ecology, and limits to recreational uses. Several studies have been
conducted in U.S. coastal waters and in the Great Lakes Region in which
the role of ambient PM deposition and runoff is
investigated.417 418 419 420 421
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    \417\ U.S. EPA (2004) National Coastal Condition Report II.
Office of Research and Development/Office of Water. EPA-620/R-03/
002. Docket EPA-HQ-OAR-2009-0472-0089.
    \418\ Gao, Y., E.D. Nelson, M.P. Field, et al. 2002.
Characterization of atmospheric trace elements on PM2.5
particulate matter over the New York-New Jersey harbor estuary.
Atmos. Environ. 36: 1077-1086. Docket EPA-HQ-OAR-2009-0472-11297.
    \419\ Kim, G., N. Hussain, J.R. Scudlark, and T.M. Church. 2000.
Factors influencing the atmospheric depositional fluxes of stable
Pb, 210Pb, and 7Be into Chesapeake Bay. J. Atmos. Chem. 36: 65-79.
Docket EPA-HQ-OAR-2009-0472-11299.
    \420\ Lu, R., R.P. Turco, K. Stolzenbach, et al. 2003. Dry
deposition of airborne trace metals on the Los Angeles Basin and
adjacent coastal waters. J. Geophys. Res. 108(D2, 4074): AAC 11-1 to
11-24. Docket EPA-HQ-OAR-2009-0472-11296.
    \421\ Marvin, C.H., M.N. Charlton, E.J. Reiner, et al. 2002.
Surficial sediment contamination in Lakes Erie and Ontario: A
comparative analysis. J. Great Lakes Res. 28(3): 437-450. Docket
EPA-HQ-OAR-2009-0472-11300.
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    Atmospheric deposition of nitrogen and sulfur contributes to
acidification, altering biogeochemistry and affecting animal and plant
life in terrestrial and aquatic ecosystems across the U.S. The
sensitivity of terrestrial and aquatic ecosystems to acidification from
nitrogen and sulfur deposition is predominantly governed by geology.
Prolonged exposure to excess nitrogen and sulfur deposition in
sensitive areas acidifies lakes, rivers and soils. Increased acidity in
surface waters creates inhospitable conditions for biota and affects
the abundance and nutritional value of preferred prey species,
threatening biodiversity and ecosystem function. Over time, acidifying
deposition also removes essential nutrients from forest soils,
depleting the capacity of soils to neutralize future acid loadings and
negatively affecting forest sustainability. Major effects include a
decline in sensitive forest tree species, such as red spruce (Picea
rubens) and sugar maple (Acer saccharum), and a loss of biodiversity of
fishes, zooplankton, and macro invertebrates.
    In addition to the role nitrogen deposition plays in acidification,
nitrogen deposition also leads to nutrient enrichment and altered
biogeochemical cycling. In aquatic systems increased nitrogen can alter
species assemblages and cause eutrophication. In terrestrial systems
nitrogen loading can lead to loss of nitrogen sensitive lichen species,
decreased biodiversity of grasslands, meadows and other sensitive
habitats, and increased potential for invasive species. For a broader
explanation of the topics treated here, refer to the description in
Section 7.1.2 of the RIA.
    Adverse impacts on soil chemistry and plant life have been observed
for areas heavily influenced by atmospheric deposition of nutrients,
metals and acid species, resulting in species shifts, loss of
biodiversity, forest decline and damage to forest productivity.
Potential impacts also include adverse effects to human health through
ingestion of contaminated vegetation or livestock (as in the case for
dioxin deposition), reduction in crop yield, and limited use of land
due to contamination.
    Atmospheric deposition of pollutants can reduce the aesthetic
appeal of buildings and culturally important articles through soiling,
and can contribute directly (or in conjunction with other pollutants)
to structural damage by means of corrosion or erosion. Atmospheric
deposition may affect materials principally by promoting and
