on-road vehicle scrappage, and (2) by shifting resources towards fuel-
saving technologies--potentially reducing the durability of new
vehicles by making them more complex.
(b) Aggregate vs. Atomic Data Source in the Literature
    One important distinction between the literatures on vehicles
scrappage is between those that use atomic vehicle data, data following
specific individual vehicles, and those that use some level of
aggregated data, data that counts the total number of vehicles of a
given type. The decision to scrap a vehicle is an atomic one--that is,
made on an individual vehicle basis. The decision relates to the cost
of maintaining a vehicle, and the value of the vehicle both on the used
car market, and as scrap metal. Generally, a used car owner will decide
to scrap a vehicle where 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.
    Recent work is able to model scrappage as an atomic decision due to
the availability of a large database of used vehicle transactions.
Following works by other authors including:

Busse, Knittel, & Zettelmeyer (2013); Sallee, West, & Fan (2010);
Alcott & Wozny (2013); and Li, Timmins, & von Haefen (2009)--Jacobsen &
van Benthem (2015) considers the impact of changes in gasoline prices
on used vehicle values and scrappage rates. In turn, they consider the
impact of an increase in used vehicle values on the scrappage rate of
those vehicles. They find that increases in gasoline price result in a
reduction in the scrappage rate of the most fuel efficient vehicles and
an increase in the scrappage rate of the least fuel efficient vehicles.
This has important implications for the validity of the average fuel
economy values linked to model years and assumed to be constant over
the life of that model year fleet within this study. Future iterations
of this study could further investigate the relationship between fuel
economy, vehicle usage, and scrappage, as noted in other places in this
discussion.
    While the decision to scrap a vehicle is made atomically, the data
available to NHTSA on scrappage rates and variables that influence
these scrappage rates are aggregate measures. This influences the best
available methods to measure the impacts of new vehicle prices on
existing vehicle scrappage. The result is that this study models
aggregate trends in vehicle scrappage and not the atomic decisions that
make up these trends. Many other works within the literature use the
same data source and general scrappage construct, such as: Walker
(1968); Park (1977), Greene & Chen (1981); Gruenspecht (1981);
Gruenspecht (1982); Feeney & Cardebring (1988); Greenspan & Cohen
(1996); Jacobsen & van Bentham (2015); and Bento, Roth, & Zhuo (2016)
all use the same aggregate vehicle registration data as the source to
compute vehicle scrappage.
    Walker (1968) and Bento, Roth, & Zhuo (2016) use aggregate data to
directly compute the elasticity of scrappage from measures of used
vehicle prices. Walker (1968) uses the ratio of used vehicle Consumer
Price Index (CPI) to repair and maintenance CPI. Bento, Roth, & Zhuo
(2016) use used vehicle prices directly. While the direct measurement
of the elasticity of scrappage is preferable in a theoretical sense,
the CAFE model does not predict future values of used vehicles, only
future prices of new vehicles. For this reason, any model compatible
with the current CAFE model must estimate a reduced form similar to
Park (1977); Gruenspecht (1981); Greenspan & Cohen (1996), who use some
form of new vehicle prices or the ratio of new vehicle prices to
maintenance and repair prices to impute some measure of the effect of
new vehicle prices on vehicle scrappage.
(c) Historical Trends in Vehicle Durability
    Waker (1968); Park (1977); Feeney & Cardebring (1988); Hamilton &
Macauley (1999); and Bento, Ruth, & Zhuo (2016) all note that vehicles
change in durability over time. Walker (1968) simply notes a
significant distinction in expected vehicle lifetimes pre- and post-
World War I. Park (1977) discusses a `durability factor' set by the
producer for each year so that different vintages and makes will have
varying expected lifecycles. Feeney & Cardebring (1988) show that
durability of vehicles appears to have generally increased over time
both in the U.S. and Swedish fleets using registration data from each
country. They also note that the changes in median lifetime between the
Swedish and U.S. fleet track well, with a 1.5 year lag in the U.S.
fleet. This lag is likely due to variation in how the data is
collected--the Swedish vehicle registry requires a title to unregister
a vehicle, and therefore gets immediate responses, where the U.S.
vehicle registry requires re-registration, which creates a lag in
reporting.
    Hamilton & Macauley (1999) argue for a clear distinction between
embodied versus disembodied impacts on vehicle longevity. They define
embodied impacts as inherent durability similar to Park's producer
supplied `durability factor' and Greenspan's `engineering scrappage'
and disembodied effects those which are environmental, not unlike
Greenspan and Cohen's `cyclical scrappage.' They use calendar year and
vintage dummy variables to isolate the effects--concluding that the
environmental factors are greater than any pre-defined `durability
factor.' Some of their results could be due to some inflexibility of
assuming model year coefficients are constant over the life of a
vehicle, and there may be some correlation between the observed life of
the later model years of their sample and the `stagflation' \276\ of
the 1970's. Bento, Ruth, & Zhuo (2016) find that the average vehicle
lifetime has increased 27% from 1969 to 2014 by sub-setting their data
into three model year cohorts. To implement these findings in the
scrappage model incorporated into the CAFE model, this study takes
pains to estimate the effect of durability changes in such a way that
the historical durability trend can be projected into the future; for
this reason, a continuous `durability' factor as a function of model
year vintage is included.
---------------------------------------------------------------------------

    \276\ Continued high inflation combined with high unemployment
and slow economic growth.
---------------------------------------------------------------------------

(d) Models of the Gruenspecht Effect Used in Other Policy Analyses
    This is not the first estimation of the `Gruenspecht Effect' for
policy considerations. In their Technical Support Document (TSD) for
the 2004 proposal to reduce greenhouse gas emissions from motor
vehicles, California Air Resources Board (CARB) outlines how they
utilized the CARBITS vehicle transaction choice model in an attempt to
capture the effect of increasing new vehicle prices on vehicle
replacement rates. They consider data from the National Personal
Transportation Survey (NPTS) as a source of revealed preferences and a
University of California (UC) study as a source of stated preferences
for the purchase and sale of household fleets under different prices
and attributes (including fuel economy) of new vehicles.
    The transaction choice model represents the addition and deletion
of a vehicle from a household fleet within a short period of time as a
``replacement'' of a vehicle, rather than as two separate actions.
Their final data set consists of 790 vehicle replacements, 292
additions, and 213 deletions; they do not include the deletions, but
assume any vehicle over 19 years old that is sold is scrapped. This
allows them to capture a slowing of vehicle replacement under higher
new vehicle prices, but because their model does not include deletions,
does not explicitly model vehicle scrappage, but assumes all vehicles
aged 20 and older are scrapped rather than resold. They calibrate the
model so that the overall fleet size is benchmarked to Emissions
FACtors (EMFAC) fleet predictions for the starting year; the simulation
then produces estimates that match the EMFAC predictions without
further calibration.
    The CARB study captures the effect on new vehicle prices on the
fleet replacement rates and offers some precedence for including some
estimate of the Gruenspecht Effect. One important thing to note is that
because vehicles that exited the fleet without replacement were
excluded, the effect of new vehicle prices on scrappage rates where the
scrapped vehicle is not replaced is not captured. Because new and used
vehicles are substitutes, it is expected that used vehicle prices will
increase with new vehicle prices. Because higher used vehicle prices
will lower the number of vehicles whose cost of maintenance is higher
than their value, it is expected that not only will

replacements of used vehicles slow, but also, that some vehicles that
would have been scrapped without replacement under lower new vehicle
prices will now remain on the road because their value will have
increased. Aggregate measures of the Gruenspecht effect will include
changes to scrappage rates both from slower replacement rates, and
slower non-replacement scrappage rates.
(2) Description of Data Sources
    NHTSA purchases proprietary data on the registered vehicle
population from IHS/Polk for safety analyses. IHS/Polk has annual
snapshots of registered vehicle counts beginning in calendar year (CY)
1975 and continuing until calendar year 2015. The data includes the
following regulatory classes as defined by NHTSA: Passenger cars, light
trucks (classes 1 and 2a), and medium and heavy-duty trucks (classes 2b
and 3). Polk separates these vehicles into another classification
scheme: Cars and trucks. Under their schema, pickups, vans, and SUVs
are treated as trucks, and all other body styles are included as cars.
In order to build scrappage models to support the model year (MY) 2021-
2026 light duty vehicle (LDV) standards, it was important to separate
these vehicle types in a way compatible with the existing CAFE model.
    There were two compatible choices to aggregate scrappage rates: (1)
By regulatory class or (2) by body style. Because for NHTSA's purposes
vans/SUVs are sometimes classified as passenger cars and sometimes as
light trucks, and there was no quick way to reclassify some SUVs as
passenger cars within the Polk dataset, NHTSA chose to aggregate
survival schedules by body style. This approach is also preferable
because NHTSA uses body style specific lifetime VMT schedules. Vehicles
experience increased wear with use; many maintenance and repair events
are closely tied to the number of miles on a vehicle. The current
version of the CAFE model considers separate lifetime VMT schedules for
cars, vans/SUVs, pickups and classes 2b and 3 vehicles. These vehicles
are assumed to serve different purposes and, as a result, are modelled
to have different average lifetime VMT patterns. These different uses
likely also result in different lifetime scrappage patterns.
    Once stratified into body style level buckets, the data can be
aggregated into population counts by vintage and age. These counts
represent the population of vehicles of a given body style and vintage
in a given calendar year. The difference between the counts of a given
vintage and vehicle type from one calendar year to the next is assumed
to represent the number of vehicles of that vintage and type scrapped
in a given year. There were a couple other important data
considerations for the calculations of the historical scrappage rates
not discussed here but discussed in detail in the PRIA Chapter 8.\277\
---------------------------------------------------------------------------

    \277\ The first is any discontinuity caused by a change in how
Polk collected their data beginning in calendar year 2010, and the
second is the use of the adjustment described in Greenspan & Cohen
(1996).
---------------------------------------------------------------------------

    For historical data on vehicle transaction prices, the models use
data from the National Automobile Dealers Association (NADA), which
records the average transaction price of all light-duty vehicles. These
transaction prices represent the prices consumers paid for new vehicles
but do not include any value of vehicles that may have been traded in
to dealers. Importantly, these transaction prices were not available by
vehicle body styles; thus, the models will miss any unique trends that
may have occurred for a particular vehicle body style. This may be
particularly relevant for pickup trucks, which observed considerable
average price increases as luxury and high option pickups entered the
market. Future models will further consider incorporating price series
that consider the price trends for cars, SUVs and vans, and pickups
separately.\278\
---------------------------------------------------------------------------

    \278\ Note: Using historical data aggregated by body styles to
capture differences in price trends by body style does not require
the assertion technology costs are or are not borne by the body
style to which they are applied. If the body-style level average
price change is used, then the assumption is manufacturers do not
cross-subsidize across body styles, whereas if the average price
change is used then the assumption is they would proportion costs
equally for each vehicle. These are implementation questions to be
worked out once NHTSA has a historical data source separating price
series by body styles, but these do not matter in the current model
which only considers the average price of all light-duty vehicles.
---------------------------------------------------------------------------

    The models use the NADA price series rather than the Bureau of
Labor Statistics (BLS) New Vehicle Consumer Price Index (CPI), used by
Park (1977) and Greenspan & Cohen (1997), because the BLS New Vehicle
CPI makes quality adjustments to the new vehicle prices. BLS assumes
that additions of safety and fuel economy equipment are a quality
adjustment to a vehicle model, which changes the good and should not be
represented as an increase in its price. While this is good for some
purposes, it presumes consumers fully value technologies that improve
fuel economy. Because it is the purpose to this study to measure
whether this is true, it is important that vehicle prices adjusted to
fully value fuel economy improving technologies, which would obscure
the ability to measure the preference for more fuel efficient and
expensive new vehicles, are not used. As further justification for
using the NADA price series over the BLS New Vehicle CPI, Park (1977)
cites a discontinuity found in the amount of quality adjustments made
to the series so that more adjustments are made over time. This could
further limit the ability for the BLS New Vehicle CPI to predict
changes in vehicle scrappage.
    Vehicle scrappage rates are also influenced by fuel economy and
fuel prices. Historical data on the fuel economy by vehicle style from
model years 1979-2016 was obtained from the 2016 EPA Motor Trends
Report.\279\ The van/SUV fuel economy values represent a sales-weighted
harmonic average of the individual body styles. Fuel prices were
obtained from Department of Energy (DOE) historical values, and future
fuel prices within the CAFE model use the Annual Energy Outlook (AEO)
future oil price projections.\280\ From these values the average cost
per 100 miles of travel for the cohort of new vehicles in a given
calendar year and the average cost per 100 miles of travel for each
used model year cohort in that same calendar year are computed.\281\ It
is expected that as the new vehicle fleet becomes more efficient
(holding all other attributes constant) that it will be more desirable,
and the demand for used vehicles should decrease (increasing their
scrappage). As a given model year cohort becomes more expensive to
operate due to increases in fuel prices, it is expected the scrappage
of that model year will increase. It is perhaps worth noting that more
efficient model year vintages will be less susceptible to changes in
fuel prices, as

absolute changes in their cost per mile will be smaller. The functional
forms of the cost per mile measures are further discussed in the model
specification subsection 3 below.
---------------------------------------------------------------------------

