For light trucks, also consistent with prior rulemakings, NHTSA is
defining fuel economy targets as shown in Equation III-2.
[GRAPHIC] [TIFF OMITTED] TR02MY22.050

Where:

TARGETFE is the fuel economy target (in mpg) applicable to a
specific vehicle model type with a unique footprint combination,
a, b, c, and d are as for passenger cars, but taking values specific
to light trucks,
e is a second minimum fuel economy target (in mpg),
f is a second maximum fuel economy target (in mpg),
g is the slope (in gpm per square foot) of a second line relating
fuel consumption (the inverse of fuel economy) to footprint, and
h is an intercept (in gpm) of the same second line.

    For the Preferred Alternative, this equation is represented
graphically as the curves in Figure III-3.
[GRAPHIC] [TIFF OMITTED] TR02MY22.051

    Although the general model of the target function equation is the
same for each vehicle category (passenger cars and light trucks) and
each model year, the parameters of the function equation differ for
cars and trucks. The actual parameters for both the Preferred
Alternative and the other regulatory alternatives are presented in
Section IV.B of this preamble.
    As has been the case since NHTSA began establishing attribute-based
standards, no vehicle need meet the specific applicable fuel economy
target, because compliance with CAFE standards is determined based on
corporate average fuel economy. In this respect, CAFE standards are
unlike, for example, Federal Motor Vehicle Safety Standards (FMVSS) and
certain vehicle criteria pollutant emissions standards where each car
must meet the requirements. CAFE standards apply to the average fuel
economy levels achieved by manufacturers' entire fleets of vehicles
produced for sale in the U.S. Safety standards apply on a vehicle-by-
vehicle basis, such that every single vehicle produced for sale in the
U.S. must, on its own, comply with minimum FMVSS. When first mandating
CAFE standards in the 1970s, Congress specified a more flexible
averaging-based approach that inherently allows some vehicles to
``under comply'' (i.e., fall short of the overall flat standard, or
fall short of their target under attribute-based standards), as long as
a manufacturer's overall fleet is in compliance.

    The required CAFE level applicable to a given fleet in a given
model year is determined by calculating the production-weighted
harmonic average of fuel economy targets applicable to specific vehicle
model configurations in the fleet, as shown in Equation III-3.
[GRAPHIC] [TIFF OMITTED] TR02MY22.052

BILLING CODE 4910-59-C
Where:

CAFErequired is the CAFE level the fleet is required to achieve,
i refers to specific vehicle model/configurations in the fleet,
PRODUCTIONi is the number of model configuration i produced for sale
in the U.S., and
TARGETFE,I is the fuel economy target (as defined above) for model
configuration i.

    Chapter 1 of the TSD describes the use of attribute-based
standards, generally, and explains the specific decision, in past rules
and for the current rule, to continue to use vehicle footprint as the
attribute over which to vary stringency. That chapter also discusses
the policy in selecting the specific mathematical function; the
methodologies used to develop the current attribute-based standards;
and methodologies previously used to reconsider the mathematical
function for CAFE standards. NHTSA refers readers to the TSD for a full
discussion of these topics.
    Several commenters supported the continued use of footprint as the
attribute on which to base fuel economy standards. Consumer
Reports,\63\ Alliance for Automotive Innovation (Auto Innovators),\64\
the Aluminum Association,\65\ and National Automobile Dealers
Association (NADA) \66\ all agreed that footprint-based standards
continue to incentivize improvements in fuel economy across all
companies and across all market segments/vehicle classes. Auto
Innovators pointed to the most recent EPA Trends Report as indicating
that any change in average vehicle footprint has been minimal at the
industry level, implying that footprint-based standards are not leading
to ``gaming'' by manufacturers seeking a less-stringent standard by
increasing their vehicles' footprints.\67\ The Aluminum Association
suggested that footprint-based standards could be beneficial for
safety, because they incentivize weight reduction in larger footprint
vehicles, which make up an increasing portion of the fleet.\68\ NADA
\69\ and International Union, United Automobile, Aerospace &
Agricultural Implement Workers of America (UAW) \70\ both stated that
footprint-based standards supported manufacturers continuing to provide
a wide range of vehicles from which consumers could choose, with UAW
stating that ``[s]imply put, to do otherwise undermines domestic
manufacturing, workers' living standards, and communities well-being.
All vehicles do not have the same function and surely our rules need to
continue to reflect this reality.'' \71\
---------------------------------------------------------------------------

    \63\ Consumer Reports, Docket No. NHTSA-2021-0053-1576-A9, at p.
7.
    \64\ Auto Innovators, Docket No. NHTSA-2021-0053-1492, at p. 47.
    \65\ The Aluminum Association (Aluminum Association), Docket No.
NHTSA-2021-0053-1518, at p. 3; Arconic Corporation (Arconic), Docket
No. NHTSA-2021-0053-1560, at p. 2 (Arconic, an individual aluminum
producer, also supported footprint-based standards).
    \66\ NADA, Docket No. NHTSA-2021-0053-1471, at p. 3.
    \67\ Auto Innovators, at p. 48.
    \68\ Aluminum Association, at p. 3.
    \69\ NADA, at p. 3.
    \70\ UAW, Docket No. NHTSA-2021-0053-0931, at p. 2.
    \71\ UAW, at p. 4.
---------------------------------------------------------------------------

    One citizen commenter, Doug Peterson (Peter Douglas), objected to
the use of footprint as the attribute on which to base fuel economy
standards, stating that a consequence of using footprint is that
``[w]asteful models are simply compensated for by more efficient models
that outperform their footprint targets, and this will become a huge
problem as more and more ZEVs enter the marketplace.'' \72\ Mr. Douglas
further commented that discouraging vehicle downsizing (as footprint-
based standards can do) was an inappropriate policy goal, because
downsizing can be a good way to reduce fuel consumption and the current
upsizing trend in the fleet is not mitigated by footprint-based
standards. He also commented that the safety concern that footprint-
based standards can address is in fact misplaced, because ``[l]arge
vehicles provide safety benefits to their occupants at the expense of
people occupying small vehicles.'' \73\
---------------------------------------------------------------------------

    \72\ Peter Douglas, Docket No. NHTSA-2021-0053-0085, at pp. 12-
13, p. 19.
    \73\ Id.
---------------------------------------------------------------------------

    NHTSA appreciates these comments but is continuing to rely on
footprint as the attribute for the final standards for MYs 2024-2026.
NHTSA notes that the first issue that Mr. Douglas raised is due to the
fact that the standards are, by law, corporate average standards, and
that ``wasteful models [being] compensated for by more efficient
models'' is difficult to avoid when standards are corporate averages--
by their nature, they enable averaging across a manufacturer's fleet.
The comments from the Aluminum Association comments, Auto Innovators,
and Mr. Douglas' further comments on the topic of footprint seem to
address one another. As Auto Innovators notes, the most recent EPA
Trends Report appears to suggest that, on average, vehicle upsizing has
been minimal at the industry (fleet) level. While footprint may not
encourage vehicle downsizing, it does reward vehicle downweighting,
which NHTSA typically refers to as ``mass reduction.'' A lighter
vehicle saves fuel compared to a heavier vehicle of the same footprint,
and thus performs better against its footprint target. NHTSA addresses
safety comments in Section V of this preamble.
    While Chapter 1 of the TSD explains why the final standards for MYs
2024-2026 continue to be footprint-based, the question has arisen
periodically of whether NHTSA should instead consider multi-attribute
standards, such as those that also depend on weight, torque, power,
towing capability, off-road capability, or a combination of such
attributes. To date, every time NHTSA has considered options for which
attribute(s) to select, the agency has concluded that a properly
designed footprint-based approach provides the best means of achieving
the basic policy goals (i.e., by increasing the likelihood of improved
fuel economy across the

entire fleet of vehicles, as noted by commenters) involved in applying
an attribute-based standard. At the same time, footprint-based
standards need also to be structured in a way that furthers the energy
and environmental policy goals of EPCA without creating inappropriate
incentives to increase vehicle size in ways that could increase fuel
consumption or compromise safety. That said, as NHTSA moves forward
with the CAFE program, and continues to refine our understanding of the
light-duty vehicle market and trends in vehicle and highway safety,
NHTSA will also continue to revisit whether other approaches (or other
ways of applying the same basic approaches) could provide better means
of achieving policy goals.
    For example, in the 2021 NAS Report, the committee recommended that
if Congress does not act to remove the prohibition at 49 U.S.C.
32902(h) on considering the fuel economy of dedicated alternative fuel
vehicles (like BEVs) in determining maximum feasible CAFE standards,
then NHTSA should account for the fuel economy benefits of ZEVs by
``setting the standard as a function of a second attribute in addition
to footprint--for example, the expected market share of ZEVs in the
total U.S. fleet of new light-duty vehicles--such that the standards
increase as the share of ZEVs in the total U.S. fleet increases.'' \74\
DOE seconded this suggestion in its comments during interagency review
of the proposal. NHTSA sought comment on whether and how NHTSA might
consider adding electrification as an attribute on which to base CAFE
standards, and specifically on the NAS committee recommendation.
---------------------------------------------------------------------------

    \74\ 2021 NAS Report, at Summary Recommendation p. 5.
---------------------------------------------------------------------------

    Two electric vehicle manufacturers supported the addition of
electrification as an attribute on which fuel economy standards could
be based. Lucid USA, Inc. (Lucid) stated that, in setting standards
based on electrification as well as footprint, NHTSA should ``consider
the battery efficiency of the electric vehicles manufactured by each
automaker, as well as the market penetration of electric vehicles in
the fleet.'' \75\ Rivian Automotive, LLC (Rivian) stated that such
``[a]pproaches . . . merit further study and eventual implementation.''
\76\ With regard to the timing of making such a change, a question on
which NHTSA specifically sought comment, Rivian commented that ``[i]t
is likely infeasible and inappropriate to implement such a change in
time for any of the model years subject to this rulemaking, but Rivian
believes development, review, and implementation of a newly conceived
multi-attribute function could take effect in the second half of this
decade, coinciding with a post-MY 2027 rule, and provide industry with
appropriate lead-time given typical product development lifecycles.''
\77\
---------------------------------------------------------------------------

    \75\ Lucid, Docket No. NHTSA-2021-0053-1584, at p. 5.
    \76\ Rivian, Docket No. NHTSA-2021-0053-1562, at p. 5.
    \77\ Id.
---------------------------------------------------------------------------

    Other commenters disagreed with adding electrification as an
attribute. Several opined that adding electrification as an attribute
seemed impermissible under 49 U.S.C. 32902(h).\78\ Auto Innovators
argued that it could create battery supply chain risks as an unintended
consequence, and that ``. . . including electrification as a fuel
economy attribute could be solidifying a dependence on foreign supply
chains that might not be reliable or have shared interests with our
country.'' \79\ American Honda Motor Co., Inc. (Honda) \80\ and Kia
Corporation (Kia) \81\ also raised the possibility of unintended
consequences and externalities. Kia further suggested that ``[i]n the
same manner that the footprint curves include many of the weight,
technology cost, and engineering analyses that go in to bringing these
vehicles online, electrification would need to have similar
considerations accounted for in the modeling assumptions,'' \82\ while
Honda stated that the agency should provide ``more than a full product
cycle (5-6 year[s]) of lead time'' to give industry time to plan for
any changes.\83\ Auto Innovators commented that it could be permissible
to limit consideration of electrification to HEVs, but ``[t]he existing
approach with footprint-based curves does not need to be modified if
one simply wants to require a more efficient gasoline-powered fleet--
whether through increased electrification or some other means.'' \84\
Jaguar Land Rover NA, LLC (JLR) offered a similar comment.\85\
---------------------------------------------------------------------------

    \78\ Auto Innovators, at 48; Stellantis, Docket No. NHTSA-2021-
0053-1527, at 12; NADA, at p. 4; Valero Energy Corporation (Valero),
Docket No. NHTSA-2021-0053-1541, at pp. 3-4; Peter Douglas, at p.
25.
    \79\ Auto Innovators, at p. 50.
    \80\ Honda, Docket No. NHTSA-2021-0053-1501, at p. 4.
    \81\ Kia, Docket No. NHTSA-2021-0053-1525, at p. 10.
    \82\ Id.
    \83\ Honda, at p. 4.
    \84\ Auto Innovators, at p. 50.
    \85\ JLR, Docket No. NHTSA-2021-0053-1505, at p. 4.
---------------------------------------------------------------------------

    Stellantis commented that ``the `percent of work' metric as
ultimately applied in the proposal is a fleet level of electrification
selected as a policy goal rather than an attribute of a particular
vehicle (like footprint) as intended by the statute.'' \86\ NADA argued
that ``[f]leet-wide standards should be technologically neutral and set
at levels that are achievable without ZEVs so as not to penalize those
OEMs (and their dealers) that choose not to aggressively develop,
produce, and push ZEVs to market.'' \87\ And finally, Securing
America's Future Energy commented that adding electrification as an
attribute just makes the program more complicated, and NHTSA should be
looking for ways to simplify it instead, perhaps via a legislative
solution.\88\
---------------------------------------------------------------------------

    \86\ Stellantis, at p. 12.
    \87\ NADA, at pp. 3-4.
    \88\ Securing America's Future Energy, Docket No. NHTSA-2021-
0053-1513, at pp. 18-19.
---------------------------------------------------------------------------

    As explained above, for this final rule, NHTSA is continuing to
base the MY 2024-2026 standards on footprint. NHTSA is not adding
electrification as an attribute at this time, based in part on comments
that raised concerns with how to implement such an approach
practically, in a way that would further EPCA's overarching goal of
energy conservation, while providing industry with appropriate lead
time to make changes to their fleet. NHTSA is also mindful of
introducing further uncertainty to the standards during this time of
rapid change in the stringency of the standards. Therefore, while NHTSA
agrees with comments suggesting that the recommendation from the NAS
committee merits further consideration, NHTSA also agrees with other
commenters who suggested that this rulemaking is not the proper one in
which to implement such a change, given the available lead time for
manufacturers to adjust their compliance approaches.