accelerating the corrosion of metals, by degrading paints, and by
deteriorating building materials such as concrete and limestone.
Particles contribute to these effects because of their electrolytic,
hygroscopic, and acidic properties, and their ability to adsorb
corrosive gases (principally sulfur dioxide).
d. Environmental Effects of Air Toxics
    Fuel combustion emissions contribute to ambient levels of
pollutants that contribute to adverse effects on vegetation. Volatile
organic compounds (VOCs), some of which are considered air toxics, have
long been suspected to play a role in vegetation damage.\422\ In
laboratory experiments, a wide range of tolerance to VOCs has been
observed.\423\ Decreases in harvested seed pod weight have been
reported for the more sensitive plants, and some studies have reported
effects on seed germination, flowering and fruit ripening. Effects of
individual VOCs or their role in conjunction with other stressors
(e.g., acidification, drought, temperature extremes) have not been well
studied. In a recent study of a mixture of VOCs including ethanol and
toluene on herbaceous plants, significant effects on seed production,
leaf water content and photosynthetic efficiency were reported for some
plant species.\424\
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    \422\ U.S. EPA. 1991. Effects of organic chemicals in the
atmosphere on terrestrial plants. EPA/600/3-91/001. Docket EPA-HQ-
OAR-2009-0472-0401.
    \423\ Cape JN, ID Leith, J Binnie, J Content, M Donkin, M
Skewes, DN Price AR Brown, AD Sharpe. 2003. Effects of VOCs on
herbaceous plants in an open-top chamber experiment. Environ.
Pollut. 124:341-343. Docket EPA-HQ-OAR-2009-0472-0357.
    \424\ Cape JN, ID Leith, J Binnie, J Content, M Donkin, M
Skewes, DN Price AR Brown, AD Sharpe. 2003. Effects of VOCs on
herbaceous plants in an open-top chamber experiment. Environ.
Pollut. 124:341-343. Docket EPA-HQ-OAR-2009-0472-0357.
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    Research suggests an adverse impact of vehicle exhaust on plants,
which has in some cases been attributed to aromatic compounds and in
other cases to nitrogen oxides.425 426 427 The impacts of
VOCs on plant reproduction may have long-term implications for
biodiversity and survival of native species near major roadways. Most
of the studies of the impacts of VOCs on vegetation have focused on
short-term exposure and few studies have focused on long-term effects
of VOCs on vegetation and the potential for metabolites of these
compounds to affect herbivores or insects.
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    \425\ Viskari E-L. 2000. Epicuticular wax of Norway spruce
needles as indicator of traffic pollutant deposition. Water, Air,
and Soil Pollut. 121:327-337. Docket EPA-HQ-OAR-2009-0472-1128.
    \426\ Ugrekhelidze D, F Korte, G Kvesitadze. 1997. Uptake and
transformation of benzene and toluene by plant leaves. Ecotox.
Environ. Safety 37:24-29. Docket EPA-HQ-OAR-2009-0472-1142.
    \427\ Kammerbauer H, H Selinger, R Rommelt, A Ziegler-Jons, D
Knoppik, B Hock. 1987. Toxic components of motor vehicle emissions
for the spruce Picea abies. Environ. Pollut. 48:235-243. Docket EPA-
HQ-OAR-2009-0472-0358.
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5. Air Quality Impacts of Non-GHG Pollutants
    Air quality modeling was performed to assess the impact of the
vehicle standards on criteria and air toxic pollutants. In this
section, we present information on current modeled levels of pollution
as well as projections for 2030, with respect to ambient
PM2.5, ozone, selected air toxics, visibility levels and
nitrogen and sulfur deposition. The air quality modeling results
indicate that the GHG standards have relatively small but measureable
impacts on ambient concentrations of these pollutants. The results are
discussed in more detail below and in Section 7.2 of the RIA. No
substantive

