a reasonable estimate of the average period over which buyers who
finance their purchases of new vehicle receive--and thus must
recognize--the monetary value of future fuel savings resulting from
higher fuel economy.
    The value of fuel savings over the first five years of a vehicle
model's lifetime that would result under each alternative fuel economy
standard is calculated using the projections of retail fuel prices
described above. It is then deducted from the technology costs incurred
by its manufacturer to produce the improvement in that model's fuel
economy estimated for each alternative standard, to determine the
increase in the ``effective price'' to buyers of that vehicle model.
The Volpe model uses these estimates of effective costs for increasing
the fuel economy of each vehicle model to identify the order in which
manufacturers would be likely to select models for the application of
fuel economy-improving technologies in order to comply with stricter
standards. The average value of the resulting increase in effective
cost from each manufacturer's simulated compliance strategy is also
used to estimate the impact of alternative standards on its total sales
for future model years.
    However, it is important to recognize that NHTSA estimates the
aggregate value to the U.S. economy of fuel savings resulting from
alternative standards--or their ``social'' value--over the entire
expected lifetimes of vehicles manufactured under those standards,
rather than over this shorter ``payback period'' we assume for their
buyers. This is discussed directly below in section f on ``Vehicle
survival and use assumptions.'' As indicated previously, the maximum
vehicle lifetimes used to analyze the effects of alternative fuel
economy standards are estimated to be 25 years for automobiles and 36
years for light trucks.
f. Vehicle Survival and Use Assumptions
    NHTSA's preliminary analysis of fuel/CO2 emissions
savings and related benefits from adopting alternative standards for MY
2011-2015 passenger cars and light trucks is based on estimates of the
resulting changes in fuel use over their entire lifetimes in the U.S.
vehicle fleet. The first step in estimating lifetime fuel consumption
by vehicles produced during a model year is to calculate the number
that is expected to remain in service during each future year after
they are produced and sold.\103\ This number is calculated by
multiplying the number of vehicles originally produced during a model
year by the proportion expected to remain in service at the age they
will have reached during each subsequent year, often referred to as a
``survival rate.''
---------------------------------------------------------------------------

    \103\ Vehicles are defined to be of age 1 during the calendar
year corresponding to the model year in which they are produced;
thus for example, model year 2000 vehicles are considered to be of
age 1 during calendar year 2000, age 1 during calendar year 2001,
and to reach their maximum age of 26 years during calendar year
2025. NHTSA considers the maximum lifetime of vehicles to be the age
after which less than 2% of the vehicles originally produced during
a model year remain in service. Applying these conventions to
vehicle registration data indicates that passenger cars have a
maximum age of 26 years, while light trucks have a maximum lifetime
of 36 years. See Lu, S., NHTSA, Regulatory Analysis and Evaluation
Division, ``Vehicle Survivability and Travel Mileage Schedules,''
DOT HS 809 952, 8-11 (January 2006). Available at http://www-
nrd.nhtsa.dot.gov/pdf/nrd-30/NCSA/Rpts/2006/809952.pdf (last
accessed April 20, 2008).
---------------------------------------------------------------------------

    The agency relies on projections of the number of passenger cars
and light trucks that will be produced during future years reported by
the EIA in its AEO Reference Case forecast.\104\ It uses updated values
of age-specific survival rates for cars and light trucks estimated from
yearly registration data for vehicles produced during recent model
years, to ensure that forecasts of the number of vehicles in use
reflect recent increases in the durability and expected life spans of
cars and light trucks.\105\
---------------------------------------------------------------------------

    \104\ The most recent edition is Energy Information
Administration, Annual Energy Outlook 2008: Early Release. Available
at http://www.eia.doe.gov/oiaf/aeo/index.html (last accessed April
20, 2008).
    \105\ Lu, S., NHTSA, Regulatory Analysis and Evaluation
Division, ``Vehicle Survivability and Travel Mileage Schedules,''
DOT HS 809 952, 8-11 (January 2006). Available at http://www-
nrd.nhtsa.dot.gov/pdf/nrd-30/NCSA/Rpts/2006/809952.pdf (last
accessed April 20, 2008). These updated survival rates suggest that
the expected lifetimes of recent-model passenger cars and light
trucks are 13.8 and 14.5 years.
---------------------------------------------------------------------------

    The next step in estimating fuel use is to calculate the total
number of miles that the cars and light trucks produced in each model
year affected by the proposed CAFE standards will be driven during each
year of their lifetimes. To

estimate total miles driven, the number of cars and light trucks
projected to remain in use during each future year (calculated as
described above) is multiplied by the average number of miles they are
expected to be driven at the age they will have reached in that year.
The agency estimated the average number of miles driven annually by
cars and light trucks of each age using data from the Federal Highway
Administration's 2001 National Household Transportation Survey
(NHTS).\106\
---------------------------------------------------------------------------

    \106\ For a description of the Survey, see http://nhts.ornl.gov/
quickStart.shtml (last accessed April 20, 2008).
---------------------------------------------------------------------------

    Finally, fuel consumption during each year of a model year's
lifetime is estimated by dividing the total number of miles its
surviving vehicles are driven by the fuel economy they are expected to
achieve under each alternative CAFE standard. Each model year's total
lifetime fuel consumption is the sum of fuel use by the cars or light
trucks produced during that model year that are projected to remain in
use during each year of their maximum life spans. In turn, the savings
in a model year's lifetime fuel use that will result from each
alternative CAFE standard is the difference between its lifetime fuel
use at the fuel economy level it attains under the Baseline
alternative, and its lifetime fuel use at the higher fuel economy level
it is projected to achieve under that alternative standard.
    To illustrate these calculations, the most recent edition of the
AEO projections that 8.52 million light trucks will be produced during
2012, and the agency's updated survival rates show that slightly more
than half of these --50.1 percent, or 4.27 million--are projected to
remain in service during the year 2027, when they will have reached an
age of 14 years. At that age, light trucks achieving the fuel economy
level required under the Baseline alternative are driven an average of
about 10,400 miles, so model year 2012 light trucks will be driven a
total of 44.4 billion miles (= 4.27 million surviving vehicles x 10,400
miles per vehicle) during 2027. Summing the results of similar
calculations for each year of their 36-year maximum lifetime, model
year 2012 light trucks will be driven a total of 1,502 billion miles
under the Baseline alternative. Under that alternative, they are
projected to achieve a test fuel economy level of 23.8 mpg, which
corresponds to actual on-road fuel economy of 19.0 mpg (= 23.8 mpg x 80
percent). Thus their lifetime fuel use under the Baseline alternative
is projected to be 79.0 billion gallons (= 1,502 billion miles divided
by 19.0 miles per gallon).
g. Growth in Total Vehicle Use
    By assuming that the annual number of miles driven by cars and
light trucks at any age will remain constant over the future, NHTSA's
procedure for estimating the number of miles driven by cars and light
trucks over their lifetimes in effect assumes that all future growth in
total vehicle-miles driven stems from increases in the number of
vehicles in service, rather than from increases in the average number
of miles they are driven each year. Similarly, because the survival
rates used to estimate the number of cars and light trucks remaining in
service to various ages are assumed to remain fixed for future model
years, growth in the total number of cars and light trucks in use is
effectively assumed to result only from increasing sales of new
vehicles. In order to determine the validity of these assumptions, the
agency conducted a detailed analysis of the causes of recent growth in
car and light truck use.
    From 1985 through 2005, the total number of miles driven (usually
referred to as vehicle-miles traveled, or VMT) by passenger cars
increased 35 percent, equivalent to a compound annual growth rate of
1.5 percent.\107\ During that time, the total number of passenger cars
registered for in the U.S. grew by about 0.3 percent annually, almost
exclusively as a result of increasing sales of new cars.\108\ Thus
growth in the average number of miles automobiles are driven each year
accounted for the remaining 1.2 percent (= 1.5 percent--0.3 percent)
annual growth in total automobile use.\109\
---------------------------------------------------------------------------

    \107\ Calculated from data reported in FHWA, Highway Statistics,
Summary to 1995, Table vm201at http://www.fhwa.dot.gov/ohim/
summary95/vm201a.xlw, (last accessed April 20, 2008).and annual
editions 1996-2005, Table VM-1 at http://www.fhwa.dot.gov/policy/
ohpi/hss/hsspubs.htm (last accessed April 20, 2008).
    \108\ A slight increase in the fraction of new passenger cars
remaining in service beyond age 10 has accounted for a small share
of growth in the U.S. automobile fleet. The fraction of new
automobiles remaining in service to various ages was computed from
R.L. Polk vehicle registration data for 1977 through 2005 by the
agency's Center for Statistical Analysis.
    \109\ See supra note [2 above here]
---------------------------------------------------------------------------

    Over this same period, total VMT by light trucks increased much
faster, growing at an annual rate of 5.1 percent. In contrast to the
causes of growth in automobile use, however, nearly all growth in light
truck use over these two decades was attributable to rapid increases in
the number of light trucks in use.\110\ In turn, growth in the size of
the nation's light truck fleet has resulted almost exclusively from
rising sales of new light trucks, since the fraction of new light
trucks remaining in service to various ages has remained stable or even
declined slightly over the past two decades.\111\
---------------------------------------------------------------------------

    \110\ FHWA data show that growth in total miles driven by ``Two-
axle, four-tire trucks,'' a category that includes most or all light
trucks used as passenger vehicles, averaged 5.1% annually from 1985
through 2005. However, the number of miles light trucks are driven
each year averaged 11,114 during 2005, almost unchanged from the
average figure of 11,016 miles during 1985. Id.
    \111\ Unpublished analysis of R.L. Polk vehicle registration
data conducted by NHTSA Center for Statistical Analysis, 2005.
---------------------------------------------------------------------------

    On the basis of this analysis, the agency tentatively concludes
that its projections of future growth in light truck VMT account fully
for the primary cause of its recent growth, which has been the rapid
increase in sales of new light trucks during recent model years.
However, the assumption that average annual use of passenger cars will
remain fixed over the future appears to ignore an important source of
recent growth in their total use, the gradual increase in the average
number of miles they are driven. To the extent that this factor
continues to represent a significant source of growth in future
passenger car use, the agency's analysis is likely to underestimate the
reductions in fuel use and related environmental impacts resulting from
stricter CAFE standards for passenger cars.\112\ The agency plans to
account explicitly for potential future growth in average annual use of
both cars and light trucks in the analysis accompanying its Final Rule
establishing CAFE standards for model years 2011-15.
---------------------------------------------------------------------------

    \112\ Assuming that average annual miles driven per automobile
will continue to increase over the future would increase the
agency's estimates of total lifetime mileage for MY 2011-18
passenger cars. Their estimated lifetime fuel use would also
increase under each alternative standard considered in this
analysis, but in inverse relation to their fuel economy. Thus
lifetime fuel use will increase by more under the No Increase
alternative than under any of the alternatives that would increase
passenger car CAFE standards, and by progressively less for the
alternatives that impose stricter standards. Taking account of this
factor would thus increase the agency's estimates of fuel savings
for those alternatives, and omitting it will cause the agency's
analysis to underestimate those fuel savings.
---------------------------------------------------------------------------

h. Accounting for the Rebound Effect of Higher Fuel Economy
    The rebound effect refers to the tendency for owners to increase
the number of miles they drive a vehicle in response to an increase in
its fuel economy, as would result from more stringent fuel economy
standards. The rebound effect occurs because an increase in a vehicle's
fuel economy reduces its owner's fuel cost for driving each mile, which
is typically the largest

single component of the cost of operating a vehicle. Even with the
vehicle's higher fuel economy, this additional driving uses some fuel,
so the rebound effect will reduce the net fuel savings that result when
the fuel economy standards require manufacturers to increase fuel
economy. The rebound effect is usually expressed as the percentage by
which annual vehicle use increases when average fuel cost per mile
driven decreases in response to a change in the marginal cost of
driving an extra mile, due either an increase in fuel economy or a
reduction in the price of fuel.
    The magnitude of the rebound effect is one of the determinants of
the actual fuel savings that are likely to result from adopting
stricter standards, and thus an important parameter affecting NHTSA's
evaluation of alternative standards for future model years. The rebound
effect can be measured directly by estimating the elasticity of vehicle
use with respect to fuel economy itself, or indirectly by the
elasticity of vehicle use with respect to fuel cost per mile
driven.\113\ When expressed as a positive percentage, either of these
parameters gives the fraction of fuel savings that would otherwise
result from adopting stricter standards, but is offset by the increase
in fuel consumption that results when vehicles with increased fuel
economy are driven more.
---------------------------------------------------------------------------