    \279\ Light-Duty Automotive Technology, Carbon Dioxide
Emissions, and Fuel Economy Trends: 1975 Through 2016, U.S. EPA
(Nov. 2016), available at https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100PKK8.pdf.
    \280\ Note: The central analysis uses the AEO reference fuel
price case, but sensitivity analysis also considers the possibility
of AEO's low and high fuel price cases.
    \281\ Work by Jacobsen and van Bentham suggests that these
initial average fuel economy values may not represent the average
fuel economy of a model year cohort as it ages--mainly, they find
that the most fuel efficient vehicles scrap earlier than the least
fuel efficient models in a given cohort. This may be an important
consideration in future endeavors that work to link fuel economy,
vehicle miles travelled (VMT), and scrappage. Studies on ``the
rebound effect'' suggest that lowering the fuel cost per driven mile
increases the demand for VMT. With more miles, a vehicle will be
worth less as its perceived remaining useful life will be shorter;
this will result in the vehicle being more likely to be scrapped. A
rebound effect is included in the CAFE model, but because reliable
data on how average VMT by age has varied over calendar year and
model year vintage is not available, expected lifetime VMT is not
included within the current dynamic scrappage model.
---------------------------------------------------------------------------

    Aggregate measures that cyclically affect the value of used
vehicles include macroeconomic factors like the real interest rate, the
GDP growth rate, unemployment rates, cost of maintenance and repairs,
and the value of a vehicle as scrap metal or as parts. Here only the
GDP growth rate is discussed, as this is the only measure included in
the final model. Extended reasoning as to why other variables are not
included in the final model in the PRIA Chapter 8 is offered, but the
discussion was omitted here for brevity in describing only the final
model. Generally economic growth will result in a higher demand for new
vehicles--cars in aggregate are normal goods--and a reduction in the
value of used vehicles. The result should be an increase in the
scrappage rate of existing vehicles so that we expect the GDP growth
rate to be an important predictor of vehicle scrappage rates.
    NHTSA sourced the GDP growth rate from the 2017 OASDI Trustees
Report.\282\ The Trustees Report offers credible projections beyond
2032. Because the purpose of building this scrappage model is to
project vehicle survival rates under different fuel economy
alternatives and the current fuel economy projections go as far forward
as calendar year 2032, using a data set that encompasses projections at
least through 2032 is an essential characteristic of any source used
for this analysis.
---------------------------------------------------------------------------

    \282\ The 2017 Annual Report of the Board of Trustees of the
Federal Old-Age and Survivors Insurance and Federal Disability
Insurance Trust Funds, Social Security Administration (2017),
available at https://www.ssa.gov/oact/tr/2017/tr2017.pdf.
---------------------------------------------------------------------------

(3) Summary of Model Estimation
    The most predictive element of vehicle scrappage is what Greenspan
and Cohen deem `engineering scrappage.' This source of scrappage is
largely determined by the age of a vehicle and the durability of a
specific model year vintage. Vehicle scrappage typically follows a
roughly logistic function with age--that is, instantaneous scrappage
increases to some peak, and then declines, with age as noted in Walker
(1968); Park (1977); Greene & Chen (1981); Gruenspecht (1981); Feeney &
Cardebring (1988); Greenspan & Cohen (1996); Hamilton & Macauley
(1999); and Bento, Roth, & Zhuo (2016). Thus, this analysis also uses a
logistic function to capture this trend of vehicle scrappage with age
but allows non-linear terms to capture any skew to the logistic
relationship. Specific details about the final and considered forms of
engineering scrappage by body styles is presented in the PRIA Chapter
8.
    The final and considered independent variables intended to capture
cyclical elements of vehicle scrappage and the considered forms of each
are discussed in PRIA Chapter 8; here only inclusion of the GDP growth
rate is discussed. The GDP growth rate is not a single-period effect;
both the current and previous GDP growth rates will affect vehicle
scrappage rates. A single year increase will affect scrappage
differently than a multi-period trend. For this reason, an optimal
number of lagged terms are included: The within-period GDP growth rate,
the previous period GDP growth rate, and the growth rate from two prior
years for the car model, while for vans/SUVs, and pickups, the current
and previous period GDP growth rate are sufficient.
    Similarly, the considered model allows that one-period changes in
new vehicle prices will affect the used vehicle market differently than
a consistent trend in new vehicle prices. The optimal number of lags is
three so that the price trend from the current year and the three prior
years influences the demand for and scrappage of used vehicles. Note:
The average lease length is three years \283\ so that the price of an
average vehicle coming off lease is estimated to affect the scrappage
rate of used vehicles--this is a major source of the newest used
vehicles that enter the used car fleet. Further, because increases in
new vehicle prices due to increased stringency of CAFE standards is the
primary mechanism through which CAFE standards influence vehicle
scrappage and the CAFE Model assumes that usage, efficiency, and safety
vary with the age of the vehicle, particular attention is paid to the
form of this effect. It is important to know the likelihood of
scrappage by the age of the vehicle to correctly account for the
additional costs of additional fatalities and increased fuel
consumption from deferred scrappage. Thus, the influence of increasing
new vehicle prices is allowed to influence the demand for used vehicles
(and reduce their scrappage) differently for different ages of vehicles
in the scrappage model. We discuss both how we determined the correct
form and number of lags for each body style in PRIA Chapter 8.
---------------------------------------------------------------------------

    \283\ See e.g., Edmunds January 2017 Lease Market Report,
Edmunds (Jan. 2017), https://dealers.edmunds.com/static/assets/articles/lease-report-jan-2017.pdf.
---------------------------------------------------------------------------

    The final cyclical factor affecting vehicle scrappage in the
preferred model is the cost per 100 miles of travel both of new
vehicles and of the vehicle which is the subject of the decision to
scrap or not to scrap. The new vehicle cost per 100 miles is defined as
the ratio of the average fuel price faced by new vehicles in a given
calendar year and the average new vehicle fuel economy for 100 miles in
the same calendar year, and varies only with calendar year:
[GRAPHIC] [TIFF OMITTED] TP24AU18.062

    The cost per 100 miles of the potentially scrapped vehicle is
described as the ratio of the average fuel price faced by that model
year vintage in a given calendar year and the average fuel economy for
100 miles of travel for that model year when it was new, and varies
both with calendar year and model year:
[GRAPHIC] [TIFF OMITTED] TP24AU18.063

    The average per-gallon fuel price faced by a model year vintage in
a given calendar year is the annual average fuel price of all fuel
types present in that model year fleet for the given calendar year,
weighted by the share of each fuel type in that model year fleet. Or
the following, where FT represents the set of fuel types present in a
given model year vintage:
[GRAPHIC] [TIFF OMITTED] TP24AU18.064

    For these variables, the best fit model includes the cost per mile
of both the new and the used vehicle for the current and prior year.
This is congruent with research that suggests consumers respond to
current fuel prices and fuel price changes. The selection process of
this form for the cost per mile and the implications is discussed in
PRIA Chapter 8.
    There are a couple other controlling factors considered in our
final model. The 2009 Car Allowance Rebate System (CARS) is not
outlined here but is outlined in PRIA Chapter 8. This program aimed to
accelerate the retirement of less fuel efficient vehicles and replace
them with more fuel efficient vehicles. Further discussion of how this
is controlled for is located in PRIA Chapter 8. Finally, evidence of
autocorrelation was found, and including three lagged values of the
dependent variable addresses the concern. Treatment of autocorrelation
is discussed in PRIA Chapter 8.
    One additional issue encountered in the estimations of scrappage
rates is that the models predict too many vehicles remain on the road
in the later years. This issue occurs because the data beyond age 15
are progressively more sparsely populated; vehicles over 15 years were
not captured in the Polk data until 1994, when each successive
collection year added an additional age of vehicles until 2005 when all
ages began to be collected. This means that for vehicles over the age
of 25 there are only 10 years of data. In order to correct for this
issue the fact that the final fleet share converges to roughly the same
share for most model years for a given vehicle type is used. The
predicted versus historical relationships seem to deviate beginning
around age 20; thus, for scrappage rates for vehicles beyond age 20 an
exponential decay function which guarantees that by age 40 the final
fleet share reaches the convergence level observed in the historical
data is applied. The application of the decay function and mathematical
definition is further defended in PRIA Chapter 8.
    A sensitivity case is also developed to isolate the magnitude of
the Greunspecht effect. The impacts on costs and benefits are presented
in section VII.H.1 of this document. In order to isolate the effect,
the price of new vehicles is held constant at CY 2016 levels. The
specific methodology used to do so is described in detail in PRIA
Chapter 8, as is the leakage implied by comparing the reference and no
Gruenspecht effect sensitivity cases. It is important to note here that
the leakage calculated ranges between 12 and 18% across regulatory
alternatives. This is in line with Jacobsen & van Bentham (2015)
estimates which put leakage for their central case between 13 and 16%.
Their high gasoline price case is more in line this analysis' central
case--with fuel prices of $3/gallon--and predicts leakage of 21%. This
further validates the scrappage model effects against examples in the
literature.
    The models used for this analysis are able to capture the
relationship for vehicle scrappage as it varies with age and how this
relationship changes with increases to new vehicle price, the cost per
mile of travel of new and used vehicles, and how the rate varies
cyclically with the GDP growth rate. It also controls for the CARS
program and checks the influence of a change in Polk's data collection
procedures. The goodness of fit measures and the plausibility of the
predictions of the model are discussed at some length in PRIA Chapter
8. In the next section, the impacts of updating the static scrappage
models to the dynamic models on average vehicle age and usage, by body
styles, and across different regulatory assumptions are discussed.
(c) What is the estimated effect on vehicle retirement and how do
results compare to previously estimated fleets and VMT?
    The expected lifetime of a car estimated using the static scrappage
schedule from the 2012 final rule, both in years and miles, is between
the expected lifetime of the dynamic scrappage model in the absence of
CAFE standards and under the baseline standards. Estimated by the
dynamic scrappage model, the average vehicle is expected to live 15.1
years under the influence of only market demand for new technology, and
15.6 years under the baseline scenario, a four percent increase.
However, given the distribution of the mileage accumulation schedule by
age, this amounts only to a two percent increase in the expected
lifetime mileage accumulation of an individual vehicle. This range is
consistent with DOT expectations in terms of direction and magnitude.
    The use of a static retirement schedule, while deemed a reasonable
approach in the past, is a limited representation of scrappage
behavior. It fails to account for increasing vehicle durability--
occurring for the last several decades--and the resulting increase in
average vehicle age in the on-road fleet, which has nearly doubled
since 1980.\284\ Thus, turning off the dynamic scrappage model
described above would not impose a perspective on the analysis that is
neutral with respect to observed scrappage behavior but would instead
represent a strong assumption that asserts important trends in the
historical record will abruptly cease or change direction.
---------------------------------------------------------------------------

    \284\ Based on data from FHWA and IHS/Polk.
---------------------------------------------------------------------------

    As discussed above, the dynamic scrappage model implemented to
support this proposal affects total fleet size through several
mechanisms. Although the model accounts for the influence of changes to
average new vehicle price and U.S. GDP growth, the most influential
mechanism, by far, is the observed trend of increasing vehicle
durability over successive model years. This phenomenon is prominently
discussed in the academic literature related to vehicle retirement,
where there is no disagreement about its existence or direction.\285\
In fact, when the CAFE model is exercised in a way that keeps average
new vehicle prices at (approximately) MY 2016 levels, the on-road fleet
grows from an initial level of 228 million in 2016 to 340 million in
2050, an increase of 49% over the 35-year period from 2016 to 2050.
---------------------------------------------------------------------------

    \285\ Waker (1968); Park (1977); Feeney & Cardebring (1988);
Hamilton & Macauley (1999); and Bento, Ruth, & Zhuo (2016) note that
vehicles change in durability over time.
---------------------------------------------------------------------------

