---------------------------------------------------------------------------

    \154\ ``For CAFE rules, feds look at aging sales data'',
Automotive News, December 22, 2011. Available at http://www.autonews.com/article/20111222/OEM11/111229956 (last accessed
Jun. 27, 2012).
---------------------------------------------------------------------------

    For this final rulemaking, the agencies have analyzed the costs and
benefits of the standards using two different forecasts of the light
vehicle fleet through MY 2025. The agencies have concluded that the
significant uncertainty associated with forecasting sales volumes,
vehicle technologies, fuel prices, consumer demand, and so forth out to
MY 2025 makes it reasonable and appropriate to evaluate the impacts of
the final CAFE and GHG standards using two baselines. One market
forecast, similar to the one used for the NPRM, uses corrected data
regarding the MY 2008 fleet, information from AEO 2011, and information
purchased from CSM. As noted above, the agencies received comments
regarding the market forecast used in the NPRM suggesting that updates
in several respects could be helpful to the agencies' analysis of final
standards; given those comments and since the agencies were already
planning to produce an updated market forecast, the final rule also
contains another market forecast using MY 2010 CAFE certification data,
information from AEO 2012, and information purchased from LMC
Automotive (formerly JD Powers Automotive).
    The two market forecasts contain certain differences, although as
will be discussed below, the differences are not significant enough to
change the agencies' decision as to the structure and stringency of the
final standards. For example, MY 2008 certification data represents the
most recent model year for which the industry's offerings were not
strongly affected by the subsequent economic recession, which may make
it reasonable to use if we believe that the future vehicle mix of
models are more likely to be reflective of the pre-recession mix than
mix of models produced after MY 2008 (e.g., in MY 2010). Also, the MY
2010-based fleet projection employs a future fleet forecast provided by
LMC Automotive, which is more current than the projection provided by
CSM in 2009. The CSM forecast, utilized for the MY 2008-based fleet
projection, appears to have been influenced by the recession, in
particular in predicting major declines in market share for some
manufacturers (e.g., Chrysler) which the agencies do not believe are
reasonably reflective of future trends.
    The MY 2010 based fleet projection, which is used in EPA's
alternative analysis and in NHTSA's co-analysis, employs a future fleet
forecast provided by LMC Automotive, which is more current than the
projection provided by CSM in 2009, and which reflects the post-
proposal MY 2010 CAFE certification data. However, this MY 2010 CAFE
data also shows effects of the economic recession. For example,
industry-wide sales were skewed down 20% compared to MY 2008 levels.
For some companies like Chrysler, Mitsubishi, and Subaru, sales were
down by 30-40% from MY 2008 levels, as documented in today's joint TSD.
For BMW, General Motors, Jaguar/Land Rover, Porsche, and Suzuki, sales
were down by more than 40%. Employing the MY 2008 vehicle data avoids
using these baseline market shifts when projecting the future fleet. On
the other hand, it also perpetuates vehicle brands and models (and
thus, their outdated fuel economy levels and engineering
characteristics) that have since been discontinued. The MY 2010 CAFE
certification data accounts for the phase-out of some brands (e.g.,
Saab, Pontiac, Hummer) \155\ and the introduction of some technologies
(e.g., Ford's Ecoboost engine), which may be more reflective of the
future fleet in this respect.
---------------------------------------------------------------------------

    \155\ Based on our review of the CAFE certification, the MY
2010-based fleet contains no Saabs, and compared to the MY 2008-
based fleet, about 90% fewer Hummers and about 75% fewer Pontiacs.
---------------------------------------------------------------------------

    Thus, given the volume of information that goes into creating a
baseline forecast and given the significant uncertainty in any
projection out to MY 2025, the agencies think that a reasonable way to
illustrate the possible impacts of that uncertainty for purposes of
this rulemaking is the approach taken here of analyzing the effects of
the final standards under both the MY 2008-based baseline and the MY
2010-based baseline. The agencies' analyses are presented in our
respective RIAs and preamble sections.
4. How did the Agencies develop the MY 2008 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 used for the NPRM and was updated for this FRM.
    There was only one change since the NPRM. A contractor working on a
market share model noted some problems with some of the 2008 MY vehicle
wheelbase data. Each of the affected vehicle's wheelbase and footprint
were corrected for the MY 2008-based fleet used for this final rule. A
more complete discussion of these changes is available in Chapter 1.3.1
of the TSD.
    The 2008 baseline fleet reflects all fuel economy technologies in
use on MY 2008 light duty vehicles as reported by manufacturers in
their CAFE certification data. The 2008 emission and fuel economy
database included data on vehicle production volume, fuel economy,
engine size, number of engine cylinders, transmission type, fuel type,
etc.; however it did not contain complete information on technologies.
Thus, the agencies relied on publicly available data like the more
complete technology descriptions from Ward's Automotive Group.\156\ 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.\157\ 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 TSD.
---------------------------------------------------------------------------

    \156\ Note that WardsAuto.com is a fee-based service, but all
information is public to subscribers.
    \157\ Motortrend.com and Edmunds.com are free, no-fee internet
sites.
---------------------------------------------------------------------------

5. How did the Agencies develop the projected MY 2017-2025 vehicle
reference fleet for the 2008 model year based fleet?
    As in the NPRM, 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 (EIAEIA
publishes a mid-term projection of national energy use called the
Annual Energy Outlook (AEO). This projection utilizes a number of
technical and econometric models which are designed to reflect both
economic and regulatory

conditions expected to exist in the future. In support of its
projection of fuel use by light-duty vehicles, EIA projects sales of
new cars and light trucks. EIA published its Early Annual Energy
Outlook for 2011 in December 2010. EIA released updated data to NHTSA
in February (Interim AEO). The final release of AEO for 2011 came out
in May 2011 and early release AEO came out in December of 2011, but for
consistency with the NPRM, EPA and NHTSA chose to use the data from
February 2011.
    The agencies used the Energy Information Administration's (EIA's)
National Energy Modeling System (NEMS) to estimate the future relative
market shares of passenger cars and light trucks. However, NEMS
methodology includes shifting vehicle sales volume, starting after
2007, away from fleets with lower fuel economy (the light truck fleet)
towards vehicles with higher fuel economies (the passenger car fleet)
in order to facilitate projected compliance with CAFE and GHG
standards. Because we use our market projection as a baseline relative
to which we measure the effects of new standards, and we attempt to
estimate the industry's ability to comply with new standards without
changing product mix (i.e., we analyze the effects of the 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 NPRM analysis, the agencies developed
a new projection of passenger car and lighttruck sales shares by
running scenarios from the Interim AEO 2011 reference case that first
deactivate the above-mentioned sales-volume shifting methodology and
then hold post-2017 CAFE standards constant at MY 2016 levels. As
discussed in Chapter 1 of the agencies' joint Technical Support
Document, incorporating these changes reduced the NEMS-projected
passenger car share of the light vehicle market by an average of about
5% during 2017-2025.
    In the AEO 2011 Interim data, EIA projects that total light-duty
vehicle sales will gradually recover from their currently depressed
levels by around 2013. In 2017, car sales are projected to be 8.4
million (53 percent) and truck sales are projected to be 7.3 million
(47 percent). Although the total level of sales of 15.8 million units
is similar to pre-2008 levels, the fraction of car sales is projected
to be higher than that existing in the 2000-2007 timeframe. This
projection reflects the impact of assumed higher fuel prices. Sales
projections of cars and trucks for future model years can be found in
Chapter 1 of the joint TSD.
    In addition to a shift towards more car sales, sales of segments
within both the car and truck markets have been changing and are
expected to continue to change. Manufacturers are introducing more
crossover utility vehicles (CUVs), which offer much of the utility of
sport utility vehicles (SUVs) but use more car-like designs. The AEO
2011 report does not, however, distinguish such changes within the car
and truck classes. In order to reflect these changes in fleet makeup,
EPA and NHTSA used a long range forecast\158\ from CSM Worldwide (CSM)
the firm which, at the time of proposal development, offered the most
detailed forecasting for the model years in question. 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.
Since proposal, the agencies have worked with LMC Automotive (formerly
J.D. Powers Forecasting) and found them to be capable of doing
forecasting of equivalent detail and are using the LMC forecast for the
2010 baseline fleet projection.
---------------------------------------------------------------------------

    \158\ The CSM Sales Forecast Excel file (``CSM North America
Sales Forecasts 2017-2025 for the Docket'') is available in the
docket (Docket EPA-HQ-OAR-2010-0799).
---------------------------------------------------------------------------

    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 II-1 and Table II-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 II-1--Annual Sales of Light-Duty Vehicles by Manufacturer in 2008 and Estimated for 2025
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                       Cars                        Light trucks                        Total
                                                         -----------------------------------------------------------------------------------------------
                                                              2008 MY         2025 MY         2008 MY         2025 MY         2008 MY         2025 MY
--------------------------------------------------------------------------------------------------------------------------------------------------------
Aston Martin............................................           1,370           1,182               0               0           1,370           1,182
BMW.....................................................         291,796         405,256          61,324         145,409         353,120         550,665
Chrysler/Fiat...........................................         703,158         436,479         956,792         331,762       1,659,950         768,241
Daimler.................................................         208,195         340,719          79,135         101,067         287,330         441,786
Ferrari.................................................           1,450           7,658               0               0           1,450           7,658
Ford....................................................         956,699       1,540,109         814,194         684,476       1,770,893       2,224,586
Geely/Volvo.............................................          65,649         101,107          32,748          42,588          98,397         143,696
GM......................................................       1,587,391       1,673,936       1,507,797       1,524,008       3,095,188       3,197,943
Honda...................................................       1,006,639       1,340,321         505,140         557,697       1,511,779       1,898,018
Hyundai.................................................         337,869         677,250          53,158         168,136         391,027         845,386
Kia.....................................................         221,980         362,783          59,472          97,653         281,452         460,436
Lotus...................................................             252             316               0               0             252             316
Mazda...................................................         246,661         306,804          55,885          61,368         302,546         368,172
Mitsubishi..............................................          85,358          73,305          15,371          36,387         100,729         109,692
Nissan..................................................         717,869       1,014,775         305,546         426,454       1,023,415       1,441,229
Porsche.................................................          18,909          40,696          18,797          11,219          37,706          51,915
Spyker/Saab.............................................          21,706          23,130           4,250           3,475          25,956          26,605
Subaru..................................................         116,035         256,970          82,546          74,722         198,581         331,692
Suzuki..................................................          79,339         103,154          35,319          21,374         114,658         124,528
Tata/JLR................................................           9,596          65,418          55,584          56,805          65,180         122,223
Tesla...................................................             800          31,974               0               0             800          31,974
Toyota..................................................       1,260,364       2,108,053         951,136       1,210,016       2,211,500       3,318,069