C. What inputs does the compliance analysis require?

    The CAFE Model applies various technologies to different vehicle
models in each manufacturer's product line to simulate how each
manufacturer might make progress toward compliance with the specified
standard. Subject to a variety of user-controlled constraints, the
model applies technologies based on their relative cost-effectiveness,
as determined by several input assumptions regarding the cost and
effectiveness of each technology, the cost of compliance (determined by
the change in CAFE or CO2 credits, CAFE-related civil
penalties, or value of CO2 credits, depending on the
compliance

program being evaluated), and the value of avoided fuel expenses. For a
given manufacturer, the compliance simulation algorithm applies
technologies either until the manufacturer runs out of cost-effective
technologies,\89\ until the manufacturer exhausts all available
technologies, or, if the manufacturer is assumed to be willing to pay
civil penalties or acquire credits from another manufacturer, until
paying civil penalties or purchasing credits becomes more cost-
effective than increasing vehicle fuel economy. At this stage, the
system assigns an incurred technology cost and updated fuel economy to
each vehicle model, as well as any civil penalties incurred/credits
purchased by each manufacturer. This compliance simulation process is
repeated for each model year included in the study period (through MY
2050 in this analysis).
---------------------------------------------------------------------------

    \89\ Generally, the model considers a technology cost-effective
if it pays for itself in fuel savings within a ``payback period''
specified as a model input (for this analysis, 30 months). Depending
on the settings applied, the model can continue to apply
technologies that are not cost-effective rather than choosing other
compliance options; if it does so, it will apply those additional
technologies in order of cost-effectiveness (i.e., most cost-
effective first).
---------------------------------------------------------------------------

    At the conclusion of the compliance simulation for a given
regulatory scenario, the system transitions between compliance
simulation and effects calculations. This is the point where the system
produces a full representation of the registered light-duty vehicle
population in the United States. The CAFE Model then uses this fleet to
generate estimates of the following (for each model year and calendar
year included in the analysis): Lifetime travel, fuel consumption,
carbon dioxide and criteria pollutant emissions, the magnitude of
various economic externalities related to vehicular travel (e.g.,
congestion and noise), and energy consumption (e.g., the economic costs
of short-term increases in petroleum prices, or social damages
associated with GHG emissions). The system then uses these estimates to
measure the benefits and costs associated with each regulatory
alternative (relative to the No-Action Alternative).
    To perform this analysis, the CAFE Model uses millions of data
points contained in several input files that have been populated by
engineers, economists, and safety and environmental program analysts at
both NHTSA and the DOT's Volpe National Transportations Systems Center
(Volpe). In addition, some of the input data come from modeling and
simulation analysis performed by experts at Argonne National Laboratory
using their Autonomie full vehicle simulation model and BatPaC battery
cost model. Other inputs are derived from other models, such as the
U.S. Energy Information Administration's (EIA's) National Energy
Modeling System (NEMS), Argonne's ``GREET'' fuel-cycle emissions
analysis model, and U.S. EPA's ``MOVES'' vehicle emissions analysis
model. As NHTSA and Volpe are both organizations within DOT, we use DOT
throughout these sections to refer to the collaborative work performed
for this analysis.
    This section and Section III.D describe the inputs that the
compliance simulation requires, including an in-depth discussion of the
technologies used in the analysis, how they are defined in the CAFE
Model, how they are characterized for vehicles that already exist in
the market, and how they can be applied to realistically simulate
manufacturers' decisions, their effectiveness, and their cost. The
inputs and analyses for the effects calculations, including economic,
safety, and environmental effects, are discussed later in Sections
III.C through III.H.
1. Overview of Inputs to the Analysis
    As discussed above, the current analysis involves estimating four
major swaths of effects. First, the analysis estimates how the
application of various combinations of technologies could impact
vehicles' costs and fuel economy levels (and CO2 emission
rates). Second, the analysis estimates how vehicle manufacturers might
respond to standards by adding fuel-saving technologies to new
vehicles. Third, the analysis estimates how changes in new vehicles
might impact vehicle sales and operation. Finally, the analysis
estimates how the combination of these changes might impact national-
scale energy consumption, emissions, highway safety, and public health.
    There are several CAFE Model input files important to the
discussion of these first two steps, and these input files are
discussed in detail later in this section and in Section III.D. The
Market Data file contains the detailed description of the vehicle
models and model configurations each manufacturer produces for sale in
the United States. The file also contains a range of other inputs that,
though not specific to individual vehicle models, may be specific to
individual manufacturers. The Technologies file identifies about six
dozen technologies to be included in the analysis, indicates when and
how widely each technology can be applied to specific types of
vehicles, provides most of the inputs involved in estimating what costs
will be incurred, and provides some of the inputs involved in
estimating impacts on vehicle fuel consumption and weight.
    The CAFE Model also makes use of databases of estimates of fuel
consumption impacts and, as applicable, battery costs for different
combinations of fuel-saving technologies.\90\ These databases are
termed the FE1 and FE2 Adjustments databases (the main database and the
database specific to plug-in hybrid electric vehicles, applicable to
those vehicles' operation on electricity) and the Battery Costs
database. DOT developed these databases using a large set of full
vehicle and accompanying battery cost model simulations developed by
Argonne National Laboratory. The Argonne simulation outputs, battery
costs, and other reference materials are also discussed in the
following sections.\91\
---------------------------------------------------------------------------

    \90\ To be used as files provided separately from the model and
loaded every time the model is executed, these databases are
prohibitively large, spanning more than a million records and more
than half a gigabyte. To conserve memory and speed model operation,
DOT has integrated the databases into the CAFE Model executable
file. When the model is run, however, the databases are extracted
and placed in an accessible location on the user's disk drive.
    \91\ The Argonne workbooks included in the docket for this
notice include 10 databases that contain the outputs of the
Autonomie full vehicle simulations, two summary workbooks of
assumptions used for the full vehicle simulations, a data
dictionary, and the lookup tables for battery costs generated using
the BatPaC battery cost model.
---------------------------------------------------------------------------

    The following discussion in this section and in Section III.D
expands on the inputs used in the compliance analysis. Further detail
is included in Chapters 2 and 3 of the TSD accompanying this notice,
and all input values relevant to the compliance analysis can be seen in
the Market Data, Technologies, fuel consumption and battery cost
database files, and Argonne summary files included in the docket for
this notice. As previously mentioned, other model input files underlie
the effects analysis, and these are discussed in detail in Sections
III.C through III.H.
2. The Market Data File
    The Market Data file contains the detailed description of the
vehicle models and model configurations each manufacturer produces for
sale in the U.S. This snapshot of the recent light duty vehicle market,
termed the analysis fleet, or baseline fleet, is the starting point for
the evaluation of different stringency levels for future fuel economy
standards. The analysis fleet provides a reference from which to
project how manufacturers could apply additional technologies to
vehicles to

cost-effectively improve vehicle fuel economy, in response to
regulatory action and market conditions.\92\ For this analysis, the MY
2020 light duty fleet was selected as the baseline for further
evaluation of the effects of different fuel economy standards. The
Market Data file also contains a range of other inputs that, though not
specific to individual vehicle models, may be specific to individual
manufacturers.
---------------------------------------------------------------------------

    \92\ The CAFE Model does not generate compliance paths a
manufacturer should, must, or will deploy. It is intended as a tool
to demonstrate a compliance pathway a manufacturer could choose. It
is almost certain all manufacturers will make compliance choices
differing from those projected by the CAFE Model.
---------------------------------------------------------------------------

    The Market Data file is an Excel spreadsheet that contains five
worksheets. Three worksheets, the Vehicles worksheet, Engines
worksheet, and Transmissions worksheet, characterize the baseline fleet
for this analysis. The three worksheets contain a characterization of
every vehicle sold in MY 2020 and their relevant technology content,
including the engines and transmissions that a manufacturer uses in its
vehicle platforms and how those technologies are shared across
platforms. In addition, the Vehicles worksheet includes baseline
economic and safety inputs linked to each vehicle that allow the CAFE
Model to estimate economic and safety impacts resulting from any
simulated compliance pathway. The remaining two worksheets, the
Manufacturers worksheet and Credits and Adjustments worksheet, include
baseline compliance positions for each manufacturer, including each
manufacturer's starting CAFE credit banks and whether the manufacturer
is willing to pay civil penalties for noncompliance with CAFE
standards, among other inputs.
    New inputs have been added for this analysis in the Vehicles
worksheet and Manufacturers worksheet. The new inputs indicate which
vehicles a manufacturer may reasonably be expected to convert to a zero
emissions vehicle (ZEV) at first redesign opportunity, to comply with
several states' ZEV program provisions. The new inputs also indicate if
a manufacturer has entered into an agreement with California to achieve
more stringent GHG emissions reductions targets than those promulgated
in the 2020 final rule.
    The following sections discuss how we built the Market Data file,
including characterizing vehicles sold in MY 2020 and their technology
content, and baseline safety, economic, and manufacturer compliance
positions. A detailed discussion of the Market Data file development
process is in TSD Chapter 2.2.
(a) Characterizing Vehicles and Their Technology Content
    The Market Data file integrates information from many sources,
including manufacturer compliance submissions, publicly available
information, and confidential business information. At times, DOT must
populate inputs using analyst judgment, either because information is
still incomplete or confidential, or because the information does not
yet exist.\93\ For this analysis DOT uses mid-MY 2020 compliance data
as the basis of the analysis fleet. The compliance data are
supplemented for each vehicle nameplate with manufacturer specification
sheets, usually from the manufacturer media website, or from online
marketing brochures.\94\ For additional information about how
specification sheets inform MY 2020 vehicle technology assignments, see
the technology specific assignments sections in Section III.D.
---------------------------------------------------------------------------

    \93\ Forward looking refresh/redesign cycles are one example of
when analyst judgement is necessary.
    \94\ The catalogue of reference specification sheets (broken
down by manufacturer, by nameplate) used to populate information in
the Market Data file is available in the docket.
---------------------------------------------------------------------------

    DOT uses the mid-MY 2020 compliance data to create a row on the
Vehicles worksheet in the Market Data file for each vehicle (or vehicle
variant \95\) that lists a certification fuel economy, sales volume,
regulatory class, and footprint. DOT identifies which combination of
modeled technologies reasonably represents the fuel saving technologies
already on each vehicle, and assigns those technologies to each
vehicle, either on the Vehicles worksheet, the Engines worksheet, or
the Transmissions worksheet. The fuel saving technologies considered in
this analysis are listed in Table III-1.
---------------------------------------------------------------------------

    \95\ The Market Data file often includes a few rows for vehicles
that may have identical certification fuel economies, regulatory
classes, and footprints (with compliance sales volumes divided out
among rows), because other pieces of information used in the CAFE
Model may be dissimilar. For instance, in the reference materials
used to create the Market Data file, for a nameplate curb weight may
vary by trim level (with premium trim levels often weighing more on
account of additional equipment on the vehicle), or a manufacturer
may provide consumers the option to purchase a larger fuel tank size
for their vehicle. These pieces of information may not impact the
observed compliance position directly, but curb weight (in relation
to other vehicle attributes) is important to assess mass reduction
technology already used on the vehicle, and fuel tank size is
directly relevant to saving time at the gas pump, which the CAFE
Model uses when calculating the value of avoided time spent
refueling.
---------------------------------------------------------------------------

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[GRAPHIC] [TIFF OMITTED] TR02MY22.054

