a
lower CO 2 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 believ e 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 CO 2 emissions and fuel economy targets under the proposed standards that
different vehicle sizes will ha ve, although we emphasize again th at these targets are not actual standards – the proposed standards are manufactur er-specific, rather than vehicle-specific.

Table II-9 Model Year 2025 CO 2 and Fuel Economy Targets for Various MY 2008 Vehicle Types

Vehicle Type Example Models Example Model Footprint

(sq. ft.) CO 2 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 59 of 426 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 CO 2 is typically 25 percent higher and real-world fuel economy is typically 20 percent lower
than the CO 2 and fuel economy target values presented here.

3. Program Flexibilities for Achieving Compliance

a. CO 2 /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 CO 2 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 CO 2 /CAFE level better than the car and/or truck standards, then the manufacturer generates credits. Conversely, if the fleet average CO 2 /CAFE level does not meet the standard, the fleet would incur debits (also referred to as a shortfall). Asin 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 limitati ons 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 60 of 426 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 CO 2 carry-forward beyond 5 years, such that any credits generate d 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 fleet s 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 transf erred 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 expectatio n 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 manufactur ers 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 tradi ng 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 ve hicles 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).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 61 of 426 manufacturers from using traded credits to meet the minimum domestic passenger car CAFE
standard. 62

b. Air Conditioning Improvement Cred its/Fuel Economy Value Increases

Air conditioning (A/C) systems contribut e 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 CO 2 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 CO 2 standard. For the current proposal, as with the MYs 2012-2016 program, manufacturers will be able to generate CO 2 -equivalent credits to use in complying with the CO 2 standards for improvements in air conditioning (A/C) systems, both for efficiency improvements (reduces tailpipe CO 2 and improves fuel consumption) and for leakage reduction or alternative, lower GWP (global warming potential) refrigerant us e (reduces hydrofluorocarbon (HFC) emissions). EPA is proposing that the maximum A/C credit available for cars is 18.8 grams/mile CO 2 and for trucks is 24.4 grams/mile CO 2 . 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 econom y benefits, because as explained above, A/C operation represents an additional load on the e ngine, 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 c ould 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 CO 2 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 year s, 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).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 62 of 426 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 CO 2 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 cont inue 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 techno logies. 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 quantifiabl e. EPA is proposing a list of technologies and credit values, where sufficient data is availabl e, 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 tec hnologies 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 ap plication and approval process. EPA isSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 63 of 426 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 ar e 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 techno logies as rapidly as possible, EPA is proposing an incentive multiplier for compliance purposes for all electric vehicles (EVs), plug-in hybrid el ectric vehicles (PHEVs), and fuel cell vehicles (FCVs) sold in MYs 2017 through 2021. This multiplier ap proach means that each EV/PHEV/FCV would
count as more than one vehicle in the manufactur er’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 th e 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 prod uction 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 t echnologies 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 64 of 426 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 EP CA/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 th en 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 CO 2 credit in the GHG program and an equivalent fuel consumption improvement value in the CAFE progr am for manufacturers that employ significant quantities of hybridization on full size pickup trucks, by including a per-vehicle CO 2 credit and fuel consumption improvement value available fo r 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 unde r 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 vehicl es in terms of the penetration of advanced technologies, which, if successful at incentivizing these “game changing technologies,” should allow additional opportunities to successfully achiev e 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 tec hnology in a manufacturer’s full size pickupSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 65 of 426 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 pr oposing that mild HEV pickup trucks would be eligible for a per vehicle credit of 10 g/mi 65 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 per 66 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 encour age 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 besi des mild and strong hybrids which can significantly reduce GHG emissions and fuel c onsumption in pickup trucks, EPA is also proposing a performance-based incentive CO 2 emissions credit and equivalent fuel consumption improvement value for full size pickup trucks that achieve a significant CO 2 reduction below/fuel economy improvement above the appli cable target. This would be available for vehicles achieving significant CO 2 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 CO 2 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 CO 2 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 CO 2 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 vehi cle 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 se tting standards for MYs 2022-2025, and given NHTSA’s obligation to conduct a separate rulemaki ng 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 informatio n 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/mileSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 66 of 426 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’ abil ity to comply, without placing decisive weight on any particular factor or proj ection. The comprehensive evaluation process will lead to final agency action by both agencies.

Consistent with the agencies’ commitment to maintaining a single national framework forregulation 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 rep licates as closely as possible, the compliance protocols associated with the ex isting CAA Tier 2 vehicle emissi on standards, and with earlier model year CAFE standards. The certification, testing, reporting, and associated compliance
activities established for the G HG program closely track those in previously existing programs
and are thus familiar to manufacturers. EPA alr eady 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 au thorities applicable to its separate in-use requirements in this program.

The compliance approach allows manufacturers to satisfy the GHG program requirements in the same genera l way they comply with previously existing applicable CAA and CAFE requirements. Manufacturers will demonstr ate 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 67 of 426 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 CO 2 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 CO 2 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 CO 2 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. Unlik e 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 fleet 67 and all manufacturers have historically done so. In the MYs 2012-2016 program, EPA’s GHG program applies to these vehicles. Ho wever, after further consideration of this

67 49 U.S.C. 32902(e)Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 68 of 426 issue, EPA is proposing the same type of exclus ion 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 manufactur ers 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 sma ll 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 comp any 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 CO 2 standards. EPA is proposing to bring
small volume manufacturers into the CO 2 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 alte rnate 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 Allowan ce 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 manufactur ers, as further discussed in Section III.B.8. With the exception of the small businesses and sm all 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.Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 69 of 426 E. Summary of Costs and Benefits for the Proposed National Program

This section summarizes the projected cost s and benefits of the proposed CAFE and GHG emissions standards. These projections he lped 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 st andards 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 ve hicles, and civil penalties), primarily because NHTSA is statutorily prohibited from consideri ng some flexibilities when establishing CAFE standards, 70 while EPA is not. These differences c ontribute to differences in the agencies’ respective estimates of costs and benefits resultin g 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 Progra m 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 potentia lly multiple state standard s 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).Summary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 70 of 426 manufacturers’ costs for complying with the proposed passenger car and light truck GHG standards are slightly higher than NHTSA’s es timates 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 co nsidering the fact that manuf acturers 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 e ffects, 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 CO 2 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 s upplemental analysis including manufacturer use
of flexibilities and advanced t echnologies currently reflects the agency’s best estimate of the potential real-world effects of the proposed CAFE standards.

Without accounting for the compliance flexib ilities 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 fuelSummary of Interagency Working Comments Received on Draft rule under EO 12866 review. Subject to further policy review.

Page 71 of 426 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 CO 2 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 soci al 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 CO 2 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 thr ee 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.SummaryofInteragencyWorkingCommentsReceivedonDraftruleunderEO12866review.Subjecttofurtherpolicyreview.Page72of426Table II-11 NHTSA’s Estimated Fuel Saved (Billion Gallons and Barrels) and CO2Emissions Avoided (mmt) under theCAFE Standards (Estimated Required)Earlier722017 2018 2019 2020 2021 2022 2023 2024 2025 TotalPassengerCarsFuel (billion gallons) 3 2 5 7 9 11 13 15 17 19 104Fuel (billion barrels) 0.09 0.06 0.12 0.17 0.22 0.27 0.31 0.36 0.42 0.46 2.47CO2(mmt) 41 26 52 76 100 122 139 158 184 202 1100LightTrucksFuel (billion gallons) 0 0 2 5 6 9 10 11 12 13 69Fuel (billion barrels) 0.01 0.01 0.05 0.11 0.15 0.21 0.24 0.27 0.29 0.32 1.65CO2(mmt) 4 5 22 49 65 93 108 118 129 141 734CombinedFuel (billion gallons) 4 3 7 12 16 20 23 26 30 33 173Fuel (billion barrels) 0.10 0.07 0.16 0.28 0.37 0.48 0.55 0.62 0.71 0.78 4.13CO2(mmt) 45 31 74 124 165 215 246 276 313 343 183472The “Earlier” column shows benefits that NHTSA forecasts manufacturers will implement in model years prior to 2017 that are inresponse to the proposedMY 2017-2025 standards. The CAFE model forecasts that manufactures will implement some technologies, and achieve benefits during vehicle redesigns thatoccur 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 manufact urers’ ability to employ comp liance flexibilities and advanced technologies for meeting the standards, NHTSA estimates the following for fuel savings and
avoided CO 2 emissions, assuming FFV credits would be used toward both the baseline and final
standards:SummaryofInteragencyWorkingCommentsReceivedonDraftruleunderEO12866review.Subjecttofurtherpolicyreview.Page74of426Table II-12 NHTSA’s Estimated Fuel Saved (Billion Gallons and Barrels) and CO2Emissions Avoided (mmt) under theCAFE Standards (Estimated Achieved)Earlier 2017 2018 2019 2020 2021 2022 2023 2024 2025 TotalPassengerCarsFuel (billion gallons) 4 2 4 6 9 10 12 14 17 20 98Fuel (billion barrels) 0.09 0.05 0.10 0.15 0.21 0.25 0.29 0.34 0.40 0.47 2.34CO2(mmt) 41 23 43 69 93 111 128 151 177 204 1040LightTrucksFuel (billion gallons) 0 1 2 4 6 8 9 10 11 13 65Fuel (billion barrels) 0.01 0.02 0.05 0.10 0.14 0.19 0.22 0.24 0.27 0.30 1.54CO2(mmt) 4 7 22 47 64 89 100 109 123 138 702CombinedFuel (billion gallons) 4 3 6 11 14 19 21 24 28 32 163Fuel (billion barrels) 0.10 0.07 0.14 0.25 0.34 0.44 0.50 0.58 0.67 0.77 3.88CO2(mmt) 45 31 65 116 157 200 227 260 300 341 1742DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL75NHTSA 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 importsbeyondthe directsavings from reduced oil purchases),73as well as some disbenefits (e.g., increased traffic congestion) caused by drivers’ tendency totravel more when the cost of driving declines (as it does whenfuel economy increases). NHTSA has estimated the total monetaryvalue to society of these benefits and disbenefits, and estimates that the proposed standards will produce significant net benefits tosociety. Using a 3 percent discount rate, NHTSA estimates that the present value of these benefits would total more than $515billionover the lives of the vehicles sold during MYs 2017-2025; usinga 7 percent discount rate, more than $419 billion. More discussionregarding monetized benefits can be found in Section IV of thisnotice and in NHTSA’s PRIA. Note that the benefit calculationin thefollowing tables includes the benefits of reducing CO2emissions,74but not the benefits of reducing other GHG emissions.73We note, of course, that reducing the amount of fuel purchased also reduces tax revenue for the Federal and state/local governments. NHTSA discusses thisissue in more detail in Chapter VIII of the PRIA.74CO2benefits for purposes of these tables are calculated using the$22/ton SCC values. Note that the net present value of reducedGHG emissions is calculateddifferently from other benefits. The same discount rate used to discount the value of damages from future emissions (SCC at 5,3, and 2.5 percent) is used tocalculate net present value of SCC for internal consistency.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL76Table II-13 NHTSA’s Discounted Benefits ($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 Total3% discount ratePassenger cars11 7 14 21 27 34 39 45 53 59 310Light trucks1 1 6 13 18 26 30 33 37 40 206Combined12 8 20 34 45 60 69 78 90 100 5157% discount ratePassenger cars9 6 12 17 22 28 32 37 44 49 254Light trucks1 1 5 10 14 21 24 27 30 33 165Combined9 7 16 27 37 49 56 64 73 81 419Considering 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 Total3% discount ratePassenger cars 10 6 12 19 26 31 36 43 51 60 293Light trucks 1 2 6 12 17 24 28 30 35 39 195Combined 11 8 17 31 43 55 63 74 86 99 4887% discount ratePassenger cars 8 5 9 15 21 25 29 35 42 49 240Light trucks 1 2 5 10 14 20 22 24 28 32 157Combined 9 7 14 25 35 45 52 60 70 81 397DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL77NHTSA attributes most of these benefits(about $451 billion at a 3 percent discountrate, or about $358 billion at a 7 percentdiscount rate, excluding consideration of compliance flexibilities and advancedtechnologies for meeting the standards) to reductionsin fuel consumption, valuing fuel (for societal purposes) at the future pre-tax prices projected in the Energy InformationAdministration’s (EIA) reference case forecast from the AnnualEnergy Outlook (AEO) . NHTSA’s PRIA accompanying thisproposal presents a detailed analysisof specific benefits of the rule.Table II-15 Summary of NHTSA’s Fuel Savings and CO2Emissions Reduction under the CAFE Standards (EstimatedRequired)Amount 3% discount rate 7% discount rateFuel savings 173 451 358CO2emissionsreductions1,834 49 49NHTSA estimates that the increases in technology application necessary to achieve the projected improvements in fueleconomy will entail considerable monetary outlays. The agencyestimates that the incremental costs for achieving the proposedCAFEstandards – that is, outlays by vehicle manufacturers over and above those required to comply with the MY 2016 CAFE standards –will total about $157 billion (i.e., during MYs 2017-2025).Table II-16 NHTSA’s Incremental Technology Cost Estimates Employed in theCAFE Model for Certain TechnologiesSubcomp. CarCompact CarMidsizeCarLargeCarPerform.SubcomPerform.CompacPerform.MidsizePerform. LargeCarMinivanLTSmallLTMidsizeLTLargeLTDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL78p. Car t Car CarVEHICLE TECHNOLOGY ICM COSTS PER VEHICLE ($)Nominalbaseline engine(for costpurposes) Inline 4 Inline 4 Inline 4 V6 Inline 4 V6 V6 V8 V6 Inline 4 V6 V8Low frictionlubricants(level 1) 16 16 16 24 16 24 24 32 24 16 24 32Engine frictionreduction(level 1) 60 60 60 90 60 90 90 120 90 60 90 120VVT—Dualcam phasing(DCP) 44 44 44 88 44 88 88 88 88 44 88 88Discretevariable valvelift (DVVL) onDOHC 160 160 160 240 160 240 240 320 240 160 240 320Cylinderdeactivation onOHV 204 204 204 204 204 204 204 204 204 204 204 204Stoichiometricgasoline directinjection 268 268 268 402 268 402 5402 536 402 268 402 536Turbocharginganddownsizing(level 1) 490 490 490 20 490 20 20 639 20 490 20 639Turbocharginganddownsizing(level 2) 26 26 26 260 26 260 260 439 260 26 260 439Cooled exhaustgasrecirculation 303 303 303 303 303 303 303 303 303 303 303 303DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL79(EGR) – (level1)Cooled exhaustgasrecirculation(EGR) – (level2) 520 520 520 (296) 520 (296) (296) (296) (296) 520 (296) (296)AdvancedDiesel 873 873 873 844 873 844 844 1693 844 873 844 16936-speed auto.trans. withimprovedinternals (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/aHighEfficiencyGearbox 248 248 248 248 248 248 248 248 248 248 248 248ShiftOptimizer 2 2 2 2 2 2 2 2 2 2 2 2Electric powersteering 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/aElectricVehicle (EarlyAdopter) 3,001 3,001 3.719 3,998 3,001 3,001 3.719 3,998 n/a 4,580 n/a n/aLow RollingResistanceTires (level 1) 7 7 7 7 7 7 7 7 7 7 7 7Low RollingResistanceTires (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 48DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL80Reduction(level 1)Aero DragReduction(level 2) 162 162 162 162 162 162 162 162 162 162 162 162DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

81 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 vehicl e’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 th e 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 OME GA 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 repr esentation 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 ar e 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 co sts 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 ve hicle, engine, and transmission, this file contains information for each technology under cons ideration as it pertains to the specific vehicle

75 The market file for the MY 2011 final rule, which incl uded 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 th e average performance of each manufacturer’s fleet of cars and light trucks, the impact of potential standards on individual manufacturer s 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 manufactu rers’ fleet composition, the stringency of an attributebased standard cannot be predicted without perfo rming analysis at this level of detail.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

82 (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 tech nologies are not applicable (due to technical considerations or engineering constraints) to a pa rticular vehicle, engine, or transmission. Theseidentifications 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, th en 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 indepe ndent 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 a ffect 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) give n the increased focus of engineering resources that will be working to solve these technical challenges.

Whether a vehicle can be equipped with a par ticular 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 down sizing 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 theDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

83 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 estimatesthrough 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 th e 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, shap e, 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, regula tions, 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 redesign ed, 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 weig ht reduction, or even something as simple as low rollingresistance tires, to a vehicle will likely require some leve l of validation and testing to ensure that the vehicle may continue to be certified as compliant with NHTSA’s Fe deral 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

84 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 technol ogy at the particular point deemed suitable. These constraints are intended to produce results consistent w ith how we assume manufacturers will apply technologies in the future base d 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 fo r 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 th at 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 an d in Chapter 3 of the TSD.

NHTSA has previously received comments sta ting 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 ma nufacturers can and will accomplish

78 In the MY 2011 final rule, NHTSA noted that the CAR re port submitted by the Allian ce, prepared by the Center for Automotive Research and EDF, stated that “For a gi ven 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 sp an from four to five years to eight to te n 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 charact eristics 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 w ith 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 th e 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 unreasonabl e (but rather simply that different manufacturers may redesign their vehicles more or less frequently, as the ra nge 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 Anal yst, Merrill Lynch research paper, May 14, 2008 and “Car Wars 2010–2013, The U.S. automotive product pipeline,” John Murphy, Research Analyst, Bank ofDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

85 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 app lied. 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 technologi cal feasibility, the agency evaluates whether each technology could be implemented on all ty pes and sizes of vehicles, and whether some differentiation is necessary in applying certain t echnologies to certain types and sizes of vehicles, and with respect to the cost incurred and fuel consumption and CO 2 emissions reduction achieved when doing so. The 2010 NAS Report differentiated technology application using eight vehicle “classes” (4 car classes and 4 truck classes). 79 NAS’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-bo dy high-performance trucks; body-on-frame small and midsize trucks; and bodyDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

86 NHTSA similarly differentiates vehicles by “subclass” for the purpose of applying technologies to “like” vehicles and assessing th eir 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, La rge 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 classe s 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 fr om 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 vehiclesDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

87 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 effe ctiveness. 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 appl ying fuel economy-improving technologies to individual vehicles. For purposes of this pr oposal, 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 categor ized can be found in the MY 2011 final rule.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

88 during the compliance analysis. The decision trees were designed and configured to allow the CAFE model to apply technologies in a cost-effec tive, 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

89 Simplified Engine Decision Tree Used in the CAFE ModelDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

90 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” effectivenes s 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. No t all estimates available in the public domain or that have been (or will be) offered for the agenci es’ 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 rule making 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 fact ors 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 modeli ng 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 ava ilable in this model year?