    \113\ Fuel cost per mile is equal to the price of fuel in
dollars per gallon divided by fuel economy in miles per gallon, so
this figure declines when a vehicle's fuel economy increases.
---------------------------------------------------------------------------

    Research on the magnitude of the rebound effect in light-duty
vehicle use dates to the early 1980s, and almost unanimously concludes
that a statistically significant rebound effect occurs when vehicle
fuel efficiency improves.\114\ The most common approach to estimating
its magnitude has been to analyze statistically household survey data
on vehicle use, fuel consumption, fuel prices (often obtained from
external sources), and other determinants of household travel demand to
isolate the response of vehicle use to higher fuel economy. Other
studies have relied on econometric analysis of annual U.S. data on
vehicle use, fuel economy, fuel prices, and other variables to identify
the response of total or average vehicle use to changes in fleet-wide
average fuel economy and its effect of fuel cost per mile driven. Two
recent studies analyzed yearly variation in vehicle ownership and use,
fuel prices, and fuel economy among individual states over an extended
time period in order to measure the response of vehicle use to changing
fuel economy.\115\
---------------------------------------------------------------------------

    \114\ Some studies estimate that the long-run rebound effect is
significantly larger than the immediate response to increased fuel
efficiency. Although their estimates of the adjustment period
required for the rebound effect to reach its long-run magnitude
vary, this long-run effect is most appropriate for evaluating the
fuel savings and emissions reductions resulting from stricter
standards that would apply to future model years.
    \115\ In effect, these studies treat U.S. states as a data
``panel'' by applying appropriate estimation procedures to data
consisting of each year's average values of these variables for the
separate states.
---------------------------------------------------------------------------

    An important distinction among studies of the rebound effect is
whether they assume that the effect is constant, or varies over time in
response to the absolute levels of fuel costs, personal income, or
household vehicle ownership. Most studies using aggregate annual data
for the U.S. assume a constant rebound effect, although some of these
studies test whether the effect can vary as changes in retail fuel
prices or average fuel economy alter fuel cost per mile driven. Many
studies using household survey data estimate significantly different
rebound effects for households owning varying numbers of vehicles,
although they arrive at differing conclusions about whether the rebound
effect is larger among households that own more vehicles. One recent
study using state-level data concludes that the rebound effect varies
directly in response to changes in personal income and the degree of
urbanization of U.S. cities, as well as fuel costs.
    In order to arrive at a preliminary estimate of the rebound effect
for use in assessing the fuel savings, emissions reductions, and other
impacts of alternative standards, NHTSA reviewed 22 studies of the
rebound effect conducted from 1983 through 2005. We then conducted a
detailed analysis of the 66 separate estimates of the long-run rebound
effect reported in these studies, which is summarized in the table
below.\116\ As the table indicates, these 66 estimates of the long-run
rebound effect range from as low as 7 percent to as high as 75 percent,
with a mean value of 23 percent.
---------------------------------------------------------------------------

    \116\ In some cases, NHTSA derived estimates of the overall
rebound effect from more detailed results reported in the studies.
For example, where studies estimated different rebound effects for
households owning different numbers of vehicles but did not report
an overall value, we computed a weighted average of the reported
values using the distribution of households among vehicle ownership
categories.
---------------------------------------------------------------------------

    Limiting the sample to 50 estimates reported in the 17 published
studies of the rebound effect yields the same range but a slightly
higher mean (24 percent), while focusing on the authors' preferred
estimates from published studies narrows this range and lowers its
average only slightly. The median estimate of the rebound effect in all
three samples, which is generally regarded as a more reliable indicator
of their central tendency than the average because it is less
influenced by unusually small and large estimates, is 22 percent. As
Table V-4 indicates, approximately two-thirds of all estimates
reviewed, of all published estimates, and of authors' preferred
estimates fall in the range of 10-30 percent.

                                 Table V-4.--Summary of Rebound Effect Estimates
----------------------------------------------------------------------------------------------------------------
                                                Number of          Range                   Distribution
       Category of estimates         Number of            ------------------------------------------------------
                                      studies   estimates     Low        High      Median      Mean    Std. Dev.
----------------------------------------------------------------------------------------------------------------
All Estimates......................         22         66         7%        75%        22%        23%        14%
Published Estimates................         17         50         7%        75%        22%        24%        14%
Authors' Preferred Estimates.......         17         17         9%        75%        22%        22%        15%
 U.S. Time-Series Estimates........          7         34         7%        45%        14%        18%         9%
Household Survey Estimates.........         13         23         9%        75%        31%        31%        16%
Pooled U.S. State Estimates........          2          9         8%        58%        22%        25%        14%
Constant Rebound Effect (1)........         15         37         7%        75%        20%        23%        16%
Variable Rebound Effect: (1).......
Reported Estimates.................         10         29        10%        45%        23%        23%        10%
Updated to 2006 (2)................         10         29         6%        46%        16%        19%       12%
----------------------------------------------------------------------------------------------------------------
(1) Three studies estimate both constant and variable rebound effects.

(2) Reported estimates updated to reflect 2006 values of vehicle use, fuel prices, fleet fuel efficiency,
  household income, and household vehicle ownership.

    The type of data used and authors' assumption about whether the
rebound effect varies over time have important effects on its estimated
magnitude. The 34 estimates derived from analysis of U.S. annual time-
series data produce a median estimate of 14 percent for the long-run
rebound effect, while the median of 23 estimates based on household
survey data is more than twice as large (31 percent), and the median of
9 estimates based on pooled state data matches that of the entire
sample (22 percent). The 37 estimates assuming a constant rebound
effect produce a median of 20 percent, while the 29 originally reported
estimates of a variable rebound effect have a slightly higher median
value (23 percent).
    In selecting a single value for the rebound effect to use in
analyzing alternative standards for future model years, NHTSA
tentatively attaches greater significance to studies that allow the
rebound effect to vary in response to changes in the various factors
that have been found to affect its magnitude. However, it is also
important to update authors' originally-reported estimates of variable
rebound effects to reflect current conditions. Recalculating the 29
original estimates of variable rebound effects to reflect current
(2006) values for retail fuel prices, average fuel economy, personal
income, and household vehicle ownership reduces their median estimate
to 16 percent.\117\ NHTSA also tentatively attaches greater
significance to the recent study by Small and Van Dender (2005), which
finds that the rebound effect tends to decline as average fuel economy,
personal income, and suburbanization of U.S. cities increase, but--in
accordance with previous studies--rises with increasing fuel
prices.\118\
---------------------------------------------------------------------------

    \117\ As an illustration, Small and Van Dender (2005) allow the
rebound effect to vary over time in response to changes in real per
capita income as well as average fuel cost per mile driven. While
their estimate for the entire interval (1966-2001) they analyze is
22 percent, updating this estimate using 2006 values of these
variables reduces the rebound effect to approximately 10 percent.
Similarly, updating Greene's 1992 original estimate of a 15 percent
rebound effect to reflect 2006 fuel prices and average fuel economy
reduces it to 6 percent. See David L. Greene, ``Vehicle Use and Fuel
Economy: How Big is the Rebound Effect?'' The Energy Journal, 13:1
(1992), 117-143. In contrast, the distribution of households among
vehicle ownership categories in the data samples used by Hensher et
al. (1990) and Greene et al. (1999) are nearly identical to the most
recent estimates for the U.S., so updating their original estimates
to current U.S. conditions changes them very little. See David A.
Hensher, Frank W. Milthorpe, and Nariida C. Smith, ``The Demand for
Vehicle Use in the Urban Household Sector: Theory and Empirical
Evidence,'' Journal of Transport Economics and Policy, 24:2 (1990),
119-137; and David L. Greene, James R. Kahn, and Robert C. Gibson,
``Fuel Economy Rebound Effect for Household Vehicles,'' The Energy
Journal, 20:3 (1999), 1-21.
    \118\ In the most recent light truck CAFE rulemaking, NHTSA
chose not to preference the Small and Van Dender study over other
published estimates of the value of the rebound effect, stating that
since it ``remains an unpublished working paper that has not been
subjected to formal peer review, ``the agency does not yet consider
the estimates it provides to have the same credibility as the
published and widely-cited estimates it relied upon.'' See 71 FR
17633 (Apr. 6, 2006). The study has subsequently been published and
peer-reviewed, so NHTSA is now prepared to ``consider it in
developing its own estimate of the rebound effect for use in
subsequent CAFE rulemakings.''
---------------------------------------------------------------------------

    Considering the empirical evidence on the rebound effect as a
whole, but according greater importance to the updated estimates from
studies allowing the rebound effect to vary--particularly the Small and
Van Dender study--NHTSA has selected a rebound effect of 15 percent to
evaluate the fuel savings and other effects of alternative standards
for the time period covered by this rulemaking. However, we do not
believe that evidence of the rebound effect's dependence on fuel prices
or household income is sufficiently convincing to justify allowing its
future value to vary in response to forecast changes in these
variables. A range extending from 10 percent to at least 20 percent--
and perhaps as high as 25 percent--appears to be appropriate for the
required analysis of the uncertainty surrounding these estimates. While
the agency selected 15 percent, it also ran sensitivity analyses at 10
and 20 percent. The results are shown in the PRIA.
i. Benefits From Increased Vehicle Use
    The increase in vehicle use from the rebound effect provides
additional benefits to their owners, who may make more frequent trips
or travel farther to reach more desirable destinations. This additional
travel provides benefits to drivers and their passengers by improving
their access to social and economic opportunities away from home. As
evidenced by their decisions to make more frequent or longer trips when
improved fuel economy reduces their costs for driving, the benefits
from this additional travel exceed the costs drivers and passengers
incur in making more frequent or longer trips.
    The amount by which the benefits from this additional travel exceed
its costs (for fuel and other operating expenses) measures the net
benefits that drivers receive from the additional travel, usually
referred to as increased consumer surplus. NHTSA's analysis estimates
the economic value of the increased consumer surplus provided by added
driving using the conventional approximation, which is one half of the
product of the decline in vehicle operating costs per vehicle-mile and
the resulting increase in the annual number of miles driven. The
magnitude of these benefits represents a small fraction of the total
benefits from the alternative fuel economy standards considered.
j. Added Costs From Congestion, Crashes and Noise
    Although it provides some benefits to drivers, increased vehicle
use associated with the rebound effect also contributes to increased
traffic congestion, motor vehicle accidents, and highway noise.
Depending on how the additional travel is distributed over the day and
on where it takes place, additional vehicle use can contribute to
traffic congestion and delays by increasing traffic volumes on
facilities that are already heavily traveled during peak periods. These
added delays impose higher costs on drivers and other vehicle occupants
in the form of increased travel time and operating expenses. Because
drivers do not take these added costs into account in deciding when and
where to travel, they must be accounted for separately as a cost of the
added driving associated with the rebound effect.
    Increased vehicle use due to the rebound effect may also increase
the costs associated with traffic accidents. Drivers may take account
of the potential costs they (and their passengers) face from the
possibility of being involved in an accident when they decide to make
additional trips. However, they probably do not consider all of the
potential costs they impose on occupants of other vehicles and on
pedestrians when accidents occur, so any increase in these ``external''
accident costs must be considered as another cost of additional
rebound-effect driving. Like increased delay costs, any increase in
these external accident costs caused by added driving is likely to
depend on the traffic conditions under which it takes place, since
accidents are more frequent in heavier traffic (although their severity
may be reduced by the slower speeds at which heavier traffic typically
moves).
    Finally, added vehicle use from the rebound effect may also
increase traffic noise. Noise generated by vehicles

causes inconvenience, irritation, and potentially even discomfort to
occupants of other vehicles, to pedestrians and other bystanders, and
to residents or occupants of surrounding property. Because these
effects are unlikely to be taken into account by the drivers whose
vehicles contribute to traffic noise, they represent additional
externalities associated with motor vehicle use. Although there is
considerable uncertainty in measuring their value, any increase in the
economic costs of traffic noise resulting from added vehicle use must
be included together with other increased external costs from the
rebound effect.
    NHTSA relies on estimates of congestion, accident, and noise costs
caused by automobiles and light trucks developed by the Federal Highway
Administration to estimate the increased external costs caused by added
driving due to the rebound effect.\119\ These estimates are intended to
measure the increases in costs from added congestion, property damages
and injuries in traffic accidents, and noise levels caused by
automobiles and light trucks that are borne by persons other than their
drivers (or ``marginal'' external costs). Updated to 2006 dollars,
FHWA's ``Middle'' estimates for marginal congestion, accident, and
noise costs caused by automobile use amount to 5.2 cents, 2.3 cents,
and 0.1 cents per vehicle-mile (for a total of 7.6 cents per mile),
while those for pickup trucks and vans are 4.7 cents, 2.5 cents, and
0.1 cents per vehicle-mile (for a total of 7.3 cents per mile).\120\,
\121\ These costs are multiplied by the annual increases in automobile
and light truck use from the rebound effect to yield the estimated
increases in congestion, accident, and noise externality costs during
each future year.
---------------------------------------------------------------------------

