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

    \328\ Note that we use cost in the CAFE Model, however many
sources also report price. We have tried to use the accurate term
throughout this section, however, note that even within the same
data source, cost and price may be used interchangeably. See Mauler,
L., F. Duffner, W. Zeier and J. Leker. 2021. Battery cost
forecasting: a review of methods and results with an outlook to
2050. Energy and Environmental Science, 4712-4739 (``However,
details on company-specific prices, costs and profit margins are not
publicly available and differences are difficult to assess.[] In
battery literature both terms are frequently used interchangeable, a
phenomenon reported earlier,[ ] which may be explained by different
perspectives on the same value, since the price paid to a battery
manufacturer represents the cost to the manufacturer of the final
product.'').
    \329\ 87 FR 25819.
    \330\ See 24-26 TSD at 286-7 (citing Nelson, Paul A., Ahmed,
Shabbir, Gallagher, Kevin G., and Dees, Dennis W. Modeling the
Performance and Cost of Lithium-Ion Batteries for Electric-Drive
Vehicles, Third Edition (ANL/CSE-19/2). Available at https://publications.anl.gov/anlpubs/2019/03/150624.pdf. (Accessed: May 31,
2023).) (``As detailed in the BatPaC model documentation, the costs
of materials, labor, and capital equipment in the model are based
upon ANL's estimates of 2018 values, `[t]hus, if BatPaC is used to
calculate the current costs of batteries at current production
levels (say 30,000 all-electric (BEV) packs per year) we expect it
to provide good estimates of current battery prices to OEMs.
Estimates done for ten years in the future should be at production
levels of 100,000 to 500,000 units per year, which will result in
lower pack prices because of the assumed increase in the degree of
plant automation.' '').
    \331\ 87 FR 25818.
    \332\ 24-26 TSD at 313.
    \333\ See, e.g., Mauler, L. et al. 2021. Battery Cost
Forecasting: A Review of Methods and Results With an Outlook to
2050. Energy and Environmental Science: pp. 4712-4739.
    \334\ 87 FR 25819-20.
---------------------------------------------------------------------------

    In particular, a 2021 study by Mauler et al., ``Battery cost
forecasting: a review of methods and results with an outlook to 2050,''
referenced above and by commenters during the last rule provided one of
the most far-reaching examinations of battery cost literature to date.
This comprehensive survey of 53 forecasts of battery pack and cell
costs included studies based on four forecasting methods: learning,
literature-based projections, expert elicitation, and bottom-up battery
pack models.\335\ Each study focused on a unique set of assumptions
that may include battery plant size and location, the plant's
production processes and overall cumulative production, battery cell
and electrode designs, and material prices. The paper identifies and
discusses these important considerations--making correlations between
resulting cost differences across battery technology considerations and
varying forecast periods between studies--and appropriately
encapsulates the battery market within technological scope.
Importantly, as discussed further below, the authors appropriately note
the uncertainty associated with predicting lithium-ion battery (LIB)
costs out through 2050.
---------------------------------------------------------------------------

    \335\ Mauler, L. et al.. Battery Cost Forecasting: A Review Of
Methods And Results With An Outlook To 2050. Energy and
Environmental Science: pp. 4712-4739. Many of these selected studies
focus on common-place LIB cathode chemistries for BEVs--such as
lithium nickel manganese cobalt oxide (NMC), lithium nickel cobalt
aluminum oxide (NCA), and lithium iron phosphate (LFP); however,
some studies investigate the future-use of battery technologies such
as solid-state (SSB) and lithium-sulfur (LSB), while other studies
examine battery applications that more broadly coincide with HEVs,
energy stationary storage (ESS), consumer electronics, and medical
devices. Thirty of the forecasts were based on bottom-up battery
models and sixteen used estimated learning curves.
---------------------------------------------------------------------------

    The authors extracted 237 estimates from the 22 studies published
over the previous 10 years that focused on LIB packs. They fitted a
central tendency curve to the estimates as a function of time up to
2050.\336\ The central tendency curve shows battery pack costs
declining from $1,014/kWh in 2010 to $234/kWh in 2020. Costs in the
fitted curve decline to $132/kWh in 2030, and progress lower to $109/
kWh in 2035, and $92/kWh in 2040. The paper's authors present the fit
curve with reference to survey battery prices from Bloomberg New Energy
Finance (BNEF), one source of battery pack prices based on survey data.
In the two articles referenced by Mauler et al. to provide comparison
data for their fitted curve, BNEF cites battery pack prices at $176/kWh
in 2018 declining to $94/kWh by 2024 (using observed historical values
to calculate a ``learning rate of around 18%. This means that for every
doubling of cumulative volume, we observe an 18% reduction in
price.''),\337\ and a more recent estimate of $137/kWh in 2020
declining to $101/kWh by 2023.\338\ Mauler et al. note that ``in the
time period between 2015 and 2020, 90% of forecasted values are more
pessimistic than observed prices. This indicates that forecasts in the
examined literature have been on the pessimistic end in the past.
Further, the persistent span of estimates above [$130/kWh, in the
surveyed literature] throughout 2050 underlines the uncertainty
associated with the prediction of LIB cost that will remain a key
challenge in the future for researchers and companies in the field.''
\339\
---------------------------------------------------------------------------

    \336\ Figure 9 of Mauler et al., 2021, at 4715. The authors
note: ``Whenever values for multiple applications are reported, the
forecast dedicated to electric vehicle batteries is preferred.''
Costs appear to be in 2020 dollars, although this is not clearly
stated in the text. The authors also ``emphasize that this should
not be considered as a literature-based forecast to 2050, but merely
as a comprehensive picture of forecasted values from the past
decade.''
    \337\ BNEF. 2019. A Behind the Scenes Take on Lithium-ion
Battery Prices. Last revised: March 5, 2019. Available at: https://about.bnef.com/blog/behind-scenes-take-lithium-ion-battery-prices/.
(Accessed: May 31, 2023).
    \338\ BNEF. 2020. Battery Pack Prices Cited Below $100/kWh for
the First Time in 2020, While Market Average Sits at $137/kWh. Last
revised: December 16, 2020. Available at: https://about.bnef.com/blog/battery-pack-prices-cited-below-100-kwh-for-the-first-time-in-2020-while-market-average-sits-at-137-kwh/. (Accessed: May 31,
2023). Note that at the time of writing (2020), BNEF was of the
opinion that ``The path to achieving $101/kWh by 2023 looks clear,
even if there will undoubtedly be hiccups, such as commodity price
increases, along the way.'').
    \339\ Mauler et al., at 4733.
---------------------------------------------------------------------------

    Much has happened since the last CAFE Model analysis and the
battery cost forecasting paper summarized above. BNEF summarized that
``[a]s demand continues to grow, battery producers and automakers are
scrambling to secure access to key metals such as lithium and nickel,
battling high prices and tight supply.'' \340\ Since the articles cited
in the Mauler paper discussed above, BNEF has revised their battery
pack price estimates for 2022 to $135/kWh,\341\ and then revised their
2022 estimate again to $138/kWh.\342\ BNEF attributed the increase in
pack costs in part to the increase in mineral costs--specifically
lithium carbonate--in addition to inflation and component cost
increases.\343\ However, BNEF also

noted that ``[t]he average battery price would have been even higher if
not for the shift to lower-cost LFP batteries, which contain no nickel
or cobalt.'' \344\ The International Energy Agency's Global EV Outlook
2023 also used estimates from BNEF, citing a value of $150 for all LIB
packs.\345\
---------------------------------------------------------------------------

    \340\ BNEF. 2022. The Race to Net Zero: The Pressures of the
Battery Boom in Five Charts. Last revised: July 21, 2022. Available
at: https://about.bnef.com/blog/race-to-net-zero-the-pressures-of-the-battery-boom-in-five-charts/. (Accessed: May 31, 2023).
    \341\ BNEF. 2022. The Race to Net Zero: The Pressures of the
Battery Boom in Five Charts. Last revised: July 21, 2022. Available
at: https://about.bnef.com/blog/race-to-net-zero-the-pressures-of-the-battery-boom-in-five-charts/. (Accessed: May 31, 2023).
    \342\ BNEF. 2022. Lithium-ion Battery Pack Prices Rise for First
Time to an Average of $151/kWh. Last revised: Dec. 6, 2022.
Available at: https://about.bnef.com/blog/lithium-ion-battery-pack-prices-rise-for-first-time-to-an-average-of-151-kwh/. (Accessed: May
31, 2023); McKerracher, C. 2022. Rising Battery Prices Threaten to
Derail the Arrival of Affordable Evs. Available at: https://www.bloomberg.com/news/articles/2022-12-06/rising-battery-prices-threaten-to-derail-the-arrival-of-affordable-evs. (Accessed: May 31,
2023). (``To arrive at the average price, BNEF gathered almost 200
survey data points from buyers and sellers of lithium-ion batteries
going into passenger Evs, commercial vehicles, buses and stationary
storage applications. The headline figure is a volume-weighted
average, so it hides a lot of variation by region and application.
The lowest prices recorded were for electric buses and commercial
vehicles in China at $131 per kWh. Average pack prices for fully
electric passenger vehicles were $138 per kWh.'').
    \343\ BNEF. 2022. Lithium-ion Battery Pack Prices Rise for First
Time to an Average of $151/kWh. Last revised: Dec. 6, 2022.
Available at: https://about.bnef.com/blog/lithium-ion-battery-pack-prices-rise-for-first-time-to-an-average-of-151-kwh/. (Accessed: May
31, 2023).
    \344\ Id.
    \345\ International Energy Agency, Global EV Outlook 2023,
available at https://www.iea.org/reports/global-ev-outlook-2023
(citing BloombergNEF, Lithium-ion Battery Prices Rise for First Time
to an Average of $151/kWh, available at https://about.bnef.com/blog/lithium-ion-battery-pack-prices-rise-for-first-time-to-an-average-of-151-kwh/). Note that $151/kWh represents an average across
multiple battery end-uses, while BNEF's estimates for battery
electric vehicle packs in particular are $138/kWh on a volume-
weighted average basis in 2022.
---------------------------------------------------------------------------

    In addition, the U.S. DOE updated modeling-based estimates of
battery pack costs from using ANL's BatPaC model. Their updated
estimates show 2022 values at $130/kWh of rated energy.\346\
Separately, a 2022 analysis of future vehicle costs sponsored by the
U.S. DOE with co-authors from ANL, Ford, GM, Electric Power Research
Institute, the National Renewable Energy Laboratory, and Chevron
compared predictions of future EV battery pack costs from 9 studies
with 3 R&D targets set by DOE and US DRIVE.\347\ They concluded that
``recent assessments of future BEV battery costs by governmental
agencies, national laboratories, the NAS, academia, consulting firms,
and automakers show this [dramatic decline in the costs of high-energy
Li-ion batteries] trend is expected to continue in the future.'' \348\
---------------------------------------------------------------------------

    \346\ ANL. 2022. BatPaC--A Spreadsheet Tool to Design a Lithium
Ion Battery and Estimate Its Production Cost. Last revised: Mar. 8,
2022. Available at: https://www.anl.gov/cse/batpac-model-software.
(Accessed: May 31, 2023). This estimate assumes a production scale
of 100,000 units per year, however as discussed further below, our
BatPaC-derived costs align extremely well with these DOE-estimated
costs. See also, Cunningham, B. U.S. Department of Energy Vehicle
Technologies Office 2023 Annual Merit Review. Overview: Batteries
R&D, DOE Modeled Battery Pack Cost. June 12, 2023; VTO Fact of the
Week #1272, Electric Vehicle Battery Pack Costs, which shows that
2022 costs are nearly 90% lower than in 2008, according to DOE
Estimates (Jan. 9, 2023). Available at https://www.energy.gov/eere/vehicles/articles/fotw-1272-january-9-2023-electric-vehicle-battery-pack-costs-2022-are-nearly. (Accessed: May 31, 2023).
    \347\ Kelly, J. et al. 2022. Cradle-to-Grave Lifecycle Analysis
of U.S. Light-Duty Vehicle-Fuel Pathways: A Greenhouse Gas Emissions
and Economic Assessment of Current (2020) and Future (2030-2035)
Technologies. ANL-22/27. ANL: Argonne, IL. p. 56.
    \348\ Id.
---------------------------------------------------------------------------

