NHTSA-2021-0053-1581, at 3, 14, 19; UCS Docket No. NHTSA-2021-0053-
1567, at 29; CBD et al., Joint Summary Comments, at 3-4, 6; CARB,
Docket No. NHTSA-2021-0053-1542, Attachment 2, at 3.
---------------------------------------------------------------------------

    IPI and CBD et al. supported their suggested estimates by arguing
that NHTSA should utilize a long-run elasticity estimate, not a short-
run elasticity estimate.\583\ IPI explained that long-run price
elasticity of demand for vehicles tends to be much lower than short run
elasticity, because, due to the limited substitution options for
personal vehicles, consumers will delay purchases when prices increase
but are likely to still purchase a vehicle down the road. CBD et al.
noted that that a long-run estimate is more appropriate because
consumers replace vehicles in the long run as they age and because it
more closely matches the timeline of this agency action in which fuel
economy standards apply years into the future. They also argued that a
``more reasonable'' price elasticity estimate would likely lead to
greater projected increases in employment than already estimated in the
proposed rule.
---------------------------------------------------------------------------

    \583\ IPI, at 26; CBD et al., Joint Summary Comments, at 6.
---------------------------------------------------------------------------

    Dr. Mark Jacobsen commented that the demand elasticity that the
agency used in the proposal is the improper measurement. Dr. Jacobsen
argued that NHTSA should instead employ a ``policy elasticity'' since
CAFE regulations will influence not only new vehicles prices but also
used vehicle prices, since the two are substitutes.\584\ Because used
vehicle prices are anticipated to increase, the change in sales in
response to increasing CAFE standards will be less than what would be
anticipated if only new vehicle prices were affected. Dr. Jacobsen
suggested the policy elasticity ranges from -0.5 in the short-run to -
0.28 in the long-run.
---------------------------------------------------------------------------

    \584\ Dr. Mark Jacobsen, Docket No. NHTSA-2021-0053-1586, at 2.
---------------------------------------------------------------------------

    In contrast, NADA expressed support for a sales elasticity of -
1.0.\585\
---------------------------------------------------------------------------

    \585\ NADA, at 11.
---------------------------------------------------------------------------

    While evaluating the concerns raised by commenters, NHTSA
identified an error in the CARs report that the agency relied upon as a
key source for selecting -1.0. The CARs report erroneously reported the
own-price elasticity of cars (-0.79) and trucks (-0.85) instead of the
long-run elasticity of all light-duty vehicles (-0.39) for Fischer
(2007). When considering the actual long-run elasticity in Fischer
(2007), the totality of the evidence presented in the CARs report no
longer supports an elasticity of -1.0. In addition, after the
publication of NHTSA's proposed rule, EPA issued a new report exploring
the effects of changes in vehicle prices that arise from due to fuel
efficiency regulations on vehicle sales. Since that report was authored
by Dr. Jacobsen, it unsurprisingly echoed his comments summarized
above, and recommended that the agency reduce the magnitude of the
sales price elasticity it uses in its analysis to the range suggested
above.\586\
---------------------------------------------------------------------------

    \586\ Chapter 4.3.2 of the FRIA accompanying this final rule
includes a detailed discussion of the interactions between new and
used vehicle markets identified in Dr. Jacobsen's report to EPA and
their implications for the sensitivity of new vehicle sales and
retirement of used vehicles to higher sales prices.
---------------------------------------------------------------------------

    For these reasons, NHTSA has elected to use a price elasticity of
sales equal to -0.4--meaning that a ten percent increase in the average
price of a new vehicle produces a four percent decrease in total
sales--for the final rule. The price change to which this elasticity is
applied is calculated as the per-vehicle average of manufacturers'
estimated costs to meet higher CAFE standards, net of the fraction of
vehicles expected lifetime fuel savings that new vehicle buyers are
assumed to value (2.5 years or 25-30 percent of lifetime savings, as
discussed in Section III.E.1. above). NADA commented that it believed
the agency's sales model was not appropriately applying the sales
elasticity to the assumed price increase and thus underestimated the
likely decline in sales.\587\ However, the agency notes that NADA's
rough sales estimates excluded any value of future fuel savings, and
that this omission was likely to have caused the divergence between
NADA's and NHTSA's estimates of changes in sales.
---------------------------------------------------------------------------

    \587\ NADA, at 12.
---------------------------------------------------------------------------

    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 followed macroeconomic cycles closely, 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 because of manufacturers' compliance actions exert
some influence on the total volume of new vehicle sales, their effects
on new vehicle sales are secondary to those of overall economic
conditions. Instead, they drive the kinds of marginal differences
between regulatory alternatives that the current sales module is
designed to simulate--making vehicles more expensive generally reduces
total sales, although only modestly.
    The first component of the sales response model is a nominal
forecast, which is a statistical model (using a small set of inputs)
that projects the size of the new vehicle market in each calendar year
in the analysis period under the baseline (No-Action Alternative). Past
reviewers expressed concerns about the possibility of econometrically
estimating an industry average price elasticity in a way that isolates
the causal effect of new vehicle prices on new vehicle sales (and
properly addresses the issue of endogeneity between sales and price).
However, the agency's current 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;

instead, it is intended to simulate the general trajectory of the
market for light duty vehicles. As discussed in more detail in Section
III below, the current economic climate and the economy's performance
during the continuing pandemic has created unusually extreme
uncertainty about this year-to-year forecast. Particularly in the near-
term, there is significant uncertainty about the pace at which the
market for automobiles will recover--and the scale and timing of the
recovery's peak--before the market returns to its long-term trend.
    The second component of the sales response model captures how price
changes affect the number of vehicles sold, by applying an assumed
price elasticity to the percentage change in average price (in each
future year) to determine the percent change in sales from its
projected baseline value. This price change does not represent an
increase/decrease over the last observed year, but rather the
percentage difference under each regulatory alternative relative to the
estimated baseline price during that year. In the baseline, the average
price is defined as the observed new vehicle price in 2019 (the last
historical year before the simulation begins) plus the average
regulatory cost associated with the No-Action Alternative.\588\ The
central analysis in this final rule simulates multiple programs
simultaneously (CAFE final standards, EPA final greenhouse gas
standards, ZEV, and the California Framework Agreements), and the
regulatory cost includes both technology costs and civil penalties paid
for non-compliance (with CAFE standards) in a model year. 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.
---------------------------------------------------------------------------

    \588\ 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.
---------------------------------------------------------------------------

    The third and final component of the sales model 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
would comprise about 70 percent of the new vehicle market by 2025,
which was internally consistent. The reality, however, has been quite
different: In MY 2020, light truck models accounted for 57 percent of
new light-duty vehicle sales.\589\ The CAFE Model includes the DFS
model in an attempt to address these market realities. The DFS
distributes the total industry sales 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 obviously cars--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. The DFS uses two functions from the National
Energy Modeling System (NEMS) used in 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.
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 2020 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
2020 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.
---------------------------------------------------------------------------

    \589\ Calculated from summary data tables accompanying EPA
Automotive Trends Report, 2021 edition, https://www.epa.gov/automotive-trends/explore-automotive-trends-data#SummaryData.
(Accessed: March 15, 2022).
---------------------------------------------------------------------------

    The DFS model shows passenger car styles gaining share with higher
fuel prices and losing them when prices are decline. Similarly, as fuel
economy increases in light truck models, which offer consumers other
desirable attributes beyond fuel economy (ride height or interior
volume, for example) their relative share increases. However, this
approach does not suggest that consumers dislike fuel economy in
passenger cars, but merely recognizes the fact that fuel economy has
diminishing returns in terms of fuel savings. As the fuel economy of
light trucks increases, the tradeoff between passenger car and light
truck purchases increasingly involves a consideration of other
attributes. The coefficients also show a relatively stronger preference
for power improvements in cars than light trucks because that is an
attribute where trucks have typically outperformed cars, just as cars
have outperformed trucks for fuel economy.
    NHTSA received a several comments about the dynamic fleet share
model. ICCT commented that the coefficient for horsepower for passenger
cars was negative, implying that passenger cars with lower fuel economy
and less power are more attractive to consumers.\590\ Both ICCT and IPI
also noted the counterintuitive sign for fuel economy, and suggested
that the model was inadequate because it estimates the share of cars
and trucks independently and fails to consider other vehicle attributes
such as sales prices.\591\ Neither IPI nor ICCT suggested revisions to
the current DFS model structure that would address these concerns.
Alternative approaches such as the simplified discrete choice model of
market share suggested by ICCT or

assuming that fleet shares remain constant could be readily
implemented, although both have potentially important drawbacks.
---------------------------------------------------------------------------

    \590\ ICCT, Appendix: Additional Comments, at 14.
    \591\ ICCT, Appendix: Additional Comments, at 14, 20; IPI, at
29.
---------------------------------------------------------------------------

    The agency agrees with ICCT that a discrete choice model calibrated
to aggregate market share data may avoid some of the challenges of
discrete choice modeling using data on individual buyers' choices but
notes that other impediments to using it would undoubtedly still
arise--for example, accounting for future changes in the classification
of some individual vehicle models, or for shifts in buyers' preferences
toward car or truck-based designs. The agency also believes that
assuming fixed fleet shares is clearly an unsatisfactory approach in
light of both gradual longer-term changes in buyers' apparent
preferences and the very rapid recent shifts in market shares for cars
and light trucks.
    NHTSA agrees that a dynamic fleet share model that includes the
attributes identified by commenters, such as IPI, would be preferable.
In fact, the agency developed a number of simplified market share
models for potential use in this analysis, each of which estimated the
shares of cars and light trucks jointly using different combinations of
attributes buyers are likely to consider when choosing among competing
models. We also attempted to incorporate vehicle prices and develop
specifications that would produce logically consistent coefficients for
each variable they included. The agency was unable to produce a model
that met all three criteria--including vehicle prices proved
particularly troublesome--and these alternative models each suffered
from their own limitations.\592\ For two main reasons, the agency
ultimately decided to retain the DFS used in the proposal instead of
employing one of the newer models it developed: First, the alternative
models did not clearly meet the criteria we established to be
considered a better model. Second, the agency feels that the DFS used
in the proposal produced logically consistent results among the
alternatives it considered in this analysis. As noted elsewhere in this
rule, isolating the impact of alternatives is more an art of internal
precision within the model than an exercise in ``external validity'' or
accuracy. The agency will continue to explore alternative DFS models
for future rulemakings.\593\
---------------------------------------------------------------------------

    \592\ See ``Exploration of alternate fleet share module'' in
Docket No. NHTSA-2021-0053.
    \593\ As with all aspects of this analysis, uncertainty abounds.
If NHTSA's current approach to modeling fleet share inaccurately
overestimates the future fleet's proportion of light trucks, then
NHTSA may have underestimated fuel savings and overestimated
emissions of the regulatory alternatives included in this analysis.
---------------------------------------------------------------------------

    Over the course of past rulemakings, many commenters have
encouraged the agency to consider vehicle attributes beyond price and
fuel economy when estimating a sales response to fuel economy
standards. Some have suggested that a more detailed representation of
the new vehicle market would enable the agency to incorporate the
effect of additional vehicle attributes on buyers' choices among
competing models, reflect consumers' differing preferences for specific
vehicle attributes, and provide the capability to simulate responses
such as strategic pricing strategies by manufacturers intended to alter
the mix of models they sell and enable them to comply with new CAFE
standards. For these purposes, nearly all of those commenters have
suggested that the agency develop a disaggregate model of buyers'
vehicle choices.\594\
---------------------------------------------------------------------------

    \594\ Comments to this effect on the proposed rule were
infrequent, and the only example generally cited much more detailed
applications or advantages of discrete choice models; see Auto
Innovators, Docket No. NHTSA-2021-0053-1492, at 56.
---------------------------------------------------------------------------