[GRAPHIC] [TIFF OMITTED] TR02MY22.055

BILLING CODE 4910-59-P

    For additional information on the characterization of these
technologies (including the cost, prevalence in the 2020 fleet,
effectiveness estimates, and considerations for their adoption) see the
appropriate technology section in Section III.D or TSD Chapter 3.
    DOT also assigns each vehicle a technology class. The CAFE Model
uses the technology class (and engine class, discussed below) in the
Market Data file to reference the most relevant technology costs for
each vehicle, and fuel saving technology combinations. We assign each
vehicle in the fleet a technology class using a two-step algorithm that
takes into account key characteristics of vehicles in the fleet
compared to the baseline characteristics of each technology class.\96\
As discussed further in Section III.C.4.b), there are ten technology
classes used in the CAFE analysis that span five vehicle types and two
performance variants. The technology class algorithm and assignment
process is discussed in more detail in TSD Chapter 2.4.2.
---------------------------------------------------------------------------

    \96\ Baseline 0 to 60 mph accelerations times are assumed for
each technology class as part of the Autonomie full vehicle
simulations. DOT calculates class baseline curb weights and
footprints by averaging the curb weights and footprints of vehicles
within each technology class as assigned in previous analyses.
---------------------------------------------------------------------------

    We also assign each vehicle an engine technology class so that the
CAFE Model can reference the powertrain costs in the Technologies file
that most reasonably align with the observed vehicle. DOT assigns
engine technology classes for all vehicles, including electric
vehicles. If an electric powertrain replaces an internal combustion
engine, the electric motor specifications may be different (and hence
costs may be different) depending on the capabilities of the internal
combustion engine it is replacing, and the costs in the technologies
file (on the engine tab) account for the power output and capability of
the gasoline or electric drivetrain.
    Parts sharing helps manufacturers achieve economies of scale,
deploy capital efficiently, and make the most of shared research and
development expenses, while still presenting a wide array of consumer
choices to the market. The CAFE Model simulates part sharing by
implementing shared engines, shared transmissions, and shared mass
reduction platforms. Vehicles sharing a part (as recognized in the CAFE
Model), will adopt fuel saving technologies affecting that part
together. To account for parts sharing across products, vehicle model/
configurations that share engines are assigned the same engine
code,\97\ vehicle model/configurations that share transmissions have
the same transmission code, and vehicles that adopt mass reduction
technologies together share the same platform. For more information
about engine codes, transmission codes, and mass reduction platforms
see TSD Chapter 3.
---------------------------------------------------------------------------

    \97\ Engines (or transmissions) may not be exactly identical, as
specifications or vehicle integration features may be different.
However, the architectures are similar enough that it is likely the
powertrain systems share R&D, tooling, and production resources in a
meaningful way.
---------------------------------------------------------------------------

    Manufacturers often introduce fuel saving technologies at a major
redesign of their product or adopt technologies at minor refreshes in
between major product redesigns. To support the CAFE Model accounting
for new fuel saving technology introduction as it relates to product
lifecycle, the Market Data file includes a projection of redesign and
refresh years for each vehicle. DOT projects future redesign years and
refresh years based on the historical cadence of that vehicle's product
lifecycle. For new nameplates, DOT considers the manufacturer's
treatment of product lifecycles for past products in similar market
segments. When considering year-by-year analysis of standards, the
sizing of redesign and refresh intervals will affect projected
compliance pathways and how quickly manufacturers can respond to
standards. TSD Chapter <IP_ADDRESS> includes additional information about
the product design cycles assumed for this action based on historical
manufacturer product design cycles.
    The Market Data file also includes information about air
conditioning (AC) and off-cycle technologies, but the information is
not currently broken out at a row level, vehicle by vehicle.\98\
Instead, historical data (and forecast projections, which are used for
analysis regardless of regulatory scenario) are listed by manufacturer,
by fleet on the Credits and Adjustments worksheet of the Market Data
file. Section III.D.8 shows model inputs specifying estimated
adjustments (all in grams/mile) for improvements to air conditioner
efficiency and other off-cycle energy consumption, and for reduced
leakage of air conditioner refrigerants with high global warming
potential (GWP). DOT estimated future values based on an expectation
that manufacturers already relying heavily on these adjustments would
continue do so, and that other manufacturers would, over time, also
approach the limits on adjustments allowed for such improvements.
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    \98\ Regulatory provisions regarding off-cycle technologies are
new, and manufacturers have only recently begun including related
detailed information in compliance reporting data. For this
analysis, though, such information was not sufficiently complete to
support a detailed representation of the application of off-cycle
technology to specific vehicle model/configurations in the MY 2020
fleet.
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(b) Characterizing Baseline Safety, Economic, and Compliance Positions
    In addition to characterizing vehicles and their technology
content, the Market Data file contains a range of other inputs that,
though not specific to individual vehicle models, may be specific to
individual manufacturers, or that characterize baseline safety or
economic information.
    First, the CAFE Model considers the potential safety effect of mass
reduction technologies and crash compatibility of different vehicle
types. Mass reduction technologies lower the vehicle's curb weight,
which may improve crash compatibility and safety, or not, depending on
the type of vehicle. DOT assigns each vehicle in the Market Data file a
safety class that best aligns with the mass-size-safety analysis. This
analysis is discussed in more detail in Section III.H of this action
and TSD Chapter 7.
    The CAFE Model also includes procedures to consider the direct
labor impacts of manufacturer's response to CAFE regulations,
considering the assembly location of vehicles, engines, and
transmissions, the percent U.S. content (that reflects percent U.S. and
Canada content),\99\ and the dealership employment associated with new
vehicle sales. The Market Data file therefore includes baseline labor
information, by vehicle. Sales volumes also influence total estimated
direct labor projections in the analysis.
---------------------------------------------------------------------------

    \99\ Percent U.S. content was informed by the 2020 Part 583
American Automobile Labeling Act Reports, appearing on NHTSA's
website.
---------------------------------------------------------------------------

    We hold the percent U.S. content constant for each vehicle row for
the duration of the analysis. In practice, this may not be the case.
Changes to trade policy and tariff policy may affect percent U.S.
content in the future. Also, some technologies may be more or less
likely to be produced in the U.S., and if that is the case, their
adoption could affect future U.S. content. NHTSA does not have data at
this time to support varying the percent U.S. content.
    We also hold the labor hours projected in the Market Data file per
unit transacted at dealerships, per unit produced for final assembly,
per unit produced for engine assembly, and per unit produced for
transmission assembly constant for the duration of the analysis, and
project that the origin

of these activities to remain unchanged. In practice, it is reasonable
to expect that plants could move locations, or engine and transmission
technologies are replaced by another fuel saving technology (like
electric motors and fixed gear boxes) that could require a meaningfully
different amount of assembly labor hours. NHTSA does not have data at
this time to support varying labor hours projected in the Market Data
file, but we will continue to explore methods to estimate the direct
labor impacts of manufacturer's responses to CAFE standards in future
analyses.
    As observed from Table III-2, manufacturers employ U.S. labor with
varying intensity. In many cases, vehicles certifying in the light
truck (LT) regulatory class have a larger percent U.S. content than
vehicles certifying in the passenger car (PC) regulatory class.
BILLING CODE 4910-59-P
[GRAPHIC] [TIFF OMITTED] TR02MY22.056

BILLING CODE 4910-59-C
    Next, manufacturers may over-comply with CAFE standards and bank
so-called over compliance credits. As discussed further in Section
III.C.7, manufacturers may use these credits later, sell them to other
manufacturers, or let them expire. The CAFE Model does not explicitly
trade credits between and among manufacturers, but staff have adjusted
starting credit banks in the Market Data file to reflect trades that
are likely to happen when the simulation begins (in MY 2020).
Considering information manufacturers have reported regarding
compliance credits, and considering recent manufacturers' compliance
positions, DOT estimates manufacturers' potential use of compliance
credits in earlier model years. This aligns to an extent that
represents how manufacturers could deplete their credit banks rather
than producing high volume vehicles with fuel saving technologies in
earlier model years. This also avoids the unrealistic application of
technologies for manufacturers in early analysis years that typically
rely on credits. For a complete discussion about how these data are
collected and assigned in the Market Data file, see TSD Chapter
<IP_ADDRESS>.
---------------------------------------------------------------------------

    \100\ Tesla does not have internal combustion engines, or multi-
speed transmissions, even thought they are identified as producing
engine and transmission systems in the United States in the Market
Data file.
---------------------------------------------------------------------------

    The Market Data file also includes assumptions about a vehicle
manufacturer's preferences towards civil penalty payments. EPCA
requires that if a manufacturer does not achieve

compliance with a CAFE standard in a given model year and cannot apply
credits sufficient to cover the compliance shortfall, the manufacturer
must pay civil penalties (i.e., fines) to the Federal Government. If
inputs indicate that a manufacturer treats civil penalty payment as an
economic choice (i.e., one to be taken if doing so would be
economically preferable to applying further technology toward
compliance), the CAFE Model, when evaluating the manufacturer's
response to CAFE standards in a given model year, will apply fuel-
saving technology only up to the point beyond which doing so would be
more expensive (after subtracting the value of avoided fuel outlays)
than paying civil penalties.
    For this analysis, DOT exercises the CAFE Model with inputs
treating all manufacturers as treating civil penalty payment as an
economic choice through MY 2023. While DOT expects that only
manufacturers with some history of paying civil penalties would
actually treat civil penalty payment as an acceptable option, the CAFE
Model does not currently simulate compliance credit trading between
manufacturers, and DOT expects that this treatment of civil penalty
payment will serve as a reasonable proxy for compliance credit
purchases some manufacturers might actually make through MY 2023. These
input assumptions for model years through 2023 reduce the potential
that the model will overestimate technology application in the model
years leading up to those for which the agency is finalizing new
standards. As in past CAFE rulemaking analyses (except that supporting
the 2020 final rule), DOT has treated manufacturers with some history
of civil penalty payment (i.e., BMW, Daimler, FCA, Jaguar-Land Rover,
Volvo, and Volkswagen) as continuing to treat civil penalty payment as
an acceptable option beyond MY 2023, but has treated all other
manufacturers as unwilling to do so beyond MY 2023. DOT believes it is
more accurate, as in past analyses besides the 2020 final rule, to
reflect the possibility that these historical payers of civil penalties
may continue to do so in the future.
    Next, the CAFE Model uses an ``effective cost'' metric to evaluate
options to apply specific technologies to specific engines,
transmissions, and vehicle model configurations. Expressed on a $/
gallon basis, the analysis computes this metric by subtracting the
estimated values of avoided fuel outlays and civil penalties from the
corresponding technology costs, and then dividing the result by the
quantity of avoided fuel consumption. The analysis computes the value
of fuel outlays over a ``payback period'' representing the
manufacturer's expectation that the market will be willing to pay for
some portion of fuel savings achieved through higher fuel economy. Once
the model has applied enough technology to a manufacturer's fleet to
achieve compliance with CAFE standards (and CO2 standards
and ZEV mandates) in a given model year, the model will apply any
further fuel economy improvements estimated to produce a negative
effective cost (i.e., any technology applications for which avoided
fuel outlays during the payback period are larger than the
corresponding technology costs). As discussed above in Section III.A
and below in Section III.C, DOT anticipates that manufacturers are
likely to act as if the market is willing to pay for avoided fuel
outlays expected during the first 30 months of vehicle operation.
    In addition, the Market Data file includes two new sets of inputs
for this analysis. In 2020, five vehicle manufacturers reached a
voluntary commitment with the state of California to improve the
emissions levels of their future nationwide fleets above levels
required by the 2020 final rule. For this analysis, compliance with
this agreement is in the baseline case for designated manufacturers.
The Market Data file contains inputs indicating whether each
manufacturer has committed to exceed Federal requirements per this
agreement.
    Finally, when considering other standards that may affect fuel
economy compliance pathways, DOT includes projected zero emissions
vehicles (ZEV) that would be required for manufacturers to meet
standards in California and Section 177 states, per the waiver granted
under the Clean Air Act. To support the inclusion of the ZEV program in
the analysis, DOT identifies specific vehicle model/configurations that
could adopt BEV technology in response to the ZEV program, independent
of CAFE standards, at the first redesign opportunity. These ZEVs are
identified in the Market Data file as future BEV200s, BEV300s, or
BEV400s. Not all announced BEV nameplates appear in the MY 2020 Market
Data file; in these cases, in consultation with CARB, DOT used the
volume from a comparable vehicle in the manufacturer's Market Data file
portfolio as a proxy. The Market Data file also includes information
about the portion of each manufacturer's sales that occur in California
and Section 177 states, which is helpful for determining how many ZEV
credits each manufacturer will need to generate in the future to comply
with the ZEV program with their own portfolio in the rulemaking
timeframe. These new procedures are described in detail below and in
TSD Chapter 2.3.
3. Simulating the Zero Emissions Vehicle Program
    California's Zero Emissions Vehicle (ZEV) program is one part of a
program of coordinated standards that the California Air Resources
Board (CARB) has enacted to control emissions of criteria pollutants
and greenhouse gas emissions from vehicles. The program began in 1990
with the low-emission vehicle (LEV) regulation,\101\ and has since
expanded to include eleven other states.102 103 These states
may be referred to as Section 177 states, in reference to Section 177
of the Clean Air Act's grant of authority to allow these states to
adopt California's air quality standards,\104\ but it is important to
note that not all Section 177 states have adopted the ZEV program
component.\105\ In the following discussion of the incorporation of the
ZEV program into the CAFE Model, any reference to the Section 177
states refers to those states that have adopted California's ZEV
program requirements.
---------------------------------------------------------------------------