Some of technologies considered are availa ble 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

91 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 make s 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 vehicl e) 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 manufact urers 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.” Unli ke 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 technolog ical 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 va lue, 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 ph ase-in cap constraint afte r 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

92 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 so me 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 ar e 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 learni ng effects, were taken into account through two kinds of mutually exclusive learning, “volum e-based” and “time-based.” NHTSA and EPA

84 See 74 FR at 14270 (Mar. 30, 2009) for further discussion and examples.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

93 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 initia l volume threshold has b een 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 re duction 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, in stead 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 th e 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 lear ning 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 implemen tation, 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 t hose 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 technologi es considered in our analysis is si mply not feasible. Instead, we have estimated the effects of learning on costs, fed those costs in to OMEGA, and reviewed the resultant penetration rates. The assumption that volumes have doubled after two years is based solely on the assump tion that year two sales are of equal or greater number than year one sales and, therefor e, 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

94 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 pe r 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-define d 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 analys is 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 CO 2 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, driv etrain or vehicle losses. Alternately, this effect may be seen when one technology shif ts 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 th e differences between one and the techno logy-specific levels of effectiveness in reducing fuel consumption. For example, not accounting fo r 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 adju st these multiplicatively comb ined effectiveness values.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

95 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, whic h 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 usi ng 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 sy nergies is similar to that in DOE’s National Energy Modeling System (NEMS). 88 Inputs to the CAFE model incorporat e 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

96 For applying incremental synergy factors in se parate 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 theincremental 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 mode l applies all available electrification, engine and transmission technologies before a pplying 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 comp leted 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 “A utonomie,” 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 technol ogy 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

97 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 CA FE 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 re quiring 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 rulema king, 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 fo r 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 (NO X ) – vehicle use $ 5,500 Nitrogen oxides (NO X ) – fuel production and $ 5,300DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

98 distribution Particulate matter (PM 2.5 ) – vehicle use $ 300,000 Particulate matter (PM 2.5 ) – fuel production and distribution $ 250,000

Sulfur dioxide (SO 2 ) $ 32,000

Annual CO 2 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 Chap ter 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 prod uction 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 constantDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

99 aspects of vehicle performance a nd 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 estimate s. Additionally, the agency notes that the technology costs included in this proposal take in to 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 maintenanc e 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 thes e 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 d ealer 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 buye rs attach to specific vehicle attributes are difficult to infer, differences in vehicle purcha se prices and buyers’ choices among competingDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

100 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 in clude 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” co sts 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 u tility 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 possi ble 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 sh ould 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 estimat ed 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 ag ency is in the process of developing a model of potential vehicle buyers’ d ecisions 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 in cludes 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

101 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 lowe r 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 vehi cle 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 achieve d 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

102 gap represents a reasonable estimate for use in evaluating the fuel savi ngs likely to result from more stringent fuel economy and CO 2 standards in MYs 2017-2025.

Table II-18. Estimated Fleet-Wide Fuel Econ omy 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 us e 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 ca rs, 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, includi ng 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 th e stakeholder meetings conducted prior to the technical assessment, confidential business information (CBI) was supplied by several manufacturers which indicated that electrically pow ered vehicles had great er variability in their on-road energy consumption than vehicles powe red by internal combustion engines, although other manufacturers suggested that the on-road/ laboratory differential attributable to electricDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

103 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 batt ery 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 responsi ble 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 engage d 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 CO 2 /mile for an average vehicle without any of the improved air conditioning technologies discussed in that rulemaking. For a 27 mpg (330 g
CO 2 /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 CO 2 emissions attributable to air cond itioner efficiency by 40 percent through the use of advanced air conditioning technologies, and th at incorporating this change would reduce the average on-road gap by about 2 percent. 95 However, air co nditioning-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: Revision s 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/200 6/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%).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

104 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 fo recasts 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 throug h 2060, the agency assumes that reta il 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 mo re 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

105 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 rema in constant when expressed in constant dollars. In contrast, EIA assumes that Federal gasoline taxes w ill 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 ar e 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 uncer tainties regarding OPEC behavior as well as future levels of oil production and demand. These alternative s cenarios 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 bene fits 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 manufact urer could expect to receive when selling theDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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 th e 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 fro m 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 re sponse 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 lifet imes 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 w ith 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

107 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 li fetimes 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 Worldw ide 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 af ter which less than 2 percent of the vehicles originally produced during a model year remain in service. Applyi ng these conventions to vehicle registration data indicates that passenger cars have a maximum age of 26 years, whil e 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 co rresponding 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 adjustme nts to account for such vehicles in the baseline.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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 an d 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 mo st 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 cal culate 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 duri ng 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 Hous ehold Travel Survey (NHTS). 103 Because these estimates reflect the historically low gasoline prices th at 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 mode l 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 futu re 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 informa tion, such as J.D. Powers, and concluded that CSM was better able to provide forecasts at the requisite leve l 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 rate s 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

109 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 consis tent 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 increa se 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 a ssuming 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 in creases it. The net effect of these two adjustments is to increase expect ed lifetime mileage by about 18 percen t significantly for both passenger cars and about 16 percent for light trucks.

106 To illustrate these calculations, the agency’s adjustment of the AE O 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 driv en 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 sim ilar 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 te st fuel economy level of 32.4 mpg, which corresponds to actual on-road fuel economy of 25.9 mpg (= 32.4 mpg × 80 per cent). 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

110 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 o ccurs 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 pri ces, 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 ma tches 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 vehi cle 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, fu el 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 st udies using aggregate annual data for the U.S.

107 Formally, the rebound effect is often expressed as the elas ticity of vehicle use with respect to the cost per mile driven. Additionally, it is consistently expressed as a posi tive 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 proba bly more appropriate for evaluating the fuel savings and emissions reductions resulting from stricter sta ndards that would apply to future model years.

109 In effect, these studies treat U.S. states as a data “p anel” by applying appropriate estimation procedures to data consisting of each year’s aver age values of these variables for the separate states.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

111 assume a constant rebound effect, although some of th ese studies test whether the effect varies as changes in retail fuel prices or average fuel effi ciency 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. Fi nally, 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. citie s, 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 fu el 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 derivedfrom 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 differen t 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 am ong vehicle ownership categories.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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 reason s: 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 ownershi p 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 resp onse 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

113 percent, and an unpublished update through 2004 pr epared 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 re bound 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 statisti cal 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 fu el 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, th e 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, wh ich average 15–25 percent. Because the lifetimes of vehicles affected by the alternative CAFE st andards 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 estim ates 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 Tr ends 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: Methodologi cal, 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 Li ght-Duty Vehicle Travel Statistics,” February 9, 2010. This paper has been accepted for an upcoming special issu e of Energy Policy, although the publication date has not yet been determined.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

114 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 historic al 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 betw een historical estimates and projected future estimates. Chapter 4.2.5 of the Joint TSD review s 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 TotalDRAFT – 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 rec over 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 ex tent, 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 estimate d 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 presumab ly 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 gallon s of fuel purchased, length of time to refuel and pay, length of time to drive to the fueling sta tion, 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 consta nt 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 drivi ng 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 re bound 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. NH TSA 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 esti mates 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

117 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 be nefits 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 pe troleum 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 fu el 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,” Ener gy 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). Han dbook of Natural Resource and Energy Economics, Vol. III. Amsterdam: North-Holland, pp. 1167–1218.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

118 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, loweri ng 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 increas ed 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 fi rms 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 pr esence to secure imported oil supplies from unstable regions, and for maintaining the strategic petroleum reserve (SPR) to cushion against resulting price increases. NHTSA r ecognizes that potential national and energy security risks exist due to the possibility of tens ion over oil supplies. Much of the world’s oil

122 The reduction in payments from U.S. oil purchasers to dom estic petroleum producers is not included as a benefit, since it represents a transfer that o ccurs entirely within the U.S. economy.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

119 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 improv ements 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 am ount of oil imported reduces those risks, and thus increases the nation’s energy security.

Similarly, while the costs for building a nd maintaining the SPR are more clearly attributable to U.S. petroleum consumption and im ports, 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 maintainin g 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 externalitie s 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_a nalysis/files/ORNL6851.pdf (last accessed October 11, 2011).

124 Leiby, Paul N. “Estimating the Energy Security Bene fits 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/Le iby2007%20Estimating%20the%20Energy%20Security%20B enefits%20of%20Reduced%2 0U.S.%20Oil%20Imports%2 0ornl-tm-2007-028%20rev20 07Jul25.pdf (last accessed October 11, 2011).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

120 comments and recommendations of the peer reviewers. 125 Finally, at the request of EPA, ORNL has repeatedly revised its estimates of external co sts 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 re duction 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 agen cies 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 (NO X ), fine particulate matter (PM 2.5 ), and sulfur oxides (SO X ). These pollutants are emitted during vehicle storage and use, as well as throughout the fuel production and distribution system. While reductions in domestic fuel refining, storage, and distributi on 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. Th e 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.

125 Peer 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

121 With the exception of SO 2 , NHTSA calculated annual emissi ons 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 SO 2 were calculated by NHTSA using average fuel sulfur content estimates suppli ed by EPA, together with the assumption that
the entire sulfur content of fuel is emitted in the form of SO 2

. 127 Total SO 2 emissions under each alternative CAFE standard were calculated by ap plying 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 re sulting 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 o ccur during each phase of fuel production and distribution, including crude oil extraction and tran sportation, fuel refining, and fuel storage and transportation. NHTSA estimates the reductions in criteria pollutant emissions from producing and distributing fuel that would occur under a lternative 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 upda te 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 es timates 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 SO 2 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/mode ling_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 fo r 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 ener gy content; these factors are then converted to mass per gallon of gasoline using the average energy content of gasoline.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

122 discussed previously to calculate the effects of reductions in fuel use on changes in imports of refined fuel and domestic refining. NHTSA’s an alysis assumes that reductions in imports of refined fuel would reduce criteria pollutant emissi ons during fuel storage and distribution only. Reductions in domestic fuel refining using impo rted 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 lo wer domestic fuel refining and distribution. 131 As indicated previously, the effect of adopting hi gher CAFE standards on total emissions of each criteria pollutant depends on the relative magnit udes of the resulting reduction in emissions from fuel refining and distribution, and the increas e 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 ai r pollutants except carbon monoxide (CO).

The net changes in direct emissions of fine particulates (PM 2.5 ) and other criteria pollutants that contribute to the formation of “secondary” fine particulates in the atmosphere (such as NO X , SO X , 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 PM 2.5 concentrations are reduced. The value of reductions in the risk of premature death due to exposure to fine particulate pollution (PM 2.5 ) 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 PM 2.5 pollution, or reductions in health and welfare impacts related to other criteria air pollutants (ozone, NO 2 , and SO 2 ) 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 travel s to U.S. refineries are approximately the same regardless of whether it travels from domestic oilfields or import termin als, 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

123 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 redu cing 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 pr ojected 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 CO 2 Emissions

Emissions of carbon dioxide and other gr eenhouse 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 truc ks, 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 th at climate changes with potentially ca tastrophic economic or environmental impacts will occur, lowering GHG emissions may also result in economic benefits that exceed the resulting reduc tion 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 econom ic 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
CO 2 . The agency estimates emissions of CO 2 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 CO 2 emissions released per gallon of fuel consumed. This calculation assumes that the entire carbon content of each fuel
is converted to CO 2 emissions during the combustion process.

132 These reflect differences in the typical geographic distribut ions of emissions of each pollutant, their contributions to ambient PM 2.5 concentrations, pollution levels (predominantly those of PM 2.5 ), and resulting changes in population exposure.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

124 NHTSA estimates emissions of CO 2 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 Tran sportation (GREET) model. For liquid fuels, NHTSA converts these rates to a per-gallon basi s 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 CO 2 emissions from fuel production and distribution. GREET supplies emission rates for electricity ge neration that are expressed as grams of CO 2 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 hybri d and battery electric vehicles. As with all
other effects of alternative CAFE standards, the reduction in CO 2 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 CO 2 emissions from supplying and using fuel energy consumed
under the baseline alternative. Unlike criteria pollutants, the agency’s estimates of CO 2 emissions include those occurring in domestic fuel production and consumption, as well as in overseas production of petroleum and refined fuel fo r 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 CO 2 Emissions

NHTSA takes the economic benefits from reducing CO 2 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 CO 2 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 CO 2 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 CO 2 in its analysis of alternative CAFE standards.

NHTSA estimates the value of the reductions in emissions of CO 2 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 CO 2 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 CO 2 emissions occurring during aDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

125 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 CO 2 in the earth’s atmosphere.

Reductions in CO 2 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 ne t 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 CO 2 emissions projected to
result from each alternative increase in CAFE standards.

NHTSA’s evaluates the economic benefits from reducing CO 2 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 CO 2 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 empl oy 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 toDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

126 as integrated assessment models), using discount rates of 2.5, 3, and 5 percent. The fourth value, which represents the 95 th percentile SCC estimate from the co mbined distribution of values generated by the three models at a 3 percent disco unt 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 a lternative 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 CO 2 Emissions for Selected Future Years (2009$ per metric ton)

Discount Rate 5% 3% 2.5% 3%

Source Average of estimates 95 t h 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 CO 2 emissions that are small relative to cumulative global emissions.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

127 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 estimat es also rise over time; for example, the average SCC at the 3 percent discount rate increases to $27 per metric ton of CO 2 by 2020 and reaches $46 per metric ton of CO 2 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 ex pected 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 th eir well-being by more today than it will in the future. The discount rate expresses the per cent 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 reflect ed 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

128 regulations that mainly affect private consumptio n should be discounted at consumers’ rate of time preference. 135

Current OMB guidance also indicates that save rs 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 wh ether vehicle manufacturers will completely recover their costs for complying with higher CA FE standards by increasing vehicle sales prices, however, NHTSA also presents benefit and cost es timates 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, th ese costs are likely to displa ce 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 th e 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 CO 2 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 CO 2 emissions (or SCC), the benefits from reducing CO 2 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 a lternative 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 ra te used by the interagency group to deri ve 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 CO 2 emissions is coincidental, and should not be interpreted as a required condition that must be satisfied in future rulemakings.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

129parameters 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 variab les, 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 po ssible 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 th e 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 re viewed and updated economic assumptions for purposes of this final rule is available in Chapte r 4 of the draft Joint TSD. In addition, all of NHTSA’s model input and output files are now publ ic 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 regardin g 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

130 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 out puts 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 documenta tion) and a cost-minimiz ing strategy in order to identify a set of technologies the manufact urer 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 tech nology application methodology has been reviewed by theDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

131 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 compliance 141 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 willin g 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. Th is ability to simulate “voluntary overcompliance” reflects the potential that manufacturers will apply some technologies to so me vehicles if doing

agency over the course of several rulemakings, and comm enters 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 abili ty—as an option—to account for credit mechanisms ( i.e. , carryforward, carry-back, transfers, and tr ades) when determining whether compli ance 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 se veral 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 tech nologies 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 re mains 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 fo r 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 techno logies before paying fines would cause unrealistically high estimates of market penetration of expensive technol ogies 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 manufac turers choosing to pay fines in its modeling analysis in order to achieve what the agency beli eves is a more realistic simulation of manufacturer decision-making. Unlike flex-fuel and other credits, NHTSA is not barred by statut e from considering fine-payment in determining maximum feasible standards under EPCA/EISA. 49 U.S.C. 32902(h).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

132 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 fres hened, 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, a nd 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 manufactur ers 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

133 emissions reductions, and costs and benefits of ach ieving 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 engi neering 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 st ill 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 give n 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

134 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 relate d 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 analys is, 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 cap abilities: (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-fue l vehicles (FFVs), (3) accounting for costs ( i.e. , “stranded capital”) related to early replacement of t echnologies, (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 (andaccompanying 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 great er 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 am ount 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.]DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

135 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 usef ulness. 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 manuf acturer 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 mo del 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 technolo gies 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 tec hnology expected to improve ride quality, and a value loss could be specified for a technology expected to reduce vehicle range.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

136 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), a nd credit trades (between manufacturers). The model accounts for EPCA-specified limitati ons 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 mo re 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 ve hicle’s fuel consumptio n 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 input s specifying the average amount of improvement anticipated, and the estimated average cost to apply the underlying technology.

Considering that past CAFE rulemakings indi cate 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 fr om 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 technol ogy, but not enough to comply with CAFE standards. In simulating this decision-maki ng, the model considers the cost to add the technology, the calculated reductio n 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 willin g 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 extensio n 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-DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

137 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 bene fits (or disbenefits) in the calcu lation 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 ne w 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 ve hicle 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—w hether 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 vehi cle choice model would be structurally and operationally similar to the integration we implem ented 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 ma nufacturers 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 ve hicle 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 repla ce the sales estimates in the original market forecast with those reflecting these model-estima ted 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, becauseDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

138 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 ra tes and corresponding incurred costs, fuel consumption, greenhouse gas and criteria polluta nt 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 refl ected in today’s proposal. Especially without knowing the characteristics of a future vehicle c hoice 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 alternativ e approaches will be facilitated by specific recommendations regarding implementation within the model’s ove rall 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 inform ation demonstrating that inclusion of such a
model would lead to more realistic estimates of co sts, 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 tota l 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

139 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 vehiclesprojected to be sold in model years 2017-2025. The benefits include impacts such as climaterelated economic benefits from reducing emissions of CO 2 (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 dr iving attributed to the rebound effect, the value of reduced refueling time needed to fill up a more fuel efficien t 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 Value a,b (Billions of 2009 dollars)

Lifetime Present Value c – 3% Discount Rate Program Costs $140 Fuel Savings $444 Benefits $117

Net Benefits d $421

Annualized Value e – 3% Discount Rate Annualized costs $6.43 Annualized fuel savings $20.3 Annualized benefits $5.36 Net benefits $19.3

Lifetime Present Value c - 7% Discount Rate Program Costs $138 Fuel Savings $347 Benefits $101

Net Benefits d $311

Annualized Value e – 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 CO 2 reduction (model average at 2.5% discount rate, 3%, and 5%; 95 th 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 wi th the marginal value deemed to be central byDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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 disc ount the value of damages from future emissions (SCC at 5, 3, and 2.5 percent) is used to cal culate 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 th is analysis, lifetime present values are calculated for the first year of each model year for MYs 201 7-2025 (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 valu e through a given time period (the lifetime of each MY in this analysis) whose summed present va lue equals the present value from which it was derived. Annualized SCC values are calculated usin g the same rate as that used to determine the SCC value while all other costs and benef its are annualized at either 3% or 7%.