    For this analysis, instead of relying on our previous methodology
of using the BatPaC model to estimate volume-based cost reductions for
battery packs, we extracted estimated learning rates from the Mauler et
al. study discussed above. Our learning rates are based on the year-
over-year cost decreases shown in the Mauler et al. study; however, we
modified the learning rate in two ways, discussed in turn.
    First, we began Mauler's 2030-2035 estimated learning rate in MY
2022, as it better aligns with our MY 2022 BatPaC-based base year cost
estimates and is reflected in the most recent BNEF survey data. To the
extent that global EV battery production has grown more rapidly than
the studies anticipated, it is reasonable to expect that learning in
manufacturing processes, economies of scale, and technological progress
have also been realized sooner than the projections anticipated.
Assuming this is the case, future learning rates will be lower than the
studies anticipated because battery manufacturing has moved farther
down the learning curve than they anticipated.
    Second, to reflect the combination of fluctuating mineral costs and
an increase in demand, we hold the battery pack cost learning curve
constant between MYs 2022 and 2025. This is a conservative assumption
that is also employed by EPA in their proposal for light duty vehicles
and medium duty vehicles beginning in MY 2027 at Section IV.C.2 and
Draft Regulatory Impact Analysis Section <IP_ADDRESS>.3. The assumption
reflects increased lithium costs since 2020 that are not expected to
decline appreciably to circa 2020 levels until additional capacity
(mining, materials processing, and cell production) comes on-line,\349\
although prices have already fallen from 2022 highs at the time of
writing. We believe that a continuation of high prices for a few years
followed by a decrease to near previous levels is reasonable because
world lithium resources are more than sufficient to supply a global EV
market and higher prices should continue to induce investment in
lithium mining and refining.350 351 That said, we recognize
the uncertainty in critical minerals prices into the near future. We
seek comment on this representation of mineral costs in the learning
curve, and any other feedback relevant to incorporating these
considerations into our modeling framework.
---------------------------------------------------------------------------

    \349\ Trading Economics. 2023. Lithium. Available at: https://tradingeconomics.com/commodity/lithium (Accessed: May 31, 2023).
    \350\ U.S. Geological Survey. 2023. Lithium Statistics and
Information. Available at: https://www.usgs.gov/centers/national-minerals-information-center/lithium-statistics-and-information.
(Accessed: May 31, 2023).
    \351\ Global lithium resources (``resources defined by U.S.G.S.
as ``[a] concentration of naturally occurring solid, liquid, or
gaseous material in or on the Earth's crust in such form and amount
that economic extraction of a commodity from the concentration is
currently or potentially feasible.'') are currently four times as
large as global reserves (``reserves'' defined by U.S.G.S. as
``[t]hat part of the reserve base that could be economically
extracted or produced at the time of determination.''), and both
have grown over time as production has increased (Figure 3). Lithium
resources are not evenly distributed geographically (Figure 4).
According to 2021 USGS estimates, Bolivia (24%), Argentina (22%),
Chile (11%), the United States (10%), Australia (8%) and China (6%)
together hold four-fifths of the world's lithium resources.
---------------------------------------------------------------------------

    Unlike our past production-based estimates for a battery learning
curve, this learning curve methodology does not explicitly assume any
particular battery chemistry is used, because the learning curve we use
aggregates assumptions from several studies and uses some assumptions
of our own. That said, we anticipate cell chemistry improvements will
happen sometime during the middle or later part of this decade. We
believe that during the rulemaking time frame, based on on-going
research and discussions with stakeholders,\352\ the industry will
continue to employ lithium-ion NMC as the predominant battery cell
chemistry for the near-term but will transition more fully to advanced
high-nickel battery chemistries \353\ like NMC811 or less-costly cell
chemistries like LFP-G during the middle or end of the decade--i.e.,
during the rulemaking timeframe. We acknowledge there are other battery
cell chemistries currently being researched that reduce the use of
cobalt, use solid opposed to liquid electrolyte, use of high silicone
content anodes or lithium-metal anodes, or even eliminate use of
lithium in the cell altogether; 354 355 however, at this
time, we do not have sufficient data to estimate cost for those
advanced battery cell chemistries. Assuming lithium-ion NMC will
continue to be used for the

near and mid-term results in reasonable estimates that are comparable
to other sources' cost projections, although we note that the outcome
of a model should not be used to justify the input assumptions.
---------------------------------------------------------------------------

    \352\ Docket Submission of Ex Parte Meetings Prior to
Publication of the Corporate Average Fuel Economy Standards for
Passenger Cars and Light Trucks for Model Years 2027-2032 and Fuel
Efficiency Standards for Heavy-Duty Pickup Trucks and Vans for Model
Years 2030-2035 Notice of Proposed Rulemaking memorandum, which can
be found under References and Supporting Material in the rulemaking
Docket No. NHTSA-2023-0022.
    \353\ Panayi, A. 2023. Into the Next Phase, the EV Market
Towards 2030--The TWh year: The Outlook for the EV & Battery Markets
in 2023. RhoMotion. Available at: https://rhomotion.com/rho-motion-seminar-series-live-q1-2023-seminar-recordings. (Accessed: May 31,
2023).
    \354\ Slowik, P. et. al. 2022. Assessment of Light-Duty Electric
Vehicle Costs and Consumer Benefits in the United States in the
2022-2035 Time Frame. International Council on Clean Transportation.
Available at: https://theicct.org/wp-content/uploads/2022/10/ev-cost-benefits-2035-oct22.pdf. (Accessed: May 31, 2023).
    \355\ Batteries News. 2022. Solid-State NASA Battery Beats The
Model Y 4680 Pack at Energy Density by Stacking all Cells in One
Case. Last revised: October 20, 2022. Available at: https://batteriesnews.com/solid-state-nasa-battery-beats-model-y-4680-pack-energy-density-stacking-cells-one-case/. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    As there are inherent uncertainties in projecting future battery
pack costs due to several factors, including the timing of the analysis
used to support this proposal, we performed several battery-related
cost sensitivity analyses. These include cases increasing and
decreasing battery pack DMCs by 20%, cases increasing and decreasing
the learning rate by 20%, and a case using the learning curve
development methodology we used for the 2022 final rule for MYs 2024-
2026 standards. These results are presented in Chapter 9 of the PRIA.
One important point that these sensitivity case results emphasize is
that because of NHTSA's inability to consider manufacturers building
EVs in response to CAFE standards during standard-setting years (i.e.,
MYs 2027-2032 for this proposal), net SCs and benefits do not change
significantly between battery cost sensitivity cases, and similarly
would not change significantly if much lower battery costs were used.
We will continue to follow Federal and international reports on battery
pack costs and seek additional comment on our battery cost estimates;
we will update these costs for the final rule analysis if better data
becomes available.
    Additional discussion in Draft TSD Chapter 3 shows that our
projected costs fall fairly well in the middle of the range of other
costs projected by various studies and organizations for future years.
Using the same approach as the rest of our analysis--that our costs
should represent an average achievable performance across the
industry--we believe that the battery DMCs with the learning curve
applied provide a reasonable representation of potential future costs
across the industry, based on the information available to us at the
time of the analysis for this proposal was completed. Figure II-26
below shows how our reference and sensitivity case cost projections
(for a 300-mile range BEV with a 70.1kWh battery pack) change over time
using different base year and learning assumptions.
[GRAPHIC] [TIFF OMITTED] TP17AU23.039

    NHTSA also continues to coordinate with DOE and EPA on assumptions
and methodology related to battery cost. During the interagency review
process for EPA's Multi-Pollutant Emissions Standards for Model Years
2027 and Later Light-Duty and Medium-Duty Vehicles proposal, which
shortly preceded the process for this proposal, EPA consulted with DOE
to incorporate battery cost learning effects that reflect the effect of
cumulative learning by considering the battery production required for
a given projected BEV penetration. In its analysis, upon recommendation
from DOE, EPA applied battery cost learning effects dependent on the
cumulative GWh of battery pack production projected in each individual
policy scenario, as described in Chapter <IP_ADDRESS>.3 of the

EPA Draft Regulatory Impact Analysis (DRIA).\356\ In other words,
learning effects were more pronounced in policy scenarios resulting in
higher rates of BEV penetration and, conversely, were less pronounced
in policy scenarios resulting in lower rates of BEV penetration.
Similar to the NHTSA analysis, the EPA cost/kWh also varies by pack
size, with larger packs having a lower cost/kWh (see Chapter <IP_ADDRESS>.2
of the EPA DRIA).\357\ Because of the way in which EPA has thus
parameterized its battery cost, which is dependent on cumulative volume
production in a given policy scenario, a direct comparison to the NHTSA
cost sensitivities shown in Figure II-27 is not straightforward. The
cost/kWh of several different pack sizes, as implemented in the EPA
analysis supporting the recent EPA proposal, are shown in Figure 30.
---------------------------------------------------------------------------

    \356\ See U.S. EPA, Multi-Pollutant Emissions Standards for
Model Years 2027 and Later Light-Duty and Medium-Duty Vehicles Draft
Regulatory Impact Analysis., EPA-420-D-23-003 (April 2023), Chapter
<IP_ADDRESS>.3.
    \357\ See U.S. EPA, Multi-Pollutant Emissions Standards for
Model Years 2027 and Later Light-Duty and Medium-Duty Vehicles Draft
Regulatory Impact Analysis., EPA-420-D-23-003 (April 2023), Chapter
<IP_ADDRESS>.2.
[GRAPHIC] [TIFF OMITTED] TP17AU23.040

    In light of the timing of EPA's analysis relative to NHTSA's
analysis, NHTSA was unable to consider the EPA approach for possible
use in the current analysis. The costs developed by EPA as depicted in
Figure II-27 above show the potential to reach significantly lower
levels than most of the costs in NHTSA's battery sensitivity cases of
Figure II-26, depending on the volume production associated with a
given policy scenario and year. As previously noted, NHTSA continues to
coordinate with DOE and EPA on battery cost assumptions and
methodology, and in light of the battery costs and methodology
published in the EPA LMDV proposal, NHTSA will consider this approach
to estimating learning effects for use in the final rule analysis.
Further analysis of battery costs similar to that proposed in EPA's
LMDV proposal, including the possible adoption of EPA's cumulative
volume-based learning approach, could result in significantly lower
battery costs than assumed in this proposed rule analysis. NHTSA
requests comment on the possibility of implementing for its final rule
analysis EPA's cumulative volume-based learning approach, and on the
methodology outlined in EPA's DRIA that EPA used to generate and
validate the cumulative GWh battery pack production-based battery pack
costs.
---------------------------------------------------------------------------

    \358\ Chart generated using EPA data, see https://www3.epa.gov/otaq/ld/2023-03-14-22-42-30-ld-central-run-to2055.zip.
---------------------------------------------------------------------------

    Recognizing that there is no way to validate costs for years that
have not yet happened, we seek comment in particular from vehicle and
battery manufacturers on any additional data they can submit
(preferably publicly) to further the conversation about battery pack
costs in the later part of this decade through the early 2030s. In
addition, we seek comment on all aspects of our methodology for
modeling base year and future year battery pack costs, and welcome data
or other information that could inform our approach for the final
rulemaking. We specifically seek comment on how the performance metrics
may change in response to shifts in chemistries used in vehicle models
driven by global policies affecting battery supply chain development,
total global production and associated learning rates, and related
sensitivity analyses.
    While batteries and relative battery components are the biggest
cost driver of electrification, non-battery electrification components,
such as electric motors, power electronics, and wiring harnesses, also
add to the total cost required to electrify a vehicle. Different
electrified vehicles have

variants of non-battery electrification components and configurations
to accommodate different vehicle classes and applications with
respective designs; for instance, some BEVs may be engineered with only
one electric motor and some BEVs may be engineered with two or even
four electric motors within their powertrain to provide all wheel drive
function. In addition, some electrified vehicle types still include
conventional powertrain components, like an ICE and traditional
transmission.
    For all electrified vehicle powertrain types, we group non-battery
electrification components into four major categories: electric motors
(or e-motors), power electronics (generally including the DC-DC
converter, inverter, and power distribution module), charging
components (charger, charging cable, and high voltage cables), and
thermal management system(s). We further group the components into
those comprising the electric traction drive system (ETDS), and all
other components. Although each manufacturer's ETDS and power
electronics vary between the same electrified vehicle types and between
different electrified vehicle types, we consider the ETDS for this
analysis to be comprised of the e-motor and inverter, power
electronics, and thermal system.
    When researching costs for different non-battery electrification
components, we found that different reports vary in components
considered and cost breakdown. This is not surprising, as vehicle
manufacturers use different non-battery electrification components in
different vehicles systems, or even in the same vehicle type, depending
on the application. In order of the component categories discussed
above, we examined the following cost teardown studies, as shown in
Table II-16.