comments were received on our plans for non-GHG air quality modeling
that were detailed in the proposal for this rule.
    We used the Community Multi-scale Air Quality (CMAQ) photochemical
model, version 4.7.1, for our analysis. This version of CMAQ includes a
number of improvements to previous versions of the model. These
improvements are discussed in Section 7.2 of the RIA.
a. Particulate Matter
i. Current Levels
    PM2.5 concentrations exceeding the level of the
PM2.5 NAAQS occur in many parts of the country. In 2005, EPA
designated 39 nonattainment areas for the 1997 PM2.5 NAAQS
(70 FR 943, January 5, 2005). These areas are composed of 208 full or
partial counties with a total population exceeding 88 million. The 1997
PM2.5 NAAQS was revised in 2006 and the 2006 24-hour
PM2.5 NAAQS became effective on December 18, 2006. On
October 8, 2009, the EPA issued final nonattainment area designations
for the 2006 24-hour PM2.5 NAAQS (74 FR 58688, November 13,
2009). These designations include 31 areas composed of 120 full or
partial counties with a population of over 70 million. In total, there
are 54 PM2.5 nonattainment areas composed of 243 counties
with a population of almost 102 million people.
ii. Projected Levels Without This Rule
    States with PM2.5 nonattainment areas are required to
take action to bring those areas into compliance in the future. Areas
designated as not attaining the 1997 PM2.5 NAAQS will need
to attain the 1997 standards in the 2010 to 2015 time frame, and then
maintain them thereafter. The 2006 24-hour PM2.5
nonattainment areas will be required to attain the 2006 24-hour
PM2.5 NAAQS in the 2014 to 2019 time frame and then be
required to maintain the 2006 24-hour PM2.5 NAAQS
thereafter. The vehicle standards finalized in this action become
effective in 2012 and therefore may be useful to states in attaining or
maintaining the PM2.5 NAAQS.
    EPA has already adopted many emission control programs that are
expected to reduce ambient PM2.5 levels and which will
assist in reducing the number of areas that fail to achieve the
PM2.5 NAAQS. Even so, our air quality modeling projects that
in 2030, with all current controls but excluding the impacts of the
vehicle standards adopted here, at least 9 counties with a population
of almost 28 million may not attain the 1997 annual PM2.5
standard of 15 [mu]g/m3 and 26 counties with a population of
over 41 million may not attain the 2006 24-hour PM2.5
standard of 35 [mu]g/m3. These numbers do not account for
those areas that are close to (e.g., within 10 percent of) the
PM2.5 standards. These areas, although not violating the
standards, will also benefit from any reductions in PM2.5
ensuring long-term maintenance of the PM2.5 NAAQS.
iii. Projected Levels With This Rule
    Air quality modeling performed for this final rule shows that in
2030 the majority of the modeled counties will see decreases of less
than 0.05 [mu]g/m3 in their annual PM2.5 design
values. The decreases in annual PM2.5 design values that we
see in some counties are likely due to emission reductions related to
lower gasoline production at existing oil refineries; reductions in
direct PM2.5 emissions and PM2.5 precursor
emissions (NOX and SOX) contribute to reductions
in ambient concentrations of both direct PM2.5 and
secondarily-formed PM2.5. The maximum projected decrease in
an annual PM2.5 design value is 0.07 [mu]g/m3 in
Harris County, TX. There are also a few counties that are projected to
see increases of no more than 0.01 [mu]g/m3 in their annual
PM2.5 design values. These small increases in annual
PM2.5 design values are likely related to downstream
emission increases. On a population-weighted basis, the average modeled
2030 annual PM2.5 design value is projected to decrease by
0.01 [mu]g/m3 due to this final rule. Those counties that
are projected to be above the annual PM2.5 standard in 2030
will see slightly larger population-weighted decreases of 0.03 [mu]g/
m3 in their design values due to this final rule.
    In addition to looking at annual PM2.5 design values, we
also modeled the impact of the standards on 24-hour PM2.5
design values. Air quality modeling performed for this final rule shows
that in 2030 the majority of the modeled counties will see changes of
between -0.05 [mu]g/m3 and +0.05 [mu]g/m3 in
their 24-hour PM2.5 design values. The decreases in 24-hour
PM2.5 design values that we see in some counties are likely
due to emission reductions related to lower gasoline production at
existing oil refineries; reductions in direct PM2.5
emissions and PM2.5 precursor emissions (NOX and
SOX) contribute to reductions in ambient concentrations of
both direct PM2.5 and secondarily-formed PM2.5.
The maximum projected decrease in a 24-hour PM2.5 design
value is 0.21 [mu]g/m3 in Harris County, TX. There are also
some counties that are projected to see increases of less than 0.05
[mu]g/m3 in their 24-hour PM2.5 design values.
These small increases in 24-hour PM2.5 design values are
likely related to downstream emission increases. On a population-
weighted basis, the average modeled 2030 24-hour PM2.5
design value is projected to decrease by 0.01 [mu]g/m3 due
to this final rule. Those counties that are projected to be above the
24-hour PM2.5 standard in 2030 will see slightly larger
population-weighted decreases of 0.05 [mu]g/m3 in their
design values due to this final rule.
b. Ozone
i. Current Levels
    8-hour ozone concentrations exceeding the level of the ozone NAAQS
occur in many parts of the country. In 2008, the EPA amended the ozone
NAAQS (73 FR 16436, March 27, 2008). The final 2008 ozone NAAQS rule
set forth revisions to the previous 1997 NAAQS for ozone to provide
increased protection of public health and welfare. EPA recently
proposed to reconsider the 2008 ozone NAAQS (75 FR 2938, January 19,
2010). Because of the uncertainty the reconsideration proposal creates
regarding the continued applicability of the 2008 ozone NAAQS, EPA has
used its authority to extend by 1 year the deadline for promulgating
designations for those NAAQS (75 FR 2936, January 19, 2010). The new
deadline is March 12, 2011. EPA intends to complete the reconsideration
by August 31, 2010. If EPA establishes new ozone NAAQS as a result of
the reconsideration, they would replace the 2008 ozone NAAQS and
requirements to designate areas and implement the 2008 NAAQS would no
longer apply.
    As of January 6, 2010 there are 51 areas designated as
nonattainment for the 1997 8-hour ozone NAAQS, comprising 266 full or
partial counties with a total population of over 122 million people.
These numbers do not include the people living in areas where there is
a future risk of failing to maintain or attain the 1997 8-hour ozone
NAAQS. The numbers above likely underestimate the number of counties
that are not meeting the ozone NAAQS because the nonattainment areas
associated with the more stringent 2008 8-hour ozone NAAQS have not yet
been designated. Table III.G.5-1 provides an estimate, based on 2005-07
air quality data, of the counties with design values greater than the
2008 8-hour ozone NAAQS of 0.075 ppm.