Volkswagen..............................................         291,483         630,163          26,999         154,284         318,482         784,447
                                                         -----------------------------------------------------------------------------------------------
    Total...............................................       8,230,568      11,541,560       5,621,193       5,708,899      13,851,761      17,250,459
--------------------------------------------------------------------------------------------------------------------------------------------------------

        Table II-2--Annual Sales of Light-Duty Vehicles by Market Segment in 2008 and Estimated for 2025
----------------------------------------------------------------------------------------------------------------
                             Cars                                                 Light trucks
----------------------------------------------------------------------------------------------------------------
                                    2008 MY         2025 MY                           2008 MY         2025 MY
----------------------------------------------------------------------------------------------------------------
Full-Size Car.................         829,896         245,355  Full-Size Pickup       1,332,335       1,002,806
Luxury Car....................       1,048,341       1,637,410  Mid-Size Pickup.         452,013         431,272
Mid-Size Car..................       2,103,108       2,713,078  Full-Size Van...          33,384          88,572
Mini Car......................         617,902       1,606,114  Mid-Size Van....         719,529         839,452
Small Car.....................       1,912,736       2,826,190  Mid-Size MAV*...         110,353         548,457
Specialty Car.................         469,324         808,183  Small MAV.......         231,265         239,065
                                ..............  ..............  Full-Size SUV*..         559,160          46,978
                                ..............  ..............  Mid-Size SUV....         436,080         338,849
                                ..............  ..............  Small SUV.......         196,424          71,827
                                ..............  ..............  Full-Size CUV*..         264,717         671,665
                                ..............  ..............  Mid-Size CUV....         923,165       1,259,483
                                ..............  ..............  Small CUV.......       1,612,029       1,875,703
                               ---------------------------------------------------------------------------------
    Total Sales**.............       6,981,307       9,836,330  ................       6,870,454       7,414,129
----------------------------------------------------------------------------------------------------------------
* MAV--Multi-Activity Vehicle, or a vehicle with a tall roof and elevated seating positions such as a Mazda5.
  SUV--Sport Utility Vehicle, CUV--Crossover Utility Vehicle.
**Total Sales are based on the classic Car/Truck definition.

    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 II-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 II-3--New and Old Car and Truck Definition in 2008, 2016, 2021, and 2025
----------------------------------------------------------------------------------------------------------------
                  Vehicle type                         2008         2016 \159\         2021            2025
----------------------------------------------------------------------------------------------------------------
Old Cars Definition.............................       6,981,307       8,576,717       8,911,173       9,836,330
New Cars Definition.............................       8,230,568      10,140,463      10,505,165      11,541,560
Old Truck Definition............................       6,870,454       7,618,459       7,277,894       7,414,129
New Truck Definition............................       5,621,193       6,054,713       5,683,902       5,708,899
----------------------------------------------------------------------------------------------------------------

    The CSM forecast provides estimates of car and truck sales by
segment and by manufacturer separately. The forecast was broken up into
two tables: one table with manufacturer volumes by year and the other
with vehicle segments percentages by year. Table II-4 and
---------------------------------------------------------------------------

    \159\ In the NPRM, MY 2016 values reported for the New Cars
Definition and Old Truck Definition were erroneously reversed.
---------------------------------------------------------------------------

    Table II--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 1.3.2 of the
TSD.

                          Table II-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.....................................................               6               6               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 II-5--CSM Segment Percentages in 2016, 2021, and 2025
----------------------------------------------------------------------------------------------------------------
                                                                       2016            2021            2025
                                                                     (percent)       (percent)       (percent)
----------------------------------------------------------------------------------------------------------------
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
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 Table II-6
below.

                                     Table II-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
represented the agencies' best forecast available at the time of its
publishing regarding the likely composition direction of the fleet. EPA
and NHTSA recognize that it is impossible to predict with certainty how
manufacturers' product offerings and sales volumes will evolve through
MY 2025 under baseline conditions--that is, without further changes in
standards after MY 2016. While the agencies have not included
variations in the market forecast as aspects of our respective
sensitivity analyses, we have conducted our central analyses twice--
once each for the MY 2008- and MY 2010-based market forecasts that
reflect differences in available vehicle models, differences in
manufacturer- and segment-level market shares, and differences in the
overall volumes of passenger cars and light trucks. In addition, as
discussed above, NHTSA's probabilistic uncertainty analysis accounts
for uncertainty regarding the relative market shares of passenger cars
and light trucks.
    The final step in the construction of the 2008 based fleet
projection 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 in the
agencies' respective RIAs.
6. How did the agencies develop the model year 2010 baseline vehicle
fleet as part of the 2010 based fleet projection?
    NHTSA and EPA also developed a baseline fleet comprised of model
year

2010 data gathered from EPA's emission and fuel economy database. This
alternative baseline fleet has the model year 2010 vehicle volumes and
attributes. The 2010 baseline fleet reflects all fuel economy
technologies in use on MY 2010 light duty vehicles as reported by
manufacturers in their CAFE certification data. The 2010 emission and
fuel economy database included data on vehicle production volume, fuel
economy, engine size, number of engine cylinders, transmission type,
fuel type, etc.; however it did not contain complete information on
technologies. Thus, as with the 2008 baseline fleet, the agencies
relied on publicly available data like the more complete technology
descriptions from Ward's Automotive Group. 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. A description of all of the
technologies used in modeling the 2010 vehicle fleet and how it was
constructed are available in Chapter 1.4 of the Joint TSD.
7. How did the Agencies develop the projected my 2017-2025 vehicle
reference fleet for the 2010 model year based fleet?
    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). EIA
published its Early Annual Energy Outlook for 2012 in December 2011.
EIA released updated data to NHTSA in February (AEO Early Release). The
final version of AEO 2012 was released June 25, 2012, after the
agencies had already completed our analyses using the early release
results.
    As the we did with the Interim 2011 AEO data, the agencies
developed a new projection of passenger car and light truck sales
shares by running scenarios from the Early Release AEO 2012 reference
case that first deactivate the above-mentioned sales-volume shifting
methodology and then hold post-2017 CAFE standards constant at MY 2016
levels. As discussed in Chapter 1 of the agencies' joint Technical
Support Document, incorporating these changes reduced the NEMS-
projected passenger car share of the light vehicle market by an average
of about 5% during 2017-2025.
    In the AEO 2012 Early Release 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
8.7 million (55 percent) and truck sales are projected to be 7.1
million (45 percent). Although the total level of sales of 15.8 million
units is similar to pre-2008 levels, the fraction of car sales is
projected to be higher than that existing in the 2000-2007 timeframe.
This projection reflects the impact of assumed higher fuel prices.
Sales projections of cars and trucks for future model years can be
found in Chapter 1.4.3 of the joint TSD.
    In addition to a shift towards more car sales, sales of segments
within both the car and truck markets have been changing and are
expected to continue to change. Manufacturers are introducing more
crossover utility vehicles (CUVs), which offer much of the utility of
sport utility vehicles (SUVs) but use more car-like designs. The AEO
2012 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 a custom long range forecast purchased from LMC
Automotive (formerly J.D. Powers Forecasting). NHTSA and EPA decided to
use the forecast from LMC for the 2010 model year based fleet for
several reasons discussed in Chapter 1 of the Joint TSD, and believe
the projection provides a useful cross-check for the forecast used for
the projections reflected in the 2008 model year based fleet. For the
public's reference, a copy of LMC's long range forecast has been placed
in the docket for this rulemaking.\160\
---------------------------------------------------------------------------

    \160\ The LMC Automotive's Sales Forecast Excel file (``LMC
North America Sales Forecasts 2017-2025 for the Docket'') is
available in the docket (Docket EPA-HQ-OAR-2010-0799).
---------------------------------------------------------------------------

    The next step was to project the LMC forecasts for relative sales
of cars and trucks by manufacturer and by market segment onto the total
sales estimates of AEO 2012. Table II-7 and Table II-8 show the
resulting projections for the reference 2025 model year and compare
these to actual sales that occurred in the baseline 2010 model year.
Both tables show sales using the traditional definition of cars and
light trucks. As discussed above, the new forecast from LMC shown in
Table II-7 shows a significant increase in Chrysler/Fiat's sales (1.6
million) from those projected by CSM (768 thousand).

                             Table II-7--Annual Sales of Light-Duty Vehicles by Manufacturer in 2010 and Estimated for 2025
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                       Cars                        Light trucks                        Total
                                                         -----------------------------------------------------------------------------------------------
                                                              2010 MY         2025 MY         2010 MY         2025 MY         2010 MY         2025 MY
--------------------------------------------------------------------------------------------------------------------------------------------------------
Aston Martin............................................             601             639               0               0             601             639
BMW.....................................................         143,638         363,380          26,788         101,013         170,426         464,394
Chrysler/Fiat...........................................         496,998         899,843         665,806         726,403       1,162,804       1,626,246
Daimler.................................................         157,453         261,242          72,393         119,090         229,846         380,332
Ferrari.................................................           1,780           1,894               0               0           1,780           1,894
Ford....................................................         940,241       1,441,350         858,798         997,694       1,799,039       2,439,045
Geely...................................................          28,223          65,883          29,719          31,528          57,942          97,411
GM......................................................       1,010,524       1,696,474         735,367       1,261,546       1,745,891       2,958,020
Honda...................................................         845,318       1,295,234         390,028         504,020       1,235,346       1,799,254
Hyundai.................................................         375,656         935,619          35,360         117,662         411,016       1,053,281
Kia.....................................................         226,157         350,765          21,721          37,957         247,878         388,723
Lotus...................................................             354             377               0               0             354             377
Mazda...................................................         249,489         262,732          61,451          53,183         310,940         315,916
Mitsubishi..............................................          54,263          67,925           9,146          15,464          63,409          83,389
Nissan..................................................         619,918         919,920         255,566         312,005         875,484       1,231,925
Porsche.................................................          11,937          17,609           3,978          19,091          15,915          36,701
Spyker..................................................               0               0               0               0               0               0
Subaru..................................................         184,587         218,870          73,665          96,326         258,252         315,196
Suzuki..................................................          25,002          48,710           3,938           4,173          28,940          52,883