    A correctly specified choice model with parameters estimated from
characteristics of individual shoppers (or households) and their
choices among vehicle models--including decisions by some not to
purchase new vehicles--offers the potential to produce consistent
forecasts of total sales of new vehicles and the shares represented by
cars and light trucks (as well as specific body styles and potentially
even individual models). Developing such a model would also provide
estimates of the value buyers attach to improved fuel economy and other
vehicle attributes that were consistent with and reflected in its
forecasts of total sales and market shares for individual vehicle
types. For these reasons, the agency has invested considerable
resources in developing such a discrete choice model of the new
automobile market, although those investments have not yet produced a
satisfactory and operational model.
    The agency's experience partly reflects the fact that discrete
choice models are highly sensitive to their data inputs and estimation
procedures, and even versions that fit well when calibrated to data
from a single period--usually a cross-section of vehicles and shoppers
or actual buyers--often produce unreliable forecasts for future
periods, which the agency's regulatory analyses invariably require.
This occurs because they are often unresponsive to relevant shifts in
economic conditions or consumer preferences, and also because it is
difficult to incorporate factors such as the introduction of new model
offerings--particularly those utilizing advances in technology or
vehicle design--or shifts in manufacturers' pricing strategies into
their representations of choices and forecasts of future sales or
market shares. For these reasons, most vehicle choice models have been
better suited for analysis of the determinants of historical variation
in sales patterns than to forecasting future sales volumes and market
shares of particular categories.
    Although these challenges have so far precluded the agency from
employing a discrete choice model in its regulatory analyses, we
believe they are not insurmountable and recognize the considerable
advantages such a model could offer.\595\ Thus, the agency intends to
continue its attempts to develop some suitable variant of such a model
for use in future fuel economy rulemakings.
---------------------------------------------------------------------------

    \595\ For an additional overview of the challenges of employing
a discrete choice model, see TSD Section 4.2.1.
---------------------------------------------------------------------------

(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.
    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 is less than the value of the vehicle as scrap
metal, plus the cost to maintain or repair the vehicle. 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 first owner.
    While scrappage decisions are made at the household level, the
agency is unaware of sufficient household data to capture scrappage at
that level. Instead, the agency 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. It is
more likely that manufacturers will defray a portion of the increased
regulatory cost across its vehicles or to other manufacturers' buyers
through the sale of credits.
    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 model year vintage. The
agency uses proprietary vehicle registration data from IHS/Polk to
compute vehicle age and durability for each model year or vintage.
Other factors affecting scrappage include fuel economy and new vehicle
prices. For historical data on new vehicle transaction prices, the
agency uses National Automobile Dealers Association (NADA) data.\596\
These data consist of the average transaction price of all light-duty
vehicles; since the transaction prices are not broken-down by body
style, the model may miss unique trends within a particular vehicle
body style. The transaction prices are the amount consumers paid for
new vehicles and exclude any trade-in value credited towards the
purchase. This may be particularly relevant for pickup trucks, which
have experienced considerable changes in average price as luxury and
high-end options entered the market over the past decade. Future models
will further consider incorporating price series that represent the
price trends for cars, SUVs and vans, and pickups separately. Vehicle
scrappage is also influenced by cyclical market trends, which the model
captures using forecasts of GDP and fuel prices.
---------------------------------------------------------------------------

    \596\ The data can be obtained from NADA. For reference, the
data for MY 2020 may be found at https://www.nada.org/nadadata/.
---------------------------------------------------------------------------

    Vehicle scrappage follows a roughly ``S-shaped'' pattern with
increasing age--that is, when a model year (or ``vintage'') is
relatively new few vehicles of its age are scrapped; progressively more
are retired as they age and accumulate use, but after some age
retirements again slow. Although fewer and fewer of the vehicles
originally produced during a model year remain on the road as they age,
the annual rate at which they are retired typically reaches a peak
sometime around age 20 and declines gradually after that.\597\ The
agency's model employs a logistic function to capture this relationship
of vehicle scrappage rates to age.
---------------------------------------------------------------------------

    \597\ The retirement rate is usually measured by the number of
vehicles originally produced during a model year that are retired
during a subsequent (calendar) year, expressed as a fraction of the
number that remained in use at its outset.
---------------------------------------------------------------------------

    Historical registration data show that vehicles produced during
more recent model years generally last longer than those from earlier
vintages, indicating that the durability of successive model years has
improved over time, although there are occasional exceptions to this
broader pattern. Annual scrappage rates for vehicles produced during
more recent model years are also observed to be lower than those of
earlier vintages up to a certain age, but are necessarily higher after
that age to account for the fact that the share of original vehicles
remaining in use ultimately converges toward the minimal share (zero,
in the extreme) observed for earlier vintages.\598\
---------------------------------------------------------------------------

    \598\ Examples of why durability may have changed are new
automakers entering the market or general changes to manufacturing
practices like switching some models from a car chassis to a truck
chassis. The agency caps model years' lifetimes at 40 years in its
accounting; by that age a slightly larger share of each successive
model year tends to remain in use, although this share so far
remains below 2 percent of those originally produced.
---------------------------------------------------------------------------

    The agency includes indicator variables for each model year in its
scrappage model to capture these historical improvements in vehicles'
durability over successive model years. Additionally, to ensure that
vehicles approaching the end of their assumed 40-year service life are
retired, the agency applies a decay function to the number remaining in
use after they reach age 30. Retirement rates for individual model
years are modeled primarily as a polynomial function of age to capture
the non-linear shape described above. The effective change in new
vehicle prices projected in the model (defined as technology costs
minus 30 months of fuel savings, as discussed previously) is also
included in the model, which produces differing scrappage rates across
regulatory alternatives since each one includes different estimates of
technology costs and fuel savings. Finally, the model also includes
year-to-year differences in U.S. GDP (to capture the effects of
macroeconomic cycles on owners' decisions to keep older vehicles in
use), fuel prices, and fuel costs for used vehicles of each age, as
well as the share of vehicles originally produced during each model
year remaining in use.
    In addition to the variables included in the scrappage model, the
agency considered several other variables that may influence scrappage
in the real world including, maintenance and repair costs, the value of
scrapped metal, vehicle characteristics, the quantity of new vehicles
purchased, higher interest rates, and unemployment. These variables
were excluded from the model either because of a lack of underlying
data or modeling constraints. Their exclusion from the model is not
intended to reflect their unimportance, but rather highlights the
practical constraints of modeling intricate decisions like scrappage.
    The agency received some comments on modeling approaches that could
explicitly represent interactions between the new and used vehicle
markets, such as the influence of prices for new models on demand for
used vehicles (and the reverse), and the relationship between scrappage
rates and consumers' decisions about replacing retired vehicles (e.g.,
Jacobsen as discussed in Section III.E.2.a) and FRIA Chapter 4.3.2). On
scrappage rates specifically, the American Fuel & Petrochemical
Manufacturers (AFPM) cautioned the agency against overestimating
scrappage rates, highlighting the effect of current macroeconomic
conditions on new and used car prices and thus on owners' decision to
retire used vehicles.\599\ While we agree with the assertion of AFPM
that scrappage rates are important in accurately representing fleet
turnover and the resulting composition of the light duty vehicle fleet,
the agency found it difficult to quantitatively isolate the effect of
economic conditions on short-term scrappage decisions from longer term
trends in vehicle durability and other factors affecting retirement
rates when developing its scrappage model. For this reason, NHTSA has
elected to maintain the existing treatment of scrappage for this rule,
but will continue to monitor

research related to both short- and long-term scrappage patterns in the
vehicle fleet.
---------------------------------------------------------------------------

    \599\ AFPM, Docket No. NHTSA-2021-0053-1530, at 18.
---------------------------------------------------------------------------

Changes in Vehicle Miles Traveled (VMT)
    The anticipated level of future vehicle use, usually measured by
the number of vehicle-miles driven annually (VMT), directly influences
most of the effects of raising fuel economy standards that decision-
makers consider in determining what standards to establish. Most
important, the amount and value of fuel saved by requiring new cars and
light trucks to achieve higher fuel economy both depend on the number
of miles they are driven each year over their lifetimes, as well as of
course on how much raising CAFE standards improves their fuel economy
and on future fuel prices. Similarly, critical indirect impacts from
raising fuel economy standards such as changes in emissions of criteria
air pollutants and greenhouse gases, potential increases in fatalities
and injuries, and congestion levels also depend directly on the
consequences of higher standards for vehicle use.
    NHTSA's CAFE Model estimates total yearly VMT as the product of
average annual usage per vehicle and the number of vehicles making up
each future year's fleet, which itself depends on new vehicle sales
during the current and previous years and owners' decisions about when
to retire used vehicles. Since cars and light trucks of different model
years (or ``vintages'') and body styles will experience different cost
increases and varying increases in their fuel economy when CAFE
standards are raised--particularly when standards increase over a
succession of model years--the costs necessary to achieve their
required fuel economy levels as well as the resulting fuel savings and
indirect benefits will differ. Vehicles originally produced during a
model year are gradually retired and the usage of those remaining in
service tends to decline as they age (at least on average), so fuel
savings and other benefits from requiring them to achieve higher fuel
economy also decline gradually over their lifetimes. In any future
calendar year, the contributions of progressively older model years to
total benefits will also decline gradually, since fewer will remain in
use and those that do will be driven less, although this pattern will
also be affected by the increases in fuel economy required for earlier
model years.\600\
---------------------------------------------------------------------------

    \600\ A vehicle's age during a future calendar year is equal to
that calendar year minus the model year in which it was originally
produced (and assumed to be sold); for example, model year 2020 cars
and light trucks will be 10 or 11 years old during calendar year
2030, depending on whether they were considered to be 0 or 1 year
old during 2020. (The agency's analysis uses the former convention,
so as an illustration, model year 2010 vehicles are considered to be
11 years old during 2020.)
---------------------------------------------------------------------------

    Thus, accounting properly for the effects of vehicle use on the
costs and benefits from establishing higher CAFE standards requires
estimates of VMT in each future calendar year accounted for by vehicles
of different types and original model years (which determines their
current age during that year). The agency estimates VMT by vehicles of
different types and ages during future calendar years as the product of
the number of vehicles of each type and age in service during that year
and their average annual use. Because vehicles' annual use throughout
their lifetimes is influenced by their fuel economy--through its effect
on the cost of driving each mile--the VMT accounted for by vehicles of
each body type and model year will vary among regulatory alternatives
that require larger increases in fuel economy from its baseline level.
    To develop estimates of average vehicle use by body type and model
year for future calendar years, the agency used odometer readings
collected at different dates for a very large sample of vehicles to
estimate average annual use at each age for cars and light trucks of
different body types (automobiles, SUVs/vans, and pickups). These
initial ``mileage accumulation schedules'' summarize how much vehicles
of each body type and age were driven during 2016, and provide a basis
to estimate how much vehicles produced during future model years will
be driven at each age throughout their lifetimes. As described in
detail in TSD Chapter 4.3, these initial schedules are adjusted to
incorporate the effects of both differences in fuel prices between 2016
and future calendar years, and differences in the fuel economy of
vehicles of each age during 2016 and those that will be of that same
during each future calendar years.
    The agency's CAFE Model uses the estimates of future sales of new
cars and light trucks and annual retirement rates for used vehicles of
different ages constructed as described previously to project the
number of vehicles of each type and age that will be in use during each
future calendar year it analyzes. It combines these with the estimates
of average vehicle use at each age for different vehicle types to
calculate their total VMT and uses the shares operating on different
fuels (gasoline, diesel, and electricity) and their on-road fuel
efficiency to estimate total consumption of each fuel. Finally, the
model applies per-mile and per-gallon emission rates to estimate total
emissions accounted for vehicles of each type and age during future
calendar years. For more aggregate reporting of costs and benefits, the
agency sums these estimates to obtain total vehicle use, fuel
consumption, emissions, and other measures by vehicle type in each
calendar year, as well as lifetime travel, fuel use, emissions, etc.
for vehicles of each type and model year.
    NHTSA's perspective is that total demand for car and light truck
travel should not vary significantly among the regulatory alternatives
it considers, since the basic travel demands of a typical household are
unlikely to be influenced much by the differences in vehicle prices or
driving costs likely to be associated with different CAFE standards.
However, the method the CAFE Model uses to calculate total VMT
described above (and in more detail in TSD Chapter 4.3), can create
modest differences in total VMT across the range of regulatory
alternatives, even without considering the potential effect of fuel
economy differences among those alternatives no vehicle use. These
arise from the effects of differences in new vehicle sales and
retirement rates for used vehicles among alternatives on the
composition of the vehicle fleet--its makeup by vehicle type and age or
original model year--during future years. Although small, these
differences in the representation of vehicle types and model years in
the future fleet can have significant impacts on the incremental costs
and benefits of different regulatory alternatives when those are
measured against the baseline.
    To prevent the estimated effects of our standards from having
unrealistic implications for household vehicle ownership or travel
demand, the agency sought in this analysis to ensure that the fuel
consumption, emissions, safety, and other impacts it reports for
different regulatory alternatives reflect only differences in total
vehicle use that are specifically attributable to their differing fuel
economy requirements, and do not incorporate differences in the number
of cars and light trucks in use under each alternative. To do this the
CAFE Model constrains the level of future vehicle use under each
regulatory alternative before applying the fuel economy rebound effect
to match values projected using the Federal Highway Administration's
VMT forecasting model. In future years where this total ``pre-rebound
effect'' VMT calculated internally by the CAFE Model differs from the
FHWA forecast, each model year cohort's average VMT