   Table III.G.5-1--Counties with Design Values Greater Than the Ozone
                                  NAAQS
------------------------------------------------------------------------
                                          Number of
                                          counties       Population \a\
------------------------------------------------------------------------
1997 Ozone Standard: Counties within               266       122,343,799
 the 54 areas currently designated
 as nonattainment (as of 1/6/10)....
2008 Ozone Standard: Additional                    156        36,678,478
 counties that would not meet the
 2008 NAAQS (based on 2006-2008 air
 quality data) \b\..................
------------------------------------------------------------------------
    Total...........................               422       159,022,277
------------------------------------------------------------------------
Notes:
\a\ Population numbers are from 2000 census data.
\b\ Area designations for the 2008 ozone NAAQS have not yet been made.
  Nonattainment for the 2008 Ozone NAAQS would be based on three years
  of air quality data from later years. Also, the county numbers in this
  row include only the counties with monitors violating the 2008 Ozone
  NAAQS. The numbers in this table may be an underestimate of the number
  of counties and populations that will eventually be included in areas
  with multiple counties designated nonattainment.

ii. Projected Levels Without This Rule
    States with 8-hour ozone nonattainment areas are required to take
action to bring those areas into compliance in the future. Based on the
final rule designating and classifying 8-hour ozone nonattainment areas
for the 1997 standard (69 FR 23951, April 30, 2004), most 8-hour ozone
nonattainment areas will be required to attain the ozone NAAQS in the
2007 to 2013 time frame and then maintain the NAAQS thereafter. As
noted, EPA is reconsidering the 2008 ozone NAAQS. If EPA promulgates
different ozone NAAQS in 2010 as a result of the reconsideration, these
standards would replace the 2008 ozone NAAQS and there would no longer
be a requirement to designate areas for the 2008 NAAQS. EPA would
designate nonattainment areas for a potential new 2010 primary ozone
NAAQS in 2011. The attainment dates for areas designated nonattainment
for a potential new 2010 primary ozone NAAQS are likely to be in the
2014 to 2031 timeframe, depending on the severity of the problem.\428\
---------------------------------------------------------------------------

    \428\ U.S. EPA 2010, Fact Sheet Revisions to Ozone Standards.
http://www.epa.gov/groundlevelozone/pdfs/fs20100106std.pdf.
---------------------------------------------------------------------------