Tata/JLR................................................          11,279          30,949          37,475          50,369          48,754          81,319
Tesla...................................................               0               0               0               0               0               0
Toyota..................................................       1,508,866       1,622,242         696,324         921,183       2,205,190       2,543,426
Volkswagen..............................................         284,046         479,423          36,327         105,009         320,373         584,432
                                                         -----------------------------------------------------------------------------------------------
    Total...............................................       7,176,330      10,981,082       4,013,850       5,473,718      11,190,180      16,454,800
--------------------------------------------------------------------------------------------------------------------------------------------------------

        Table II-8--Annual Sales of Light-Duty Vehicles by Market Segment in 2010 and Estimated for 2025
----------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------------------------------------------
                             Cars                 Light Trucks
----------------------------------------------------------------------------------------------------------------
                                    2010 MY         2025 MY                           2010 MY         2025 MY
----------------------------------------------------------------------------------------------------------------
Compact Conventional..........       2,107,568       2,380,540  Compact CUV.....       1,201,018       1,172,645
Compact Premium Conventional..         498,107         868,582  Compact MPV.....         250,816         409,034
Compact Premium Sporty........          45,373          59,523  Compact Premium          154,808         204,204
                                                                 CUV.
Compact Sporty................         136,464         170,121  Compact Utility.         216,634         234,737
Large Conventional............         485,656         832,113  Large Pickup....         992,473       1,426,193
Large Premium Conventional....          61,291         187,898  Large Premium             72,411         139,719
                                                                 Utility.
Large Premium Sporty..........           8,551          21,346  Large Utility...         164,416         323,992
Midsize Conventional..........       1,742,494       3,353,080  Large Van.......          17,516          31,198
Midsize Premium Conventional..         176,193         412,950  Midsize CUV.....         825,743       1,351,888
Midsize Premium Sporty........          27,023          67,005  Midsize Pickup..         288,508         443,502
Midsize Sporty................         244,895         257,865  Midsize Premium          333,790         493,977
                                                                 CUV.
Sub-Compact Conventional......         336,971         748,210  Midsize Premium           18,584          33,087
                                                                 Utility.
Unity Class *.................           7,351           7,820  Midsize Utility.         267,035         331,291
                                ..............  ..............  Midsize Van.....         508,491         492,280
                               ---------------------------------------------------------------------------------
    Total Sales * *...........       5,877,937       9,367,054  ................       5,312,243       7,087,746
----------------------------------------------------------------------------------------------------------------
* Unity Class--Is a special class created by the EPA for luxury brands that were not covered by the forecast.
* * Total Sales are based on the classic Car/Truck definition.

    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 II-9 shows the different volumes for car and
trucks based on the new and old NHTSA definition. The table shows the
difference in 2010, 2021, and 2025 to give a feel for how the change in
definition changes the car/truck split.

                 Table II-9--New and Old Car and Truck definition in 2010, 2016, 2021, and 2025
----------------------------------------------------------------------------------------------------------------
                  Vehicle type                         2010            2016            2021            2025
----------------------------------------------------------------------------------------------------------------
Old Cars Definition.............................       6,016,063       8,725,700       8,898,400       9,525,700
New Cars Definition.............................       7,176,330      10,227,185      10,310,594      10,981,082
----------------------------------------------------------------------------------------------------------------
Old Truck Definition............................       5,174,117       7,136,500       6,831,700       6,929,100
New Truck Definition............................       4,013,850       5,635,015       5,419,506       5,473,718
----------------------------------------------------------------------------------------------------------------

    The LMC forecast provides estimates of car and truck sales by
manufacturer segment and by manufacturer separately. The forecast was
broken up into two tables: one table with manufacturer volumes by year
and the other with vehicle segments percentages by year. Table II-10 is
an example of the data received from LMC. The task of estimating future
sales using these tables is complex. Table II-11 is the LMC projected
volumes for each manufacturer.
    Table II-12 has the LMC segment percentages for 2016, 2021, and
2025. We used a new methodology that is different than we used for the
2008 fleet projection. A detailed description of how the projection
process was done is found in Chapter 1 of the TSD.

               Table II-10--Example of the LMC Segmented Chrysler Volumes in 2016, 2021, and 2025
----------------------------------------------------------------------------------------------------------------
            Manufacturer                     LMC segment               2016            2021            2025
----------------------------------------------------------------------------------------------------------------
Chrysler/Fiat......................  Compact Basic..............               0               0               0
Chrysler/Fiat......................  Compact Conventional.......          66,300          80,131          90,032
Chrysler/Fiat......................  Compact CUV................          66,861          73,867          79,812
Chrysler/Fiat......................  Compact MPV................          42,609          73,673         108,134

Chrysler/Fiat......................  Compact Premium                      32,080          36,654          40,287
                                      Conventional.
Chrysler/Fiat......................  Compact Premium CUV........          10,780          11,229          11,811
Chrysler/Fiat......................  Compact Premium Sporty.....             164             151             140
Chrysler/Fiat......................  Compact Utility............         227,901         249,383         274,171
Chrysler/Fiat......................  Large Conventional.........         182,468         231,692         251,766
Chrysler/Fiat......................  Large Pickup...............         334,980         366,592         382,492
Chrysler/Fiat......................  Large Van..................          19,981          20,639          21,569
Chrysler/Fiat......................  Midsize Conventional.......         106,105         108,965         112,637
Chrysler/Fiat......................  Midsize CUV................          82,615          90,608          95,281
Chrysler/Fiat......................  Midsize Pickup.............          31,246          42,374          48,862
Chrysler/Fiat......................  Midsize Premium                       9,078          13,074          15,891
                                      Conventional.
Chrysler/Fiat......................  Midsize Premium CUV........          10,983          19,432          24,749
Chrysler/Fiat......................  Midsize Premium Sporty.....           4,132           3,753           3,728
Chrysler/Fiat......................  Midsize Sporty.............               0               0               0
Chrysler/Fiat......................  Midsize Utility............         219,206         185,386         162,149
Chrysler/Fiat......................  Midsize Van................         181,402         155,543         145,019
Chrysler/Fiat......................  Sub-Compact Conventional...          77,361          75,478          79,533
Chrysler/Fiat......................  Unity Class*...............           3,163           3,163           3,163
----------------------------------------------------------------------------------------------------------------
* Note: Unity Class is created by EPA to account for luxury brands.

                          Table II-11 LMC Manufacturer Volumes in 2016, 2021, and 2025
----------------------------------------------------------------------------------------------------------------
                          Manufacturer                                 2016            2021            2025
----------------------------------------------------------------------------------------------------------------
Aston Martin....................................................             601             601             601
BMW.............................................................         411,137         441,500         461,752
Daimler.........................................................         354,175         385,197         404,899
Chrysler/Fiat...................................................       1,709,415       1,841,787       1,951,226
Ford............................................................       2,692,193       2,818,737       2,935,409
Geely...........................................................          91,711          97,548         100,912
GM..............................................................       3,382,343       3,532,217       3,676,282
Honda...........................................................       1,635,473       1,758,092       1,838,444
Hyundai.........................................................       1,325,712       1,378,186       1,438,427
Lotus...........................................................             354             354             354
Mazda...........................................................         309,864         308,298         318,450
Mitsubishi......................................................          69,397          80,028          87,468
Nissan..........................................................       1,221,374       1,247,279       1,288,609
Subaru..........................................................         313,619         321,934         339,206
Spyker..........................................................  ..............  ..............  ..............
Suzuki..........................................................          44,935          48,861          52,594
Tata/JLR........................................................          83,824          87,169          89,011
Toyota..........................................................       2,492,707       2,582,404       2,658,145
Volkswagen......................................................         608,484         604,255         619,274
----------------------------------------------------------------------------------------------------------------

                          Table II-12--LMC Segment Percentages in 2016, 2021, and 2025
----------------------------------------------------------------------------------------------------------------
                                                                       2016            2021            2025
                           LMC segment                               (percent)       (percent)       (percent)
----------------------------------------------------------------------------------------------------------------
Unity Class*....................................................            0.04            0.04            0.04
Compact Basic...................................................            0.00            0.00            0.00
Compact Conventional............................................           12.44           12.07           12.03
Compact CUV.....................................................            7.74            7.38            7.30
Compact MPV.....................................................            2.61            2.47            2.56
Compact Premium Conventional....................................            4.59            4.68            4.69
Compact Premium CUV.............................................            1.49            1.54            1.55
Compact Premium Sporty..........................................            0.41            0.34            0.31
Compact Sporty..................................................            0.95            0.91            0.88
Compact Utility.................................................            1.37            1.45            1.53
Large Conventional..............................................            3.95            4.27            4.27
Large Pickup....................................................           12.62           12.95           12.92
Large Premium Conventional......................................            0.88            0.95            0.98
Large Premium Pickup............................................            0.00            0.00            0.00
Large Premium Sporty............................................            0.09            0.11            0.11
Large Premium Utility...........................................            0.91            0.91            0.91
Large Utility...................................................            2.32            2.21            2.11
Large Van.......................................................            2.24            2.34            2.40
Midsize Conventional............................................           16.49           17.04           17.17
Midsize CUV.....................................................            9.28            8.84            8.92
Midsize Pickup..................................................            2.56            2.79            2.89

Midsize Premium Conventional....................................            2.06            2.18            2.21
Midsize Premium CUV.............................................            2.87            3.08            3.11
Midsize Premium Sporty..........................................            0.40            0.36            0.34
Midsize Premium Utility.........................................            0.23            0.22            0.22
Midsize Sporty..................................................            1.59            1.41            1.33
Midsize Utility.................................................            2.57            2.42            2.16
Midsize Van.....................................................            3.53            3.32            3.21
Sub-Compact Conventional........................................            3.77            3.72            3.85
----------------------------------------------------------------------------------------------------------------
* Note: Unity Class is created by EPA to account for luxury brands.

    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 LMC forecast. These sales splits are shown in Table II-13
below.