is adjusted up or down so that the two estimates match. This process
ensures that any differences in total VMT among regulatory alternatives
is attributable to the fuel economy rebound effect. It also ensures
that the forecasts of total VMT for future years constructed using the
``bottom up'' process of estimating VMT separately for each vehicle
type and age and summing the results, as described immediately above,
are consistent with forecasts of aggregate VMT that are based on an
underlying theory of household travel demand and independent forecasts
of its demographic and economic determinants.
    The agency's analysis of this final rule begins with the year 2020
and relies on actual data rather than forecasts for that year wherever
possible. The elements of the analysis that rely most heavily on
macroeconomic inputs--aggregate demand for VMT, new vehicle sales, and
used vehicle retirement rates--all reflect the economy's unexpectedly
rapid return to pre-pandemic levels of activity and expected future
growth, and these conditions prevail under each of the regulatory
alternatives considered. The Federal Highway Administration (FHWA)
publishes annual estimates of VMT for the light-duty vehicle fleet;
while FHWA's definition of light-duty vehicles differs slightly from
those subject to CAFE standards, over the period from 2016 through 2019
FHWA's estimates of VMT have agreed closely with those generated
internally by NHTSA's CAFE Model.601 602 In 2020, however,
the effects of the COVID pandemic--including sharply reduced demand for
travel and mandated travel restrictions--reduced light-duty VMT
significantly from its 2019 level, and this decline persisted through
much of 2021.
---------------------------------------------------------------------------

    \601\ See Highway Statistics 2017, Table VM-1, available at
https://www.fhwa.dot.gov/policyinformation/statistics/2017/vm1.cfm.
(Accessed: March 15, 2022) FHWA's estimates of VMT include travel by
light-duty trucks up to 10,000 lbs. GVW, while the CAFE program
excludes trucks with GVWs exceeding 8,500 lbs. FHWA reported light-
duty VMT of 2.86 trillion for calendar year 2016, while NHTSA's
model generated an internal estimate of 2.85 trillion VMT by
vehicles subject to CAFE standards. The two estimates did not
compare as closely for subsequent years, but never differed by more
than 2 percent.
    \602\ NHTSA's estimates of total VMT rely on estimates of
average annual mileage for light-duty vehicles at each age,
calibrated to 2016 data, together with the number of registered
light-duty vehicles at each age. Chapter 4 of the TSD accompanying
this rulemaking describes these data and the process NHTSA uses to
estimate total VMT in detail.
---------------------------------------------------------------------------

    Although this downturn in travel activity was accurately reflected
in FHWA's published estimates of light-duty vehicle travel for the year
2020 and monthly travel volumes during 2021, it was not captured in the
VMT estimates produced internally by NHTSA's CAFE Model because those
rely on vehicle use and registration estimates that could not readily
be adjusted to account for sharply reduced commuting, shopping, and
recreational travel or for restrictions on vehicle use that were
imposed in some locations. To avoid the problems that relying on the
models' internally generated forecasts for 2020 and 2021 would have
caused, the agency's analysis for this final rule relied on FHWA's
published estimate of light-duty VMT for 2020 and extrapolated the
volumes reported in that agency's monthly travel updates through
October of 2021 to develop an estimate of annual VMT for 2021.
    The fuel economy rebound effect--a specific example of the well-
documented energy efficiency rebound effect for energy-consuming
capital goods--refers to the tendency of motor vehicles' use to
increase when their fuel economy is improved and the fuel cost to drive
each mile declines as a result. A regulatory alternative that
establishes more stringent CAFE standards than those assumed to prevail
under the baseline scenario will increase the fuel economy of new cars
and light trucks, thereby reducing their pre-mile fuel consumption and
fuel costs and increasing the number of miles they are driven annually
over their lifetimes. The assumed magnitude of this fuel economy
rebound effect influences the overall costs and benefits associated
with each regulatory alternative considered, as well as the estimates
of its effects on fatalities and other safety measures. Thus, its
value--together with fuel prices, technology costs, and other
analytical inputs--is part of the body of information that agency
decision-makers have considered in selecting the CAFE standards this
final rule establishes. By magnifying the effect of higher fuel economy
on vehicle use, larger values of the fuel economy rebound effect also
reduce the economic and environmental benefits associated with
increased fuel efficiency.
    The agency received a number of comments on the value of the
rebound effect. Most commenters argued that the agency rebound
selection of 15 percent was too high and suggested that the literature
supported a rebound magnitude ranging from 5 to 10 percent; most
commenters supported using a rebound of 10 percent.\603\ A few
commenters argued that an even lower value such as 5 percent should be
used instead.\604\ While Auto Innovators did not comment directly on
the agency's choice of 15 percent, it argued that the agency's estimate
of rebound did not take into consideration of ``attribute
substitution,'' whereby a household will buy a less fuel efficient
vehicle as their second vehicle and will make a decision on which
vehicle to use depending on the purpose for any particular trip.\605\
The agency notes that Auto Innovators did not provide any guidance on
the likely direction of this ``attribute substitution'' effect--which
is not clear a priori--in its comment, nor provide any suggestions for
how to account for it in the analysis.
---------------------------------------------------------------------------

    \603\ See California Attorney General et al., Docket No. NHTSA-
2021-0053-1526-A1, at 2; UCS, Docket No. NHTSA-2021-0053-1567-A1, at
32; CBD et al., Joint Summary Comments, at 2-3; ICCT, A1, at 14;
Lucid, Docket No. NHTSA-2021-0053-1584-A1, at 6; IPI, at 35-37; and
CARB, Docket No NHTSA-2021-0053-1521-A2, at 2-3.
    \604\ See e.g., CFA, Docket No. NHTSA-2021-0053-1535, at 4-5.
    \605\ Auto Innovators, at 93-94.
---------------------------------------------------------------------------

    ICCT commented in general support of the methodology used to
construct the vehicle mileage accumulation schedules, but suggested
that the agency could further improve them by considering how increased
durability of successive models could cause newer vehicles to be driven
more as they age than their older counterparts.\606\ The agency notes
that ICCT is correct that increased durability can increase VMT. NHTSA
captures this possibility in the scrappage model, where more recent
model years tend to be retained in service longer, and also in its
application of the fuel economy rebound effect, where vehicles
featuring higher fuel economy are assumed to be used more intensively
throughout their lifetimes. The agency notes that the data and methods
it used to develop the mileage accumulation schedules capture the
increasing durability of recent model year to some extent, because as
described in detail in TSD Chapter 4.3 those data include a range of
model years observed over several decades, and increased durability is
not a recent phenomenon. Treating model years as a ``panel'' when
estimating the pattern of vehicle use with age explicitly accounts for
both increases in the fraction of vehicles produced during successive
model years that remain in use at each age and any accompanying
increase in the average use of vehicles of different ages.
---------------------------------------------------------------------------

    \606\ ICCT, at 22-23.
---------------------------------------------------------------------------

    Several of the commenters also seemed to suggest that we should not
consider the impacts of rebound driving at all since they are freely
chosen.\607\ We note that rebound driving is an expected

result of this final rule, and that understanding how increased fuel
efficiency will affect additional mobility deserves consideration even
if there is an offsetting mobility benefit. In addition, the question
of whether and how to consider the rebound effect and its consequences
is an aspect of the agency's determination of what standard represents
the ``maximum feasible,'' which is a separate question from the more
technical issue of what the appropriate value for the rebound effect
should be in the analysis.
---------------------------------------------------------------------------

    \607\ See, e.g., CBD et al., at 17.
---------------------------------------------------------------------------

    As described in detail in TSD Chapter 4.3.5, the agency conducted a
thorough and detailed review of recent research on the fuel economy
rebound effect, which includes several new estimates it had not
previously considered and also incorporates statistical uncertainty
surrounding different estimates. The agency's updated review shows that
research measuring the response of vehicle use to fuel economy itself
suggests a rebound effect ranging from 5 to 15 percent, while studies
examining the association of vehicle use to fuel costs of driving
suggest that the rebound effect is most likely to lie in the range from
10 to 20 percent.
    Based on this updated analysis, the agency selected a rebound
effect of 10 percent for this analysis, because it was well-supported
by the totality of the evidence and aligned closely with the response
of total vehicle use to fuel costs incorporated in FHWA's forecasting
model (approximately 14 percent). This value is also consistent with
the value used in EPA's recent final rule. To recognizing the wide
range of uncertainty surrounding the true value of the fuel economy
rebound effect, we also examine the sensitivity of estimated impacts to
values ranging from 5 to 20 percent.
    To calculate levels of total light-duty that incorporate the fuel
economy rebound effect, the CAFE Model interprets the assumed magnitude
of the rebound effect as an elasticity of average vehicle use with
respect to fuel cost per mile, and applies this to changes in fuel
costs resulting from the higher fuel economy levels each regulatory
alternative requires. It then adds the resulting proportional increases
in average vehicle use to their values under the No-Action Alternative,
as previously adjusted to reconcile the CAFE Model's estimate of total
VMT with that produced by FHWA's travel forecasting model. TSD Chapter
4.3 provides an extensive discussion of how the agency calculates
changes in VMT to account for the rebound effect.
    Jacobsen and Liao commented on the agency's procedures for
estimating VMT and incorporating the rebound effect, noting that while
still in progress, their recent research shows that by raising prices
for new cars and light trucks, higher CAFE standards increase the
depreciation cost their owners incur in driving each mile.\608\ They
assert that the response of vehicle use to higher per-mile
depreciations costs outweighs its response to the reduction in fuel
costs from required increases in their fuel economy, although they do
not report empirical results demonstrating this effect. These
commenters also argue that the reduction in sales of new vehicles in
response to higher new car and light truck prices will reinforce this
effect, because households owning fewer vehicles will drive less in
total as complementarity between the number of vehicles households own
and their trip-making frequency operates in reverse. They argue that as
these two effects interact with the usual fuel economy rebound effect,
higher CAFE standards will reduce average vehicle use on balance rather
than increasing it as the agency estimates.\609\
---------------------------------------------------------------------------

    \608\ Jacobsen and Liao, NHTSA-2021-0053-0065, at 1.
    \609\ Jacobsen and Liao, at 2.
---------------------------------------------------------------------------