    The historical data show the size of the registered vehicle
population (i.e., the on-road fleet) growing by about 60% in the 35
years between 1980 and

2015.\286\ In the 35 years between 2016 and 2050, our simulation shows
the on-road fleet growing from about 230 million vehicles to about 345
million vehicles when the market adopts only the amount of fuel
economy, which it naturally demands. The simulated growth over this
period is about 50% from today's level, rather than the 60% observed in
the historical data over the last 35 years. Under the baseline
regulatory scenario, the growth over the next 35 years is simulated to
be about 54%--still short of the observed growth over a comparable
period of time. In fact, the simulated annual growth rate in the size
of the on-road fleet in this analysis, about 1.3%, is lower than the
long-term average annual growth rate of about two percent dating back
to the 1970s.\287\
---------------------------------------------------------------------------

    \286\ There are two measurements of the size of the registered
vehicle population that are considered to be authoritative. One is
produced by the Federal Highway Administration, and the other by
R.L. Polk (now part of IHS). The Polk measurement shows fleet growth
between 1980 and 2015 of about 85%, while the FHWA measurement shows
a slower growth rate over that period, only about 60%.
    \287\ Based on calculations using Polk's National Vehicle
Population Profile (NVPP).
---------------------------------------------------------------------------

    Additionally, there are inherent precision limitations in measuring
something as vast and complex as the registered vehicle population. For
decades, the two authoritative sources for the size of the on-road
fleet have been R.L. Polk (now IHS/Polk) and FHWA. For two decades
these two sources differed by more than 10% each year, only lately
converging to within a few percent of each other. These discrepancies
over the correct interpretation of the data by each source have
consistently represented differences of more than 10 million vehicles.
    The total number of new vehicles projected to enter the fleet is
slightly higher than the historical trend (though the impact of the
great recession makes it hard to say by how much). More generally, the
projections used in the analysis cover long periods of time without
exhibiting the kinds of fluctuation that are present in the historical
record. For example, the forecast of GDP growth in our analysis posits
a world in which the United States sees uninterrupted positive annual
growth in real GDP for four decades. The longest such period in the
historical record is 17 years and still included several years of low
(but positive) growth during that interval.
    Over such a long period of time, in the absence of deep insight
into the future of the U.S. auto industry, it is sensible to assume
that the trends observed over the course of decades are likely to
persist. Analyzing fuel economy standards requires an understanding of
the mechanisms that influence new vehicle sales, the size of the on-
road fleet, and vehicle miles traveled. It is upon these mechanisms
that the policy acts: Increasing/decreasing new vehicle prices changes
the rate at which new vehicles are sold, changing the attributes and
prices of these vehicles influences the rates at which all used
vehicles are retired, the overall size of the on-road fleet determines
the total amount of VMT, which in turn affects total fuel consumption,
fatalities, and other externalities. The fact that DOT's bottom-up
approach produces results in line with historical trends is both
expected and intended.
    This is not to say that all details of this new approach will be
immediately intuitive for reviewers accustomed to results that do not
include a dynamic sales model or dynamic scrappage model, much less
results that combine the two. For example, some reviewers may observe
that today's analysis shows that, compared to the baseline standards,
the proposed standards produce a somewhat smaller on-road fleet (i.e.,
fewer vehicles in service) despite somewhat increased sales of new
vehicles (consistent with reduced new vehicle prices) and decreased
prices for used vehicles. While it might be natural to assume that
reduced prices of new vehicles and increased sales should lead to a
larger on-road fleet, in our modelling, the increased sales are more
than offset by the somewhat accelerated scrappage that accompanies the
estimated decrease in new vehicle prices. This outcome represents an
on-road fleet that is both smaller and a little younger on average
(relative to the baseline) and ``turns over'' more quickly.
    To further test the validity of the scrappage model, a dynamic
forecast was constructed for calendar years 2005 through 2015 to see
how well it predicts the fleet size for this period. The last true
population the scrappage model ``sees'' is the 2005 registered vehicle
population. It then takes in known production volumes for the new model
year vehicles and dynamically estimates instantaneous scrappage rates
for all registered vehicles at each age for CYs 2006-2015, based only
on the observed exogenous values that inform the model (GDP growth
rate, observed new vehicle prices, and cost per mile of operation),
fleet attributes of the vehicles (body style, age, cost per mile of
operation), and estimated scrappage rates at earlier ages. Within this
exercise, the scrappage model relies on its own estimated values as the
previous scrappage rates at earlier ages, forcing any estimation errors
to propagate through to future years. This exercise is discussed
further in PRIA Chapter VII. While the years of the recession represent
a significant shock to the size of the fleet, briefly reversing many
years of annual growth, the model recovers quickly and produces results
within one percent of the actual fleet size, as it did prior to the
recession.
    In order to compare the magnitudes of the sales and scrappage
effects across different fuel economy standards considered it is
important to define comparable measures. The sales effect in a single
calendar year is simply the difference in new vehicle sales across
alternatives. However, the scrappage effect in a single calendar year
is not simply the change in fleet size across regulatory alternatives.
The scrappage model predicts the probability that a vehicle will be
scrapped in the next year conditional on surviving to that age; the
absolute probability that a vehicle survives to a given age is
conditional on the scrappage effect for all previous analysis years. In
other words, if successive calendar years observe lower average new
vehicle prices, the effect of increased scrappage on fleet size will
accumulate with each successive calendar year--because fewer vehicles
survived to previous ages, the same probability of scrappage would
result in a smaller fleet size for the following year as well, though
fewer vehicles will have been scrapped than in the previous year.
    To isolate the number of vehicles not scrapped in a single calendar
year because of the change in standards, the first step is to calculate
the number of vehicles scrapped in every calendar year for both the
proposed standards and the baseline; this is calculated by the inter-
annual change in the size of the used vehicle fleet (vehicles ages 1-
39) for each alternative. The difference in this measure across
regulatory alternatives represents the change in vehicle scrappage
because of a change in the standards. The resulting scrappage effect
for a single calendar year can be compared to the difference across
regulatory alternatives in new vehicle sales for the same calendar year
as a comparison of the relative magnitudes of the two effects. In most
years, under the proposed standards relative to the baseline standards,
the analysis shows that for each additional new vehicles sold, two to
four used vehicles are removed from the fleet. Over the time period of
the analysis these predicted differences in the numbers of vehicles
accumulate, resulting in a maximum of

seven million fewer vehicles by CY 2033 for the proposed CAFE standards
relative to the augural standards, and nine million fewer vehicles by
CY 2035 for the proposed GHG standards relative to the current GHG
standards. Tables 11-29 and 11-30 in the PRIA show the difference in
the fleet size by calendar year for the proposed standards relative to
the augural standards for the CAFE and GHG programs, respectively.
    To understand why the sales and scrappage effects do not perfectly
offset each other to produce a constant fleet size across regulatory
alternatives it is important to remember that the decision to buy a new
vehicle and the decision to scrap a used vehicle are often not made by
the same household as a joint decision. The average length of initial
ownership for new vehicles is approximately 6.5 years (and increasing
over time). Cumulative scrappage up to age seven is typically less than
10%of the initial fleet. This suggests that most vehicles belong to
more than one household over the course of their lifetimes. The
household that is deciding whether or not to purchase a new vehicle is
rarely the same household deciding whether or not to scrap a vehicle.
So a vehicle not scrapped in a given year is seldom the direct
substitute for a new vehicle purchased by that household. Considering
this, it is not expected that for every additional vehicle scrapped,
there is also an additional new one sold, under the proposed standards
relative to the baseline standards.
    Further, while sales and scrappage decisions are both influenced by
changes in new vehicle prices, the mechanism through which these
decisions change are different for the two effects. A decrease in
average new vehicle prices will directly increase the demand for new
vehicles along the same demand curve. This decrease in new vehicle
prices will cause a substitution towards new vehicles and away from
used vehicles, shifting the entire demand curve for used vehicles
downwards. This will decrease both the equilibrium prices of used
vehicles, as shown in Figure 8-16 of the PRIA. Since the decision to
scrap a vehicle in a given year is closely related to the difference
between the vehicle's value and the cost to maintain it, if the value
of a vehicle is lower than the cost to maintain it, the current owner
will not choose to maintain the vehicle for their own use or for resale
in the used car market, and the vehicle will be scrapped. That is, a
current owner will only supply a vehicle to the used car market if the
price of the vehicle is greater than the cost of supplying it. Lowering
the equilibrium price of used vehicles will lower the increase the
number of scrapped vehicles, lowering the supply of used vehicles, and
decreasing the equilibrium quantity. The change in new vehicle sales is
related to demand of new vehicles at a given price, but the change in
used vehicle scrappage is related to the shift in the demand curve for
used vehicles, and the resulting change in the quantity current owners
will supply; these effects are likely not exactly offsetting.
    Our models indicate that the ratio of the magnitude of the
scrappage effect to the sales effect is greater than one so that the
fleet grows under more stringent scenarios. However, it is important to
remember that not all vehicles are driven equally; used vehicles are
estimated to deliver considerably less annual travel than new vehicles.
Further, used vehicles only have a portion of their original life left
so that it will take more than one used vehicle to replace the full
lifetime of a new vehicle, at least in the long-run. The result of the
lower annual VMT and shorter remaining lifetimes of used vehicles, is
that although the fleet is 1.5% bigger in CY 2050 for the augural
baseline than it is for the proposed standards, the total non-rebound
VMT for CY 2050 is 0.4% larger in the augural baseline than in the
proposed standards. This small increase in VMT is consistent with a
larger fleet size; if more used vehicles are supplied, there likely is
some small resulting increase in VMT.
    Our models face some limitations, and work will continue toward
developing methods for estimating vehicle sales, scrappage, and mileage
accumulation. For example, our scrappage model assumes that the average
VMT for a vehicle of a particular vintage is fixed--that is, aside from
rebound effects, vehicles of a particular vintage drive the same amount
annually, regardless of changes to the average expected lifetimes. The
agencies seek comment on ways to further integrate the survival and
mileage accumulation schedules. Also, our analysis uses sales and
scrappage models that do not dynamically interact (though they are
based on similar sets of underlying factors); while both models are
informed by new vehicle prices, the model of vehicle sales does not
respond to the size and age profile of the on-road fleet, and the model
of vehicle scrappage rates does not respond to the quantity of new
vehicles sold. As one potential option for development, the potential
for an integrated model of sales and scrappage, or for a dynamic
connection between the two models will be considered. Comment is sought
on both the sales and scrappage models, on potential alternatives, and
on data and methods that may enable practicable integration of any
alternative models into the CAFE model.
7. Accounting for the Rebound Effect Caused by Higher Fuel Economy
(a) What is the rebound effect and how is it measured?
    Amending and establishing fuel economy and GHG standards at a
lesser stringency than the augural standards for future model years
will lead to comparatively lower fuel economy for new cars and light
trucks, thus increasing the amount of fuel they consume in traveling
each mile than they would under the augural standard. The resulting
increase in their per-mile fuel and total driving costs will lead to a
reduction in the number of miles they are driven each year over their
lifetimes, and example of the rebound effect that is usually associated
with energy efficiency improvements working in reverse. The fuel
economy rebound effect--a specific example of the energy efficiency
rebound effect for the case of motor vehicles--refers to the well-
documented tendency of vehicles' use to increase when their fuel
economy is improved and the cost of driving each mile declines as a
result.
(b) What does the literature say about the magnitude of this effect?
    Table-II-43 summarizes estimates of the fuel economy rebound effect
for light-duty vehicles from studies conducted through 2008, when the
agencies originally surveyed research on this subject.\288\ After
summarizing all of the estimates reported in published and other
publicly-available research available at that time, it distinguishes
among estimates based on the type of data used to develop them. As the
table reports, estimates of the rebound effect ranged from 6% to as
high as 75%, and the range spanned by published estimates was nearly as
wide (7-75%).
---------------------------------------------------------------------------

    \288\ Complete references to the studies summarized in Table 8-2
are included in the PRIA, and many of the unpublished studies are
available in the docket for this rulemaking.