    EPA has already adopted many emission control programs that are
expected to reduce ambient ozone levels and assist in reducing the
number of areas that fail to achieve the ozone NAAQS. Even so, our air
quality modeling projects that in 2030, with all current controls but
excluding the impacts of the vehicle standards, up to 16 counties with
a population of almost 35 million may not attain the 2008 ozone
standard of 0.075 ppm (75 ppb). These numbers do not account for those
areas that are close to (e.g., within 10 percent of) the 2008 ozone
standard. These areas, although not violating the standards, will also
be impacted by changes in ozone as they work to ensure long-term
maintenance of the ozone NAAQS.
iii. Projected Levels With This Rule
    We do not expect this rule to have a meaningful impact on ozone
concentrations, given the small magnitude of the ozone impacts and the
fact that much of the impact is due to ethanol assumptions that are
independent of this rule. Our modeling projects increases in ozone
design value concentrations in many areas of the country and decreases
in ozone design value concentrations in a few areas. However, the
increases in ozone design values are not due to the standards finalized
in this rule, but are related to our assumptions about the volume of
ethanol that will be blended into gasoline. The ethanol volumes will be
occurring as a result of the recent Renewable Fuel Standards (RFS2)
rule.\429\
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    \429\ EPA 2010, Renewable Fuel Standard Program (RFS2)
Regulatory Impact Analysis. EPA-420-R-10-006, February 2010. Docket
EPA-HQ-OAR-2009-0472-11332. See also 75 FR 14670, March 26, 2010.
---------------------------------------------------------------------------

    The ethanol volume assumptions are discussed in the introduction to
Section III.G of this preamble. We attribute decreased fuel consumption
and production from this program to gasoline only, while assuming
constant ethanol volumes in our reference and control cases. Holding
ethanol volumes constant while decreasing gasoline volumes increases
the market share of 10% ethanol (E10) in the control case. However, the
increased E10 market share is projected to occur regardless of this
rule; in the RFS2 analysis we project 100% E10 by 2014. The air quality
impacts of this effect are included in our analyses for the recent RFS2
rule. As the RFS2 analyses indicate, increasing usage of E10 fuels
(when compared with E0 fuels) can increase NOX emissions and
thereby increase ozone concentrations, especially in NOX-
limited areas where relatively small amounts of NOX enable
ozone to form rapidly.\430\
---------------------------------------------------------------------------

    \430\ Sections <IP_ADDRESS>.2 and <IP_ADDRESS> of the Renewable Fuel
Standard Program (RFS2) Regulatory Impact Analysis, EPA-420-R-10-
006, February 2010. Docket EPA-HQ-OAR-2009-0472-11332.
---------------------------------------------------------------------------

    The majority of the ozone design value increases are less than 0.1
ppb. The maximum projected increase in an 8-hour ozone design value is
0.25 ppb in Richland County, South Carolina. As mentioned above there
are some areas which see decreases in their ozone design values. The
decreases in ambient ozone concentration are likely due to projected
upstream emissions decreases in NOX and VOCs from reduced
gasoline production. The maximum decrease projected in an 8-hour ozone
design value is 0.22 ppb in Riverside County, California. On a
population-weighted basis, the average modeled 8-hour ozone design
values are projected to increase by 0.01 ppb in 2030 and the design
values for those counties that are projected to be above the 2008 ozone
standard in 2030 will see population-weighted decreases of 0.10 ppb.
c. Air Toxics
i. Current Levels
    The majority of Americans continue to be exposed to ambient
concentrations of air toxics at levels which have the potential to
cause adverse health effects.\431\ The levels of air toxics to which
people are exposed vary depending on where people live and work and the
kinds of activities in which they engage, as discussed in detail in
U.S. EPA's most recent Mobile Source Air Toxics Rule.\432\ According to
the National Air Toxic Assessment

(NATA) for 2002,\433\ mobile sources were responsible for 47 percent of
outdoor toxic emissions, over 50 percent of the cancer risk, and over
80 percent of the noncancer hazard. Benzene is the largest contributor
to cancer risk of all 124 pollutants quantitatively assessed in the
2002 NATA and mobile sources were responsible for 59 percent of benzene
emissions in 2002. Over the years, EPA has implemented a number of
mobile source and fuel controls resulting in VOC reductions, which also
reduce benzene and other air toxic emissions.
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    \431\ U.S. EPA (2009) 2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/. Docket EPA-HQ-OAR-2009-0472-
11321.
    \432\ U.S. Environmental Protection Agency (2007). Control of
Hazardous Air Pollutants from Mobile Sources; Final Rule. 72 FR
8434, February 26, 2007. Docket EPA-HQ-OAR-2009-0472-0271, 0271.1
and 0271.2.
    \433\ U.S. EPA (2009) 2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/. Docket EPA-HQ-OAR-2009-0472-
11321.
---------------------------------------------------------------------------