    The agency agrees that higher per-mile depreciation costs are
likely by themselves to reduce vehicle use but notes that only some
fraction of vehicles' total depreciation costs owes to their usage,
with the remainder attributable to the passage of time and
technological progress in new vehicle designs and utility. Empirical
estimates of this breakdown are scarce, so it is difficult to assess
how large the increase in per-mile depreciation costs associated with a
given increase in new vehicles' prices might be. We also note that
increasing durability of new cars and light trucks over time tends to
reduce the depreciation costs associated with their use, simply because
their lifetime use-related depreciation is distributed over a larger
number of miles. The agency notes further that the increases in new car
and light truck prices it estimates will occur as consequences of the
alternatives it considered for this analysis are quite modest,
particularly after they are adjusted to reflect their buyers' assumed
valuation of the higher fuel economy they provide. Combined with their
increased durability and the fact that only a fraction of their higher
prices is reflected in increased use-related depreciation, the implied
increases in their per-mile depreciation costs are likely to be
extremely small. Finally, we also note that empirical estimates of the
fuel economy rebound effect generally do not control for potential
increases in vehicles' purchase prices and accompanying depreciation
costs. As a consequence, the association between higher fuel economy
(or lower per-mile fuel costs) and higher per-mile depreciation is
likely to be incorporated to some extent in estimates the rebound
effect, in which case they can be interpreted as the combined or net
effect of these countervailing changes on vehicle use.
4. Changes to Fuel Consumption
    The agency combines modeled fuel economy levels with age and body-
style VMT estimates to determine changes in fuel consumption over time
and across alternatives. The agency computes the amount of fuel
consumed by dividing expected total travel by predicted MPG at the
vehicle level and then aggregates to produce estimates of total fuel
consumed in each alternative.\610\
---------------------------------------------------------------------------

    \610\ Total value of fuel consumed is computed across all fuel
types and draws fuel price values (e.g., retail prices for gasoline
and electricity) from the set of model inputs.
---------------------------------------------------------------------------

F. Simulating Environmental Impacts of Regulatory Alternatives

    In estimating the environmental impacts of each regulatory
alternative we considered, the agency accounted for the projected
application of many fuel-saving technologies to vehicles that could
continue to use only gasoline or diesel fuel (including hybrid electric
vehicles that do not require external charging), as well as the
projected increased application of plug-in hybrid electric vehicles
and, with some analytical constraints, battery electric vehicles.\611\
By reducing overall energy consumption and the production and use of
petroleum-based fuels, the alternatives the agency considered would
thus have important consequences for the environment and public health.
These occur because each alternative would reduce tailpipe emissions of
both GHGs and criteria air pollutants during vehicle operation, as well
as ``upstream'' emissions that occur during petroleum extraction,
transportation, and refining to produce fuel, as well as during the
transportation, storage, and distribution of refined fuel. In turn,
reduced emissions of GHGs and air pollutants would improve
environmental quality, reduce the health consequences of

exposure to air pollution (whether climate-exacerbated or not), and
mitigate economic damages attributable to changes in the global climate
and air pollution levels.
---------------------------------------------------------------------------

    \611\ This document and FRIA do not consider the potential for
manufacturers to respond to new standards for MYs 2024-2026 by
introducing new BEV models in MYs 2024-2026. However, the
accompanying Supplemental Environmental Impact Analysis (SEIS) does
account for such potential introductions of new BEV models in these
model years.
---------------------------------------------------------------------------

    This section provides an overview of how we develop the assumptions
and parameters used to estimate emissions of criteria air pollutants,
greenhouse gases, and air toxics. It also describes how we develop and
apply estimates of the air quality and climate-related impacts of these
emissions and their consequences for human health, focusing
particularly on the rule's effects on emissions of criteria air
pollutants that cause poor air quality and can damage human health. The
agency's analysis utilizes the ``emissions inventory'' approach to
estimate these impacts. Vehicle-related emissions inventories are often
described as three-legged stools, since they depend on measures of
vehicle activity (i.e., miles traveled, hours operated, or gallons of
fuel burned), the number of vehicles in use, and emission factors per
unit of vehicle activity.
    An emissions factor is a rate that measures the quantity of a
pollutant released to the atmosphere per unit of vehicle activity.\612\
This analysis relies on vehicle-miles traveled (VMT) as its measure of
vehicle activity, and emission rates are measured by emissions (in mass
units) per vehicle-mile; the vehicle-related or ``tailpipe'' emission
inventory for most pollutants is the product of their per-mile
emissions factor and the appropriate estimate of the number of miles
traveled. Exceptions include tailpipe emissions of sulfur oxides
(SOX) and carbon dioxide (CO2), which are
estimated by applying emissions factors per gallon of fuel consumed
derived from the chemical properties of different fuels to the
appropriate values of fuel consumption in gallons. Vehicle activity
levels--both the number of miles traveled and the number of gallons of
fuel consumed--are generated by the CAFE Model (as described in
Sections III.E.3. and 4. above), while the per-mile and per-gallon
emission factors have been extracted from other models developed by
other Federal agencies. In this rulemaking, vehicle-related emissions
also include those that occur throughout the process of supplying fuel
and other forms of vehicle energy (such as electric power), and these
are termed upstream emissions. The agency estimates these upstream
emissions from the volume or energy content of fuel supplied and
consumed by cars and light trucks, together with factors that express
emissions of air pollutants and GHGs in mass per unit of fuel volume
(usually grams per gallon) or fuel energy (e.g., grams per million Btu)
supplied. Total upstream emissions of each pollutant are estimated as
the product of the number of gallons of fuel supplied and the relevant
per-gallon emission factor, or as the product of total energy supplied
and emissions per unit of energy produced and delivered.
---------------------------------------------------------------------------

    \612\ U.S. EPA, Basics Information of Air Emissions Factors and
Quantification, https://www.epa.gov/air-emissions-factors-and-quantification/basic-information-air-emissions-factors-and-quantification. (Accessed: March 15, 2022)
---------------------------------------------------------------------------

    For this rule, vehicle tailpipe (sometimes called ``downstream'')
and upstream emission factors as well as estimates of total emissions
from both sources were developed independently using separate data
sources. Tailpipe emission factors are estimated from the highway
emissions model developed for use in regulatory analysis by the U.S.
Environmental Protection Agency's (EPA) National Vehicle and Fuel
Emissions Laboratory, known as the Motor Vehicle Emission Simulator
(MOVES). Upstream emission factors are estimated from a lifecycle
emissions model developed by the U.S. Department of Energy's (DOE)
Argonne National Laboratory, the Greenhouse Gases, Regulated Emissions,
and Energy Use in Transportation (GREET) Model.\613\ For this final
rule, we updated the CAFE Model to utilize data from the most current
versions of each model, MOVES 3 and GREET 2021.
---------------------------------------------------------------------------

    \613\ U.S. Department of Energy, Argonne National Laboratory,
Greenhouse gases, Regulated Emissions, and Energy use in
Transportation (GREET) Model, Last Update: 11 Oct. 2021, https://greet.es.anl.gov/. (Accessed: March 15, 2022) Upstream emission
factors for criteria air pollutants may be undercounted, but are
nonetheless important.
---------------------------------------------------------------------------

    Adverse human health outcomes caused by exposure to harmful
accumulations of criteria air pollutants, such as asthma episodes and
respiratory or cardiovascular distress requiring hospitalization, are
generally reported as incidences per ton of emissions of each pollutant
(or its chemical precursors). The incidence per ton values used to
estimate changes in health impacts were developed using several EPA
studies and recently updated to better account for the specific sources
of emissions estimated by the CAFE Model. Finally, EPA also applies
estimates of the affected population's willingness to pay to avoid each
incidence of these adverse health impacts and sums the results to
obtain estimates of the economic cost of air pollutant emissions in
dollars per ton, which can be interpreted as estimates of the economic
benefit from reducing each ton of emissions of different pollutants.
Chapter 5 of the TSD accompanying this final rule includes a detailed
discussion of the procedures we used to simulate the environmental
impacts of the different regulatory alternatives that were considered,
and the implementation of these procedures within the CAFE Model is
discussed in detail in the supporting Model Documentation. Further
discussion of how the health impacts of upstream and tailpipe emissions
of criteria air pollutants have been monetized and the resulting values
used in this analysis can be found in Section III.G.2.b)(2). The Final
SEIS accompanying this analysis also includes a detailed discussion of
both criteria pollutant and GHG emissions and their impacts on human
health as well as on the natural environment.
1. Activity Levels Used To Calculate Emissions Impacts
    The CAFE Model estimates the annual number of miles driven (VMT)
for each individual car and light truck model produced in every future
model year at each age over their lifetimes, which extend for a maximum
of 40 years. Since a vehicle's age is equal to the current calendar
year minus the model year in which it was originally produced, the age
span of each vehicle model's lifetime corresponds to a sequence of 40
calendar years beginning in the calendar year corresponding to the
model year it was produced.\614\ These estimates reflect the gradual
decline in the fraction of each car and light truck model's original
model year production volume that is expected to remain in service
during each year of its lifetime, as well as the well-documented
decline in their typical use as they age. Using this relationship, the
CAFE Model calculates total VMT for cars and light trucks in service
during each calendar year spanned in this analysis.
---------------------------------------------------------------------------

    \614\ In practice, many vehicle models bearing a given model
year designation become available for sale in the preceding calendar
year, and their sales can extend through the calendar year following
their designated model year as well. However, the CAFE Model does
not attempt to distinguish between model years and calendar years;
vehicles bearing a model year designation are assumed to be produced
and sold in that same calendar year.
---------------------------------------------------------------------------

    Based on these estimates, the model also calculates quantities of
each type of fuel or energy, including gasoline, diesel, and
electricity, consumed in each calendar year. By combining these with
estimates of each model's fuel or energy efficiency, the model also
estimates the quantity and energy content of each type of fuel consumed
(including gasoline, diesel, and electricity) by cars and light trucks
at

each age, or viewed another way, during each calendar year of their
lifetimes. As with the accounting of VMT, these estimates of annual
fuel or energy consumption for each vehicle model and model year
combination are combined to calculate the total volume of each type of
fuel or energy consumed during each calendar year, as well as its
aggregate energy content.
    The procedures the CAFE Model uses to estimate annual VMT for
individual car and light truck models produced during each model year
over their lifetimes and to combine these into estimates of annual
fleet-wide travel during each future calendar year, together with the
sources of its estimates of their survival rates and average use at
each age, are described in detail in Section III.E.2. The data and
procedures it employs to convert these estimates of VMT to fuel and
energy consumption by individual model, and to aggregate the results to
calculate total consumption and energy content of each fuel type during
future calendar years, are also described in detail in that same
section.
    The model documentation accompanying this final rule also describes
these procedures in detail.\615\ The quantities of travel and fuel
consumption estimated for the cross section of model years and calendar
years constitutes a set of ``activity levels'' based on which the model
calculates emissions. The model does so by multiplying activity levels
by emission factors. As indicated in the previous section, the
resulting estimates of vehicle use (VMT), fuel consumption, and fuel
energy content are combined with emission factors drawn from various
sources to estimate emissions of GHGs, criteria air pollutants, and
airborne toxic compounds that occur throughout the fuel supply and
distribution process, as well as during vehicle operation, storage, and
refueling. Emission factors measure the mass of each GHG, or criteria
pollutant emitted per vehicle-mile of travel, gallon of fuel consumed,
or unit of fuel energy content. The following sections identifies the
sources of these emission factors and explains in detail how the CAFE
Model applies them to its estimates of vehicle travel, fuel use, and
fuel energy consumption to estimate total annual emissions of each GHG,
criteria pollutant, and airborne toxic.
---------------------------------------------------------------------------

    \615\ CAFE Model documentation is available at https://www.nhtsa.gov/corporate-average-fuel-economy/compliance-and-effects-modeling-system.
---------------------------------------------------------------------------

2. Simulating Upstream Emissions Impacts
    Building on the methodology for simulating upstream emissions
impacts used in prior CAFE rules, this final rule analysis uses
emissions factors developed with the U.S. Department of Energy's
Greenhouse gases, Regulated Emissions, and Energy use in Transportation
(GREET) Model, specifically GREET 2021.\616\ The analysis includes
emissions impacts estimates for regulated criteria pollutants,\617\
greenhouse gases,\618\ and air toxics.\619\
---------------------------------------------------------------------------

    \616\ U.S. Department of Energy, Argonne National Laboratory,
Greenhouse gases, Regulated Emissions, and Energy use in
Transportation (GREET) Model, Last Update: 11 Oct. 2021, https://greet.es.anl.gov/.
    \617\ Carbon monoxide (CO), volatile organic compounds (VOCs),
nitrogen oxides (NOX), sulfur oxides (SOX),
and particulate matter with 2.5-micron ([micro]m) diameters or less
(PM2.5).
    \618\ Carbon dioxide (CO2), methane (CH4),
and nitrous oxide (N2O).
    \619\ Acetaldehyde, acrolein, benzene, butadiene, formaldehyde,
diesel particulate matter with 10-micron ([micro]m) diameters or
less (PM10).
---------------------------------------------------------------------------