    \101\ California Air Resource Board (CARB), Zero-Emission
Vehicle Program. California Air Resources Board. https://ww2.arb.ca.gov/our-work/programs/zero-emission-vehicle-program/about. (Accessed: February 16, 2022)
    \102\ Through 2020, the Section 177 states that had adopted the
ZEV program included Colorado, Connecticut, Maine, Maryland,
Massachusetts, New Jersey, New York, Oregon, Rhode Island, Vermont,
and Washington. See Vermont Department of Environmental
Conservation, Zero Emission Vehicles. https://dec.vermont.gov/air-quality/mobile-sources/zev. (Accessed: February 16, 2022)
    \103\ The states of Minnesota, Nevada, and Virginia have
recently adopted ZEV standards, which will go into effect for MY
2025. As discussed in this section, reflecting these three states'
adoption of ZEV mandates would have only negligibly impacted the
agency's national-scale modeling. See Green Car Reports, Minnesota
adopts California EV mandate, https://www.greencarreports.com/news/1133027_minnesota-adopts-california-ev-mandate-makes-it-tougher-for-plug-in-compliance-cars (accessed: February 16, 2022); State of
Nevada Climate Initiative, Adopt Low-and Zero-Emissions Passenger
Vehicle Standards, https://climateaction.nv.gov/policies/lev-zev
(accessed: February 16, 2022); Green Car Reports, Virginia becomes
15th Clean Cars State, https://www.greencarcongress.com/2021/03/20210330-virginia.html (accessed: February 16, 2022).
    \104\ Section 177 of the Clean Air Act allows other states to
adopt California's new motor vehicle emission standards, if
specified criteria are met.
    \105\ At the time of writing, Delaware and Pennsylvania are the
two states that have adopted the LEV standards, but not the ZEV
portion.
---------------------------------------------------------------------------

    In their comments on the NPRM, Rivian stated that our ZEV program
modeling should include Minnesota, Virginia, and Nevada as ZEV states,
as those states have recently adopted the

regulation.\106\ We have not included those states as part of the ZEV
program in the modeling, but have ascertained that reflecting these
three states' adoption of ZEV mandates would have only negligibly
impacted the agency's national-scale modeling. Furthermore, the ZEV
standards for these states go into effect only beginning in MY 2025,
which created an inconsistency with our current modeling approach.
---------------------------------------------------------------------------

    \106\ Rivian, Docket ID No. NHTSA-2021-0053-1562, at p. 2.
---------------------------------------------------------------------------

    To account for the ZEV program, and particularly as other states
have recently adopted California's ZEV standards, DOT includes the main
provisions of the ZEV program in the CAFE Model's analysis of
compliance pathways. As explained below, incorporating the ZEV program
into the model includes converting vehicles that have been identified
as potential ZEV candidates into battery-electric vehicles (BEVs) at
the first redesign opportunity, so that a manufacturer's fleet meets
calculated ZEV credit requirements. Since ZEV program compliance
pathways happen independently from the adoption of fuel saving
technology in response to increasing CAFE standards, the ZEV program is
considered in the baseline of the analysis, and in all other regulatory
alternatives.
    Through its ZEV program, California requires that all manufacturers
that sell cars within the state meet ZEV credit standards. The current
credit requirements are calculated based on manufacturers' California
sales volumes. Manufacturers primarily earn ZEV credits through the
production of BEVs, fuel cell vehicles (FCVs), and transitional zero-
emissions vehicles (TZEVs), which are vehicles with partial
electrification, namely plug-in hybrids (PHEVs). Total credits are
calculated by multiplying the credit value each ZEV receives by the
vehicle's volume.
    The ZEV and PHEV/TZEV credit value per vehicle is calculated based
on the vehicle's range; ZEVs may earn up to four credits each and PHEVs
with a US06 all-electric range capability of 10 mi or higher receive an
additional 0.2 credits on top of the credits received based on all-
electric range.\107\ The maximum PHEV credit amount available per
vehicle is 1.10.\108\ Note however that CARB only allows intermediate-
volume manufacturers to meet their ZEV credit requirements through PHEV
production.\109\
---------------------------------------------------------------------------

    \107\ US06 is one of the drive cycles used to test fuel economy
and all-electric range, specifically for the simulation of
aggressive driving. See https://www.epa.gov/vehicle-and-fuel-emissions-testing/dynamometer-drive-schedules for more information,
as well as Section III.C.4 and Section III.D.3.d). (Accessed: March
6, 2022)
    \108\ 13 California Code of Regulations (CCR) 1962.2(c)(3).
    \109\ 13 CCR 1962.2(c)(3).
---------------------------------------------------------------------------

    DOT's method for simulating the ZEV program involves several steps;
first, DOT calculates an approximate ZEV credit target for each
manufacturer based on the manufacturer's national sales volumes, share
of sales in Section 177 states, and the CARB credit requirements. Next,
DOT identifies a general pathway to compliance that involves accounting
for manufacturers' potential use of ZEV overcompliance credits or other
credit mechanisms, and the likelihood that manufacturers would choose
to comply with the requirements with BEVs rather than PHEVs or other
types of compliant vehicles, in addition to other factors. For this
analysis, as discussed further below, DOT consulted with CARB to
determine reasonable assumptions for this compliance pathway. Finally,
DOT identifies vehicles in the MY 2020 analysis fleet that
manufacturers could reasonably adapt to comply with the ZEV standards
at the first opportunity for vehicle redesign, based on publicly
announced product plans and other information. Each of these steps is
discussed in turn, below, and a more detailed description of DOT's
simulation of the ZEV program is included in TSD Chapter 2.3.
    The CAFE Model is designed to present outcomes at a national scale,
so the ZEV analysis considers the Section 177 states as a group as
opposed to estimating each state's ZEV credit requirements
individually. To capture the appropriate volumes subject to the ZEV
requirement, DOT calculates each manufacturer's total market share in
Section 177 states. DOT also calculates the overall market share of
ZEVs in Section 177 states, in order to estimate as closely as
possible, the number of predicted ZEVs we expect all manufacturers to
sell in those states. These shares are then used to scale down
national-level information in the CAFE Model to ensure that we
represent only Section 177 states in the final calculation of ZEV
credits that we project each manufacturer to earn in future years.
    DOT uses MY 2019 National Vehicle Population Profile (NVPP) from
IHS Markit--Polk to calculate these percentages.\110\ These data
include vehicle characteristics such as powertrain, fuel type,
manufacturer, nameplate, and trim level, as well as the state in which
each vehicle is sold, which allows staff to identify the different
types of ZEVs manufacturers sell in the Section 177 state group.
---------------------------------------------------------------------------

    \110\ National Vehicle Population Profile (NVPP) 2020, IHS
Markit--Polk. At the time of the analysis, MY 2019 data from the
NVPP contained the most current estimate of market shares by
manufacturer, and best represented the registered vehicle population
on January 1, 2020.
---------------------------------------------------------------------------

    We calculate sales volumes for the ZEV credit requirement based on
each manufacturer's future assumed market share in Section 177 states.
DOT decided to carry each manufacturer's ZEV market shares forward to
future years, after examination of past market share data from MY 2016,
from the 2017 version of the NVPP.\111\ Comparison of these data to the
2020 version showed that manufacturers' market shares remain fairly
constant in terms of geographic distribution. Therefore, we determined
that it was reasonable to carry forward the recently calculated market
shares to future years.
---------------------------------------------------------------------------

    \111\ National Vehicle Population Profile (NVPP) 2017, IHS
Markit--Polk.
---------------------------------------------------------------------------

    We calculate total credits required for ZEV compliance by
multiplying the percentages from CARB's ZEV requirement schedule by the
Section 177 state volumes. CARB's credit percentage requirement
schedule for the years covered in this analysis begins at 9.5 percent
in 2020 and ramps up in increments to 22 percent by 2025.\112\ Note
that the requirements do not currently change after 2025.\113\
---------------------------------------------------------------------------

    \112\ See 13 CCR 1962.2(b). The percentage credit requirements
are as follows: 9.5 percent in 2020, 12 percent in 2021, 14.5
percent in 2022, 17 percent in 2023, 19.5 percent in 2024, and 22
percent in 2025 and onward.
    \113\ 13 CCR 1962.2(b).
---------------------------------------------------------------------------

    We generate national sales volume predictions for future years
using the Compliance Report, a CAFE Model output file that includes
simulated sales by manufacturer, fleet, and model year. We use a
Compliance Report that corresponds to the baseline scenario of 1.5
percent per year increases in standards for both passenger car and
light truck fleets. The resulting national sales volume predictions by
manufacturer are then multiplied by each manufacturer's total market
share in the Section 177 states to capture the appropriate volumes in
the ZEV credits calculation. Required credits by manufacturer, per
year, are determined by multiplying the Section 177 state volumes by
CARB's ZEV credit percentage requirement. These required credits are
subsequently added to the CAFE Model inputs as targets for manufacturer
compliance with ZEV standards in the CAFE baseline.
    The estimated ZEV credit requirements serve as a target for
simulating ZEV compliance in the baseline. To achieve this, DOT
determines a modeling philosophy for ZEV pathways, reviews various
sources

for information regarding upcoming ZEV programs, and inserts those
programs into the analysis fleet inputs. As manufacturers can meet ZEV
standards in a variety of different ways, using various technology
combinations, the analysis must include certain simplifying assumptions
in choosing ZEV pathways. We made these assumptions in conjunction with
guidance from CARB staff. The following sections discuss the approach
used to simulate a pathway to ZEV program compliance in this analysis.
    First, DOT targeted 2025 compliance, as opposed to assuming
manufacturers would perfectly comply with their credit requirements in
each year prior to 2025. This simplifying assumption was made upon
review of past history of ZEV credit transfers, existing ZEV credit
banks, and redesign schedules. DOT focused on integrating ZEV
technology throughout that timeline with the target of meeting 2025
obligations; thus, some manufacturers are estimated to over-comply or
under-comply, depending on their individual situations, in the years
2021-2024.
    Second, DOT determined that the most reasonable way to model ZEV
compliance would be to allow under-compliance in certain cases and
assume that some manufacturers would not meet their ZEV obligation on
their own in 2025. Instead, these manufacturers were assumed to prefer
to purchase credits from another manufacturer with a credit surplus.
Reviews of past ZEV credit transfers between manufacturers informed the
decision to make this simplifying assumption.\114\ CARB advised that
for these manufacturers, the CAFE Model should still project that each
manufacturer meet approximately 80 percent of their ZEV requirements
with technology included in their own portfolio. Manufacturers that
were observed to have generated many ZEV credits in the past or had
announced major upcoming BEV initiatives were projected to meet 100
percent of their ZEV requirements on their own, without purchasing ZEV
credits from other manufacturers.\115\
---------------------------------------------------------------------------

    \114\ See https://ww2.arb.ca.gov/our-work/programs/advanced-clean-cars-program/zev-program/zero-emission-vehicle-credit-balances
for past credit balances and transfer information. (Accessed:
February 16, 2022)
    \115\ The following manufacturers were assumed to meet 100-
percent ZEV compliance: Ford, General Motors, Hyundai, Kia, Jaguar
Land Rover, and Volkswagen Automotive. Tesla was also assumed to
meet 100 percent of its required standards, but the analyst team did
not need to add additional ZEV substitutes to the baseline for this
manufacturer.
---------------------------------------------------------------------------

    Third, DOT agreed that manufacturers would meet their ZEV credit
requirements in 2025 though the production of BEVs. As discussed above,
manufacturers may choose to build PHEVs or FCVs to earn some portion of
their required ZEV credits. However, DOT projected that manufacturers
would rely on BEVs to meet their credit requirements, based on reviews
of press releases and industry news, as well as discussion with CARB.
Since nearly all manufacturers have announced some plans to produce
BEVs at a scale meaningful to future ZEV requirements, DOT agreed that
this was a reasonable assumption.\116\ Furthermore, as CARB only allows
intermediate-volume manufacturers to meet their ZEV credit requirements
through the production of PHEVs, and the volume status of these few
manufacturers could change over the years, assuming BEV production for
ZEV compliance is the most straightforward path.
---------------------------------------------------------------------------

    \116\ See TSD Chapter 2.3 for a list of potential BEV programs
recently announced by manufacturers.
---------------------------------------------------------------------------

