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 proposal
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),\49\ 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.
---------------------------------------------------------------------------

    \49\ 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.

[GRAPHIC] [TIFF OMITTED] TP03SE21.038

    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 MYs. 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 MYs. 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 this data is collected and assigned
in the Market Data file, see TSD Chapter <IP_ADDRESS>.
---------------------------------------------------------------------------

    \50\ Tesla does not have internal combustion engines, or multi-
speed transmissions, even though 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 model year 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 model
year 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
proposing 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 model year 2023, but has
treated all other manufacturers as unwilling to do so beyond model year
2023.
    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.
    We seek comment on whether this expectation is appropriate, or
whether some other amount of time should be used. If commenters believe
a different amount of time should be used for the payback assumption,
it would be most helpful if commenters could define the amount of time,
provide an explanation of why that amount of time is preferable,
provide any data or information on which the amount of time is based,
and provide any discussion of how changing this assumption would
interact with other elements in the analysis.
    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 fuel
economy 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,
within the low-emission vehicle (LEV) regulation,\51\ and has since
expanded to include eleven other states.\52\ 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,\53\ but it is important to note
that not all Section 177 states have adopted the ZEV program
component.\54\ 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.
---------------------------------------------------------------------------

    \51\ California Air Resource Board (CARB), Zero-Emission Vehicle
Program. California Air Resources Board. Accessed April 12, 2021.
https://ww2.arb.ca.gov/our-work/programs/zero-emission-vehicle-program/about.
    \52\ At the time of writing, the Section 177 states that have
adopted the ZEV program are Colorado, Connecticut, Maine, Maryland,
Massachusetts, New Jersey, New York, Oregon, Rhode Island, Vermont,
and Washington. See Vermont Department of Environmental
Conservation, Zero Emission Vehicles. Accessed April 12, 2021.
https://dec.vermont.gov/air-quality/mobile-sources/
zev#:~:text=To%20date%2C%2012%20states%20have,ZEVs%20over%20the%20nex
t%20decade.
    \53\ Section 177 of the Clean Air Act allows other states to
adopt California's air quality standards.
    \54\ At the time of writing, Delaware and Pennsylvania are the
two states that have adopted the LEV standards, but not the ZEV
portion.
---------------------------------------------------------------------------

    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 4 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.\55\ The maximum PHEV credit amount available per
vehicle is 1.10.\56\ Note however that CARB only allows intermediate-
volume manufacturers to meet their ZEV credit requirements through PHEV
production.\57\
---------------------------------------------------------------------------

    \55\ US06 is one of the drive cycles used to test fuel economy
and all-electric range, specifically for the simulation of
aggressive driving. See Dynamometer Drive Schedules [verbar] Vehicle
and Fuel Emissions Testing [verbar] U.S. EPA for more information,
as well as Section III.C.4 and Section III.D.3.d).
    \56\ 13 CCR 1962.2(c)(3).
    \57\ 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 model year 2019 National Vehicle Population Profile (NVPP)
from IHS Markit--Polk to calculate these percentages.\58\ 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. DOT
may make use of future Polk data in updating the analysis for the final
rule and may include other states that join the ZEV program after the
publication of this proposal, if necessary.
---------------------------------------------------------------------------

    \58\ National Vehicle Population Profile (NVPP) 2020, IHS
Markit--Polk. At the time of the analysis, model year 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 model
year 2016, from the 2017 version of the NVPP.\59\ 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.
---------------------------------------------------------------------------

    \59\ 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% in 2020
and ramps up in increments to 22% by 2025.\60\ Note that the
requirements do not currently change after 2025.\61\
---------------------------------------------------------------------------

    \60\ See 13 CCR 1962.2(b). The percentage credit requirements
are as follows: 9.5% in 2020, 12% in 2021, 14.5% in 2022, 17% in
2023, 19.5% in 2024, and 22% in 2025 and onward.
    \61\ 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% 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.\62\ CARB advised that for these manufacturers,
the CAFE Model should still project that each manufacturer meet
approximately 80% 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% of their ZEV requirements on
their own, without purchasing ZEV credits from other manufacturers.\63\
---------------------------------------------------------------------------

    \62\ 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.
    \63\ The following manufacturers were assumed to meet 100% ZEV
compliance: Ford, General Motors, Hyundai, Kia, Jaguar Land Rover,
and Volkswagen Automotive. Tesla was also assumed to meet 100% 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.\64\ 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.
---------------------------------------------------------------------------

    \64\ 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.\65\
---------------------------------------------------------------------------

    \65\ 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.\66\ 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. DOT may reassign some manufacturer's ZEV programs in
the analysis fleet for the final rule based on stakeholder comments or
other public information releases that occur in time for the final rule
analysis.
---------------------------------------------------------------------------

    \66\ 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.\67\ 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.
---------------------------------------------------------------------------

    \67\ BEV300s are 300-mile range battery-electric vehicles. See
Section III.D.3.b) for further information regarding electrification
fleet assignments.
---------------------------------------------------------------------------

    BEVs, especially BEVs with smaller battery packs and less range,
are less likely to meet all the performance needs of traditional pickup
truck owners today. However, new markets for BEVs may emerge,
potentially 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. Comments from
manufacturers are solicited on this issue.
    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 the 2020 model year
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 model year 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.\68\ 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.
---------------------------------------------------------------------------

    \68\ 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% 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.\69\
---------------------------------------------------------------------------

    \69\ 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,\70\ 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),\71\ to determine how the individual components
interact.
---------------------------------------------------------------------------

    \70\ 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.
    \71\ 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'' or ``HWFET''), 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.\72\ 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.
---------------------------------------------------------------------------

    \72\ 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. The next sections discuss the details of the
technology effectiveness analysis input specifications and assumptions.
NHTSA seeks comment on the following discussion.
(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.\73\ These inputs consist of engine technologies, transmission
technologies, powertrain electrification, lightweighting, 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 1.1 million individual
vehicle technology combination models. For additional discussion on the
full vehicle modeling used in this analysis see TSD Chapter 2.
---------------------------------------------------------------------------