    The upstream emissions factors included in the CAFE Model input
files include parameters for 2020 through 2050 in five-year intervals
(e.g., 2020, 2025, 2030, and so on). For gasoline and diesel fuels,
each analysis year includes upstream emissions factors for the four
following upstream emissions processes: Petroleum extraction, petroleum
transportation, petroleum refining, and fuel transportation, storage,
and distribution (TS&D). In contrast, the upstream electricity
emissions factor is only a single value per analysis year. We briefly
discuss the components included in each upstream emissions factor here,
and a more detailed discussion is included in Chapter 5 of the TSD
accompanying this rule and the CAFE Model Documentation.
    The first step in the process for calculating upstream emissions
includes any emissions related to the extraction, recovery, and
production of petroleum-based feedstocks, namely conventional crude
oil, oil sands, and shale oils. Then, the petroleum transportation
process accounts for the transport processes of crude feedstocks sent
for domestic refining. The petroleum refining calculations are based on
the aggregation of fuel blendstock processes rather than the crude
feedstock processes, like the petroleum extraction and petroleum
transportation calculations. The final upstream process after refining
is the transportation, storage, and distribution (TS&D) of the finished
fuel product.
    The upstream gasoline and diesel emissions factors are aggregated
in the CAFE Model based on the share of fuel savings leading to reduced
domestic oil fuel refining and the share of reduced domestic refining
from domestic crude oil.\620\ The CAFE Model applies a fuel savings
adjustment factor to the petroleum refining process and a combined fuel
savings and reduced domestic refining adjustment to both the petroleum
extraction and petroleum transportation processes for both gasoline and
diesel fuels and for each pollutant. These adjustments are consistent
across fuel types, analysis years, and pollutants, and are unchanged
from the previous CAFE analyses. Additional discussion of the
methodology for estimating the share of fuel savings leading to reduced
domestic oil refining is located in Chapter <IP_ADDRESS> of the TSD.
---------------------------------------------------------------------------

    \620\ Upstream emissions are underestimated to the extent that
they do not account for any toxic pollutants (like mercury) and
criteria pollutants (i.e., from refining/production in Mexico/
Canada, as such pollutants can cross boundaries), as well as certain
greenhouse gas emissions, that originate outside the borders of the
United States and are attributable to changes in gasoline
consumption as a result of these standards.
---------------------------------------------------------------------------

    Upstream electricity emissions factors are also calculated using
GREET 2021. GREET 2021 projects a national default electricity
generation mix for transportation use from the latest Annual Energy
Outlook (AEO) data.\621\ As discussed above, the CAFE Model uses a
single upstream electricity factor for each analysis year.
---------------------------------------------------------------------------

    \621\ For this CAFE analysis, this was AEO 2021, released
February 3, 2021, https://www.eia.gov/outlooks/archive/aeo21.
---------------------------------------------------------------------------

    The Environmental Defense Fund (EDF) submitted comments to the
Draft SEIS docket stating that NHTSA's estimates of reductions in
global GHG emissions associated with lower domestic consumption of
gasoline and diesel and its consequences for U.S. imports of crude
petroleum should incorporate empirical estimates of the specific
sources of U.S. imports that would be reduced and the rates of GHG
emissions associated with producing crude petroleum at each of those
sources and transporting it to the U.S. for refining.\622\
---------------------------------------------------------------------------

    \622\ EDF, NHTSA-2021-0054-0016, at pp. 4-5.
---------------------------------------------------------------------------

    We do not have the detailed production and supply modeling
capability that would be necessary to estimate reductions in U.S.
imports of crude petroleum from specific sources, and the global nature
of the market for crude petroleum suggests that those reductions are
unlikely to be proportional to the volumes currently imported from
different sources, as EDF

appears to assume. The global nature of the market for crude petroleum
also means that reductions in U.S. purchases from specific sources
would not necessarily be met by corresponding reductions in petroleum
production and associated GHG emissions at those locations, since those
producers' reduced exports to the U.S. might simply be redirected to
supply other purchasers.
    In light of this situation, we believe the most reasonable
assumption to use for estimating reductions in global GHG emissions
associated with lower U.S. petroleum imports and global production is
to apply the emission factors associated with crude petroleum
production at different global locations and with current
transportation patterns, weighted by each location's projected
contribution to future global production. This is in fact the
assumption implicitly reflected in the agency's reliance on GHG
emission factors for crude petroleum transportation and distribution
derived using GREET. Even this assumption is likely to lead to an
overestimate of the reduction in global GHG emissions, since it implies
that the estimated decline in U.S. imports will be fully reflected in
an overall reduction in global petroleum production, rather than being
partly or fully absorbed by other oil-consuming nations. We have
therefore elected to retain this assumption and its current procedure
for estimating reduced GHG emissions from petroleum production. These
assumptions are discussed in further detail in Section 0.
    EDF also commented that that NHTSA's estimates of reductions in
domestic emissions of criteria air pollutants resulting from lower U.S.
production and consumption of transportation fuels and its assumed
effect on U.S. petroleum imports should include reductions in emissions
that occur during the transportation of imported petroleum ``. . . on
U.S. soil or within established distances from our borders where
emissions still affect U.S. ambient air quality.'' This would include
emissions by tanker ships operating within U.S. Emission Control Areas
(ECAs, which can extend as far as 200 miles from U.S. shores),
including those to which petroleum is transferred when large oceangoing
tankers cannot enter some U.S. ports, as well as emissions by
petroleum-carrying barges, rail tank cars, and pipelines operating
within U.S. borders.
    In fact, our analysis does include emissions that occur during
transportation of crude petroleum as domestic emissions associated with
petroleum imports. In effect, it assumes that transportation modes and
shipment distances for moving crude petroleum from U.S. coastal ports
to domestic refineries are similar to those for moving domestically
extracted crude petroleum from oilfields or other domestic petroleum
production facilities to U.S. refineries. Thus, some reductions in
emissions that occur during transportation of imported crude petroleum
within U.S. coastal and interior areas are included in the agency's
estimates of total reductions in domestic emissions of criteria
pollutants attributable to reduced U.S. petroleum imports. The agency
believes this approach provides a satisfactory substitute for detailed
estimation of movement distances and shipment modes for carrying
imported crude petroleum from ports to refineries. This is discussed
further in TSD Chapter 5.2 and TSD Chapter <IP_ADDRESS>.
3. Simulating Tailpipe Emissions Impacts
    Tailpipe emission factors are generated using a regulatory model
for on-road emission inventories from the U.S. Environmental Protection
Agency, the Motor Vehicle Emission Simulator (MOVES3), November 2020
release. MOVES3 is a state-of-the-science, mobile-source emissions
inventory model for regulatory applications.\623\ MOVES3 tailpipe
emission factors have been incorporated into the CAFE parameters, and
these updates supersede tailpipe data previously provided by EPA from
MOVES2014 for past CAFE analyses. MOVES3 accounts for a variety of
processes related to emissions impacts from vehicle use, examples
include exhaust and evaporative processes, among others.\624\
---------------------------------------------------------------------------

    \623\ U.S. Environmental Protection Agency, Office of
Transportation and Air Quality, Motor Vehicle Emission Simulator
(MOVES), Last Updated: September 2021, https://www.epa.gov/moves/latest-version-motor-vehicle-emission-simulator-moves. For the CAFE
analysis, MOVES 3.0.1 was used to generate the emission factors.
    \624\ For CAFE modeling, the post-processing of emission factors
for PM2.5 included exhaust processes (running, start,
crankcase running, and crankcase start) and excluded brake and tire
wear.
---------------------------------------------------------------------------

    The CAFE Model uses tailpipe emissions factors for all model years
from 2020 to 2060 for criteria pollutants and air toxics. To maintain
continuity in the historical inventories, only emission factors for MYs
2020 and after were updated; all emission factors prior to MY 2020 were
unchanged from previous CAFE rulemakings. In addition, the updated
tailpipe data in the current CAFE reference case no longer account for
any fuel economy improvements or changes in vehicle miles traveled from
the 2020 final rule. In order to avoid double-counting effects from the
previous rulemaking in the current rulemaking, the tailpipe baseline
backs out 1.5 percent year-over-year stringency increases in fuel
economy, and 0.3 percent VMT increases assumed each year (20 percent
rebound on the 1.5 percent improvements in stringency). Note that the
MOVES3 data do not cover all the model years and ages required by the
CAFE Model; MOVES only generates emissions data for vehicles made in
the last 30 model years for each calendar year being run. This means
emissions data for some calendar year and vehicle age combinations are
missing. To remedy this, we take the last vehicle age that has
emissions data and forward fill those data for the following vehicle
ages. Due to incomplete available data for years prior to MY 2020,
tailpipe emission factors for MY 2019 and earlier have not been
modified and continue to utilize MOVES2014 data.
    For tailpipe CO2 emissions, these factors are defined
based on the fraction of each fuel type's mass that represents carbon
(the carbon content) along with the mass density per unit of the
specific type of fuel. To obtain the emission factors associated with
each fuel, the carbon content is then multiplied by the mass density of
a particular fuel as well as by the ratio of the molecular weight of
carbon dioxide to that of elemental carbon. This ratio, a constant
value of 44/12, measures the mass of carbon dioxide that is produced by
complete combustion of mass of carbon contained in each unit of fuel.
The resulting value defines the emission factor attributed to
CO2 as the amount of grams of CO2 emitted during
vehicle operation from each type of fuel. This calculation is repeated
for gasoline, E85, diesel, and compressed natural gas (CNG) fuel types.
In the case of CNG, the mass density and the calculated CO2
emission factor are denoted as grams per standard cubic feet (scf),
while for the remainder of fuels, these are defined as grams per gallon
of the given fuel source. Since electricity and hydrogen fuel types do
not cause CO2 emissions to be emitted during vehicle
operation, the carbon content, and the CO2 emission factors
for these two fuel types are assumed to be zero. The mass density,
carbon content, and CO2 emission factors for each fuel type
are defined in the Parameters file.
    The CAFE Model calculates CO2 tailpipe emissions
associated with vehicle operation of the surviving on-road fleet by
multiplying the number of gallons (or scf for CNG) of a specific fuel
consumed by the CO2 emissions factor

for the associated fuel type. More specifically, the amount of gallons
or scf of a particular fuel are multiplied by the carbon content and
the mass density per unit of that fuel type, and then the model applies
the ratio of carbon dioxide emissions generated per unit of carbon
consumed during the combustion process.\625\
---------------------------------------------------------------------------

    \625\ Chapter 3, Section 4 of the CAFE Model Documentation
provides additional description for calculation of CO2
tailpipe emissions with the model.
---------------------------------------------------------------------------

4. Estimating Health Impacts From Changes in Criteria Pollutant
Emissions
    The CAFE Model computes select health impacts resulting from three
criteria pollutants: NOX, SOX,\626\ and
PM2.5. Out of the six criteria pollutants currently
regulated, NOX, SOX, and PM2.5 are
known to be emitted regularly from mobile sources and have the most
adverse effects to human health. These health impacts include several
different morbidity measures, as well as a mortality estimate, and are
measured by the number of instances predicted to occur per ton of
emitted pollutant.\627\ The model reports total health impacts by
multiplying the estimated tons of each criteria pollutant by the
corresponding health incidence per ton value. The inputs that inform
the calculation of the total tons of emissions resulting from criteria
pollutants are discussed above. This section discusses how the health
incidence per ton values were obtained. See Section III.G.2.b)(2) and
Chapter 6.2.2 of the TSD accompanying this notice for information
regarding the monetized damages arising from these health impacts.
---------------------------------------------------------------------------