    Fourth, to account for the new BEV programs announced by some
manufacturers, DOT identified vehicles in the 2020 fleet that closely
matched the upcoming BEVs, by regulatory class, market segment, and
redesign schedule. DOT made an effort to distribute ZEV candidate
vehicles by CAFE regulatory class (light truck, passenger car), by
manufacturer, in a manner consistent with the 2020 manufacturer fleet
mix. Since passenger car and light truck mixes by manufacturer could
change in response to the CAFE policy alternative under consideration,
this effort was deemed necessary in order to avoid redistributing the
fleet mix in an unrealistic manner. However, there were some exceptions
to this assumption, as some manufacturers are already closer to meeting
their ZEV obligation through 2025 with BEVs currently produced, and
some manufacturers underperform their compliance targets more so in one
fleet than another. In these cases, DOT deviated from keeping the LT/PC
mix of BEVs evenly distributed across the manufacturer's
portfolio.\117\
---------------------------------------------------------------------------

    \117\ The GM light truck and passenger car distribution is one
such example.
---------------------------------------------------------------------------

    DOT then identified future ZEV programs that could plausibly
contribute towards the ZEV requirements for each manufacturer by 2025.
To obtain this information, DOT examined various sources, including
trade press releases, industry announcements, and investor reports. In
many cases, these BEV programs are in addition to programs already in
production.\118\ Some manufacturers have not yet released details of
future electric vehicle programs at the time of writing, but have
indicated goals of reaching certain percentages of electric vehicles in
their portfolios by a specified year. In these cases, DOT reviewed the
manufacturer's current fleet characteristics as well as the
aspirational information in press releases and other news in order to
make reasonable assumptions about the vehicle segment and range of
those future BEVs. No changes in BEV program assumptions were made
between the NPRM and this document.
---------------------------------------------------------------------------

    \118\ Examples of BEV programs already in production include the
Nissan Leaf and the Chevrolet Bolt.
---------------------------------------------------------------------------

    Overall, analysts assumed that manufacturers would lean towards
producing BEV300s rather than BEV200s, based on the information
reviewed and an initial conversation with CARB.\119\ Phase-in caps were
also considered, especially for BEV200, with the understanding that the
CAFE Model will always pick BEV200 before BEV300 or BEV400, until the
quantity of BEV200s is exhausted. See Section III.D.3.c) for details
regarding BEV phase-in caps.
---------------------------------------------------------------------------

    \119\ BEV300s are 300-mile range battery-electric vehicles. See
Section III.D.3.b) for further information regarding electrification
fleet assignments.
---------------------------------------------------------------------------

    BEVs with smaller battery packs and less range are less likely to
meet all the performance needs of traditional pickup truck owners
today, such as long-range towing. However, longer-range BEV pickups are
being introduced, and may be joined by new markets in the form of
electric delivery trucks and some light-duty electric truck
applications in state and local government. The extent to which BEVs
will be used in these and other new markets is difficult to project.
DOT did identify certain trucks as upcoming BEVs for ZEV compliance,
and these BEVs were expected to have higher ranges, due to the specific
performance needs associated with these vehicles. Outside of the ZEV
inputs described here, the CAFE Model does not handle the application
of BEV technology with any special considerations as to whether the
vehicle is a pickup truck or not.
    Finally, in order to simulate manufacturers' compliance with their
particular ZEV credits target, 142 rows in the analysis fleet were
identified as substitutes for future ZEV programs. As discussed above,
the analysis fleet summarizes the roughly 13.6 million light-duty
vehicles produced and sold in the United States in MY 2020 with more
than 3,500 rows, each reflecting

information for one vehicle type observed. Each row includes the
vehicle's nameplate and trim level, the sales volume, engine,
transmission, drive configuration, regulatory class, projected redesign
schedule, and fuel saving technologies, among other attributes.
    As the goal of the ZEV analysis is to simulate compliance with the
ZEV program in the baseline, and the analysis fleet only contains
vehicles produced during MY 2020, DOT identified existing models in the
analysis fleet that shared certain characteristics with upcoming BEVs.
DOT also focused on identifying substitute vehicles with redesign years
similar to the future BEV's introduction year. The sales volumes of
those existing models, as predicted for 2025, were then used to
simulate production of the upcoming BEVs. DOT identified a combination
of rows that would meet the ZEV target, could contribute productively
towards CAFE program obligations (by manufacturer and by fleet), and
would introduce BEVs in each manufacturer's portfolio in a way that
reasonably aligned with projections and announcements. DOT tagged each
of these rows with information in the Market Data file, instructing the
CAFE Model to apply the specified BEV technology to the row at the
first redesign year, regardless of the scenario or type of CAFE or GHG
simulation.
    The CAFE Model does not optimize compliance with the ZEV mandate;
it relies upon the inputs described in this section in order to
estimate each manufacturer's resulting ZEV credits. The resulting
amount of ZEV credits earned by manufacturer for each model year can be
found in the CAFE Model's Compliance file.
    Not all ZEV-qualifying vehicles in the U.S. earn ZEV credits, as
they are not all sold in states that have adopted ZEV regulations. In
order to reflect this in the CAFE Model, which only estimates sales
volumes at the national level, the percentages calculated for each
manufacturer are used to scale down the national-level volumes.
Multiplying national-level ZEV sales volumes by these percentages
ensures that only the ZEVs sold in Section 177 states count towards the
ZEV credit targets of each manufacturer.\120\ See Section 5.8 of the
CAFE Model Documentation for a detailed description of how the model
applied these ZEV technologies and any changes made to the model's
programming for the incorporation of the ZEV program into the baseline.
---------------------------------------------------------------------------

    \120\ The single exception to this assumption is Mazda, as Mazda
has not yet produced any ZEV-qualifying vehicles at the time of
writing. Thus, the percentage of ZEVs sold in Section 177 states
cannot be calculated from existing data. However, Mazda has
indicated its intention to produce ZEV-qualifying vehicles in the
future, so DOT assumed that 100 percent of future ZEVs would be sold
in Section 177 states for the purposes of estimating ZEV credits in
the CAFE Model.
---------------------------------------------------------------------------

    As discussed above, DOT made an effort to distribute the newly
identified ZEV candidates between CAFE regulatory classes (light truck
and passenger car) in a manner consistent with the proportions seen in
the 2020 analysis fleet, by manufacturer. As mentioned previously,
there were a few exceptions to this assumption in cases where
manufacturers' regulatory class distribution of current or planned ZEV
programs clearly differed from their regulatory class distribution as a
whole.
    In some instances, the regulatory distribution of flagged ZEV
candidates leaned towards a higher portion of PCs. The reasoning behind
this differs in each case, but there is an observed pattern in the 2020
analysis fleet of fewer BEVs being light trucks, especially pickups.
The 2020 analysis fleet contains no BEV pickups in the light truck
segment. The slow emergence of electric pickups could be linked to the
specific performance needs associated with pickup trucks. However, the
market for BEVs may emerge in unexpected ways that are difficult to
project. Examples of this include anticipated electric delivery trucks
and light-duty electric trucks used by state and local governments. Due
to these considerations, DOT tagged some trucks as BEVs for ZEV, and
expected that these would generally be of higher ranges.
    TSD Chapter 2.3 includes more information about the process we use
to simulate ZEV program compliance in this analysis.
4. Technology Effectiveness Values
    The next input we use to simulate manufacturers' decision-making
processes for the year-by-year application of technologies to specific
vehicles are estimates of how effective each technology would be at
reducing fuel consumption. For this analysis, we use full-vehicle
modeling and simulation to estimate the fuel economy improvements
manufacturers could make to a fleet of vehicles, considering the
vehicles' technical specifications and how combinations of technologies
interact. Full-vehicle modeling and simulation uses physics-based
models to predict how combinations of technologies perform as a full
system under defined conditions. We use full vehicle simulations
performed in Autonomie, a physics-based full-vehicle modeling and
simulation software developed and maintained by the U.S. Department of
Energy's Argonne National Laboratory.\121\
---------------------------------------------------------------------------

    \121\ Islam, E.S., A. Moawad, N. Kim, R. Vijayagopal, and A.
Rousseau. A Detailed Vehicle Simulation Process to Support CAFE
Standards for the MY 2024-2026 Analysis. ANL/ESD-21/9 (hereinafter,
Autonomie model documentation).
---------------------------------------------------------------------------

    A model is a mathematical representation of a system, and
simulation is the behavior of that mathematical representation over
time. In this analysis, the model is a mathematical representation of
an entire vehicle,\122\ including its individual components such as the
engine and transmission, overall vehicle characteristics such as mass
and aerodynamic drag, and the environmental conditions, such as ambient
temperature and barometric pressure. We simulate the model's behavior
over test cycles, including the 2-cycle laboratory compliance tests (or
2-cycle tests),\123\ to determine how the individual components
interact.
---------------------------------------------------------------------------

    \122\ Each full vehicle model in this analysis is composed of
sub-models, which is why the full vehicle model could also be
referred to as a full system model, composed of sub-system models.
    \123\ EPA's compliance test cycles are used to measure the fuel
economy of a vehicle. For readers unfamiliar with this process, it
is like running a car on a treadmill following a program--or more
specifically, two programs. The ``programs'' are the ``urban
cycle,'' or Federal Test Procedure (abbreviated as ``FTP''), and the
``highway cycle,'' or Highway Fuel Economy Test (abbreviated as
``HFET''), and they have not changed substantively since 1975. Each
cycle is a designated speed trace (of vehicle speed versus time)
that all certified vehicles must follow during testing. The FTP is
meant roughly to simulate stop and go city driving, and the HFET is
meant roughly to simulate steady flowing highway driving at about 50
mph.
---------------------------------------------------------------------------

    Using full-vehicle modeling and simulation to estimate technology
efficiency improvements has two primary advantages over using single or
limited point estimates. An analysis using single or limited point
estimates may assume that, for example, one fuel economy-improving
technology with an effectiveness value of 5 percent by itself and
another technology with an effectiveness value of 10 percent by itself,
when applied together achieve an additive improvement of 15 percent.
Single point estimates generally do not provide accurate effectiveness
values because they do not capture complex relationships among
technologies. Technology effectiveness often differs significantly
depending on the vehicle type (e.g., sedan versus pickup truck) and the
way in which the technology interacts with other technologies on the
vehicle, as different technologies may provide different incremental
levels of fuel economy improvement if implemented alone or in
combination with other technologies. Any

oversimplification of these complex interactions leads to less accurate
and often overestimated effectiveness estimates.
    In addition, because manufacturers often implement several fuel-
saving technologies simultaneously when redesigning a vehicle, it is
difficult to isolate the effect of individual technologies using
laboratory measurement of production vehicles alone. Modeling and
simulation offer the opportunity to isolate the effects of individual
technologies by using a single or small number of baseline vehicle
configurations and incrementally adding technologies to those baseline
configurations. This provides a consistent reference point for the
incremental effectiveness estimates for each technology and for
combinations of technologies for each vehicle type. Vehicle modeling
also reduces the potential for overcounting or undercounting technology
effectiveness.
    An important feature of this analysis is that the incremental
effectiveness of each technology and combinations of technologies
should be accurate and relative to a consistent baseline vehicle. For
this analysis, the baseline absolute fuel economy value for each
vehicle in the analysis fleet is based on CAFE compliance data for each
make and model.\124\ The absolute fuel economy values of the full
vehicle simulations are used only to determine incremental
effectiveness and are never used directly to assign an absolute fuel
economy value to any vehicle model or configuration. For subsequent
technology changes, we apply the incremental effectiveness values of
one or more technologies to the baseline fuel economy value to
determine the absolute fuel economy achieved for applying the
technology change.
---------------------------------------------------------------------------

    \124\ See Section III.C.2 for further discussion of CAFE
compliance data in the Market Data file.
---------------------------------------------------------------------------