                                    Table II-13--Car and Truck Volumes and Split Based on NHTSA New Truck Definition
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                        2016      2017      2018      2019      2020      2021      2022      2023      2024      2025
--------------------------------------------------------------------------------------------------------------------------------------------------------
Car Volume*.........................................    10,227    10,213    10,089    10,140    10,194    10,311    10,455    10,594    10,812    10,981
Truck Volume*.......................................     5,635     5,599     5,516     5,522     5,436     5,420     5,432     5,413     5,435     5,474
Car Split...........................................     64.5%     64.6%     64.7%     64.7%     65.2%     65.5%     65.8%     66.2%     66.5%     66.7%
Truck Split.........................................     35.5%     35.4%     35.3%     35.3%     34.8%     34.5%     34.2%     33.8%     33.5%     33.3%
--------------------------------------------------------------------------------------------------------------------------------------------------------
\*\ In thousands.

    The final step in the construction of the 2010 model year based
fleet involves applying additional technology to individual vehicle
models--that is, technology beyond that already present in MY 2010----
reflecting already-promulgated standards through MY 2016, and
reflecting the assumption that MY 2016 standards would continue to
apply in each model year through MY 2025. A description of the
agencies' modeling work to develop their respective final reference (or
adjusted baseline) fleets appear in the agencies' respective RIAs.
8. What are the Differences in the Sales Volumes and Characteristics of
the MY 2008 Based and the MY 2010 Based Fleets Projections?
    Table II-14 is the difference in actual and projected sales volumes
between the 2010 based and the 2008 based fleet forecast. This summary
table is the most convenient way to compare the projections from CSM
and LMC, since the forecasting companies use different segmentations of
vehicles. It also provides a comparison of the two AEO forecasts since
the projections are normalized to AEO's total volume of cars and trucks
in each year of the projection. The table shows a total projected
reduction from the 2008 fleet to the 2010 fleet in 2025 of .5 million
cars and .8 million trucks. The largest manufacturer changes in the
2025 model projections are for Chrysler and Toyota. The newer
projection increases Chrysler's total vehicles by .9 million vehicles,
while it decreases Toyota's total vehicles by .8 million.
    The table also shows that the total actual reduction in cars from
2008 MY to 2010 MY is 1.0 million vehicles, and the reduction in trucks
is 1.6 million vehicles.

                                     Table II-14--Differences in Annual Sales of Light-Duty Vehicles by Manufacturer
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                       Cars                        Light trucks                        Total
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                           2010-2008 MY       2025 MY      2010-2008 MY       2025 MY      2010-2008 MY       2025 MY
--------------------------------------------------------------------------------------------------------------------------------------------------------
Aston Martin............................................            -769            -543               0               0            -769            -543
BMW.....................................................        -148,158         -41,876         -34,536         -44,396        -182,694         -86,271
Chrysler/Fiat...........................................        -206,160         463,364        -290,986         394,641        -497,146         858,005
Daimler.................................................         -50,742         -79,477          -6,742          18,023         -57,484         -61,454
Ferrari.................................................             330          -5,764               0               0             330          -5,764
Ford....................................................         -16,458         -98,759          44,604         313,218          28,146         214,459
Geely...................................................         -37,426         -35,224          -3,029         -11,060         -40,455         -46,285
GM......................................................        -576,867          22,538        -772,430        -262,462      -1,349,297        -239,923
Honda...................................................        -161,321         -45,087        -115,112         -53,677        -276,433         -98,764
Hyundai.................................................          37,787         258,369         -17,798         -50,474          19,989         207,895
Kia.....................................................           4,177         -12,018         -37,751         -59,696         -33,574         -71,713
Lotus...................................................             102              61               0               0             102              61
Mazda...................................................           2,828         -44,072           5,566          -8,185           8,394         -52,256
Mitsubishi..............................................         -31,095          -5,380          -6,225         -20,923         -37,320         -26,303
Nissan..................................................         -97,951         -94,855         -49,980        -114,449        -147,931        -209,304
Porsche.................................................          -6,972         -23,087         -14,819           7,872         -21,791         -15,214
Spyker..................................................          -21706          -23130           -4250           -3475          -25956          -26605
Subaru..................................................          68,552         -38,100          -8,881          21,604          59,671         -16,496

Suzuki..................................................         -54,337         -54,444         -31,381         -17,201         -85,718         -71,645
Tata/JLR................................................           1,683         -34,469         -18,109          -6,436         -16,426         -40,904
Tesla...................................................            -800          -31974               0               0            -800          -31974
Toyota..................................................         248,502        -485,811        -254,812        -288,833          -6,310        -774,643
Volkswagen..............................................          -7,437        -150,740           9,328         -49,275           1,891        -200,015
                                                         -----------------------------------------------------------------------------------------------
    Total...............................................      -1,054,238        -560,478      -1,607,343        -235,181      -2,661,581        -795,659
--------------------------------------------------------------------------------------------------------------------------------------------------------

    Table II-15 shows the change in volumes between the two forecasts
for cars and trucks based on the new and old NHTSA definition. The
table shows the change to give a feel for how the change in definition
impacts the car/truck split. Many factors impact the changes shown here
including differences in AEO, differences in the number of SUV and CUV
vehicles becoming cars, and the future volume projected by CSM and LMC.

         Table II-15--Differences in New and Old Car and Truck definition in 2008, 2016, 2021, and 2025
----------------------------------------------------------------------------------------------------------------
                  Vehicle type                       2010-2008         2016            2021            2025
----------------------------------------------------------------------------------------------------------------
Old Cars Definition.............................        -965,244         148,983         -12,773        -310,630
New Cars Definition.............................      -1,054,238          86,722        -194,571        -560,478
Old Truck Definition............................      -1,696,337        -481,959        -446,194        -485,029
New Truck Definition............................      -1,607,343        -419,698        -264,396        -235,181
----------------------------------------------------------------------------------------------------------------

    Table II-16 is the changes in car and truck split due to the
difference between the 2010 and 2008 forecast. The table shows that the
different AEO forecasts, CSM and LMC projections have an insignificant
impact on the car and truck split.

                             Table II-16--Differences in Car and Truck Volumes and Split Based on NHTSA New Truck Definition
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                               2016       2017       2018       2019       2020       2021       2022       2023       2024       2025
--------------------------------------------------------------------------------------------------------------------------------------------------------
Car Volume \*\............................         87        225        184        144        -98       -194       -281       -374       -446       -561
Truck Volume \*\..........................       -419       -220       -155        -61       -168       -264       -272       -274       -241       -235
Car Split.................................       1.9%       1.4%       1.1%       0.5%       0.5%       0.6%       0.5%       0.3%       0.0%      -0.2%
Truck Split...............................      -1.9%      -1.4%      -1.1%      -0.5%      -0.5%      -0.6%      -0.5%      -0.3%       0.0%       0.2%
--------------------------------------------------------------------------------------------------------------------------------------------------------
\*\ in thousands.

    The joint TSD contains further comparisons of the two projections
at the end of Chapter 1.
    So, given all of the discussion above, the agencies have created
these two baselines to illustrate possible uncertainty in the future
market forecast. The industry-wide differences between the forecasts
are relatively minor, even if there are some fairly significant
differences for individual manufacturers. Analysis under both baselines
supports the agencies' respective decisions as to the stringency of the
final standards, as discussed further in Sections III and IV below.

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 promulgating attribute-based CAFE and
CO2 standards that are defined by a mathematical function.
EPCA, as amended by EISA, expressly requires that CAFE standards for
passenger cars and light trucks be based on one or more vehicle
attributes related to fuel economy, and be expressed in the form of a
mathematical function.\161\ 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,\162\ in the recent
GHG standards for heavy duty pickups and vans using a work factor
attribute,\163\ and in the MYs 2012-2016 GHG rule itself which used
vehicle footprint as the attribute). As for the MYs 2012-2016
rulemaking, public comments on the MYs 2017-2025 proposal widely
supported attribute-based standards for both agencies' standards as
further discussed in section II.C.2.
---------------------------------------------------------------------------

    \161\ 49 U.S.C. 32902(a)(3)(A).
    \162\ 69 FR 38958 (June 29, 2004).
    \163\ 76 FR 57106, 57162-64, (Sept. 15, 2011).
---------------------------------------------------------------------------

    Under an attribute-based standard, every vehicle model has a
performance target (fuel economy and CO2 emissions for CAFE
and CO2 emissions standards, respectively), the level of
which depends on the vehicle's attribute (for this final rule,
footprint, as discussed below). Each manufacturers' fleet average
standard is determined by the production-weighted \164\ average (for
CAFE, harmonic average) of those targets.
---------------------------------------------------------------------------

    \164\ Production for sale in the United States.
---------------------------------------------------------------------------

    The agencies believe that an attribute-based standard is preferable
to a single-industry-wide average standard in the

context of CAFE and CO2 standards for several reasons.
First, if the shape is chosen properly, every manufacturer is more
likely to be required to continue adding more fuel efficient technology
each year across their fleet, because the stringency of the compliance
obligation will depend on the particular product mix of each
manufacturer. Therefore a maximum feasible attribute-based standard
will tend to require greater fuel savings and CO2 emissions
reductions overall than would a maximum feasible flat standard (that
is, a single mpg or CO2 level applicable to every
manufacturer).
    Second, depending on the attribute, attribute-based standards
reduce the incentive for manufacturers to respond to CAFE and
CO2 standards in ways harmful to safety.\165\ Because each
vehicle model has its own target (based on the attribute chosen),
properly fitted attribute-based standards provide little, if any,
incentive to build smaller vehicles simply to meet a fleet-wide
average, because the smaller vehicles will be subject to more stringent
compliance targets.\166\
---------------------------------------------------------------------------

    \165\ The 2002 NAS Report described at length and quantified the
potential safety problem with average fuel economy standards that
specify a single numerical requirement for the entire industry. See
2002 NAS Report at 5, finding 12. Ensuing analyses, including by
NHTSA, support the fundamental conclusion that standards structured
to minimize incentives to downsize all but the largest vehicles will
tend to produce better safety outcomes than flat standards.
    \166\ Assuming that the attribute is related to vehicle size.
---------------------------------------------------------------------------

    Third, attribute-based standards provide a more equitable
regulatory framework for different vehicle manufacturers.\167\ A single
industry-wide average standard imposes disproportionate cost burdens
and compliance difficulties on the manufacturers that need to change
their product plans to meet the standards, and puts no obligation on
those manufacturers that have no need to change their plans. As
discussed above, attribute-based standards help to spread the
regulatory cost burden for fuel economy more broadly across all of the
vehicle manufacturers within the industry.
---------------------------------------------------------------------------