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

Most studies reported more than one empirical estimate, and the authors
of published studies typically identified the single estimate in which
they were most confident; these preferred estimates spanned only a
slightly narrower range (9-75%).
[GRAPHIC] [TIFF OMITTED] TP24AU18.065

    Despite their wide range, these estimates displayed a strong
central tendency, as Table-II-43 also shows. The average values of all
estimates, those that were published, and authors' preferred estimates
from published studies were 22-23%, and the median estimates in each
category were close to these values, indicating nearly symmetric
distributions. The estimates in each category also clustered fairly
tightly around their respective average values, as shown by their
standard deviations in the table's last column. When classified by the
type of data they relied on, U.S. aggregate time-series data produced
slightly smaller values (averaging 18%) than did panel-type data for
individual states (23%) or household survey data (25%). In each
category, the median estimate was again quite close to the average
reported value, and comparing the standard deviations of estimates
based on each type of data again suggests a fairly tight scatter around
their respective means.
    Of these studies, a then recently-published analysis by Small & Van
Dender (2007), which reported that the rebound effect appeared to be
declining over time in response to increasing income of drivers, was
singled out. These authors theorized that rising income increased the
opportunity cost of drivers' time, leading them to be less responsive
over time to reductions in the fuel cost of driving each mile. Small
and Van Dender reported that while the rebound effect averaged 22% over
the entire time period they analyzed (1967-2001), its value had
declined by half--or to 11%--during the last five years they studied
(1997-2001). Relying primarily on forecasts of its continued decline
over time, the analysis reduced the 20% rebound effect that NHTSA used
to analyze the effects of CAFE standards for light trucks produced
during model years 2005-07 and 2008-11 to 10% for their analysis of
CAFE and GHG standards for MY 2012-16 passenger cars and light trucks.
    Table-II-44 summarizes estimates of the rebound effect reported in
research that has become available since the agencies' original survey,
which extended through 2008, and the following discussion briefly
summarizes the approaches used by these more recent studies. Bento et
al. (2009) combined demographic characteristics of more than 20,000
U.S. households, the manufacturer and model of each vehicle they owned,
and their annual usage of each vehicle from the 2001 National Household
Travel Survey with detailed data on fuel economy and other attributes
for each vehicle model obtained from commercial publications. The
authors aggregated vehicle models into 350 categories representing
combinations of manufacturer, vehicle type, and age, and use the
resulting data to estimate the parameters of a complex model of
households' joint choices of the number and types of vehicles to own,
and their annual use of each vehicle.

[GRAPHIC] [TIFF OMITTED] TP24AU18.066

    Bento et al. estimate the effect of vehicles' operating costs per
mile, including fuel costs, which depend in part on each vehicle's fuel
economy, as well as maintenance and insurance expenses, on households'
annual use of each vehicle they own. Combining the authors' estimates
of the elasticity of vehicle use with respect to per-mile operating
costs with the reported fraction of total operating costs accounted for
by fuel (slightly less than one-half) yields estimates of the rebound
effect. The resulting values vary by household composition, vehicle
size and type, and vehicle age, ranging from 21 to 38%, with a
composite estimate of 34% for all households, vehicle models, and ages.
The smallest values apply to new luxury cars, while the largest
estimates are for light trucks and households with children, but the
implied rebound effects differ little by vehicle age.
    Barla et al. (2009) analyzed the responses of car and light truck
ownership, vehicle travel, and average fuel efficiency to variation in
fuel prices and aggregate economic activity (measured by gross product)
using panel-type data for the 10 Canadian provinces over the period
from 1990 through 2004. The authors estimated a system of equations for
these three variables using statistical procedures appropriate for
models where the variables of interest are simultaneously determined
(that is, where each variable is one of the factors explaining
variation in the others). This procedure enabled them to control for
the potential ``reverse influence'' of households' demand for vehicle
travel on their choices of how many vehicles to own and their fuel
efficiency levels when estimating the effect of variation in fuel
efficiency on vehicle use.
    Their analysis found that provincial-level aggregate economic
activity had moderately strong effects on car and light truck ownership
and use but that fuel prices had only modest effects on driving and the
average fuel efficiency of the light-duty vehicle fleet. Each of these
effects became considerably stronger over the long term than in the
year when changes in economic activity and fuel prices initially
occurred, with three to five years typically required for behavioral
adjustments to stabilize. After controlling for the joint relationship
among vehicle ownership, driving demand, and the fuel efficiency of
cars and light trucks, Barla et al. estimated elasticities of average
vehicle use with respect to fuel efficiency that corresponded to a
rebound effect of eight percent in the short run, rising to nearly 20%
within five years. A notable feature of their analysis was that
variation in average fuel efficiency among the individual Canadian
provinces and over the time period they studied was adequate to
identify its effect on vehicle use, without the need to combine it with
variation in fuel prices in order to identify its effect.
    Wadud et al. (2009) combine data on U.S. households' demographic
characteristics and expenditures on gasoline over the period 1984-2003
from the Consumer Expenditure Survey with data on gasoline prices and
an estimate of the average fuel economy of

vehicles owned by individual households (constructed from a variety of
sources). They employ these data to explore variation in the
sensitivity of individual households' gasoline consumption to
differences in income, gasoline prices, the number of vehicles owned by
each household, and their average fuel economy. Using an estimation
procedure intended to account for correlation among unmeasured
characteristics of households and among estimation errors for
successive years, the authors explore variation in the response of fuel
consumption to fuel economy and other variables among households in
different income categories and between those residing in urban and
rural areas.
    Dividing U.S. households into five equally-sized income categories,
Wadud et al. estimate rebound effects ranging from 1-25%, with the
smallest estimates (8% and 1%) for the two lowest income categories,
and significantly larger estimates for the middle (18%) and two highest
income groups (18 and 25%). In a separate analysis, the authors
estimate rebound effects of seven percent for households of all income
levels residing in U.S. urban areas and 21% for rural households.
    West & Pickrell (2011) analyzed data on more than 100,000
households and 300,000 vehicles from the 2009 Nationwide Household
Transportation Survey to explore how households owning multiple
vehicles chose which of them to use and how much to drive each one on
the day the household was surveyed. Their study focused on how the type
and fuel economy of each vehicle a household owned, as well as its
demographic characteristics and location, influenced household members'
decisions about whether and how much to drive each vehicle. They also
investigated whether fuel economy and fuel prices exerted similar
influences on vehicle use, and whether households owning more than one
vehicle tended to substitute use of one for another--or vary their use
of all of them similarly--in response to fluctuations in fuel prices
and differences in their vehicles' fuel economy.
    Their estimates of the fuel economy rebound effect ranged from as
low as nine percent to as high as 34%, with their lowest estimates
typically applying to single-vehicle households and their highest
values to households owning three or more vehicles. They generally
found that differences in fuel prices faced by households who were
surveyed on different dates or who lived in different regions of the
U.S. explained more of the observed variation in daily vehicle use than
did differences in vehicles' fuel economy. West and Pickrell also found
that while the rebound effect for households' use of passenger cars
appeared to be quite large--ranging from 17% to nearly twice that
value--it was difficult to detect a consistent rebound effect for SUVs.
    Anjovic & Haas (2012) examined variation in vehicle use and fuel
efficiency among six European nations over an extended period (1970-
2006), using an elaborate model and estimation procedure intended to
account for the existence of common underlying trends among the
variables analyzed and thus avoid identifying spurious or misleading
relationships among them. The six nations included in their analysis
were Austria, Germany, Denmark, France, Italy, and Sweden; the authors
also conducted similar analyses for the six nations combined. The
authors focused on the effects of average income levels, fuel prices,
and the fuel efficiency of each nation's fleet of cars on the total
distance they were driven each year and their total fuel energy
consumption. They also tested whether the responses of energy
consumption to rising and falling fuel prices appeared to be symmetric
in the different nations.
    Anjovic and Haas report a long-run aggregate rebound effect of 44%
for the six nations their study included, with corresponding values for
individual nations ranging from a low of 19% (for Austria) to as high
as 56% (Italy). These estimates are based on the estimated response of
vehicle use to variation in average fuel cost per kilometer driven in
each of the six nations and for their combined total. Other information
reported in their study, however, suggests lower rebound effects; their
estimates of the response of total fuel energy consumption to fuel
efficiency appear to imply an aggregate rebound effect of 24% for the
six nations, with values ranging from as low as 0-3% (for Austria and
Denmark) to as high as 70% (Sweden), although the latter is very
uncertain. These results suggest that vehicle use in European nations
may be somewhat less sensitive to variation in driving costs caused by
changes in fuel efficiency than to changes in driving costs arising
from variation in fuel prices, but they find no evidence of asymmetric
responses of total fuel consumption to rising and falling prices. Using
data on household characteristics and vehicle use from the 2009
Nationwide Household Transportation Survey (NHTS), Su (2012) analyzes
the effects of locational and demographic factors on household vehicle
use and investigates how the magnitude of the rebound effect varies
with vehicles' annual use. Using variation in the fuel economy and per-
mile cost of and detailed controls for the demographic, economic, and
locational characteristics of the households that owned them (e.g.,
road and population density) and each vehicle's main driver (as
identified by survey respondents), the author employs specialized
regression methods to capture the variation in the rebound effect
across 10 different categories of vehicle use.
    Su estimated the overall rebound effect for all vehicles in the
sample averaged 13%, and that its magnitude varied from 11-19% among
the 10 different categories of annual vehicle use. The smallest rebound
effects were estimated for vehicles at the two extremes of the
distribution of annual use--those driven comparatively little, and
those used most intensively--while the largest estimated effects
applied to vehicles that were driven slightly more than average.
Controlling for the possibility that high-mileage drivers respond to
the increased importance of fuel costs by choosing vehicles that offer
higher fuel economy narrowed the range of Su's estimated rebound
effects slightly (to 11-17%), but did not alter the finding that they
are smallest for lightly- and heavily-driven vehicles and largest for
those with slightly above average use.
    Linn (2013) also uses the 2009 NHTS to develop a linear regression
approach to estimate the relationship between the VMT of vehicles
belonging to each household and a variety of different factors: Fuel
costs, vehicle characteristics other than fuel economy (e.g.,
horsepower, the overall ``quality'' of the vehicle), and household
characteristics (e.g., age, income). Linn reports a fuel economy
rebound effect with respect to VMT of between 20-40%.
    One interesting result of the study is that when the fuel
efficiency of all vehicles increases, which would be the long-run
effect of rising fuel efficiency standards, two factors have opposing
effects on the VMT of a particular vehicle. First, VMT increases when
that vehicle's fuel efficiency increases. But the increase in the fuel
efficiency of the household's other vehicles causes the vehicle's own
VMT to decrease. Because the effect of a vehicle's own fuel efficiency
is larger than the other vehicles' fuel efficiency, VMT increases if
the fuel efficiency of all vehicles increases proportionately. Linn
also finds that VMT responds much more strongly to vehicle fuel economy
than to gasoline prices, which is at variance with the Hymel et al. and
Greene results discussed above.