    \119\ These estimates were developed by FHWA for use in its 1997
Federal Highway Cost Allocation Study; see http://www.fhwa.dot.gov/
policy/hcas/final/index.htm (last accessed April 20, 2008).
    \120\ See Federal Highway Administration, 1997 Federal Highway
Cost Allocation Study, http://www.fhwa.dot.gov/policy/hcas/final/
index.htm, Tables V-22, V-23, and V-24 (last accessed April 20,
2008).
    \121\ The Federal Highway Administration's estimates of these
costs agree closely with some other recent estimates. For example,
recent published research conducted by Resources for the Future
(RFF) estimates marginal congestion and external accident costs for
increased light-duty vehicle use in the U.S. to be 3.5 and 3.0 cents
per vehicle-mile in year-2002 dollars. See Ian W.H. Parry and
Kenneth A. Small, ``Does Britain or the U.S. Have the Right Gasoline
Tax?'' Discussion Paper 02-12, Resources for the Future, 19 and
Table 1 (March 2002). Available at http://www.rff.org/rff/Documents/
RFF-DP-02-12.pdf (last accessed April 20, 2008).
---------------------------------------------------------------------------

k. Petroleum Consumption and Import Externalities
    U.S. consumption and imports of petroleum products also impose
costs on the domestic economy that are not reflected in the market
price for crude petroleum, or in the prices paid by consumers of
petroleum products such as gasoline. In economics literature on this
subject, these costs include (1) higher prices for petroleum products
resulting from the effect of U.S. oil import demand on the world oil
price; (2) the risk of disruptions to the U.S. economy caused by sudden
reductions in the supply of imported oil to the U.S.; and (3) expenses
for maintaining a U.S. military presence to secure imported oil
supplies from unstable regions, and for maintaining the strategic
petroleum reserve (SPR) to cushion against resulting price
increases.\122\ Higher U.S. imports of crude oil or refined petroleum
products increase the magnitude of these external economic costs, thus
increasing the true economic cost of supplying transportation fuels
above the resource costs of producing them. Conversely, reducing U.S.
imports of crude petroleum or refined fuels or reducing fuel
consumption can reduce these external costs. Any reduction in their
total value that results from improved light truck fuel economy
represents an economic benefit of setting more stringent CAFE standards
in addition to the value of fuel savings and emissions reductions
itself.
---------------------------------------------------------------------------

    \122\ See, e.g., Bohi, Douglas R. and W. David Montgomery
(1982). Oil Prices, Energy Security, and Import Policy Washington,
DC: Resources for the Future, Johns Hopkins University Press; Bohi,
D. R., and M. A. Toman (1993). ``Energy and Security: Externalities
and Policies,'' Energy Policy 21:1093-1109; and Toman, M. A. (1993).
``The Economics of Energy Security: Theory, Evidence, Policy,'' in
A. V. Kneese and J. L. Sweeney, eds. (1993). Handbook of Natural
Resource and Energy Economics, Vol. III. Amsterdam: North-Holland,
pp. 1167-1218.
---------------------------------------------------------------------------

    Increased U.S. oil imports can impose higher costs on all
purchasers of petroleum products, because the U.S. is a sufficiently
large purchaser of foreign oil supplies that changes in U.S. demand can
affect the world price. The effect of U.S. petroleum imports on world
oil prices is determined by the degree of OPEC monopoly power over
global oil supplies, and the degree of monopsony power over world oil
demand exerted by the U.S. The combination of these two factors means
that increases in domestic demand for petroleum products that are met
through higher oil imports can cause the price of oil in the world
market to rise, which imposes economic costs on all other purchasers in
the global petroleum market in excess of the higher prices paid by U.S.
consumers.\123\ Conversely, reducing U.S. oil imports can lower the
world petroleum price, and thus generate benefits to other oil
purchasers by reducing these ``monopsony costs.''
---------------------------------------------------------------------------

    \123\ For example, if the U.S. imports 10 million barrels of
petroleum per day at a world oil price of $20 per barrel, its total
daily import bill is $200 million. If increasing imports to 11
million barrels per day causes the world oil price to rise to $21
per barrel, the daily U.S. import bill rises to $231 million. The
resulting increase of $31 million per day ($231 million minus $200
million) is attributable to increasing daily imports by only 1
million barrels. This means that the incremental cost of importing
each additional barrel is $31, or $10 more than the newly-increased
world price of $21 per barrel. This additional $10 per barrel
represents a cost imposed on all other purchasers in the global
petroleum market by U.S. buyers, in excess of the price they pay to
obtain those additional imports.
---------------------------------------------------------------------------

    Although the degree of current OPEC monopoly power is subject to
debate, the consensus appears to be that OPEC remains able to exercise
some degree of control over the response of world oil supplies to
variation in world oil prices, so that the world oil market does not
behave completely competitively.\124\ The extent of U.S. monopsony
power is determined by a complex set of factors including the relative
importance of U.S. imports in the world oil market, and the sensitivity
of petroleum supply and demand to its world price among other
participants in the international oil market. Most evidence appears to
suggest that variation in U.S. demand for imported petroleum continues
to exert some influence on world oil prices, although this influence
appears to be limited.\125\
---------------------------------------------------------------------------

    \124\ For a summary see Leiby, Paul N., Donald W. Jones, T.
Randall Curlee, and Russell Lee, Oil Imports: An Assessment of
Benefits and Costs, ORNL-6851, Oak Ridge National Laboratory,
November 1, 1997, 17. Available at http://pzl1.ed.ornl.gov/
ORNL6851.pdf (last accessed April 20, 2008).
    \125\ Id. 18-19.
---------------------------------------------------------------------------

    The second component of external economic costs imposed by U.S.
petroleum imports arises partly because an increase in oil prices
triggered by a disruption in the supply of imported oil reduces the
level of output that the U.S. economy can produce. The reduction in
potential U.S. economic output depends on the extent and duration of
the increases in petroleum product prices that result from a disruption
in the supply of imported oil, as well as on whether and how rapidly
these prices return to pre-disruption levels. Even if prices for
imported oil return completely to their original levels, however,
economic output will be at least temporarily reduced from the level
that would have been possible without a disruption in oil supplies.
    Because supply disruptions and resulting price increases tend to
occur

suddenly rather than gradually, they can also impose costs on
businesses and households for adjusting their use of petroleum products
more rapidly than if the same price increase had occurred gradually
over time. These adjustments impose costs because they temporarily
reduce economic output even below the level that would ultimately be
reached once the U.S. economy completely adapted to higher petroleum
prices. The additional costs to businesses and households reflect their
inability to adjust prices, output levels, and their use of energy and
other resources quickly and smoothly in response to rapid changes in
prices for petroleum products.
    Since future disruptions in foreign oil supplies are an uncertain
prospect, each of these disruption costs must be adjusted by the
probability that the supply of imported oil to the U.S. will actually
be disrupted. The ``expected value'' of these costs-- the product of
the probability that an oil import disruption will occur and the costs
of reduced economic output and abrupt adjustment to sharply higher
petroleum prices--is the appropriate measure of their magnitude. Any
reduction in these expected disruption costs resulting from a measure
that lowers U.S. oil imports represents an additional economic benefit
beyond the direct value of savings from reduced purchases of petroleum
products.
    While the vulnerability of the U.S. economy to oil price shocks is
widely thought to depend on total petroleum consumption rather than on
the level of oil imports, variation in imports is still likely to have
some effect on the magnitude of price increases resulting from a
disruption of import supply. In addition, changing the quantity of
petroleum imported into the U.S. may also affect the probability that
such a disruption will occur. If either the size of the likely price
increase or the probability that U.S. oil supplies will be disrupted is
affected by oil imports, the expected value of the costs from a supply
disruption will also depend on the level of imports.
    Businesses and households use a variety of market mechanisms,
including oil futures markets, energy conservation measures, and
technologies that permit rapid fuel switching to ``insure'' against
higher petroleum prices and reduce their costs for adjusting to sudden
price increases. While the availability of these market mechanisms has
likely reduced the potential costs of disruptions to the supply of
imported oil, consumers of petroleum products are unlikely to take
account of costs they impose on others, so these costs are probably not
reflected in the price of imported oil. Thus changes in oil import
levels probably continue to affect the expected cost to the U.S.
economy from potential oil supply disruptions, although this component
of oil import costs is likely to be significantly smaller than
estimated by studies conducted in the wake of the oil supply
disruptions during the 1970s.
    The third component of the external economic costs of importing oil
into the U.S. includes government outlays for maintaining a military
presence to secure the supply of oil imports from potentially unstable
regions of the world and to protect against their interruption. Some
analysts also include outlays for maintaining the U.S. Strategic
Petroleum Reserve (SPR), which is intended to cushion the U.S. economy
against the consequences of disruption in the supply of imported oil,
as additional costs of protecting the U.S. economy from oil supply
disruptions.
    NHTSA believes that while costs for U.S. military security may vary
over time in response to long-term changes in the actual level of oil
imports into the U.S., these costs are unlikely to decline in response
to any reduction in U.S. oil imports resulting from raising future CAFE
standards for passenger cars and light trucks. U.S. military activities
in regions that represent vital sources of oil imports also serve a
broader range of security and foreign policy objectives than simply
protecting oil supplies, and as a consequence are unlikely to vary
significantly in response to changes in the level of oil imports
prompted by higher standards.
    Similarly, while the optimal size of the SPR from the standpoint of
its potential influence on domestic oil prices during a supply
disruption may be related to the level of U.S. oil consumption and
imports, its actual size has not appeared to vary in response to recent
changes in oil imports. Thus while the budgetary costs for maintaining
the Reserve are similar to other external costs in that they are not
likely to be reflected in the market price for imported oil, these
costs do not appear to have varied in response to changes in oil import
levels.
    In analyzing benefits from its recent actions to increase light
truck CAFE standards for model years 2005-07 and 2008-11, NHTSA relied
on a 1997 study by Oak Ridge National Laboratory (ORNL) to estimate the
value of reduced economic externalities from petroleum consumption and
imports.\126\ More recently, ORNL updated its estimates of the value of
these externalities, using the analytic framework developed in its
original 1997 study in conjunction with recent estimates of the
variables and parameters that determine their value.\127\ These include
world oil prices, current and anticipated future levels of OPEC
petroleum production, U.S. oil import levels, the estimated
responsiveness of oil supplies and demands to prices in different
regions of the world, and the likelihood of oil supply disruptions.
ORNL prepared its updated estimates of oil import externalities for use
by EPA in evaluating the benefits of reductions in U.S. oil consumption
and imports expected to result from its Renewable Fuel Standard Rule of
2007 (RFS).\128\
---------------------------------------------------------------------------

    \126\ Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and
Russell Lee, Oil Imports: An Assessment of Benefits and Costs, ORNL-
6851, Oak Ridge National Laboratory, November 1, 1997. Available at
http://pzl1.ed.ornl.gov/ORNL6851.pdf (last accessed April 20, 2008).
    \127\ Leiby, Paul N. ``Estimating the Energy Security Benefits
of Reduced U.S. Oil Imports,'' Oak Ridge National Laboratory, ORNL/
TM-2007/028, Revised July 23, 2007. Available at http://
pzl1.ed.ornl.gov/energysecurity.html (click on link below ``Oil
Imports Costs and Benefits'') (last accessed April 20, 2008).
    \128\ 72 FR 23899 (May 1, 2007).
---------------------------------------------------------------------------

    The updated ORNL study was subjected to a detailed peer review by
experts selected by EPA, and its estimates of the value of oil import
externalities were subsequently revised to reflect their comments and
recommendations.\129\ Specifically, reviewers recommended that ORNL
increase its estimates of the sensitivity of oil supply by non-OPEC
producers and oil demand by nations other than the U.S. to changes in
the world oil price, as well as reduce its estimate of the sensitivity
of U.S. gross domestic product (GDP) to potential sudden increases in
world oil prices.
---------------------------------------------------------------------------

    \129\ Peer Review Report Summary: Estimating the Energy Security
Benefits of Reduced U.S. Oil Imports, ICF, Inc., September 2007.
---------------------------------------------------------------------------