             Table II-16--Cost Estimates for Different Electrified Vehicle Components, by Powertrain
----------------------------------------------------------------------------------------------------------------
                                                        UBS MY 2016 chevy                         EPA-sponsored
     Non-battery electrical        EETT \359\ roadmap     bolt teardown                            FEV report
           components              report  (2017$ in    (2017$ in DMC year      Assumptions      (updated 2021$
                                     DMC year 2017)           2017)                               for analysis)
----------------------------------------------------------------------------------------------------------------
ETDS............................  $18/kW.............  $17.76/kW..........  Based on e-motor           $19.80/kW
                                                                             peak power.
On-Board Charger................  no information       85/kW..............  Based on vehicle            93.54/kW
                                   provided..                                requirement (7 kW
                                                                             for BEVs, 2 kW
                                                                             for PHEVs).
DC to DC Converter..............  no information       90/kW..............  Based on converter         100.94/kW
                                   provided.                                 rated power (2
                                                                             kW).
High Voltage Cables and Charging  no information       450................  Fixed cost rated              495.21
 Cords for BEVs and PHEVs.         provided.                                 for 360V.
High Voltage Cables for Strong    no information       no information       Fixed cost........            100.44
 Hybrids.                          provided.            provided.
----------------------------------------------------------------------------------------------------------------

    Using the best available estimate for each component from the
different reports captures components in most manufacturer's systems
but not all; we believe, however, that this is a reasonable metric and
approach for this analysis, given the non-standardization of
electrified powertrain designs and subsequent component specifications.
Other sources we used for non-battery electrification component costs
include an EPA-sponsored FEV teardown of a 2013 Chevrolet Malibu ECO
with eAssist for some BISG component costs,\360\ which we validated
against a 2019 Dodge Ram eTorque system's publicly available retail
price,\361\ and the 2015 NAS report.\362\ Broadly, our total BISG
system cost, including the battery, fairly matches these other cost
estimates.
---------------------------------------------------------------------------

    \359\ Electrical and Electronics Technical Team.
    \360\ Light Duty Vehicle Technology Cost Analysis 2013 Chevrolet
Malibu ECO with eAssist BAS Technology Study, FEV P311264 (Contract
no. EP-C-12-014, WA 1-9).
    \361\ Colwell, K.C. 2019. The 2019 Ram 1500 eTorque Brings Some
Hybrid Tech, If Little Performance Gain, to Pickups. Last revised:
Mar. 14, 2019. Available at: https://www.caranddriver.com/reviews/a22815325/2019-ram-1500-etorque-hybrid-pickup-drive. (Accessed: May
31, 2023).
    \362\ 2015 NAS report, at p. 305.
---------------------------------------------------------------------------

    As discussed in Section II.C, our technology costs account for
three variables: retail price equivalence (RPE), which is 1.5 times the
DMC, the technology learning curve, and the adjustment of the dollar
value to 2021$ for this analysis. While HDPUVs have larger non-battery
electrification componentry than LDVs, the cost calculation methodology
is identical, in that the $/kW metric is the same, but the absolute
costs are higher. As a result, HDPUVs and LDVs share the same non-
battery electrification DMCs.
    For the non-battery electrification component learning curves, in
both the LD and HDPUV fleets, we used cost information from ANL's 2016
Assessment of Vehicle Sizing, Energy Consumption, and Cost through
Large-Scale Simulation of Advanced Vehicle Technologies report.\363\
The report provides estimated cost projections from the 2010 lab year
to the 2045 lab year for individual vehicle
components.364 365 We considered the component costs used in
electrified vehicles, and determined the learning curve by evaluating
the year over year cost change for those components. ANL published a
2020 and a 2022 version of the same report; however, those versions did
not include a discussion of the high and low-cost estimates for the
same components.366 367 Our learning estimates generated
using the 2016 report align in the middle of these two ranges, and
therefore we continue to apply the learning curve estimates based on
the 2016 report. There are many sources that we could have picked to
develop learning curves for non-battery electrification component
costs, however given the uncertainty surrounding extrapolating costs
out to

MY 2050, we believe these learning curves provide a reasonable
estimate.
---------------------------------------------------------------------------

    \363\ Moawad, A. et al. 2016. Assessment of Vehicle Sizing,
Energy Consumption and Cost Through Large Scale Simulation of
Advanced Vehicle Technologies. ANL/ESD-15/28. Available at: https://www.osti.gov/biblio/1245199. (Accessed: May 31, 2023).
    \364\ ANL/ESD-15/28 at p. 116.
    \365\ DOE's lab year equates to five years after a model year,
e.g., DOE's 2010 lab year equates to MY 2015.
    \366\ Islam, E. et al. 2020. Energy Consumption and Cost
Reduction of Future Light-Duty Vehicles through Advanced Vehicle
Technologies: A Modeling Simulation Study Through 2050. ANL/ESD-19/
10.
    \367\ Islam, E. et al. 2022. A Comprehensive Simulation Study to
Evaluate Future Vehicle Energy and Cost Reduction Potential. ANL/
ESD-22/6.
---------------------------------------------------------------------------

    In summary, we calculate total electrified powertrain costs by
summing individual component costs, which ensures that all technologies
in an electrified powertrain appropriately contribute to the total
system cost. We combine the costs associated with the ICE (if
applicable) and transmission, non-battery electrification components
like the electric machine, and battery pack to create a full-system
cost. Chapter <IP_ADDRESS> of the Draft TSD presents the total costs for
each electrified powertrain option, broken out by the components we
discussed throughout this section. In addition, the chapter discusses
where to find each of the component costs in the CAFE Model's various
input files.
4. Road Load Reduction Paths
    No car or truck uses energy (whether gas or otherwise) 100%
efficiently when it is driven down the road. If the energy in a gallon
of gas is thought of as a pie, the amount of energy ultimately
available from that gallon to propel a car or truck down the road would
only be a small slice. So where does the lost energy go? Most of it is
lost due to thermal and frictional loses in the engine and drivetrain
and drag from ancillary systems (like the air conditioner, alternator
generator, various pumps, etc.). The rest is lost to what engineers
call road loads. For the most part, road loads include wind resistance
(or aerodynamics), drag in the braking system, and rolling resistance
from the tires. At low speeds, aerodynamic losses are very small, but
as speeds increases these loses rapidly become dramatically higher than
any other road load. Drag from the brakes in most cars is practically
negligible. ROLL losses can be significant: at low speeds ROLL losses
can be more than aerodynamic losses. Whatever energy is left after
these road loads are spent on accelerating the vehicle anytime a its
speed increases. This is where reducing the mass of a vehicle is
important to efficiency because the amount of energy to accelerate the
vehicle is always directly proportional to a vehicle's mass. All else
being equal, reduce a car's mass and better fuel economy is guaranteed.
However, keep in mind that at freeway speeds, aerodynamics plays a more
dominant role in determining fuel economy than any other road load or
than vehicle mass.
    We include three road load reducing technology paths in this
analysis: the MR Path, Aerodynamic Improvements (AERO) Path, and ROLL
Path. For all three vehicle technologies, we assign baseline fleet
technologies and identify adoption features based on the vehicle's body
style. The LD fleet body styles we include in the analysis are
convertible, coupe, sedan, hatchback, wagon, SUV, pickup, minivan, and
van. The HDPUV fleet body styles include chassis cab, cutaway, fleet
SUV, work truck, and work van. Figure II-28 and Figure II-29 show the
LD and HDPUV fleet body styles used in the analysis.
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    As expected, the road load forces described above operate
differently based on a vehicle's body style, and the technology
adoption features and effectiveness values reflect this. The following
sections discuss the three Road Load Reduction Paths.
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    \368\ For this proposal, vehicles were divided between the LD
and HDPUV fleets solely on their gross vehicle weight rating (GVWR)
being above or below 8,500 lbs. We will revisit the distribution of
vehicles in the final rule to include the distinction for MDPVs.

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

a. Mass Reduction
    MR is a relatively cost-effective means of improving fuel economy,
and vehicle manufacturers are expected to apply various MR technologies
to meet fuel economy standards. Vehicle manufacturers can reduce
vehicle mass through several different techniques, such as modifying
and optimizing vehicle component and system designs, part
consolidation, and adopting materials that are conducive to MR
(advanced high strength steel (AHSS), aluminum, magnesium, and plastics
including carbon fiber reinforced plastics).
    For the LD fleet portion of this analysis, we considered five
levels of MR technology (MR1-MR5) that include increasing amounts of
advanced materials and MR techniques applied to the vehicle's
glider.\369\ The subsystems that may make up a vehicle glider include
the vehicle body, chassis, interior, steering, electrical accessory,
brake, and wheels systems. We accounted for mass changes associated
with powertrain changes separately.\370\ We considered two levels of MR
(MR1--MR2) and a baseline (MR0) for the HDPUV fleet. We use fewer
levels because vehicles within the HD fleets are built for a very
different duty cycle \371\ and tend to be larger and heavier. Moreover,
there are different vehicle parameters, like towing capacity, that
drive vehicle mass in the HD fleet rather than, for example, NVH
(noise, vibration and harshness) performance in the LD fleet.
Similarly, HDPUV MR is assumed to come from the glider,\372\ and
powertrain MR occurs during the Autonomie modeling. Our estimates of
how manufacturers could reach each level of MR technology in the LD and
HDPUV analyses, including a discussion of advanced materials and MR
techniques, can be found in Chapter 3.4 of the Draft TSD.
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    \369\ Note that in the previous analysis, there was a sixth
level of mass reduction available as a pathway to compliance. For
this analysis, this pathway was removed because it relied on
extensive use of carbon fiber composite technology to an extent that
is only found in purpose-built racing cars and a few hundred road
legal sports cars costing hundreds of thousands of dollars. Draft
TSD Chapter 3.4 provides additional discussion on the decision to
include five mass reduction levels in this analysis.
    \370\ Glider mass reduction can sometimes enable a smaller
engine while maintaining performance neutrality. Smaller engines
typically weigh less than bigger ones. We captured any changes in
the resultant fuel savings associated with powertrain mass reduction
and downsizing via the Autonomie simulation. Autonomie calculates a
hypothetical vehicle's theoretical fuel mileage using a mass
reduction to the vehicle curb weight equal to the sum of mass
savings to the glider plus the mass savings associated with the
downsized powertrain.
    \371\ HD vans that are used for package delivery purposes are
frequently loaded to GVWR. However, LD passenger cars are never
loaded to GVWR. Operators of HD vans have an economic motivation to
load their vehicles to GVWR. In contrast studies show that between
38% and 82% of passenger cars are used soley to transport their
drivers. (Bureau of Transportation Studies, 2011, FHWA Publication
No. FHWA-PL-18-020, 2019).
    \372\ We also assumed that an HDPUV glider comprises 71 percent
of a vehicle's curb weight, based on a review of mass reduction
technologies in the 2010 Transportation Research Board and National
Research Council's ``Technologies and Approaches to Reducing the
Fuel Consumption of Medium- and Heavy-Duty Vehicles.'' See
Transportation Research Board and National Research Council. 2010.
Technologies and Approaches to Reducing the Fuel Consumption of
Medium- and Heavy-Duty Vehicles. Washington, DC: The National
Academies Press. At page 120-121. Available at: https://nap.nationalacademies.org/12845/. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    We assigned baseline MR levels to vehicles in both the LD and HDPUV
analysis fleets by using regression analyses that consider a vehicle's
body design \373\ and body style, in addition to several variables
about the vehicle, like footprint, power, bed length (for pickup
trucks), and battery pack size (if applicable), among other factors. We
have been improving on the LD regression analysis since the 2016 Draft
Technical Assessment Report (TAR) and continue to find that it
reasonably estimates MR technology levels of vehicles in the analysis
fleet. We developed a similar regression for the HDPUV fleet for this
analysis using the factors described above and other applicable HDPUV
attributes and found that it similarly appropriately assigns baseline
MR technology levels. Chapter 3.4 of the Draft TSD contains a full
description of the regression analyses used for each fleet and examples
of results of the regression analysis for select vehicles.
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    \373\ The body design categories we used are 3-box, 2-box, HD
pickup, and HD van. A 3-box can be explained as having a box in the
middle for the passenger compartment, a box in the front for the
engine and a box in the rear for the luggage compartment. A 2-box
has a box in front for the engine and then the passenger and luggage
box are combined into a single box.
---------------------------------------------------------------------------

    There are several ways we ensure that the CAFE Model considers MR
technologies like manufacturers might apply them in the real world.
Given the degree of commonality among the vehicle models built on a
single platform, manufacturers do not have complete freedom to apply
unique technologies to each vehicle that shares the platform. While
some technologies (e.g., low rolling resistance tires) are very nearly
``bolt-on'' technologies, others involve substantial changes to the
structure and design of the vehicle, and therefore often necessarily
affect all vehicle models that share that platform. In most cases, MR
technologies are applied to platform level components and therefore the
same design and components are used on all vehicle models that share
the platform. Each vehicle in the analysis fleet is associated with a
specific platform. A platform ``leader'' in the analysis fleet is a
vehicle variant of a given platform that has the highest level of MR
technology in the analysis fleet. As the model applies technologies, it
will ``level up'' all variants on a platform to the highest level of MR
technology on the platform. For example, if a platform leader is
already at MR3 in MY 2022, and a ``follower'' starts at MR0 in MY 2022,
the follower will get MR3 at its next redesign (unless the leader is
redesigned again before that time, and further increases the MR level
associated with that platform, then the follower would receive the new
MR level).
    In addition to leader-follower logic for vehicles that share the
same platform, we also restrict MR5 technology to platforms that
represent 80,000 vehicles or fewer. The CAFE Model will not apply MR5
technology to platforms representing high volume sales, like a
Chevrolet Traverse, for example, where hundreds of thousands of units
are sold per year. We use this particular adoption feature and the
80,000-unit threshold in particular, to model several relevant
considerations. First, we assume that MR5 would require a significant
amount of carbon fiber technology.\374\ There is high global demand
from a variety of industries for a limited supply of carbon fibers;
specifically, aerospace, military/defense, and industrial applications
demand most of the carbon fiber currently produced. Today, only roughly
10 percent of the global dry fiber supply goes to the automotive
industry, which translates to the global supply base only being able to
support approximately 70,000 cars.\375\ In addition, the production
process for carbon fiber is significantly different than for
traditional vehicle materials. We use this adoption feature as a proxy
for stranded capital (i.e., when manufacturers amortize research,
development, and tooling expenses over many years) from leaving the
traditional processes, and to represent the significant paradigm change
to tooling

and equipment that would be required to support molding carbon fiber
panels. There are no other adoption features for MR in the LD analysis,
and no adoption features for MR in the HDPUV analysis.
---------------------------------------------------------------------------