Table II-23 shows EPA’s estimated lifetime fuel savings and CO 2 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 ca rs and trucks may change the distribution of the fuel savings and GHG emission impacts betwee n 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 CO 2 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, primaril y because of the possibility of paying civil penalties in lieu of applying technology in NHTSA ’s program, which is required by EPCA.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL141Table II-23 EPA’s Estimated 2017-2025 Model YearLifetime Fuel Saved and GHG Emissions Avoided2017MY2018MY2019MY2020MY2021MY2022MY2023MY2024MY2025MYTotalCarsFuel (billion gallons) 1 3 6 8 11 13 16 19 21 99Fuel (billion barrels) 0.0 0.1 0.1 0.2 0.3 0.3 0.4 0.4 0.5 2.4CO2EQ (mmt) 17 44 71 101 131 160 186 213 241 1,163LightTrucksFuel (billion gallons) 12347911131566Fuel (billion barrels) 0.0 0.0 0.1 0.1 0.2 0.2 0.3 0.3 0.4 1.6CO2EQ (mmt) 12 26 38 51 88 113 136 159 181 805CombinedFuel (billion gallons) 269121823273237165Fuel (billion barrels) 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.8 0.9 3.9CO2EQ (mmt) 29 70 108 151 220 273 322 372 422 1,967DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

142 Table II-24 shows EPA’s estimated lifetime disc ounted 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 CO 2 , 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 omitte d 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. Th e benefits include all benefits considered by EPA such as GHG reductions, PM benefits, ener gy 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 associat ed 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 Mode l Year Lifetime Discounted Benefits Assuming the $22/ton SCC Value a,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 CO 2 (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 analys is 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 consis tency. Refer to Section III.H for more detail.

c Monetized GHG benefits exclude the value of reductions in non-CO 2 GHG emissions (HFC, CH 4 and N 2 O) expected under this final rule. Although EPA has not monetized the benefits of reductions in these non-CO 2 emissions, the value of these reductions should not be interpreted as zero. Rather, the reductions in non-CO 2 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-CO 2 emissions and CO 2 emissions, and specifies a goal to develop methods to value non-CO 2 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

143 Table II-25 shows EPA’s estimated lifetime fuel savings, lifetime CO 2 emission reductions, and the monetized net present values of those fuel savings and CO 2 emission reductions. The fuel savings and CO 2 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 CO 2 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 CO 2 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, CO 2 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

CO 2 e emission reductions (CO 2 portion valued assuming $22/ton
CO 2 in 2010) 1,967 MMT CO 2 e $46.4 a,b

a $46.4 billion for 1,743 MMT of reduced CO 2 emissions. As noted in Section III.H, the $22/ton (2009$) value applies to 2010 emissions and grows larger over time. M onetized GHG benefits exclude the value of reductions in non-CO 2 GHG emissions (HFC, CH 4 and N 2 O) expected under this proposed rule. Although EPA has not monetized the benefits of reductions in these non-CO 2 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-CO 2 emissions and CO 2 emissions, and specifies a goal to develop methods to value non-CO 2 emissions in future analyses.

b Note that net present value of reduced CO 2 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 cons istency. Refer to Section III.H for more detail.

Table II-26 shows EPA’s estimated incrementa l 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

144 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 th e first year of each model year; the “S um” 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 Case a (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’ resp ective 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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 pot ential CAFE standards, the CAFE model does not determine the CAFE standards that NHTSA propos es 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 CA FE 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 inst alled, 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 mode l 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 manufactur ers, 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 standa rds, 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 anal ysis conducted that relied in part on confidential manufacturer product plans cannot be made pub lic, as prohibited under 49 CFR part 512.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

146 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 schedul e 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 establ ish separate CAFE standards for passenger cars and light trucks 153 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 technologi cal 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.”).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

147 Besides the requirement that standards be maximum feasible for the fleet in question, EPCA/EISA also contains several other requirement s. 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-footp rint 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 standa rds for each model year at the level determined
to be the maximum feasible leve l for that model year. Generall y 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 Determ ining 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 consider able latitude in interpreting them. NHTSA interprets the four statutory factors as set forth below.

(1) Technological Feasibility

“Technological feasibility” refers to whet her 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 establis hed. Thus, the agency is not lim ited in determining the level of new standards to technology that is already be ing 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

148 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 re member, 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 “technolo gical feasibility” can drive standards higher by assuming the use of tec hnologies that are not yet commercial, “maximum feasible” is still also defined in terms of economic practicability, for example, which might caut ion 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” balanc ing 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 un reasonable 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 partic ularly 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-savin g 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

149 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 manufactur er 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 pres ent 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 ve hicle 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 generall y sought to ensure the economic practicability of standards in part by setting them at or near the capabilit y 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 th e 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 proj ected to be cost-effectiv e 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 situatio ns 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 consideratio n 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; manufact urers’ 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 (9 th Cir. 2008).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

150 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 years 166 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 or iginally 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

151 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 chan ges 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, th at 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 “oth er 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 ru lemaking 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 reduct ions in emissions of criteria pollutants and CO 2

. 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

152 Projected future fuel prices are a critical i nput 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 disr uption in commercial oil supplies threaten the U.S. economy, to allow the United States to me et 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 a nd distribution that result from lower fuel consumption will reduce U.S. emis sions of various pollutants, additional vehicle use associated
with the rebound effect 170 from higher fuel economy will increa se emissions of th ese pollutants. Thus, the net effect of stricter CAFE standards on emissions of each pollutant depends on the relative magnitudes of its reduced emissions in fu el refining and distribution, and increases in its
emissions from vehicle use. 171 Fuel savings from stricter CAFE standards also result in lower
emissions of CO 2 , the main greenhouse gas emitted as a result of refining, distribution, and use of transportation fuels. Reducing fuel consumpt ion reduces carbon dioxide emissions directly, because the primary source of transportation-related CO 2 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

153 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 qua ntities 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 eco nomy 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 ve hicle 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 st andards, NHTSA assesses the potential safety impacts and considers them in balancing the st atutory considerations and to determine the
maximum feasible leve l of the standards.

Under the universal or “flat” CAFE standard s 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 (9 th 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

154 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 faci litate 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 speci al calculation procedure that results in those vehicles being assigned a high fuel economy level.

The effect of the prohibitions against consider ing 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 thos e 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 re main described above remain true compliance
flexibilities.

On the other hand, NHTSA does not believe th at 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

155 what standards would be maximum feasible. In order to better meet EPCA’s overarching purpose of energy conservation, the agency is therefore consid ering 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 woul d similarly consider manufacturers’ ability to raise their fuel economy using off-cycle technologi es 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 whic h 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 sta ndards 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 prohibi tion against including the defined statutory
credits in our determination of maximum feasible standards as ap plying only to the flexibilities expressly identified in 49 U.S.C. 32902(h), NHTSA must, for the fi rst 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 cr edit 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 calcu lation 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 make s 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 ec onomy attributable to dual-fueled automobilesDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

156 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 ec onomy 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 Sta ndards 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 ch ange 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 (9 th Cir. 2008).

180 Id

. CAS , 1338 (quoting Chevron U.S.A., Inc. v. Natural Resources Defense Council, Inc., 467 U.S. 837, 845).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

157 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 di scussed 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 au thority 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 fo r 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 en sure 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

158 improvements occur during that time frame. To help commenters’ c onsideration of this question, Section IV.E presents illustrative leve ls 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 au thority to set universal standards for domestically-manufactured passenger cars at the le vel 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 co uld 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 a nd light trucks and “accept[ed] at least the possibility that . . . [it] could be reasonably interp reted 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 fu lly 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

159 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 fl eet that may be more than offs et by shifts the other way in another manufacturer’s fleet. However, in th is 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 mi nimum 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 mini mum 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 wo uld 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 provid ed 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 st andards 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 me t 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 stringen cy that larger manufactur ers 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

160 their inability to spread compliance burden across a larger product lineup, and difficulty in obtaining fuel economy-improving technolog ies quickly from suppli ers. 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, wher e 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 perm issible 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 depa rture 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 woul d 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 ar e 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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 c onsidering the four factors of technological feasibility, economic practicability, the eff ect 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 inte grated 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 th at 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 evalua ted, 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 byDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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 (i n 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:

ܧܨܣܥ ௗ௜௥௘௨௥௘௤ ൌ ∑ ܱܰܫܶܥܷܦܴܱܲ ௜ ௜ ∑ ܱܰܫܶܥܷܦܴܱܲ ௜ ܶܧܩܴܣܶ ௜ ௜

PRODUCTION i 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 TARGET i 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 re quired levels based on NHTSA’s current projection of manufacturers’ vehicle fleets in MYs 2017–2025. Actual re quired 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 curv es, as defined by the constrained linear function, and as embedded in the function for the sales-weighted harmonic av erage, are the real “standards” being proposed today.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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 “f loor” reflecting the MY 2016 standard, after accounting for estimated adju stments reflecting increased air conditioner efficiency. This prevents the target at any f ootprint from declining betw een model years. The
resultant mathematical function is as follows:

ܶܧܩܴܣܶ

ൌ ܣܯܺ ൮ 1

ܰܫܯ ቂ ܺܣܯ ቀ ݀, ൅ ܰܶܫܱܱܴܶܲܨ ൈ ܿ 1

ܽ ቁ

, 1 ܾ ቃ , 1

ܰܫܯ ൤ ܺܣܯ ቀ ݄, ൅ ܰܶܫܱܱܴܶܲܨ ൈ ݃ 1

݁ ቁ

, 1 ݂ ൨ ൲

The proposed standards for light trucks are, th erefore, 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 followingDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL165Figure II-5. Fuel Economy Targets for MYs 2012–2016 and 2017–2025 Passenger Cars2025303540455055606530 35 40 45 50 55 60 65 70 75 80

Fuel Economy Target (mpg)Footprint (sf)20252024202320222021202020192018201720162015201420132012DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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/Fiat 190 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
VW 191 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 th e MY 2016 passenger car market, NHTSA again estimates that the average requi red 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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/Fiat 192 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
VW 193 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

168 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 st andard 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 theminimum 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 resp ected, 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 finalDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

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 otherassumptions, that might make such minimum standards relevant to help ensure that currentlyexpected fuel economy improvements occur duri ng 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 observeDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

170 potential market changes during the implementation of the MYs 2012-2016 standards and
consider additional minimum standards in a fu ture 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 S ection 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 f ootprint-dependent targets shown graphically below for MYs 2017-2025. MYs 2012-2016 final standards are shown for comparison.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL171Figure II-6. Fuel Economy Targets forMYs 2012–2016 and 2017–2025 Light Trucks2025303540455055606530 35 40 45 50 55 60 65 70 75 80

Fuel Economy Target (mpg)Footprint (sf)20252024202320222021202020192018201720162015201420132012DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 172 of 426 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 leve ls 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 averag e 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 agen cy’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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 173 of 426 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
VW 196 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/Fiat 197 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 174 of 426 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
VW 198 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 necessari ly 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 pass enger 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: sp ecifically, technological feasibility, economic practicability, the effect of other motor vehicl e 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 n eed of the nation to conserve more energy and might potentially be technologically feasible (in the narrowest se nse) during those model years, but based on our analysis and the evidence presented by the indust ry, we tentatively conclude that hi gher 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 es timates necessarily do not perfectly capture all of the factors of economic practicability, and this conclusion re garding net benefits versus economic practicability is similar to the conclusion reach ed 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 alternativ es as required under NEPA and the need of the nation to conserve energy, we do not believe they would have repres ented 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 en ergy security and climate ch ange concerns. They would also have lower net benefits th an the Preferred Alternative.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 175 of 426 conclude that the correct balancing of the relevant factors that the agency must consider in determining the maximum feasible standards r ecognizes 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 moneti zed 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 c onsider the findings of an Environmental Impact Statement (EIS) for “major Federal actio ns 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 with in 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 careful ly 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: techno logical feasibility, economic practicability, theDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 176 of 426 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 rulemakings 202 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 factor s 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 fact ors 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 di fferent 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 cons ider the ability of manufacturers to take advantage of several statutory provisions that facilitate compliance wi th 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 fu el 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 th at used by the agency in the MY 201 2-2016 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 am ount, without reference to any yearly changes in the fleet mix. In the 2012-2016 rule, the annual increases for the st ringency 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 177 of 426 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 near ly 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 discusse d 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 Draf t EIS analyzes a “No Action Alternative,” which assumes that, for MYs 2017 and beyond, NHTSA woul d set standards at the same level as MY 2016. The No Action Alternative provides a ba seline 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 id entify 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 ava ilability 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 technologi cal 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 recogn ize 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 represen t balancings carefully consider ed 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 le ad to the greatest net benefits over the entire rulemaking periodDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 178 of 426 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 th e 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 ai r-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 prac ticability, and to which the most stringent alternatives give little weight. “Economic pract icability” 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 gaug e during times of uncertain fuel prices. 205 In a rulemaking such as the present one, dete rmining economic practicability re quires 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 179 of 426 fuel economy in addition to other vehicle attribut es. 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 tech nology, 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 so me 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. Standa rds 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 deve lop 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 distin ction 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 th e 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 th at 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 regula tory alternatives; but it also indicated thatDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 180 of 426 manufacturers would not be able to apply t hose 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 practicabilit y 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, technolo gy 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 Cumula tive Lifetime Fuel Savings and Average Vehicle Cost Increase

Cumulative Average Cumulative AverageDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 181 of 426 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 economi c consequences. Standards that are lower thanthat point would likely not be maximum feasible, because such standards would leave fuelsaving technologies on the table unnecessarily; st andards 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 co st-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 theDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 182 of 426 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 anal ysis 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 theo retical technological feasibility of achieving these levels, various manufacturers would likely l ack 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 al ternatives 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 Chrysle r - - - - - 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 CO 2 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 183 of 426 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 alternativ es 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 th at we had not conducted sufficient analysis at the time to narrow the range of potential stringenci es 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 comi ng months to refine our proposal. Based on our
initial consideration of how the factors might be bala nced to determine th e 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 na tion 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 fl exibilities and provisions, in or der to get feedback from the
manufacturer stakeholders. Manufacturer stakeh olders provided comments, much of which was confidential business information, which included projections of how they might comply withDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 184 of 426 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 simila r 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 manufactur ers 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 integrat e 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 coul d create a sustainable business ca se under passenger car standards that increased at the rate required by the 4% alternative.

Regarding light trucks, most manufacturers expr essed significantly greater concerns over the 4% alternative for light trucks than for passenger cars. Many manufacturers argued that increases in light truck standard stringency shou ld 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 significan t increases in stringency in the MY 2012-2016

207 Feedback from these stakeholder meetings is summarized in secti on IV.B and documents that are referenced in that section.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 185 of 426 program 208 in combination with redesign schedules wo uld 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 standa rds 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 th eir ability to apply tec hnologies 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 an d consumer groups, consistently stated that stringent standards are technically achievable and crit ical 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 interest s stressed the need to carefully consider economic impacts and the op portunity 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 shoul d 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, co nducted additional independent analyses, and deliberated over the feedback received on the agencies’ analyses. NHTSA considered individual manufacturers’ redesign cycles and, where availabl e, 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 fl eet fuel economy would be below what they anticipated their required fuel economy level would be in MY 2016, and th at they currently expect that they will need to employ available flexibilities to comply with that standard.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 186 of 426 opportunity for industry to develop and implement technologies that would not adversely affect utility. NHTSA considered feedback on cons umer 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 assu mptions 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 stakeholde rs 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 b illion for the 4% alternative), and achieves only $20 billion less in total benefits than the 4% alte rnative (estimated total benefits are $310 billion for the preferred alternative and $330 billion fo r the 4% alternative), a very small difference given that benefits are spread across the entire lifet imes of all vehicles subject to the standards. The analysis also shows that the lifetime cumula tive 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 billi on gallons for the 4% alternative).

At the same time, the increase in average ve hicle 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 andDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 187 of 426 development into the more advan ced 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 nece ssary 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 implem ent technologies that do not maintain vehicleDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 188 of 426 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 sustai ned 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 co ncludes 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 altern ative has 48 percent lo wer 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 bi llion for the preferred alternative and $293 billion for th e 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 impr ovements in fuel effici ency initially envisioned over the entire period and supported by other stakeholders. NHTSA believes that theseDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 189 of 426 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 be lieve that standards set at the stated levels for
MYs 2017-2021 and the incentives fo r 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 CO 2 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 th e 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 dete rmination 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 tech nologies, 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 avai lability of these technologies.

211 We note, however, that the alignment is based on the a ssumption that manufacturers im plement 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 manufactur er 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 190 of 426 and jointly deliberate on inform ation 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 st andards 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 repr esented 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 requireme nts 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 dete rmination of maximum feasible standards.

213 We underscore that the agency’s tentative decision re garding 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 191 of 426 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 ec onomy levels under laboratory conditions, NHTSA estimates that these requirements would tr anslate 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 44.8DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 192 of 426 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 f actors the agency cannot consider for purposes of determining maximum feasible CAFE standards, 214 average achieved fuel economy levels would correspondingly increase through MY 2025, bu t that manufacturers would, on average, under-comply 215 in some model years and over-comply 216 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 ec onomy levels under laboratory conditions, NHTSA estimates that these requirements would tr anslate 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 th e ability of manufacturers to earn and use credits for over-compliance, in determining the ma ximum feasible stringency of CAFE standards.