    \626\ Any reference to SOX in this section refers to
the sum of sulfur dioxide (SO2) and sulfate particulate
matter (pSO4) emissions, following the methodology of the EPA papers
cited.
    \627\ The complete list of morbidity impacts estimated in the
CAFE Model is as follows: acute bronchitis, asthma exacerbation,
cardiovascular hospital admissions, lower respiratory symptoms,
minor restricted activity days, non-fatal heart attacks, respiratory
emergency hospital admissions, respiratory emergency room visits,
upper respiratory symptoms, and work loss days.
---------------------------------------------------------------------------

    The Final SEIS associated with this document also includes a
detailed discussion of the criteria pollutants and air toxics analyzed
and their potential health effects. Consistent with past analyses, we
have performed full-scale photochemical air quality modeling and
presented those results in the Final SEIS. That analysis provides
additional assessment of the human health impacts from changes in
PM2.5 and ozone associated with this rule. We note that
compliance with CAFE standards is based on the average performance of
manufacturers' production for sale throughout the U.S., and that the
FRIA involves sensitivity analysis spanning a range of model inputs,
many of which impact estimates of future emissions from passenger cars
and light trucks. Chapter 6 of the FRIA includes a discussion of
overall changes in health impacts associated with criteria pollutant
changes across the different rulemaking scenarios.
    In previous rulemakings, health impacts were split into two
categories based on whether they arose from upstream emissions or
tailpipe emissions. In the current analysis, these health incidence per
ton values have been updated to reflect the differences in health
impacts arising from each emission source sector, according to the
latest publicly available EPA reports that appropriately correspond to
these sectors. Five different upstream emission source sectors
(petroleum extraction, petroleum transportation, refineries, fuel
transportation, storage and distribution, and electricity generation)
are now represented. The tailpipe source sector is now disaggregated
based on fuel and vehicle type. As the health incidences for the
different source sectors are all based on the emission of one ton of
the same pollutants, NOX, SOX, and
PM2.5, the differences in the incidence per ton values arise
from differences in the geographic distribution of the pollutants, a
factor which affects the number of people impacted by the
pollutants.\628\
---------------------------------------------------------------------------

    \628\ See Environmental Protection Agency (EPA). 2018.
Estimating the Benefit per Ton of Reducing PM2.5
Precursors from 17 Sectors. https://www.epa.gov/sites/production/files/2018-02/documents/sourceapportionmentbpttsd_2018.pdf.
---------------------------------------------------------------------------

    The CAFE Model health impacts inputs are based partially on the
structure of EPA's 2018 TSD, Estimating the Benefit per Ton of Reducing
PM2.5 Precursors from 17 Sectors (referred to here as the
2018 EPA source apportionment TSD),\629\ which reported benefit per ton
values for the years 2016, 2020, 2025, and 2030.\630\ For the years in
between the source years used in the input structure, the CAFE Model
applies values from the closest source year. For instance, 2020 values
are applied for 2020-2022, and 2025 values are applied for 2023-2027.
For further details, see the CAFE Model documentation, which contains a
description of the model's computation of health impacts from criteria
pollutant emissions.
---------------------------------------------------------------------------

    \629\ Environmental Protection Agency (EPA). 2018. Estimating
the Benefit per Ton of Reducing PM2.5 Precursors from 17
Sectors. https://www.epa.gov/sites/production/files/2018-02/documents/sourceapportionmentbpttsd_2018.pdf.
    \630\ As the year 2016 is not included in this analysis, the
2016 values were not used.
---------------------------------------------------------------------------

    Despite efforts to be as consistent as possible between the
upstream emissions sectors utilized in the CAFE Model with the 2018 EPA
source apportionment TSD, the need to use up-to-date sources based on
newer air quality modeling updates led to the use of multiple papers.
In addition to the 2018 EPA source apportionment TSD used in the 2020
final rule, we used additional EPA sources and conversations with EPA
staff to appropriately map health incidence per ton values to the
appropriate CAFE Model emissions source category. Very recently, EPA
updated its approach to estimating the benefits of changes in
PM2.5 and ozone,631 632 as well as the associated
changes in health impacts per ton. These updates were based on
information drawn from the recent 2019 PM2.5 and 2020 Ozone
Integrated Science Assessments (ISAs), which were reviewed by the Clean
Air Science Advisory Committee (CASAC) and the
public.633 634 EPA has not updated its health incidence
estimates for mobile sources to reflect these updates in time for this
analysis. Instead, based on the recommendation of EPA staff, we use the
same PM2.5 BPT estimates and health incidence values that we
used in the NPRM, to ensure consistency between the values
corresponding to different source sectors. The estimates used are based
on the review of the 2009 PM ISA \635\ and 2012 p.m. ISA Provisional
Assessment \636\ and include

a mortality risk estimate derived from the Krewski et al. (2009) \637\
analysis of the American Cancer Society (ACS) cohort and nonfatal
illnesses consistent with benefits analyses performed for the analysis
of the final Tier 3 Vehicle Rule (79 FR 23414, April 28, 2014),\638\
the final 2012 p.m. NAAQS Revision (78 FR 3154, Jan. 15, 2013),\639\
and the final 2017-2025 Light-duty Vehicle GHG Rule (77 FR 62624, Oct.
15, 2012).\640\ We expect this lag in updating our health incidence and
BPT estimates to have only a minimal impact on total PM benefits, since
the underlying mortality risk estimate based on the Krewski study is
identical to an updated PM2.5 morality risk estimate derived
from an expanded analysis of the same ACS cohort. We are aware of EPA's
work to update its mobile source BPT and health incidence estimates to
reflect these recent updates for use in future rulemaking analyses, and
we will work further with EPA in future rulemakings to update and
synchronize approaches.
---------------------------------------------------------------------------

    \631\ U.S. Environmental Protection Agency (U.S. EPA). 2021a.
Regulatory Impact Analysis for the Final Revised Cross-State Air
Pollution Rule (CSAPR) Update for the 2008 Ozone NAAQS. EPA-452/R-
21-002. March.
    \632\ U.S. Environmental Protection Agency (U.S. EPA). 2021b.
Estimating PM2.5- and Ozone-Attributable Health Benefits.
Technical Support Document (TSD) for the Final Revised Cross-State
Air Pollution Rule Update for the 2008 Ozone Season NAAQS. EPA-HQ-
OAR-2020-0272. March.
    \633\ U.S. Environmental Protection Agency (U.S. EPA). 2019a.
Integrated Science Assessment (ISA) for Particulate Matter (Final
Report, 2019). U.S. Environmental Protection Agency, Washington, DC,
EPA/600/R-19/188, 2019.
    \634\ U.S. Environmental Protection Agency (U.S. EPA). 2019a.
Integrated Science Assessment (ISA) for Ozone and Related
Photochemical Oxidants (Final Report). U.S. Environmental Protection
Agency, Washington, DC, EPA/600/R-20/012, 2020.
    \635\ U.S. Environmental Protection Agency (U.S. EPA). 2009.
Integrated Science Assessment for Particulate Matter (Final Report).
EPA-600-R-08-139F. National Center for Environmental Assessment-RTP
Division, Research Triangle Park, NC. December. Available at: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546.
    \636\ U.S. Environmental Protection Agency (U.S. EPA). 2012.
Provisional Assessment of Recent Studies on Health Effect of
Particulate Matter Exposure. EPA/600/R-12/056F. National Center for
Environmental Assessment-RTP Division, Research Triangle Park, NC.
December. Available at: https://cfpub.epa.gov/ncea/isa/recordisplay.cfm?deid=247132.
    \637\ Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, Y.
Shi, et al. 2009. Extended Follow-Up and Spatial Analysis of the
American Cancer Society Study Linking Particulate Air Pollution and
Mortality. HEI Research Report, 140, Health Effects Institute,
Boston, MA.
    \638\ U.S. Environmental Protection Agency (2014). Control of
Air Pollution from Motor Vehicles: Tier 3 Motor Vehicle Emission and
Fuel Standards Final Rule: Regulatory Impact Analysis, Assessment
and Standards Division, Office of Transportation and Air Quality,
EPA-420-R-14-005, March 2014. Available on the internet: http://www3.epa.gov/otaq/documents/tier3/420r14005.pdf.
    \639\ U.S. Environmental Protection Agency. (2012). Regulatory
Impact Analysis for the Final Revisions to the National Ambient Air
Quality Standards for Particulate Matter, Health and Environmental
Impacts Division, Office of Air Quality Planning and Standards, EPA-
452-R-12-005, December 2012. Available on the internet: http://www3.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf.
    \640\ U.S. Environmental Protection Agency (U.S. EPA). (2012).
Regulatory Impact Analysis: Final Rulemaking for 2017-2025 Light-
Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average
Fuel Economy.
---------------------------------------------------------------------------

    The basis for the health impacts from the petroleum extraction
sector is a 2018 oil and natural gas sector paper written by EPA staff
(Fann et al.), which estimates health impacts for this sector in the
year 2025.\641\ This paper defines the oil and gas sector's emissions
not only as arising from petroleum extraction but also from
transportation to refineries, while the CAFE/GREET component is
composed of only petroleum extraction. After consultation with the
authors of the EPA paper, we determined that these are the best
available estimates for the petroleum extraction sector,
notwithstanding this difference. Specific health incidences per
pollutant were not reported in the paper, so EPA staff sent BenMAP
health incidence files for the oil and natural gas sector upon request.
DOT staff then calculated per ton values based on these files and the
tons reported in the Fann et al. paper.\642\ The only available health
impacts corresponded to the year 2025. Rather than trying to
extrapolate, these 2025 values were used for all the years in the CAFE
Model structure: 2020, 2025, and 2030.\643\ This simplification implies
an overestimate of damages in 2020 and an underestimate in 2030.\644\
---------------------------------------------------------------------------

    \641\ Fann, N., Baker, K. R., Chan, E., Eyth, A., Macpherson,
A., Miller, E., & Snyder, J. (2018). Assessing Human Health
PM2.5 and Ozone Impacts from U.S. Oil and Natural Gas
Sector Emissions in 2025. Environmental science & technology,
52(15), 8095-8103 (hereinafter, Fann et al.).
    \642\ Nitrate-related health incidents were divided by the total
tons of NOX projected to be emitted in 2025, sulfate-
related health incidents were divided by the total tons of projected
SOX, and EC/OC (elemental carbon and organic carbon)
related health incidents were divided by the total tons of projected
EC/OC. Both Fann et al. and the 2018 EPA source apportionment TSD
define primary PM2.5 as being composed of elemental
carbon, organic carbon, and small amounts of crustal material. Thus,
the EC/OC BenMAP file was used for the calculation of the incidents
per ton attributable to PM2.5.
    \643\ These three years are used in the CAFE Model structure
because it was originally based on the estimate provided in the 2018
EPA source apportionment TSD.
    \644\ See EPA. 2018. Estimating the Benefit per Ton of Reducing
PM2.5 Precursors from 17 Sectors. https://www.epa.gov/sites/production/files/2018-02/documents/sourceapportionmentbpttsd_2018.pdf, p. 9.
---------------------------------------------------------------------------

    We understand that uncertainty exists around the contribution of
VOCs to PM2.5 formation in the modeled health impacts from
the petroleum extraction sector; however, based on feedback to the 2020
final rule, we believe that the updated health incidence values
specific to petroleum extraction sector emissions may provide a more
appropriate estimate of potential health impacts from that sector's
emissions than the previous approach of applying refinery sector
emissions impacts to the petroleum extraction sector. For further
discussion of the BPT estimates corresponding to the health effects
discussed in this section, see Section III.G.2.b)(2).
    The petroleum transportation sector and fuel TS&D sector do not
correspond to any one EPA source sector in the 2018 EPA source
apportionment TSD, so we use a weighted average of multiple different
EPA sectors to determine the health impact per ton values for those
sectors. We use a combination of different EPA mobile source sectors
from two different papers, the 2018 EPA source apportionment TSD,\645\
and a 2019 mobile source sectors paper (Wolfe et al.) \646\ to generate
these values. The health incidence per ton values associated with the
refineries sector and electricity generation sector are drawn solely
from the 2018 EPA source apportionment TSD.
---------------------------------------------------------------------------