    Like Su and Linn, Liu et al. (2014) employed the 2009 NHTS to
develop an elaborate model of an individual household's choices about
how many vehicles to own, what types and ages of vehicles to purchase,
and how much combined driving to do using all of them. Their analysis
used a complex mathematical formulation and statistical methods to
represent and measure the interdependence among households' choices of
the number, types, and ages of vehicles to purchase, as well as how
intensively to use them.
    Liu et al. employed their model to simulate variation in
households' total vehicle use to changes in their income levels,
neighborhood characteristics, and the per-mile fuel cost of driving
averaged over all vehicles each household owns. The complexity of the
relationships among the number of vehicles owned, their specific types
and ages, fuel economy levels, and use incorporated in their model
required them to measure these effects by introducing variation in
income, neighborhood attributes, and fuel costs, and observing the
response of households' annual driving. Their results imply a rebound
effect of approximately 40% in response to significant (25-50%)
variation in fuel costs, with almost exactly symmetrical responses to
increases and declines.
    A study of the rebound effect by Frondel et al. (2012) used data
from travel diaries recorded by more than 2,000 German households from
1997 through 2009 to estimate alternative measures of the rebound
effect, and to explore variation in their magnitude among households.
Each household participating in the survey recorded its automobile
travel and fuel purchases over a period of one to three years and
supplied information on its composition and the personal
characteristics of each of its members. The authors converted
households' travel and fuel consumption to a monthly basis, and used
specialized estimation procedures (quantile and random-effects panel
regression) to analyze monthly variation in their travel and fuel use
in relation to differences in fuel prices, the fuel efficiency of each
vehicle a household owned, and the fuel cost per mile of driving each
vehicle.
    Frondel et al. estimate four separate measures of the rebound
effect, three of which capture the response of vehicle use to variation
in fuel efficiency, fuel price, and fuel cost per mile traveled, and a
fourth capturing the response of fuel consumption to changes in fuel
price. Their first three estimates range from 42% to 57%, while their
fourth estimate corresponds to a rebound effect of 90%. Although their
analysis finds no significant variation of the rebound effect with
household income, vehicle ownership, or urban versus rural location, it
concludes that the rebound effect is substantially larger for
households that drive less (90%) than for those who use their vehicles
most intensively (56%).
    Gillingham (2014) analyzed variation in the use of approximately
five million new vehicles sold in California from 2001 to 2003 during
the first several years after their purchase, focusing particularly on
how their use responded to geographic and temporal variation in fuel
prices. His sample consisted primarily of personal or household
vehicles (87%) but also included some that were purchased by
businesses, rental car companies, and government agencies. Using
county-level data, he analyzed the effect of differences in the monthly
average fuel price paid by their drivers on variation in their monthly
use and explored how that effect varied with drivers' demographic
characteristics and household incomes.
    Gillingham's analysis did not include a measure of vehicles' fuel
economy or fuel cost per mile driven, so he could not measure the
rebound effect directly, but his estimates of the effect of fuel prices
on vehicle use correspond to a rebound effect of 22-23% (depending on
whether he controlled for the potential effect of gasoline demand on
its retail price). His estimation procedure and results imply that
vehicle use requires nearly two years to adjust fully to changes in
fuel prices. He found little variation in the sensitivity of vehicle
use to fuel prices among car buyers with different demographic
characteristics, although his results suggested that it increases with
their income levels.
    Weber & Farsi (2014) analyzed variation in the use of more than
70,000 individual cars owned by Swiss households who were included in a
2010 survey of travel behavior. Their analysis focuses on the
simultaneous relationships among households' choices of the fuel
efficiency and size (weight) of the vehicles they own, and how much
they drive each one, although they recognize that fuel efficiency
cannot be chosen independently of vehicle weight. The authors employ a
model specification and statistical estimation procedures that account
for the likelihood that households intending to drive more will
purchase more fuel-efficient cars but may also choose more spacious and
comfortable--and thus heavier--models, which affects their fuel
efficiency indirectly, since heavier vehicles are generally less fuel-
efficient. The survey data they rely on includes both owners' estimates
of their annual use of each car and the distance it was actually driven
on a specific day; because they are not closely correlated, the authors
employ them as alternative measures of vehicle use to estimate the
rebound effect, but this restricts their sample to the roughly 8,100
cars for which both measures are available. Weber and Farsi's estimates
of the rebound effect are extremely large: 75% using estimated annual
driving and 81% when they measure vehicle use by actual daily driving.
Excluding vehicle size (weight) and limiting the choices that
households are assumed to consider simultaneously to just vehicles'
fuel efficiency and how much to drive approximately reverses these
estimates, but both are still very large. Using a simpler procedure
that does not account for the potential effect of driving demand on
households' choices among vehicle models of different size and fuel
efficiency produces much smaller values for the rebound effect: 37%
using annual driving and 19% using daily travel. The authors interpret
these latter estimates as likely to be too low because actual on-road
fuel efficiency has not improved as rapidly as suggested by the
manufacturer-reported measure they employ. This introduces an error in
their measure that may be related to a vehicle's age, and their more
complex estimation procedure may reduce its effect on their estimates.
Nevertheless, even their lower estimates exceed those from many other
studies of the rebound effect, as Table 8-2 shows.
    Hymel, Small, & Van Dender (2010)--and more recently, Hymel & Small
(2015)--extended the simultaneous equations analysis of time-series and
state-level variation in vehicle use originally reported in Small & Van
Dender (2007) and to test the effect of including more recent data. As
in the original 2007 study, both subsequent extensions found that the
fuel economy rebound effect had declined over time in response to
increasing personal income and urbanization but had risen during
periods when fuel prices increased. Because they rely on the response
of vehicle use to fuel cost per mile to estimate the rebound effect,
however, none of these three studies is able to detect whether its
apparent decline in response to rising income levels over time truly
reflects its effect on drivers' responses to changing fuel economy--the
rebound effect itself--or simply captures the effect of rising income
on their sensitivity to fuel

prices.\289\ These updated studies each revised Small and Van Dender's
original estimate of an 11% rebound effect for 1997-2011 upward when
they included more recent experience: To 13% for the period 2001-04,
and subsequently to 18% for 2000-2009.
---------------------------------------------------------------------------

    \289\ DeBorger et al. (2016) analyze the separate effects of
variation in household income on the sensitivity of their vehicle
use to fuel prices and the fuel economy of vehicles they own. Their
results imply the decline in the fuel economy rebound effect with
income reported in Small & Van Dender (2007) and its subsequent
extensions appears to result entirely from a reduction in drivers'
sensitivity to fuel prices as their incomes rise, rather than from
any effect of rising income on the sensitivity of vehicle use to
improving fuel economy; i.e., on the fuel economy rebound effect
itself.
---------------------------------------------------------------------------

    In their 2015 update, Hymel and Small hypothesized that the recent
increase in the rebound effect could be traced to a combination of
expanded media coverage of changing fuel prices, increased price
volatility, and an asymmetric response by drivers to variation in fuel
costs. The authors estimated that about half of the apparent increase
in the rebound effect for recent years could be attributed to greater
volatility in fuel prices and more media coverage of sudden price
changes. Their results also suggest that households curtail their
vehicle use within the first year following an increase in fuel prices
and driving costs, while the increase in driving that occurs in
response to declining fuel prices--and by implication, to improvements
in fuel economy--occurs more slowly.
    West et al. (2015) attempted to infer the fuel economy rebound
effect using data from Texas households who replaced their vehicles
with more fuel-efficient models under the 2009 ``Cash for Clunkers''
program, which offered sizeable financial incentives to do so. Under
the program, households that retired older vehicles with fuel economy
levels of 18 miles per gallon (MPG) or less were eligible for cash
incentives ranging from $3,500-4,000, while those retiring vehicles
with higher fuel economy were ineligible for such rebates. The authors
examined the fuel economy, other features, and subsequent use of new
vehicles households in Texas purchased to replace older models that
narrowly qualified for the program's financial incentives because their
fuel economy was only slightly below the 18 MPG threshold. They then
compared these to the fuel economy, features, and use of new vehicles
that demographically comparable households bought to replace older
models, but whose slightly higher fuel economy--19 MPG or above--made
them barely ineligible for the program.
    The authors reported that the higher fuel economy of new models
that eligible households purchased in response to the generous
financial incentives offered under the ``Cash for Clunkers'' did not
prompt their buyers to use them more than the older, low-MPG vehicles
they replaced. They attributed this apparent absence of a fuel economy
rebound effect--which they described as an ``attribute-adjusted''
measure of its magnitude--to the fact that eligible households chose to
buy less expensive, smaller, and lower-performing models to replace
those they retired. Because these replacements offered lower-quality
transportation service, their buyers did not drive them more than the
vehicles they replaced.
    The applicability of this result to the proposal's analysis is
doubtful because previous regulatory analyses assumed that
manufacturers could achieve required improvements in fuel economy
without compromising the performance, carrying and towing capacity,
comfort, or safety of cars and light trucks from recent model
years.\290\ While this may be technically true, doing so would come at
a combined greater cost. If this argument is correct, then amending
future standards at a reduced stringency from their previously-adopted
levels would lead to less driving attributable to rebound, and should
therefore not lead to artificial constraints in new vehicles' other
features that offset the reduction in their use stemming from lower
fuel economy.
---------------------------------------------------------------------------

    \290\ As discussed, this does not mean attributes of future cars
and light trucks will be anything close to those manufacturers could
have offered if lower standards had remained in effect. Instead, the
agencies asserted features other than fuel economy could be
maintained at the levels offered in recent model years--that
features will not likely be removed, but may not be improved.
---------------------------------------------------------------------------

    Most recently, De Borger et al. (2017) analyze the response of
vehicle use to changes in fuel economy among a sample of nearly 350,000
Danish households owning the same model vehicle, of which almost one-
third replaced it with a different model sometime during the period
from 2001 to 2011. By comparing the changes in households' driving from
the early years of this period to its later years among those who
replaced their vehicles during the intervening period to the changes in
driving among households who kept their original vehicles, the authors
attempted to isolate the effect of changes in fuel economy on vehicle
use from those of other factors. They measured the rebound effect as
the change in households' vehicle use in response to differences in the
fuel economy between vehicles they had owned previously and the new
models they purchased to replace them, over and above any change in
vehicle use among households who did not buy new cars (and thus saw no
change in fuel economy).
    These authors' data enabled them to control for the effects of
changes over time in household characteristics and vehicle features
other than fuel economy that were likely to have contributed to
observed changes in vehicle use. They also employed complex statistical
methods to account for the fact that some households replacing their
vehicles may have done so in anticipation of changes in their driving
demands (rather than the reverse), as well as for the possibility that
some households who replaced their cars may have done so because their
driving behavior was more sensitive to fuel prices than other
households. Their estimates ranged from 8-10%, varying only minimally
among alternative model specifications and statistical estimation
procedures or in response to whether their sample was restricted to
households that replaced their vehicles or also included households
that kept their original vehicles throughout the period.\291\ Finally,
De Borger et al. found no evidence that the rebound effect is smaller
among lower-income households than among their higher-income
counterparts.
---------------------------------------------------------------------------

    \291\ This latter result suggests their estimates were not
biased by any tendency for households whose demographic
characteristics, economic circumstances, or driving demands changed
over the period in ways that prompted them to replace their vehicles
with models offering different fuel economy.
---------------------------------------------------------------------------

(c) What value have the agencies assumed in this rule?
    On the basis of all of the evidence summarized here, a fuel economy
rebound effect of 20% has been chosen to analyze the effects of the
proposed action. This is a departure from the 10% value used in
regulatory analyses for MYs 2012-2016 and previous analyses for MYs
2017-2025 CAFE and GHG standards and represents a return to the value
employed in the analyses for MYs 2005-2011 CAFE standards. There are
several reasons the estimate of the fuel economy rebound effect for
this analysis has been increased.
    First, the 10% value is inconsistent with nearly all research on
the magnitude of the rebound effect, as Table-II-43 and Table-II-44
indicate. Instead, it is based almost exclusively