    As an example, if a Ford F-150 2-wheel drive crew cab and short bed
in the analysis fleet has a fuel economy value of 30 mpg for CAFE
compliance, 30 mpg will be considered the reference absolute fuel
economy value. A similar full vehicle model node in the Autonomie
simulation may begin with an average fuel economy value of 32 mpg, and
with incremental addition of a specific technology X its fuel economy
improves to 35 mpg, a 9.3 percent improvement. In this example, the
incremental fuel economy improvement (9.3 percent) from technology X
would be applied to the F-150's 30 mpg absolute value.
    We determine the incremental effectiveness of technologies as
applied to the thousands of unique vehicle and technology combinations
in the analysis fleet. Although, as mentioned above, full-vehicle
modeling and simulation reduces the work and time required to assess
the impact of moving a vehicle from one technology state to another, it
would be impractical--if not impossible--to build a unique vehicle
model for every individual vehicle in the analysis fleet. Therefore, as
discussed in the following sections, the Autonomie analysis relies on
ten vehicle technology class models that are representative of large
portions of the analysis fleet vehicles. The vehicle technology classes
ensure that key vehicle characteristics are reasonably represented in
the full vehicle models.
    We sought comment on the full vehicle modeling and simulation
assumptions used for this analysis and received some comments specific
to individual technologies, which are discussed further in the
individual technology subsections in final rule Section III.D. However,
we did not receive any comments on our use of Autonomie itself. The
next sections discuss the details of the technology effectiveness
analysis input specifications and assumptions that we continued to use
for this final rule analysis.
(a) Full Vehicle Modeling and Simulation
    As discussed above, for this analysis we use Argonne's full vehicle
modeling tool, Autonomie, to build vehicle models with different
technology combinations and simulate the performance of those models
over regulatory test cycles. The difference in the simulated
performance between full vehicle models, with differing technology
combination, is used to determine effectiveness values. We consider
over 50 individual technologies as inputs to the Autonomie
modeling.\125\ These inputs consist of engine technologies,
transmission technologies, powertrain electrification, light-weighting,
aerodynamic improvements, and tire rolling resistance improvements.
Section III.D broadly discusses each of the technology groupings
definitions, inputs, and assumptions. A deeper discussion of the
Autonomie modeled subsystems, and how inputs feed the sub models
resulting in outputs, is contained in the Autonomie model documentation
that accompanies this analysis. The 50 individual technologies, when
considered with the ten vehicle technology classes, result in over 1
million individual vehicle technology combination models. For
additional discussion on the full vehicle modeling used in this
analysis see TSD Chapter 2.
---------------------------------------------------------------------------

    \125\ See Autonomie model documentation; ANL--All
Assumptions_Summary_NPRM_022021.xlsx; ANL--Data Dictionary January
2021.xlsx.
---------------------------------------------------------------------------

    While Argonne built full-vehicle models and ran simulations for
many combinations of technologies, it did not simulate literally every
single vehicle model/configuration in the analysis fleet. Not only
would it be impractical to assemble the requisite detailed information
specific to each vehicle/model configuration, much of which would
likely only be provided on a confidential basis, doing so would
increase the scale of the simulation effort by orders of magnitude.
Instead, Argonne simulated ten different vehicle types, corresponding
to the five ``technology classes'' generally used in CAFE analysis over
the past several rulemakings, each with two performance levels and
corresponding vehicle technical specifications (e.g., small car, small
performance car, pickup truck, performance pickup truck, etc.).
    Technology classes are a means of specifying common technology
input assumptions for vehicles that share similar characteristics.
Because each vehicle technology class has unique characteristics, the
effectiveness of technologies and combinations of technologies is
different for each technology class. Conducting Autonomie simulations
uniquely for each technology class provides a specific set of
simulations and effectiveness data for each technology class. In this
analysis the technology classes are compact cars, midsize cars, small
SUVs, large SUVs, and pickup trucks. In addition, for each vehicle
class there are two levels of performance attributes (for a total of 10
technology classes). The high performance and low performance vehicles
classifications allow for better diversity in estimating technology
effectiveness across the fleet.
    For additional discussion on the development of the vehicle
technology classes used in this analysis and the attributes used to
characterize each vehicle technology class, see TSD Chapter 2.4 and the
Autonomie model documentation.
    Before any simulation is initiated in Autonomie, Argonne must
``build'' a vehicle by assigning reference technologies and initial
attributes to the components of the vehicle model representing each
technology class. The reference technologies are baseline

technologies that represent the first step on each technology pathway
used in the analysis. For example, a compact car is built by assigning
it a baseline engine (DOHC, VVT, PFI), a baseline transmission (AT5), a
baseline level of aerodynamic improvement (AERO0), a baseline level of
rolling resistance improvement (ROLL0), a baseline level of mass
reduction technology (MR0), and corresponding attributes from the
Argonne vehicle assumptions database like individual component weights.
A baseline vehicle will have a unique starting point for the simulation
and a unique set of assigned inputs and attributes, based on its
technology class. Argonne collected over a hundred baseline vehicle
attributes to build the baseline vehicle for each technology class. In
addition, to account for the weight of different engine sizes, like 4-
cylinder versus 8-cylinder or turbocharged versus naturally aspirated
engines, Argonne developed a relationship curve between peak power and
engine weight based on the A2Mac1 benchmarking data. Argonne uses the
developed relationship to estimate mass for all engines. For additional
discussion on the development and optimization of the baseline vehicle
models and the baseline attributes used in this analysis see TSD
Chapter 2.4 and the Autonomie model documentation.
    The next step in the process is to run a powertrain sizing
algorithm that ensures the built vehicle meets or exceeds defined
performance metrics, including low-speed acceleration (time required to
accelerate from 0-60 mph), high-speed passing acceleration (time
required to accelerate from 50-80 mph), gradeability (the ability of
the vehicle to maintain constant 65 miles per hour speed on a six
percent upgrade), and towing capacity. Together, these performance
criteria are widely used by the automotive industry as metrics to
quantify vehicle performance attributes that consumers observe and that
are important for vehicle utility and customer satisfaction.
    As with conventional vehicle models, electrified vehicle models
were also built from the ground up. For MY 2020, the U.S. market has an
expanded number of available hybrid and electric vehicle models. To
capture improvements for electrified vehicles for this analysis, DOT
applied a mass regression analysis process that considers electric
motor weight versus electric motor power (similar to the regression
analysis for internal combustion engine weights) for vehicle models
that have adopted electric motors. Benchmarking data for hybrid and
electric vehicles from the A2Mac1 database were analyzed to develop a
regression curve of electric motor peak power versus electric motor
weight.\126\
---------------------------------------------------------------------------

    \126\ See Autonomie model documentation, Chapter 5.2.10,
Electric Machines System Weight.
---------------------------------------------------------------------------

    We maintain performance neutrality in the full vehicle simulations
by resizing engines, electric machines, and hybrid electric vehicle
battery packs at specific incremental technology steps. To address
product complexity and economies of scale, engine resizing is limited
to specific incremental technology changes that would typically be
associated with a major vehicle or engine redesign. This is intended to
reflect manufacturers' comments to DOT on how they consider engine
resizing and product complexity, and DOT's observations on industry
product complexity. A detailed discussion on powertrain sizing can be
found in TSD Chapter 2.4 and in the Autonomie model documentation.
    After all vehicle class and technology combination models have been
built, Autonomie simulates the vehicles' performance on test cycles to
calculate the effectiveness improvement of adding fuel-economy-
improving technologies to the vehicle. Simulating vehicles' performance
using tests and procedures specified by Federal law and regulations
minimizes the potential variation in determining technology
effectiveness.
    For vehicles with conventional powertrains and micro hybrids,
Autonomie simulates the vehicles per EPA 2-cycle test procedures and
guidelines.\127\ For mild and full hybrid electric vehicles and FCVs,
Autonomie simulates the vehicles using the same EPA 2-cycle test
procedure and guidelines, and the drive cycles are repeated until the
initial and final state of charge are within a SAE J1711 tolerance. For
PHEVs, Autonomie simulates vehicles per similar procedures and
guidelines as prescribed in SAE J1711.\128\ For BEVs Autonomie
simulates vehicles per similar procedures and guidelines as prescribed
in SAE J1634.\129\
---------------------------------------------------------------------------

    \127\ 40 CFR part 600.
    \128\ PHEV testing is broken into several phases based on SAE
J1711: Charge-sustaining on the city cycle and HWFET cycle, and
charge-depleting on the city and HWFET cycles.
    \129\ SAE J1634. ``Battery Electric Vehicle Energy Consumption
and Range Test Procedure.'' July 12, 2017.
---------------------------------------------------------------------------

    We received comments from The International Council on Clean
Transportation (ICCT) regarding the application of the engine sizing
algorithm, and when it is applied in relation to vehicle road load
improvement technologies. ICCT stated that, ``[d]ue to the large
uncertainties in when and how to downsize engines for the variety of
vehicles, the only acceptable solution is to always model the
appropriate amount of engine downsizing to maintain performance.''
\130\
---------------------------------------------------------------------------

    \130\ ICCT, Docket No. NHTSA-2021-0053-1581-A1, at p. 5.
---------------------------------------------------------------------------

    We disagree with the comment implying that engine resizing is
required for every technology change on a vehicle platform. We believe
that this would artificially inflate effectiveness relative to cost.
Manufacturers have repeatedly and consistently conveyed that the costs
for redesign and the increased manufacturing complexity resulting from
continual resizing engine displacement for small technology changes
preclude them from doing so. NHTSA believes that it would not be
reasonable or cost-effective to expect resizing powertrains for every
unique combination of technologies, and even less reasonable and cost-
effective for every unique combination of technologies across every
vehicle model due to the extreme manufacturing complexity that would be
required to do so.\131\ In addition, a 2011 NAS report stated that
``[f]or small (under 5 percent [of curb weight]) changes in mass,
resizing the engine may not be justified, but as the reduction in mass
increases (greater than 10 percent [of curb weight]), it becomes more
important for certain vehicles to resize the engine and seek secondary
mass reduction opportunities.'' \132\
---------------------------------------------------------------------------

    \131\ For more details, see comments and discussion in the 2020
Rulemaking Preamble Section VI.B.3.a)(6) Performance Neutrality.
    \132\ National Research Council 2011. Assessment of Fuel Economy
Technologies for Light-Duty Vehicles. Washington, DC: The National
Academies Press. https://doi.org/10.17226/12924 (hereinafter, 2011
NAS Report), at 107.
---------------------------------------------------------------------------

    We also believe that ICCT's comment regarding Autonomie's engine
resizing process is further addressed by the Autonomie's powertrain
calibration process. We do agree that the powertrain should be re-
calibrated for every unique technology combination and this calibration
is performed as part of the transmission shift initializer
routine.\133\ Autonomie runs the shift initializer routine for every
unique Autonomie full vehicle model configuration and generates
customized transmission shift maps. The algorithms' optimization is
designed to balance minimization of energy consumption and vehicle
performance.
---------------------------------------------------------------------------

    \133\ See FRM ANL Model Documentation at Paragraph <IP_ADDRESS>.
---------------------------------------------------------------------------

(b) Performance Neutrality
    The purpose of the CAFE analysis is to examine the impact of
technology

application that can improve fuel economy. When the fuel economy-
improving technology is applied, frequently the manufacturer must
choose how the technology will affect the vehicle. The advantages of
the new technology can either be completely applied to improving fuel
economy or be used to increase vehicle performance while maintaining
the existing fuel economy, or some mix of the two effects.
Historically, vehicle performance, historically equated with
horsepower, has improved over the years as more technology is applied
to the fleet. The average horsepower is the highest that it has ever
been; all vehicle types have improved horsepower by at least 43 percent
compared to the 1978 model year, and pickup trucks have improved by 49
percent.\134\ Fuel economy has also improved, but the horsepower and
acceleration trends show that not 100 percent of technological
improvements have been applied to fuel savings. While future trends are
uncertain, the past trends suggest that vehicle performance is unlikely
to decrease, as it seems reasonable to assume that customers will, at a
minimum, demand vehicles that offer the same utility as today's fleet.
---------------------------------------------------------------------------

    \134\ ``The 2021 EPA Automotive Trends Report, Greenhouse Gas
Emissions, Fuel Economy, and Technology since 1975,'' EPA-420-R-21-
023, November 2021, at pp. 20-7 (hereinafter, 2021 EPA Automotive
Trends Report).
---------------------------------------------------------------------------

    For this rulemaking analysis, we analyzed technology pathways
manufacturers could use for compliance that attempt to maintain vehicle
attributes, utility, and performance. Using this approach allows us to
assess the costs and benefits of potential standards under a scenario
where consumers continue to get the similar vehicle attributes and
features, other than changes in fuel economy. The purpose of
constraining vehicle attributes is to simplify the analysis and reduce
variance in other attributes that consumers may value across the
analyzed regulatory alternatives. This allows for a streamlined
accounting of costs and benefits by not requiring the values of other
vehicle attributes.
    To confirm minimal differences in performance metrics across
regulatory alternatives, we analyzed the sales-weighted average 0-60
mph acceleration performance of the entire simulated vehicle fleet for
MYs 2020 and 2029. The analysis compared performance under the baseline
standards and Preferred Alternative. For the NPRM, this analysis
identified that the analysis fleet under the No-Action Alternative in
MY 2029 had a 0.77 percent worse 0-60 mph acceleration time than under
the Preferred Alternative; in other words, the alternative with the
higher fuel economy standards also showed greater acceleration and
performance. For the final rule analysis, using the similar approach
yielded a 0.0615 percent better (as compared to the baseline) 0-60 mph
acceleration time, indicating there is minimal difference in
performance between the alternatives. This assessment shows that for
this analysis, the performance difference is minimal across regulatory
alternatives and across the simulated model years, which allows for
fair, direct comparison among the alternatives. Further details about
this assessment can be found in TSD Chapter 2.4.5.
    Overall, commenters were supportive of our approach to maintaining
performance neutrality and the metrics we use to accomplish this.
Commenters said we should continue to improve our methodologies for
maintaining performance neutrality.\135\ Auto Innovators stated that
``[t]he [a]gencies have historically sought to maintain the performance
characteristics of vehicles modeled with fuel economy-improving
technologies.'' They added that they ``appreciate that the [a]gencies
continue to consider high- speed acceleration, gradeability, towing,
range, traction, and interior room (including headroom) in the analysis
when sizing powertrains and evaluating pathways for road-load
reductions.'' Finally, they stated that ``[a]ll of these parameters
should be considered separately, not just in combination. (For example,
we do not support an approach where various acceleration times are
added together to create a single `performance' statistic.
Manufacturers must provide all types of performance, not just one or
two to the detriment of others.).''
---------------------------------------------------------------------------