    \167\ 2002 NAS Report at 4-5, finding 10.
---------------------------------------------------------------------------

    Fourth, attribute-based standards better respect economic
conditions and consumer choice as compared to single-value standards. A
flat, or single value, standard encourages a certain vehicle size fleet
mix by creating incentives for manufacturers to use vehicle downsizing
as a compliance strategy. Under a footprint-based standard,
manufacturers have the incentive to invest in technologies that improve
the fuel economy of the vehicles they sell rather than shifting their
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 adopting, 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 promulgating CAFE and CO2
standard curves that are based on vehicle footprint, which has an
observable correlation to fuel economy and emissions. There are several
policy and technical reasons why NHTSA and EPA believe that footprint
is the most appropriate attribute on which to base the standards for
the vehicles covered by this rulemaking, even though some other vehicle
attributes (notably curb weight) are better correlated to fuel economy
and emissions.
    First, in the agencies' judgment, from the standpoint of vehicle
safety, it is important that the CAFE and CO2 standards be
set in a way that does not encourage manufacturers to respond by
selling vehicles that are less safe. While NHTSA's research of
historical crash data also indicates that reductions in vehicle mass
tend to compromise overall highway safety, reductions in vehicle
footprint do so to a much greater extent. If footprint-based standards
are defined in a way that creates a relatively uniform burden for
compliance for vehicles of all sizes, then footprint-based standards
should not create incentives for manufacturers to downsize their fleets
as a strategy for compliance which could compromise societal safety, or
to upsize their fleets which might reduce the program's fuel savings
and GHG emission reduction benefits. Footprint-based standards also
enable manufacturers to apply weight-efficient materials and designs to
their vehicles while maintaining footprint, as an effective means to
improve fuel economy and reduce GHG emissions. On the other hand,
depending on their design, weight-based standards can create
disincentives for manufacturers to apply weight-efficient materials and
designs. This is because weight-based standards would become more
stringent as vehicle mass is reduced. The agencies discuss mass
reduction and its relation to safety in more detail in Preamble section
II.G.
    Further, although we recognize that weight is better correlated
with fuel economy and CO2 emissions than is footprint, we
continue to believe that there is less risk of ``gaming'' (changing the
attribute(s) to achieve a more favorable target) by increasing
footprint under footprint-based standards than by increasing vehicle
mass under weight-based standards--it is relatively easy for a
manufacturer to add enough weight to a vehicle to decrease its
applicable fuel economy target a significant amount, as compared to
increasing vehicle footprint. We also continue to agree with concerns
raised in 2008 by some commenters to 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 \168\
that the possibility of gaming an attribute-based standard is lowest
with footprint-based standards, as opposed to weight-based or multi-
attribute-based standards. Specifically, standards that incorporate
weight, torque, power, towing capability, and/or off-road capability in
addition to footprint would not only be more complex, but by providing
degrees of freedom with respect to more easily-adjusted attributes,
they could make it less certain that the future fleet would actually
achieve the average fuel economy and CO2 reduction levels
projected by the agencies.\169\ This is not to say that a footprint-
based system will eliminate gaming, or that a footprint-based system
eliminates the possibility that manufacturers will change vehicles in
ways that compromise occupant protection. Such risks cannot be
completely avoided, and in the agencies' judgment, footprint-based
standards achieved the best balance among affected considerations.
---------------------------------------------------------------------------

    \168\ See 74 FR 14359 (Mar. 30, 2009).
    \169\ However, for heavy-duty pickups and vans not covered by
today's standards, the agencies determined that use of footprint and
work factor as attributes for heavy duty pickup and van GHG and fuel
consumption standards could reasonably avoid excessive risk of
gaming. See 76 FR 57106, 57161-62 (Sept. 15, 2011).
---------------------------------------------------------------------------

    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. The agencies recognize that a recent
independent analysis, discussed below, suggests that the NPRM form of
the MY 2014 standards could, under some circumstances posited by the
authors, induce some increases in vehicle footprint. Underlining the
potential uncertainty, considering a range of scenarios, the authors
obtained a wide range of results in their analyses. As discussed in
later in this section,

slopes of the linear relationships underlying today's standards are
within the range of technically reasonable analyses of the
relationships between fuel consumption and footprint, and the agencies
continue to expect that there will not be significant shifts in the
distribution of footprints as a direct consequence of this final rule.
The agencies also recognize that some attribute-based standards in
other countries/regions use attributes other than footprint and that
there could be benefits for some manufacturers if there was greater
international harmonization of fuel economy and GHG standards for
light-duty vehicles, but this is largely a question of how stringent
standards are and how they are tested and enforced. It is entirely
possible that footprint-based and weight-based systems can coexist
internationally and not present an undue burden for manufacturers if
they are carefully crafted. Different countries or regions may find
different attributes appropriate for basing standards, depending on the
particular challenges they face--from fuel prices, to family size and
land use, to safety concerns, to fleet composition and consumer
preference, to other environmental challenges besides climate change.
The agencies anticipate working more closely with other countries and
regions in the future to consider how fuel economy and related GHG
emissions test procedures and standards might be approached in ways
that least burden manufacturers while respecting each country's need to
meet its own particular challenges.
    In the NPRM, the agencies stated that we continue to find that
footprint is the most appropriate attribute upon which to base the
proposed standards, but recognizing strong public interest in this
issue, we sought comment on whether the agencies should consider
setting standards for the final rule based on another attribute or
another combination of attributes. The agencies also specifically
requested that the commenters address the concerns raised in the
paragraphs above regarding the use of other attributes, and explain how
standards should be developed using the other attribute(s) in a way
that contributes more to fuel savings and CO2 reductions
than the footprint-based standards, without compromising safety.
    The agencies received several comments regarding the attribute(s)
upon which post-MY 2016 CAFE and GHG standards should be based. The
National Auto Dealers Association (NADA) \170\ and the Consumer
Federation of America (CFA) \171\ expressed support for attribute-based
standards, generally, indicating that such standards accommodate
consumer preferences, level the playing field between manufacturers,
and remove the incentive to push consumers into smaller vehicles. Many
commenters, including automobile manufacturers, NGOs, trade
associations and parts suppliers (e.g., General Motors,\172\ Ford,\173\
American Chemistry Council,\174\ Alliance of Automobile
Manufacturers,\175\ International Council on Clean Transportation,\176\
Insurance Institute for Highway Safety,\177\ Society of the Plastics
Industry,\178\ Aluminum Association,\179\ Motor and Equipment
Manufacturers Association,\180\ and others) expressed support for the
continued use of vehicle footprint as the attribute upon which to base
CAFE and CO2 standards, citing advantages similar to those
mentioned by NADA and CFA. Conversely, the Institute for Policy
Integrity (IPI) at the New York University School of Law questioned
whether non-attribute-based (flat) or an alternative attribute basis
would be preferable to footprint-based standards as a means to increase
benefits, improve safety, reduce ``gaming,'' and/or equitably
distribute compliance obligations.\181\ IPI argued that, even under
flat standards, credit trading provisions would serve to level the
playing field between manufacturers. IPI acknowledged that NHTSA,
unlike EPA, is required to promulgate attribute-based standards, and
agreed that a footprint-based system could be at much less risk of
gaming than a weight-based system. IPI suggested that the agencies
consider a range of options, including a fuel-based system, and select
the approach that maximizes net benefits. Ferrari and BMW suggested
that the agencies consider weight-based standards, citing the closer
correlation between fuel economy and footprint, and BMW further
suggested that weight-based standards might facilitate international
harmonization (i.e., between U.S. standards and related standards in
other countries).\182\ Porsche commented that the footprint attribute
is not well suited for manufacturers of high performance vehicles with
a small footprint.\183\
---------------------------------------------------------------------------

    \170\ NADA, Docket No. NHTSA-2010-0131-0261, at 11.
    \171\ CFA, Docket No. EPA-HQ-OAR-2010-0799-9419 at 810, 44.
    \172\ GM, Docket No. NHTSA-2010-0131-0236, at 2.
    \173\ Ford, Docket No. NHTSA-2010-0131-0235, at 8.
    \174\ ACC, Docket No. EPA-HQ-OAR-2010-0799-9517 at 2.
    \175\ Alliance, Docket No. NHTSA-2010-0131-0262, at 85.
    \176\ ICCT, Docket No. NHTSA-2010-0131-0258, at 48.
    \177\ IIHS, Docket No. NHTSA-2010-0131-0222, at 1.
    \178\ SPI, Docket No. EPA-HQ-OAR-2010-0799-9492 at 4.
    \179\ Aluminum Association, Docket No. NHTSA-2010-0131-0226, at
1.
    \180\ MEMA, Docket No. EPA-HQ-OAR-2010-0799-9478 at 1.
    \181\ IPI, Docket No. EPA-HQ-OAR-2010-0799-11485 at 13-15.
    \182\ BMW, Docket No. NHTSA-2010-0131-0250, at 3.
    \183\ Porsche, Docket No. EPA-HQ-OAR-2010-0799-9264.
---------------------------------------------------------------------------