    After making the revisions recommended by peer reviewers, ORNL's
updated estimates of the monopsony cost associated with U.S. oil
imports range from $5.22 to $9.68 per barrel, with a most likely
estimate of $7.41 per barrel. These estimates imply that each gallon of
fuel saved as a result of adopting higher CAFE standards will reduce
the monopsony costs of U.S. oil imports by $0.124 to $0.230 per gallon,
with the actual value most likely to be $0.176 per gallon saved. ORNL's
updated and revised estimates of the increase in the expected costs
associated with oil supply disruptions to the U.S. and the resulting
rapid increase in prices for petroleum products amount to $4.54 to
$5.84 per barrel, although its

most likely estimate of $4.59 per barrel is very close to the lower end
of this range. According to these estimates, each gallon of fuel saved
will reduce the expected costs disruptions to the U.S. economy by
$0.108 to $0.139, with the actual value most likely to be $0.109 per
gallon.
    The updated and revised ORNL estimates suggest that the combined
reduction in monopsony costs and expected costs to the U.S. economy
from oil supply disruptions resulting from lower fuel consumption total
$0.232 to $0.370 per gallon, with a most likely estimate of $0.286 per
gallon. This represents the additional economic benefit likely to
result from each gallon of fuel saved by higher CAFE standards, beyond
the savings in resource costs for producing and distributing each
gallon of fuel saved. NHTSA employs this midpoint estimate in its
analysis of the benefits from fuel savings projected to result from
alternative CAFE standards for model years 2011-15. It also analyzes
the effect on these benefits estimates from variation in this value
over the range from $0.232 to $0.370 per gallon of fuel saved.
    NHTSA's analysis of benefits from alternative CAFE standards does
not include cost savings from either reduced outlays for U.S. military
operations or maintaining a smaller SPR among the external benefits of
reducing gasoline consumption and petroleum imports by means of
tightening future standards. This view concurs with that of both the
original ORNL study of economic costs from U.S. oil imports and its
recent update, which conclude that savings in government outlays for
these purposes are unlikely to result from reductions in consumption of
petroleum products and oil imports on the scale of those likely to
result from the alternative increases in CAFE standards considered for
model years 2011-15.
l. Air Pollutant Emissions
(i) Impacts on Criteria Air Pollutant Emissions
    While reductions in domestic fuel refining and distribution that
result from lower fuel consumption will reduce U.S. emissions of
criteria pollutants, additional vehicle use associated with the rebound
effect from higher fuel economy will increase emissions of these
pollutants. Thus the net effect of stricter CAFE standards on emissions
of each criteria pollutant depends on the relative magnitudes of its
reduced emissions in fuel refining and distribution, and increases in
its emissions from vehicle use. Because the relationship between
emissions rates (emissions per gallon refined of fuel or mile driven)
in fuel refining and vehicle use is different for each criteria
pollutant, the net effect of fuel savings from the proposed standards
on total emissions of each pollutant is likely to differ. Criteria air
pollutants emitted by vehicles and during fuel production 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).
    The increase in emissions of these pollutants from additional
vehicle use due to the rebound effect is estimated by multiplying the
increase in total miles driven by vehicles of each model year and age
by age-specific emission rates per vehicle-mile for each pollutant.
NHTSA developed these emission rates using EPA's MOBILE6.2 motor
vehicle emissions factor model.\130\ Emissions of these pollutants also
occur during crude oil extraction and transportation, fuel refining,
and fuel storage and distribution. The reduction in total emissions
from each of these sources thus depends on the extent to which fuel
savings result in lower imports of refined fuel, or in reduced domestic
fuel refining. To a lesser extent, they also depend on whether any
reduction in domestic gasoline refining is translated into reduced
imports of crude oil or reduced domestic extraction of petroleum.
---------------------------------------------------------------------------

    \130\ U.S. Environmental Protection Agency, MOBILE6 Vehicle
Emission Modeling Software, available at http://www.epa.gov/otaq/
m6.htm#m60 (last accessed April 20, 2008).
---------------------------------------------------------------------------

    Based on analysis of changes in U.S. gasoline imports and domestic
gasoline consumption forecast in AEO's 2008 Early Release, NHTSA
tentatively estimates that 50 percent of fuel savings resulting from
higher CAFE standards will result in reduced imports of refined
gasoline, while the remaining 50 percent will reduce domestic fuel
refining.\131\ The reduction in domestic refining is assumed to leave
its sources of crude petroleum unchanged from the mix of 90 percent
imports and 10 percent domestic production projected by AEO.
---------------------------------------------------------------------------

    \131\ Estimates of the response of gasoline imports and domestic
refining to fuel savings from stricter standards are variable and
highly uncertain, but our preliminary analysis indicates that under
any reasonable assumption about these responses, the magnitude of
the net change in criteria pollutant emissions (accounting for both
the rebound effect and changes in refining emissions) is extremely
low relative to their current total.
---------------------------------------------------------------------------

    NHTSA proposes to estimate reductions in criteria pollutant
emissions from gasoline refining and distribution using emission rates
obtained from Argonne National Laboratories' Greenhouse Gases and
Regulated Emissions in Transportation (GREET) model.\132\ The GREET
model provides separate estimates of air pollutant emissions that occur
in four phases of fuel production and distribution: crude oil
extraction, crude oil transportation and storage, fuel refining, and
fuel distribution and storage.\133\ We tentatively assume that
reductions in imports of refined fuel would reduce criteria pollutant
emissions during fuel storage and distribution only. Reductions in
domestic fuel refining using imported crude oil as a feedstock are
tentatively assumed to reduce emissions during crude oil transportation
and storage, as well as during gasoline refining, distribution, and
storage, because less of each of these activities would be occurring.
Similarly, reduced domestic fuel refining using domestically-produced
crude oil is tentatively assumed to reduce emissions during all phases
of gasoline production and distribution.\134\
---------------------------------------------------------------------------

    \132\ Argonne National Laboratories, The Greenhouse Gas and
Regulated Emissions from Transportation (GREET) Model, Version 1.8,
June 2007, available at http://www.transportation.anl.gov/software/
GREET/index.html (last accessed April 20, 2008).
    \133\ Emissions that occur during vehicle refueling at retail
gasoline stations (primarily evaporative emissions of volatile
organic compounds, or VOCs) are already accounted for in the
``tailpipe'' emission factors used to estimate the emissions
generated by increased light truck use. GREET estimates emissions in
each phase of gasoline production and distribution in mass per unit
of gasoline energy content; these factors are then converted to mass
per gallon of gasoline using the average energy content of gasoline.
    \134\ In effect, this assumes that the distances crude oil
travels to U.S. refineries are approximately the same regardless of
whether it travels from domestic oilfields or import terminals, and
that the distances that gasoline travels from refineries to retail
stations are approximately the same as those from import terminals
to gasoline stations.
---------------------------------------------------------------------------

    The net changes in emissions of each criteria pollutant are
calculated by adding the increases in their emissions that result from
increased vehicle use and the reductions that result from lower
domestic fuel refining and distribution. The net change in emissions of
each criteria pollutant is converted to an economic value using
estimates of the economic costs per ton emitted (which result primarily
from damages to human health) developed by EPA and submitted to the
federal Office of Management and Budget for review. For certain
criteria pollutants, EPA estimates different per-ton costs for
emissions from vehicle use than for emissions of the same pollutant
during fuel production, reflecting differences in their typical
geographic distributions,

contributions to ambient pollution levels, and resulting population
exposure.
(ii) Reductions in CO2 Emissions
    Fuel savings from stricter CAFE standards also result in lower
emissions of carbon dioxide (CO2), the main greenhouse gas emitted as a
result of refining, distribution, and use of transportation fuels.\135\
Lower fuel consumption reduces carbon dioxide emissions directly,
because the primary source of transportation-related CO2
emissions is fuel combustion in internal combustion engines. NHTSA
tentatively estimates reductions in carbon dioxide emissions resulting
from fuel savings by assuming that the entire carbon content of
gasoline, diesel, and other fuels is converted to carbon dioxide during
the combustion process.\136\
---------------------------------------------------------------------------

    \135\ For purposes of this rulemaking, NHTSA estimated emissions
of vehicular CO2 emissions, but did not estimate vehicular emissions
of methane, nitrous oxide, and hydroflourocarbons. Methane and
nitrous oxide account for less than 3 percent of the tailpipe GHG
emissions from passenger cars and light trucks, and CO2
emissions accounted for the remaining 97 percent. Of the total
(including non-tailpipe) GHG emissions from passenger cars and light
trucks, tailpipe CO2 represents about 93.1 percent,
tailpipe methane and nitrous oxide represent about 2.4 percent, and
hydroflourocarbons (i.e., air conditioner leaks) represent about 4.5
percent. Calculated from U.S CO2. EPA, Inventory of U.S>
Greenhouse Gas Emissions and Sinks 1990-2006, EPA430-R-08-05, April
15, 2008. Available at http://www.epa.gov/climatechange/emissions/
downloads/08_CR.pdf, Table 215. (Last accessed April 20, 2008.)
    \136\ This assumption results in a slight overestimate of carbon
dioxide emissions, since a small fraction of the carbon content of
gasoline is emitted in the forms of carbon monoxide and unburned
hydrocarbons. However, the magnitude of this overestimate is likely
to be extremely small. This approach is consistent with the
recommendation of the Intergovernmental Panel on Climate Change for
``Tier 1'' national greenhouse gas emissions inventories. Cf.
Intergovernmental Panel on Climate Change, 2006 Guidelines for
National Greenhouse Gas Inventories, Volume 2, Energy, p. 3.16.
---------------------------------------------------------------------------

    Reduced fuel consumption also reduces carbon dioxide emissions that
result from the use of carbon-based energy sources during fuel
production and distribution.\137\ NHTSA currently estimates the
reductions in CO2 emissions during each phase of fuel
production and distribution using CO2 emission rates
obtained from the GREET model, using the previous assumptions about how
fuel savings are reflected in reductions in each phase. The total
reduction in CO2 emissions from the improvement in fuel
economy under each alternative CAFE standard is the sum of the
reductions in emissions from reduced fuel use and from lower fuel
production and distribution.
---------------------------------------------------------------------------

    \137\ NHTSA did not, for purposes of this proposed rulemaking,
attempt to estimate changes in ``upstream'' emissions of greenhouse
gases (GHGs) other than CO2. This was because carbon
dioxide from final combustion itself accounts for nearly 97 percent
of the total CO2-equivalent emissions from petroleum
production and use, even with other GHGs that result from those
activities (principally methane and nitrous oxide) weighted by their
higher global warming potentials (GWPs) relative to CO2.
Calculated from U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions
and Sinks 1990-2006, EPA430-R-08-05, April 15, 2008. Available at
http://epa.gov/climatechange/emissions/downloads/08_CR.pdf, Tables
3-3, 3-39, and 3-41. (Last accessed April 20, 2008.)
---------------------------------------------------------------------------

    NHTSA has not attempted to estimate changes in emissions of other
greenhouse gases, in particular methane, nitrous oxide, and
hydrofluorocarbons. The agency invites comment on the importance and
potential implications of doing so under NEPA.
(iii) Economic value of reductions in CO2 emissions
    NHTSA has taken the economic benefits of reducing CO2
emission into account in this rulemaking, both in developing proposed
CAFE standards and in assessing the economic benefits of each
alternative that was considered. As noted above, the Ninth Circuit
found in CBD that NHTSA had been arbitrary and capricious in deciding
not to monetize the benefit of reducing CO2 emissions,
saying that the agency had not substantiated the conclusion in its
April 2006 final rule that the appropriate course was not to monetize
(i.e., quantify the value of) carbon emissions reduction at all.
    To this end, NHTSA reviewed published estimates of the ``social
cost of carbon emissions'' (SCC). The SCC refers to the marginal cost
of additional damages caused by the increase in expected climate
impacts resulting from the emission of each additional metric ton of
carbon, which is emitted in the form of CO2.\138\ It is
typically estimated as the net present value of the impact over some
time period (100 years or longer) of one additional ton of carbon
emitted into the atmosphere. Because accumulated concentrations of
greenhouse gases in the atmosphere and the projected impacts on global
climate are increasing over time, the economic damages resulting from
each additional ton of CO2 emissions in future years are
believed to be greater as a result. Thus estimates of the SCC are
typically reported for a specific year, and these estimates are
generally larger for emissions in more distant future years.
---------------------------------------------------------------------------

    \138\ Carbon itself accounts for 12/44, or about 27%, of the
mass of carbon dioxide (12/44 is the ratio of the molecular weight
of carbon to that of carbon dioxide). Thus each ton of carbon
emitted is associated with 44/12, or 3.67, tons of carbon dioxide
emissions. Estimates of the SCC are typically reported in dollars
per ton of carbon, and must be divided by 3.67 to determine their
equivalent value per ton of carbon dioxide emissions.
---------------------------------------------------------------------------