    \645\ Environmental Protection Agency (EPA). 2018. Estimating
the Benefit per Ton of Reducing PM2.5 Precursors from 17
Sectors. https://www.epa.gov/sites/production/files/2018-02/documents/sourceapportionmentbpttsd_2018.pdf.
    \646\ Wolfe et al. 2019. Monetized health benefits attributable
to mobile source emissions reductions across the United States in
2025. https://pubmed.ncbi.nlm.nih.gov/30296769/.
---------------------------------------------------------------------------

    IPI expressed concern that the agency's domestic fuel refining
share assumptions cause an underestimate in the health effects counted
in this analysis.\647\ For discussion of NHTSA's domestic fuel refining
assumptions, see Section III.G.2.b)(3), TSD Chapter 5.2, and TSD
Chapter 6.2.
---------------------------------------------------------------------------

    \647\ IPI, Docket No. NHTSA-2021-0053-1579, at 39.
---------------------------------------------------------------------------

    The CAFE Model follows a similar process for computing health
impacts resulting from tailpipe emissions as it does for calculating
health impacts from upstream emissions. Previous rulemakings used the
2018 EPA source apportionment TSD as the source for the health
incidence per ton, matching the CAFE Model tailpipe emissions inventory
to the ``on-road mobile sources sector'' in the TSD. However, a more
recent EPA paper from 2019 (Wolfe et al.) \648\ computes monetized
damage costs per ton values at a more disaggregated level, separating
on-road mobile sources into multiple categories based on vehicle type
and fuel type. Wolfe et al. did not report incidences per ton, but that
information was obtained through communications with EPA staff. The
Center for Biological Diversity, Chesapeake Bay Foundation,
Conservation Law Foundation, Earthjustice, Environmental Law & Policy
Center, Natural Resources Defense Council, Public Citizen, Inc., Sierra
Club, and Union of Concerned Scientists, in their joint summary
comments, stated that the estimates of the benefits of PM2.5
reductions have been improved with the addition of the Wolfe et al.
paper.\649\ We agree, and continue to use these sources in the final
rulemaking analysis as the categories are more expansive and specific
than the original 2018 source.
---------------------------------------------------------------------------

    \648\ Wolfe et al. 2019. Monetized health benefits attributable
to mobile source emissions reductions across the United States in
2025. https://pubmed.ncbi.nlm.nih.gov/30296769/.
    \649\ CBD et al., Docket No. NHTSA-2021-0053-1572, at 5.
---------------------------------------------------------------------------

    The Wisconsin Department of Natural Resources (WDNR) stated that
``NHTSA

should work with EPA to offset any increases in sulfur dioxide
emissions associated with the rule'' and that ``NHTSA should work with
EPA to offset any short-term increases in NOX and VOC
emissions associated with the rule,'' specifically citing the on-road
emissions that contribute to ozone formation in Wisconsin. Furthermore,
they state that ``NHTSA's analysis should be updated to reflect EPA's
revised area designations for the 2015 ozone NAAQs.'' \650\
---------------------------------------------------------------------------

    \650\ WDNR, Docket No. NHTSA-2021-0053-0059, at 2, 4.
---------------------------------------------------------------------------

    While this final rulemaking will result in small short-term
increases in criteria pollutants, the number of vehicle re-fueling
events and emissions of certain criteria pollutants and precursors the
emissions impact will vary from area to area depending on factors such
as the composition of the local vehicle fleet and the amount of
gasoline produced in the area. As discussed further in the Final SEIS,
criteria pollutant impacts are by their nature diffuse and
indeterminate, which makes the assessment of any potential mitigation
measures difficult; however, NHTSA does not have jurisdiction to
regulate criteria and air toxic pollutant emissions. However, as
discussed further in the Final SEIS, NHTSA did update the Final SEIS
analysis to reflect EPA's revised area designations for the 2015 ozone
NAAQS, including nonattainment area designations in Wisconsin and the
Chicago area.
    The Alliance for Automotive Innovation and CEI expressed the
concern that the analysis overstates health effects. The Alliance
argued that reductions in PM2.5 emissions ``will not provide
public health benefits that are additive to the emissions reductions
accomplished by EPA's mobile-source and stationary-source programs for
criteria air pollutants.'' \651\ CEI objected to counting benefits from
a reduction in PM emissions in areas that are not classified as
nonattainment areas.\652\ As EPA stated in their recent GHG final rule
for MYs 2023-2026 (86 FR 74434, Dec. 30, 2021),\653\ NAAQS are set with
an ``adequate margin of safety'' but this ``does not represent a zero-
risk standard.'' As such, it is important to count health benefits from
reductions in criteria pollutants, regardless of whether they occur in
nonattainment areas or not. Furthermore, the relative magnitude of the
health benefits in our analysis is minimal compared to the other costs
and benefits and does not significantly change net benefits.
---------------------------------------------------------------------------

    \651\ Auto Innovators, Docket No. NHTSA-2021-0053-1492, at 90.
    \652\ Competitive Enterprise Institute, Docket No. NHTSA-2021-
0053-1546, at 3.
    \653\ EPA. Revised 2023 and Later Model Year Light-Duty Vehicle
Greenhouse Gas Emissions Standards: Response to Comments (EPA-420-R-
21-027, December 2021) pp. 15-31.
---------------------------------------------------------------------------

    We are aware of other limitations of using national values of
health incidences per ton associated with the BPT approach, which we
discuss extensively in prior rules, the NPRM, and Chapter 5 of the TSD.
That said, we believe that the BPT approach provides a reasonable
estimate of how different levels of CAFE standards may impact public
health.
    The methodology for generating values for each emissions category
in the CAFE Model is discussed in further detail in Chapter 5 of the
TSD. The Parameters file contains all of the health impact per ton of
emissions values used in this final rule.

G. Simulating Economic Impacts of Regulatory Alternatives

    This section summarizes the agency's approach for measuring the
economic costs and benefits that will result from establishing
alternative CAFE standards for future model years. The benefit and cost
measures the agency uses are important considerations, because as
Office of Management and Budget (OMB) Circular A-4 states, benefits and
costs reported in regulatory analyses must be defined and measured
consistently with economic theory, and should also reflect how
alternative regulations are anticipated to change the behavior of
producers and consumers from a baseline scenario.\654\ For CAFE
standards, those include vehicle manufacturers, buyers of new cars and
light trucks, owners of used vehicles, and suppliers of fuel, all of
whose behavior is likely to respond in complex ways to the level of
CAFE standards that DOT establishes for future model years.
---------------------------------------------------------------------------

    \654\ White House Office of Management and Budget, Circular A-4:
Regulatory Analysis, September 17, 2003 (https://obamawhitehouse.archives.gov/omb/circulars_a004_a-4/), Section E.
---------------------------------------------------------------------------

    It is important to report the benefits and costs of this final rule
in a format that conveys useful information about how those impacts are
generated and also distinguishes the impacts of those economic
consequences for private businesses and households from the effects on
the remainder of the U.S. economy. A reporting format will accomplish
this objective to the extent that it clarifies who incurs the benefits
and costs of the final rule, and shows how the economy-wide or
``social'' benefits and costs of the final rule are composed of its
direct effects on vehicle producers, buyers, and users, plus the
indirect or ``external'' benefits and costs it creates for the general
public.
    Table III-37 and Table III-38 present the incremental economic
benefits and costs of the final rule and the alternatives (described in
detail in Section IV) to increase CAFE standards for MYs 2024-26 at
three percent and seven percent discount rates in a format that is
intended to meet these objectives. The tables include costs that are
transfers between different economic actors--these will appear as both
a cost and a benefit in equal amounts (to separate affected parties).
Societal cost and benefit values shown elsewhere in this document do
not show costs that are transfers for the sake of simplicity but report
the same net societal costs and benefits. The final rule and the
alternatives would increase costs to manufacturers for adding
technology necessary to enable new cars and light trucks to comply with
fuel economy and emission regulations. It may also increase fine
payments by manufacturers who would have achieved compliance with the
less demanding baseline standards. Manufacturers are assumed to
transfer these costs on to buyers by charging higher prices; although
this reduces their revenues, on balance, the increase in compliance
costs and higher sales revenue leaves them financially unaffected.
Since the analysis assumes that manufacturers are left in the same
economic position regardless of the standards, they are excluded from
the tables.
---------------------------------------------------------------------------

    \655\ Average SC-GHG values are constructed using a 3 percent
discount rate and are discounted back to present value using a 3
percent discount rate.
---------------------------------------------------------------------------

BILLING CODE 4910-59-P

[GRAPHIC] [TIFF OMITTED] TR02MY22.105

[GRAPHIC] [TIFF OMITTED] TR02MY22.106

BILLING CODE 4910-59-C
    Compared to the baseline standards, the analysis shows that buyers
of new cars and light trucks will incur higher purchasing prices and
financing costs, which will lead to some buyers dropping out of the new
vehicle market. Drivers of new vehicles will also experience a slight
uptick in the risk of being injured in a crash because of mass
reduction technologies employed to meet the increased standards. While
this effect is not statistically significant, NHTSA provides these
results for transparency, and to demonstrate that their inclusion does
not affect NHTSA's policy decision. Because of the increasing price of
new vehicles, some owners may delay retiring and replacing their older
vehicles with newer models. In effect, this will transfer some driving
that would have been done in newer vehicles under the baseline scenario
to older models within the legacy fleet, thus increasing costs for
injuries (both fatal and less severe) and property damages sustained in
motor vehicle crashes. This stems from the fact that cars and light
trucks have become progressively more protective in crashes over time
(and also slightly less prone to certain types of crashes, such as
rollovers). Thus, shifting some travel from newer to older models would
increase injuries and damages sustained by drivers and passengers
because they are traveling in less safe vehicles and not because it
changes the risk profiles of drivers themselves. These costs are
largely driven by assumptions regarding consumer valuation of fuel
efficiency and an assumption that more fuel-efficient vehicles are less
preferable to consumers than their total cost to improve fuel economy.
The agency examines alternate assumptions regarding consumer valuation,
as well as other assumptions that influence our safety impact estimates
in a sensitivity analysis that can be found in the accompanying FRIA.
    In exchange for these costs, consumers will benefit from new cars
and light trucks with better fuel economy. Drivers will experience
lower costs as a consequence of new vehicles' decreased fuel
consumption, and from fewer refueling stops required because of their
increased driving range. They will experience mobility benefits as they