    Regarding the comments from IPI, as IPI appears to acknowledge,
EPCA/EISA expressly requires that CAFE standards be attribute-based and
defined in terms of mathematical functions. Also, NHTSA has, in fact,
considered and reconsidered options other than footprint, over the
course of multiple CAFE rulemakings conducted throughout the past
decade. When first contemplating attribute-based systems, NHTSA
considered attributes such as weight, ``shadow'' (overall area),
footprint, power, torque, and towing capacity. NHTSA also considered
approaches that would combine two or potentially more than two such
attributes. To date, every time NHTSA (more recently, with EPA) has
considered options for light-duty vehicles, the agency has concluded
that a properly designed footprint-based approach provides the best
means of achieving the basic policy goals (i.e., by reducing
disparities between manufacturers' compliance burdens, increasing the
likelihood of improved fuel economy and reduced GHG emissions across
the entire spectrum of footprint targets; and by reducing incentives
for manufacturers to respond to standards by reducing vehicle size in
ways that could compromise overall highway safety) involved in applying
an attribute-based standards, and at the same time structuring
footprint-based standards in a way that furthers the energy and
environmental policy goals of EPCA and the CAA by not creating
inappropriate incentives to increase vehicle size in ways that could
increase fuel consumption and GHG emissions. As to IPI's suggestion to
use fuel type as an attribute, although neither NHTSA nor EPA have
presented quantitative analysis of standards that differentiate between
fuel type, such standards would effectively use fuel type to identify
different subclasses of vehicles, thus requiring mathematical
functions--not addressed by IPI's comments--to

recombine these fuel types into regulated classes. Insofar as EPCA/EISA
already specifies how different fuel types are to be treated for
purposes of calculating fuel economy and CAFE levels, and moreover,
insofar as the EISA revisions to EPCA removed NHTSA's previously-clear
authority to set separate CAFE standards for different classes of light
trucks, using fuel type to further differentiate subclasses of vehicles
could conflict with the intent, and possibly the letter, of NHTSA's
governing statute. Finally, in the agencies' judgment, while regarding
IPI's suggestion that the agencies select the attribute-based approach
that maximizes net benefits may have merit, net benefits are but one of
many considerations which lead to the setting of the standard. Also,
such an undertaking would be impracticable at this time, considering
that the mathematical forms applied under each attribute-based approach
would also need to be specified, and that the agencies lack methods to
reliably quantify the relative potential for induced changes in vehicle
attributes.
    Regarding Ferrari's and BMW's comments, as stated previously, in
the agencies' judgment, footprint-based standards (a) discourage
vehicle downsizing that might compromise occupant protection, (b)
encourage the application of technology, including weight-efficient
materials (e.g., high-strength steel, aluminum, magnesium, composites,
etc.), and (c) are less susceptible than standards based on other
attributes to ``gaming'' that could lead to less-than-projected energy
and environmental benefits. It is also important to note that there are
many differences between both the standards and the on-road light-duty
vehicle fleets in Europe and the United States. The stringency of
standards, independent of the attribute used, is another factor that
influences harmonization. While the agencies agree that international
harmonization of test procedures, calculation methods, and/or standards
could be a laudable goal, again, harmonization is not simply a function
of the attribute upon which the standards are based. Given the
differences in the on-road fleet, in fuel composition and availability,
in regional consumer preferences for different vehicle characteristics,
in other vehicle regulations besides for fuel economy/CO2
emissions, and in the balance of program goals given all of these
factors in the model years affected, among other things, it would not
necessarily be expected that the CAFE and GHG emission standards would
align with standards of other countries. Thus, the agencies continue to
judge vehicle footprint to be a preferable attribute for the same
reasons enumerated in the proposal and reiterated above.
    Finally, as explained in section III.B.6 and documented in section
III.D.6 below, EPA agrees with Porsche that the MY2017 GHG standards,
and the GHG standards for the immediately succeeding model years, pose
special challenges of feasibility and (especially) lead time for
intermediate volume manufacturers, in particular for limited-line
manufacturers of smaller footprint, high performance passenger cars. It
is for this reason that EPA has provided additional lead time to these
manufacturers. NHTSA, however, is providing no such additional lead
time. As required under EISA/EPCA, manufacturers continue--as since the
1970s--to have the option of paying civil penalties in lieu of
achieving compliance with the standards, and NHTSA is uncertain as to
what authority would allow it to promulgate separate standards for
different classes of manufacturers, having raised this issue in the
proposal and having received no legal analysis with suggestions from
Porsche or other commenters.
3. How have the agencies changed the mathematical functions for the MYs
2017-2025 standards, and why?
    By requiring NHTSA to set CAFE standards that are attribute-based
and defined by a mathematical function, NHTSA interprets Congress as
intending that the post-EISA standards to be data-driven--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.\184\ EPA is also
setting attribute-based CO2 standards defined by similar
mathematical functions, for the reasonable technical and policy grounds
discussed below and in Section II of the preamble to the proposed
rule,\185\ and which supports a harmonization with the CAFE standards.
---------------------------------------------------------------------------

    \184\ 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.
    \185\ See 76 FR 74913 et seq. (Dec. 1, 2011).
---------------------------------------------------------------------------

    The relationship between fuel economy (and GHG emissions) and
footprint, though directionally clear (i.e., fuel economy tends to
decrease and CO2 emissions tend to increase with increasing
footprint), is theoretically vague and quantitatively uncertain; in
other words, not so precise as to a priori yield only a single possible
curve.\186\ 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 since the statutes do not dictate a particular
mathematical function for curve shape. For example, curve shapes that
might have some theoretical basis could lead to perverse outcomes
contrary to the intent of the statutes to conserve energy and reduce
GHG emissions.\187\ Thus, the decision of how to set the target curves
cannot always be just about most ``clearly'' using a mathematical
function to define the relationship between fuel economy and the
attribute; it often has to reflect legitimate policy judgments, where
the agencies adjust the function that would define the relationship in
order to achieve environmental goals, reduce petroleum consumption,
encourage application of fuel-saving technologies, not adversely affect
highway safety, reduce disparities of manufacturers' compliance burdens
(increasing the likelihood of improved fuel economy and reduced GHG
emissions across the entire spectrum of footprint targets), preserve
consumer choice, etc. This is true both for the decisions that guide
the mathematical function defining the sloped portion of the target
curves, and for the separate decisions that guide the agencies' choice
of ``cutpoints'' (if any) that define the fuel economy/CO2
levels and 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.
---------------------------------------------------------------------------

    \186\ In fact, numerous manufacturers have confidentially shared
with the agencies what they describe as ``physics based'' curves,
with each OEM showing significantly different shapes, and footprint
relationships. The sheer variety of curves shown to the agencies
further confirm the lack of an underlying principle of ``fundamental
physics'' driving the relationship between CO2 emission
or fuel consumption and footprint, and the lack of an underlying
principle to dictate any outcome of the agencies' establishment of
footprint-based standards.
    \187\ For example, if the agencies set weight-based standards
defined by a steep function, the standards might encourage
manufacturers to keep adding weight to their vehicles to obtain less
stringent targets.
---------------------------------------------------------------------------

    The next sections examine the policy concerns that the agencies
considered in developing the target curves that define

the MYs 2017-2025 CAFE and CO2 standards presented in this
final rule, and the technical work supporting selection of the curves
defining those standards.
4. What curves are the agencies promulgating for MYs 2017-2025?
    The mathematical functions for the MYs 2017-2025 curves are
somewhat changed from the functions for the MYs 2012-2016 curves, in
response to comments received from stakeholders pre-proposal in order
to address technical concerns and policy goals that the agencies judge
more significant in this rulemaking than in the prior one, given their
respective timeframes, and have retained those same mathematical
functions for the final rule as supported by commenters. This section
discusses the methodology the agencies selected as, at this time, best
addressing those technical concerns and policy goals, given the various
technical inputs to the agencies' current analyses. Below the agencies
discuss how the agencies determined the cutpoints and the flat portions
of the MYs 2017-2025 target curves. We also note that both of these
sections address only how the curves were fit to fuel consumption and
CO2 emission values determined using the city and highway
test procedures, and that in determining respective regulatory
alternatives, the agencies made further adjustments to the curves to
account for improvements to mobile air conditioners.
    Thus, recognizing that there are many reasonable statistical
methods for fitting curves to data points that define vehicles in terms
of footprint and fuel economy, as in past rules, the agencies added
equivalent levels of technology to the baseline fleet as a starting
point for the curve analysis. The agencies continue to believe that
this is a valid method to adjust for technology differences between
actual vehicle models in the MY 2008 and MY 2010 fleets. The
statistical method for fitting that curve, however, was revisited by
the agencies in this rule. For the NPRM, the agencies chose to fit the
proposed standard curves using an ordinary least-squares formulation,
on sales-weighted data, using a fleet that has had technology applied,
and after adjusting the data for the effects of weight-to-footprint, as
described below. This represented a departure from the statistical
approach for fitting the curves in MYs 2012-2016, as explained in the
next section. The agencies considered a wide variety of reasonable
statistical methods in order to better understand the range of
uncertainty regarding the relationship between fuel consumption (the
inverse of fuel economy), CO2 emission rates, and footprint,
thereby providing a range within which decisions about standards would
be potentially supportable. In preparing for analysis supporting
today's final rule, the agencies updated analytical inputs, including
by developing two market forecasts (as discussed above in Section II.B
of the preamble and in Chapter 1 of the joint TSD). Using all of this
information, the agencies repeated the curve fitting analysis, once for
each market forecast. The agencies obtained results that were broadly
similar, albeit not identical, to those supporting the NPRM. Results
obtained for the NPRM and for today's final rule span similar regions
in footprint--fuel economy space, areas within which it would be
technically reasonable to select specific linear relationships upon
which to base new attribute-based standards. The agencies thus believe
it is reasonable to finalize the curves as proposed. This updated
analysis is presented in Chapter 2 of the joint TSD.
a. What concerns were the agencies looking to address that led them to
change from the approach used for the MYs 2012-2016 curves?
    During the year and a half between when the MYs 2012-2016 final
rule was issued and when the MYs 2017-2025 NPRM was issued, NHTSA and
EPA 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 production of small vehicles,
because steeper curves tend to result in more stringent targets for
smaller vehicles. Most full-line manufacturers have argued that a
passenger car curve similar in slope to the MY 2016 passenger car curve
would be appropriate for future model years, but that the light truck
curve should be revised to be less difficult for manufacturers selling
the largest full-size pickup trucks. These manufacturers argued that
the MY 2016 light truck curve was not ``physics-based,'' and that in
order for future tightening of standards to be feasible for full-line
manufacturers, the truck curve for later model years should be steeper
and extended further (i.e., made less stringent) into the larger
footprints. The agencies do not agree that the MY 2016 light truck
curve was somehow deficient in lacking a ``physics basis,'' or that it
was somehow overly stringent for manufacturers selling large pickups--
manufacturers making these arguments presented no ``physics-based''
model to explain how fuel economy should depend on footprint.\188\ The
same manufacturers indicated that they believed that the light truck
standard should be somewhat steeper after MY 2016, primarily because,
after more than ten years of progressive increases in the stringency of
applicable CAFE standards, large pickups would be less capable of
achieving further improvements without compromising load carrying and
towing capacity. The related issue of the stringency of the CAFE and
GHG standards for light trucks is discussed in sections and III.D and
IV.F of the preamble to this final rule.
---------------------------------------------------------------------------

    \188\ See footnote 186
---------------------------------------------------------------------------