    \135\ RV Industry Association, Docket No. NHTSA-2021-0053-0053,
at 4; Auto Innovators, Docket No. NHTSA-2021-0053-1492, at p. 62.
---------------------------------------------------------------------------

    The RV Industry Association commented that the agency should
include towing capacity considerations for large SUVs because of the
public's reliance on large SUVs for RV towing.\136\ Currently, our
analysis assumes that SUVs are primarily used for carrying passengers
and cargo and towing is not their primary function, in contrast to how
full-size pickups are characterized in the analysis. Other aspects of
the analysis capture potential performance limitations for SUVs such as
limiting the adoption of technologies that could be considered less
practical for SUVs. For example, for some larger SUVs with higher power
density requirements, we limit HCR engine technologies and power-split
strong hybrid powertrains. For more details on these limitations, see
Section III.D.1.c) of this preamble for each technology pathway.
---------------------------------------------------------------------------

    \136\ RV Industry Association, at p. 4.
---------------------------------------------------------------------------

    For this final rule analysis, we continued to use the same
methodology for modeling full vehicles and maintaining performance
neutrality. As such, the estimated compliance costs reflect the
assumption that manufacturers will resize powertrains or make other
adjustments to maintain performance while increasing fuel economy. We
will continue to monitor performance neutrality metrics and their
incorporation as part of future analyses.
(c) Implementation in the CAFE Model
    The CAFE Model uses two elements of information from the large
amount of data generated by the Autonomie simulation runs: Battery
costs, and fuel consumption on the city and highway cycles. We combine
the fuel economy information from the two cycles to produce a composite
fuel economy for each vehicle, and for each fuel used in dual fuel
vehicles. The fuel economy information for each simulation run is
converted into a single value for use in the CAFE Model.
    In addition to the technologies in the Autonomie simulation, the
CAFE Model also incorporated a handful of technologies not explicitly
simulated in Autonomie. These technologies' performance either could
not be captured on the 2-cycle test, or there were no robust data
usable as an input for full-vehicle modeling and simulation. The
specific technologies are discussed in the individual technology
sections below and in TSD Chapter 3. To calculate fuel economy
improvements attributable to these additional technologies, estimates
of fuel consumption improvement factors were developed and scale
multiplicatively when applied together. See TSD Chapter 3 for a
complete discussion on how these factors were developed. The Autonomie-
simulated results and additional technologies are combined, forming a
single dataset used by the CAFE Model.
    Each line in the CAFE Model dataset represents a unique combination
of technologies. We organize the records using a unique technology
state vector, or technology key (tech key), that describes the
technology content associated with each unique record. The modeled 2-
cycle fuel economy (miles per gallon) of each combination is converted
into fuel consumption (gallons per mile) and then normalized relative
to a baseline tech key. The improvement factors used by the model

are a given combination's fuel consumption improvement relative to the
baseline tech key in its technology class.
    The tech key format was developed by recognizing that most of the
technology pathways are unrelated and are only logically linked to
designate the direction in which technologies are allowed to progress.
As a result, it is possible to condense the paths into groups based on
the specific technology. These groups are used to define the technology
vector, or tech key. The following technology groups defined the tech
key: Engine cam configuration (CONFIG), VVT engine technology (VVT),
VVL engine technology (VVL), SGDI engine technology (SGDI), DEAC engine
technology (DEAC), non-basic engine technologies (ADVENG), transmission
technologies (TRANS), electrification and hybridization (ELEC), low
rolling resistance tires (ROLL), aerodynamic improvements (AERO), mass
reduction levels (MR), EFR engine technology (EFR), electric accessory
improvement technologies (ELECACC), LDB technology (LDB), and SAX
technology (SAX). This summarizes to a tech key with the following
fields: CONFIG; VVT; VVL; SGDI; DEAC; ADVENG; TRANS; ELEC; ROLL; AERO;
MR; EFR; ELECACC; LDB; SAX. It should be noted that some of the fields
may be blank for some tech key combinations. These fields will be left
visible for the examples below, but blank fields may be omitted from
tech keys shown elsewhere in the documentation.
    As an example, a technology state vector describing a vehicle with
a SOHC engine, variable valve timing (only), a 6-speed automatic
transmission, a belt-integrated starter generator, rolling resistance
(level 1), aerodynamic improvements (level 2), mass reduction (level
1), electric power steering, and low drag brakes, would be specified as
``SOHC; VVT; ; ; ; ; AT6; BISG; ROLL10; AERO20; MR1; ; EPS; LDB ; .''
\137\
---------------------------------------------------------------------------

    \137\ In the example tech key, the series of semicolons between
VVT and AT6 correspond to the engine technologies which are not
included as part of the combination, while the gap between MR1 and
EPS corresponds to EFR and the omitted technology after LDB is SAX.
The extra semicolons for omitted technologies are preserved in this
example for clarity and emphasis and will not be included in future
examples.
---------------------------------------------------------------------------

    Once a vehicle is assigned (or mapped) to an appropriate tech key,
adding a new technology to the vehicle simply represents progress from
a previous tech key to a new tech key. The previous tech key refers to
the technologies that are currently in use on a vehicle. The new tech
key is determined, in the simulation, by adding a new technology to the
combination represented by the previous state vector while
simultaneously removing any technologies that are superseded by the
newly added one.
    For example, start with a vehicle with the tech key: SOHC; VVT;
AT6; BISG; ROLL10; AERO20; MR1; EPS; LDB. Assume the simulation is
evaluating PHEV20 as a candidate technology for application on this
vehicle. The new tech key for this vehicle is computed by removing
SOHC, VVT, AT6, and BISG technologies from the previous state
vector,\138\ and adding PHEV20, resulting a tech key that looks like
this: PHEV20; ROLL10; AERO20; MR1; EPS; LDB.
---------------------------------------------------------------------------

    \138\ For more discussion of how the CAFE Model handles
technology supersession, see S4.5 of the CAFE Model Documentation.
---------------------------------------------------------------------------

    From here, the simulation obtains a fuel economy improvement factor
for the new combination of technologies and applies that factor to the
fuel economy of a vehicle in the analysis fleet. The resulting
improvement is applied to the original compliance fuel economy value
for a discrete vehicle in the analysis fleet.
5. Defining Technology Adoption in the Rulemaking Timeframe
    As discussed in Section III.C.2, starting with a fixed analysis
fleet (for this analysis, the MY 2020 fleet indicated in manufacturers'
early CAFE compliance data), the CAFE Model estimates ways each
manufacturer could potentially apply specific fuel-saving technologies
to specific vehicle model/configurations in response to, among other
things (such as fuel prices), CAFE standards, CO2 standards,
commitments some manufacturers have made to CARB's ``Framework
Agreements,'' and ZEV mandates imposed by California and several other
states. The CAFE Model follows a year-by-year approach to simulating
manufacturers' potential decisions to apply technology, accounting for
multiyear planning within the context of estimated schedules for future
vehicle redesigns and refreshes during which significant technology
changes may most practicably be implemented.
    The modeled technology adoption for each manufacturer under each
regulatory alternative depends on this representation of multiyear
planning, and on a range of other factors represented by other model
characteristics and inputs, such as the logical progression of
technologies defined by the model's technology pathways; the
technologies already present in the analysis fleet; inputs directing
the model to ``skip'' specific technologies for specific vehicle model/
configurations in the analysis fleet (e.g., because secondary axle
disconnect cannot be applied to 2-wheel-drive vehicles, and because
manufacturers already heavily invested in engine turbocharging and
downsizing are unlikely to abandon this approach in favor of using high
compression ratios); inputs defining the sharing of engines,
transmissions, and vehicle platforms in the analysis fleet; the model's
logical approach to preserving this sharing; inputs defining each
regulatory alternative's specific requirements; inputs defining
expected future fuel prices, annual mileage accumulation, and valuation
of avoided fuel consumption; inputs defining the estimated efficacy and
future cost (accounting for projected future ``learning'' effects) of
included technologies; inputs controlling the maximum pace the
simulation is to ``phase in'' each technology; and inputs further
defining the availability of each technology to specific technology
classes.
    Two of these inputs--the ``phase-in cap'' and the ``phase-in start
year''--apply to the manufacturer's entire estimated production and,
for each technology, define a share of production in each model year
that, once exceeded, will stop the model from further applying that
technology to that manufacturer's fleet in that model year. The
influence of these inputs varies with regulatory stringency and other
model inputs. For example, setting the inputs to allow immediate 100
percent penetration of a technology will not guarantee any application
of the technology if stringency increases are low and the technology is
not at all cost effective. Also, even if these are set to allow only
very slow adoption of a technology, other model aspects and inputs may
nevertheless force more rapid application than these inputs, alone,
would suggest (e.g., because an engine technology propagates quickly
due to sharing across multiple vehicles, or because BEV application
must increase quickly in response to ZEV requirements). For this
analysis, nearly all of these inputs are set at levels that do not
limit the simulation at all.
    As discussed below, for the most advanced engines (advanced
cylinder deactivation, variable compression ratio, variable
turbocharger geometry, and turbocharging with cylinder deactivation),
we have specified phase-in caps and phase-in start years that limit the
pace at which the analysis shows the technology being adopted in

the rulemaking timeframe. For example, this analysis applies a 34-
percent phase-in cap and MY 2019 phase-in start year for advanced
cylinder deactivation (ADEAC), meaning that in MY 2021 (using a MY 2020
fleet, the analysis begins simulating further technology application in
MY 2021), the model will stop adding ADEAC to a manufacturer's MY 2021
fleet once ADEAC reaches more than 68-percent penetration, because 34%
x (2021-2019) = 34% x 2 = 68%.
    We apply phase-in caps and corresponding start years to prevent the
simulation from showing unlikely rates of applying battery-electric
vehicles (BEVs), such as showing that a manufacturer producing very few
BEVs in MY 2020 could plausibly replace every product with a 300- or
400-mile BEV by MY 2025. Also, as discussed in Section III.D.4, we
apply phase-in caps and corresponding start years intended to ensure
that the simulation's plausible application of the highest included
levels of mass reduction (20 and 28.2 percent reductions of vehicle
``glider'' weight) do not, for example, outpace plausible supply of raw
materials and development of entirely new manufacturing facilities.
    These model logical structures and inputs act together to produce
estimates of ways each manufacturer could potentially shift to new
fuel-saving technologies over time, reflecting some measure of
protection against rates of change not reflected in, for example,
technology cost inputs. This does not mean that every modeled solution
would necessarily be economically practicable. Using technology
adoption features like phase-in caps and phase-in start years is one
mechanism that can be used so that the analysis better represents the
potential costs and benefits of technology application in the
rulemaking timeframe.
6. Technology Costs
    DOT estimates present and future costs for fuel-saving technologies
taking into consideration the type of vehicle, or type of engine if
technology costs vary by application. These cost estimates are based on
three main inputs. First, we estimate direct manufacturing costs
(DMCs), or the component and labor costs of producing and assembling
the physical parts and systems, assuming high volume production. DMCs
generally do not include the indirect costs of tools, capital
equipment, financing costs, engineering, sales, administrative support
or return on investment. DOT accounts for these indirect costs via a
scalar markup of direct manufacturing costs (the retail price
equivalent, or RPE). Finally, costs for technologies may change over
time as industry streamlines design and manufacturing processes. To
reflect this, DOT estimates potential cost improvements with learning
effects (LE). The retail cost of equipment in any future year is
estimated to be equal to the product of the DMC, RPE, and LE.
Considering the retail cost of equipment, instead of merely direct
manufacturing costs, is important to account for the real-world price
effects of a technology, as well as market realities.
(a) Direct Manufacturing Costs
    Direct manufacturing costs (DMCs) are the component and assembly
costs of the physical parts and systems that make up a complete
vehicle. The analysis uses agency-sponsored tear-down studies of
vehicles and parts to estimate the DMCs of individual technologies, in
addition to independent tear-down studies, other publications, and
confidential business information. In the simplest cases, the agency-
sponsored studies produce results that confirm third-party industry
estimates and align with confidential information provided by
manufacturers and suppliers. In cases with a large difference between
the tear-down study results and credible independent sources, DOT
scrutinized the study assumptions, and sometimes revised or updated the
analysis accordingly.
    Due to the variety of technologies and their applications, and the
cost and time required to conduct detailed tear-down analyses, the
agency did not sponsor teardown studies for every technology. In
addition, we consider some fuel-saving technologies that are pre-
production or are sold in very small pilot volumes. For those
technologies, DOT could not conduct a tear-down study to assess costs
because the product is not yet in the marketplace for evaluation. In
these cases, DOT relied upon third-party estimates and confidential
information from suppliers and manufacturers; however, there are some
common pitfalls with relying on confidential business information to
estimate costs. The agency and the source may have had incongruent or
incompatible definitions of ``baseline.'' The source may have provided
DMCs at a date many years in the future, and assumed very high
production volumes, important caveats to consider for agency analysis.
In addition, a source, under no contractual obligation to DOT, may
provide incomplete and/or misleading information. In other cases,
intellectual property considerations and strategic business
partnerships may have contributed to a manufacturer's cost information
and could be difficult to account for in the CAFE Model as not all
manufacturers may have access to proprietary technologies at stated
costs. The agency carefully evaluates new information in light of these
common pitfalls, especially regarding emerging technologies.
    While costs for fuel-saving technologies reflect the best estimates
available today, technology cost estimates will likely change in the
future as technologies are deployed and as production is expanded. For
emerging technologies, DOT uses the best information available at the
time of the analysis and will continue to update cost assumptions for
any future analysis. The discussion of each category of technologies in
Section III.D (e.g., engines, transmissions, electrification) and
corresponding TSD Chapter 3 summarizes the specific cost estimates DOT
applied for this analysis.
(b) Indirect Costs (Retail Price Equivalent)
    As discussed above, direct costs represent the cost associated with
acquiring raw materials, fabricating parts, and assembling vehicles
with the various technologies manufacturers are expected to use to meet
future CAFE standards. They include materials, labor, and variable
energy costs required to produce and assemble the vehicle. However,
they do not include overhead costs required to develop and produce the
vehicle, costs incurred by manufacturers or dealers to sell vehicles,
or the profit manufacturers and dealers make from their investments.
All of these items contribute to the price consumers ultimately pay for
the vehicle. These components of retail prices are illustrated in Table
III-3 below.