    \374\ See the Final TSD for CAFE Standards for MYs 2024-2026,
and Chapter 3.4 of the Draft TSD accompanying this rulemaking for
more information about carbon fiber.
    \375\ Sloan, J. 2020. Carbon Fiber Suppliers Gear up for Next
Generation Growth. Available at: https://www.compositesworld.com/articles/carbon-fiber-suppliers-gear-up-for-next-gen-growth.
(Accessed: May 31, 2023).
---------------------------------------------------------------------------

    In the Autonomie simulations, MR technology is simulated as a
percentage of mass removed from the specific subsystems that make up
the glider. The mass of subsystems that make up the vehicle's glider is
different for every technology class, based on glider weight data from
the A2Mac1 database \376\ and two NHTSA-sponsored studies that examined
light-weighting a passenger car and light truck. We account for MR from
powertrain improvements separately from glider MR. Autonomie considers
several components for powertrain MR, including engine downsizing, and,
fuel tank, exhaust systems, and cooling system light-weighting.\377\
With regard to the LDV fleet, the 2015 NAS report suggested an engine
downsizing opportunity exists when the glider mass is light-weighted by
at least 10 percent. The 2015 NAS report also suggested that 10 percent
light-weighting of the glider mass alone would boost fuel economy by 3
percent and any engine downsizing following the 10 percent glider MR
would provide an additional 3 percent increase in fuel economy.\378\
The NHTSA light-weighting studies applied engine downsizing (for some
vehicle types but not all) when the glider weight was reduced by 10
percent. Accordingly, the analysis limits engine resizing to several
specific incremental technology steps; important for this discussion,
engines in the analysis are only resized when MR of 10 percent or
greater is applied to the glider mass, or when one powertrain
architecture replaces another architecture. For the HDPUV analysis, we
do not allow engine downsizing at any MR level. This is because HDPUV
designs are sized with the maximum GVWR and GCWR in mind, as discussed
earlier in this section. We are objectively controlling the vehicles'
utility and performance by this method in Autonomie. For example, if
more MR technology is applied to a HD van, the payload capacity
increases while maintaining the same maximum GVWR and GCWR.\379\ The
lower laden weight enables these vehicles to improve fuel efficiency by
increased capacity. A summary of how the different MR technology levels
improve fuel consumption is shown in Figure II-30, Figure II-31, and
Figure II-32 below.
---------------------------------------------------------------------------

    \376\ A2Mac1: Automotive Benchmarking. Available at: https://portal.a2mac1.com/. (Accessed: May 31, 2023). The A2Mac1 database
tool is widely used by industry and academia to determine the bill
of materials (a list of the raw materials, sub-assemblies, parts,
and quantities needed to manufacture an end-product) and mass of
each component in the vehicle system.
    \377\ Although we do not acount for mass reduction in
transmissions, we do reflect design improvements as part of mass
reduction when going from, for example, an older AT6 to a newer AT8
that has similar if not lower mass.
    \378\ National Research Council. 2015. Cost, Effectiveness, and
Deployment of Fuel Economy Technologies for Light-Duty Vehicles. The
National Academies Press: Washington DC. Available at: https://doi.org/10.17226/21744. (Accessed: May 31, 2023).
    \379\ Transportation Research Board and National Research
Council. 2010. Technologies and Approaches to Reducing the Fuel
Consumption of Medium- and Heavy-Duty Vehicles. The National
Academies Press: Washington, DC. p. 116. Available at: https://nap.nationalacademies.org/12845/. (Accessed: May 31, 2023).
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    Our MR costs are based on two NHTSA light-weighting studies--the
teardown of a MY 2011 Honda Accord and a MY 2014 Chevrolet Silverado
pickup truck \380\--and the 2021 NAS report.\381\ The costs for MR1-MR4
rely on the light-weighting studies, while the cost of MR5 references
the carbon fiber costs provided in the 2021 NAS report. The same cost
curves are used for the HDPUV analysis, however, we used linear
interpolation to shift the HDPUV MR2 curve (by roughly a factor of 20)
to account for the fact that MR2 in the HDPUV analysis represents a
different level than MR2 in the LD analysis. Unlike the other
technologies in our analysis that have a fixed technology cost (for
example, it costs about $3,000 to add a AT10L3 transmission to a LD SUV
or pickup truck in MY 2027), the cost of MR is calculated on a dollar
per pound saved basis based on a vehicle's starting weight. Put another
way, for a given vehicle platform, a baseline mass is assigned using
the aforementioned regression model. The amount of mass to reach each
of the five levels of MR is calculated by the CAFE Model based on this
baseline number and then multiplied by the dollar per pound saved
figure for each of the five MR levels. The dollar per pound saved
figure increases at a nearly linear rate going from MR0 to M4. However,
this figure increases steeply going from MR4 to MR5 because the
technology cost to realize the associated mass savings level is an
order of magnitude larger. This dramatic increase is reflected by all
three studies we relied on for MR costing, and we believe that it
reasonably represents what manufacturers would expect to pay for
including increasing amounts of carbon fiber on their vehicles. For the
HDPUV analysis, there is also a significant cost increase from MR1 to
MR2. This is because the MR going from MR1 to MR2 in the HDPUV fleet
analysis is a larger step than going from MR1 to MR2 for the LD fleet
analysis--5% to 7.5% off the glider compared to 1.4% to 13%. More MR
demands higher costs.
---------------------------------------------------------------------------

    \380\ DOT HS 811 666, Singh, H., Final Report, Mass Reduction
for Light-Duty Vehicles for Model Years 2017-2025, 2012; DOT HS 812
487, Singh, H., Davies, J., Kramer, D. Fisher, A., Paramasuwom, M.,
Mogal, V., . . . and Ganesan, V., Mass Reduction for Light-Duty
Vehicles for Model Years 2017-2025, 2018.
    \381\ This analysis applied the cost estimates per pound derived
from passenger cars to all passenger car segments, and the cost
estimates per pound derived from full-size pickup trucks to all
light-duty truck and SUV segments. The cost estimates per pound for
carbon fiber (MR5) were the same for all segments.
---------------------------------------------------------------------------

    Like past analyses, we considered several options for MR technology
costs. Again, we determined that the NHTSA-sponsored studies accounted
for significant factors that we believe are important to include our
analysis, including materials considerations (material type and gauge,
while considering real-world constraints such as manufacturing and
assembly methods and complexity), safety (including the Insurance
Institute for Highway Safety's (IIHS) small overlap tests), and
functional performance (including towing and payload capacity, noise,
vibration, and harshness (NVH)+, and gradeability in the pickup truck
study).
b. Aerodynamic Improvements
    The energy required for a vehicle to overcome wind resistance, or
more formally what is known as aerodynamic drag, ranges from minimal at
low speeds to incredibly significant at highway speeds.\382\ Reducing a
vehicle's aerodynamic drag is, therefore, an effective way to reduce
the vehicle's fuel consumption. Aerodynamic drag is characterized as
proportional to the frontal area (A) of the vehicle and a factor called
the coefficient of drag (Cd). The coefficient of drag
(Cd) is a dimensionless value that represents a moving
object's resistance against air, which depends on the shape of the
object and flow conditions. The frontal area (A) is the cross-sectional
area of the vehicle as viewed from the front. Aerodynamic drag of a
vehicles is often expressed as the product of the two values,
CdA, which is also known as the drag area of a vehicle. The
force imposed by aerodynamic drag increases with the square of vehicle
velocity, accounting for the largest contribution to road loads at
higher speeds.\383\
---------------------------------------------------------------------------

    \382\ 2015 NAS Report, at 207.
    \383\ See, e.g., Pannone, G. 2015. Technical Analysis of Vehicle
Load Reduction Potential for Advanced Clean Cars, Final Report.
April 2015. Available at: https://ww2.arb.ca.gov/sites/default/files/2020-04/13_313_ac.pdf (Accessed: May 31, 2023). The graph on
page 20 shows how at higher speeds the aerodyanmic force becomes the
dominant load force.
---------------------------------------------------------------------------

    Manufacturers can reduce aerodynamic drag either by reducing the
drag coefficient or reducing vehicle frontal area, which can be
achieved by passive or active aerodynamic technologies. Passive
aerodynamics refers to aerodynamic attributes that are inherent to the
shape and size of the vehicle. Passive attributes can include the shape
of the hood, the angle of the windscreen, or even overall vehicle ride
height. Active aerodynamics refers to technologies that variably deploy
in response to driving conditions. Example of active aerodynamic
technologies are grille shutters, active air dams, and

active ride height adjustment. Manufacturers may employ both passive
and active aerodynamic technologies to improve aerodynamic drag values.
    There are four levels of aerodynamic improvement (over the baseline
AERO0) available in the LD analysis (AERO5, AERO10, AERO15, AERO20),
and two levels of improvements available for the HDPUV analysis
(AERO10, AERO20). There are fewer levels available for the HDPUV
analysis because HDPUVs have less diversity in overall vehicle shape;
prioritization of vehicle functionality forces a boxy shape and limits
incorporation of many of the ``shaping''--based aerodynamic
technologies, such as smaller rear-view mirrors, body air flow, rear
diffusers, and so on. Refer back to Figure II-28 and Figure II-29 for a
visual of each body style considered in the LD and HDPUV analyses.
    Each AERO level associates with 5, 10, 15, or 20 percent
aerodynamic drag improvement values over a baseline computed for each
vehicle body style. These levels, or bins, respectively correspond to
the level of aerodynamic drag reduction over the baseline, e.g.,
``AERO5'' corresponds to the 5 percent aerodynamic drag improvement
value over the baseline, and so on. While each level of aerodynamic
drag improvement is technology agnostic--that is, manufacturers can
ultimately choose how to reach each level by using whatever
technologies work for the vehicle--we estimated a pathway to each
technology level based on data from a NRC of Canada-sponsored wind
tunnel testing program. The program included an extensive review of
production vehicles utilizing aerodynamic drag improvement
technologies, and industry comments.384 385 Our example
pathways for achieving each level of aerodynamic drag improvements is
discussed in Chapter 3.5 of the Draft TSD.
---------------------------------------------------------------------------

    \384\ Larose, G. et al. 2016. Evaluation of the Aerodynamics of
Drag Reduction Technologies for Light-duty Vehicles--a Comprehensive
Wind Tunnel Study. SAE International Journal of Passenger Cars--
Mechanical Systems 9(2): pp. 772-784. Available at: https://doi.org/10.4271/2016-01-1613. (Accessed: May 31, 2023).
    \385\ Larose, G. et al. 2016. Evaluation of the Aerodynamics of
Drag Reduction Technologies for Light-duty Vehicles--a Comprehensive
Wind Tunnel Study. SAE International Journal of Passenger Cars--
Mechanical Systems 9(2): pp. 772-784. Available at: https://doi.org/10.4271/2016-01-1613. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    We assigned baseline aerodynamic drag reduction technology levels
based on vehicle body styles.\386\ We computed an average coefficient
of drag based on vehicle body styles, using coefficient of drag data
from the MY 2015 analysis fleet for the LD analysis, and data from the
MY 2019 Chevy Silverado and MY 2020 Ford Transit and the MY 2022 Ford
e-Transit for cargo vans for the HDPUV analysis. Different body styles
offer different utility and have varying levels of baseline form drag.
This analysis considers both frontal area and body style as
unchangeable utility factors affecting aerodynamic forces; therefore,
the analysis assumes all reduction in aerodynamic drag forces come from
improvement in the drag coefficient. Then we used drag coefficients for
each vehicle in the baseline fleet to establish a baseline aerodynamic
technology level for each vehicle. We compared the vehicle's drag
coefficient to the calculated drag coefficient by body style mentioned
above, to assign baseline levels of aerodynamic drag reduction
technology. We were able to find most vehicles' drag coefficients in
manufacturer's publicly available specification sheets, however in
cases where we could not find that information, we used engineering
judgment to assign the baseline technology level.
---------------------------------------------------------------------------

    \386\ These assignments do not necessarily match the body styles
that manufacturers use for marketing purposes. Instead, we make
these assignments based on engineering judgment and the categories
used in our modeling, considering how this affects a vehicle's AERO
and vehicle technology class assignments.
---------------------------------------------------------------------------