    In developing the curve shapes for the proposed rule, the agencies
were aware of the current and prior technical concerns raised by OEMs
concerning the effects of the stringency on individual manufacturers
and their ability to meet the standards with available technologies,
while producing vehicles at a cost that allowed them to recover the
additional costs of the technologies being applied. Although we
continued to believe that the methodology for fitting curves for the
MYs 2012-2016 standards was technically sound, we recognized
manufacturers' concerns regarding their abilities to comply with a
similarly shallow curve after MY 2016 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 the economic losses that could be incurred if curve shapes unduly
discourage market shifts--including vehicle upsizing--that have vehicle
buyers value. In addition, the agencies sought to improve the balance
of compliance burdens among manufacturers, and thereby increase the
likelihood of improved fuel economy and reduced GHG emissions across
the entire spectrum of footprint targets. 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, potentially
compromising highway safety.
     Flatter standards potentially impact the utility of
vehicles by providing an incentive for vehicle downsizing.
     Steeper footprint-based standards may create incentives to
upsize

vehicles, thus increasing the possibility that fuel economy and
greenhouse gas reduction benefits will be less than expected.
     Given the same industry-wide average required fuel economy
or CO2 level, flatter standards tend to place greater
compliance burdens on full-line manufacturers.
     Given the same industry-wide average required fuel economy
or CO2 level, steeper standards tend to place greater
compliance burdens on limited-line manufacturers (depending of course,
on which vehicles are being produced).
     If cutpoints are adopted, given the same industry-wide
average required fuel economy, moving small-vehicle cutpoints to the
left (i.e., up in terms of fuel economy, down in terms of
CO2 emissions) discourages the introduction of small
vehicles, and reduces the incentive to downsize small vehicles in ways
that could compromise overall highway safety.
     If cutpoints are adopted, given the same industry-wide
average required fuel economy, moving large-vehicle cutpoints to the
right (i.e., down in terms of fuel economy, up in terms of
CO2 emissions) better accommodates the design requirements
of larger vehicles--especially large pickups--and extends the size
range over which downsizing is discouraged.
    All of these were policy goals that required weighing and
consideration. Ultimately, the agencies did not agree that the MY 2017
target curves for the proposal, on a relative basis, should be made
significantly flatter than the MY 2016 curve,\189\ as we believed 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.
---------------------------------------------------------------------------

    \189\ While ``significantly'' flatter is subjective, the year
over year change in curve shapes is discussed in greater detail in
Section II.C.6.a and Chapter 2 of the joint TSD.
---------------------------------------------------------------------------

    Nonetheless, the agencies recognized full-line OEM concerns and
tentatively concluded that further increases in the stringency of the
light truck standards would be more feasible if the light truck curve
was made steeper than the MY 2016 truck curve and the right (large
footprint) cut-point was extended over time to larger footprints. This
conclusion was supported by the agencies' technical analyses of
regulatory alternatives defined using the curves developed in the
manner described below.
    The Alliance, GM, and the UAW commented in support of the
reasonableness of the agencies' proposals regarding the shape and slope
of the curves and how they were developed, although the Alliance stated
that the weighting and regression analysis used to develop the curves
for MYs 2022-2025 should be reviewed during the mid-term evaluation
process.
    Other commenters objected to specific aspects of the agencies'
approach to developing the curves. ACEEE provided extensive comments,
arguing generally that agencies appeared to be proposing curve choices
in response to subjective policy concerns (namely, protecting large
trucks) rather than on a sound technical basis.\190\ ACEEE recommended
that the agencies choose ``the most robust technical approach,'' and
then make policy-driven adjustments to the curves for a limited time as
necessary, and explain the curves in those terms, revisiting this issue
for the final rule.\191\
---------------------------------------------------------------------------

    \190\ ACEEE comments, Docket No. EPA-HQ-OAR-2010-0799-9528 at 6.
    \191\ Id.
---------------------------------------------------------------------------

    The agencies reaffirm the reasonable technical and policy basis for
selecting the truck curve. Three primary drivers form this technical
basis: (a) The largest trucks have unique equipment and design, as
described in the Ford comment referenced below in section II.C.4.f; (b)
the agencies agree with those large truck manufacturers who indicated
in discussions prior to the proposal that they believed that the light
truck standard should be somewhat steeper after MY 2016, primarily
because, after more than ten recent years of progressive increases in
the stringency of applicable CAFE standards (after nearly ten years
during which Congress did not allow NHTSA to increase light truck CAFE
standards), manufacturers of large pickups would have limited options
to comply with more stringent standards without resorting to
compromising large truck load carrying and towing capacity; and (c)
given the relatively few platforms which comprise the majority of the
sales at the largest truck footprints, the agencies were concerned
about requiring levels of average light truck performance that might
lead to overly aggressive technology penetration rates in this
important segment of the work fleet. Specifically, the agencies were
concerned at proposal, and remain concerned about issues of lead time
and cost with regard to manufacturers of these work vehicles. As noted
later in this chapter, while the largest trucks are a small segment of
the overall truck fleet, and an even smaller segment of the overall
fleet, \192\ these changes to the truck slope have been made in order
to provide a clearer path toward compliance for manufacturers of these
vehicles, and reduce the potential that new standards would lead these
manufacturers to choose to downpower, modify the structure, or
otherwise reduce the utility of these work vehicles.
---------------------------------------------------------------------------

    \192\ The agencies' market forecast used at proposal 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, In the MY2010 based market forecast, there are 14
vehicle configurations with a total volume of 130,000 vehicles or
less during any MY in the 2017-2025 time frame. This is a similarly
small portion of the overall number of vehicle models or vehicle
sales.
---------------------------------------------------------------------------

    As discussed in the NPRM and in Chapter 2 of the TSD, as well as in
section III.D and IV.E below, we considered all of the utilized methods
of normalizing (including not normalizing) fuel economy levels and the
different methods for fitting functional forms to the footprint and
fuel economy and CO2 levels, to be technically reasonable
options. We indicated that, within the range spanned by these
technically reasonable options, the selection of curves for purposes of
specifying standards involves consideration of technical concerns and
policy implications. Having considered the above comments on the
estimation and selection of curves, we have not changed our judgment
about the process--that is, that the agencies can make of policy-
informed selection within the range spanned by technically reasonable
quantitative methods. We disagree with ACEEE's portrayal of this
involving the ``protection'' of large trucks. We have selected a light
truck slope that addresses real engineering aspects of large light
trucks and real fleet aspects of the manufacturers producing these
trucks, and sought to avoid creating an incentive for such
manufacturers to reduce the hauling and towing capacity of these
vehicles, an undesirable loss of utility. Such concerns are applicable
much more directly to light trucks than to passenger cars. The
resulting curves are well within the range of curves we have estimated.
The steeper slope at the right hand of the truck curve recognizes the
physical differences in these larger vehicles \193\ and the fleet
differences in

manufacturers that produce them. Further, we disagree with ACEEE's
suggestion that the agencies should commit to a particular method for
selecting curves; as the approaches we have considered demonstrate that
the range of technically reasonable curve fitting methods spans a wide
range, indicating uncertainty that could make it unwise to ``lock in''
a particular method for all future rulemakings. The agencies plan on
observing fleet trends in the future to see if there are any unexpected
shifts in the distribution of technology and utility within the
footprint range for both cars and trucks.
---------------------------------------------------------------------------

    \193\ As Ford Motor Company detailed, in its public comments,
``towing capability generally requires increased aerodynamic drag
caused by a modified frontal area, increased rolling resistance, and
a heavier frame and suspension to support this additional
capability.'' Ford further noted that these vehicles further require
auxiliary transmission oil coolers, upgraded radiators, trailer
hitch connectors and wiring harness equipment, different steering
ratios, upgraded rear bumpers and different springs for heavier
tongue load (for upgraded towing packages), body-on-frame (vs.
unibody) construction (also known as ladder frame construction) to
support this capability and an aggressive duty cycle, and lower axle
ratios for better pulling power/capability.
---------------------------------------------------------------------------

    We note that comments by CBD, ACEEE, NACAA, and an individual,
Yegor Tarazevich, referenced a 2011 study by Whitefoot and Skerlos,
``Design incentives to increase vehicle size created from the U.S.
footprint-based fuel economy standards.'' \194\ This study concluded
that MY 2014 standards, as proposed, ``create an incentive to increase
vehicle size except when consumer preference for vehicle size is near
its lower bound and preference for acceleration is near its upper
bound.'' \195\ The commenters who cited this study generally did so as
part of arguments in favor of flatter standards (i.e., curves that are
flatter across the range of footprints) for MYs 2017-2025. While the
agencies consider the concept of the Whitefoot and Skerlos analysis to
have some potential merits, it is also important to note that, among
other things, the authors assumed different inputs than the agencies
actually used in the MYs 2012-2016 rule regarding the baseline fleet,
the cost and efficacy of potential future technologies, and the
relationship between vehicle footprint and fuel economy.
---------------------------------------------------------------------------

    \194\ Available at Docket No. EPA-HQ-OAR-2010-0799.
    \195\ page 410.
---------------------------------------------------------------------------