    There is substantial variation among different authors' estimates
of the SCC, much of which can be traced to differences in their
underlying assumptions about several variables. These include the
sensitivity of global temperatures and other climate attributes to
increasing atmospheric concentrations of greenhouse gases, discount
rates applied to future economic damages from climate change, whether
damages sustained by developing regions of the globe should be weighted
more heavily than damages to developed nations, how long climate
changes persist once they occur, and the economic valuation of specific
climate impacts.\139\
---------------------------------------------------------------------------

    \139\ For a discussion of these factors, see Yohe, G.W., R.D.
Lasco, Q.K. Ahmad, N.W. Arnell, S.J. Cohen, C. Hope, A.C. Janetos
and R.T. Perez, 2007: Perspectives on climate change and
sustainability. Climate Change 2007: Impacts, Adaptation and
Vulnerability. Contribution of Working Group II to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change,
M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and
C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, pp.
821-824.
---------------------------------------------------------------------------

    Taken as a whole, recent estimates of the SCC may underestimate the
true damage costs of carbon emissions because they often exclude
damages caused by extreme weather events or climate response scenarios
with low probabilities but potentially extreme impacts, and may
underestimate the climate impacts and damages that could result from
multiple stresses on the global climatic system. At the same time,
however, many studies fail to consider potentially beneficial impacts
of climate change, and do not adequately account for how future
development patterns and adaptations could reduce potential impacts
from climate change or the economic damages they cause.
    Given the uncertainty surrounding estimates of the SCC, the use of
any single study may not be advisable since its estimate of the SCC
will depend on many assumptions made by its authors. The Working Group
II's contribution to the Fourth Assessment Report of the United Nations
Intergovernmental Panel on Climate Change (IPCC)\140\ notes that:
---------------------------------------------------------------------------

    \140\ Climate Change 2007--Impacts, Adaptation and
Vulnerability, Contribution of Working Group II to the Fourth
Assessment Report of the IPCC, 17. Available at http://www.ipcc-
wg2.org (last accessed ).

    The large ranges of SCC are due in the large part to differences
in assumptions regarding climate sensitivity, response lags, the
treatment of risk and equity, economic and non-economic impacts, the
---------------------------------------------------------------------------
inclusion of potentially catastrophic losses, and discount rates.

    Although the IPCC does not recommend a single estimate of the SCC,
it does cite the Tol (2005) study on four separate occasions (pages 17,
65, 813, 822) as the only available survey of the peer-reviewed
literature that has itself been subjected to peer review. Tol developed
a probability function using the SCC estimates of the peer reviewed
literature and found estimates ranging from less than zero to over $200
per metric ton of carbon. In an effort to resolve some of the
uncertainty in reported estimates of climate damage costs from carbon
emissions, Tol (2005) reviewed and summarized one hundred and three
estimates of the SCC from 28 published studies. He concluded that when
only peer-reviewed studies published in recognized journals are
considered, ``* * * climate change impacts may be very uncertain but is
unlikely that the marginal damage costs of carbon dioxide emissions
exceed $50 per [metric] ton carbon [about $14 per metric ton of
CO2].'' \141\ He also concluded that the costs may be less
than $14.
---------------------------------------------------------------------------

    \141\ Tol, Richard. The marginal damage costs of carbon dioxide
emissions: an assessment of the uncertainties. Energy Policy 33
(2005) 2064-2074, 2072. The summary SCC estimates reported by Tol
are assumed to be denominated in U.S. dollars of the year of
publication, 2005.
---------------------------------------------------------------------------

    Because of the number of assumptions required by each study, the
wide range of uncertainty surrounding these assumptions, and their
critical influence on the resulting estimates of climate damage costs,
some studies have undoubtedly produced estimates of the SCC that are
unrealistically high, while others are likely to have estimated values
that are improbably low. Using a value for the SCC that reflects the
central tendency of estimates drawn from many studies reduces the
chances of relying on a single estimate that subsequently proves to be
biased.
    It is important to note that estimates of the SCC almost invariably
include the value of worldwide damages from potential climate impacts
caused by carbon dioxide emissions, and are not confined to damages
likely to be suffered within the U.S. In contrast, the other estimates
of costs and benefits of increasing fuel economy included in this
proposal include only the economic values of impacts that occur within
the U.S. For example, the economic value of reducing criteria air
pollutant emissions from overseas oil refineries is not counted as a
benefit resulting from this rule, because any reduction in damages to
health and property caused by overseas emissions are unlikely to be
experienced within the U.S.
    In contrast, the reduced value of transfer payments from U.S. oil
purchasers to foreign oil suppliers that results when lower U.S. oil
demand reduces the world price of petroleum (the reduced ``monopsony
effect'') is counted as a benefit of reducing fuel use.\142\ If the
agency's analysis was conducted from a worldwide rather than a U.S.
perspective, however, the benefit from reducing air pollution overseas
would be included, while reduced payments from U.S. oil consumers to
foreign suppliers would not.
---------------------------------------------------------------------------

    \142\ The reduction in payments from U.S. oil purchasers to
domestic petroleum producers is not included as a benefit, since it
represents a transfer that occurs entirely within the U.S. economy.
---------------------------------------------------------------------------

    In order to be consistent with NHTSA's use of exclusively domestic
costs and benefits in prior CAFE rulemakings, the appropriate value to
be placed on changes climate damages caused by carbon emissions should
be one that reflects the change in damages to the United States alone.
Accordingly, NHTSA notes that the value for the benefits of reducing
CO2 emissions might be restricted to the fraction of those
benefits that are likely to be experienced within the United States.
    Although no estimates of benefits to the U.S. itself that are
likely to result from reducing CO2 emissions are currently
available, NHTSA expects that if such values were developed, the agency
would employ those rather than global benefit estimates in its
analysis. NHTSA also anticipates that if such values were developed,
they would be lower than comparable global values, since the U.S. is
likely to sustain only a fraction of total global damages resulting
from climate change.
    In the meantime, the agency has elected to use the IPCC estimate of
$43 per metric ton of carbon as an upper bound on the benefits
resulting from reducing each metric ton of U.S. emissions.\143\ This
corresponds to approximately $12 per metric ton of CO2 when
expressed in 2006 dollars. This estimate is based on the 2005 Tol
study.\144\ The Tol study is cited repeatedly as an authoritative
survey in various IPCC reports, which are widely accepted as
representing the general consensus in the scientific community on
climate change science. Since the IPCC estimate includes the worldwide
costs of potential damages from carbon dioxide emissions, NHTSA has
elected to employ it as an upper bound on the estimated value of the
reduction in U.S. domestic damage costs that is likely to result from
lower CO2 emissions.\145\
---------------------------------------------------------------------------

    \143\ The estimate of $43 per ton of carbon emissions is
reported by Tol (p. 2070) as the mean of the ``best'' estimates
reported in peer-reviewed studies (see fn. 144). It thus differs
from the mean of all estimates reported in the peer-reviewed studies
surveyed by Tol. The $43 per ton value is also attributed to Tol by
IPCC Working Group II (2007), p. 822.
    \144\ Tol's more recent (2007) and inclusive survey has been
published online with peer-review comments. The agency has elected
not to rely on the estimates it reports, but will consider doing so
in its analysis of the final rule if the survey has been published,
and will also consider any other newly-published evidence.
    \145\ For purposes of comparison, we note that in the rulemaking
to establish CAFE standards for MY 2008-11 light trucks, NRDC
recommended a value of $10 to $25 per ton of CO2
emissions reduced by fuel savings and both Environmental Defense and
Union of Concerned Scientists recommended a value of $50 per ton of
carbon (equivalent to about $14 per ton of CO2
emissions).
---------------------------------------------------------------------------

    The IPCC Working Group II Fourth Assessment Report (2007, p. 822)
further suggests that the SCC of carbon is growing at an annual 2.4
percent growth rate, based on estimated increases in damages from
future emissions reported in published studies. NHTSA has also elected
to apply this growth rate to Tol's original 2005 estimate. Thus by
2011, the agency estimates that the upper bound on the benefits of
reducing CO2 emissions will have reached about $14 per
metric ton of CO2, and will continue to increase by 2.4
percent annually thereafter.
    In setting a lower bound, the agency agrees with the IPCC Working
Group II (2007) report that ``significant warming across the globe and
the locations of significant observed changes in many systems
consistent with warming is very unlikely to be due solely to natural
variability of temperatures or natural variability of the systems''
(pp. 9). Although this finding suggests that the global value of
economic benefits from reducing carbon dioxide emissions is unlikely to
be zero, it does not necessarily rule out low or zero values for the
benefit to the U.S. itself from reducing emissions.
    For most of the analysis it performed to develop this proposal,
NHTSA required a single estimate for the value of reducing
CO2 emissions. The agency thus elected to use the midpoint
of the range from $0 to $14 (or $7.00) per metric ton of CO2
as the initial value for the year 2011, and assumed that this value
would grow at 2.4 percent annually thereafter. This estimate is
employed for the analyses conducted using the Volpe CAFE model to
support development of the proposed standards. The agency also
conducted sensitivity analyses of the benefits from reducing
CO2 emissions using both the upper ($14 per metric ton) and
lower ($0 per metric ton) bounds of this range.
    NHTSA seeks comment on its tentative conclusions for the value of

the SCC, the use of a domestic versus global value for the economic
benefit of reducing CO2 emissions, the rate at which the
value of the SCC grows over time, the desirability of and procedures
for incorporating benefits from reducing emissions of greenhouse gases
other than CO2, and any other aspects of developing a
reliable SCC value for purposes of establishing CAFE standards.
m. The Value of Increased Driving Range
    Improving vehicles' fuel economy may also increase their driving
range before they require refueling. By reducing the frequency with
which drivers typically refuel their vehicles, and by extending the
upper limit of the range they can travel before requiring refueling,
improving fuel economy thus provides some additional benefits to their
owners. (Alternatively, if manufacturers respond to improved fuel
economy by reducing the size of fuel tanks to maintain a constant
driving range, the resulting cost saving will presumably be reflected
in lower vehicle sales prices.)
    No direct estimates of the value of extended vehicle range are
readily available, so NHTSA's analysis calculates the reduction in the
annual number of required refueling cycles that results from improved
fuel economy, and applies DOT-recommended values of travel time savings
to convert the resulting time savings to their economic value.\146\ As
an illustration of how the value of extended refueling range is
estimated, a typical small light truck model has an average fuel tank
size of approximately 20 gallons. Assuming that drivers typically
refuel when their tanks are 20 percent full (i.e., 4 gallons in
reserve), increasing this model's actual on-road fuel economy from 24
to 25 mpg would extend its driving range from 384 miles (= 16 gallons x
24 mpg) to 400 miles (= 16 gallons x 25 mpg). Assuming that it is
driven 12,000 miles/year, this reduces the number of times it needs to
be refueled each year from 31.3 (= 12,000 miles per year/384 miles per
refueling) to 30.0 (= 12,000 miles per year/400 miles per refueling),
or by 1.3 refuelings per year.
---------------------------------------------------------------------------

    \146\ See Department of Transportation, Guidance Memorandum,
``The Value of Saving Travel Time: Departmental Guidance for
Conducting Economic Evaluations,'' Apr. 9, 1997. Available at http:/
/ostpxweb.dot.gov/policy/Data/VOT97guid.pdf (last accessed October
20, 2007); update available at http://ostpxweb.dot.gov/policy/Data/
VOTrevision1--2-11-03.pdf (last accessed October 20, 2007).
---------------------------------------------------------------------------

    Weighted by the nationwide mix of urban (about 2/3) and rural
(about 1/3) driving and average vehicle occupancy for all driving trips
(1.6 persons), the DOT-recommended value of travel time per vehicle-
hour is $24.00 (in 2006 dollars).\147\ Assuming that locating a station
and filling up requires ten minutes, the annual value of time saved as
a result of less frequent refueling amounts to $5.20 (calculated as 10/
60 x 1.3 x $24.00). This calculation is repeated for each future
calendar year that vehicles of each model year affected by the
alternative CAFE standards proposed in this rule would remain in
service. Like fuel savings and other benefits, however, the value of
this benefit declines over a model year's lifetime, because a smaller
number of vehicles originally produced during that model year remain in
service each year, and those remaining in service are driven fewer
miles.
n. Discounting Future Benefits and Costs
---------------------------------------------------------------------------