    We also look at vehicle body style and vehicle horsepower to
determine which types of vehicles can adopt different aerodynamic
technology levels. For the LD analysis, AERO15 and AERO20 cannot be
applied to minivans, and AERO20 cannot be applied to convertibles,
pickup trucks, and wagons. We also do not allow application of AERO15
and AERO20 technology to vehicles with more than 780 horsepower. There
are two main types of vehicles that inform this threshold: performance
ICE vehicles and high-power BEVs. In the case of the former, we
recognize that manufacturers tune aerodynamic features on these
vehicles to provide desirable downforce at high speeds and to provide
sufficient cooling for the powertrain, rather than reducing drag,
resulting in middling drag coefficients despite advanced aerodynamic
features. Therefore, manufacturers may have limited ability to improve
aerodynamic drag coefficients for high performance vehicles with ICEs
without reducing horsepower. Only 4,047 units of sales volume in the
baseline fleet include limited application of aerodynamic technologies
due to ICE vehicle performance.\387\
---------------------------------------------------------------------------

    \387\ See the Market Data Input File.
---------------------------------------------------------------------------

    In the case of high-power BEVs, the 780-horsepower threshold is set
above the highest peak system horsepower present on a BEV in the 2020
fleet. We originally set this threshold based on vehicles in the MY
2020 fleet in parallel with the 780-horsepower ICE limitation. For this
analysis, the restriction does not have any functional effect because
the only BEVs that have above 780-horsepower in the MY 2022 analysis
fleet--the Tesla Model S and X Plaid, and variants of the Lucid Air--
are already assigned AERO20 as a baseline technology state and there
are no additional levels of AERO technology left for those vehicles to
adopt. Note that these high horsepower BEVs have extremely large
battery packs to meet both performance and range requirements. These
bigger battery packs make the vehicles heavier, which means they do not
have the same downforce requirements as a similarly situated high-
horsepower ICE vehicle. Broadly speaking, BEVs have different
aerodynamic behavior and considerations than ICE vehicles, allowing for
features such as flat underbodies that significantly reduce drag.\388\
BEVs are therefore more likely to achieve higher AERO levels, so the
horsepower threshold is set high enough that it does not restrict
AERO15 and AERO20 application. BEVs that do not currently use high AERO
technology levels are generally bulkier (e.g., SUVs or trucks) or lower
budget vehicles.
---------------------------------------------------------------------------

    \388\ 2020 EPA Automotive Trends Report, at p. 227.
---------------------------------------------------------------------------

    There are no additional adoption features for aerodynamic
improvement technologies in the HDPUV analysis. We limited the range of
technology options for reasons discussed above, but both AERO
technology levels are available to all HDPUV body styles.
    Figure II-33, Figure II-34, and Figure II-35 show the potential
fuel consumption improvement from the baseline AERO0 technology. For
example, the AERO20 values shown represent the range of potential fuel
consumption improvement values that could be achieved through the
replacement of AERO0 technology with AERO20 technology for every
technology key that is not restricted from using AERO20. We use the
change in fuel consumption values between entire technology keys, and
not the individual technology effectiveness values. Using the change
between whole technology keys captures the

complementary or non-complementary interactions among technologies.
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    We carried forward the established AERO technology costs previously
used in the 2020 final rule and again into the MY 2024-2026 standards
analysis,\389\ and updated those costs to the dollar-year used in this
analysis. For LD AERO improvements, the cost to achieve AERO5 is
relatively low, as manufacturers can make most of the improvements
through body styling changes. The cost to achieve AERO10 is higher than
AERO5, due to the addition of several passive aerodynamic technologies,
and consecutively the cost to achieve AERO15 and AERO20 are much higher
than AERO10 due to use of both passive and active aerodynamic
technologies. The two AERO technology levels available for HDPUVs are
similar in technology type and application to LDVs in the same
technology categories, specifically light trucks. Because of this
similarity, and unlike other technology areas that are required to
handle higher loads or greater wear, aerodynamics technologies can be
almost directly ported between fleets. As a result, there is no
difference in technology cost between LD and HDPUV fleets for this
analysis. The cost estimates are based on CBI submitted by the
automotive industry in advance of the 2018 CAFE NPRM, and on our
assessment of manufacturing costs for specific aerodynamic
technologies. See the 2018 PRIA for discussion of the cost
estimates.\390\ We received no additional comments from stakeholders
regarding the costs established in the 2018 PRIA during the MY 2024-
2026 standards analysis and continued to use the established costs for
this analysis. Draft TSD Chapter 3.5 contains additional discussion of
aerodynamic improvement technology costs, and costs for all technology
classes across all MYs are in the CAFE Model's Technologies Input File.
---------------------------------------------------------------------------

    \389\ See the FRIA accompanying the 2020 final rule, Chapter
VI.C.5.e.
    \390\ See the PRIA accompanying the 2018 NPRM, Chapter
<IP_ADDRESS>.2.1.2 for a discussion of these cost estimates.
---------------------------------------------------------------------------

c. Low Rolling Resistance Tires
    Tire rolling resistance burns additional fuel when driving. As a
car or truck tire rolls, at the point the tread touches the pavement,
the tire flattens-out to create what tire engineers call the contact
patch. The rubber in the contact patch deforms to mold to the tiny
peaks and valleys of the payment. The interlock between the rubber and
these tiny peaks and valleys creates grip. Every time the contact patch
leaves the road surface as the tire rotates, it must recover to its
original shape and then as the tire goes all the way around it must
create a new contact patch that molds to a new piece of road surface.
However, this molding and repeated re-molding action takes energy. Just
like when a person stretches a rubber band it takes work, so does
deforming the rubber and the tire to form the contact patch. When
thinking about the efficiency of driving a car down the road, this
means that not all the energy produced by a vehicle's engine can go
into propelling the vehicle forward. Instead, some small, but
appreciable, amount goes into deforming the tire and creating the
contact patch repeatedly. This also explains why tires with low
pressure have higher rolling resistance than properly inflated tires.
When the tire pressure is low, the tire deforms more to create the
contact patch which is the same as stretching the rubber farther in the
analogy above. The larger deformations burn up even more energy and
results in worse fuel mileage. Lower-rolling-resistance tires have
characteristics that reduce frictional losses associated with the
energy dissipated mainly in the deformation of the tires under load,
thereby improving fuel economy.
    We use three levels of low rolling resistance tire technology for
LDVs and two levels for HDPUVs. Each level of low rolling resistance
tire technology reduces rolling resistance by 10 percent from an
industry-average baseline rolling resistance coefficient (RRC) value of
0.009.\391\ While the industry-average baseline RRC is based on
information from LDVs, we also determined that baseline is appropriate
for HDPUVs. RRC data from a NHTSA-sponsored study shows that similar
vehicles across the LD and HDPUV categories have been able to achieve
similar RRC improvements. See Chapter 3.6 of the Draft TSD for more
information on this comparison. Table II-17 shows the LD and HDPUV low
rolling resistance technology options and their associated RRC.
---------------------------------------------------------------------------

    \391\ See Technical Analysis of Vehicle Load Reduction by
CONTROLTEC for California Air Resources Board (April 29, 2015). We
determined the industry-average baseline RRC using a CONTROLTEC
study prepared for the CARB, in addition to considering CBI
submitted by vehicle manufacturers prior to the 2018 LD NPRM
analysis. The RRC values used in this study were a combination of
manufacturer information, estimates from coast down tests for some
vehicles, and application of tire RRC values across other vehicles
on the same platform. The average RRC from surveying 1,358 vehicle
models by the CONTROLTEC study is 0.009. The CONTROLTEC study
compared the findings of their survey with values provided by the
U.S. Tire Manufacturers Association for original equipment tires.
The average RRC from the data provided by the U.S. Tire
Manufacturers Association is 0.0092, compared to the average of
0.009 from CONTROLTEC.

 Table II-17--Tire Rolling Resistance Technologies and Their Associated
                  Rolling Resistance Coefficient (RRC)
------------------------------------------------------------------------
                                                              Rolling
                                                            resistance
                       Technology                           coefficient
                                                           (RRC)  (N/N)
------------------------------------------------------------------------
ROLL0...................................................          0.0090
ROLL10..................................................          0.0081
ROLL20..................................................          0.0072
ROLL30..................................................          0.0063
------------------------------------------------------------------------

    We have been using ROLL10 and ROLL20 in the last several CAFE Model
analyses. New for this analysis is ROLL30 for the LD fleet. In past
rulemakings, we did not consider ROLL30 due to lack of widespread
commercial adoption of ROLL30 tires in the fleet within the rulemaking
timeframe, despite commenters' argument on availability of the
technology on current vehicle models and possibility that there would
be additional tire improvements over the next decade.\392\ Comments we
received during the comment period for the last CAFE rule also
reflected the application of ROLL30 by OEMs, although they discouraged
considering the technology due to high cost and possible wet traction
reduction. With increasing use of ROLL30 application by
OEMs,393 394 395 and material selection making it possible
to design low rolling resistance independent of tire wet grip
(discussed in detail in Chapter 3.6 of the Draft TSD), we now consider
ROLL30 as a viable future technology during this rulemaking period. We
believe that the tire industry is in the process of moving automotive
manufacturers towards higher levels of rolling resistance technology in
the vehicle fleet. We believe that at this time, the emerging tire
technologies that would achieve 30 percent improvement in rolling
resistance, like changing tire profile, stiffening tire walls, or
adopting improved tires along with active chassis control, among other
technologies, will be available for commercial adoption in

the fleet during this rulemaking timeframe.
---------------------------------------------------------------------------

    \392\ NHTSA-2018-0067-11985.
    \393\ Docket No. NHTSA-2021-0053-0010, Evaluation of Rolling
Resistance and Wet Grip Performance of OEM Stock Tires Obtained from
NCAP Crash Tested Vehicles Phase One and Two, Memo to Docket--
Rolling Resistance Phase One and Two.
    \394\ Technical Analysis of Vehicle Load Reduction by CONTROLTEC
for California Air Resources Board (April 29, 2015).
    \395\ NHTSA DOT HS 811 154.
---------------------------------------------------------------------------

    However, we did not consider ROLL30 for the HDPUV fleet, for
several reasons. We do not believe that HDPUV manufacturers will use
ROLL30 tires because of the significant added cost for the technology
while they would see more fuel efficiency benefits from powertrain
improvements. As discussed further below, our cost estimates for ROLL30
technology--which incorporate both technology and materials costs--are
approximately double the costs of ROLL20. In addition, a significant
majority of the HDPUV fleet currently employs no low rolling resistance
tire technology. We believe that HDPUV manufacturers will still move
through ROLL10 and ROLL20 technology in the rulemaking timeframe. That
said, we welcome any data or feedback from stakeholders showing a
pathway to ROLL30 (i.e., vehicles that can achieve a RRC value of
0.0063) for HDPUVs.
    Assigning low rolling resistance tire technology to the baseline
fleet is difficult because RRC data is not part of tire manufacturers'
publicly released specifications, and because vehicle manufacturers
often offer multiple wheel and tire packages for the same nameplate.
Consistent with previous rules, we used a combination of CBI data, data
from a NHTSA-sponsored ROLL study, and assumptions about parts-sharing
to assign tire technology in the baseline fleet. A slight majority of
vehicles (52.9%) in the baseline LD fleet do not use any ROLL
improvement technology, while 16.2% of baseline vehicles use ROLL10 and
24.9% of baseline vehicles use ROLL20. Only 6% of vehicles in the
baseline LD fleet use ROLL30. Most (74.5%) vehicles in the HDPUV fleet
do not use any ROLL improvement technology, and 3.0% and 22.5% use
ROLL10 and ROLL20, respectively.
    The CAFE Model can apply ROLL technology at either a vehicle
refresh or redesign. We recognize that some vehicle manufacturers
prefer to use higher RRC tires on some performance cars and SUVs. Since
most of performance cars have higher torque, to avoid tire slip, OEMs
prefer to use higher RRC tires for these vehicles. Like the aerodynamic
technology improvements discussed above, we applied ROLL technology
adoption features based on vehicle horsepower and body style. All
vehicles in the LD and HDPUV fleets that have below 350hp can adopt all
levels of ROLL technology.
    Table II-18 shows that all LDVs under 350hp can adopt ROLL
technology, and as vehicle hp increases, fewer vehicles can adopt the
highest levels of ROLL technology. Note that ROLL30 is not available
for vehicles in the HDPUV fleet not because of an adoption feature, but
because it is not included in the ROLL technology pathway.