    \73\ 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, port fuel injection (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.\74\
---------------------------------------------------------------------------

    \74\ 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.\75\ 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.\76\ For BEVs Autonomie simulates
vehicles per similar procedures and guidelines as prescribed in SAE
J1634.\77\
---------------------------------------------------------------------------

    \75\ 40 CFR part 600.
    \76\ 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.
    \77\ SAE J1634. ``Battery Electric Vehicle Energy Consumption
and Range Test Procedure.'' July 12, 2017.
---------------------------------------------------------------------------

(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, often 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 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 42 percent compared to the 1978 model year, and
pickup trucks have improved by 48 percent.\78\ 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
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.
---------------------------------------------------------------------------

    \78\ ``The 2020 EPA Automotive Trends Report, Greenhouse Gas
Emissions, Fuel Economy, and Technology since 1975,'' EPA-420-R-21-
003, January 2021 [hereinafter 2020 EPA Automotive Trends Report].
---------------------------------------------------------------------------

    For this rulemaking analysis, DOT analyzed technology pathways
manufacturers could use for compliance that attempt to maintain vehicle
attributes, utility, and performance. Using this approach allows DOT 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 that trade off with fuel economy.
    To confirm minimal differences in performance metrics across
regulatory alternatives, DOT 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. This analysis identified that the
analysis fleet under no action standards in MY 2029 had a 0.77 percent
worse 0-60 mph acceleration time than under the preferred alternative,
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.
(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. DOT
combines 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 was 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. DOT organizes 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 ; .''
\79\
---------------------------------------------------------------------------

    \79\ 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,\80\ and adding PHEV20, resulting a tech key that looks like
this: PHEV20; ROLL10; AERO20; MR1; EPS; LDB.
---------------------------------------------------------------------------

    \80\ 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 MY 2020 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 model year 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 Agreement'', 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; and 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%
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),
DOT has 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% 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% penetration, because 34% x (2021-2019) = 34% x 2
= 68%.
    This analysis also applies phase-in caps and corresponding start
years to prevent the simulation from showing inconceivable 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, this analysis applies 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% 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, direct manufacturing costs (DMCs), or the
component and labor costs of producing and assembling the physical
parts and systems, are estimated 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. Absent a
Government mandate, motor vehicle manufacturers will not undertake
expensive development and production efforts to implement technologies
without realistic prospects of consumers being willing to pay enough
for such technology to allow for the manufacturers to recover their
investment.
(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 used 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 produced results that confirmed third-party industry
estimates and aligned with confidential information provided by
manufacturers and suppliers. In cases with a large difference between
the tear-down study results and credible independent sources, 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, some fuel-saving technologies were considered 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] TP03SE21.039

    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.\81\ 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.\82\
---------------------------------------------------------------------------

    \81\ 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.
    \82\ 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, NC.
    Spinney, B.C., Faigin, B., Bowie, N., & St. 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.
---------------------------------------------------------------------------

    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] TP03SE21.040

    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 recommended RPEs of 1.5 for suppliers and 2.0 for in-house
production be used to estimate total costs.\83\ The Alliance of
Automobile Manufacturers also advocates these values as appropriate
markup factors for estimating costs of technology changes.\84\ In their
2015 report, the National Academy of Sciences recommend 1.5 as an
overall RPE markup.\85\ An RPE of 2.0 has also been adopted by a
coalition of environmental and research groups (Northeast States Center
for a Clean Air Future (NESCCAF), International Council on Clean
Transportation (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.
---------------------------------------------------------------------------

    \83\ Effectiveness and Impact of Corporate Average Fuel Economy
Standards, Washington, DC--The National Academies Press; NRC, 2011.
    \84\ Communication from Chris Nevers (Alliance) 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.
    \85\ 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 [hereinafter 2015 NAS report].
---------------------------------------------------------------------------

    Table III-4--Alternate Estimates of the RPE \86\
---------------------------------------------------------------------------

    \86\ 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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[GRAPHIC] [TIFF OMITTED] TP03SE21.041

    The RPE has thus enjoyed widespread use and acceptance by a variety
of governmental, academic, and industry organizations.
    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 (75 FR 25324, May
7, 2010). 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 Final Regulatory Impact Analysis (FRIA) for
the 2020 final rule.\87\
---------------------------------------------------------------------------

    \87\ Final Regulatory Impact Analysis, The Safer Affordable
Fuel-Efficient (SAFE) Vehicles Rule for Model Year 2021-2026
Passenger Cars and Light Trucks, USDOT, EPA, March 2020, at 354-76.
---------------------------------------------------------------------------

(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
    Manufacturers make improvements to production processes over time,
which often result in lower costs. ``Cost learning'' reflects the
effect of experience and volume on the cost of production, which
generally results in better utilization of resources, leading to higher
and more efficient production. As manufacturers gain experience through
production, they refine production techniques, raw material and
component sources, and assembly methods to maximize efficiency and
reduce production costs. Typically, a representation of this cost
learning, or learning curves, reflects initial learning rates that are
relatively high, followed by slower learning as additional improvements
are made and production efficiency peaks. This eventually produces an
asymptotic shape to the learning curve, as small percent decreases are
applied to gradually declining cost levels. These learning curve
estimates are applied to various technologies that are used to meet
CAFE standards.
    We estimate cost learning by considering methods established by
T.P. Wright and later expanded upon by J.R. Crawford.88 89
Wright, examining aircraft production, found that every doubling of
cumulative production of airplanes resulted in decreasing labor hours
at a fixed percentage. This fixed percentage is commonly referred to as
the progress rate or progress ratio, where a lower rate implies faster
learning as cumulative

production increases. J.R. Crawford expanded upon Wright's learning
curve theory to develop a single unit cost model, that estimates the
cost of the nth unit produced given the following information is known:
(1) Cost to produce the first unit; (2) cumulative production of n
units; and (3) the progress ratio.
---------------------------------------------------------------------------