on the finding of the 2007 study by Small and Van Dender that the
rebound effect had been declining over time in response to drivers'
rising incomes and on extending that decline through future years using
an assumption of steady income growth. As indicated above, however,
subsequent extensions of Small and Van Dender's original research have
produced larger estimates of the rebound effect for recent years: While
their original study estimated the rebound effect at 11% for 1997-2001,
the 2010 update by Hymel, Small, and Van Dender reported a value of 13%
for 2004, and Hymel and Small's 2015 update estimated the rebound
effect at 18% for 2003-09. Further, the issues with state-level
measures of vehicle use, fuel consumption, and fuel economy identified
previously raise some doubt about the reliability of these studies'
estimates of the rebound effect.
    At the same time, the continued increases in income that were
anticipated to produce a continued decline in the rebound effect have
not materialized. The income measure (real personal income per Capita)
used in these analyses has grown only approximately one percent
annually over the past two decades and is projected to grow at
approximately 1.5% for the next 30 years, in contrast to the two to
three percent annual growth assumed by the agencies when developing
earlier forecasts of the future rebound effect. Further, another recent
study by DeBorger et al. (2016) analyzed the separate effects of
variation in household income on the sensitivity of their vehicle use
to fuel prices and the fuel economy of vehicles they own. These
authors' results indicate that the decline in the fuel economy rebound
effect with income reported in Small & Van Dender (2007) and subsequent
research results entirely from a reduction in drivers' sensitivity to
fuel prices as their incomes rise rather than from any effect of rising
income on the sensitivity of vehicle use to fuel economy itself. This
latter measure, which DeBorger et al. find has not changed
significantly as incomes have risen over time, is the correct measure
of the fuel economy rebound effect, so their analysis calls into
question its assumed sensitivity to income.
    Some studies of households' use of individual vehicles also find
that the fuel economy rebound effect increases with the number of
vehicles they own. Because vehicle ownership is strongly associated
with household income, this common finding suggests that the overall
value of the rebound effect is unlikely to decline with rising incomes
as the agencies had previously assumed. In addition, buyers of new cars
and light trucks belong disproportionately to higher-income households
that already own multiple vehicles, which further suggests that the
higher values of the rebound effect estimated by many studies for such
households are more relevant for analyzing use of newly-purchased cars
and light trucks.
    Finally, research on the rebound effect conducted since the
agencies' original 2008 review of evidence almost universally reports
estimates in the 10-40% (and larger) range, as Table-II-43 shows. Thus,
the 20% rebound effect used in this analysis more accurately represents
the findings from both the studies considered in 2008 review and the
more recent analyses.
(1) What are the implications of the rebound effect for VMT?
    The assumed rebound effect not only influences the use of new
vehicles in today's analysis but also affects the response of the
initial registered vehicle population to changes in fuel price
throughout their remaining useful lives. The fuel prices used in this
analysis are lower than the projections used to inform the 2012 Final
Rule but generally increase from today's level over time. As they do
so, the rebound effect acts as a price elasticity of demand for
travel--as the cost-per-mile of travel increases, owners of all
vehicles in the registered population respond by driving less. In
particular, they drive 20% less than the difference between the cost-
per-mile of travel when they were observed in calendar year 2016 and
the relevant cost-per-mile at any future age. For the new vehicles
subject to this proposal (and explicitly simulated by the CAFE model),
fuel economies increase relative to MY 2016 levels, and generally
improve enough to offset the effect of rising fuel prices--at least
during the years covered by the proposal. For those vehicles, the
difference between the initial cost-per-mile of travel and future
travel costs is negative. As the vehicles become less expensive to
operate, they are driven more (20% more than the difference between
initial and present travel costs, precisely). Of course, each of the
regulatory alternatives considered in the analysis would result in
lower fuel economy levels for vehicles produced in model year 2020 and
later than if the baseline standards remained in effect, so total VMT
is lower under these alternatives than under the baseline.
(2) What is the mobility benefit that accrues to vehicle owners?
    The increase in travel associated with the rebound effect produces
additional benefits to vehicle owners, which 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. As evidenced by the fact that they elect to make
more frequent or longer trips when the cost of driving declines, the
benefits from this added travel exceed drivers' added outlays for the
fuel it consumes (measured at the improved level of fuel economy
resulting from stricter CAFE standards). The amount by which the
benefits from this increased driving travel exceed its increased fuel
costs measures the net benefits they receive from the additional
travel, usually are referred to as increased consumer surplus.
    NHTSA's analysis estimates the economic value of the decreased
consumer surplus provided by reduced driving using the conventional
approximation, which is one half of the product of the increase in
vehicle operating costs per vehicle-mile and the resulting decrease in
the annual number of miles driven. Because it depends on the extent of
the change in fuel economy, the value of economic impacts from
decreased vehicle use changes by model year and varies among
alternative CAFE standards.
(d) Societal Externalities Associated With CAFE Alternatives
(1) Energy Security Externalities
    Higher U.S. fuel consumption will produce a corresponding increase
in the nation's demand for crude petroleum, which is traded actively in
a worldwide market. The U.S. accounts for a large enough share of
global oil consumption that the resulting boost in global demand will
raise its worldwide price. The increase in global petroleum prices that
results from higher U.S. demand causes a transfer of revenue to oil
producers worldwide from not only buyers of new cars and light trucks,
but also other consumers of petroleum products in the U.S. and
throughout the world, all of whom pay the higher price that results.
    Although these effects will be tempered by growing U.S. oil
production, uncertainty in the long-term import-export balance makes it
difficult to precisely project how these effects might change in
response to that increased production. Growing U.S. petroleum
consumption will also increase potential costs to all U.S. petroleum
users from possible interruptions in the global supply of petroleum or
rapid increases in global oil prices, not all of which are borne by

the households or businesses who increase their petroleum consumption
(that is, they are partly ``external'' to petroleum users). If U.S.
demand for imported petroleum increases, it is also possible that
increased military spending to secure larger oil supplies from unstable
regions of the globe will be necessary.
    These three effects are often referred to collectively as ``energy
security externalities'' resulting from U.S. petroleum consumption, and
increases in their magnitude are sometimes cited as potential social
costs of increased U.S. demand for oil. To the extent that they
represent real economic costs that would rise incrementally with
increases in U.S. petroleum consumption of the magnitude likely to
result from less stringent CAFE and GHG standards, these effects
represent potential additional costs of this proposed action. Chapter 7
of the Regulatory Impact Analysis for this proposed action defines each
of these energy security externalities in detail, assesses whether its
magnitude is likely to change as a consequence of this action, and
identifies whether that change represents a real economic cost or
benefit of this action.
(2) Environmental Externalities
    The change in criteria pollutant emissions that result from changes
in vehicle usage and fuel consumption is estimated as part of this
analysis. Criteria air pollutants include carbon monoxide (CO),
hydrocarbon compounds (usually referred to as ``volatile organic
compounds,'' or VOC), nitrogen oxides (NOX), fine
particulate matter (PM2.5), and sulfur oxides
(SOX). These pollutants are emitted during vehicle storage
and use, as well as throughout the fuel production and distribution
system. While increases in domestic fuel refining, storage, and
distribution that result from higher fuel consumption will increase
emissions of these pollutants, reduced vehicle use associated with the
fuel economy rebound effect will decrease their emissions. The net
effect of less stringent CAFE standards on total emissions of each
criteria pollutant depends on the relative magnitudes of increases in
its emissions during fuel refining and distribution, and decreases in
its emissions resulting from additional vehicle use. Because the
relationship between emissions in fuel refining and vehicle use is
different for each criteria pollutant, the net effect of increased fuel
consumption from the proposed standards on total emissions of each
pollutant is likely to differ.
    The social damage costs associated with changes in the emissions of
criteria pollutants and CO2 was calculated, attributing
benefits and costs to the regulatory alternatives considered based on
the sign of the change in each pollutant. In previous rulemakings, the
agencies have considered the social cost of CO2 emissions
from a global perspective, accumulating social costs for CO2
emissions based on adverse outcomes attributable to climate change in
any country. In this analysis, however, the costs of CO2
emissions and resulting climate damages from both domestic and global
perspectives were considered. Chapter 9 of the Regulatory Impact
Analysis provides a detailed discussion of how the agencies estimate
changes in emissions of criteria air pollutants and CO2 and
reports the values the agencies use to estimate benefits or costs
associated with those changes in emissions.
(3) Traffic Externalities (Congestion, Noise)
    Increased vehicle use associated with the rebound effect also
contributes to increased traffic congestion and highway noise. To
estimate the economic costs associated with these consequences of added
driving, the estimates of per-mile congestion and noise costs caused by
increased use of automobiles and light trucks developed previously by
the Federal Highway Administration (FHWA) were applied. These values
are intended to measure the increased costs resulting from added
congestion and the delays it causes to other drivers and passengers and
noise levels contributed by automobiles and light trucks. NHTSA
previously employed these estimates in its analysis accompanying the MY
2011 final CAFE rule as well as in its analysis of the effects of
higher CAFE standards for MY 2012-16 and MY 2017-2021. After reviewing
the procedures used by FHWA to develop them and considering other
available estimates of these values and recognizing that no commenters
have addressed these costs directly in their comments on previous
rules, the values continue to be appropriate for use in this proposal.
For this analysis, FHWA's estimates of per-mile costs are multiplied by
the annual increases in automobile and light truck use from the rebound
effect to yield the estimated increases in total congestion and noise
externality costs during each year over the lifetimes of the cars and
light trucks in the on-road fleet. Due to the fact that this proposal
represents a decrease in stringency, the fuel economy rebound effect
results in fewer miles driven under the action alternatives relative to
the baseline, which generates savings in congestion and road noise
relative to the baseline.

F. Impact of CAFE Standards on Vehicle Safety

    In past CAFE rulemakings, NHTSA has examined the effect of CAFE
standards on vehicle mass and the subsequent effect mass changes will
have on vehicle safety. While setting standards based on vehicle
footprint helps reduce potential safety impacts associated with CAFE
standards as compared to setting standards based on some other vehicle
attribute, footprint-based standards cannot entirely eliminate those
impacts. Although prior analyses noted that there could also be impacts
because of other factors besides mass changes, those impacts were not
estimated quantitatively.\292\ In this current analysis, the safety
analysis has been expanded to include a broader and more comprehensive
measure of safety impacts, as discussed below. A number of factors can
influence motor vehicle fatalities directly by influencing vehicle
design or indirectly by influencing consumer behavior. These factors
include:
---------------------------------------------------------------------------

    \292\ NHTSA included a quantification of rebound-associated
safety impacts in its Draft TAR analysis, but because the scrappage
model is new for this rulemaking, did not include safety impacts
associated with the effect of standards on new vehicle prices and
thus on fleet turnover. The fact that the scrappage model did not
exist previously does not mean that the effects that it aims to show
were not important considerations, simply that the agency was unable
to account for them quantitatively prior to the current analysis.
---------------------------------------------------------------------------

    (1) Changes, which affect the crashworthiness of vehicles impact
other vehicles or roadside objects, in vehicle mass made to reduce fuel
consumption. NHTSA's statistical analysis of historical crash data to
understand effects of vehicle mass and size on safety indicates
reducing mass in light trucks generally improves safety, while reducing
mass in passenger cars generally reduces safety. NHTSA's crash
simulation modeling of vehicle design concepts for reducing mass
revealed similar trends.\293\
---------------------------------------------------------------------------

    \293\ DOT HS 812051a--Methodology for evaluating fleet
protection of new vehicle designs Application to lightweight vehicle
designs, DOT HS 812051b Methodology for evaluating fleet protection
of new vehicle designs_Appendices.
---------------------------------------------------------------------------

    (2) The delay in the pace of consumer acquisition of newer safer
vehicles that results from higher vehicle prices associated with
technologies needed to meet higher CAFE standards. Because of a
combination of safety regulations and voluntary safety improvements,
passenger vehicles have become safer over time. Compared to prior
decades, fatality rates have declined significantly

because of technological safety improvements as well as behavioral
shifts such as increased seat belt use. The results of this analysis
project that vehicle prices will be nearly $1,900 higher under the
augural CAFE standards compared to the preferred alternative that would
hold stringency at MY 2020 levels in MYs 2021-2026. This will induce
some consumers to delay or forgo the purchase of newer safer vehicles
and slow the transition of the on-road fleet to one with the improved
safety available in newer vehicles. This same factor can also shift the
mix of passenger cars and light trucks.
    (3) Increased driving because of better fuel economy. The ``rebound
effect'' predicts consumers will drive more when the cost of driving
declines. More stringent CAFE standards reduce vehicle operating costs,
and in response, some consumers may choose to drive more. Driving more
increases exposure to risks associated with on-road transportation, and
this added exposure translates into higher fatalities.
    Although all three factors influence predicted fatality levels that
may occur, only two of them, the changes in vehicle mass and the
changes in the acquisition of safer vehicles--are actually imposed on
consumers by CAFE standards. The safety of vehicles has improved over
time and is expected to continue improving in the future commensurate
with the pace of safety technology innovation and implementation and
motor vehicle safety regulation. Safety improvements will likely
continue regardless of changes to CAFE standards. However, its pace may
be modified if manufacturers choose to delay or forgo investments in
safety technology because of the demand CAFE standards impose on
research, development, and manufacturing budgets. Increased driving
associated with rebound is a consumer choice. Improved CAFE will reduce
driving costs, but nothing in the higher CAFE standards compels
consumers to drive additional miles. If consumers choose to do so, they
are making a decision that the utility of more driving exceeds the
marginal operating costs as well as the added crash risk it entails.
Thus, while the predicted fatality impacts with all three factors
embedded into the model are measured, the fatalities associated with
consumer choice decisions are accounted for separately from those
resulting from technologies implemented in response to CAFE regulations
or economic limitations resulting from CAFE regulation. Only those
safety impacts associated with mass reduction and those resulting from
higher vehicle prices are directly attributed to CAFE standards.\294\
This is reflected monetarily by valuing extra rebound miles at the full
value of their added driving cost plus the added safety risk consumers
experience, which completely offsets the societal impact of any added
fatalities from this voluntary consumer choice.
---------------------------------------------------------------------------

    \294\ It could be argued fatalities resulting from consumer's
decision to delay the purchase of newer safer vehicles is also a
market decision implying consumers fully accept the added safety
risk associated with this delay and value the time value of money
saved by the delayed purchase more than this risk. This scenario is
likely accurate for some purchasers. For others, the added cost may
represent a threshold price increase effectively preventing them
from being financially able to purchase a new vehicle. Presently
there is no way to determine the proportion of lost sales reflected
by these two scenarios. The added driving from the rebound effect
results from a positive benefit of CAFE, which reduces the cost of
driving. By contrast, the effect of retaining older vehicles longer
results from costs imposed on consumers, which potentially limit
their purchase options. Thus, fatalities are attributed to retaining
older vehicles due to CAFE but not those resulting from decisions to
drive more. Comments are sought on this assumption.
---------------------------------------------------------------------------