                                                    Table II-18--When Can ROLL Technology Be Applied?
--------------------------------------------------------------------------------------------------------------------------------------------------------
          Technology                                     Light Duty                                                      HDPUV
--------------------------------------------------------------------------------------------------------------------------------------------------------
    Engine horsepower (hp)          <350            >=350           >=405          >=500           <350          >=350          >=405          >=500
--------------------------------------------------------------------------------------------------------------------------------------------------------
ROLL0........................  All body        All body        All body        All body       All body       All body       All body       All body
                                styles.         styles.         styles.         styles.        styles.        styles.        styles.        styles.
ROLL10.......................  All body        All body        All body        --Pickup       All body       All body       All body       --Work truck.
                                styles.         styles.         styles.         truck.         styles.        styles.        styles.
ROLL20.......................  All body        All body        --Pickup truck  No body        All body       All body       --Work truck.  No body
                                styles.         styles.        --SUV.........   styles.        styles.        styles.       --Work van...   styles.
                                                               --Van.........                                               --Fleet SUV..
                                                               --Minivan.....                                               --Chassis Cab
                                                                                --Cutaway....
ROLL30.......................  All body        --Pickup truck  No body styles  No body        All body       N/A..........  N/A..........  N/A.
                                styles.        --Sport                          styles.        styles.
                                                Utility.
                                               --Van.........
                                               --Minivan.....
--------------------------------------------------------------------------------------------------------------------------------------------------------

    Figure II-36, Figure II-37, and Figure II-38 show how effective the
different levels of ROLL technology are at improving vehicle fuel
consumption.
BILLING CODE 4910-59-P

[GRAPHIC] [TIFF OMITTED] TP17AU23.048

[GRAPHIC] [TIFF OMITTED] TP17AU23.049

[GRAPHIC] [TIFF OMITTED] TP17AU23.050

BILLING CODE 4910-59-C
    DMCs and learning rates for ROLL10 and ROLL20 are the same as prior
analyses,\396\ but are updated to the dollar-year used in this
analysis. In the absence of ROLL30 DMCs from tire manufacturers,
vehicle manufacturers, or studies, to develop the DMC for ROLL30 we
extrapolated the DMCs for ROLL10 and ROLL20. We seek comment on this
approach, and if we receive updated information from tire or vehicle
manufacturers, or other studies, we will update it for future analyses.
In addition, we used the same DMCs for the LD and HDPUV analyses. This
is because the original cost of a potentially heaver or sturdier HDPUV
tire is already accounted for in the baseline MSRP of a HDPUV in our
baseline, and the DMC represents the added cost of the improved tire
technology. In addition, as discussed above, LD and HDPUV tires are
often interchangeable. We believe that the added cost of each tire
technology accurately represents the price difference that would be
experienced by the different fleets. ROLL technology costs are
discussed in detail in Chapter 3.6 of the Draft TSD, and ROLL
technology costs for all vehicle technology classes can be found in the
CAFE Model's Technologies Input File.
---------------------------------------------------------------------------

    \396\ See NRC/NAS Special Report 286, Tires and Passenger
Vehicle Fuel Economy: Informing Consumers, Improving Performance
(2006); Corporate Average Fuel Economy for MY 2011 Passenger Cars
and Light Trucks, Final Regulatory Impact Analysis (March 2009), at
V-137; Joint Technical Support Document: Rulemaking to Establish
Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate
Average Fuel Economy Standards (April 2010), at 3-77; Draft
Technical Assessment Report: Midterm Evaluation of Light-Duty
Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel
Economy Standards for Model Years 2022-2025 (July 2016), at 5-153
and 154, 5-419. In brief, the estimates for ROLL10 are based on the
incremental $5 value for four tires and a spare tire in the NAS/NRC
Special Report and confidential manufacturer comments that provided
a wide range of cost estimates. The estimates for ROLL20 are based
on incremental interpolated ROLL10 costs for four tires (as NHTSA
and EPA believed that ROLL20 technology would not be used for the
spare tire), and were seen to be generally fairly consistent with
CBI suggestions by tire suppliers.
---------------------------------------------------------------------------

5. Simulating AC Efficiency and Off-Cycle Technologies
    Off-cycle and AC efficiency technologies can provide fuel economy
benefits in real-world vehicle operation, but the traditional 2-cycle
test procedures (i.e., FTP and HFET) used to measure fuel economy
cannot fully capture those benefits.\397\ Off-cycle technologies can
include, but are not limited to, thermal control technologies, high-
efficiency alternators, and high-efficiency exterior lighting. As an
example, manufacturers can claim a benefit for thermal control
technologies like active seat ventilation and solar reflective surface
coating, which help to regulate the temperature within the vehicle's
cabin--making it more comfortable for the occupants and reducing the
use of low-efficiency heating, ventilation, and air-conditioning (HVAC)
systems. AC efficiency technologies are technologies that reduce the
operation of or the loads on the compressor, which pressurizes AC
refrigerant. The less the compressor operates or the more efficiently
it operates, the less load the compressor places on the engine or
battery storage system, resulting in better fuel efficiency. AC
efficiency technologies can include, but are not limited to, blower
motor controls, internal heat exchangers, and improved condensers/
evaporators.
---------------------------------------------------------------------------

    \397\ See 49 U.S.C 32904(c) (``The Administrator shall measure
fuel economy for each model and calculate average fuel economy for a
manufacturer under testing and calculation procedures prescribed by
the Administrator. The Administrator shall use the same procedures
for passenger automobiles the Administrator used for model year 1975
(weighted 55 percent urban cycle and 45 percent highway cycle), or
procedures that give comparable results.'').
---------------------------------------------------------------------------

    Vehicle manufacturers have the option to generate credits for off-
cycle technologies and improved AC systems under the EPA's
CO2 program and receive a fuel consumption improvement value
(FCIV) equal to the value of the benefit not captured on the 2-cycle
test under NHTSA's CAFE program. The FCIV is not a ``credit'' in the
NHTSA CAFE program--unlike, for example, the statutory overcompliance
credits prescribed in 49 U.S.C. 32903--but FCIVs increase the reported
fuel economy of a manufacturer's fleet, which is used to determine
compliance. EPA applies FCIVs during determination of a fleet's final
average

fuel economy reported to NHTSA.\398\ We only calculate and apply FCIVs
at a manufacturer's fleet level, and the improvement is based on the
volume of the manufacturer's fleet that contains qualifying
technologies.
---------------------------------------------------------------------------

    \398\ 49 U.S.C. 32904. Under EPCA, the Administrator of the EPA
is responsible for calculating and measuring vehicle fuel economy.
---------------------------------------------------------------------------

    We currently do not model AC efficiency and off-cycle technologies
in the CAFE Model like we model other vehicle technologies, for several
reasons. Each time we add a technology option to the CAFE Model's
technology pathways we increase the number of Autonomie simulations by
approximately a hundred thousand. This means that to add just five AC
efficiency and five off-cycle technology options would double our
Autonomie simulations to around two million total simulations. In
addition, 40 CFR 600.512-12 does not require manufacturers to submit
information regarding AC efficiency and off-cycle technologies on
individual vehicle models in their FMY reports to EPA and NHTSA.\399\
In their FMY reports, manufacturers are only required to provide
information about AC efficiency and off-cycle technology application at
the fleet level. However, starting with MY 2023, manufacturers are
required to submit AC efficiency and off-cycle technology data to NHTSA
in the new CAFE Projections Reporting Template for PMY, MMY and
supplementary reports. Once we begin evaluating manufacturer
submissions in the CAFE Projections Reporting Template we may
reconsider in future analyses how off-cycle and AC efficiency
technologies are evaluated in the analysis. However, developing a
robust methodology for including off-cycle and AC efficiency
technologies in the analysis depends on manufacturers giving us robust
data.
---------------------------------------------------------------------------

    \399\ 40 CFR 600.512-12.
---------------------------------------------------------------------------

    Instead, the CAFE Model applies predetermined AC efficiency and
off-cycle benefits to each manufacturer's fleet after the CAFE Model
applies traditional technology pathway options. The CAFE Model attempts
to apply pathway technologies and AC efficiency and off-cycle
technologies in a way that both minimizes cost and allows the
manufacturer to meet a given level of CAFE standard without over or
under complying. The predetermined benefits that the CAFE Model applies
for AC efficiency and off-cycle technologies are based on EPA's 2022
Trends Report and CBI compliance data from vehicle manufacturers. We
started with each manufacturer's latest reported values and
extrapolated the values to the regulatory cap on benefits that
manufacturers are allowed to claim, considering each manufacturer's
fleet composition (i.e., passenger cars versus light trucks) and
historic AC efficiency and off-cycle technology use. In general, data
shows that manufacturers apply less off-cycle technology to passenger
cars than pickup trucks, and our input assumptions reflect that.
Additional details about how we determined AC efficiency and off-cycle
technology application rates are discussed Chapter 3.7 of the Draft
TSD.
    New for this analysis, we also developed a methodology for
considering BEV AC efficiency and off-cycle technology application. We
did this because the analytical ``no-action'' baseline against which we
measure the costs and benefits of our standards includes an appreciable
number of BEVs. Because BEVs are not equipped with a traditional engine
or transmission, they cannot benefit from off-cycle technologies like
engine idle start-stop, active transmission and engine warm-up, and
high efficiency alternator technologies. However, BEVs still benefit
from technologies like high efficiency lighting, solar panels, active
aerodynamic improvement technologies, and thermal control technologies.
We calculated the maximum off-cycle benefit that the model could apply
for each manufacturer and each MY based on off-cycle technologies that
could be applied to BEVs and the percentage of BEVs in each
manufacturer's fleet. Note that we do not include PHEVs in this
calculation, because they still use a conventional engine and
transmission. We discuss additional details and assumptions for this
calculation in Chapter 3.7 of the Draft TSD.
    Note that we do not model AC efficiency and off-cycle technology
benefits for HDPUVs. We have received petitions for off-cycle benefits
for HDPUVs from manufacturers, but to date, none have been approved.
    Because the CAFE Model applies AC efficiency and off-cycle
technology benefits independent of the technology pathways, we must
account for the costs of those technologies independently as well. We
generated costs for these technologies on a dollars per gram of
CO2 per mile ($ per g/mi) basis, as AC efficiency and off-
cycle technology benefits are applied in the CAFE Model on a gram per
mile basis (as in the regulations). Like the last CAFE analysis, we
used data from EPA's Proposed Determination TSD and the 2012 Joint
NHTSA/EPA TSD, updated to 2018$ with an indirect cost markup and
relatively flat learning rate applied. We did not have time to update
these costs to 2021$, but will do so for the final rule, and we expect
the impact to be minimal. Additional details and assumptions used for
A/C Efficiency and off-cycle costs is discussed in Chapter 3.7 of the
Draft TSD.

E. Consumer Responses to Manufacturer Compliance Strategies

    The previous subsections in Section II have so far discussed how
manufacturers might respond to changes to the standards. While the
technology analysis is informative of the different compliance
strategies available to manufactures, the tangible costs and benefits
that accrue because of the standards are dependent on how consumers
respond to the decisions made by manufacturers. Many, if not most, of
the benefits and costs resulting from changes to standards are private
benefits that accrue to the buyers of new vehicles, produced in the MYs
under consideration. These benefits and costs largely flow from the
changes to vehicle ownership and operating costs that result from
improved fuel economy, and the cost of the technology required to
achieve those improvements. The remaining benefits are also derived
from how consumers use--or do not use--vehicles. Since they are not
borne directly by the consumer who purchases or operates the new
vehicle, we categorize these as ``external'' benefits, even if they do
not necessarily meet the economic definition of an externality. The
next few subsections walk through how the analysis models how consumers
respond to changes to vehicles implemented by manufacturers to respond
to the CAFE and HDPUV FE standards. NHTSA seeks comment on the
following discussion.
1. Macroeconomic and Consumer Behavior Assumptions
    This proposal includes a comprehensive economic analysis of the
impacts of the proposed standards. Most of the effects measured are
influenced by macroeconomic conditions that are exogenous to the
agency's influence. For example, fuel prices are mainly determined by
global supply and demand, and yet they partially determine how much
fuel efficiency technology manufacturers will apply to U.S. vehicles,
how much consumers are willing to pay for a new vehicle, the amount of
travel in which all users engage, and the value of each gallon saved
from higher standards. Constructing these forecasts requires robust
projections of macroeconomic variables that span the timeframe of the
analysis, including real GDP, consumer