    \88\ Wright, T.P., Factors Affecting the Cost of Airplanes.
Journal of Aeronautical Sciences, Vol. 3 (1936), at 124-25.
Available at http://www.uvm.edu/pdodds/research/papers/others/1936/wright1936a.pdf.
    \89\ Crawford, J.R., Learning Curve, Ship Curve, Ratios, Related
Data, Burbank, California-Lockheed Aircraft Corporation (1944).
---------------------------------------------------------------------------

    As pictured in Figure III-5, Wright's learning curve shows the
first unit is produced at a cost of $1,000. Initially cost per unit
falls rapidly for each successive unit produced. However, as production
continues, cost falls more gradually at a decreasing rate. For each
doubling of cumulative production at any level, cost per unit declines
20 percent, so that 80 percent of cost is retained. The CAFE Model uses
the basic approach by Wright, where cost reduction is estimated by
applying a fixed percentage to the projected cumulative production of a
given fuel economy technology.
[GRAPHIC] [TIFF OMITTED] TP03SE21.042

    The analysis accounts for learning effects with model year-based
cost learning forecasts for each technology that reduces direct
manufacturing costs over time. We evaluate the historical use of
technologies, and reviews industry forecasts to estimate future volumes
to develop the model year-based technology cost learning curves.
    The following section discusses the development of model year-based
cost learning forecasts for this analysis, including how the approach
has evolved from the 2012 rulemaking for MY 2017-2025 vehicles, and how
the progress ratios were developed for different technologies
considered in the analysis. Finally, we discuss how these learning
effects are applied in the CAFE Model.
(1) Time Versus Volume-Based Learning
    For the 2012 joint CAFE and GHG rulemaking, DOT developed learning
curves as a function of vehicle model year.\90\ Although the concept of
this methodology is derived from Wright's cumulative production volume-
based learning curve, its application for CAFE technologies was more of
a function of time. More than a dozen learning curve schedules were
developed, varying between fast and slow learning, and assigned to each
technology corresponding to its level of complexity and maturity. The
schedules were applied to the base year of direct manufacturing cost
and incorporate a percentage of cost reduction by model year, declining
at a decreasing rate through the technology's production life. Some
newer technologies experience 20 percent cost reductions for
introductory model years, while mature or less complex technologies
experience 0-3 percent cost reductions over a few years.
---------------------------------------------------------------------------

    \90\ 77 FR 62624 (Oct. 15, 2012).
---------------------------------------------------------------------------

    In their 2015 report to Congress, the National Academy of Sciences
(NAS) recommended NHTSA should ``continue to conduct and review
empirical evidence for the cost reductions that occur in the automobile
industry with volume, especially for large-volume technologies that
will be relied on to meet the CAFE/GHG standards.'' \91\
---------------------------------------------------------------------------

    \91\ 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.
---------------------------------------------------------------------------

    In response, we incorporated statically projected cumulative volume
production data of fuel economy-improving technologies, representing an
improvement over the previously used time-based method. Dynamic
projections of cumulative production are not feasible with current CAFE
Model capabilities, so one set of projected cumulative production data
for most vehicle technologies was developed for the purpose of
determining cost impact. We obtained historical cumulative production
data for many technologies produced and/or sold in the U.S. to
establish a starting point for learning schedules. Groups of similar
technologies or technologies of similar complexity may share identical
learning schedules.
    The slope of the learning curve, which determines the rate at which
cost reductions occur, has been estimated using research from an
extensive literature review and automotive cost tear-down reports (see
below). The slope of the learning curve is derived from the progress
ratio of manufacturing automotive and other mobile source technologies.
(2) Deriving the Progress Ratio Used in This Analysis
    Learning curves vary among different types of manufactured
products. Progress ratios can range from 70 to 100

percent, where 100 percent indicates no learning can be achieved.\92\
Learning effects tend to be greatest in operations where workers often
touch the product, while effects are less substantial in operations
consisting of more automated processes. As automotive manufacturing
plant processes become increasingly automated, a progress ratio towards
the higher end would seem more suitable. We incorporated findings from
automotive cost-teardown studies with EPA's 2015 literature review of
learning-related studies to estimate a progress ratio used to determine
learning schedules of fuel economy-improving technologies.
---------------------------------------------------------------------------

    \92\ Martin, J., ``What is a Learning Curve?'' Management and
Accounting Web, University of South Florida, available at: https://www.maaw.info/LearningCurveSummary.htm.
---------------------------------------------------------------------------

    EPA's literature review examined and summarized 20 studies related
to learning in manufacturing industries and mobile source
manufacturing.\93\ The studies focused on many industries, including
motor vehicles, ships, aviation, semiconductors, and environmental
energy. Based on several criteria, EPA selected five studies providing
quantitative analysis from the mobile source sector (progress ratio
estimates from each study are summarized in Table III-5, below).
Further, those studies expand on Wright's learning curve function by
using cumulative output as a predictor variable, and unit cost as the
response variable. As a result, EPA determined a best estimate of 84
percent as the progress ratio in mobile source industries. However, of
those five studies, EPA at the time placed less weight on the Epple et
al. (1991) study, because of a disruption in learning due to incomplete
knowledge transfer from the first shift to introduction of a second
shift at a North American truck plant. While learning may have
decelerated immediately after adding a second shift, we note that unit
costs continued to fall as the organization gained experience operating
with both shifts. We recognize that disruptions are an essential part
of the learning process and should not, in and of themselves, be
discredited. For this reason, the analysis uses a re-estimated average
progress ratio of 85 percent from those five studies (equally
weighted).
---------------------------------------------------------------------------