215 “Under-compliance” with CAFE standard s 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-complianc e” occurs through multi-year planning: manufacturers apply some “extra” technology in early model years ( e.g., MY 2014) in order to carry that tech nology forward and thereby facilitate compliance in later model years ( e.g., MY 2016).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 193 of 426 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 additio nal 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 billio n 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 sufficien tly advantageous with respect to the ability to comply with CAFE standards in later model years. For example, fo r 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 technol ogies 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 standa rd 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 ne ed apply if standards remain unchanged after MY 2016, and that this additional technology would yield further fu el economy improvements of up to 1.3 mpg, depending on pickup configuration.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 194 of 426 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 CO 2 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 frac tion 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 ligh t 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 duri ng 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 productsDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 195 of 426 outside the U.S., which would in turn lead to a modest increase in emissions of greenhouse gases, criteria air pollutants, and airborne toxi cs 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 potentia l 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 fl eet-wide fuel consumption, the agency also estimates that the new CAFE standards for MYs 2017–2025 would lead to corresponding
reductions in CO 2 emissions from the U.S. light-duty vehicle fleet. Specifically, NHTSA
estimates that total annual CO 2 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, wh en the proposed standards would take effect, through 2060.

Table II-59. Reduction in Fleet-Wide Carb on 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 CO 2 emissions, together with corresponding reductions in other GHG emissions from fuel production and us e, 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 2100DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 196 of 426 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 vehi cle 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 in crease 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 agen cy’s analysis assumes that all changes in upstream emissions result from a decrease in petroleum production and transport, the anal ysis 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 197 of 426 Vehicle

class Source of emissions Criteria air pollutant

Nitrogen

oxides (NO X ) Particulate

matter (PM 2.5 ) Sulfur oxides (SO X ) 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 th e estimated reductions in selected PM 2.5 -related human health impacts that are expected to result from reduced population exposure to unhealthful atmospheric concentrations of PM 2.5 . 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 PM 2.5 -related dollar-per-ton estimates that reflect the quantifiable reductions in health impacts likely to result from reduced population exposure to particular matter (PM 2.5 ). They do not include all he alth 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 pollut ants (particularly ozone) and air toxics.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 198 of 426 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 PM 2.5 , ozone, and air toxics is very complex. Full-scale photochemical modeling would provide the necessary spatial an d 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 standa rds could entail significant additional technology beyond the levels that could be applied under baseline CAFE standards ( i.e. , the application of MY 2016 CAFE st andards to MYs 2017-2025). This additional technology will
lead to increases in costs to manufacturers and ve hicle buyers, as well as fuel savings to vehicle buyers. Also, as discussed above, NHTSA estim ates 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 co mpliance 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 co rresponding 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)DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 199 of 426 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 547DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 200 of 426 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 1,370DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 201 of 426 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 2022-2025DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 202 of 426 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 wher e 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:DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 203 of 426 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 woul d 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 re lated to this issue.

As mentioned above, these estimated costs derive primarily from the additional application of technology under the proposed st andards. 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.,DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 204 of 426 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 hi gh efficiency transmissions (denoted below by “HETRANS”) under the proposed standards, a nd 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%)DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 205 of 426 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%DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 206 of 426 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%DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 207 of 426 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%DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 208 of 426 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 a dditional application of technology will lead to a total of nearly $157 billion in incremental outlays during MY s 2017–2025 (and model years leading up to MY 2017) for additional technology attrib utable 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 5,487DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 209 of 426 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 exceptions 219 —to hold vehicle performance, capacity, and utility constant when applying fuel-saving technologies to vehicles, the analysis imputes no cost to any actual reducti ons in vehicle performance, capacity, and utility

219 For example, the agencies have assumed no cost chan ges due to our assumption that HEV towing capability is not maintained; due to potential drivability issues with th e P2 HEV; and due to potential drivability and NVH issues with the shift optimizer.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 210 of 426 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 benefi ts for meeting higher CAFE standards includes estimates of the economic value of potential change s in motor vehicle fatalities that could result from reductions in the size or weight of vehicl es, 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 associat ed with higher vehicle manufacturing costs. In either case, the tec hnology outlays reported here will not correctly represent the costs of meeting higher CAFE st andards. Similarly, NHTSA’s estimates of increased costs of congestion, accidents, and no ise 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 incr eased 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 be nefits are attributable to reductions in fuel consumption; fuel savings are valued using for ecasts 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 CO 2 and criteria pollutant emissions, the value of additional travel (induced by the rebound effect), an d the social costs of additional congestion, accidents, and noise attrib utable 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 estimate s that at the discount rates of 3 percent prescribed in OMB guidance for regulatory analys is, 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 2017-2021 220

220 Unless otherwise indicated, all tables in Section IV re port benefits calculated using the Reference Case input assumptions, with future benefits resulting from reducti ons in carbon dioxide emissions discounted at the 3 percent rate prescribed in the interagency guidance on the social cost of carbon.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 211 of 426 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 ec onomy—the difference between the total benefits that result from higher fuel economy and the tech nology 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 main tain consistency with the discount rate used to develop the reference case estimate of the soci al cost of carbon. All other future benefits reported in these tables are discounted using the 7 percent rate.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 212 of 426 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 agen cy estimates that when the benefits of the proposed standards are discounted at a 3 percent ra te, 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 achiev e 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 15DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 213 of 426 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 co mply 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 act ually 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 overestimatio n in the agency’s estimates of benefits from requiring higher fuel economy stems from it s 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 under estimate 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 undere stimated the value of fuel sa vings 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 supplyDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 214 of 426 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 PM 2.5 emissions. Although this is likely to be the most significant component of health benefits from redu ced 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 (N 2 O), 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

PM 2.5 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. Th e models also may not adequately capture certain other impacts, including potentially abrupt change s in climate associated with thresholds that govern climate system responses, interregional inte ractions 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 modelyears combined, using both discount rates prescribed by OMB regulatory guidance. Tables IV66 and 67 report technology outlays, each separa te 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 Impac t Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, United States Govern ment, February 2010. Available in Docket No. NHTSA– 2009–0059.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 215 of 426 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 CO 2 emissions 223 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
NO X 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
SO X 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 CO 2 emissions at a 3 percent annual rate. Additionally, we not e 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 216 of 426 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 CO 2 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
NO X 0.0 0.0 0.0 0.0 0.2

PM 1.1 1.3 1.4 1.7 8.3
SO X 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” re sulting from additional dr iving) 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 2017-2021DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 217 of 426 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 CO 2 emissions 224 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
NO X 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
SO X 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 CO 2 emissions at a 3 percent annual rate. Additionally, we not e 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 218 of 426 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 CO 2 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
NO X 0.0 0.0 0.0 0.0 0.2

PM 0.9 1.0 1.1 1.3 6.6
SO X 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 re flect the availability and use of certain flexibility mechanisms, such as compliance credits and credit trading, because EPCA prohibits NHTSA from considering the effect s of those mechanisms in settin g 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 219 of 426 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 gall ons—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 153DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 220 of 426 The agency similarly estimates CO 2 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 signi ficant 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, tota l 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 dies el engines and plug-in hybrid electric vehicles lead to differences in the percentage changes in fuel consumption and carbon dioxid e emissions between the with- and without-credit cases.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 221 of 426 Because NHTSA’s analysis indicated that th ese EPCA provisions will modestly reduce fuel savings and related benefits, the agency’s es timate of the present value of total benefits will be $488 billion when discounted at a 3 percent annu al 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 truc ks 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 wh en 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 397DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 222 of 426 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 provisi ons 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 263DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 223 of 426 The agency has performed several sensitivity analyses to examine important assumptions. All sensitivity analyses were based on the “sta ndard 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 se nsitivity 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 CO 2 benefits: The main analysis uses $22 per ton discounted at a 3 percent discount rate to quantify the benefits of reducing CO 2 emissions and $0.174 per gallon to quantify the benefits of reducing fu el 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 CO 2 emissions. 226 These are the 2010 values, which increase over time.
These values can be translated into cen ts per gallon by multiplying by 0.0089, 227 giving the following values:

 ($4.86 per ton CO 2 ) x 0.0089 = $0.043 per gallon discounted at 5%

 ($22.00 per ton CO 2 ) x 0.0089 = $0.196 per gallon discounted at 3% (used in the main analysis)

 ($36.13 per ton CO 2 ) x 0.0089 = $0.322 per gallon discounted at 2.5%

 And a 95 th percentile estimate of

 ($66.88 per ton CO 2 ) 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 assume s there is no loss in value to consumers resulting from vehicles that have an increas e 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 accura te to the nearest cent; rath er, 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 CO 2 is 44. 1 gallon of gas weighs 2,819 grams, of that 2,433 grams are carbon. One ton of CO 2 /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 CO 2 *0.0089 = 1 gallon of gasoline.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 224 of 426 sensitivity analysis assumes that there is a 25, or 50 percent loss in value to consumers – equivalent to the assumption that cons umers 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 sensit ivity 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 225 of 426 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 [$/(lb / %MR)] Example Unit Direct Manufacturing Cost 1 [$/lb] Example Total Direct Manufacturing Cost 2 [$/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 ve hicles that they believe consumers would value and be willing to pay for. Using para meter 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%UnitCost[$/lb]

Percent of Mass Reduction 

### Mass Reduction Cost Sensitivity Bounds

Low Bound NPRM Central Analysis Upper Bound Slope = 4.32 Slope = 2.59

Slope = 6.05DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 226 of 426 of one year for purposes of calculating the value of future fuel savings when simulating whether manufacturers would apply additio nal 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 t echnology 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 su fficiently 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 smallerwith a marketdriven response; rather, it means that costs, effects, and benefits would be at least as great, but would be partially attributable no t 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 CO 2 Value Reference 3% 10% $5 0¢/gal $36/ton CO 2 Value Reference 3% 10% $36 0¢/gal $67/ton CO 2 Value Reference 3% 10% $67 0¢/gal 50% Consumer Benefit Reference 3% 10% $22 0¢/gal 75% Consumer Benefit Reference 3% 10% $22 0¢/galDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 227 of 426 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 re duction 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 CO 2 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 potent ial 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 CO 2 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 CO 2 emissions reductions predicted by the sensitivity analysis as fuel price increases, wh ich initially may seem counterintuitive, we note that there are some counterbalancing factors occurri ng. As fuel price increases, people will drive less and so fuel savings and CO 2 emissions reductions may decrease.

The agency performed two additional sensitivit y 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 comparedDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 228 of 426 to the RPE markup factor from variable cost of 1.5. Next, the agency conducted a separate sensitivity analysis using values that were de rived from the 2011 NAS Report. This analysis used an RPE markup factor of 1.5 for non-electri fication 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 r ecent or near-term future model years. As noted above, the cost learning factors used for the sens itivity case are different fr om 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, NHTS A 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 -0.51DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 229 of 426 Max Net Benefits 44.31 44.48 0.18

Table II-106. Sensitivity Analyses (Achieved mpg, Per-Vehicle Cost, Net Benefits, Fuel Saved, & CO 2 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 woul d increase vehicle prod uction 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 fo r 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 fuelDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 230 of 426 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 change s 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 manufacturer s 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 an alysis without attemptin g 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 whensimulating 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 ownershi p. 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 underc ompliance 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 th is question; however, NHTSA regards this “counterfactual” case as simply unknowable.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 231 of 426 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 genera lly consistent with manufacturers’ confidential and, in some cases, public statements. Manu facturers 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 w ould 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 ev en” within a relatively short time—generally the first two to four years of vehicle ownership.

NHTSA considers it not only f easible 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 vehicl e 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 vehicl e 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 manufact urers 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 historical ly been the case, in part because recentlypromulgated requirements regard ing vehicle labels will provide clearer information regardingDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 232 of 426 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 pl ans—shifts consistent with expect ations 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 ru lemaking analysis in a manner that ignores the possibility that in the future, manufacturers wi ll voluntarily apply more technology than the minimum necessary to comply with CAFE standard s. 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 eco nomy 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 wouldDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 233 of 426 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 th e 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 billi on gallons estimated when potential voluntary overcompliance is not taken into account:

Table II-111. Fuel Saved (billion gallons) un der 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) un der 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 152DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 234 of 426 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, theref ore, 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 volunt ary overcompliance attributes some of the fuel savings to the market. The same holds for the at tribution of costs, other effects, and monetized benefits—inclusion of voluntary overcompliance do es 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 CO 2 emissions reductions attri butable 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 slight ly greater outlays for additional technology under the proposed standards when potential voluntary overcompliance is taken into account. This increase is attributable to slight incr eases 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 235 of 426 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 fo llowing 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 declin e 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 bill ion estimate of total benefits when potential voluntary overcompliance is not taken into account:DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 236 of 426 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 whenvoluntary 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 immedi ately 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 237 of 426 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 CO 2 emissions (and correspondingly, in total and net benefits) attributable to today’s proposed standards, do not indicate that fuel consumption and CO 2 emissions will be higher when potential voluntary overcompliance with standards is taken into accoun t 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 CO 2 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 sensitivit y 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 regardi ng 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 inform ation regarding NHTSA’s sensitivit y analyses for this proposed rule, please see Chapter X of NHTSA’s PRIA.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 238 of 426 5. Social Benefits, Private Benefits, and Potential Unquantifie d 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 co sts 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 redu ctions 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 exclus ively by the initial purchasers and subsequent
owners of vehicle models whose fuel econom y 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 tr ucks 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 pr esent 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 fuelDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 239 of 426 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 Savi ngs 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 Savi ngs 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, valu e, 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 si tuation might persist is that the market for vehicle fuel economy does not appear to work perfectly, in which case properly designed CAFE standardsDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 240 of 426 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 vehi cle 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 ec onomics, 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 empi rical 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 manufactur ers 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 prosp ect 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 a nd 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%20th e%20Energy%20Paradox.pdf (last accessed Sept. 26, 2011). For relevant background, with an empha sis on the importance of salience and attention, see Kahneman, D. Thinking, Fast and Slow (2011).

231 Mutulinggan, S., C.Corbett, S.Benzarti, and B. Oppenhe im. “Investment in Energy Efficiency by Small and Medium-Size Firms: An Empirical Analysis of the A doption of Process Improvement Recommendations” (2011), available at http://papers.ssrn.com/sol3/p apers/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, Te rrence 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). “Fue l 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 uncertaintyDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 241 of 426 The well-known finding that as gas prices rise, consumers show more willingness to pay for fuel-efficient vehicles is not necessarily inco nsistent 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 purch asing 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 attri butes. 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 ap parent 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 vehicl e buyer will experience net savings from the proposed standards, yet it is not simple to reconc ile 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 se veral 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 predic ts that certain losses will l oom 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 Economics 121(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’ Mark ets, Market Imperfections, and Energy Efficiency.” Energy Policy 22(10): 811–818.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 242 of 426 but were not available for application to new ve hicles 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 driv e, differences in their driving styles affect the fuel economy they expect to achieve, and vary ing 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 eff ects 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 isolat e the component of a new vehicle’s selling price that is related to its fuel economy, and compare thes e 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 prev ent 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 persistent ly 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 vehi cle 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 po ssible that deliberate decisions by manufacturersDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 243 of 426 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 offe r more fuel-efficient models within those segments. Of course, this possibility is most realistic if it is also as sumed 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 de cided to purchase a mi nivan, 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 de mand 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 th ey discount future fuel savings using higher rates than those prescribed by OMB for evalua ting 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 differen ces 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 feat ures the higher fuel economy levels required by

236 This is the range of comb ined city and highway fuel economy levels from lowest (Toyota Sienna AWD) to highest (Honda Odyssey) available for model year 201 0; http://www.fueleconomy.gov/feg/bestworstEPAtrucks.htm (last accessed September 26, 2011).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 244 of 426 the proposed rule, when those fuel savings are evaluated over different time horizons. The table then compares these values to the agency’s esti mates 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 aff ect private consumption d ecisions, 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 OMBestimates 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 245 of 426 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 disc ount 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 disc ount 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 pl ausible 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, howev er, 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 exte nding 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 fi nance 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/Cu rrent (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 Re serve Statistical Release G.19, Consumer Credit. Available at http ://www.federalreserve.gov/releases/g19/Current (last accessed September 27, 2011).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 246 of 426 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 thei r 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 ove r 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 consid er when deciding whether to purchase more fuelefficient vehicles. Readily imaginable combina tions 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 perhap s even unwilling—to purcha se 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 su ch 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 improve d 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 247 of 426 Another possibility is that achieving the fu el 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 i ndividual 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 competi ng 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 attribut es are of sufficient value, or if the range of
vehicles offered ensures that vehicles with thos e attributes will continue to be offered, then vehicle buyers will still have the opportunity to ch oose 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 allowan ces 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 t echnologies that manufacturers could employ to increase the fuel efficiency of cars or light truc ks. Although NHTSA has revised its estimates of manufacturers’ costs for some technologies signifi cantly for use in this rulemaking, these revised estimates are still intended to represent costs that would allow manufacturers to maintain the performance, safety, carrying cap acity, 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 make rs 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 combinat ions 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 wouldDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 248 of 426 change those other vehicle characteristics if co nsumers 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 ex isting 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 agen cy will continue to investigate new empirical literature addressing this question as it become s available, and seeks comment on all of the relevant questions.

NHTSA acknowledges the possibility that it ha s incorrectly characterized the impact on the market of the CAFE standards this rule prop oses, 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 res ources 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. Re presenting the change in fuel tax revenues in effect as an economy-wide cost is necessary to of fset the portion of fuel savings included in line 1 that represents savings in fuel tax payments by consumers. This prevents the savi ngs in tax revenues from being counted as a benefit from the economy-wide perspective.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 249 of 426 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 emi ssions 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 th e 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. Th e 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 featur es 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 outla ys 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 $61.20DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 250 of 426 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 CO 2 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 – Pass enger 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 CO 2 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 beli eves 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 buye rs and subsequent owners of the cars and light trucks to which the higher CAFE standards it propos es would apply. It is also possible that the agency has failed to develop cost estimates that do not require manufacturers to make changes inDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 251 of 426 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 unqua ntifiable) 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 standard s 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 ha ve 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 pri ce) 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 an d suppliers to devote increased attention to methods of improving vehicle fuel economy.

This increased attention will stimulate add itional research and engineering, and we anticipate that, over time, innovative approaches to reducing the fuel consumption of light dutyDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 252 of 426 vehicles will emerge. These innov ative 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 ef fects 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 theproposed 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 cons ider attempting to account for them in the final rule analysis.