use newly purchased cars and light trucks more in response to their
lower operating costs. On balance, consumers of new cars and light
trucks produced during the model years subject to this final rule will
experience significant economic benefits.
    Table III-37 and Table III-38 also show that the changes in fuel
consumption and vehicle use resulting from this final rule will in turn
generate both benefits and costs to society writ large. These impacts
are ``external,'' in the sense that they are by-products of decisions
by private firms and individuals that alter vehicle use and fuel
consumption but are experienced broadly throughout society rather than
by the firms and individuals who indirectly cause them. In terms of
costs, additional driving by consumers of new vehicles in response to
their lower operating costs will increase the external costs associated
with their contributions to traffic delays and noise levels in urban
areas, and these additional costs will be experienced throughout much
of the society. While most of the risk of additional driving or
delaying purchasing a newer vehicle are internalized by those who make
those decisions, a portion of the costs are borne by other road users.
Finally, since owners of new vehicles will be consuming less fuel, they
will pay less in fuel taxes.
    Society will also benefit from more stringent standards. Increased
fuel efficiency will reduce the amount of petroleum-based fuel consumed
and refined domestically, which will decrease the emissions of carbon
dioxide and other greenhouse gases that contribute to climate change,
and, as a result, the U.S. (and the rest of world) will avoid some of
the economic damages from future changes in the global climate.
Similarly, reduced fuel production and use will decrease emissions of
more localized air pollutants (or their chemical precursors), and the
resulting decrease in the U.S. population's exposure to harmful levels
of these pollutants will lead to lower costs from its adverse effects
on health. Decreasing consumption and imports of crude petroleum for
refining lower volumes of gasoline and diesel will also create some
benefits throughout the U.S., in potential gains in energy security as
businesses and households that are dependent on fuel are less subject
to sudden and sharp changes in energy prices.
    On balance, Table III-37 and Table III-38 show that both consumers
and society as a whole will experience net economic benefits from the
final rule. The following subsections will briefly describe the
economic costs and benefits considered by the agency. For a complete
discussion of the methodology employed and the results, see TSD Chapter
6 and FRIA Chapter 6, respectively. The safety implications of the
final rule--including the monetary impacts--are addressed in Section
III.H.
1. Private Costs and Benefits
(a) Costs to Consumers
(1) Technology Costs
    The final rule and the alternatives would increase costs to
manufacturers for adding technology necessary to enable new cars and
light trucks to comply with fuel economy and emission regulations.
Manufacturers are assumed to transfer these costs on to buyers by
charging higher prices. See Section III.C.6 and TSD Chapter 2.6.
(2) Consumer Sales Surplus
    Buyers who would have purchased a new vehicle with the baseline
standards in effect but decide not to do so in response to the changes
in new vehicles' prices due to more stringent standards in place will
experience a decrease in welfare. The collective welfare loss to those
``potential'' new vehicle buyers is measured by the forgone consumer
surplus they would have received from their purchase of a new vehicle
in the baseline.
    Consumer surplus is a fundamental economic concept and represents
the net value (or net benefit) a good or service provides to consumers.
It is measured as the difference between what a consumer is willing to
pay for a good or service and the market price. OMB Circular A-4
explicitly identifies consumer surplus as a benefit that should be
accounted for in cost-benefit analysis. For instance, OMB Circular A-4
states the ``net reduction in total surplus (consumer plus producer) is
a real cost to society,'' and elsewhere elaborates that consumer
surplus values be monetized ``when they are significant.'' \656\
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    \656\ OMB Circular A-4, at 37-38.
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    Accounting for the portion of fuel savings that the average new
vehicle buyer demands, and holding all else equal, higher average
prices should depress new vehicle sales and by extension reduce
consumer surplus. The inclusion of consumer surplus is not only
consistent with OMB guidance, but with other parts of the regulatory
analysis. For instance, we calculate the increase in consumer surplus
associated with increased driving that results from the decrease in the
cost per mile of operation under more stringent regulatory
alternatives, as discussed in Section III.G.1.b)(3). The surpluses
associated with sales and additional mobility are inextricably linked
as they capture the direct costs and benefits accrued by purchasers of
new vehicles. The sales surplus captures the welfare loss to consumers
when they forgo a new vehicle purchase in the presence of higher prices
and the additional mobility measures the benefit increased mobility
under lower operating expenses.
    The agency estimates the loss of sales surplus based on the change
in quantity of vehicles projected to be sold after adjusting for
quality improvements attributable to fuel economy. For additional
information about consumer sales surplus, see TSD Chapter 6.1.2.
(3) Ancillary Costs of Higher Vehicle Prices
    Some costs of purchasing and owning a new or used vehicle scale
with the value of the vehicle. Where fuel economy standards increase
the transaction price of vehicles, they will affect both the absolute
amount paid in sales tax and the average amount of financing required
to purchase the vehicle. Further, where they increase the MSRP, they
increase the appraised value upon which both value-related registration
fees and a portion of insurance premiums are based. The analysis
assumes that the transaction price is a set share of the MSRP, which
allows calculation of these factors as shares of MSRP.
    For this final rule, NHTSA has revised its estimates of these
ancillary costs to correct some mistakes in their accounting. First,
NHTSA excludes financing costs from the per-vehicle analysis. The
availability of vehicle financing is, if anything, a benefit to
consumers that would lower the cost to consumers of fuel-economy
technology by spreading out the costs over time. Second, NHTSA has
reduced its estimate of insurance costs to avoid a double-counting
issue it identified. Specifically, a portion of the insurance premium
goes to covering replacement vehicles and including that portion of the
insurance cost would be duplicative with estimates of the upfront
technology cost on the replacement vehicle (which is already captured
in the analysis and discussed above). For a detailed explanation of how
the agency estimates these costs, see TSD Chapter 6.1.1.
    These costs are included in the consumer per-vehicle cost-benefit
analysis but are not included in the societal cost-benefit analysis
because they are assumed to be transfers from

consumers to governments, financial institutions, and insurance
companies.
(b) Benefits to Consumers
(1) Fuel Savings
    The primary benefit to consumers of increasing CAFE standards are
the additional fuel savings that accrue to new vehicle owners. Fuel
savings are calculated by multiplying avoided fuel consumption by fuel
prices. Each vehicle of a given body style is assumed to be driven the
same as all the others of a comparable age and body style in each
calendar year. The ratio of that cohort's VMT to its fuel efficiency
produces an estimate of fuel consumption. The difference between fuel
consumption in the baseline, and in each alternative, represents the
gallons (or energy) saved. Under this assumption, our estimates of fuel
consumption from increasing the fuel economy of each individual model
depend only on how much its fuel economy is increased, and do not
reflect whether its actual use differs from other models of the same
body type. Neither do our estimates of fuel consumption account for
variation in how much vehicles of the same body type and age are driven
each year, which appears to be significant (see TSD Chapter 4.3.2).
Consumers save money on fuel expenditures at the average retail fuel
price (fuel price assumptions are discussed in detail in TSD Chapter
4.1.2), which includes all taxes and represents an average across
octane blends. For gasoline and diesel, the included taxes reflect both
the Federal tax and a calculated average state fuel tax. Expenditures
on alternative fuels (E85 and electricity, primarily) are also included
in the calculation of fuel expenditures, on which fuel savings are
based. And while the included taxes net out of the social benefit cost
analysis (as they are a transfer), consumers value each gallon saved at
retail fuel prices including any additional fees such as taxes. See TSD
Chapter 6.1.3 for additional details. In the TSD, the agency considers
the possibility that several of the assumptions made about vehicle use
could lead to imprecision in projecting fuel savings. The agency notes
that these simplifying assumptions are necessary to model fuel savings
and likely have minimal impact to the accuracy of this analysis.
    CBD et al. commented that NHTSA underestimates the fuel savings in
the analysis. CBD et al. argued that NHTSA needs to account for any
fuel savings that may be achieved if CAFE standards cause gasoline
prices to fall due to decreasing demand.\657\ The agency acknowledges
that if fuel prices do decrease as a result of this rule, the analysis
could understate the amount of fuel savings. However, given how
pervasive fuel price projections are within the analysis, other
estimates would be incorrect as well. For example, our model assumes
that manufacturers will apply technology if the fuel savings in the
first 30 months exceeds the technology costs. If prices drop as a
result of better fuel economy, our standards would have a larger,
negative impact on sales as fewer technology costs are `worth it' in
the eyes of consumers. It is not readily apparent, then, whether
holding fuel prices constant across alternatives would increase or
decrease the net benefits attributable to the standards. Modeling fuel
prices that respond dynamically is currently outside the ability of the
model. Furthermore, since fuel prices are influenced by many different
factors--many of which are outside the purview of United States--it's
not clear if modeling gas prices dynamically would enhance the agency's
analysis.
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    \657\ CBD et al., Appendix, Docket No. NHTSA-2021-0053-1572, at
31.
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(2) Refueling Benefit
    Increasing CAFE standards, all else being equal, affects the amount
of time drivers spend refueling their vehicles in several ways. First,
they increase the fuel economy of ICE vehicles produced in the future,
which increases vehicle range and decreases the number of refueling
events for those vehicles. Conversely, to the extent that more
stringent standards increase the purchase price of new vehicles, they
may reduce sales of new vehicles and scrappage of existing ones,
causing more VMT to be driven by older and less efficient vehicles,
which require more refueling events for the same amount of VMT driven.
Finally, sufficiently stringent standards may also change the number of
electric vehicles that are produced, and shift refueling to occur at a
charging station or at a residence, rather than at the pump--changing
per-vehicle lifetime expected refueling costs.
    We estimate these savings by calculating the amount of refueling
time avoided--including the time it takes to find, refuel, and pay--and
multiplying it by DOT's value of time of travel savings estimate. For a
full description of the methodology, refer to TSD Chapter 6.1.4.
(3) Additional Mobility
    Any increase in travel demand provides benefits that reflect the
value to drivers and other vehicle occupants of the added--or more
desirable--social and economic opportunities that become accessible
with additional travel. Under the alternatives in this analysis, the
fuel cost per mile of driving would decrease as a consequence of the
higher fuel economy levels they require, thus increasing the number of
miles that buyers of new cars and light trucks would drive as a
consequence of the well-documented fuel economy rebound effect.
    The fact that drivers and their passengers elect to make more
frequent or longer trips to gain access to these opportunities when the
cost of driving declines demonstrates that the benefits they gain by
doing so exceed the costs they incur. At a minimum, the benefits must
equal the cost of the fuel consumed to travel the additional miles (or
they would not have occurred). The cost of that energy is subsumed in
the simulated fuel expenditures, so it is necessary to account for the
benefits associated with those miles traveled here. But the benefits
must also offset the economic value of their (and their passengers')
travel time, other vehicle operating costs, and the economic cost of
safety risks due to the increase in exposure that occurs with
additional travel. The amount by which the benefits of this additional
travel exceeds its economic costs measures the net benefits drivers and
their passengers experience, usually referred to as increased consumer
surplus.
    TSD Chapter 6.1.5 explains the agency's methodology for calculating
additional mobility. The benefit of additional mobility over and above
its costs is measured by the change in consumers' surplus. This is
calculated using the rule of one-half, and is equal to one-half of the
change in fuel cost per mile times the increase in vehicle miles
traveled due to the rebound effect.
    In contrast to the societal cost-benefit analysis, calculation of
average costs and benefits to consumers is done on a per-vehicle basis
and is intended to describe how alternative standards affect the costs
and benefits of owning vehicles from the consumers' perspective. The
mobility costs and benefits per vehicle are affected by the assumption
that total VMT before adding the rebound effect will be the same in the
baseline and all alternative cases (See TSD Chapter 4.3.1). Because the
standards affect vehicle sales and scrappage which changes the number
of vehicles in the alternative cases, the

CAFE Model changes VMT per vehicle in the alternative cases to maintain
a constant total non-rebounded VMT. When vehicle sales decrease in the
alternative cases, VMT per vehicle increases. IPI and Drs. Jacobsen and
Liao of the University of California at San Diego (UCSD) commented that
changes in the size and age composition of the vehicle stock will
change total VMT.\658\ IPI suggested VMT will change only ``slightly,''
while the UCSD commenters suggest reallocating only 50 percent of the
difference in non-rebounded VMT between the baseline and alternative
cases. We recognize that the assumption of constant non-rebounded VMT
is an approximation, and we may consider the possibility of refining
this method in the future.
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    \658\ IPI, at 30; Jacobsen and Liao, at 2.
---------------------------------------------------------------------------

    When the size of the vehicle stock decreases in the alternative
cases, VMT and fuel cost per vehicle increase. Because maintaining
constant non-rebounded VMT assumes consumers are willing to pay the
full cost of the reallocated vehicle miles, we offset the increase in
fuel cost per vehicle by adding the product of the reallocated VMT and
fuel cost per mile to the mobility value. This corrects an error in the
NPRM per vehicle analysis, which included the fuel cost per vehicle of
reallocated miles but not the mobility benefit per vehicle. Because we
do not estimate other changes in cost per vehicle that could result
from the reallocated miles (e.g., maintenance, depreciation, etc.) we
do not estimate the portion of the transferred mobility benefits that
would correspond to consumers' willingness to pay for those costs. We
do not estimate the consumers' surplus associated with the reallocated
miles because there is no change in total non-rebounded VMT and thus no
change in consumers' surplus per consumer.
2. External Costs and Benefits
(a) Costs
(1) Congestion and Noise
    Increased vehicle use associated with the rebound effect also
contributes to increased traffic congestion and highway noise. Although
drivers obviously experience these impacts themselves, they do not
fully value the costs these impacts impose on other road users and
surrounding residents, just as they do not fully value the emissions
impacts of their own driving. Congestion and noise costs are largely
``external'' to the vehicle owners whose decisions about how much,
where, and when to drive more in response to changes in fuel economy
create these costs. Thus, unlike changes in the fuel costs drivers
incur or the safety risks they assume when they decide to travel more,