    \93\ Cost Reduction through Learning in Manufacturing Industries
and in the Manufacture of Mobile Sources, United States
Environmental Protection Agency (2015). Prepared by ICF
International and available at https://19january2017snapshot.epa.gov/sites/production/files/2016-11/documents/420r16018.pdf.
[GRAPHIC] [TIFF OMITTED] TP03SE21.043

    In addition to EPA's literature review, this progress ratio
estimate was informed based on findings from automotive cost-teardown
studies. NHTSA routinely performs evaluations of costs of previously
issued Federal Motor Vehicle Safety Standards (FMVSS) for new motor
vehicles and equipment. NHTSA engages contractors to perform detailed
engineering ``tear-down'' analyses for representative samples of
vehicles, to estimate how much specific FMVSS add to the weight and
retail price of a vehicle. As part of the effort, the agency examines
cost and production volume for automotive safety technologies. In
particular, we estimated costs from multiple cost tear-down studies for
technologies with actual production data from the Cost and weight added
by the Federal Motor Vehicle Safety Standards for MY 1968-2012
passenger cars and LTVs (2017).\99\
---------------------------------------------------------------------------

    \94\ Argote, L., Epple, D., Rao, R. D., & Murphy, K., The
acquisition and depreciation of knowledge in a manufacturing
organization--Turnover and plant productivity, Working paper,
Graduate School of Industrial Administration, Carnegie Mellon
University (1997).
    \95\ Benkard, C. L., Learning and Forgetting--The Dynamics of
Aircraft Production, The American Economic Review, Vol. 90(4), at
1034-54 (2000).
    \96\ Epple, D., Argote, L., & Devadas, R., Organizational
Learning Curves--A Method for Investigating Intra-Plant Transfer of
Knowledge Acquired through Learning by Doing, Organization Science,
Vol. 2(1), at 58-70 (1991).
    \97\ Epple, D., Argote, L., & Murphy, K., An Empirical
Investigation of the Microstructure of Knowledge Acquisition and
Transfer through Learning by Doing, Operations Research, Vol. 44(1),
at 77-86 (1996).
    \98\ Levitt, S. D., List, J. A., & Syverson, C., Toward an
Understanding of Learning by Doing--Evidence from an Automobile
Assembly Plant, Journal of Political Economy, Vol. 121 (4), at 643-
81 (2013).
    \99\ Simons, J. F., Cost and weight added by the Federal Motor
Vehicle Safety Standards for MY 1968-2012 Passenger Cars and LTVs
(Report No. DOT HS 812 354). Washington, DC--National Highway
Traffic Safety Administration (November 2017), at 30-33.
---------------------------------------------------------------------------

    We chose five vehicle safety technologies with sufficient data to
estimate progress ratios of each, because these technologies are large-
volume technologies and are used by almost all vehicle manufacturers.
Table III-6 includes these five technologies and yields an average
progress rate of 92 percent.

[GRAPHIC] [TIFF OMITTED] TP03SE21.044

    For the final progress ratio used in the CAFE Model, the five
progress rates from EPA's literature review and five progress rates
from NHTSA's evaluation of automotive safety technologies results were
averaged. This resulted in an average progress rate of approximately 89
percent. We placed equal weight on progress ratios from all 10 sources.
More specifically, we placed equal weight on the Epple et al. (1991)
study, because disruptions have more recently been recognized as an
essential part in the learning process, especially in an effort to
increase the rate of output.
(3) Obtaining Appropriate Baseline Years for Direct Manufacturing Costs
    DOT obtained direct manufacturing costs for each fuel economy-
improving technology from various sources, as discussed above. To
establish a consistent basis for direct manufacturing costs in the
rulemaking analysis, we adjusted each technology cost to MY 2018
dollars. For each technology, the DMC is associated with a specific
model year, and sometimes a specific production volume, or cumulative
production volume. The base model year is established as the MY in
which direct manufacturing costs were assessed (with learning factor of
1.00). With the aforementioned data on cumulative production volume for
each technology and the assumption of a 0.89 progress ratio for all
automotive technologies, we can solve for an implied cost for the first
unit produced. For some technologies, we used modestly different
progress ratios to match detailed cost projections if available from
another source (for instance, batteries for plug-in hybrids and battery
electric vehicles).
    This approach produces reasonable estimates for technologies
already in production, and some additional steps are required to set
appropriate learning rates for technologies not yet in production.
Specifically, for technologies not yet in production in MY 2017, the
cumulative production volume in MY 2017 is zero, because manufacturers
have not yet produced the technologies. For pre-production cost
estimates in previous CAFE rulemakings, we often relied on confidential
business information sources to predict future costs. Many sources for
pre-production cost estimates include significant learning effects,
often providing cost estimates assuming high volume production, and
often for a timeframe late in the first production generation or early
in the second generation of the technology. Rapid doubling and re-
doubling of a low cumulative volume base with Wright's learning curves
can provide unrealistic cost estimates. In addition, direct
manufacturing cost projections can vary depending on the initial
production volume assumed. Accordingly, we carefully examined direct
costs with learning, and made adjustments to the starting point for
those technologies on the learning curve to better align with the
assumptions used for the initial direct cost estimate.
(4) Cost Learning Applied in the CAFE Model
    For this analysis, we applied learning effects to the incremental
cost over the null technology state on the applicable technology tree.
After this step, we calculated year-by-year incremental costs over
preceding technologies on the tech tree to create the CAFE Model
inputs.\100\ The shift from incremental cost accounting to absolute
cost accounting in recent CAFE analyses made cost inputs more
transparently relatable to detailed model output, and relevant to this
discussion, made it easier to apply learning curves in the course of
developing inputs to the CAFE Model.
---------------------------------------------------------------------------

    \100\ These costs are located in the CAFE Model Technologies
file.
---------------------------------------------------------------------------