    The safety component of CAFE analysis has evolved over time. In the
2012 final rule, the analysis accounted for the change in projected
fatalities attributable to mass reduction of new vehicles. The model
assumed that manufacturers would choose mass reduction as a compliance
method across vehicle classes such that the net effect of mass
reduction on fatalities was zero. However, in the 2016 draft Technical
Assessment Report, DOT made two consequential changes to the analysis
of fatalities associated with the CAFE standards. In particular, first,
the modelling assumed that mass reduction technology was available to
all vehicles, regardless of net safety impact, and second, it accounted
for the incremental safety costs associated with additional miles
traveled due to the rebound effect. The current analysis extends the
analysis to report incremental fatality impacts associated with
additional miles traveled due to the rebound effect, and identifies the
increase in fatalities associated with additional driving separately
from changes in fatalities attributable other sources.\295\
---------------------------------------------------------------------------

    \295\ Drivers who travel additional miles are assumed to
experience benefits that at least offset the costs they incur in
doing so, including the increased safety risks they face. Thus while
the number of additional fatalities resulting from increased driving
is reported, the associated costs are not included among the social
costs of the proposal.
---------------------------------------------------------------------------

    The current analysis adds another element: The effect that higher
new vehicle prices have on new vehicle sales and on used vehicle
scrappage, which influences total expected fatalities because older
vehicle vintages are associated with higher rates of involvement in
fatal crashes than newer vehicles. Finally, a dynamic fleet share model
also predicts the effects of changes in the standards on the share of
light trucks and passenger cars in future model year light-duty vehicle
fleets. Vehicles of different body styles have different rates of
involvement in fatal crashes, so that changing the share of each in the
projected future fleet has safety impacts; the implied safety effects
are captured in the current modelling. The agencies seek comment on
changes to the safety analysis made in this proposal, they seek
particular comment on the following changes:

    (1) The sales scrappage models as independent models: Two
separate models capture the effects of new vehicle prices on new
vehicle demand and used vehicle retirement rates--the sales model
and the scrappage model, respectively. We seek public comment on the
methods used for each of these models, in particular we seek comment
on:

 The assumptions and variables included in the independent
models
 The techniques and data used to estimate the independent
models
 The structure and implementation of the independent models
    (2) Integration of the sales and scrappage models: The new sales
and scrappage models use many of the same predictors, but are not
directly integrated. We seek public comment on, and data supporting
whether integrating the two models is appropriate.
    (3) Integration of the scrappage rates and mileage accumulation:
The current model assumes that annual mileage accumulation and
scrappage rates are independent of one another. We seek public
comment on the appropriateness of this assumption, and data that
would support developing an interaction between scrappage rates and
mileage accumulation, or testing whether such an interaction is
important to include.
    (4) Increased risk of older vehicles: The observed increase in
crash and injury risk associated with older vehicles is likely due
to a combination of vehicle factors and driver factors. For example,
older vehicles are less crashworthy because in general they're
equipped with fewer or less modern safety features, and drivers of
older cars are on average younger and may be less skilled drivers or
less risk-averse than drivers of new vehicles. We fit a model which
includes both an age and vintage affect, but assume that the age
effect is entirely a result of changes in average driver
demographics, and not impacted by changes in CAFE or GHG standards.
We seek comment on this approach for attributing increased older
vehicle risk. Is the analysis likely to overestimate or
underestimate the safety benefits under the proposed alternative?
    (5) Changes in the mix of light trucks and passenger cars: The
dynamic fleet share model predicts changes in the future share of
light truck and passenger car vehicles. Changes in the mix of
vehicles may result in

increased or decreased fatalities. Does the dynamic fleet share
model reasonably capture consumers' decisions about how they
substitute between different types and sizes of vehicles depending
on changes in fuel economy, relative and absolute prices, and other
vehicle attributes? We seek comment on whether our safety analysis
provides a reasonable estimate of the effects of changes in fleet
mix on future fatalities.

1. Impact of Weight Reduction on Safety
    The primary goals of CAFE and CO2 standards are reducing
fuel consumption and CO2 emissions from the on-road light-
duty vehicle fleet; in addition to these intended effects, the
potential of the standards to affect vehicle safety is also
considered.\296\ As a safety agency, NHTSA has long considered the
potential for adverse safety consequences when establishing CAFE
standards, and under the CAA, EPA considers factors related to public
health and human welfare, including safety, in regulating emissions of
air pollutants from mobile sources.
---------------------------------------------------------------------------

    \296\ In this rulemaking document, ``vehicle safety'' is defined
as societal fatality rates per vehicle mile of travel (VMT),
including fatalities to occupants of all vehicles involved in
collisions, plus any pedestrians. Injuries and property damage are
not within the scope of the statistical models discussed in this
section because of data limitations (e.g., limited information on
observed or potential relationships between safety standards and
injury and property damage outcomes, consistency of reported injury
severity levels). Rather, injuries and property damage are
represented within the CAFE model through adjustment factors based
on observed relationships between societal costs of fatalities and
societal injury and property damage costs.
---------------------------------------------------------------------------

    Safety trade-offs associated with fuel economy increases have
occurred in the past, particularly before NHTSA CAFE standards were
attribute-based; past safety trade-offs may have occurred because
manufacturers chose at the time, in response to CAFE standards, to
build smaller and lighter vehicles. Although the agency now uses
attribute-based standards, in part to protect against excessive vehicle
downsizing, the agency must be mindful of the possibility of related
safety trade-offs in the future. In cases where fuel economy
improvements were achieved through reductions in vehicle size and mass,
the smaller, lighter vehicles did not fare as well in crashes as
larger, heavier vehicles, on average.
    Historically, as shown in FARS data analyzed by NHTSA, the safest
cars generally have been heavy and large, while cars with the highest
fatal-crash rates have been light and small. The question, then, is
whether past is necessarily a prologue when it comes to potential
changes in vehicle size (both footprint and ``overhang'') and mass in
response to the more stringent future CAFE and GHG standards.
    Manufacturers stated they will reduce vehicle mass as one of the
cost-effective means of increasing fuel economy and reducing
CO2 to meet standards, and this approach is incorporated
this expectation into the modeling analysis supporting the standards.
Because the analysis discerns a historical relationship between vehicle
mass, size, and safety, it is reasonable to assume these relationships
will continue in the future.
(a) Historical Analyses of Vehicle Mass and Safety
    Researchers have been using statistical analysis to examine the
relationship of vehicle mass and safety in historical crash data for
many years and continue to refine their techniques. In the MY 2012-2016
final rule, the agencies stated we would conduct further study and
research into the interaction of mass, size, and safety to assist
future rulemakings and start to work collaboratively by developing an
interagency working group between NHTSA, EPA, DOE, and CARB to evaluate
all aspects of mass, size, and safety. The team would seek to
coordinate government-supported studies and independent research to the
greatest extent possible to ensure the work is complementary to
previous and ongoing research and to guide further research in this
area.
    The agencies also identified three specific areas to direct
research in preparation for future CAFE/CO2 rulemaking
regarding statistical analysis of historical data. First, NHTSA would
contract with an independent institution to review statistical methods
NHTSA and DRI used to analyze historical data related to mass, size,
and safety, and to provide recommendations on whether existing or other
methods should be used for future statistical analysis of historical
data. This study would include a consideration of potential near
multicollinearity in the historical data and how best to address it in
a regression analysis. The 2010 NHTSA report (hereinafter 2010 Kahane
report) was also peer reviewed by two other experts in the safety
field--Farmer (Insurance Institute for Highway Safety) and Lie (Swedish
Transport Administration).\297\
---------------------------------------------------------------------------

    \297\ All three peer reviews are available in Docket No. NHTSA-
2010-0152, Relationships Between Fatality Risk, Mass, and Footprint,
https://www.regulations.gov/docket?D=NHTSA-2010-0152.
---------------------------------------------------------------------------

    Second, NHTSA and EPA, in consultation with DOE, would update the
MY 1991-1999 database where safety analyses in the NPRM and final rule
are based with newer vehicle data and create a common database that
could be made publicly available to address concerns that differences
in data were leading to different results in statistical analyses by
different researchers.
    And third, to assess if the design of recent model year vehicles
incorporating various mass reduction methods affect relationships among
vehicle mass, size, and safety, the agencies sought to identify
vehicles using material substitution and smart design and to assess if
there is sufficient crash data involving those vehicles for statistical
analysis. If sufficient data exists, statistical analysis would be
conducted to compare the relationship among mass, size, and safety of
these smart design vehicles to vehicles of similar size and mass with
more traditional designs.
    By the time of the MY 2017-2025 final rule, significant progress
was made on these tasks: The independent review of recent and updated
statistical analyses of the relationship between vehicle mass, size,
and crash fatality rates had been completed. NHTSA contracted with the
University of Michigan Transportation Research Institute (UMTRI) to
conduct this review, and the UMTRI team led by Green evaluated more
than 20 papers, including studies done by NHTSA's Kahane, Wenzel of the
U.S. Department of Energy's Lawrence Berkeley National Laboratory,
Dynamic Research, Inc., and others. UMTRI's basic findings are
discussed in Chapter 11 of the PRIA accompanying this NPRM.
    Some commenters in recent CAFE rulemakings, including some vehicle
manufacturers, suggested designs and materials of more recent model
year vehicles may have weakened the historical statistical
relationships between mass, size, and safety. It was agreed that the
statistical analysis would be improved by using an updated database
reflecting more recent safety technologies, vehicle designs and
materials, and reflecting changes in the vehicle fleet. An updated
database was created and employed for assessing safety effects for that
final rule. The agencies also believed, as UMTRI found, different
statistical analyses may have produced different results because they
used slightly different datasets for their analyses.
    To try to mitigate this issue and to support the current
rulemaking, NHTSA created a common, updated database for statistical
analysis consisting of crash data of model years 2000-2007 vehicles in
calendar years 2002-2008, as

compared to the database used in prior NHTSA analyses, which was based
on model years 1991-1999 vehicles in calendar years 1995-2000. The new
database was the most up-to-date possible, given the processing lead
time for crash data and the need for enough crash cases to permit
statistically meaningful analyses. NHTSA made the preliminary version
of the new database, which was the basis for NHTSA's 2011 preliminary
report (hereinafter 2011 Kahane report), available to the public in May
2011, and an updated version in April 2012 (used in NHTSA's 2012 final
report, hereinafter 2012 Kahane report),\298\ enabling other
researchers to analyze the same data and hopefully minimize
discrepancies in results because of inconsistencies across
databases.\299\
---------------------------------------------------------------------------

    \298\ Those databases are available at ftp://ftp.nhtsa.dot.gov/CAFE/.
    \299\ See 75 FR 25324, 25395-25396 (May 7, 2010) (for a
discussion of planned statistical analyses).
---------------------------------------------------------------------------