    \147\ The hourly wage rate during 2006 is estimated to be
$24.00. Personal travel (94.4 percent of urban travel) is valued at
50 percent of the hourly wage rate. Business travel (5.6 percent or
urban travel) is valued at 100 percent of the hourly wage rate. For
intercity travel, personal travel (87 percent) is valued at 70
percent of the wage rate, while business travel (13 percent) is
valued at 100 percent of the wage rate. The resulting values of
travel time are $12.67 for urban travel and $17.66 for intercity
travel, and must be multiplied by vehicle occupancy (1.6) to obtain
the estimate value of time per vehicle hour.
---------------------------------------------------------------------------

    Discounting future fuel savings and other benefits is intended to
account for the reduction in their value to society when they are
deferred until some future date rather than received immediately. The
discount rate expresses the percent decline in the value of these
benefits--as viewed from today's perspective--for each year they are
deferred into the future. NHTSA uses a rate of 7 percent per year to
discount the value of future fuel savings and other benefits to analyze
the potential impacts of alternative CAFE standards. However, the
agency also performed an alternative analysis of benefits from
alternative increases in CAFE standards using a 3 percent discount
rate, and seeks comment on whether the standards should be set using a
3 percent rate instead of a 7 percent rate.
    There are several reasons that NHTSA relies primarily on 7 percent
as the appropriate rate for discounting future benefits from increased
CAFE standards. First, OMB Circular A-4 indicates that this rate
reflects the economy-wide opportunity cost of capital.\148\ It also
states that this ``is the appropriate discount rate whenever the main
effect of a regulation is to displace or alter the use of capital in
the private sector.''\149\ We believe that a substantial portion of the
cost of this regulation may come at the expense of other investments
the auto manufacturers might otherwise make. Several large
manufacturers are resource-constrained with respect to their
engineering and product-development capabilities. As a result, other
uses of these resources will be foregone while they are required to be
applied to technologies that improve fuel economy.
---------------------------------------------------------------------------

    \148\ Office of Management and Budget, Circular A-4,
``Regulatory Analysis,'' September 17, 2003, 33. Available at http:/
/www.whitehouse.gov/omb/circulars/a004/a-4.pdf (last accessed Feb.
14, 2008).
    \149\ Id.
---------------------------------------------------------------------------

    Second, 7 percent also appears to be an appropriate rate to the
extent that the costs of the regulation come at the expense of
consumption as opposed to investment. NHTSA believes that financing
rates on vehicle loans represent an appropriate discount rate, because
they reflect the opportunity costs faced by consumers when buying
vehicles with greater fuel economy and a higher purchase price. Most
new and used vehicle purchases are financed, and because most of the
benefits from higher fuel economy standards accrue to vehicle
purchasers in the form of fuel savings, the appropriate discount rate
is the interest rate buyers pay on loans to finance their vehicle
purchases.\150\
---------------------------------------------------------------------------

    \150\ Some empirical evidence also demonstrates that used car
purchasers are willing to pay higher prices for greater fuel
economy; see, e.g., James A. Kahn, ``Gasoline Price Expectations and
the Used Automobile Market: A Rational Expectations Asset Price
Approach,'' Quarterly Journal of Economics, Vol. 101 (May 1986),
323-339.
---------------------------------------------------------------------------

    According to the Federal Reserve, the interest rate on new car
loans made through commercial banks has closely tracked the rate on 10-
year treasury notes, but exceeded it by about 3 percent.\151\ The
official Administration forecast is that real (or inflation-adjusted)
interest rates on 10-year treasury notes will average about 3 percent
through 2016, implying that 6 percent is a reasonable forecast for the
real interest rate on new car loans.\152\ In turn, the interest rate on
used car loans

made through automobile financing companies has closely tracked the
rate on new car loans made through commercial banks, but exceeded it by
about 3 percent.\153\ (We consider rates on loans that finance used car
purchases, because some of the fuel savings resulting from improved
fuel economy accrue to used car buyers.) Given the 6 percent estimate
for new car loans, a reasonable forecast for used car loans is thus 9
percent.
---------------------------------------------------------------------------

    \151\ See Federal Reserve Bank, Statistical Release H.15,
Selected Interest Rates (Weekly) (click on ``Historical Data,'' then
``Treasury constant maturities,'' then ``10-year, monthly''),
available at http://www.federalreserve.gov/Releases/H15/data/
Monthly/H15_TCMNOM_Y10.txt (last accessed February 13, 2008); and
Federal Reserve Bank, Statistical Release G.19, Consumer Credit,
(click on ``Historical Data,'' then ``Terms of Credit'') available
at http://www.federalreserve.gov/releases/g19/hist/cc_hist_tc.html
(last accessed February 13, 2008).
    \152\ See The White House, Joint Press Release of the Council of
Economic Advisors, the Department of the Treasury, and the Office of
Management and Budget, November 29, 2007, available at http://
www.whitehouse.gov/news/releases/2007/11/20071129-4.html (last
accessed February 13, 2008).
    \153\ See supra [2 above here] and Federal Reserve Bank,
Statistical Release G.20, Finance Companies, (click on ``Historical
Data,'' then ``Terms of Credit'') available at http://
www.federalreserve.gov/releases/g20/hist/fc_hist_tc.html (last
accessed February 13, 2008).
---------------------------------------------------------------------------

    Because the benefits of fuel economy accrue to both new and used
car owners, a discount rate between 6 percent and 9 percent is thus
appropriate for evaluating future benefits resulting from more
stringent fuel economy standards. Assuming that new car buyers discount
fuel savings at 6 percent for 5 years (the average duration of a new
car loan) \154\ and that used car buyers discount fuel savings at 9
percent for 5 years (the average duration of a used car loan), \155\
the single constant discount rate that yields equivalent present value
fuel savings is very close to 7 percent.
---------------------------------------------------------------------------

    \154\ Id.
    \155\ Id.
---------------------------------------------------------------------------

    However, NHTSA also seeks comment on whether a discount rate of 3
percent would be more appropriate for this proposed rulemaking. OMB
Circular A-4 also states that when regulation primarily and directly
affects private consumption (e.g., through higher consumer prices for
goods and services), instead of primarily affecting the allocation of
capital, a lower discount rate may be appropriate. The alternative
discount rate that is most appropriate in this case is the social rate
of time preference, which refers to the rate at which society discounts
future consumption to determine its value at the present time. The rate
that savers are willing to accept to defer consumption into the future
when there is no risk that borrowers will fail to pay them back offers
one possible measure of the social rate of time preference. As noted
above, the real rate of return on long-term government debt, which has
averaged around 3 percent over the last 30 years, provides a reasonable
estimate of this value.
    In the context of CAFE standards for motor vehicles, the
appropriate discount rate depends on one's view of how the costs and
benefits of more stringent standards are distributed between vehicle
manufacturers and consumers. Given that the discount rate plays a
significant role in determining the level of the standards under a
``social optimization'' context, NHTSA conducted an analysis of what
the standards and associated costs and benefits would be if the future
benefits were discounted at 3 percent. The results of this analysis can
be found in the PRIA. We estimated that following the same methods and
criteria discussed below, but applying a 3 percent discount rate rather
than a 7 percent discount rate, would suggest standards reaching about
33.6 mpg (average required fuel economy among both passenger cars and
light trucks) in MY2015, 2 mpg higher than the 31.6 mpg average
resulting from the standards we are proposing based on a 7 percent
discount rate. The more stringent standards during MY2011-MY2015 would
reduce CO2 emissions by 672 million metric tons (mmt), or 29 percent
more than the 521 mmt achieved by the proposed standards. On the other
hand, we estimated that standards increasing at this pace would require
about $85b in technology outlays during MY2011-MY2015, or 89 percent
more than the $45b in technology outlays associated with the standards
proposed today.
    Thus, although our proposed standards are based on a 7 percent
discount rate, NHTSA seeks comment on whether it should set standards
based on discount rate assumptions of 3 percent, instead of 7 percent.
o. Accounting for Uncertainty in Benefits and Costs
    In analyzing the uncertainty surrounding its estimates of benefits
and costs from alternative CAFE standards, NHTSA has considered
alternative estimates of those assumptions and parameters likely to
have the largest effect. These include the projected costs of fuel
economy-improving technologies and their expected effectiveness in
reducing vehicle fuel consumption, forecasts of future fuel prices, the
magnitude of the rebound effect, the reduction in external economic
costs resulting from lower U.S. oil imports, the value to the U.S.
economy of reducing carbon dioxide emissions, and the discount rate
applied to future benefits and costs. The range for each of these
variables employed in the uncertainty analysis is presented in the
section of this document discussing each variable.
    The uncertainty analysis was conducted by assuming independent
normal probability distributions for each of these variables, using the
low and high estimates for each variable as the values below which 5
percent and 95 percent of observed values are believed to fall. Each
trial of the uncertainty analysis employed a set of values randomly
drawn from each of these probability distributions, assuming that the
value of each variable is independent of the others. Benefits and costs
of each alternative standard were estimated using each combination of
variables. A total of 1,000 trials were used to establish the likely
probability distributions of estimated benefits and costs for each
alternative standard.

B. How Has NHTSA Used the Volpe Model To Select the Proposed Standards?

1. Establishing a Continuous Function Standard
    NHTSA's analysis supporting determination of the proposed
continuous function standard builds on the analysis that supported the
determination of the standards in NHTSA's 2006 light truck final rule.
That process involved three steps.\156\
---------------------------------------------------------------------------

    \156\ See 71 FR 17596-97 (Apr. 6, 2006) for a more complete
discussion of this process.
---------------------------------------------------------------------------

    In ``phase one,'' NHTSA added fuel saving technologies to each
manufacturer's fleet, model by model, for a model year until the net
benefit from doing so reached its maximum value (i.e., until the
incremental cost of improving its fuel economy further just equals the
incremental value of fuel savings and other benefits from doing so).
This was done for each of the seven largest manufacturers. Data points
representing each vehicle's size and ``optimized'' fuel economy from
the light truck fleets of those manufacturers were then combined into a
single data set.
    In ``phase two,'' a preliminary continuous function was
statistically fitted through these data points, subject to constraints
at the upper and lower ends of the footprint range.
    Once a preliminary continuous function was statistically fitted to
the data for a model year, ``phase three'' was performed. In that
phase, the level of the function was adjusted to maximize net benefits,
that is, the preliminary continuous function was raised or lowered
until industry-wide (limited to the seven largest manufacturers)
benefits were maximized.
    For NHTSA's 2006 light truck rulemaking, the optimization procedure
was applied in its entirety only for MY 2011. The levels of the
functions for MYs 2008-2010 were set at levels producing incremental
costs approximately equivalent to those produced by the alternative
Unreformed

CAFE standards promulgated for those model years in the same
rulemaking.
    Analysis conducted by NHTSA to prepare for the current proposed
rulemaking revealed several opportunities to refine the procedure
described above before applying it to this action, which spans several
model years. The resultant procedure is described below.
2. Calibration of Initial Continuous Function Standards
    For the optimized standards, the first step in the current
procedure involves all three phases described above. Separately, for
each of the seven largest manufacturers, the agency determined the
level of additional technology that would maximize net benefits. The
agency then combined the resultant fleets and used standard statistical
analysis procedures to specify a continuous function (i.e., a function
without abrupt changes) with asymptotes \157\ set at the average fuel
economy levels of the smallest and largest vehicles in this
``optimized'' fleet.\158\
---------------------------------------------------------------------------

    \157\ Some functions are not bounded. For example, a line that
is not flat will increase in one direction without limit and will,
in the other direction, decrease without limit. The continuous
function applied by the agency is of a form with upper and lower
boundaries. Even as vehicle footprint declines or increases, the
function's value (in mpg or grams/mile) will never exceed or fall
below a specific value. These upper and lower limits are called
asymptotes.
    \158\ Consistent with EPCA, the passenger car and light truck
fleets were analyzed separately. For passenger cars, the agency
determined the asymptotes of the continuous function by calculating
the average fuel economy of the smallest 8 percent and the largest 5
percent of the fleet. For light trucks, the agency considered the
smallest 11 percent and the largest 10 percent of the fleet. These
cohorts were determined by identifying gaps in the distribution of
vehicles according to footprint.
---------------------------------------------------------------------------