    We grouped certain technologies, such as advanced engines, advanced
transmissions, and non-battery electric components and assigned them to
the same learning schedule. While these grouped technologies differ in
operating characteristics and design, we chose to group them based on
their complexity, technology integration, and economies of scale across
manufacturers. The low volume of certain advanced technologies, such as
hybrid and electric technologies, poses a significant issue for
suppliers and prevents them from producing components needed for
advanced transmissions and other technologies at more efficient high
scale production. The technology groupings consider market
availability, complexity of technology integration, and production
volume of the technologies that can be implemented by manufacturers and
suppliers. For example, technologies like ADEAC and VCR are grouped
together; these technologies were not in production or were only in
limited introduction in MY 2017 and are planned to be introduced in
limited production by a few manufacturers. The details of these
technologies are discussed in Section III.D.
    In addition, we expanded model inputs to extend the explicit
simulation of technology application through MY 2050. Accordingly, we
updated the learning curves for each technology group to cover MYs
through 2050. For MYs 2017-2032, we expect incremental improvements in
all technologies, particularly in electrification technologies because
of increased production volumes, labor efficiency, improved
manufacturing methods, specialization, network building, and other
factors. While these and other factors contribute to continual cost
learning, we believe that many fuel economy-improving technologies
considered in this rule will approach a flat learning level by the
early 2030s. Specifically, older and less complex internal combustion
engine technologies and transmissions will reach a flat learning curve
sooner when compared to electrification technologies, which have more
opportunity for improvement. For batteries and non-battery
electrification components, we estimated a steeper learning curve that

will gradually flatten after MY 2040. For a more detailed discussion of
the electrification learning curves, see Section III.D.3.
    Each technology in the CAFE Model is assigned a learning schedule
developed from the methodology explained previously. For example, the
following chart shows learning rates for several technologies
applicable to midsize sedans, demonstrating that while we estimate that
such learning effects have already been almost entirely realized for
engine turbocharging (a technology that has been in production for many
years), we estimate that significant opportunities to reduce the cost
of the greatest levels of mass reduction (e.g., MR5) remain, and even
greater opportunities remain to reduce the cost of batteries for HEVs,
PHEVs, BEVs. In fact, for certain advanced technologies, we determined
that the results predicted by the standard learning curves progress
ratio was not realistic, based on unusual market price and production
relationships. For these technologies, we developed specific learning
estimates that may diverge from the 0.89 progress rate. As shown in
Figure III-6, these technologies include: turbocharging and downsizing
level 1 (TURBO1), variable turbo geometry electric (VTGE), aerodynamic
drag reduction by 15 percent (AERO15), mass reduction level 5 (MR5), 20
percent improvement in low-rolling resistance tire technology (ROLL20)
over the baseline, and battery integrated starter/generator (BISG).
[GRAPHIC] [TIFF OMITTED] TP03SE21.045

(e) Cost Accounting
    To facilitate specification of detailed model inputs and review of
detailed model outputs, the CAFE Model continues to use absolute cost
inputs relative to a known base component cost, such that the estimated
cost of each technology is specified relative to a common reference
point for the relevant technology pathway. For example, the cost of a
7-speed transmission is specified relative to a 5-speed transmission,
as is the cost of every other transmission technology. Conversely, in
some earlier versions of the CAFE Model, incremental cost inputs were
estimated relative to the technology immediately preceding on the
relevant technology pathway. For our 7-speed transmission example, the
incremental cost would be relative to a 6-speed transmission. This
change in the structure of cost inputs does not, by itself, change
model results, but it does make the connection between these inputs and
corresponding outputs more transparent. The CAFE Model Documentation
accompanying our analysis presents details of the structure for model
cost inputs.\101\ The individual technology sections in Section III.D
provide a detailed discussion of cost accounting for each technology.
---------------------------------------------------------------------------

    \101\ CAFE Model Documentation, S4.7.
---------------------------------------------------------------------------

7. Manufacturer's Credit Compliance Positions
    This proposed rule involves a variety of provisions regarding
``credits'' and other compliance flexibilities. Some regulatory
provisions allow a manufacturer to earn ``credits'' that will

be counted toward a vehicle's rated CO2 emissions level, or
toward a fleet's rated average CO2 or CAFE level, without
reference to required levels for these average levels of performance.
Such flexibilities effectively modify emissions and fuel economy test
procedures or methods for calculating fleets' CAFE and average
CO2 levels. Other provisions (for CAFE, statutory
provisions) allow manufacturers to earn credits by achieving CAFE or
average CO2 levels beyond required levels; these provisions
may hence more appropriately be termed ``compliance credits.'' We
described in the 2020 final rule how the CAFE Model simulates these
compliance credit provisions for both the CAFE program and for EPA's
CO2 standards.\102\ For this analysis, we modeled the no-
action and action alternatives as a set of CAFE standards in place
simultaneously with EPA baseline (i.e., 2020 final) CO2
standards, related CARB agreements with five manufacturers, and ZEV
mandates in place in California and some other states. The modeling of
CO2 standards and standard-like contractual obligations
includes our representation of applicable credit provisions.
---------------------------------------------------------------------------

    \102\ See 85 FR 24174, 24303 (April 30, 2020).
---------------------------------------------------------------------------

    EPCA has long provided that, by exceeding the CAFE standard
applicable to a given fleet in a given model year, a manufacturer may
earn corresponding ``credits'' that the same manufacturer may, within
the same regulatory class, apply toward compliance in a different model
year. EISA amended these provisions by providing that manufacturers
may, subject to specific statutory limitations, transfer compliance
credits between regulatory classes and trade compliance credits with
other manufacturers. The CAA provides the EPA with broad standard-
setting authority for the CO2 program, with no specific
directives regarding CO2 standards or CO2
compliance credits.
    EPCA also specifies that NHTSA may not consider the availability of
CAFE credits (for transfer, trade, or direct application) toward
compliance with new standards when establishing the standards
themselves.\103\ Therefore, this analysis excludes model years 2024-
2026 from those in which carried-forward or transferred credits can be
applied for the CAFE program.
---------------------------------------------------------------------------