Effects on Vehicle Costs

Any action that increases the cost of new vehi cles 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 propo sed 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. Th ese costs ar e 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 thepast that premium (higher octane) fuel may be necessary if certain advanced fuel economyimproving technologies are required by stringen t CAFE standards. The agencies have not assumed in our development of technology costs that premium fuel would be required.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 253 of 426 We intend to attempt to quantif y these additional costs for th e 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 aggregatevehicle 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 al so 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 comm enters 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 com position 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 ve hicles will to some extent be impacted by the standards. Compositional effects might be im portant 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 ve hicle prices, the fuel efficiency of new vehicle models, and total sales of new vehicles. If the va lue of fuel savings resulting from improved fuel efficiency to the typical potential buyer of a ne w vehicle outweighs the average increase in new models’ prices, sales of new vehicles will rise , while scrappage rates of used vehicles willDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 254 of 426 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 CO 2 emissions. However, if potential buyers value future fuel savings result ing from the increased fuel efficiency of new
models at less than the increase in their average selling price, sa les of new vehicles will decline,
as will the rate at which used vehicles are reti red 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 th e 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 volume s) may increase or decrease the cost of blended gasoline and diesel, depending on crude oil prices and tax subsidies offered forrenewable 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 bl ended with more ethanol emit slightly fewer greenhouse gas emissions per gallon. In addition, there might be corresponding benefit lossesDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 255 of 426 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 consum ption 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 sensitivit y 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 experi ence larger fuel savings and eco nomic benefits th an the average values reported in this NPRM, while those w ho drive less than our average estimates will experience smaller fuel savings and benefits. NHT SA 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 th e 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 so me 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 ru le 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 de sign 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 dete rmining which vehicles would be subject to which CO 2 standards.

243 EPCA 501(2), 89 Stat. 901, codified at 49 U.S.C. 32901(a).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 256 of 426 manufactured “primarily” for transporting up to te n 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), respec tively. 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 differen t 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 recl assify 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 ve hicles 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 capabi lity, if they also meet the “significan t 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 vehicl e 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 transporti ng passengers must be classified as a passenger car, the statute as currently written clearly provides that vehicl es 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 257 of 426 generally less capable of achieving higher fuel ec onomy levels as compared to the vehicles in the passenger car fleet. 2WD SUVs are the vehicles th at 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 gr ound clearance requirements for “o ff-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 cla ssifying 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 fl at to allow greater cargo-carrying capacity. NHTSA has received comments in the past arguing that vehicl es with three or more rows of seats, unless they are capable of transporting more than 10 in dividuals, 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 be yond. 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 defini tions that expanded cargo space is a fundamentally “truck-like”
characteristic. Much of th e public reaction to this definition, wh ich is mixed, tends to be visceral and anecdotal – for example, for parents with mini vans 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 sp rung up entirely in resp onse to the regulation, rather than in response to the consumer’s n eed for utility. If we believe, for the sake of argument, that the agency’s decision might be reasonable from both a policy and a legalDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 258 of 426 perspective whether we decided to change the defi nition 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 th e 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 heav y for their footprint and thus comparatively less fuel economy-capable, while the truck fleet achieve d 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 CO 2 emissions (equivalent to about 1 percent of the reduction in
CO 2 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 forw ard to consumers by increasing vehicle costs, NHTSA estimated that vehicle pri ces 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 259 of 426 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 po int 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 refitt ed curves, considering the vehicles in question, we might have obtained a somewhat steeper pass enger 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 (mov ing 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 technol ogically 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 shou ld 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 pa ssenger cars. At the same time, NHTSA also concluded that because Congress explicitly de signated 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 260 of 426 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 fi rst 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 co mmenters in the MY 2011 rulemaking that manufacturers may respond to this tighter classi fication by ceasing to build 2WD versions of SUVs, which could reduce fuel savings. In resp onse 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 m easure 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 prescrip tive with regard to enforcement. EPCA and EISA also clearly specify a number of flexibilitie s 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 co uld 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 261 of 426 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 CA FE program, EPA is proposing: 1) fuel economy performance adjustments due to improvements in air conditioning system efficiency; 2) utilization of “game changing” technologies in stalled 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 read er’s reference, the proposed new incentives and calculations, but we also refer readers to Sectio n 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 subsequentmodel 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 potentia l 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 262 of 426 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 readil y 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 w ith the manufacturer prio r 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 31 st 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 betw een 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 exp ected to simplify data submissi ons to NHTSA, improve the quality of the agency’s data, expedite public reporting, improve audit verifications and testing, and enable more efficient tracking of manufacturer s’ CAFE credits with greater transparency.

249 See http://www.nhtsa.gov/DOT/NHTSA/Vehicle%20Saf ety/Test%20Procedures/Associated%20Files/TP-537- 01.pdfDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 263 of 426 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 manufactur er’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 ma y 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 pl an indicating it will allocate existing credits, or if it does not have sufficient credits available in that fleet, how it wi ll 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 ap prove a credit allocation plan unless it finds the proposed credits are unavaila ble 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 a ssessment 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 264 of 426 truck), manufactured for that model year. The amount of the penalty may not be reduced except
under the unusual or extr eme 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 Vehicl e Safety Act, EPCA does not provide for recall and remedy in the event of a noncompliance. The presence of recall and remedy provisions 251 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 th e 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 th e 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 t hose 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 fl eet average is to meet the applicable standard.

Thus, under EPCA, there is no such thi ng 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-complian ce 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 initia te compliance proceedings. 49 CFR part 536 contains the detailed regulations governing the us e and application of CAFE credits authorized by 49 U.S.C. 32903.

251 49 U.S.C. 30120, Remedies for defects and noncompliance.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 265 of 426 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 cancurrently use to achieve compliance with CAFE standards beyond applying fuel economyimproving technologies: (1) Building dual- and al ternative-fueled vehicles; (2) banking (carryforward and carry-back), trading, and transferring credits earned for exceeding fuel economy standards; and (3) paying civil penalties. We not e 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, wh ich 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 gaso line 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 266 of 426 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 (ft 3 ) 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 ft 3 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-fuel ed 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 dedi cated 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 th at EPA will be treating dual- and alternative-fueled vehicles under its GHG program similarly to the way EP CA/EISA provides for CAFE through MY 2015, but for MY 2016, EPA established CO 2 emission levels for alternative fuel ve hicles based on measurement of actual CO2 emissions during testing, plus a manufacturer demonstrat ion 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 fuel s. Because EPCA/EISA provides the explicit CAFE measurement methodology for EPA to use for dedicated ve hicles and dual-fueled vehicles through MY 2019, we explained in the MYs 2012-2016 final rule that the CAFE pr ogram would not require that vehicles manufactured for the purpose of obtaining the credit actually be run on the alternative fuel.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 267 of 426 avoid the absurd result of dual-fueled vehicles’ fuel economy being measured like that of conventional gasoline vehicles. If the overarchin g 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 contin ue 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 an d 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 inst ead 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 possib le. 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 c onsumers 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 buildin g 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

-DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 268 of 426 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 fu el 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 provisio ns in EISA authorizing NHTSA to establish by regulation a credit trading program and directing it to establish by regula tion 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 (i ncluding, 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 “transfe rring” 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 269 of 426 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 fi nes 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 asa 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 targ et 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 cons ervation 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 qua ntitative 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 drawbacksDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 270 of 426 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 in sufficient 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 prov ide, 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 in formation 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 prop osed 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 incentiv es 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 tradeo ffs 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 applicati on of these “game changing” technologies for large pickups, both to save more fuel and to help provide a bridge for industry to more stringent lighttruck 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 beDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 271 of 426 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 si ze 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 de finitions for passenger automobiles and trucks and to guide the agency and manufacturers in cl assifying vehicles. NHTSA proposes to add a definition in Part 523.2 defining the characteristic s 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 penetrati on 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 stro ng 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 si ze 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 woul d 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. Th ese adjustments are consistent with the GHG

258 NHTSA is not prohibited from considering this availa bility of this incentive in determining the maximum feasible levels of stringency for the light truck standard s, because it is not one of the statutory flexibilities enumerated in 49 U.S.C. 32902(h).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 272 of 426 credits under EPA’s program of 10 g/mi CO 2 for mild hybrid pickups and 20 g/mi CO 2 for strong hybrid pickups. The specified adjustment in the fuel consumption performance would then be subtracted from the fuel consumption performance te st 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 CO 2 and 20 g/mi CO 2 , respectively, for beating the applicable CO 2 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 appl icable 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 calculat ions. 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 c onservation. EPA is not proposing to allow
manufacturers to claim CAFE-related benefits fo r reducing A/C leakage or switching to an A/CDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 273 of 426 refrigerant with a lower global warming poten tial, 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 CO 2 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 vehicl e 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. Conver ting to miles per gallon might assist the reader accustomed to thinking in those terms, but w ould actually complicate the discussion since fuel economy, unlike CO 2 /fuel consumption, is not linear, and fuel economy values would be inversely related to CO 2 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 CO 2 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 CO 2 in grams per minute (g/min) while the vehicl e 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 a dd 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 274 of 426 A/C are compared to a pre-determined baseline emissions level. 260 The fuel consumption performance is determined using the following equation: grams CO 2 /mi = 1/mpg * 8,887.

The second test, the “modified idle test,” uses th e 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 ha ve a higher idle test result (in CO 2 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 ex tensive 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 opera ting 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 CO 2 emissions in grams per mile (g/mi), and requires that baseline emissions
be measured in addition to emissions from vehicl es with improved A/C systems, unlike the idle test where baseline emissions are pre-determined by EPA.

Manufacturers then use the results of the test ing 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 CO 2 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 CO 2 emissions. In testing, average car and truck A/C CO 2 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 fuelDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 275 of 426 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 CO 2 credit value that can be claimed by the manufacturer

TCV1 = the maximum CO 2 credit value available, equal to the sum of the reduction for the A/C technologies used, and

ITP = the increase in CO 2 emissions shown on the idle test.

Finally, if the vehicle has a test result highe r 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 CO 2 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:

െ 20.5 ൌ ݈݀݋݄ݏ݁ݎ݄ܶ	ݐݏܶ݁	݈݀݁ܫ ሺ ݐ݈ܽܿ݉݁݊݌ݏ݅ܦ ൈ 1.58 ሻ

If the increased CO 2 emissions determined from the idle test procedure is less than or equal to the Idle Test Threshold , the total fuel consumption im provement value that can be claimed by the manufacturer shall be determined by summing the maximum value in Table
IV.I.1. If the increased CO 2 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 276 of 426 ܶܥܸ ൌ ܶܥܸ ଵ ൈ ൤1 െ ൬ ܫܶܲ െ ܫܶܶ

6.4 ൰൨

Where:

TCV = the total CO 2 credit value that can be claimed;

TCV 1 = the sum of the CO 2 credits for the technologies used in the A/C system;

ITP = the increased CO 2 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 higher 262 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 higher 263 20% 0.000113 0.000156

Blower motor control which limit 15% 0.000090 0.000124

262 Deviations from this temperature are allowe d if accompanied by an engineering analysis.

263 Deviations from this temperature are allowe d if accompanied by an engineering analysis.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 277 of 426 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 s ubject 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 fu el 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 CO 2 emissions, but for which the CO 2 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 inte nded to encourage the introduction of off-cycle technologies that achieve real-world benefits. Th e 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 determin ation is affirmative, the manufacture must follow the 5-cycle procedures to determine the CO 2 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, toDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 278 of 426 demonstrate the benefit and determine the appropriate CO 2 gram per mile credit. The demonstration program must be robust, verifiable, and capable of demonstrating the real-world emissions benefit of the technology with st rong statistical significan ce. The non-5-cycle approach includes an opportunity for public comment as part of the approval process.

EPA has been encouraged by automakers’ inte rest 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 techno logies 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 pr ogram 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 adjustme nt 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-define d 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 opti on should significantly simplify the program for manufacturers and provide certainty that improvement values may be generated through the use of pre-approved technologies. For improvement s from technologies not on the pre-defined list, EPA is proposing to clarify the step-by-step ap plication 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 0.0003DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 279 of 426 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 us ed 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 wi ll 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 is0.000416 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 techno logy 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 cond ition 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 theDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 280 of 426 technologies for that model year exceeded the per centage 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 t echnologies 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 an d bring the technologies into the mainstream. For solar roof panels and electric heat circul ation pumps, which are HE V-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-bas ed 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 basi s, allowing manufacturers the flexibility to focus off-cycle technologi es 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 a pproval 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 31 st prior to the model year, and mid-model year reports by July 31 st 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 di fference 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 whichDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 281 of 426 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 submis sions 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 expe dite the tracking and reporting of CAFE credits. The agency also plans to eventually develop an XML schema for submitting CAFE reports electronically that will av ailable through its website. Ultimately, the XML schema would be used as part of the new database system NHTS A 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 format s 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) a nd 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-mode l year reports from 12 manuf acturers. Each of the manufacturers also provided hardcopy reports.

266 49 CFR 537.7(c)(4)(xvi)(B).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 282 of 426 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 classificati on determination. By incorporating all the requirements into one section, the agency believes the cla ssification process will become significantly more accurate and efficient. NHTSA seeks comment on this proposed change.

c. Base Tire Definition

Beginning in model year 2011, manufacturer s 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 “b ase tire” is insufficiently descriptive, and may lead to inconsistencies among manufacturers’ base tire selections. In meetings relating to CAFE enforcement, manufacturers have stated th at 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 EP A does not effectively prevent differences in their interpretations. Some manufacturers, for exampl e, 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 283 of 426 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 va lues 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 c onfigurations 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 appro aches for designating base tires, NHTSA is proposing to modify its definition for base tir e 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 fo r 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 co nfiguration, 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 va lues 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 284 of 426 Since NHTSA only requires a breakdown of foot print 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 dete rminations 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 act ion” 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 otherw ise 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 arisi ng 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 probabilis tic uncertainty analysis for this proposal. TheDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 285 of 426 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 Environm ental 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 po tential environmental impacts of alternative mile per gallon (mpg) levels that will be consid ered 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 environmenta l 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 IP CC 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 c ites these sources and the studies they review frequently.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 286 of 426 Because of the link between the transpor tation sector and GHG emissions, NHTSA recognizes the need to consider the possible imp acts on climate and global climate change in the analysis of the effects of thes e fuel economy standards. NHTS A also recognizes the difficulties and uncertainties involved in such an impact analysis. Accordingly, consistent with CEQ regulations on addressing incomplete or unava ilable information in environmental impact analyses, NHTSA has reviewed existing credible sc ientific 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
CO 2 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 o ccur. As discussed below, NHTSA presumes
that these reductions in climate effects will be re flected 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 “whi ch operates primarily with in 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 ec onomic 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 follo wing is NHTSA’s statement providing the factual basis for the certification (5 U.S.C. 605(b)).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 287 of 426 If adopted, the proposal would directly aff ect nineteen large single stage motor vehicle
manufacturers. 273 Based on our preliminary assessment, th e 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 Admini stration’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 pe tition 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 ear ly 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, Hyunda i, Kia, Lotus, Mazda, Mitsubishi, Nissan, Porsche, Subaru, Suzuki, Tata (Jaguar Land Rover), Toyota, and Volkswagen/Audi.

274 64 FR 43255 (Aug. 10, 1999).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 288 of 426 NHTSA solicits comment on this proposed act ion from State and local officials. The agency believes that it is unnece ssary 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 explanati on 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 stat utorily 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 289 of 426 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 13045 276 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 crit eria, 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 br eathing 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 childre n, 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 290 of 426 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 at tention 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 ef fective 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 in consistent 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 voluntaryconsensus 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 Sta ndards 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 291 of 426 10. Executive Order 13211

Executive Order 13211 278 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 a nd 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 292 of 426 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 subm itting 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 293 of 426 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 st anding 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 sa les 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 apassenger compartment. Note that a vehicle lacking some components of the cab is a cabcomplete vehicle if it substantially includes the cab.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 294 of 426 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 pa sses through the rearmost point on the rearmost seat and elsewhere by the automobile’s interior surfaces.

Class 2b vehicles are vehicles with a gr oss 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 requ ires 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) attach ed 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 sta nding 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, “wh eelbase” 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 295 of 426 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 meetin g either of the fo llowing 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 vehicl es 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

Page 296 of 426 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

Page 297 of 426 (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 st anding, measured with the automobile at curb weight, the wheel parallel to the vehicle's longit udinal 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 cl assified as a vocationa l 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 298 of 426 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 fo llows, 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;

N i is the number (sum) of the ith passenger automobile model produced by the manufacturer; and

T i 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

ܽ ൅ ቀ 1 ܾ െ 1

ܽ ቁ ݁ ሺ ௫ି௖ ሻ ௗ


### 1൅݁ ሺ ௫ି௖ ሻ ௗ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.

T ABLE II

– P ARAMETERS FOR THE P ASSENGER A UTOMOBILE F UEL E CONOMY T ARGETS

Model year Parameters

a (mpg) b (mpg) c (gal/mi/ft 2 ) 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:



### ܧܨܣܥ ௗ௜௥௘௨௥௘௤ 

### ൌ ∑ ܱܰܫܶܥܷܦܴܱܲ ௜ ௜ 

### ∑ ܱܰܫܶܥܷܦܴܱܲ ௜ 

### ܶܧܩܴܣܶ ௜ ௜

Where:

CAFE required 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;

Production i 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;

TARGET i 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.