confidence, U.S. population, and real disposable personal income.
    The analysis presented along with this proposal employs fuel price
forecasts developed by the EIA's NEMS. EIA is an agency within the U.S.
DOE which collects, analyzes, and disseminates independent and
impartial energy information to promote sound policymaking, efficient
markets, and public understanding of energy and its interaction with
the economy and the environment. EIA uses NEMS to produce its AEO,
which presents forecasts of future fuel prices, among many other
energy-related variables. The analysis uses the 2022 EIA forecasts of
fuel prices and electricity prices.
    The analysis also uses IHS Markit Global Insight forecasts of U.S.
population, GDP, total number of households, and disposable personal
income. We chose to use these estimates as they are the same estimates
employed by EIA to construct their AEO projections. The agency uses a
forecast of consumer confidence to project sales from the IHS Markit
Global Insight long-term macroeconomic model.
    While these macroeconomic assumptions are important inputs to the
analysis, they are also subject to the most uncertainty--particularly
over the full lifetimes of the vehicles affected by this proposed rule.
The agency uses low and high cases from the AEO as bounding cases for
fuel price sensitivity analyses. The purpose of the sensitivity
analyses, discussed in greater detail in Chapter 9 of the PRIA, is not
to posit a more credible future state of the world than the central
case assumes--we assume the central case is the most likely future
state of the world--but rather to measure the degree to which important
outcomes can change under different assumptions about fuel prices.
    The first year simulated in this analysis is 2022, though it is
based on observational data (rather than forecasts) to the greatest
extent possible. The elements of the analysis that rely most heavily on
the macroeconomic inputs--aggregate demand for VMT, new vehicle sales,
used vehicle retirement rates--all reflect the continued return to pre-
pandemic growth rates (in all the regulatory alternatives). See Chapter
4.1 of the Draft TSD for a more complete discussion of the
macroeconomic assumptions made for the analysis.
    Another key assumption that permeates throughout the analysis is
how much consumers are willing to pay for fuel economy. Increased fuel
economy offers vehicle owners savings through reduced fuel expenditures
throughout the lifetime of a vehicle. If buyers fully value the savings
in fuel costs that result from driving (and potentially re-selling)
vehicles with higher fuel economy and manufacturers supply all
improvements in fuel economy that buyers demand, market-determined
levels of fuel economy would reflect both the cost of improving it and
the private benefits from doing so. In that case, regulations on fuel
economy would only be necessary to reflect environmental or other
benefits other than to buyers themselves. But if consumers instead
undervalue future fuel savings or are otherwise unable to purchase
their optimal levels of fuel economy due to market failures, they will
underinvest in fuel economy and manufacturers would spend too little on
fuel-saving technology (or deploy its energy-saving benefits to improve
vehicles' other attributes). In that case, more stringent fuel economy
standards could lead manufacturers to adopt improvements in fuel
economy that not only reduce external costs from producing and
consuming fuel to appropriate levels but also improve consumer welfare.
    Increased fuel economy offers vehicle owners significant potential
savings. The analysis shows that the value of prospective fuel savings
exceeds manufacturers' technology costs to comply with the preferred
alternatives for HDPUVs and light trucks discounted at 3 percent, and
the fuel savings for passenger automobiles pays back a significant
portion of the upfront costs. It would seem reasonable to assume that
well-informed vehicle shoppers, if without time constraints or other
barriers to rational decision-making, will recognize the full value of
fuel savings from purchasing a model that offers higher fuel economy,
since they would enjoy an equivalent increase in their disposable
income and the other consumption opportunities it affords them; or for
commercial operators, higher fuel efficiency would free up additional
capital for either higher profits or additional business ventures. If
consumers did value the full amount of fuel savings, more fuel-
efficient vehicles would functionally be less costly for consumers to
own when considering both their initial purchase prices and subsequent
operating costs, thus making the models that manufacturers are likely
to offer under stricter alternatives more attractive than those
available under the No-Action Alternative.
    Recent econometric research is divided between studies concluding
that consumers value most or all of the potential savings in fuel costs
from driving higher-mpg vehicles, and those concluding that consumers
significantly undervalue expected fuel savings. More circumstantial
evidence appears to show that consumers do not fully value the expected
lifetime fuel savings from purchasing higher-mpg models. Although the
average fuel economy of new light vehicles reached an all-time high in
MY 2021 of 25.4 mpg,\400\ this is still significantly below the fuel
economy of the fleet's most efficient vehicles that are readily
available to consumers.\401\ Manufacturers have repeatedly informed the
agency that consumers only value between 2 to 3 years-worth of fuel
savings when making purchasing decisions. And in the last CAFE
rulemaking, the Environmental Defense Fund commented with a Consumer
Reports article indicating that 64 percent of consumers ranked fuel
economy as extremely or very important, and viewed fuel economy as the
attribute that has the most room for improvement, but only 29% of those
same respondents would be willing to pay for technology that paid back
over a period in excess of 3 years with the average consumer willing to
pay for fuel economy that recouped the upfront costs between 2 and 3
years.\402\
---------------------------------------------------------------------------

    \400\ See EPA 2022 Automotive Trends Report at 5. Available at
https://www.epa.gov/system/files/documents/2022-12/420r22029.pdf.
(Accessed: May 31, 2023).
    \401\ Id. At 9.
    \402\ 87 FR 25856.
---------------------------------------------------------------------------

    The potential for buyers to voluntarily forego improvements in fuel
economy that offer savings exceeding their initial costs is one example
of what is often termed the ``energy-efficiency gap.'' This appearance
of such a gap, between the level of energy efficiency that would
minimize consumers' overall expenses and what they actually purchase,
is typically based on engineering calculations that compare the initial
cost for providing higher energy efficiency to the discounted present
value of the resulting savings in future energy costs. There has long
been an active debate about why such a gap might arise and whether it
actually exists. Economic theory predicts that economically rational
individuals will purchase more energy-efficient products only if the
savings in future energy costs they offer promise to offset their
higher initial costs.
    On the other hand, behavioral economics has documented numerous
situations in which the decision-making of consumers differs in
important ways from the from the predictions of the standard model of
rational consumer behavior, especially for choices under

uncertainty.\403\ The future value of purchasing a model that offers
higher fuel economy is uncertain for several reasons, but particularly
because the mileage any particular consumer experiences will generally
differ from that shown on fuel economy labels, potential buyers may be
uncertain how much they will actually drive a new vehicle, future
resale prices may be uncertain, and future fuel prices are highly
uncertain. Recent research indicates that typical consumers exhibit
several behavioral departures from the rational economic model, some of
which could explain undervaluing of fuel economy to an extent roughly
consistent with the agency's assumed 30-month payback rule. These
include loss aversion (valuing potential losses more than potential
gains when faced with an uncertain choice), present bias (the tendency
to use DRs that decrease over time, also known as hyperbolic
discounting), certainty bias (a preference for certain over uncertain
options) and inattention or satisficing.\404\ Behavioral economic
theory also differs from rational economic theory by recognizing that
consumers' preferences may change depending on the context of a choice.
In addition, behavioral economics recognizes that by conscious
deliberation or learning by experience consumers can overrule behaviors
that differ from the rational economic model. There are also a variety
of classic externalities that could prevent consumers in an unregulated
market from fully purchasing levels of fuel efficiency that will
deliver net present savings, including informational asymmetries
between consumers, dealerships, and manufacturers; market power; first-
mover disadvantages for both consumers and manufacturers; principal-
agent split incentives between vehicle purchasers and vehicle drivers;
and positional externalities.\405\
---------------------------------------------------------------------------

    \403\ e.g. Dellavigna, S. 2009. Psychology and economics:
Evidence from the field. Journal of Economic Literature. 47(2): pp.
315-372.
    \404\ Satisficing is when a consumer finds a solution that meets
enough of their requirements instead of searching for a vehicle that
optimizes their utility.
    \405\ For a discussion of these potential market failures, see
Rothschild, R. and Schwartz, J. (2021) ``Tune Up: Fixing Market
Failures to Cut Fuel Costs and Pollution from Cars and Trucks''
Institute for Policy Integrity. New York University School of Law.
---------------------------------------------------------------------------

    If the behavioral explanation for how potential new buyers choose
fuel economy is more accurate than the rational economic model, there
could be important implications for our cost-benefit analysis. Because
preferences can be context dependent, some consumers may view the
decision whether to buy a model offering increased fuel economy in a
market without increasing fuel economy standards as a risky choice,
because their return from the purchase will vary with their future
travel activity and gasoline prices. In contrast, if the fuel economies
of most new vehicles are increasing in response to higher standards,
they may view the relative risk/reward of purchasing a vehicle with
higher fuel economy more favorably. When fuel economy standards
increase incrementally over several years, consumers' experience might
lead them to conclude that the value of fuel savings was worth the
higher cost to purchase more fuel-efficient models, even if that was
not their initial view. Such differences from rational economic theory
could affect NHTSA's estimates of the impacts of raising standards on
new vehicle sales as well as the usage and retirement rates of used
vehicles, with important implications for safety, emissions, and
employment, as well as for the welfare of producers and consumers.
    The analysis assumes that potential buyers value only the
undiscounted savings in fuel costs from purchasing a higher-mpg model
they expect to realize over the first 30 months (i.e., 2.5 years) they
own it. NHTSA feels that 30 months is supported by the totality of
present literature and is consistent with manufacturer assumptions
about consumer demand. Depending on the DR buyers are assumed to apply,
this amounts to 25-30% of the expected savings in fuel costs over its
entire lifetime. These savings would offset only a fraction of the
expected increase in new vehicle prices that NHTSA estimates will be
required for manufacturers to recover their increased costs for making
required improvements to fuel economy. NHTSA seeks comment on whether
30 months of undiscounted fuel savings is an appropriate measure for
the analysis of consumer willingness to pay for fuel economy. The
assumption also has important implications for other outcomes of the
model, including for VMT, safety, and air pollution emissions
projections, and NHTSA has included a handful of sensitivity cases to
examine the impacts of higher and lower payback periods on the
analysis. If commenters believe a different amount of time should be
used for the payback assumption, it would be most helpful to NHTSA if
commenters could define the amount of time, provide an explanation of
why that amount of time is preferable, and provide any data or
information on which the amount of time is based. These concepts are
explored more thoroughly in Chapter <IP_ADDRESS> of the Draft TSD and
Chapter 2.4 of the PRIA.
    It is possible that commercial operators, to the extent they act as
profit-maximizing entities could value the tradeoff between long-term
fuel savings and upfront capital differently than the average non-
commercial consumer. However, both commercial and non-commercial
consumers may face their own set of market failures and other
constraints that may prevent them from purchasing in an un-regulated
market the level of fuel efficiency that may maximize their private net
benefits. Additionally, the CAFE Model is unable to distinguish between
these two types of purchasers. Given this constraint, NHTSA believes
that using the same payback period for the HDPUV fleet as for the LD
fleet made sense. Similar to the LD analysis, the agency is including
several sensitivity cases testing alternative payback assumptions for
HDPUVs.
2. Fleet Composition
    The composition of the on-road fleet--and how it changes in
response to the standards--determines many of the costs and benefits of
the proposal. For example, how much fuel the LD fleet consumes is
dependent on the number and efficiency of new vehicles sold, older (and
less efficient) vehicles retired, and how much those vehicles are
driven.
    Until the 2020 final rule, all previous CAFE rulemaking analyses
used static fleet forecasts that were based on a combination of
manufacturer compliance data, public data sources, and proprietary
forecasts (or product plans submitted by manufacturers). When
simulating compliance with regulatory alternatives, those analyses
projected identical sales and retirements across the alternatives, for
each manufacturer down to the make/model level--where the exact same
number of each model variant was assumed to be sold in a given MY under
both the least stringent alternative (typically the baseline) and the
most stringent alternative considered (intended to represent ``maximum
technology'' scenarios in some cases).
    However, a fleet forecast is unlikely to be representative of a
broad set of regulatory alternatives with significant variation in the
cost of new vehicles. Several commenters on previous regulatory actions
and peer reviewers of the CAFE Model encouraged consideration of the
potential impact of fuel efficiency standards on new vehicle