    \103\ 49 U.S.C. 32902(h)(3).
---------------------------------------------------------------------------

    The ``unconstrained'' perspective acknowledges that these
flexibilities exist as part of the program and, while not considered by
NHTSA in setting standards, are nevertheless important to consider when
attempting to estimate the real impact of any alternative. Under the
``unconstrained'' perspective, credits may be earned, transferred, and
applied to deficits in the CAFE program throughout the full range of
model years in the analysis. The Draft Supplemental Environmental
Impact Statement (SEIS) accompanying this proposed rule, like the
corresponding SEIS analysis, presents ``unconstrained'' modeling
results. Also, because the CAA provides no direction regarding
consideration of any CO2 credit provisions, this analysis
includes simulation of carried-forward and transferred CO2
credits in all model years.
    The CAFE Model, therefore, does provide means to simulate
manufacturers' potential application of some compliance credits, and
both the analysis of CO2 standards and the NEPA analysis of
CAFE standards do make use of this aspect of the model. On the other
hand, 49 U.S.C. 32902(h) prevents NHTSA from, in its standard setting
analysis, considering the potential that manufacturers could use
compliance credits in model years for which the agency is establishing
maximum feasible CAFE standards. Further, as discussed below, we also
continue to find it appropriate for the analysis largely to refrain
from simulating two of the mechanisms allowing the use of compliance
credits.
    The CAFE Model's approach to simulating compliance decisions
accounts for the potential to earn and use CAFE credits as provided by
EPCA/EISA. The model similarly accumulates and applies CO2
credits when simulating compliance with EPA's standards. Like past
versions, the current CAFE Model can simulate credit carry-forward
(i.e., banking) between model years and transfers between the passenger
car and light truck fleets but not credit carry-back (i.e., borrowing)
from future model years or trading between manufacturers.
    While NHTSA's ``unconstrained'' evaluation can consider the
potential to carry back compliance credits from later to earlier model
years, past examples of failed attempts to carry back CAFE credits
(e.g., a MY 2014 carry back default leading to a civil penalty payment)
underscore the riskiness of such ``borrowing.'' Recent evidence
indicates manufacturers are disinclined to take such risks, and we find
it reasonable and prudent to refrain from attempting to simulate such
``borrowing'' in rulemaking analysis.
    Like the previous version, the current CAFE Model provides a basis
to specify (in model inputs) CAFE credits available from model years
earlier than those being explicitly simulated. For example, with this
analysis representing model years 2020-2050 explicitly, credits earned
in the model year 2015 are made available for use through the model
year 2020 (given the current five-year limit on carry-forward of
credits). The banked credits are specific to both the model year and
fleet in which they were earned.
    To increase the realism with which the model transitions between
the early model years (MYs 2020-2023) and the later years that are the
subject of this action, we have accounted for the potential that some
manufacturers might trade credits earned prior to 2020 to other
manufacturers. However, the analysis refrains from simulating the
potential that manufacturers might continue to trade credits during and
beyond the model years covered by this action. In 2018 and 2020, the
analysis included idealized cases simulating ``perfect'' (i.e., wholly
unrestricted) trading of CO2 compliance credits by treating
all vehicles as being produced by a single manufacturer. Even for
CO2 compliance credit trading, these scenarios were not
plausible, because it is exceedingly unlikely that some pairs of
manufacturers would trade compliance credits. NHTSA did not include
such cases for CAFE compliance credits, because EPCA provisions (such
as the minimum domestic passenger car standard requirement) make such
scenarios impossible. At this time, we remain concerned that any
realistic simulation of such trading would require assumptions
regarding which specific pairs of manufacturers might trade compliance
credits, and the evidence to date makes it clear that the credit market
is far from fully ``open.''
    We also remain concerned that to set standards based on an analysis
that presumes the use of program flexibilities risks making the
corresponding actions mandatory. Some flexibilities--credit carry-
forward (banking) and transfers between fleets in particular--involve
little risk because they are internal to a manufacturer and known in
advance. As discussed above, credit carry-back involves significant
risk because it amounts to borrowing against future improvements,
standards, and production volume and mix. Similarly, credit trading
also involves significant risk, because the ability of manufacturer A
to acquire credits from manufacturer B depends not just on manufacturer
B actually earning the expected amount of credit, but also on
manufacturer B being willing to trade with manufacturer A, and on
potential interest by other manufacturers. Manufacturers' compliance
plans have