    Were the agencies to use the Whitefoot and Skerlos methodology
(e.g., methods to simulate manufacturers' potential decisions to
increase vehicle footprint) with the actual inputs to the MYs 2012-2016
rules, the agencies would likely obtain different findings. Underlining
the potential uncertainty, the authors obtained a wide range of results
in their analyses. Insofar as Whitefoot and Skerlos found, for some
scenarios, that manufacturers might respond to footprint-based
standards by deliberately increasing vehicle footprint, these findings
are attributable to a combination of (a) the assumed baseline market
characteristics, (b) the assumed cost and fuel economy impacts involved
in increasing vehicle footprint, (c) the footprint-based fuel economy
targets, and (d) the assumed consumer preference for vehicle size.
Changes in any of these assumptions could yield different analytic
results, and potentially result in different technical implications for
agency action. As the authors note when interpreting their results:
``Designing footprint-based fuel-economy standards in practice such
that manufacturers have no incentive to adjust the size of their
vehicles appears elusive at best and impossible at worst.''
    Regarding the cost impacts of footprint increases, that authors
make an ad hoc assumption that changes in footprint would incur costs
linearly, such that a 1% change in footprint would entail a 1% increase
in production costs. The authors refer to this as a conservative
assumption, but present no supporting evidence. The agencies have not
attempted to estimate the engineering cost to increase vehicle
footprint, but we expect that it would be considerably nonlinear, with
costs increasing rapidly once increases available through small
incremental changes--most likely in track width--have been exhausted.
Moreover, we expect that were a manufacturer to deliberately increase
footprint in order to ease compliance burdens, it would confine any
significant changes to coincide with vehicle redesigns, and engaging in
multiyear planning, would consider how the shifts would impact
compliance burdens and consumer desirability in ensuing model years.
With respect to the standards promulgated today, the standards become
flatter over time, thereby diminishing any ``reward'' for deliberately
increasing footprint beyond normal market expectations.
    Regarding the fuel economy impacts of footprint increases, the
authors present a regression analysis based on which increases in
footprint are estimated to entail increases in weight which are, in
turn, estimated to entail increases in fuel consumption. However, this
relationship was not the relationship the agencies used to develop the
MY 2014 standards the authors examine in that study. Where the target
function's slope is similar to that of the tendency for fuel
consumption to increase with footprint, fuel economy should tend to
decrease approximately in parallel with the fuel economy target,
thereby obviating the ``benefit'' of deliberate increases in vehicle
footprint. The agencies' analysis supporting today's final rule
indicates relatively wide ranges wherein the relationship between fuel
consumption and footprint may reasonably be specified.
    As part of the mid-term evaluation and future NHTSA rulemaking, the
agencies plan to further investigate methods to estimate the potential
that standards might tend to induce changes in the footprint. The
agencies will also continue to closely monitor trends in footprint (and
technology penetration) as manufacturers come into compliance with
increasing levels of the footprint standards.
b. What methodologies and data did the agencies consider in developing
the MYs 2017-2025 curves?
    In considering how to address the various policy concerns discussed
in the previous sections, the agencies revisited the data and performed
a number of analyses using different combinations of the various
statistical methods, weighting schemes, adjustments to the data and the
addition of technologies to make the fleets less technologically
heterogeneous. As discussed above, in the agencies' judgment, there is
no single ``correct'' way to estimate the relationship between
CO2 or fuel consumption and footprint--rather, each
statistical result is based on the underlying assumptions about the
particular functional form, weightings and error structures embodied in
the representational approach. These assumptions are the subject of the
following discussion. This process of performing many analyses using
combinations of statistical methods generates many possible outcomes,
each embodying different potentially reasonable combinations of
assumptions and each thus reflective of the data as viewed through a
particular lens. The choice of a proposed standard developed by a given
combination of these statistical methods was 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. As discussed below, for
today's final rule, the agencies used updated information to repeat
these analyses, found that results were generally similar and spanned a
similarly wide range, and found that the curves underlying the

proposed standards were well within this range.
c. What information did the agencies use to estimate a relationship
between fuel economy, CO2 and footprint?
    For each fleet, the agencies began with the MY 2008-based market
forecast developed to support the proposal (i.e., the baseline fleet),
with vehicles' fuel economy levels and technological characteristics at
MY 2008 levels.\196\ For today's final rule, the agencies made minor
corrections to this market forecast, and also developed a MY 2010-based
market forecast. The development, scope, and content of these market
forecasts are discussed in detail in Chapter 1 of the joint Technical
Support Document supporting the rulemaking.
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    \196\ While the agencies jointly conducted this analysis, the
coefficients ultimately used in the slope setting analysis are from
the CAFE model.
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d. What adjustments did the agencies evaluate?
    The agencies believe one possible approach is to fit curves to the
minimally adjusted data shown above (the approach still includes sales
mix adjustments, which influence results of sales-weighted
regressions), much as DOT did when it first began evaluating potential
attribute-based standards in 2003.\197\ However, the agencies have
found, as in prior rulemakings, that the data are so widely spread
(i.e., when graphed, they fall in a loose ``cloud'' rather than tightly
around an obvious line) that they indicate a relationship between
footprint and CO2 and fuel consumption that is real but not
particularly strong. Therefore, as discussed below, the agencies also
explored possible adjustments that could help to explain and/or reduce
the ambiguity of this relationship, or could help to support policy
outcomes the agencies judged to be more desirable.
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    \197\ 68 FR 74920-74926.
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i. Adjustment to Reflect Differences in Technology
    As in prior rulemakings, the agencies consider technology
differences between vehicle models to be a significant factor producing
uncertainty regarding the relationship between CO2/fuel
consumption and footprint. Noting that attribute-based standards are
intended to encourage the application of additional technology to
improve fuel efficiency and reduce CO2 emissions, the
agencies, in addition to considering approaches based on the unadjusted
engineering characteristics of MY 2008 vehicle models, therefore also
considered approaches in which, as for previous rulemakings, technology
is added to vehicles for purposes of the curve fitting analysis in
order to produce fleets that are less varied in technology content.
    The agencies adjusted the baseline fleet for technology by adding
all technologies considered, except for the most advanced high-BMEP
(brake mean effective pressure) gasoline engines, diesel engines, ISGs,
strong HEVs, PHEVs, EVs, and FCVs. The agencies included 15 percent
mass reduction on all vehicles.\198\
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    \198\ As described in the preceding paragraph, applying
technology in this manner helps to reduce the effect of technology
differences across the vehicle fleet. The particular technologies
used for the normalization were chosen as a reasonable selection of
technologies which could potentially be used by manufacturers over
this time period.
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ii. Adjustments Reflecting Differences in Performance and ``Density''
    For the reasons discussed above regarding revisiting the shapes of
the curves, the agencies considered adjustments for other differences
between vehicle models (i.e., inflating or deflating the fuel economy
of each vehicle model based on the extent to which one of the vehicle's
attributes, such as power, is higher or lower than average).
Previously, NHTSA had rejected such adjustments because they imply that
a multi-attribute standard may be necessary, and the agencies judged
most multi-attribute standards to be more subject to gaming than a
footprint-only standard.199,200 Having considered this issue
again for purposes of this rulemaking, NHTSA and EPA conclude the need
to accommodate in the target curves the challenges faced by
manufacturers of large pickups currently outweighs these prior
concerns. Therefore, the agencies also evaluated curve fitting
approaches through which fuel consumption and CO2 levels
were adjusted with respect to weight-to-footprint alone, and in
combination with power-to-weight. While the agencies examined these
adjustments for purposes of fitting curves, the agencies are not
promulgating a multi-attribute standard; the proposed fuel economy and
CO2 targets for each vehicle are still functions of
footprint alone. No adjustment will be used in the compliance process.
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    \199\ 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 be calculated as follows: target = f(SWA),
where target is the fuel economy target applicable to a given
vehicle model and SWA = footprint + torque1/1.5 + weight
1/2.5. (NHTSA-2008-0089-0174.)
    \200\ 74 FR 14359.
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    For the proposal, the agencies also examined some differences
between the technology-adjusted car and truck fleets in order to better
understand the relationship between footprint and CO2/fuel
consumption in the agencies' MY 2008 based forecast. The agencies
investigated the relationship between HP/WT and footprint in the
agencies' MY 2008-based market forecast. On a sales weighted basis,
cars tend to become proportionally more powerful as they get larger. In
contrast, there is a minimally positive relationship between HP/WT and
footprint for light trucks, indicating that light trucks become only
slightly more powerful as they get larger.
    This analysis, presented in chapter <IP_ADDRESS> of the joint TSD,
indicated that vehicle performance (power-to-weight ratio) and
``density'' (curb weight divided by footprint) are both correlated to
fuel consumption (and CO2 emission rate), and that these
vehicle attributes are also both related to vehicle footprint. Based on
these relationships, the agencies explored adjusting the fuel economy
and CO2 emission rates of individual vehicle models based on
deviations from ``expected'' performance or weight/footprint at a given
footprint; the agencies inflated fuel economy levels of vehicle models
with higher performance and/or weight/footprint than the average of the
fleet would indicate at that footprint, and deflated fuel economy
levels with lower performance and/or weight. While the agencies
considered this technique for purposes of fitting curves, the agencies
are not promulgating a multi-attribute standard, as the proposed fuel
economy and CO2 targets for each vehicle are still functions
of footprint alone. No adjustment will be used in the compliance
process.
    For today's final rule, the agencies repeated the above analyses,
using the corrected MY 2008-based market forecast and, separately, the
MY 2010-based market forecasts. As discussed in section 2.6 of the
joint TSD and further detailed in a memorandum available at Docket No.
NHTSA-2010-0131-0325, doing so produced results similar to the analysis
used in the proposal.
    The agencies sought comment on the appropriateness of the
adjustments described in Chapter 2 of the joint TSD, particularly
regarding whether these adjustments suggest that standards should be
defined in terms of other attributes in addition to footprint, and
whether they may encourage changes other than encouraging the
application of technology to improve fuel economy

and reduce CO2 emissions. The agencies also sought comment
regarding whether these adjustments effectively ``lock in'' through MY
2025 relationships that were observed in MY 2008.
    ACEEE objected to the agencies' adjustments to the truck curves,
arguing that if the truck slope needs to be adjusted for ``density,''
then that suggests that the MY 2008-based market forecast used to build
up the reference fleet must be ``incorrect and show * * *
unrealistically low pickup truck fuel consumption, due to the
overstatement of the benefits of certain technologies.'' \201\ ACEEE
stated that ``If that is the case, the agencies should revisit the
adjustments made to generate the reference fleet and remove
technologies from pickups that are not suited to those trucks,'' which
``would be a far more satisfactory approach than the speculative and
non-quantitative approach of adjusting for vehicle density.'' \202\
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    \201\ ACEEE comments, Docket No. EPA-HQ-OAR-2010-0799-9528 at 3-
4.
    \202\ Id.
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    ACEEE further stated that ``the fuel consumption trend that the
density adjustment is meant to correct appears in the unadjusted fleet
as well as the technology-adjusted fleet of light trucks (TSD Figures
2-1 and 2-2),'' which they argued is evidence that ``the flattening of
fuel consumption at higher footprints is not a byproduct of unrealistic
technology adjustments, but rather a reflection of actual fuel economy
trends in today's market.'' \203\ ACEEE stated that therefore it did
not make sense to adjust the fuel consumption of ``low-density'' trucks
upwards before fitting the curve.\204\ ACEEE pointed out that it would
appear that trucks' HP-to-weight ratio should be higher than the
agencies' analysis indicated, and stated that the weight-based EU
CO2 standard curves are adjusted for HP-to-weight, which
resulted in flatter curves, and which are intended to avoid
incentivizing up-weighting.\205\ ACEEE argued that by not choosing this