    In the 2006 light truck final rule, NHTSA created an attribute-
based fuel economy standard based upon a continuous function using a
logistic curve. The 2006 rulemaking, and its antecedent advanced notice
of proposed rulemaking, contain an extended discussion of alternative
approaches, including a bin-based system and different potential
curves. As discussed below, that final rule explains NHTSA's decision
to promulgate a standard based on a logistic (``S shaped'') curve with
constrained asymptotes (upper and lower limits).
    Although we did not explicitly discuss it in the MY 2008-2011 light
truck rulemaking, NHTSA now wishes to explain that any continuous
function with lower asymptotes, as was promulgated in the last
rulemaking and is proposed in this rulemaking, provides an absolute
lower fuel economy level which guards against manufacturers having an
unlimited economic incentive to upsize their vehicles in order to lower
their fuel economy requirement. As vehicle footprint continues to
increase, decreases in the corresponding fuel economy target become
progressively smaller, such that the target approaches but never
reaches the value of the lower asymptote. Because the required level of
CAFE is the harmonic average of targets applicable to a manufacturer's
vehicle models, the value of the standard can approach but will never
fall to the value of this lower asymptote, no matter how far the
manufacturer's product mix shifts toward larger vehicles. This will
limit any loss of fuel savings due to manufacturer decisions to upsize
their vehicles.
    In a perfect world, NHTSA would develop the continuous functions
for setting passenger car and light truck standards by letting the
vehicle attribute (footprint) completely control the shape of the
curves used for the functions in a way that provides the clearest
observed relationship between this attribute and its fuel economy. But,
NHTSA must balance many real world practical and public policy aspects
in order to ensure that the standards are achieving the purpose set
forth by EPCA and EISA. In developing the Agency's last light truck
rule, the curve used to fit the data (attribute versus fuel economy)
was a sales-weighted least-squares logistic curve. During this
rulemaking, as NHTSA continued to look for ways to improve its standard
setting methodology, consideration was given to other methods that
could be used to develop the continuous functions. One such method that
NHTSA explored and is using in this proposal is unweighted analysis of
the data using the Mean Absolute Deviation (MAD) statistical procedure.
Unweighted regression involves counting each vehicle model once, rather
than as many times as vehicles included in that model are to be
produced. MAD involves weighting deviations from predicted values based
on their absolute rather than squared magnitude. As discussed below,
NHTSA has tentatively concluded that, compared to sales-weighted least-
squares analysis, unweighted MAD is better suited to data with wide
disparities in weight (i.e., sales volumes) and with many outliers.
    In establishing footprint-based CAFE standards, the agency does not
have the sole objective of seeking to reflect a clear engineering
relationship between footprint and fuel economy. Attributes other than
footprint would be more closely correlated with fuel economy. The
agency's objective is to make CAFE regulations more consistent with
public policy goals, in particular (1) a rebalancing of requirements
such that full-line manufacturers are not disproportionately burdened
and (2) the establishment of an incentive that discourages
manufacturers from responding to CAFE standards in ways that could
compromise occupant protection and highway safety. While it is helpful
that the attribute--in this case footprint--has an observed
relationship to fuel economy, it is not necessary that this
relationship be isolated from accompanying relationships (e.g., between
weight and fuel economy) that can be better related to estimable
physical processes. Similarly, it is more important that the functional
form for the attribute-based standard yield desirable outcomes than
that it singly seek a clear foundation in estimable physical processes.
    In general, public policy considerations and available vehicle data
combine to suggest that the fuel economy standard should be generally
downward sloping (on a fuel economy basis) with respect to NHTSA's
chosen attribute, vehicle footprint. The arguments that favor an
attribute-based system (maintaining consumer choice, protecting safety,
more equitable distribution of costs, reducing the cost of regulation)
all argue for a downward sloping curve. Larger vehicles should, in
principle, have higher drag, weigh more, and therefore have greater
inertia than otherwise identical smaller vehicles. Hence, all other
factors remaining equal, larger vehicles should have lower fuel economy
than smaller vehicles. Therefore, the selection of vehicle footprint as
the reference attribute should produce downward sloping curves. Also,
the tendency of larger vehicles to have lower fuel economy than smaller
vehicles should provide some disincentive to shift to larger vehicles
rather than adding technology; although doing so would tend to reduce
the required CAFE level, it would also tend to reduce the achieved CAFE
level.
    However, vehicle data, by itself, does not necessarily define what
functional form that the curve ought to take. In the 2006 light truck
rulemaking, NHTSA considered linear, quadratic, exponential,
unconstrained logistic, and constrained logistic functions as possible
alternatives. For light trucks, the various approaches produced broadly
similar standards through the most commonly used vehicle sizes, but
drastically different standards at the high and low ends of the range.
     Linear functions produced very high fuel economy standards
for the

smallest vehicles, and low standards for the largest vehicles.
     The quadratic function generated a minimum at about 75
square feet, and then perversely turned upward for vehicles with larger
footprints. The standard for very small vehicles was unreasonably high.
     The exponential and unconstrained logistic functions
produced unreasonably high standards for small vehicles, but flattened
out for larger vehicles.
     The constrained logistic function provided a broadly
linear downward-sloping through the most commonly used vehicle sizes,
along with basically flat standards for very large and very small
vehicles.
    On this basis, NHTSA believed that, while the data did not dictate
a particular functional form, public policy considerations made the
constrained logistic function particularly attractive. The
considerations include:
     A relatively flat standard for larger vehicles acts as a
de facto `backstop' for the standard in the event that future market
conditions encourage manufacturers to build very large vehicles.
Nothing prevents manufacturers from building larger vehicles. With a
logistic curve, however, vehicles upsizing beyond some limit face a
flat standard that is increasingly difficult to meet.
     A constrained logistic curve doesn't impose unachievable
fuel economy standards on vehicles that have unusually small
footprints, thus continuing to keep manufacturing fuel-efficient small
vehicles available as a compliance option.
     A curve fitted without upper and lower constraints could
reach very high fuel economy levels for small vehicles and very low
fuel economy vehicles for large vehicles. While such a curve might
produce similar required CAFE levels for the industry as a whole, it
could have a particular adverse impact on manufacturers that specialize
in very small vehicles, for example, two-seater sports cars. By the
same token, it could require little or nothing of manufacturers
specializing in very large vehicles.
     The transition from the `flat' portions of the curve to
the `slope' portions of the curve is smooth and gradual, reducing the
incentive for manufacturers to achieve compliance through marginal
changes in vehicle size.
     The inflection points are set by the data and can
potentially vary from year to year, rather than being chosen by NHTSA.
    On the other hand, a constrained logistic curve shares with other
functional forms a risk of an excessively steep or excessively flat
slope. The slope of the compliance curve may be considered as `too
steep' for public policy purposes when manufacturers can achieve
appreciable reductions in compliance costs by marginally increasing the
size of a vehicle's footprint--e.g., the cost of compliance from
upsizing is lower than other cost-effective compliance methods open to
manufacturers.
    A slope is `too flat' for public policy purposes when it negates
the advantages of an attribute-based system: Where the standard doesn't
meaningfully vary with respect to changes in the underlying attribute,
it cannot be said to be an attribute-based system within the meaning of
the statute.
    NHTSA chose footprint as the best attribute for an attribute-based
standard in part because we believed changing a vehicle's footprint
would involve significant costs for manufacturers, probably requiring a
redesign of the vehicle.
    While ``too steep'' or ``too flat'' inevitably cannot be defined
with precision, they need to be kept in mind.
    For the proposed standards, the agency defined the continuous
function using the following formula:
[GRAPHIC] [TIFF OMITTED] TP02MY08.007

Where:

T = the fuel economy target (in mpg)
a = the maximum fuel economy target (in mpg)
b = the minimum fuel economy target (in mpg)
c = the footprint value (in square feet) at which the fuel economy
target is midway between a and b \159\
---------------------------------------------------------------------------

    \159\ That is, the midpoint.
---------------------------------------------------------------------------

d = the parameter (in square feet) defining the rate at which the
value of targets decline from the largest to smallest values
e = 2.718\160\
---------------------------------------------------------------------------

    \160\ For the purpose of the Reformed CAFE standard, we are
carrying e out to only three decimal places.
---------------------------------------------------------------------------

x = footprint (in square feet, rounded to the nearest tenth) of the
vehicle model

    NHTSA invites comment regarding the relative importance of the
curve as a means of (1) providing a basis for describing the observed
relationship between footprint and fuel economy, (2) providing a basis
for describing a theoretical physical relationship (assuming one can be
defined) between footprint and fuel economy, and (3) providing socially
desirable incentives to manufacturers. The agency further invites
comment on functional forms that would be consistent with each of these
purposes.
    As for analysis of the light truck rule promulgated in 2006, NHTSA
constrained this function by determining the maximum and minimum
targets (a and b) and then holding those targets constant while using
statistical techniques to fit the other two coefficients (c and d) in
this equation.
    In the current analysis for passenger cars, the upper and lower
asymptotes are based on the smallest three percent and largest four
percent, respectively, of the fleet. These reflect footprint values
defining distinct cohorts outside the bulk of the fleet, and correspond
to footprint values of less than 39.5 square feet (i.e., up to the
approximate size of a Honda Fit) and greater than 52.5 square feet
(i.e., at least as great as the approximate size of a Toyota Avalon),
respectively:

[GRAPHIC] [TIFF OMITTED] TP02MY08.008

    For light trucks, the upper asymptote (i.e., the highest mpg value
of the continuous function defining fuel economy targets) is based on
the smallest (in terms of footprint) eleven percent of the fleet, and
the lower asymptote is based on the largest six percent of the fleet.
These cohorts correspond to footprint values of less than 44.5 square
feet (i.e., up to the approximate size of a Honda CR-V) and greater
than 72.5 square feet (i.e., comprised primarily of extended vans and
long-bed pickup trucks), respectively:

[GRAPHIC] [TIFF OMITTED] TP02MY08.009

    NHTSA invites comment on the identification of vehicle cohorts for
purposes of establishing upper and lower limits (asymptotes) bounding
the attribute-based standard. After updating its baseline market
forecast in consideration of new product plan information from
manufacturers, the agency plans to reevaluate these cohorts for both
passenger cars and light trucks before promulgating a final rule, and
notes that changes in approach could lead to changes in stringency.
    Given the above asymptotes, fitting the above functional form to
the ``optimized'' passenger car fleet resulted in the following initial
continuous functions:

[GRAPHIC] [TIFF OMITTED] TP02MY08.010

    For each model year, NHTSA then raised or lowered the resultant
continuous function until net benefits were maximized for the seven
largest manufacturers (in total). Without subsequent recalibrations
discussed below, this produced the following continuous functions for
passenger cars:

[GRAPHIC] [TIFF OMITTED] TP02MY08.011

    The agency followed the same procedures for setting light truck
standards and doing so resulted in the following continuous functions:

[GRAPHIC] [TIFF OMITTED] TP02MY08.012

    In fitting the continuous function, NHTSA considered a range of
statistical estimation techniques. In the 2006 light truck rulemaking,
NHTSA estimated the parameters of the logistic function using fuel
consumption (measured in gallons per mile) for each vehicle produced in
a particular model year, weighted by sales.
    For this rulemaking, we observed that estimated fuel consumption
functions for passenger cars were significantly affected by several
outliers--a small number of popular vehicles that had significantly
higher fuel economy than the fleet as a whole and, even more so, than
vehicles of similar footprint. For passenger cars, the function, as
estimated by weighted ordinary least squares, was exceptionally steep
within the range considered. This observation, in turn, led NHTSA to
consider alternative approaches to statistically fitting the continuous
function.
    Among the options considered by NHTSA were the following: dropping
the outlying vehicles from the estimation process, weighted and
unweighted ordinary least squares, and weighted and unweighted mean
absolute deviation (MAD). MAD is a statistical procedure that has been
demonstrated to produce more efficient parameter estimates in the
presence of significant outliers.\161\ As examples, the following two
charts show the MY2015 passenger car and light truck fleets after the
application of technologies to each manufacturer's fleet. These charts
reveal numerous outliers for the passenger car fleet and, to a lesser
extent, the light truck fleet:
---------------------------------------------------------------------------

    \161\ In the case of a dataset not drawn from a sample with a
Gaussian, or normal, distribution, there is often a need to employ
robust estimation methods rather than rely on least-squares approach
to curve fitting. The least-squares approach has, as an underlying
assumption, that the data are drawn from a normal distribution, and
hence fits a curve using a sum-of-squares method to minimize errors.
This approach will, in a sample drawn from a non-normal
distribution, give excessive weight to outliers by making their
presence felt in proportion to the square of their distance from the
fitted curve, and, hence, distort the resulting fit. With outliers
in the sample, the typical solution is to use a robust method such
as a minimum absolute deviation, rather than a squared term, to
estimate the fit (see, e.g., ``AI Access: Your Access to Data
Modeling,'' at http://www.aiaccess.net/English/Glossaries/GlosMod/
e_gm_O_Pa.htm#Outlier). The effect on the estimation is to let
the presence of each observation be felt more uniformly, resulting
in a curve more representative of the data (see, e.g., Peter
Kennedy, A Guide to Econometrics, 3rd edition, 1992, MIT Press,
Cambridge, MA).