T ABLE III

– P ARAMETERS FOR THE P ASSENGER A UTOMOBILE F UEL E CONOMY T ARGETS
, MY S 2012-2025

Model year Parameters

a (mpg) b (mpg) c (gal/mi/ft 2 ) 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:

T ABLE IV

– M INIMUM F UEL E CONOMY S TANDARDS FOR D OMESTICALLY M ANUFACTURED P ASSENGER A UTOMOBILES
, MY S 2011-2021

Model year Minimum standard
2011........................... .................. 27.8
2012........................... .................. 30.7
2013........................... .................. 31.4
2014........................... .................. 32.1DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 301 of 426 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 ye ars 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 perfor mance 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:

A PPENDIX TO P ART 531

– E XAMPLE OF C ALCULATING C OMPLIANCE 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:

A PPENDIX T ABLE I

Model type Description Actual measured fuel economy (mpg) Volum

e Group Carline name Basic engine (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 hatchbac k 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

N OTE TO A PPENDIX T ABLE 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:

A PPENDIX T ABLE 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 (ft 2 ) 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 35.95DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 303 of 426 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

N OTE TO A PPENDIX T ABLE 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 ′ ܾ݈݅݁݋݉݋ݐݑܣ	ݎ݁݊݃݁ݏݏܲܽ	݅ܿݐݏ݉݁݋ܦ	ݏ ݈݀݁݋ܯ	݈ܾ݈݅ܿܽ݁݌݌ܣ	ݎ݋݂	݊݋݅ݐܿݑ݀݋ݎܲ ሻݎܻ݁ܽ

∑ ሺ ݌ݑ݋ݎܩ ଵ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଵ ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽ ൅ ݌ݑ݋ݎܩ ଶ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଶ ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽ ௜ ⋯ ൅ ݌ݑ݋ݎܩ ଽ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଽ ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽ ሻ

ൌ ሺ27,500ሻ

ሺ 1500 35.01 ൅ 2000 35.14 ൅ 2000 35.08 ൅ 1000 35.95 ൅ 3000 35.81 ൅ 8000 30.33 ൅ 2000 29.99 ൅ 5000 29.52 ൅ 3000 29.79 ሻ

= 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 ′ ܾ݈݅݁݋݉݋ݐݑܣ	ݎ݁݊݃݁ݏݏܲܽ	݅ܿݐݏ݉݁݋ܦ	ݏ ݈݀݁݋ܯ	݈ܾ݈݅ܿܽ݁݌݌ܣ	ݎ݋݂	݊݋݅ݐܿݑ݀݋ݎܲ ሻݎܻ݁ܽ

∑ ሺ ݌ݑ݋ݎܩ ଵ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଵ ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ ൅ ݌ݑ݋ݎܩ ଶ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଶ ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ ௜ ⋯ ൅ ݌ݑ݋ݎܩ ଽ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଽ ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ ሻDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 304 of 426 ൌ ሺ27,500ሻ

ሺ 1500

34.0 ൅ 2000 34.6 ൅ 2000 33.8 ൅ 1000 34.4 ൅ 3000 32.9 ൅ 8000 32.2 ൅ 2000 33.1 ൅ 5000 30.6 ൅ 3000 28.5 ሻ

= 32.0 mpg

N OTE TO A PPENDIX F IGURE 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:
T ABLE I

Model year 2-wheel drive light trucks 4-wheel drive light trucks Limited product line light trucks Captive 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

T ABLE 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 17.5DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 305 of 426 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

T ABLE 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

T ABLE 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........................................................................... 23.5DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 306 of 426 Figure 1:


### ൌ ݈݁ݒ݁ܮ_ݕ݉݋݊݋ܿܧ_݈݁ݑܨ_݁݀ݎ݅ݑݍܴ݁ ܰ

∑ ܰ ௜ 

### ܶ

௜ ௜

Where:

N is the total number (sum) of ligh t trucks produced by a manufacturer;

N i is the number (sum) of the ith light truck model type produced by a manufacturer; and

T i 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 ݁ ሺ ௫ି௖ ሻ ௗ


### 1൅݁ ሺ ௫ି௖ ሻ ௗ 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.

T ABLE V

– P ARAMETERS FOR THE L IGHT T RUCK F UEL E CONOMY T ARGETS FOR MY S 2008-2011

Model year Parameters

a (mpg) b (mpg) c (gal/mi/ft 2 ) 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:



### ܧܨܣܥ ௗ௜௥௘௨௥௘௤ 

### ൌ ∑ ܱܰܫܶܥܷܦܴܱܲ ௜ ௜ 

### ∑ ܱܰܫܶܥܷܦܴܱܲ ௜ 

### ܶܧܩܴܣܶ ௜ ௜

Where:

CAFE required 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.

Production i 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;

TARGET i 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.

T ABLE VI

– P ARAMETERS FOR THE L IGHT T RUCK F UEL E CONOMY T ARGETS FOR MY S 2012-2016

Model year Parameters

a (mpg) b (mpg) c (gal/mi/ft 2 ) 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: ൮ ܺܣܯ ൌ ܶܧܩܴܣܶ 1 ݀, ൅ ܰܶܫܱܱܴܶܲܨ ൈ ܿቀ ܺܣܯቂ ܰܫܯ 1

ܽ ቁ , 1 ܾ ቃ , 1 ൅ ܰܶܫܱܱܴܶܲܨ ൈ ݃ቀ ܺܣܯ൤ ܰܫܯ ݄ 1

݁ ቁ , 1 ݂ ൨ ൲DRAFT – 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.

T ABLE VII

– P ARAMETERS FOR THE L IGHT T RUCK F UEL E CONOMY T ARGETS FOR MY S 2017-2025

Model

year Parameters

a (mpg) b (mpg) c (gal/mi/ft 2 ) d (gal/mi) e (mpg) f (mpg) g (gal/mi/ft 2 ) 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 li ght 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 fo r that model year according to Figures 2 and 3 and the appropriate values in Table VI.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 309 of 426 (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 NHT SA 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 year s 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:

A PPENDIX TO P ART 533

– E XAMPLE OF C ALCULATING C OMPLIANCE UNDER § 533.5( I )

Assume a hypothetical manufacturer (Manufact urer X) produces a fleet of light trucks in MY 2012 as follows:

A PPENDIX T ABLE I

Model type Description Actual measured fuel economy (mpg) Volume Group Carline

name Basic engine (L) Transmission classDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 310 of 426 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

N OTE TO A PPENDIX T ABLE 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:

A PPENDIX T ABLE 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 (ft 2 ) Volume Fuel

economy

target standard

(mpg) Group 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 23.79DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 311 of 426 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

N OTE TO A PPENDIX T ABLE 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 ′ ݅݃ܮ		ݏ  ሻݎܻ݁ܽ	݈݀݁݋ܯ	݈ܾ݈݅ܿܽ݁݌݌ܣ	ݎ݋݂	݊݋݅ݐܿݑ݀݋ݎܲ		ܿ݇ݑݎܶ	ݐ

∑ ሺ ݌ݑ݋ݎܩ ଵ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଵ ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽ ൅ ݌ݑ݋ݎܩ ௔ଶ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଶ ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽ ௜ ⋯ ൅ ݌ݑ݋ݎܩ ଵଵ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଵଵ ݀ݎܽ݊݀ܽݐܵ	ݐ݃݁ݎܶܽ ሻ

ൌ ሺ6,700ሻ

ሺ 800 27.30 ൅ 200 27.44 ൅ 300 23.79 ൅ 400 23.79 ൅ 400 22.27 ൅ 400 23.79 ൅ 500 23.79 ൅ 500 23.68 ൅ 1600 23.76 ൅ 800 22.27 ൅ 800 22.27 ሻ

= 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 =DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 312 of 426 ൌ ݎ݁ݎݑݐ݂ܽܿݑܽ݊ܯሺ ′ ݅݃ܮ	ݏ  ሻݎܻ݁ܽ	݈݀݁݋ܯ	݈ܾ݈݅ܿܽ݁݌݌ܣ	ݎ݋݂	݊݋݅ݐܿݑ݀݋ݎܲ	ܿ݇ݑݎܶ	ݐ

∑ ሺ ݌ݑ݋ݎܩ ଵ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଵ ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ ൅ ݌ݑ݋ݎܩ ଶ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଶ ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ ௜ ⋯ ൅ ݌ݑ݋ݎܩ ଵଵ ݊݋݅ݐܿݑ݀݋ݎܲ ݌ݑ݋ݎܩ ଵଵ ݉ܽ݊ܿ݁ݎ݋݂ݎܲ݁ ሻ

ൌ ሺ27,500ሻ

ሺ 1500

34.0 ൅ 2000 34.6 ൅ 2000 33.8 ൅ 1000 34.4 ൅ 3000 32.9 ൅ 8000 32.2 ൅ 2000 33.1 ൅ 5000 30.6 ൅ 3000 28.5 ሻ

= 23.3 mpg

N OTE TO A PPENDIX F IGURE 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 pa rt 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 numb er 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 mu ltiply 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 = VMT
u 

## * MPG
ae 

## * MPG
se 

## VMT e 

## * MPG
au 

## * MPG
su

Where:

A = Adjustment factor applied to traded and transferred credits;DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 313 of 426 VMT e = Lifetime vehicle miles traveled as prov ided in the following table for the model year and compliance category in which the credit was earned;

VMT u = Lifetime vehicle miles traveled as pr ovided 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

MPG se = Required fuel economy standard for the originating (earning) manufacturer, compliance category, and model year in which the credit was earned;

MPG ae = Actual fuel economy for the originating manufacturer, compliance category, and model year in which the credit was earned;

MPG su = Required fuel economy standard for the user (buying) manufacturer, compliance category, and model year in which the credit is used for compliance; and

MPG au = 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 pass enger 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 314 of 426 * * * * * (b) If a manufacturer's calculated fuel economy for a particular compliance category, including any statutorily-required calculations fo r alternative fuel and dual fuel vehicles, is higher or lower than the applicable fuel ec onomy 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 vehicl es) 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 purpos es 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 JerseyAvenue, 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 315 of 426 * * * * *

(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 paragr aph (b)(3)(i) and (ii) of this s ection 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 th e 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;DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 316 of 426 (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 acq uire 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;DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 317 of 426 (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 thos e 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 desi gnated 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 forwar d-most point of installation of those seats to the rear of the automobile's interior.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 318 of 426 (ii) For an automobile capable of off-highw ay operation, identify which of the features below qualify the vehicle as o ff-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 supplementar y 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 testingDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 319 of 426 requirements may continue post-certification. Useful life standards may apply an adjustment
factor to account for vehicle emission control deteri oration 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 CO 2 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 calcu lating 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

Page 320 of 426 to pay fines in lieu of compliance with the st andards. 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 st andard 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-calle d 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 CO 2 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 specifi ed 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 cond itioning emissions, as well as proposed standards
for methane and N 2 O, 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 multipli er for advanced technology vehi cles, and takes those flexibilities into account in its technical analysis and modeling supporting its proposal. EPCA specifies a number of particular compliance flexibilities fo r CAFE, and expressly prohibits NHTSA from considering the impacts of those statutory complian ce flexibilities in setting the CAFE standard
so that the manufacturers’ election to avail th emselves of the permitted flexibilities remains strictly voluntary. 282 The Clean Air Act, on the other hand, contains no such prohibition. TheseDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 321 of 426 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 CO 2 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 CO 2 emission credits manufacturers may be able to transfer under the CAA program, recognizing that manufacturers must comply with both the pr oposed CAFE standards and the proposed EPA standards. However, it is possible that in so me specific circumstances the EPCA limit on CAFE
credit transfers could c onstrain 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-relate d technologies). The standards set by each agency will drive the kind and degree of penetratio n 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 agenci es’ 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 fe asibility, 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 technologi es, the appropriate lead time for introduction of technology, and based on this the feasibility and pr acticability 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 considerati on 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 intoDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 322 of 426 consideration NHTSA’s CAFE standards in determ ining appropriate action under section 202(a).
This is consistent with the S upreme 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 revi ewing 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 su pports 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 coordi nated fashion, and will provide regulatory compatibility that allows auto manufacturers to build a single natio nal 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 wh ich 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 in itial 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 recogni ze the significant increase in the application of technology that the pr oposed standards would require across a high percentage of vehicles, which will require the manufacturers to devote co nsiderable engineering and development resources before 2017 laying the critical foundation for the widespread deployment of upgraded technology across a hi gh percentage of the 2017-2025 fleet. This clearly will be challenging for au tomotive manufacturers and thei r 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 th ese standards with respect to the expected reductions in GHGs and oil consumption and, fou nd them to be very significant in magnitude. The agencies considered other factors such as the impacts on noise, energy, and vehicularDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 323 of 426 congestion. The impact on safety was also given careful consideration. Moreover, the agencies quantified the various costs and benefits of the pro posed standards, to the extent practicable. The agencies’ analyses to date indicate that the over all quantified benefits of the proposed standards far outweigh the projected costs. All of these f actors support the reasonableness of the proposed standards. See section III (proposed GHG standard s) 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 fu ture. 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 co sts and effectiveness, the economic assumptions the agencies included in their analyses, a desc ription 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 RI A, 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 releasedDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 324 of 426 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 fina l 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 th e 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 sale s 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 flee t volumes. The third step was to modify that
MY 2017-2025 reference fleet such that it reflect s 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 ag encies’ 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, developmen t 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 sta ndards 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 sta ndards in this preamble.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 325 of 426 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 wh ich 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 th e 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 repres entative 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 repres entativeness. To the extent the MY 2010 data becomes available during the comment period the ag encies 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 informa tion 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 Sy stem (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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 326 of 426 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 dist ribution 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 agen cies 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 (In terim 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 Informati on 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 include s shifting vehicle sales volume, starting after , away from fleets with lower fuel economy (the lig ht-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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 327 of 426 and GHG standards. Because we use our market projection as a baseline relative to which we measure the effects of new standards, and we atte mpt 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 ligh t 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 th an that existing in the 2000- timeframe. This projection reflects the impact of assumed higher fu el 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 segm ents within both the car and truck markets have been changing and are expected to continue to change. Manufacturers are introducing more crossover utility vehicles (CUV s), which offer much of the utility of sport utility vehicles (SUVs) but use more car-like de signs. 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 for ecasting for that time frame. NHTSA and EPA
decided to use the forecast from CSM for several re asons 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 Amer ica Sales Forecasts 2017-2025 for the Docket”) is available in the docket (Docket EPA-HQ-OAR-2010-0799).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 328 of 426 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 Vehicl es 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 17,250,459DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 329 of 426 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 ro of and elevated seating posit ions 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 withDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 330 of 426 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%DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 331 of 426 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 re presents the agencies’ best technical judgment regarding the likely composition direction of th e fleet. EPA and NHTSA recognize that it is impossible to predict with certainty how manufac turers’ product offering s and sales volumes will evolve through MY 2025 under baseline cond itions—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 th e 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 CO 2 standards that areDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 332 of 426 defined by a mathematical function. EPCA, as am ended 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 ve hicle model has a performance target (fuel economy and CO 2 emissions for CAFE and CO 2 emissions standards, respectively), the level of which depends on the vehicle’s attribute (for this pr oposal, footprint, as discussed below). Each manufacturers’ fleet average standard is determined by the production-weighted 292 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 CO 2 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 part icular product mix of each manufacturer.
Therefore a maximum feasible attr ibute-based standard will tend to require greater fuel savings
and CO 2 emissions reductions overall than would a maximum feasible flat standard (that is, a single mpg or CO 2 level applicable to every manufacturer).

Second, depending on the attribute, attribute-based standards reduce the incentive for
manufacturers to respond to CAFE and CO 2 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, sup port 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 333 of 426 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 diffi culties on the manufacturers that need to change their product plans to meet the standard s, 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 eco nomic conditions and c onsumer 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 vehi cle downsizing as a compliance strategy. Under a footprint-based standard, manufacturers are required to invest in technologies that improve the fuel economy of th e 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 CO 2 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 be lieve that footprint is the most appropriate attribute on which to base the standards, even th ough some other vehicle attributes (notably curb weight) are better correlated to fuel economy and emissions.

First, in the agencies’ judgment, from the sta ndpoint of vehicle safety, it is important that
the CAFE and CO 2 standards be set in a way that does not encourage manufacturers to respond by selling vehicles that are 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 CO 2 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
CO 2 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 334 of 426 (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 compar ed 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 rule 296
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 fre edom 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 CO 2 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. Howe ver, the agencies continue to believe that there will not be significant shifts in this distributi on as a direct consequence of this proposed rule. The agencies also recognize that some internationa l attribute-based standard s 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 weig ht-based systems can coexist internationally and not present an undue burden for manufacturers if th ey 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 st rong 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 attrib utes. If commenters suggest that the agencies
should consider another attribute or another comb ination 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 335 of 426 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 CO 2 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 poin t 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 standa rds extend without limit) and transposed these constrained/piecewise linear functions vertically ( i.e ., on a gpm or CO 2 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 disp roportionally large change in stringency.

299 75 FR at 25362DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 336 of 426 downward) to produce the fleetwide fuel economy and CO 2 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 amathematical 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 CO 2 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 supp orts 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 CO 2 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 c ould 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 ca nnot 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/CO 2 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 shar ed with the agencies what they describe as “physics based” curves, with each OEM showing significantly differen t shapes, and footprint relationships. The sheer variety of curves shown to the agencies furthe r confirm the lack of an underlying principle of “fundamental physics” driving the relationship between CO 2 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 sta ndards defined by a steep function, the standards might encourage manufacturers to keep adding weight to thei r vehicles to obtain less stringent targets.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 337 of 426 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 CO 2 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 pro cess of reexamining potential mathematical functions, how the agencies have defined the data, and how the agencies explored statistical curve-fitting methodologies in or der 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 con cerns 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
CO 2 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 adjustme nts 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 foot print and fuel economy, the agencies have chosen for this proposed rule to fit curves using an ordi nary 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 statis tical methods in order to better understand the range of uncertainty regarding the relationship between fuel consumption (the inverse of fuel economy), CO 2 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 finalDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 338 of 426 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 appropri ate for future model year s, but that the light
truck curve should be revised to be less difficul t 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 “p hysics 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 belie ved 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 compro mising 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 me et 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 con tinue to believe that the methodology for fitting
curves for the MY2012-2016 standards was techni cally 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 302DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 339 of 426  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 gr eenhouse gas reduction benefits will be less than expected.

 Given the same industry-wide average required fuel economy or CO 2 standard, flatter standards tend to place greater compliance burdens on full-line manufacturers

 Given the same industry-wide average required fuel economy or CO 2 standard, steeper standards tend to place greater compliance burdens on limited-line manufacturers (depending of course, on which vehicles are being produced).