already evidenced cases of compliance credit trades that were planned
and subsequently aborted, reinforcing our judgment that, like credit
banking, credit trading involves too much risk to be included in an
analysis that informs decisions about the stringency of future
standards.
    As discussed in the CAFE Model Documentation, the model's default
logic attempts to maximize credit carry-forward--that is, to ``hold
on'' to credits for as long as possible. If a manufacturer needs to
cover a shortfall that occurs when insufficient opportunities exist to
add technology to achieve compliance with a standard, the model will
apply credits. Otherwise, the manufacturer carries forward credits
until they are about to expire, at which point it will use them before
adding technology that is not considered cost-effective. The model
attempts to use credits that will expire within the next three years as
a means to smooth out technology applications over time to avoid both
compliance shortfalls and high levels of over-compliance that can
result in a surplus of credits. Although it remains impossible
precisely to predict the manufacturer's actual earning and use of
compliance credits, and this aspect of the model may benefit from
future refinement as manufacturers and regulators continue to gain
experience with these provisions, this approach is generally consistent
with manufacturers' observed practices.
    NHTSA introduced the CAFE Public Information Center (PIC) to
provide public access to a range of information regarding the CAFE
program,\104\ including manufacturers' credit balances. However, there
is a data lag in the information presented on the CAFE PIC that may not
capture credit actions across the industry for as much as several
months. Furthermore, CAFE credits that are traded between manufacturers
are adjusted to preserve the gallons saved that each credit
represents.\105\ The adjustment occurs at the time of application
rather than at the time the credits are traded. This means that a
manufacturer who has acquired credits through trade, but has not yet
applied them, may show a credit balance that is either considerably
higher or lower than the real value of the credits when they are
applied. For example, a manufacturer that buys 40 million credits from
Tesla may show a credit balance in excess of 40 million. However, when
those credits are applied, they may be worth only 1/10 as much--making
that manufacturer's true credit balance closer to 4 million than 40
million (e.g., when another manufacturer uses credits acquired from
Tesla, the manufacturer may only be able to offset a 1 mpg compliance
shortfall, even though the credits' ``face value'' suggests the
manufacturer could offset a 10 mpg compliance shortfall).
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    \104\ CAFE Public Information Center, https://one.nhtsa.gov/cafe_pic/cafe_pic_home.htm (last visited May 11, 2021).
    \105\ CO2 credits for EPA's program are denominated
in metric tons of CO2 rather than gram/mile compliance
credits and require no adjustment when traded between manufacturers
or fleets.
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    Specific inputs accounting for manufacturers' accumulated
compliance credits are discussed in TSD Chapter <IP_ADDRESS>.
    In addition to the inclusion of these existing credit banks, the
CAFE Model also updated its treatment of credits in the rulemaking
analysis. EPCA requires that NHTSA set CAFE standards at maximum
feasible levels for each model year without consideration of the
program's credit mechanisms. However, as recent CAFE rulemakings have
evaluated the effects of standards over longer time periods, the early
actions taken by manufacturers required more nuanced representation.
Accordingly, the CAFE Model now provides means to exclude the simulated
application of CAFE compliance credits only from specific model years
for which standards are being set (for this analysis, 2024-2026), while
allowing CAFE credits to be applied in other model years.
    In addition to more rigorous accounting of CAFE and CO2
compliance credits, the model also accounts for air conditioning
efficiency and off-cycle adjustments. NHTSA's program considers those
adjustments in a manufacturer's compliance calculation starting in MY
2017, and specific estimates of each manufacturer's reliance on these
adjustments are discussed above in Section III.C.2.a). Because air
conditioning efficiency and off-cycle adjustments are not credits in
NHTSA's program, but rather adjustments to compliance fuel economy,
they may be included under either a ``standard setting'' or
``unconstrained'' analysis perspective.
    The manner in which the CAFE Model treats the EPA and CAFE A/C
efficiency and off-cycle credit programs is similar, but the model also
accounts for A/C leakage (which is not part of NHTSA's program). When
determining the compliance status of a manufacturer's fleet (in the
case of EPA's program, PC and LT are the only fleet distinctions), the
CAFE Model weighs future compliance actions against the presence of
existing (and expiring) CO2 credits resulting from over-
compliance with earlier years' standards, A/C efficiency credits, A/C
leakage credits, and off-cycle credits.
    The model currently accounts for any off-cycle adjustments
associated with technologies that are included in the set of fuel-
saving technologies explicitly simulated as part of this proposal (for
example, start-stop systems that reduce fuel consumption during idle or
active grille shutters that improve aerodynamic drag at highway speeds)
and accumulates these adjustments up to the cap. As discussed further
in Section III.D.8, this analysis considers that some manufacturers may
apply up to 15.0 g/mi of off-cycle credit by MY 2032. We considered the
potential to model the application of off-cycle technologies
explicitly. However, doing so would require data regarding which
vehicle models already possess these improvements as well as the cost
and expected value of applying them to other models in the future. Such
data are currently too limited to support explicit modeling of these
technologies and adjustments.
    When establishing maximum feasible fuel economy standards, NHTSA is
prohibited from considering the availability of alternatively fueled
vehicles,\106\ and credit provisions related to AFVs that significantly
increase their fuel economy for CAFE compliance purposes. Under the
``standard setting'' perspective, these technologies (pure battery
electric vehicles and fuel cell vehicles \107\) are not available in
the compliance simulation to improve fuel economy. Under the
``unconstrained'' perspective, such as is documented in the SEIS, the
CAFE Model considers these technologies in the same manner as other
available technologies and may apply them if they represent cost-
effective compliance pathways. However, under both perspectives, the
analysis continues to include dedicated AFVs that could be produced in
response to CAFE standards outside the model years for which standards
are being set, or for other reasons (e.g., ZEV mandates, as accounted
for in this analysis).
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    \106\ 49 U.S.C. 32902(h).
    \107\ Dedicated compressed natural gas (CNG) vehicles should
also be excluded in this perspective but are not considered as a
compliance strategy under any perspective in this analysis.
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    EPCA also provides that CAFE levels may, subject to limitations, be
adjusted upward to reflect the sale of flexible fuel vehicles (FFVs).
Because these adjustments ended in model year 2020, this analysis
assumes no manufacturer

will earn FFV credits within the modeling horizon.
    Also, the CAA provides no direction regarding consideration of
alternative fuels, and EPA has provided that manufacturers selling
PHEVs, BEVs, and FCVs may, when calculating fleet average
CO2 levels, ``count'' each unit of production as more than a
single unit. The CAFE Model accounts for these ``multipliers.'' For
example, under EPA's current regulation, when calculating the average
CO2 level achieved by its MY 2019 passenger car fleet, a
manufacturer may treat each 1,000 BEVs as 2,000 BEVs. When calculating
the average level required of this fleet, the manufacturer must use the
actual production volume (in this example, 1,000 units). Similarly, the
manufacturer must use the actual production volume when calculating
compliance credit balances.
    There were no natural gas vehicles in the baseline fleet, and the
analysis did not apply natural gas technology due to cost
effectiveness. The application of a 2.0 multiplier for natural gas
vehicles for MYs 2024-2026 would have no impact on the analysis because
given the state of natural gas vehicle refueling infrastructure, the
cost to equip vehicles with natural gas tanks, the outlook for
petroleum prices, and the outlook for battery prices, we have little
basis to project more than an inconsequential response to this
incentive in the foreseeable future.