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    NHTSA requests comment on the best method for statistically fitting
the continuous function.
    There are good theoretical arguments for using an unweighted
(rather than weighted) analysis. Although the purpose of the attribute-
based standard is to discourage downsizing (because of safety
implications) and more equitably distribute compliance burdens among
manufacturers, we strive to develop the curves based on the observed
physical relationship between vehicle size (i.e., footprint) and fuel
economy. The curve developed using unweighted sales data better
reflects this relationship.
    However, the process by which we select the stringency (as distinct
from the form) of the standard must consider sales volumes because the
standards are based on sales-weighted average performance. Therefore,
even if we use unweighted analysis develop the form of the standard, we
would continue to evaluate the standard's stringency (and, therefore,
its costs and benefits) based on sales-weighted average calculations
done on a manufacturer-by-manufacturer basis.
    There is already precedent for using unweighted data to produce
curves that are descriptive of engineering relationships. In NHTSA's
Preliminary Regulatory Impact Analysis for FMVSS 216 roof crush
standards, a series of force-versus-deflection curves were produced for
individual vehicle models and then averaged together. In that case, the
agency was seeking observed relationships that reflect engineering
possibilities, rather than a profile of the existing sales fleet.
    In terms of relative emphasis on different vehicle models, the
distinction between unweighted and weighted analysis is profound in the
light vehicle market, in part because of the way ``models'' are defined
for purposes of CAFE. The highest-selling passenger car model
represents 356,000 units, and the lowest-selling model represents only
5 units. As a group, the five lowest-selling models represent only 305
units. Thus, weighted analysis places more than 1,000 times the
emphasis on the highest-selling model than on the five lowest-selling
models, and more than 70,000 times the emphasis than on the single
lowest-selling model. The following histograms show the broader
distributions of models and sales with respect to model-level sales
(first for passenger cars, then for light trucks):

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    For purposes of setting the stringency of the corporate average
fuel economy standard, this is vital because enforcement is based on
the sales-weighted average. However, for purposes of developing a curve
intended to represent fuel economy levels achieved at a given
footprint, weighted analysis effectively ignores many models.
    On the other hand, unweighted estimation is depending on the
definition of a ``model''. Manufacturers will sometimes offer
substantially similar vehicles with different badges (i.e., Ford
Taurus/Mercury Sable) as two different models. The distinction between
differing ``options packages'' on a single model and two distinct
models is inevitably a bit blurry. When estimating fuel economy
standards using a sales-weighted regression, this distinction is not
material, since the estimation process will produce substantially the
same results independently of the number of distribution of those sales
into larger or smaller numbers of models. In unweighted estimation,
however, dividing a particular vehicle family into a larger number of
distinct models give that family some extra influence in the analysis.
Nonetheless, considering that such parsing less than does sales
weighting. NHTSA has tentatively concluded that unweighted estimation
remains preferable to sales-weighted estimation, but invites comment on
whether and, if so how substantially similar vehicles should be
combined for purposes of fitting an attribute-based function when using
unweighted estimation.
    The following charts show, for MY2015 passenger cars and light
trucks, how the use of sales-weighted least-squares estimation compares
to the proposed approach, which uses unweighted mean absolute
deviation. For passenger cars, the curve resulting from proposed
approach is somewhat shallower than the curve resulting from sales-
weighted least squares estimation. For light trucks, the curve
resulting from proposed approach is somewhat steeper:
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    NHTSA invites comment on the relative merits of unweighted and
weighted estimation, as well as on the other curve fitting options
(e.g., the use of mean absolute deviation) raised here. The agency
plans to reevaluate curve fitting approaches for both passenger cars
and light trucks before promulgating a final rule, and notes that
changes in approach could lead to changes in stringency and impacts on
different manufacturers.

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3. Adjustments To Address Policy Considerations
    NHTSA believes that the resultant curve characteristics discussed
above are empirically correct in that they correspond to the footprint
and fuel economy values of the fleet obtained by adding fuel saving
technologies to each manufacturer's fleet until the net benefit from
doing so reached its maximum value.
    However, there are three issues (described above) which may tend to
reduce the effectiveness of fuel economy regulation over time. These
concerns are:
     Curve crossings;
     Excessive steepness of the passenger car curve;
     Risk of upsizing.
    In this rule, NHTSA proposes a solution to the curve crossing
issue, requests comment on various methods of reducing the steepness of
the passenger car, and examines the potential for upsizing generally
under the provisions of this proposed rule.
a. Curve Crossings
    For both passenger cars and light trucks, NHTSA observed some curve
crossings from one model year to the next (i.e., for the same
footprint, some targets fell below the levels attained in the previous
model year), as revealed in the above charts. The upper limit of the MY
2012 passenger car curve falls slightly (about 0.1 mpg) below the MY
2011 value. For light trucks, the lower asymptote in MY 2012 is 0.9 mpg
below the lower asymptote in MY 2011. This was not observed during the
last round of light truck rulemaking because reformed CAFE was fully
implemented only in MY 2011. During the transition period (MYs 2008-
2010), the standards were set at levels equivalent in cost to
unreformed CAFE. However, for this rulemaking, because the projected
fleet composition changes between model years and the fuel economy
target function is optimized in every model year, the initial
continuous functions do not change monotonically (i.e., in only one
direction--increasing) from year to

year at every footprint value. Given the availability of lead time and
the importance of improving fuel economy, NHTSA has decided that, in
the setting of the standards, we should ensure that the fuel economy
targets do not fall from one year to the next at any footprint value.
    To address the year-to-year fluctuations in the functions, which
may lead to these curve crossings, NHTSA recalibrated each continuous
function to prevent it from crossing the continuous function from any
previous model year. In doing so, the agency attempted to avoid
continuous functions that would artificially encourage the product mix
to approximate that of earlier years. Instead, the agency recalibrated
by gradually shifting the initial continuous functions for each model
year toward the initial continuous function determined above for the
product mix for MY 2015. For both passenger cars and light trucks, the
agency adjusted each of the four coefficients in the formula
determining the continuous function such that regular steps were taken
year by year between the values determined above for MY 2011 and those
for MY 2015. For example, the inflection point (the coefficient
determining the footprint at which the target falls halfway between its
minimum and maximum values) defining the light truck target function
was increased by 0.034 square feet annually from 51.9 square feet in MY
2011 to 52.1 square feet in MY 2015.
    NHTSA also recalibrated the continuous function for each model year
by adding, as needed, anti-backsliding constraints that prevent the
function from either (a) yielding an industry wide average level of
CAFE lower than that for the preceding model year, (b) for a given
footprint, having targets that fall below the level of previous year,
and (c) having an asymptote lower than that of the preceding model
year. The ``decision tree'' for determining for each model year the
need for each of these constraints is summarized below in Figure V 16.

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    The industry-wide average CAFE is prevented from decreasing between
model years in order to prevent standards from falling below the level
that was determined to be achievable for the model year before. To
allow the industry-wide CAFE level to fall between successive model
years would be to promulgate a standard that, notwithstanding
maximizing net benefits, falls below what the agency has determined to
be feasible in previous years. In a model year in which simple
maximization of net benefits would have caused this to occur, NHTSA
shifted the resultant curve upward (without changing the curve's shape)
in order to produce an industry-wide CAFE equal to that of the
preceding model year.
    Application of the decision tree shown above results in the
following target functions for passenger cars and light trucks,
respectively. These target functions are identical to those shown below
in Section VI, which discusses the standards proposed today by NHTSA:
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b. Steep Curves for Pasenger Cars
    NHTSA has developed a set of attribute-based curves for passenger
cars for this proposal consistent with the methodology used in the
2008-2011 light duty truck rule. However, unlike the relatively
gradually sloped curve related fuel economy to footprint for trucks,
our analysis for cars when utilizing a constained logistic curve
produces a comparatively steep ``S''-shaped curve for passenger cars.
This occurs primarily because--unlike trucks--current passenger car
sales include vehicles with a wide range of fuel economy spanning a
relatively narrow footprint range. Consequently, there is a relatively
steep curve applied to the middle range of footprint values with a more
rapid change of slope in the tails to flatten curve and thus satisfy
the constrained logistic functional form.
    In this rule, NHTSA is proposing a relatively ``steep'' curve. The
agency has considered and experimented with several methods of reducing
the steepness of the passenger car curve. However, each of these
approaches has created challenges that may potentially be worse than
the problem they are trying to cure. The Agency is questioning whether
the steep slope portion of the curve could potentially motivate vehicle
manufacturers to reduce their compliance obligation under the standard
by slightly increasing its footprint when they redesign their vehicles.
We do not know the extent to which this is a real problem, but the
agency has considered this possibility and has worked to minimize
steepness of the slope while maintaining the scientific integrity
behind our methodology.
    However, any attempt to ``fix'' the steepness of the passenger car
curve appears to come at a price: First, flattening the curve by any
particular method will move the curve away from the actual vehicle
data. Second, flatter curves are generally place greater compliance
burdens on full-line manufacturers than comparatively stringent (in
terms of average require CAFE) standards. Furthermore, NHTSA believes
that this could increase the overall costs required to achieve a given
amount of fuel savings and societal

benefits, and it increases the risk that NHTSA would need to return to
a ``least capable manufacturer'' approach in order to ensure economic
practicability. Doing so would likely reduce stringency, and reduce
fuel savings. In deciding on a particular approach, NHTSA must balance
the certainty of high costs and lost fuel savings through a less
``efficient'' standard against the risk that the steepness of the curve
might stimulate manufacturers to evade the standard over time by
redesigning their vehicles over time.
    In proposing the steep curve for this rule, NHTSA has tentatively
decided that the cures that we have identified come at too high a
price, i.e., lost stringency or undesirable side effects. However,
NHTSA requests comment on these and other potential solutions to reduce
the steepness of the proposed car curves for passenger cars.
    Some of the approaches considered or tested by NHTSA include:
    Linear standards. When the fuel consumption of vehicles with added
technologies is plotted against footprint, we note a roughly linear
relationship over the existing range of footprint values. Hence, a
simple alternative to the current constrained logistic function would
be to estimate a linear form of the curve with the sales data. However,
NHTSA is concerned that such an approach may result in very low fuel
economy standards for the largest footprint vehicles, very high fuel
economy standards for the smallest vehicles, and loss of the inherent
backstop properties of the constrained logistic function.
    In addition, the slope of a line estimated through a ``cloud'' of
data may be very sensitive to the exact characteristics of vehicles
with the largest and smallest footprints. It may turn out that small
changes in vehicle characteristics in the tails could shift the slope
of a linear estimate. Further, it may be impossible to materially
adjust the slope of a linear standard in future years without accepting
curve crossing. The following two charts compare linear regression
results for MY2015 to the curves proposed today by NHTSA. The result
for passenger cars illustrates the concern regarding behavior at large
and small footprints. Over the range of footprints in which light
trucks are expected to be offered in MY2015, the result for light
trucks shows less difference from the proposed curve.
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    Constrained linear standards. Another possible approach would be to
retain the flattened tails proposed today but reduce the steepness of
the middle portion by allowing it to directly reflect a linear
relationship. This approach could be likened to a simplification or
linearization of the constrained logistic function. The same minima and
maxima would be used to bound the vertical extent of the linear form.
The following two charts suggest that, at least for the MY2015
passenger car and light truck fleets considered today, a constrained
linear standard would, compared to the standard proposed today, likely
result in a similar distribution of compliance burdens among
manufacturers (because the stringency at each footprint would be
similar):
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    However, the agency remains concerned that the slope could exhibit
greater year-to-year variation than the proposed logistic form
(although further analysis would be required in order to address this
concern). Also, as discussed in the preamble to the 2006 Federal
Register notice regarding light truck CAFE standards, the agency
remains concerned that the upper and lower ``kinks'' in the function
could offer unexpected incentives for manufacturers to redesign
vehicles with footprints close to the kink-point.
    Dual Attribute Approaches. A third possible solution would be to
use additional attribute-based information to spread out the
distribution of passenger cars across the x-axis. In effect, this
approach uses a second attribute to normalize the footprint-fuel
economy relationship. This second attribute might be horsepower,
weight, or horsepower-to-weight.
    In analyzing the expected passenger car market, NHTSA observes that
the ratio of engine horsepower to vehicle weight generally increases
with increasing footprint. Higher power-to-weight ratios tend to imply
lower fuel economy, as the engine is typically larger and operating
less efficiently under driving conditions applicable to certification.
Thus, the fuel consumption versus footprint curves for passenger cars
reflect this relationship. For trucks, there does not appear to be a
relationship between footprint and the power-to-weight ratio. For
passenger cars, then, adjusting fuel consumption values to normalize