 If cutpoints are adopted, given the same industry-wide average required fuel economy, moving small-vehicle cutpoints to the left ( i.e. , up in terms of fuel economy, down in terms of CO 2 emissions) discourages the introduction of small vehicles, and reduces the incentive to downsize small vehicles in ways that 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 CO 2 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 th at required trade-offs, and in determining the curves they also required balance against the comments from th e 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 signi ficantly 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 stringe ncy 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 alte rnatives 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 po licy 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, adjust ments 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 ye ar change in curve shapes is discussed in greater detail in Section III.C.7.a and Chapter 2 of the joint TSD.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 340 of 426 above, in the agencies’ judgment, there is no single “correct” way to estimate the relationship
between CO 2 or fuel consumption and footprint – rather, each statistical result is based on the underlying assumptions about the particular func tional form, weightings and error structures embodied in the representational approach. These assumptions are the subject of the following discussion. This process of performing many an alyses 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 gi ven 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, CO 2 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 mi x 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 CO 2 and fuel consumption that is real but not particularly stro ng. 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 di fferences in technology

As in prior rulemakings, the agencies cons ider technology differences between vehicle models to be a significant factor producing un certainty regarding the relationship between

306 While the agencies jointly conducted th is analysis, the coefficients ultimately used in the slope setting analysis are from the CAFE model.

307 68 FR 74920-74926.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 341 of 426 CO 2 /fuel consumption and footprint. Noting that attribute-based standards are intended to encourage the applicatio n of additional technology to impr ove fuel efficiency and reduce CO 2 emissions, the agencies, in addition to cons idering approaches based on the unadjusted engineering characteristics of MY 2008 vehicle m odels, 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 othe r 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 CO 2 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 CO 2 targets for each vehicle are still f unctions 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 CO 2 /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 + torque 1/1.5 + weight 1/2.5 . (NHTSA-2008-0089-0174). While the standards the agencies are proposing for MY 2017-2025 are not multi-attributes, that is the ta rget is only a function of footprint, we are proposing curve shapes that were developed considering more than one attribute.

309 74 FR 14359.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 342 of 426 contrast, there is a minimally positive relationship between HP/WT and footprint for light trucks, indicating that light trucks become only sligh tly 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 “d ensity” (curb weight divided by footprint) are both correlated to fuel consumption (and CO 2 emission rate), and that these vehicle attributes are also both related to vehicle footprint. Base d on these relationships, the agencies explored adjusting the fuel economy and CO 2 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 th at 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 CO 2 targets for each
vehicle are still functions of foot print 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 CO 2 emissions. The agencies also seek comment regarding whether these adjustments effectively “lock in” through MY 20 25 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 with out 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 + torque 1/1.5 + weight 1/2.5 . (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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 343 of 426 performance (power/weight). Using these data se ts, the agencies tested a range of regression methodologies, each judged to be possibly reasonab le 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), rath er 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 outliers 313 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 OL S 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 whet her it is appropriate to classify these vehicles as outliers. Unlike in traditional datasets, these ve hicles’ 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 s hould be near zero, particularly towards high CO 2 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 agenci es concluded that it would appropriate to
reconsider the treatment of these vehi cles 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 aver age values. This was the inte nded 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 344 of 426 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) un it 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 unweig hted regression, vehicle sales do not matter. The agencies note that the light truck market fo recast shows MY 2025 sales of 218,000 units for Toyota’s 2WD Sienna, and shows 66 model config urations 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 Si enna and Prius more than a thousand times the consideration of many vehicle model configuratio ns. 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 othe r 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 345 of 426 In the interest of taking a fresh look at appropriate methodologies as promised in the lastfinal 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 CO 2 /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 techno logy 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 persp ective 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 dete rmine 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 CO 2 ) 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 ob tained 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 a nd without sales weighting, with and without the application of technology, and with various adjustments to the gpm variable prior to running a regression.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 346 of 426 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, t echnical, 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 pr ior 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/CO 2 and footprint.

Similarly, for the agencies’ current MY 2008-based market-forecast and the agencies’ current estimates of future technology effectivene ss, 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 manufacturer s) to adjust the slope of the curve defining fuel economy and CO 2 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 th e 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 salesDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 347 of 426 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 previous ly, a steeper slope relaxes the stringency of targets for larger vehicles re lative 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 wit hout a limit at the sma llest footprints, the function—whether logistic or linear—can reach va lues 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 func tion, without a limit at th e largest footprints, the function may provide no floor on required fuel economy. Also, the safety considerations that support the provision of a disincentive for downsizi ng 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 ma rket, 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 againDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 348 of 426 proposing to cut off the sloped portion of the passenger car function at 41 square feet, consistent with the MYs 2012-2016 rulemaking. The agenci es 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 agenci es 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 othe r hand, the agencies note that some manufacturers are introducing mini cars not reflected in the agen cies 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 po rtion of the light truck function at the same footprint, 41 square feet, although we recognize that no light trucks are currently offered belowDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 349 of 426 41 square feet. With regard to the upper cutpoint, the agencies heard from a number of manufacturers during the discussions leading up to th is 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 ta rgets 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 CO 2 levels (as discussed immediately below). In disaggregating the pickup truck model data, the im pact of setting the cut point at 66 square feet after model year 2016 became clearer to the agencies.

In the agencies’ view, there is legitimate basi s 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 so me 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 ma nufacturers to add fuel economyimproving/CO 2 -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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 350 of 426 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 high er 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/CO 2 -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 point s, 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 351 of 426 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 consideratio ns 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, offsettin g 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 CO 2 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
CO 2 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 adju stments), an absolute offset would eventually ( i.e. , at very high average stringencies) produce ne gative (gpm or g/mi) targets. Relative offsets avoid this potential outcome. Relative offsets do cause curves to become, on a fuel consumption
and CO 2 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 CO 2 emissions, a curve that is flatter by the same relative amou nt should be equally challengi ng in terms of the potential to achieve compliance through the addition of fuel-saving technology.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 352 of 426 On this basis, and considering that the “fla ttening” occurs gradually for the regulatory alternatives the agencies have evaluated, the agen cies 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 agen cies invite comment on these conclusions, and on any other means that might avoid the potential outcomes—in particular, negative fuel consumption and CO 2 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 calculati on procedures that do not reflect potential improvements in air conditioning system efficien cy, 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 ar e increasing the stringency of the target curves based on the agencies’ assessment of the capab ility 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 CO 2 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 th e target curves by additive stringency increases (or vertical shifts) in the curves.

For the GHG target curves, the offset for ai r 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 analysesDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 353 of 426 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 re ly 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 considerab le 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 2012-2 016 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 associ ated with new technologies since both the MYs 2012-2016 final
rule and the TAR. We continue to use the indir ect 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 354 of 426 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 prop osal 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 und erstanding of the technologies available to the manufacturers. This analysis includes an assessment of the cost, effectiveness, availability, development time, and manufacturability of vari ous 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 t echnologies that manufacturers could use to improve the fuel economy and reduce CO 2 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 pro duct 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 consid ered 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 comb ined 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 CO 2 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 355 of 426 currently in an initial stage of research because of the uncertainty involved in the availability and feasibility of implementing these t echnologies 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 t echnologies (such as mass reduction, tires and aerodynamic treatments); and electrification technol ogies (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 th e following new technologies added to the list since the last final rule: the P2 hybrid, a newl y emerging hybridization technology that was also considered in the 2010 TAR; continued improvements in gasoline engines, with greater efficiencies and downsizing; continued signifi cant 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 fu el 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 gr oups rather than four – hybridization is one category, and “electrification/accessories” is another.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 356 of 426 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 efficien cy, 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 us e 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 th e 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-DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 357 of 426 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 great ly 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 sy stem 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-se lected, 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 th at 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 358 of 426 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 CO 2 emissions. New for this proposal (and also marking an advance over low rolling resistance tires considered during the heavy duty greenhouse gas rulemaking, see 76 FR at 57207, 57229) is a second level of lower rolling resistance tires that reduce frictional losses ev en further. The first level of low rolling resistance tires will have 10 percent rolling resistance reduction while the 2 nd 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 ar ea. 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 treatment s, the agencies have included a second level of aerodynamic technologies which coul d 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 re lative 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 el ectric 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 asse ssing 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 agenciesDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 359 of 426 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 a nd Hybrid Technologies Considered

Electric power steering (EPS)/ Elect ro-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 al ternators, electrically driven (i.e., on-demand) water pumps and cooling fans. This excludes other electrical accessories such as electric oil pumps and elect rically 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 rege neration 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 capab ility 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 enhan ced 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 byDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 360 of 426 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 e ngine. In addition, a P2 hybrid wouldtypically 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 replaci ng 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 impr oves both the transmission torque capacity for heavy-duty applications and reduces fuel consumption and CO 2 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 pe rmanently connected to the vehicle’s final drive and always turns with the wheels. Th e 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 e ither agency as an enabling technology in the analysis supporting this proposal, (the agencies evaluate the P2 hybrid technology discussed above wher e 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 th an 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 referr ed to as Range Extended Electric Vehicles (REEV). In this MYs 2017-2025 analysis, PH EVs with several all-electric ranges—both a 20 mile and a 40 mile all-electric range—have been included as potential technologies.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 361 of 426 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 an d hydrogen fuel. Fuel cells are electro-chemical devices that directly convert reactants (hydroge n 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 th an 10 million CNG vehicles wo rldwide, 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 sectio n, 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 sin ce the basis of the direct manufacturing cost estimates have not changed; however, the costs ha ve been updated to more recent dollars, the learning effects have resulted in further cost reduc tions 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 main tenance 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 el ectric vehicles (which lack routine engine maintenance) or the replacement of automatic transmissions with simpler dual-clutchDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 362 of 426 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) relate d 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 co sts 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 pr oposal 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, ve hicle 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 th e future because of uncertainties in predicting commodities (and raw material) prices, labor rate s, 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 feasib ility 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 conf igurations. FEV’s methodology was documented in a report published as part of the MY 2012-2016 rulemaking, detailing the costing of the firstDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 363 of 426 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 be low list) were document ed in follow-up FEV reports made available in the public docket for the MY 2012-2016 rulemaking. 322

Since then, FEV’s work under th is 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 Fu sion. (The latter served as a baseline vehicle for comparison.) In addition to providing powersplit HEV costs, the results for individual components in these vehicles we re 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 an d 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 Anal ysis, Power-Split and P2 Hybrid Electric Vehicle Case Studies”, EPA-420-R-11-017, November 2011October 3, 2011.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 364 of 426 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 th is 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, th ey 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 co sts for HEVs, PHEVs, and EVs since both the 2012-2016 final rule and the 2010 TAR. First, electrified vehicle tech nologies 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 ap plications) versus EVs (high energy density applications) need to be considered appropriatel y 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 365 of 426 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 ve hicle 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 hybridizati on 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 app lications. 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 batter y 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 pl ay 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 Determ ining the Manufacturing Costs of Lithium-Ion Batteries for PHEVs,” 24 th World Battery, Hybrid and Fuel Cell Electri c Vehicle Symposium and Exposition EVS-24, Stavenger, Norway, May 13-16, 2009 (www.evs24.org). Cite ANL BatPaC peer review report.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 366 of 426 ANL model utilized in this report. DOE has re viewed 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 we ll 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 th e final rule the agencies will consider any new studies that become available, including two stud ies 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 estimate d by applying indirect cost multipliers (ICM)
to direct cost estimates. ICMs were derive d by EPA as a basis for estimating the impact on indirect costs of individual vehicle technology ch anges 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 variat ion 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 co sts. 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, Glor ia 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 #]DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 367 of 426 particular technology, if the new regulatory-induced technology change is not one expected to be
marketed to consumers. Because ICMs calcula ted 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 tec hnologies, specifically hybrid technologies (for near term ICMs), and plug-in hybrid battery and full electric vehicle techno logies ( 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 co st 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 esti mates 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 al l 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 em pirical 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 technologi es, or for groups of specific technologies. Thus applying a single average RPE to any give n 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, Assessme nt 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 proc ess for the second, modified Delphi panel, and reviewed the results as they were developed, but did not serve on the panel.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 368 of 426 generally received little to no co mment 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-s pecific markup factors” where “this problem” spoke to the issue of estimating technology-s pecific 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 Economics 335 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 d ecreasing 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, a nd 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, Assessme nt and Standards Division, Office of Transportation and Air Quality, U.S. Environmental Protection Agency, August 2009 (Docket item #XX).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 369 of 426 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 hist orical RPE data indicates that, although there is year to year variation, the average RPE has rema ined 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 aver age 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 majo r change in vehicle design, plant operations, or manufacturer’s product mix, such as a shift in cons umer 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 th e 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 re gard 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 bound ing 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 cap ital analysis with potential for major impacts on results are:

337 FEV, Inc., “Potential Stranded Capital Analysis on EP A Light-Duty Technology Cost Analysis”, Contract No. EP-C-07-069 Work Assignment 3-3. November 2011. FEV stra nded capital report [update with actual title, etc] [xx Kopinski, Docket item #]DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 370 of 426 • 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 manufacturer s 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 371 of 426 However, we have employed some new terminology in an effort to eliminate some confusion that existed with our old terminology. This ne w 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 cons ider 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 cu rve 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 an d 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 co nsulting 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 cons idered, 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-synergiesDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 372 of 426 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 technolo gies 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 fro m 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 CO 2 emissions) due to the application of low rolling resistance tires depends not only on the unique characteristics of the tires originally on the vehicl e, 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 CO 2 emissions, but it is also highly depend ent 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 Interi m Joint TAR and the MYs 2012-2016 final rule. To review the parameters and assumptions the agen cies used in the 2012-2016 final rule, pleaseDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 373 of 426 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 econom y 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 im provements, and thus of manufacturers’ compliance with stricter standards. Curren tly 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 ma intaining 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 374 of 426 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 econom y 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 no t 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 theprogram, 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-savi ng technology should preclude future improvements in performance, safety, or other attributes, though it is possibl e that the costs of these additions may be affected by the presence of fuel-saving technology.

340 U.S. Environmental Protection Agency, Final Techni cal 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 Protect ion 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 anal ysis. 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 375 of 426 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 Administrati on’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 Ch apter 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 th e 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 du ring 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 HighwayDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 376 of 426 Administration’s 2001 National Household Travel Survey (NHTS), 342 adjusted to account for the effects on vehicle use of subseq uent 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 th e 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 driv ers, 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 th e 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 c onsumer 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 increas e in the annual number of miles driven.

• Added costs from congestion, accidents, and noise – Although it provides benefits to drivers as described above, increased vehi cle 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.b ts.gov/programs/national_household_travel_survey/ (last accessed Sept. 9, 2011).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 377 of 426 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 acci dents, 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 unst able 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 CO 2 emissions, the energy security benefits estimated to result from this program exclude the value of this monopsony effect. In contrast, the macroeconomic di sruption 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 par ticular missions or activities is difficult. Also, historical variation in U.S. military co sts 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/i ndex.htm (last accessed Sept. 9, 2011).

344 See, e.g., Bohi, Douglas R. and W. David Montgomery (1 982). 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," Ener gy 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 378 of 426 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 securi ty 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 fa ll 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 i ssues 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 a nd distribution include carbon monoxide (CO), hydrocarbon compou nds (usually referred to as “volatile organic compounds,” or VOC), nitrogen oxides (NO X ), fine particulate matter (PM 2.5 ), and sulfur oxides (SO X ). Although reductions in domestic fuel refining and distribution that result from lower fuel cons umption will reduce U.S. emissions of these pollutants, additional vehicle use associated with the rebound effect, and additional electricity production will increas e emissions. Thus the net effect of stricter standards on emissions of each criteria pollutant depends on the relative magnitudes of reduced emissions from fu el refining and distribution, and increases in emissions resulting from added vehicle use. The agencies’ analysis assumes that the per-mile emission rates fo r 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 ai r 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 re fining is assumed to utilize 90 percent imported crude petroleum and 10 percent domestically-produced crude petrol eum as feedstocks. Together, these assumptions imply that each gallon of fuel saved will reduce imports of refined fuel and crude petr oleum by 0.50 gallons + 0.50 gallons*90 percent = 0.50 gallons + 0.45 gallons = 0.95 gallons.

346 Leiby, Paul. Oak Ridge National Laborat ory. “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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 379 of 426 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 PM 2.5 using a “benefit-per-ton” method. These PM 2.5 -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 PM 2.5 , or one ton of other pollutants that contribute to atmospheric levels of PM 2.5 (such as NO X , SO X , 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 PM 2.5

.

o Impacts on greenhouse gas (GHG) emissions - NHTSA estimates reductions in emissions of carbon dioxide (CO 2 ) 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 CO 2 emissions released per gallon of fuel consumed. EPA directly calculates reductions in total CO 2 emissions from the projected reductions in CO 2 emissions by each vehicle subject to the proposed rule. 348 Both agencies also calculate the impact on CO 2 emissions that occur during fuel production and distribution resulting from lo wer fuel consumption, as well as the emission impacts due to changes in electricity production. Although CO 2 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 hi gher “potency” as GHGs than that of CO 2 itself. EPA and NHTSA therefore also estimate the change in upstream and downstream emissions of non-CO 2 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 CO 2 emissions – EPA and NHTSA assigned a dollar value to reductions in CO 2 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 obse rved, “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 CO 2 content of certification gasoline is estimate d to be 8,887 grams per gallon, while that of diesel fuel is estimated to be a pproximately 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 380 of 426 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, pro cedures 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 theGreenhouse 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 Syst em (NASS), NHTSA was able to better estimate parameters associated with refue ling 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 no t covered in the Interi m 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 Executi ve 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 381 of 426 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 accoun t 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; spec ific 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 sensitiv ity of their estimates of the benefits and costs associated with this proposed rule to va riation 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 agenci es 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 indir ect 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, ma ss 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 produ ced, 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 382 of 426 technology benefits, fuel prices, the rebound effect , and military security costs. This information is provided in Chapter XII of NHTSA’s PRIA. Th ese uncertainty parameters are consistent with
those used in the MY 2012-2016 final rule. Th e 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 CO 2 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 CO 2 ($/metric ton), 2017 (2009$)

Discount Rate 5% 3% 2.5% 3%
Source of Estimate Mean of Estimated Values 95 t h percentile estimate 2017 Estimate $6 $26 $41 $78

F. Air Conditioning Efficiency CO 2 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 CO 2 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 383 of 426 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 pr ogram 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 st ringency of the two-cycle tailpipe CO 2 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 al so accounted for expected A/C efficiency improvements in determining the maximum feasible CAFE standards. The agencies discuss these proposed CO 2 credits/fuel consumption improvement values be low and in more detail in the Joint TSD (Chapter 5). EPA discusses add itional proposed GHG A/C leakage credits that are unrelated to
CO 2 and fuel consumption (though they are part of EPA’s CO 2 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 CO 2 emissions and fuelconsumption. Further discussions of the A/C, off-cycle, and the advanced technology for pickup truck incentive programs are provided below.