prices and sales, the changes to compliance strategies that those
shifts could necessitate, and the downstream impact on vehicle
retirement rates. In particular, the continued growth of the utility
vehicle segment causes changes within some manufacturers' fleets as
sales volumes shift from one region of the footprint curve to another,
or as mass is added to increase the ride height of a vehicle on a sedan
platform to create a crossover utility vehicle, which exists on the
same place of the footprint curve as the sedan upon which it might be
based.
    The analysis accompanying this proposal, like the 2020 and 2022
rulemakings, dynamically simulates changes in the vehicle fleet's size,
composition, and usage as manufacturers and consumers respond to
regulatory alternatives, fuel prices, and macroeconomic conditions. The
analysis of fleet composition is comprised of two forces, how new
vehicle sales--the flow of new vehicles into the registered
population--change in response to regulatory alternatives, and the
influence of economic and regulatory factors on vehicle retirement
(otherwise known as scrappage). Below are brief descriptions of how the
agency models sales and scrappage. For a full explanation, refer to
Chapter 4.2 of the Draft TSD. Particularly given the broad uncertainty
discussed in Chapter 4.2 of the Draft TSD, NHTSA seeks comment on the
discussion below and the associated discussions in the TSD, on the
internal structure of the sales and scrappage modules, and whether and
how to change the sales and scrappage analyses for the final rule.
a. Sales
    For the purposes of regulatory evaluation, the relevant sales
metric is the difference between alternatives rather than the absolute
number of sales in any of the alternatives. As such, the sales response
model currently contains three parts: a nominal forecast that provides
the level of sales in the baseline (based upon macroeconomic inputs,
exclusively), a price elasticity that creates sales differences
relative to that No-Action alternative in each year, and a fleet share
model that produces differences in the passenger car and light truck
market share in each alternative. For a more detailed description of
these three parts, see Chapter 4.2 of the Draft TSD.
    The current baseline sales module reflects the idea that total new
vehicle sales are primarily driven by conditions in the economy that
are exogenous to the automobile industry. Over time, new vehicle sales
have been cyclical--rising when prevailing economic conditions are
positive (periods of growth) and falling during periods of economic
contraction. While the kinds of changes to vehicle offerings that occur
as a result of manufacturers' compliance actions exert some influence
on the total volume of new vehicle sales, they are not determinative.
Instead, they drive the kinds of marginal differences between
regulatory alternatives that the current sales module is designed to
simulate--more expensive vehicles, generally, reduce total sales but
only marginally.
    The first component of the sales response model is the nominal
forecast, which is a function with a small set of macroeconomic inputs
that determines the size of the new vehicle market in each CY in the
analysis for the baseline. It is of some relevance that this
statistical model is intended only as a means to project a baseline
sales series for LDVs. The nominal forecast model does not include
prices and is not intended for statistical inference around the
question of price response in the new vehicle market. NHTSA's
projection oscillates by MY at the beginning of the analysis before
settling on a constant trend in the 2030s. This result seems consistent
with the continued response to the pandemic and to supply chain
challenges. NHTSA's projections for most MYs fall between AEO 2021 and
2022 forecasts, which were run as sensitivity cases. NHTSA will
continue to monitor macroeconomic data and new vehicle sales and update
its baseline forecast as appropriate.
    The baseline HDPUV fleet is modeled differently. NHTSA considered
using a statistical model drawn from the LD specification to project
new HDPUV sales but reasoned that the mix of HDPUV buyers and vehicles
was sufficiently different that an alternative approach was required.
Due to a lack of historical and future data on the changing customer
base in the HDPUV market (e.g., the composition of commercial and
personal users) and uncertainty around vehicle classification at the
LDV and HDPUV margin, NHTSA chose to rely on an exogenous forecast path
from the AEO to project sales. To align with the technology used to
create the model fleet, NHTSA used compliance data from multiple MYs to
estimate aggregate sales for MY 2022 and then applied year-over-year
growth rates taken from the AEO forecast to project aggregate sales for
subsequent MYs. Since the first year of the analysis, MY 2022, was
constructed using compliance data spanning nearly a decade, the
aggregate number of sales for the simulated fleet in MY 2022 was lower
than the MY 2022 AEO forecast. To align with the AEO projections, the
agency applied an upward adjustment to the HDPUV growth rate of 2
percent for MYs 2023-2025, and 2.5 percent for MYs 2026-2028. Instead
of adjusting the fleet size to match AEO's in MY2022, the agency
elected to phase-in the increase in growth rates over a span of years
to reflect that HDPUV production may continue to face supply
constraints resulting from the COVID pandemic in the near future but
should return to normal sometime later in the decade. NHTSA seeks
comment on this approach, and whether it should implement an approach
similar to how NHTSA models LDV sales.
    The second component of the sales response model captures how price
changes affect the number of vehicles sold. NHTSA applies a price
elasticity to the percentage change in average price (in each year).
The price change does not represent an increase/decrease over the last
observed year, but rather the percentage change relative to the
baseline for that year. In the baseline, the average price is defined
as the observed new vehicle price in 2022 (the last historical year
before the simulation begins) plus the average regulatory cost
associated with the No-Action Alternative for each MY.\406\ The central
analysis in this proposal simulates multiple programs simultaneously
(CAFE and HDPUV FE final standards, EPA final GHG standards, ZEV, and
the California Framework Agreement), and the regulatory cost includes
both technology costs and civil penalties paid for non-compliance (with
CAFE standards) in a MY. We also subtract any IRA tax credits that a
vehicle may qualify for from the regulatory costs.\407\ Because the
elasticity assumes no perceived change in the quality of the product,
and the vehicles produced under different regulatory scenarios have
inherently different operating costs, the price metric must account for
this difference. The price to which the elasticity is applied in this
analysis represents the residual price change between scenarios after
accounting for

2.5 years' worth of fuel savings to the new vehicle buyer.
---------------------------------------------------------------------------

    \406\ The CAFE Model currently operates as if all costs incurred
by the manufacturer as a consequence of meeting regulatory
requirements, whether those are the cost of additional technology
applied to vehicles in order to improve fleetwide fuel economy or
civil penalties paid when fleets fail to achieve their standard, are
``passed through'' to buyers of new vehicles in the form of price
increases.
    \407\ For additional details about how we model tax credits, see
Section II.C.5b above.
---------------------------------------------------------------------------

    The price elasticity is also specified as an input, and for this
analysis the agency assumes an elastic response of -0.4--meaning that a
five percent increase in the average price of a new vehicle produces a
two percent decrease in total sales. As explained in Chapter <IP_ADDRESS> of
the Draft TSD, NHTSA selected this elasticity because of the totality
of present evidence. NHTSA seeks comment on this assumption and has
included several sensitivity cases testing alternative values.
    The third and final component of the sales model, which only
applies to the LD fleet, is the dynamic fleet share module (DFS). Some
commenters to previous rules noted that the market share of SUVs
continues to grow, while conventional passenger car body-styles
continue to lose market share. For instance, in the 2012 final rule,
the agencies projected fleet shares based on the continuation of the
baseline standards (MYs 2012-2016) and a fuel price forecast that was
much higher than the realized prices since that time. As a result, that
analysis assumed passenger car body-styles comprising about 70 percent
of the new vehicle market by 2025. The reality, however, has been quite
different; in 2021, passenger cars represented only 22% of new vehicle
sales.\408\ Since the 2020 rule, NHTSA has incorporated a DFS into the
CAFE Model in an attempt to address these market realities.
---------------------------------------------------------------------------

    \408\ See Bureau of Transportation Statistics. 2023. National
Transportation Statics. Table 1-17. Avaliable at: https://www.bts.gov/content/new-and-used-passenger-car-sales-and-leases-thousands-vehicles. (Accessed May 31, 2023).
---------------------------------------------------------------------------

    For the 2020 and 2022 rulemakings, NHTSA used a DFS model crafted
from two functions from the NEMS used for the 2017 AEO to independently
estimate the share of passenger cars and light trucks, respectively,
given average new market attributes (fuel economy, horsepower, and curb
weight) for each group and current fuel prices, as well as the prior
year's market share and prior year's attributes. The two independently
estimated shares are then normalized to ensure that they sum to one.
However, as the agency explained in the 2022 final rulemaking, that
approach had several drawbacks including the model having
counterintuitive signs, the exclusion a variable for price, and an
overestimation of the fleet share of passenger automobile as currently
observed.\409\
---------------------------------------------------------------------------

    \409\ 84 FR 25861 (May 2, 2022).
---------------------------------------------------------------------------

    For this proposal, NHTSA has revised its approach to modeling the
DFS. The baseline fleet share projection is derived from the agency's
own compliance data for the 2022 fleet, and the 2022 AEO projections
for later MYs. To reconcile differences in the initial 2022 shares,
NHTSA projected the fleet share forward using the annual changes from
2022 predicted by AEO and applied these to the agency's own compliance
fleet shares for MY 2022.\410\ The fleet is distributed across two
different body-types: ``cars'' and ``light trucks.'' While there are
specific definitions of ``passenger cars'' and ``light trucks'' that
determine a vehicle's regulatory class, the distinction used in this
phase of the analysis is more simplistic. All body-styles that are
commonly considered a car--sedans, coupes, convertibles, hatchbacks,
and station wagons--are defined as ``cars'' for the purpose of
determining fleet share. Everything else--SUVs, smaller SUVs
(crossovers), vans, and pickup trucks--are defined as ``light
trucks''--even though they may not be treated as such for compliance
purposes.
---------------------------------------------------------------------------

    \410\ For example if AEO PC share grows from 40 percent in one
year to 50 percent in the next (25 percent growth), and our
compliance PC share in that year is 44 percent then the predicted
share in the next year would be 55 percent (11 points or 25 percent
higher).
---------------------------------------------------------------------------

    These shares are applied to the total industry sales derived in the
first stage of the sales response. This produces total industry volumes
of car and light truck body styles. Individual model sales are then
determined from there based on the following sequence: (1) individual
manufacturer shares of each body style (either car or light truck)
times the total industry sales of that body style, then (2) each
vehicle within a manufacturer's volume of that body-style is given the
same percentage of sales as appear in the 2022 fleet. This implicitly
assumes that consumer preferences for particular styles of vehicles are
determined in the aggregate (at the industry level), but that
manufacturers' sales shares of those body styles are consistent with MY
2022 sales. Within a given body style, a manufacturer's sales shares of
individual models are also assumed to be constant over time. This
approach implicitly assumes that manufacturers are currently pricing
individual vehicle models within market segments in a way that
maximizes their profit. Without more information about each OEM's true
cost of production and operation, fixed and variables costs, and both
desired and achievable profit margins on individual vehicle models,
there is no basis to assume that strategic shifts within a
manufacturer's portfolio will occur in response to standards.
    Similar to the second component of the sales module, the DFS then
applies an elasticity to the change in price between alternatives and
the No-Action Alternative to determine the change in fleet share. NHTSA
uses the net regulatory cost differential (costs minus fuel savings) in
a logistic model to capture the changes in fleet share between
passenger cars and light trucks, with a price coefficient of -0.000042.
NHTSA selected this methodology and price coefficient based on academic
literature.\411\ When the total regulatory costs of passenger
automobiles minus fuel savings exceeds that of light-trucks, the market
share of light-trucks will rise relative to passenger automobiles. For
example, a $100 net regulatory cost increase in passenger automobiles
relative to light trucks would produce a ~.1% shift in market share
towards light trucks assuming light trucks initially represented 60% of
the fleet. NHTSA seeks comment on how it is modeling the DFS in this
proposal, and more specifically seeks input to the elasticity NHTSA is
using.
---------------------------------------------------------------------------

    \411\ The agency describes this literature review and the
calibrated logit model in more detail in the accompanying docket
memo ``Calibrated Estimates for Projecting Light-Duty Fleet Share in
the CAFE Model''.
---------------------------------------------------------------------------

    The approach for this proposal to modeling changes in fleet share
addresses several key concerns raised by NHTSA in its prior rulemaking.
There are no longer any counterintuitive signs, and the model now
directly considers the impacts of changes in price. While the model
applies fuel savings in determining the relative changes in prices
between passenger cars and light-trucks, the current approach does not
explicitly consider the utility of fuel economy when determining the
respective market share of passenger automobiles and light trucks. In
prior rules, NHTSA has speculated that the rise in light-truck market
share may be attributable to the increased utility that light-trucks
provide their operators, and as the fuel economy between the different
body-styles diminished, light-trucks have become an even more
attractive option. As explained in a docket memo, NHTSA has been unable
to create a comprehensive model that includes the variables in NEMS,
price, and fuel economy that behaves appropriately. NHTSA is
considering applying an elasticity to the changes in fuel economy
directly to capture this change in utility. NHTSA seeks comment on
whether this alternative approach is appropriate.

b. Scrappage
    New and used vehicles are substitutes. When the price of a good's
substitute increases/decreases, the demand curve for that good shifts
upwards/downwards and the equilibrium price and quantity supplied also
increases/decreases. Thus, increasing the quality-adjusted price of new
vehicles will result in an increase in equilibrium price and quantity
of used vehicles. Since, by definition, used vehicles are not being
``produced'' but rather ``supplied'' from the existing fleet, the
increase in quantity must come via a reduction in their scrappage
rates. Practically, when new vehicles become more expensive, demand for
used vehicles increases (and they become more expensive). Because used
vehicles are more valuable in such circumstances, they are scrapped at
a lower rate, and just as rising new vehicle prices push marginal
prospective buyers into the used vehicle market, rising used vehicle
prices force marginal prospective buyers of used vehicles to acquire
older vehicles or vehicles with fewer desired attributes. The effect of
fuel economy standards on scrappage is partially dependent on how
consumers value future fuel savings and our assumption that consumers
value only the first 30 months of fuel savings when making a purchasing
decision.
    Many competing factors influence the decision to scrap a vehicle,
including the cost to maintain and operate it, the household's demand
for VMT, the cost of alternative means of transportation, and the value
that can be attained through reselling or scrapping the vehicle for
parts. A car owner will decide to scrap a vehicle when the value of the
vehicle minus the cost to maintain or repair the vehicle is less than
the value as scrap metal. In other words, the owner gets more value
from scrapping the vehicle than continuing to drive it, or from selling
it. Typically, the owner that scraps the vehicle is not the original
vehicle owner.
    While scrappage decisions are made at the household level, NHTSA is
unaware of sufficient household data to sufficiently capture scrappage
at that level. Instead, NHTSA uses aggregate data measures that capture
broader market trends. Additionally, the aggregate results are
consistent with the rest of the CAFE Model as the model does not
attempt to model how manufacturers will price new vehicles; the model
instead assumes that all regulatory costs to make a particular vehicle
compliant are passed onto the purchaser who buys the vehicle.
    The most predictive element of vehicle scrappage is ``engineering
scrappage.'' This source of scrappage is largely determined by the age
of a vehicle and the durability of a specific MY vintage. NHTSA uses
proprietary vehicle registration data from IHS/Polk to estimate vehicle
age and durability. Other factors include fuel economy and new vehicle
prices. For historical data on new vehicle transaction prices, NHTSA
uses National Automobile Dealers Association (NADA) Data.\412\ The data
consist of the average transaction price of all LDVs; since the
transaction prices are not broken-down by body style, the model may