[GRAPHIC] [TIFF OMITTED] TR02MY22.057

    To estimate the impact of higher vehicle prices on consumers, both
direct and indirect costs must be considered. To estimate total
consumer costs, DOT multiplies direct manufacturing costs by an
indirect cost factor to represent the average price for fuel-saving
technologies at retail.
    Historically, the method most commonly used to estimate indirect
costs of producing a motor vehicle has been the retail price equivalent
(RPE). The RPE markup factor is based on an examination of historical
financial data contained in 10-K reports filed by manufacturers with
the Securities and Exchange Commission (SEC). It represents the ratio
between the retail price of motor vehicles and the direct costs of all
activities that manufacturers engage in.
    Figure III-4 indicates that for more than three decades, the retail
price of motor vehicles has been, on average, roughly 50 percent above
the direct cost expenditures of manufacturers. This ratio has been
remarkably consistent, averaging roughly 1.5 with minor variations from
year to year over this period. At no point has the RPE markup exceeded
1.6 or fallen below 1.4.\139\ During this time frame, the average
annual increase in real direct costs was 2.5 percent, and the average
annual increase in real indirect costs was also 2.5 percent. Figure
III-4 illustrates the historical relationship between retail prices and
direct manufacturing costs.\140\
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    \139\ Based on data from 1972-1997 and 2007. Data were not
available for intervening years, but results for 2007 seem to
indicate no significant change in the historical trend.
    \140\ Rogozhin, A., Gallaher, M., & McManus, W., 2009,
Automobile Industry Retail Price Equivalent and Indirect Cost
Multipliers. Report by RTI International to Office of Transportation
Air Quality. U.S. Environmental Protection Agency, RTI Project
Number 0211577.002.004, February, Research Triangle Park, N.C.
Spinney, B.C., Faigin, B., Bowie, N., & S. Kratzke, 1999, Advanced
Air Bag Systems Cost, Weight, and Lead Time analysis Summary Report,
Contract NO. DTNH22-96-0-12003, Task Orders--001, 003, and 005.
Washington, DC, U.S. Department of Transportation.
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    An RPE of 1.5 does not imply that manufacturers automatically mark
up each vehicle by exactly 50 percent. Rather, it means that, over
time, the competitive marketplace has resulted in pricing structures
that average out to this relationship across the entire industry.
Prices for any individual model may be marked up at a higher or lower
rate depending on market demand. The consumer who buys a popular
vehicle may, in effect, subsidize the installation of a new technology
in a less marketable vehicle. But, on average, over time and across the
vehicle fleet, the retail price paid by consumers has risen by about
$1.50 for each dollar of direct costs incurred by manufacturers.

[GRAPHIC] [TIFF OMITTED] TR02MY22.058

    It is also important to note that direct costs associated with any
specific technology will change over time as some combination of
learning and resource price changes occurs. Resource costs, such as the
price of steel, can fluctuate over time and can experience real long-
term trends in either direction, depending on supply and demand.
However, the normal learning process generally reduces direct
production costs as manufacturers refine production techniques and seek
out less costly parts and materials for increasing production volumes.
By contrast, this learning process does not generally influence
indirect costs. The implied RPE for any given technology would thus be
expected to grow over time as direct costs decline relative to indirect
costs. The RPE for any given year is based on direct costs of
technologies at different stages in their learning cycles, and that may
have different implied RPEs than they did in previous years. The RPE
averages 1.5 across the lifetime of technologies of all ages, with a
lower average in earlier years of a technology's life, and, because of
learning effects on direct costs, a higher average in later years.
    The RPE has been used in all NHTSA safety and most previous CAFE
rulemakings to estimate costs. In 2011, the National Academy of
Sciences (NAS) recommended RPEs of 1.5 for suppliers and 2.0 for in-
house production be used to estimate total costs.\141\ Auto Innovators,
formerly known as the Alliance of Automobile Manufacturers, also
advocated these values as appropriate markup factors for estimating
costs of technology changes.\142\ In their 2015 report, NAS recommended
1.5 as an overall RPE markup.\143\ An RPE of 2.0 has also been adopted
by a coalition of environmental and research groups (NESCCAF, ICCT,
Southwest Research Institute, and TIAX-LLC) in a report on reducing
heavy truck emissions, and 2.0 is recommended by the U.S. Department of
Energy for estimating the cost of hybrid-electric and automotive fuel
cell costs (see Vyas et al. (2000) in Table III-4 below). Table III-4
below also lists other estimates of the RPE. Note that all RPE
estimates vary between 1.4 and 2.0, with most in the 1.4 to 1.7 range.
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    \141\ Effectiveness and Impact of Corporate Average Fuel Economy
Standards, Washington, DC--The National Academies Press; NRC, 2011.
    \142\ Communication from Chris Nevers (Auto Innovators) to
Christopher Lieske (EPA) and James Tamm (NHTSA), http://www.regulations.gov Docket ID Nos. NHTSA-2018-0067; EPA-HQ-OAR-2018-
0283, p .143.
    \143\ National Research Council 2015. Cost, Effectiveness, and
Deployment of Fuel Economy Technologies for Light Duty Vehicles.
Washington, DC: The National Academies Press. https://doi.org/10.17226/21744 (hereafter, ``2015 NAS Report''). (Accessed: February
16, 2022)

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[GRAPHIC] [TIFF OMITTED] TR02MY22.059

    The RPE has thus enjoyed widespread use and acceptance by a variety
of governmental, academic, and industry organizations.
---------------------------------------------------------------------------

    \144\ Duleep, K.G. ``2008 Analysis of Technology Cost and Retail
Price.'' Presentation to Committee on Assessment of Technologies for
Improving Light Duty Vehicle Fuel Economy, January 25, Detroit, MI.;
Jack Faucett Associates, September 4, 1985. Update of EPA's Motor
Vehicle Emission Control Equipment Retail Price Equivalent (RPE)
Calculation Formula. Chevy Chase, MD--Jack Faucett Associates;
McKinsey & Company, October 2003. Preface to the Auto Sector Cases.
New Horizons--Multinational Company Investment in Developing
Economies, San Francisco, CA.; NRC (National Research Council),
2002. Effectiveness and Impact of Corporate Average Fuel Economy
Standards, Washington, DC--The National Academies Press; NRC, 2011.
Assessment of Fuel Economy Technologies for Light Duty Vehicles.
Washington, DC--The National Academies Press; Cost, Effectiveness,
and Deployment of Fuel Economy Technologies in Light Duty Vehicles.
Washington, DC--The National Academies Press, 2015; Sierra Research,
Inc., November 21, 2007, Study of Industry-Average Mark-Up Factors
used to Estimate Changes in Retail Price Equivalent (RPE) for
Automotive Fuel Economy and Emissions Control Systems, Sacramento,
CA--Sierra Research, Inc.; Vyas, A. Santini, D., & Cuenca, R. 2000.
Comparison of Indirect Cost Multipliers for Vehicle Manufacturing.
Center for Transportation Research, Argonne National Laboratory,
April. Argonne, Ill.
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    In past rulemakings, a second type of indirect cost multiplier has
also been examined. Known as the ``Indirect Cost Multiplier'' (ICM)
approach, ICMs were first examined alongside the RPE approach in the
2010 rulemaking regarding standards for MYs 2012-2016. Both methods
have been examined in subsequent rulemakings.
    Consistent with the 2020 final rule, we continue to employ the RPE
approach to account for indirect manufacturing costs. The RPE accounts
for indirect costs like engineering, sales, and administrative support,
as well as other overhead costs, business expenses, warranty costs, and
return on capital considerations. A detailed discussion of indirect
cost methods and the basis for our use of the RPE to reflect these
costs is available in the FRIA for the 2020 final rule.\145\
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    \145\ FRIA, The Safer Affordable Fuel-Efficient (SAFE) Vehicles
Rule for Model Year 2021-2026 Passenger Cars and Light Trucks,
USDOT, EPA, March 2020, at pp. 354-76.
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    The Consumer Federation of America (CFA) noted that the inputs we
use for indirect costs produce less optimistic results than those used
by EPA. They cite these differing results as evidence that our analysis
should use the EPA values. CFA states that, ``EPA's benefit cost ratios
are much higher affirming that their analysis is more appropriate.''
\146\ CFA provided no new data or discussion to justify a conclusion
that their preferred values are justified empirically, and NHTSA
continues to believe that an RPE of 1.5 is the most justified by
empirical evidence and research, without regard to the outcomes that a
different RPE would produce. We have provided a full description of the
basis for choosing the indirect cost values that we use in Chapter
2.6.2 of the TSD accompanying this final rule, as well as in the FRIA
accompanying the 2020 final rule. In addition, we note that the RPE
value of 1.5 was also used by EPA in its regulatory impact analysis to
calculate RPE-inclusive vehicle manufacturer costs.\147\
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    \146\ CFA, Docket No. NHTSA-2021-0053-1535, at p. 5.
    \147\ FRIA, Revised 2023 and Later Model Year Light-Duty Vehicle
GHG Emissions Standards: Regulatory Impact Analysis, US EPA,
December 2021, at pp. 4-8.
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(c) Stranded Capital Costs
    The idea behind stranded capital is that manufacturers amortize
research, development, and tooling expenses over many years, especially
for engines and transmissions. The traditional production life-cycles
for transmissions and engines have been a decade or longer. If a
manufacturer launches or updates a product with fuel-saving technology,
and then later replaces that technology with an unrelated or different
fuel-saving technology before the equipment and research and
development investments have been fully paid off, there will be
unrecouped, or stranded, capital costs. Quantifying stranded capital
costs accounts for such lost investments.
    As DOT has observed previously, manufacturers may be shifting their
investment strategies in ways that may alter how stranded capital could
be considered. For example, some suppliers sell similar transmissions
to multiple manufacturers. Such arrangements allow manufacturers to
share in capital expenditures or amortize expenses more quickly.
Manufacturers share parts on vehicles around the globe, achieving
greater scale and greatly affecting tooling strategies and costs.
    As a proxy for stranded capital in recent CAFE analyses, the CAFE
Model has accounted for platform and engine sharing and includes
redesign and refresh cycles for significant and less significant
vehicle updates. This analysis continues to rely on the CAFE Model's
explicit year-by-year accounting for estimated refresh and redesign
cycles, and shared vehicle platforms and engines, to moderate the
cadence of technology adoption and thereby limit the implied occurrence
of stranded capital and the need to account for it explicitly. In
addition, confining some manufacturers to specific advanced technology
pathways through technology adoption features acts as a proxy to
indirectly account for stranded capital. Adoption features specific to
each technology, if applied on a manufacturer-by-manufacturer basis,
are

discussed in each technology section. The agency will monitor these
trends to assess the role of stranded capital moving forward.
(d) Cost Learning