1. Proposed Air Conditioning CO 2 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 CO 2 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 system s that affect vehicle efficiency: the engine, electrical, transmission, and vehi cle systems. The simulation model is supported by data from a wide range of sources; Chapter 2 of the Draf t Regulatory Impact Analysis discusses its development in more detail.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 384 of 426 The agencies have identified several technologies that are key to the amount of fuel a
vehicle consumes and thus the amount of CO 2 it emits. Most of thes e 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 CO 2 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 CO 2 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 CO 2 (and thus allow calculation of fuel consumption) before and after the incorporation of the improved technologies. Progress toward such a test continues. As me ntioned in the introduction to this section, the primary vehicle emissions and fuel consumption te st, 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 CO 2 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 CO 2 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 info rmed EPA (in meetings subsequent to the 2012- 2016 final rule) that the Idle Test does not accurately capture the improvements from many ofDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 385 of 426 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 CO 2 emissions as there is significant real-world driving activity at idle, and the Idle Test significantly exercise s 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 Jo int 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 quan tify 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 t echnology 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 386 of 426 2. Off-Cycle CO 2 Credits

For MYs 2012-2016, EPA provided an option for manufacturers to generate adjustments (credits) for employing new and innovative technologies that achieve CO 2 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 pr ograms. 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 proposi ng 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 ap ply 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 emis sions 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 comp licated 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 st ifle 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 CO 2 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 consumptionDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 387 of 426 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 thereview 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 th e CAFE program based on off-cycle technology would be equivalent to the off-cycle credit a llowed 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 th ese technologies in setting the standards, EPA and NHTSA will reevaluate their positions. Ho wever, 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 consumpt ion improvement values. Therefore, effectively the off-cycle credits and fuel consumption impr ovement 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 determin ing the appropriate CO 2 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 orDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 388 of 426 releases the brake. This includes HEVs and PH EVs (but not EVs). In addition, active grill shutters is just one of variou s 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 th ese 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 S ections 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 t echnologies, and other factors. The proposed standards are appropriate recognizing that thes e 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 cont ext 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 fi ve-cycle benefit for the purposes of assigning credit and fuel consumption improvement values fo r 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 MOVES 353 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 othe rwise be employed, beyond the level that the 2- cycle standards already do, thus we are now allo wing credits and fuel consumption improvement values to be generated where the technology achie ves 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 o ff-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. Th is model contains (in its database) a wide variety of fleet and activity data as well as na tional ambient temperature conditions.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 389 of 426 improvements be determined at the fleet level by converting the CO 2 credits determined under the EPA program (in metric tons of CO 2 ) 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 CO 2 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 t echnologies 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 technol ogies 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 desc ription 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)DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 390 of 426 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 c ould bring additional GHG reductions over the 5-cycle drive test (or in the real world) compar ed 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 Recove ry, and Solar Roof Panels. In an effort to streamline the process for approving off-cycle cr edits, 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) provide d 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 resu lt in additional GHG reduction benefits that cannot be fully captured on the combined FTP/High way 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. Exampl es include Engine Start-Stop, Electric Heater Circulation Pump, Active Engine/Transmission Warm-Up, and Solar Control. For these types of technologies, the overall GHG re duction 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 toDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 391 of 426 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 cr edits and corresponding equivalent fuel consumption improvement values for manufacturers that hybridize a significant quantity of their full size pickup trucks, or use other te chnologies that significantly reduce CO 2 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 stan dards. 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 consumptio n improvement value is conditioned on a minimum penetration of the technologies in a manu facturer’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 en sure 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 CO 2

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 CO 2 credits due to each incentive program from metric tons of CO 2 to a fleetwide CAFE improvement value. The fuel consumpti on values are presented to give the reader some context and explain the relationship between CO 2 and fuel consumption improvements.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 392 of 426 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 prot rusions, 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 vehicl e 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 defin itions 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 CO 2 credit (equal to a 0.001125 gal/mi fuel consumption improvement value) during MYs 2017-2021 if the mild HEV te chnology is used on a minimum percentage of

355 As discussed in TSD Section 5.3.1, EPA is seeking co mment 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 al so known as dimension L506 in Society of Automotive Engineers Procedure J1100. The pickup body length at th e 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 ma nufacturer as the maximum weight of a loaded vehicle and trailer, consistent with good engi neering 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 define d in 40 CFR 86.1803, consistent with the provisions of 40 CFR 1037.140.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 393 of 426 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 CO 2 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 qu alifies 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 be low the applicable target. 359 EPA, in coordination with NHTSA, proposes this credit to be either 10 g/mi CO 2 (equivalent to 0.001125 gal/mi for the CAFE program) or 20 g/mi CO 2 (equivalent to 0.002250 gal/mi for the CAFE program) for pickups achieving 15 percent or 20 percent, respectively, better CO 2 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 CO 2 level to be compared with the targ et would also include any A/C related credits generated by the vehicle. Further deta ils 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 im provement value until MY 2021 unless its CO 2 level increases or fuel economy decreases. The 20 g/mi CO 2 (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 CO 2 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 CO 2 performance compared to the applicable CO 2 vehicle target for both CO 2 credits and corresponding CAFE fuel consumption improvement values. As with A/C and offcycle credits, EPA would convert the total CO 2 credits due to the pick-up incentive program from metric tons of CO 2 to a fleetwide equivalent CAFE improvement value.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 394 of 426 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 manufactur er’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 ga l/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 pr oposed 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 sta ndards 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 st andards 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. Histor ically, as shown in FARS data analyzed by

360 In this rulemaking document, “vehicle safety” is define d as societal fatality rates per vehicle miles traveled (VMT), which include fatalities to occupants of all the vehi cles 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 se tting the 1987-1989 passenger car standards, “NHTSA has always examined the safety consequences of the CAFE st andards 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 395 of 426 NHTSA, the safest cars generally have been heavy and large, wh ile 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 CO 2 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 relations hip 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 fleet s, 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 ar eas 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 fo otprint-based standards create no incentive to
downsize vehicles does not mean that manufact urers 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 cu rve, 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. A dditionally, 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 pri ce advantage. Because of these two reasons, the agencies believe that the incentive to in crease the sales of vehicles smaller than 41 sq uare feet due to this rulemaking, if any, is small. See Section II.C.1 above and Chapter 1 of the draf t Joint TSD for more information on the agencies’ choice of “cut-off” points for the footprint-based target curves.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 396 of 426 believe the target curves and the incentives th ey 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. Th e 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 ma de 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 compar tment 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 th e 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 397 of 426 analyzing potential future CAFE and GHG standard s. 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 qu estion from several angles. First, we are using a statistical approach to study the effect of ve hicle 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 cons idered as the agencies’ best estimate of potential mass-safety effects. The agencies recognize that negative safety effects estimated based on the historical relationships could pot entially 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 ne w challenges these lighter vehicles might bring to vehicle safety and potential countermeasures available to manage those challenges effectively.

The sections below discuss more specifically th e 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, si ze 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 i ndependent 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 wi th 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 histori cal data. This study will include a consideration ofDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 398 of 426 potential near multicollinearity in the historical da ta 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 fo r 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 vehi cles that are using material su bstitution and smart design, and to try to assess if there is sufficient crash data invo lving 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 vehicl es to vehicles of similar size and mass with more traditional designs.

Significant progress has been made on thes e tasks since the MY 2012-2016 final rule, as follows: The independent review of recent and upd ated statistical analyses of the relationship between vehicle mass, size, and crash fatality rates has been completed. NHTSA contracted with the University of Michigan Tran sportation Research Institute (U MTRI) 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 mate rials of more recent model year vehicles may
have weakened the historical statistical rela tionships 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 be lieve, as UMTRI also found, that different statistical analyses may have had different resu lts because they each used slightly different datasets for their analyses. In order to try to mitigate this pr oblem 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.”DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 399 of 426 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 awar e 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 tw o 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 examin e 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. Casual ty 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 ti me to inform this NPRM. The completed final peer reviewed-report on both assessments will be av ailable 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 co mments 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 in vestigate 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...DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 400 of 426 Design” to result in vehicle mass reduction, selec ting analytical pairs of vehicles, and using theappropriate 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 in sufficient to produce consistent results that could be used to support conclusions regarding hi storical 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. De partment 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 an d 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 Res earch 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), Sco tt Schmidt of th e 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 addresse d the two expansive sets of issues – statistical evidence of the roles of mass and size on safety, and engineering r ealities – 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”)DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 401 of 426 The first session focused on previous and ongoi ng 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 pe r 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 usag e 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#VehicleDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 402 of 426 The separation of key parameters was also discussed as a challenge to the analyses, as vehicle size has historically been highly correlat ed with vehicle mass. Presenters had varying approaches for dealing with th e 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 “per haps the effect of we ight 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 subset s with similar footprint values) ma y 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 avoi d 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 presen ters noted the difficulties of projecting past
data into the future as new tec hnologies 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 50 th anniversary crash test of a 1959 Chevrolet Bel Air and 2009 Chevrolet Malibu graphically demo nstrated 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 cont emporary larger, heavier vehicles graphically showed the significance of vehicle mass and size.

Kahane presented results from his 2010 report 371 that found that a scenario which took
some mass out of heavier vehicles but little or no mass out of th e 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 ma y be more indicative of future crashes. There is apparent consistency with ot her presentations (e.g., Padmanab an, Nusholtz) th at 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

.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 403 of 426 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, result ing in inflated variance with wrong signs and magnitudes in the results. Her re sults 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 review 372 of a set of statistical analyses of relationships between vehicle curb weight, th e 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 sta tistical 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 fa tality 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 genera lly lead to improved inference, assuming the

372 The review is independent in the sense that it was conduc ted by an outside third party without any interest in the reported outcome.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 404 of 426 data and model assumptions are appropriate. In that regard, the disaggregate logistic regression model used by NHTSA in the 2003 report 373 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 comp licated than is necessary based on the data being analyzed, and summing regression coeffici ents 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. Includi ng 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 multicollin earity, other than to say th at 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 si ze. Kahane has comb ined 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 differen ces 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 w ith mass reduction whil e 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 thatDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 405 of 426 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 une quivocally 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 stabili ty 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 fa tality 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 anal ysis were that mass reduction in lighter cars, even while holding footprint constant, woul d significantly increase so cietal fatality risk,
whereas mass reduction in the heavier LTVs would significantly reduce net societal fatality risk, because it would reduce the fatality risk of occu pants 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

.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 406 of 426 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 reduci ng 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 estimate s somewhere between the initial DRI and NHTSA
results. 375

Immediately after issuing the final rule fo r 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 relation ships 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 te chnologies, 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 Ze llner, J. W. (2003). A Further Assessment of the Eff ects 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 1985-19 98 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 Pa ssenger Vehicle Size and Weight on Safety,” NHTSA Workshop on Vehicle Mass-Size-Safety , Washington, February 25, 2011, http://www.nhtsa.gov/staticfiles/rulemak ing/pdf/MSS/MSSworks hop_VanAuken.pdfDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 407 of 426 databases available to the public 376 , 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 Report 378 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), whic h are SUVs of unibody construction, often but not always built upon a platform shared with pass enger 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 pe riod 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. Th e annual target populatio n of fatalities or the annual fatality distribution baseline 380 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 Fatal ity 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 onl y 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 408 of 426 for the safety statistics analysis, but is taken in to account later in the Volpe model analysis, since all vehicles in the future will be equipped with ESC 381

.
For the 2011 report, vehicles are now grouped into five classes rather than four: passenger cars (including both 2-door and 4-door car s) 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 esti mated 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 mainta ining 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-m odels 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 safe ty standard, and behavior changes anticipated.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 409 of 426 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 b ecause 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 a nd 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 safe ty performance were discontinued by 2000 or during 2000-. Also, the tendency of light, small vehicl es 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-pro tection 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 CU Vs 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 th e previous report’s “lower-estimate scenario,”DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 410 of 426 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 propor tion 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, redu ction 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 ha rmful and often beneficial, because the lighter vehicles respond more quickly to braking and st eering and are often more stable because their center of gravity is lower. Offsetting that benefi t is the continuing historical tendency of lighter
and smaller vehicles to be driven less well – al though 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 pred ict 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 somewh at 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 ma ss, 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%).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 411 of 426 statistical analysis has, over the past years, been able to accommodate mass increases of this magnitude, perhaps it will also succeed in modelin g 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 an d 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 3,10 6 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 specificat ions 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 fata lity 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 th e 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 in cluding vehicle make in the model increases theDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 412 of 426 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 i nduced 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 pe rcentage 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 st rongly correlated in the same regression model (referred to as multicollinearity) can lead to in accurate 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 consider ed 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 specificat ion, 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 wa s 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 heav er 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 th eir 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 sim ilar footprint. The analysis indicates that mass reduction does not consistently increase risk acr oss 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 413 of 426 vehicle type and crash type. Mass reduction increases risk in a majority of footprint deciles for 13 of the 27 crash and vehicle combinations, bu t few of these increases are statistically significant. On the other hand, mass reduction decr eases 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 R 2 , 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 footprin t 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 mo del explain the range in risk, equivalent to the

R 2 statistic in a linear regression model, does not exist. (SAS does generate a pseudo-R 2 value for logistic regression models; in almost all of the NHTSA regres sion 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 vehicl e, 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 R 2 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 increas es, 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 R 2 of 0.43, consistent with NHTSA’s basic regression results for the heavier LTVs (societal risk increases as mass increases). LBNL also examined the relationshi p 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 th e 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 substantiall y 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 414 of 426 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 limi ted 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 importan t 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 attri bute-based standards will likely facilitate the design of vehicles such that manufacturers may reduce ma ss 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 7600Predicted US fatality risk (crash fdatalities per 1010VMT)

Curb weight (lbs) 2-dr cars
4-dr cars Small pickups Fullsize pickups
SUVs
CUVs
MinivansDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 415 of 426 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 st atistical 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 av ailable, 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 po tential multicollinearity issues. . The agencies
will continue to monitor issues with multicolli nearity in our analyses, and hope that outside researchers will do the same. And finally, base d 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 CA FE 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 ve hicle 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 recen t 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 contin ued improvements in driver (and passenger)DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 416 of 426 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 cr ash 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 wh ich 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, manufactur ers will invest more and more resources in developing increasingly lightweight vehicle design s 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 co nsiderable 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 technol ogies – 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 417 of 426 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. A nother 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 Technolo gy (MIT) Materials Systems Laboratory’s research 385 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 processbetween 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 associat ed with it. NHTSA intends to have this study completed and peer reviewed before July 2012, in time for it to pl ay 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 feas ible 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, Pr ocess cost modeling: Strategic engineering and economic evaluation of materials technologies, JOM Journal of th e Minerals, Metals and Materials Society, Volume 59, Number 10, 21-32. Available at http://msl.mit.edu/pubs/docs/Field_Kirch ainCM_StratEvalMatls.pdf (last accessed Aug. 22, 2011).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 418 of 426 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 detaile d 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 Mode l Year Vehicle Program”. This study builds upon the low development (20% mass reduction) design in the 2010 Lotus Engineering study “An Assessment of Ma ss 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. Th e 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 wi ll provide an updated design with crash simulation, detailed costing and manufacturin g 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 rela tionship 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 (I CCT). 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 feasibleamounts 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 419 of 426 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 engineer ing 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 Alli ance of Automobile Manufacturer s, Guy Nusholtz of Chrysler, and Frank Field of the Massachusetts Institute of Technology.

The second session explored what degree of we ight reduction and occupant protection are feasible from technical, economic, and manuf acturing perspectives. Field emphasized that technical feasibility alone does not constitute feasibility in the cont ext 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 mate rials and good designs will be necessary to reduce fatalities. For exampl e, 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 wi despread 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 estima te 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 aDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 420 of 426 3/2 power law) were not generally consistent wi th 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 isolatin g 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 influe nces, 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 we ight, and felt that effective design and driver attributes may start to dominate size and we ight 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 ma terial and component supply chains and manufacturing infrastructure. Schmidt described an industry perspective on the complicated decisions that must be made in the face of regul atory change, such as evaluating goals, gains, and timing.

Field and Schmidt noted that the introduction of technical innovations is generally an innate development process involv ing both tactical and strategic considerations that balance
desired vehicle attributes with economic and t echnical 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 inclusi on 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 wide spread 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 implemen tations. Regulatory changes will affect how these tradeoffs are made and these risks are managed.

Koichi Kamiji presented data showing in in creased 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 applicat ion of certain optimized it designs can improve safety even when controlling for weight and size.

A philosophical theme developed that explor ed the ethics of consciously allowing the
total societal harm associated with mass reduc tion to approach the anticipated benefits ofDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 421 of 426 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 real ity 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 estimat es 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 es timates 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 consumpt ion/emissions impacts of improved CAFE/GHG
standards. We note that the amount of mass re duction selected for this rulemaking is based on our assumptions about how much is technologi cally 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 airbag s, 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 forDRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 422 of 426 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 th an 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 fa talities, while applying mass reduction to passenger
cars will increase fatalities. The CAFE model us es coefficients from the Kahane study along with the mass reduction level applied to each vehi cle 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 applie d 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 th e 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_St ratEvalMatls.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 se em 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 423 of 426 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 lif etime 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 regu latory 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-R elated 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. Abou t 1.4 million 2 wheel drive SUVs have been redefined as passenger cars instead of light trucks. The Kahane 2011 anal ysis 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).DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 424 of 426 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 assu med 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 incr ease; a negative number means th at 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 techno logical advances could potentially mitigate the safety effects estimated for this rulemaking: ligh tweight 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 t echnologies 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 w ill 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 lightweig hted 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 sa fety. 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 425 of 426 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 belie ves 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 manuf acturer 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 a ssumes 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 pr oposed standards will have no effe ct 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.DRAFT – DELIBERATIVE – PRIVILEGED AND CONFIDENTIAL

Page 426 of 426 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 repor t 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 di scussed 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.”