D. Technology Pathways, Effectiveness, and Cost

    Vehicle manufacturers meet increasingly more stringent fuel economy
standards by applying increasing levels of fuel-economy-improving
technologies to their vehicles. An appropriate characterization of the
technologies available to manufacturers to meet fuel economy standards
is, therefore, an important input required to assess the levels of
standards that manufacturers can achieve. Like previous CAFE standards
analyses, this proposal considers over 50 fuel-economy-improving
technologies that manufacturers could apply to their MY 2020 fleet of
vehicles to meet proposed levels of CAFE standards in MYs 2024-2026.
The characterization of these technologies, the technology
effectiveness values, and technology cost assumptions build on work
performed by DOT, EPA, the National Academy of Sciences, and other
Federal and state government agencies including the Department of
Energy's Argonne National Laboratory and the California Air Resources
Board.
    After spending approximately a decade refining the technology
pathways, effectiveness, and cost assumptions used in successive CAFE
Model analyses, DOT has developed guiding principles to ensure that the
CAFE Model's simulation of manufacturer compliance pathways results in
impacts that we would reasonably expect to see in the real world. These
guiding principles are as follows:
    Even though the analysis considers over 50 individual technologies,
the fuel economy improvement from any individual technology must be
considered in conjunction with the other fuel-economy-improving
technologies applied to the vehicle. For example, there is an obvious
fuel economy benefit that results from converting a vehicle with a
traditional internal combustion engine to a battery electric vehicle;
however, the benefit of the electrification technology depends on the
other road load reducing technologies (i.e., mass reduction,
aerodynamic, and rolling resistance) on the vehicle.
    Technologies added in combination to a vehicle will not result in a
simply additive fuel economy improvement from each individual
technology. As discussed in Section III.C.4, full vehicle modeling and
simulation provides the required degree of accuracy to project how
different technologies will interact in the vehicle system. For
example, as discussed further in Sections III.D.1 and III.D.3, a
parallel hybrid architecture powertrain improves fuel economy, in part,
by allowing the internal combustion engine to spend more time operating
at efficient engine speed and load conditions. This reduces the
advantage of adding advanced internal combustion engine technologies,
which also improve fuel economy, by broadening the range of speed and
load conditions for the engine to operate at high efficiency. This
redundancy in fuel savings mechanism results in a reduced effectiveness
improvement when the technologies are added to each other.
    The effectiveness of a technology depends on the type of vehicle
the technology is being applied to. For example, applying mass
reduction technology results in varying effectiveness as the absolute
mass reduced is a function of the starting vehicle mass, which varies
across technology classes. See Section III.D.4 for more details.
    The cost and effectiveness values for each technology should be
reasonably representative of what can be achieved across the entire
industry. Each technology model employed in the analysis is designed to
be representative of a wide range of specific technology applications
used in industry. Some vehicle manufacturer's systems may perform
better and cost less than our modeled systems and some may perform
worse and cost more. However, employing this approach will ensure that,
on balance, the analysis captures a reasonable level of costs and
benefits that would result from any manufacturer applying the
technology.
    The baseline for cost and effectiveness values must be identified
before assuming that a cost or effectiveness value could be employed
for any individual technology. For example, as discussed further in
Section III.D.1.d) below, this analysis uses a set of engine map models
that were developed by starting with a small number of baseline engine
configurations, and then, in a very systematic and controlled process,
adding specific well-defined technologies to create a new map for each
unique technology combination.
    The following sections discuss the engine, transmission,
electrification, mass reduction, aerodynamic, tire rolling resistance,
and other vehicle technologies considered in this analysis. Each
section discusses how we define the technology in the CAFE Model,\108\
how we assigned the technology to vehicles in the MY 2020 analysis
fleet used as a starting point for this analysis, any adoption features
applied to the technology so the analysis better represents
manufacturers' real-world decisions, the technology effectiveness
values, and technology cost.
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    \108\ Note, due to the diversity of definitions industry
sometimes employs for technology terms, or in describing the
specific application of technology, the terms defined here may
differ from how the technology is defined in the industry.
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    Please note that the following technology effectiveness sections
provide examples of the range of effectiveness values that a technology
could achieve when applied to the entire vehicle system, in conjunction
with the other fuel-economy-improving technologies already on or also
applied at the same time to the vehicle. To see the incremental
effectiveness values for any particular vehicle moving from one
technology key to a more advanced technology key, see the FE_1 and FE_2
Adjustments files that are integrated in the CAFE Model executable
file. Similarly, the technology costs provided in each section are
examples of absolute costs seen in specific model years (MYs 2020,
2025, and 2030 for most technologies), for specific vehicle classes. To
see all absolute technology costs used in the analysis across all model
years, see the Technologies file.

NHTSA seeks comment on the following discussion.
1. Engine Paths
    For this analysis, the extensive variety of light duty vehicle
internal combustion (IC) engine technologies are classified into
discrete engine technology paths. These paths are used to model the
most representative characteristics, costs, and performance of the
fuel-economy improving technologies most likely available during the
rulemaking time frame, MYs 2024-2026. Due to uncertainties in the cost
and capabilities of emerging technologies, some new and pre- production
technologies are not part of this analysis. We did not include
technologies unlikely to be feasible in the rulemaking timeframe,
technologies unlikely to be compatible with U.S. fuels, or technologies
for which there was not appropriate data available to allow the
simulation of effectiveness across all vehicle technology classes in
this analysis.
    The following sections discuss IC engine technologies considered in
this analysis, general technology categories used by the CAFE Model,
and how the engine technologies are assigned in the MY 2020 analysis
fleet. The following sections also discuss adoption features applicable
to engine technologies, engine technologies' effectiveness when
combined in a full vehicle model, and the engine technologies' costs.
(a) Engine Modeling in the CAFE Model
    DOT models IC engine technologies that manufacturers can use to
improve fuel economy. Some engine technologies can be incorporated into