    Since the publication of the MYs 2017-2025 final rule, NHTSA has
sponsored, and is sponsoring, new studies and research to inform the
current CAFE and CO2 rulemaking. In addition, the National
Academy of Sciences published a new report in this area.\300\
Throughout the rulemaking process, NHTSA's goal is to publish as much
of our research as possible. In establishing standards, all available
data, studies, and information objectively without regard to whether
they were sponsored by the agencies, will be considered.
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    \300\ Cost, Effectiveness and Deployment of Fuel Economy
Technologies for Light-Duty Vehicles, National Academy of Sciences
(2015).
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    Undertaking these tasks has helped come closer to resolving ongoing
debates in statistical analysis research of historical crash data. It
is intended that these conclusions will be applied going forward in
future rulemakings, and it is believed the research will assist the
public discussion of the issues. Specific historical analyses (in
addition to NHTSA's own analysis) on vehicle mass and safety used to
support this rulemaking include:
     The 2011 and 2013 NHTSA Workshops on Vehicle Mass, Size,
and Safety;
     the University of Michigan Transportation Research
Institute (UMTRI) independent review of a set of statistical
relationships between vehicle curb weight, footprint variables (track
width, wheelbase), and fatality rates from vehicle crashes;
     the 2012 Lawrence Berkeley National Laboratory (LBNL)
Phase 1 and Phase 2 reports on the sensitivity of NHTSA's baseline
results and casualty risk per VMT;
     the 2012 DRI reports on, among other things, the effects
of mass reduction on crash frequency and fatality risk per crash;
     LBNL's subsequent review of DRI's study;
     the 2015 National Academy of Sciences Report; and
     the 2017 NBER working paper analyzing the relationships
among traffic fatalities, CAFE standards, and distributions of MY 1989-
2005 light-duty vehicle curb weights.
    A detailed discussion of each analysis is discussed in Chapter 11
of the PRIA accompanying this proposed rule.
(b) Recent NHTSA Analysis Supporting CAFE Rulemaking
    As mentioned previously, NHTSA and EPA's 2012 joint final rule for
MYs 2017 and beyond set ``footprint-based'' standards, with footprint
being defined as roughly equal to the wheelbase multiplied by the
average of the front and rear track widths. Basing standards on vehicle
footprint ideally helps to discourage vehicle manufacturers from
downsizing their vehicles; the agencies set higher (more stringent)
mile per gallon (mpg) targets for smaller-footprint vehicles but would
not similarly discourage mass reduction that maintains footprint while
potentially improving fuel economy. Several technologies, such as
substitution of light, high-strength materials for conventional
materials during vehicle redesigns, have the potential to reduce weight
and conserve fuel while maintaining a vehicle's footprint and
maintaining or possibly improving the vehicle's structural strength and
handling.
    In considering what technologies are available for improving fuel
economy, including mass reduction, an important corollary issue for
NHTSA to consider is the potential effect those technologies may have
on safety. NHTSA has thus far specifically considered the likely effect
of mass reduction that maintains footprint on fatal crashes. The
relationship between a vehicle's mass, size, and fatality risk is
complex, and it varies in different types of crashes. As mentioned
above, NHTSA, along with others, has been examining this relationship
for more than a decade.\301\
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    \301\ A complete discussion of the historical analysis of
vehicle mass and safety is located in Chapter 10 of the PRIA
accompanying this proposed rulemaking.
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    The safety chapter of NHTSA's April 2012 final regulatory impact
analysis (FRIA) of CAFE standards for MY 2017-2021 passenger cars and
light trucks included a statistical analysis of relationships between
fatality risk, mass, and footprint in MY 2000-2007 passenger cars and
LTVs (light trucks and vans), based on calendar year (CY) 2002-2008
crash and vehicle-registration data; \302\ this analysis was also
detailed in the 2012 Kahane report.
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    \302\ Kahane, C.J. Relationships Between Fatality Risk, Mass,
and Footprint in Model Year 2000-2007 Passenger Cars and LTVs--Final
Report, National Highway Traffic Safety Administration (Aug. 2012),
available at https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811665.
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    The principal findings and conclusions of the 2012 Kahane report
were mass reduction in the lighter cars, even while holding footprint
constant, would significantly increase fatality risk, whereas mass
reduction in the heavier LTVs would reduce societal fatality risk by
reducing the fatality risk of occupants of lighter vehicles colliding
with those heavier LTVs. NHTSA concluded, as a result, any reasonable
combination of mass reductions that held footprint constant in MY 2017-
2021 vehicles--concentrated, at least to some extent, in the heavier
LTVs and limited in the lighter cars--would likely be approximately
safety-neutral; it would not significantly increase fatalities and
might well decrease them.
    NHTSA released a preliminary report (2016 Puckett and Kindelberger
report) on the relationship between fatality risk, mass, and footprint
in June 2016 in advance of the Draft TAR. The preliminary report
covered the same scope as the 2012 Kahane report, offering a detailed
description of the databases, modeling approach, and analytical results
on relationships among vehicle size, mass, and fatalities that informed
the Draft TAR. Results in the Draft TAR and the 2016 Puckett and
Kindelberger report are consistent with results in the 2012 Kahane
report; chiefly, societal effects of mass reduction are small, and mass
reduction concentrated in larger vehicles is likely to have a
beneficial effect on fatalities, while mass reduction concentrated in
smaller vehicles is likely to have a detrimental effect on fatalities.
    For the 2016 Puckett and Kindelberger report and Draft TAR, NHTSA,
working closely with EPA and the DOE, performed an updated statistical
analysis of relationships between fatality rates, mass and footprint,
updating the crash and exposure databases to the latest available model
years. The agencies analyzed updated databases that included MY 2003-
2010 vehicles in CY 2005-2011 crashes. For this proposed

rule, databases are the most up-to-date possible (MY 2004-2011 vehicles
in CY 2006-2012), given the processing time for crash data and the need
for enough crash cases to permit statistically meaningful analyses. As
in previous analyses, NHTSA has made the new databases available to the
public on its website, enabling other researchers to analyze the same
data and hopefully minimizing discrepancies in results that would have
been because of inconsistencies across databases.
(c) Updated Analysis for This Rulemaking
    The basic analytical method used to analyze the impacts of weight
reduction on safety in this proposed rule is the same as in NHTSA's
2012 Kahane report, 2016 Puckett and Kindelberger report, and the Draft
TAR: The agency analyzed cross sections of the societal fatality rate
per billion vehicle miles of travel (VMT) by mass and footprint, while
controlling for driver age, gender, and other factors, in separate
logistic regressions by vehicle class and crash type. ``Societal''
fatality rates include fatalities to occupants of all the vehicles
involved in the collisions, plus any pedestrians.
    The temporal range of the data is now MY 2004-2011 vehicles in CY
2006-2012, updated from previous databases of MY 2000-2007 vehicles in
CY 2002-2008 (2012 Kahane Report) and MY 2003-2010 vehicles in CY 2005-
2011 (2016 Puckett and Kindelberger report and Draft TAR). NHTSA
purchased a file of odometer readings by make, model, and model year
from Polk that helped inform the agency's improved VMT estimates. As in
the 2012 Kahane report, 2016 Puckett and Kindelberger report, and the
Draft TAR, the vehicles are grouped into three classes: Passenger cars
(including both two-door and four-door cars); CUVs and minivans; and
truck-based LTVs.
    There are nine types of crashes specified in the analysis. Single-
vehicle crashes include first-event rollovers, collisions with fixed
objects, and collisions with pedestrians, bicycles and motorcycles.
Two-vehicle crashes include collisions with: heavy-duty vehicles; car,
CUV, or minivan < 3,187 pounds (the median curb weight of other, non-
case, cars, CUVs and minivans in fatal crashes in the database); car,
CUV, or minivan >= 3,187 pounds; truck-based LTV < 4,360 pounds (the
median curb weight of other truck-based LTVs in fatal crashes in the
database); and truck-based LTV >= 4,360 pounds. An additional crash
type includes all other fatal crash types (e.g., collisions involving
more than two vehicles, animals, or trains). Splitting the ``other''
vehicles into a lighter and a heavier group permits more accurate
analyses of the mass effect in collisions of two light vehicles.
Grouping partner-vehicle CUVs and minivans with cars rather than LTVs
is more appropriate because their front-end profile and rigidity more
closely resembles a car than a typical truck-based LTV.
    The curb weight of passenger cars is formulated, as in the 2012
Kahane report, 2016 Puckett and Kindelberger report, and Draft TAR, as
a two-piece linear variable to estimate one effect of mass reduction in
the lighter cars and another effect in the heavier cars. The boundary
between ``lighter'' and ``heavier'' cars is 3,201 pounds (which is the
median mass of MY 2004-2011 cars in fatal crashes in CY 2006-2012, up
from 3,106 for MY 2000-2007 cars in CY 2002-2008 in the 2012 NHTSA
safety database, and up from 3,197 for MY 2003-2010 cars in CY 2005-
2011 in the 2016 NHTSA safety database).
    Likewise, for truck-based LTVs, curb weight is a two-piece linear
variable with the boundary at 5,014 pounds (again, the MY 2004-2011
median, higher than the median of 4,594 for MY 2000-2007 LTVs in CY
2002-2008 and the median of 4,947 for MY 2003-2010 LTVs in CY 2005-
2011). Curb weight is formulated as a simple linear variable for CUVs
and minivans. Historically, CUVs and minivans have accounted for a
relatively small share of new-vehicle sales over the range of the data,
resulting in less crash data available than for cars or truck-based
LTVs.
    For a given vehicle class and weight range (if applicable),
regression coefficients for mass (while holding footprint constant) in
the nine types of crashes are averaged, weighted by the number of
baseline fatalities that would have occurred for the subgroup MY 2008-
2011 vehicles in CY 2008-2012 if these vehicles had all been equipped
with electronic stability control (ESC). The adjustment for ESC, a
feature of the analysis added in 2012, takes into account results will
be used to analyze effects of mass reduction in future vehicles, which
will all be ESC-equipped, as required by NHTSA's regulations.
    Techniques developed in the 2011 (preliminary) and 2012 (final)
Kahane reports have been retained to test statistical significance and
to estimate 95 percent confidence bounds (sampling error) for mass
effects and to estimate the combined annual effect of removing 100
pounds of mass from every vehicle (or of removing different amounts of
mass from the various classes of vehicles), while holding footprint
constant.
    NHTSA considered the near multicollinearity of mass and footprint
to be a major issue in the 2010 Kahane report \303\ and voiced concern
about inaccurately estimated regression coefficients.\304\ High
correlations between mass and footprint and variance inflation factors
(VIF) have not changed from MY 1991-1999 to MY 2004-2011; large
vehicles continued to be, on the average, heavier than small vehicles
to the same extent as in the previous decade.\305\
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    \303\ Kahane, C. J. Relationships Between Fatality Risk, Mass,
and Footprint in Model Year 1991-1999 and Other Passenger Cars and
LTVs (Mar. 24, 2010), in Final Regulatory Impact Analysis: Corporate
Average Fuel Economy for MY 2012-MY 2016 Passenger Cars and Light
Trucks, National Highway Traffic Safety Administration (Mar. 2010)
at 464-542.
    \304\ Van Auken and Green also discussed the issue in their
presentations at the NHTSA Workshop on Vehicle Mass-Size-Safety in
Washington, DC February 25, 2011. More information on the NHTSA
Workshop on Vehicle Mass-Size-Safety is available at https://one.nhtsa.gov/Laws-&-Regulations/CAFE-%E2%80%93-Fuel-Economy/NHTSA-Workshop-on-Vehicle-Mass%E2%80%93Size%E2%80%93Safety.
    \305\ Greene, W. H. Econometric Analysis 266-68 (Macmillan
Publishing Company 2d ed. 1993); Paul D. Allison, Logistic
Regression Using the SAS System 48-51 (SAS Institute Inc. 2001). VIF
scores are in the 6-9 range for curb weight and footprint in NHTSA's
new database--i.e., in the somewhat unfavorable 2.5-10 range where
near multicollinearity begins to become a concern in logistic
regression analyses.
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    Nevertheless, multicollinearity appears to have become less of a
problem in the 2012 Kahane, 2016 Puckett and Kindelberger/Draft TAR,
and current NHTSA analyses. Ultimately, only three of the 27 core
models of fatality risk by vehicle type in the current analysis
indicate the potential presence of effects of multicollinearity, with
estimated effects of mass and footprint reduction greater than two
percent per 100-pound mass reduction and one-square-foot footprint
reduction, respectively; these three models include passenger cars and
CUVs in first-event rollovers, and CUVs in collisions with LTVs greater
than 4,360 pounds. This result is consistent with the 2016 Puckett and
Kindelberger report, which also found only three cases out of 27 models
with estimated effects of mass and footprint reduction greater than two
percent per 100-pound mass reduction and one-square-foot footprint
reduction.
    Table II-45 presents the estimated percent increase in U.S.
societal fatality risk per 10 billion VMT for each 100-

pound reduction in vehicle mass, while holding footprint constant, for
each of the five vehicle classes:
[GRAPHIC] [TIFF OMITTED] TP24AU18.067

    None of the estimated effects have 95-percent confidence bounds
that exclude zero, and thus are not statistically significant at the
95-percent confidence level. Two estimated effects are statistically
significant at the 85-percent level. Societal fatality risk is
estimated to: (1) Increase by 1.2 percent if mass is reduced by 100
pounds in the lighter cars; and (2) decrease by 0.61 percent if mass is
reduced by 100 pounds in the heavier truck-based LTVs. The estimated
increases in societal fatality risk for mass reduction in the heavier
cars and the lighter truck-based LTVs, and the estimated decrease in
societal fatality risk for mass reduction in CUVs and minivans are not
significant, even at the 85-percent confidence level.
    Confidence bounds estimate only the sampling error internal to the
data used in the specific analysis that generated the point estimate.
Point estimates are also sensitive to the modification of components of
the analysis, as discussed at the end of this section. However, this
degree of uncertainty is methodological in nature rather than
statistical.
    It is useful to compare the new results in Table II-45 to results
in the 2012 Kahane report (MY 2000-2007 vehicles in CY 2002-2008) and
the 2016 Puckett and Kindelberger report and Draft TAR (MY 2003-2010
vehicles in CY 2005-2011), presented in Table II-46 below:
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    \306\ Median curb weights in the 2012 Kahane report: 3,106
pounds for cars, 4,594 pounds for truck-based LTVs. Median curb