ii. Projected Levels
    Our modeling indicates that the GHG standards have relatively
little impact on national average ambient concentrations of the modeled
air toxics. Additional detail on the air toxics results can be found in
Section <IP_ADDRESS> of the RIA.
d. Nitrogen and Sulfur Deposition
i. Current Levels
    Over the past two decades, the EPA has undertaken numerous efforts
to reduce nitrogen and sulfur deposition across the U.S. Analyses of
long-term monitoring data for the U.S. show that deposition of both
nitrogen and sulfur compounds has decreased over the last 17 years
although many areas continue to be negatively impacted by deposition.
Deposition of inorganic nitrogen and sulfur species routinely measured
in the U.S. between 2004 and 2006 were as high as 9.6 kilograms of
nitrogen per hectare per year (kg N/ha/yr) and 21.3 kilograms of sulfur
per hectare per year (kg S/ha/yr). The data show that reductions were
more substantial for sulfur compounds than for nitrogen compounds.
These numbers are generated by the U.S. national monitoring network and
they likely underestimate nitrogen deposition because neither ammonia
nor organic nitrogen is measured. In the eastern U.S., where data are
most abundant, total sulfur deposition decreased by about 44% between
1990 and 2007, while total nitrogen deposition decreased by 25% over
the same time frame.\434\
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    \434\ U.S. EPA. U.S. EPA's 2008 Report on the Environment (Final
Report). U.S. Environmental Protection Agency, Washington, DC, EPA/
600/R-07/045F (NTIS PB2008-112484). Docket EPA-HQ-OAR-2009-0472-
11298. Updated data available online at: http://cfpub.epa.gov/eroe/
index.cfm?fuseaction=detail.viewInd&ch=46&subtop=341&lv=list.listByCh
apter&r=201744.
---------------------------------------------------------------------------

ii. Projected Levels
    Our air quality modeling does not show substantial overall
nationwide impacts on the annual total sulfur and nitrogen deposition
occurring across the U.S. as a result of the vehicle standards required
by this rule. For sulfur deposition the vehicle standards will result
in annual percent decreases of 0.5% to more than 2% in locations with
refineries as a result of the lower output from refineries due to less
gasoline usage. These locations include the Texas and Louisiana
portions of the Gulf Coast; the Washington DC area; Chicago, IL;
portions of Oklahoma and northern Texas; Bismarck, North Dakota;
Billings, Montana; Casper, Wyoming; Salt Lake City, Utah; Seattle,
Washington; and San Francisco, Los Angeles, and San Luis Obispo,
California. The remainder of the country will see only minimal changes
in sulfur deposition, ranging from decreases of less than 0.5% to
increases of less than 0.5%. For a map of 2030 sulfur deposition
impacts and additional information on these impacts, see Section
<IP_ADDRESS> of the RIA. The impacts of the vehicle standards on nitrogen
deposition are minimal, ranging from decreases of up to 0.5% to
increases of up to 0.5%.
e. Visibility
i. Current Levels
    As mentioned in Section III.G.5.a, millions of people live in
nonattainment areas for the PM2.5 NAAQS. These populations,
as well as large numbers of individuals who travel to these areas, are
likely to experience visibility impairment. In addition, while
visibility trends have improved in mandatory class I Federal areas, the
most recent data show that these areas continue to suffer from
visibility impairment. In summary, visibility impairment is experienced
throughout the U.S., in multi-State regions, urban areas, and remote
mandatory class I Federal areas.
ii. Projected Levels
    Air quality modeling conducted for this final rule was used to
project visibility conditions in 138 mandatory class I Federal areas
across the U.S. in 2030. The results show that all the modeled areas
will continue to have annual average deciview levels above background
in 2030.\435\ The results also indicate that the majority of the
modeled mandatory class I Federal areas will see no change in their
visibility, but some mandatory class I Federal areas will see
improvements in visibility due to the vehicle standards and a few
mandatory class I Federal areas will see visibility decreases. The
average visibility at all modeled mandatory class I Federal areas on
the 20% worst days is projected to improve by 0.002 deciviews, or
0.01%, in 2030. Section <IP_ADDRESS>.2 of the RIA contains more detail on
the visibility portion of the air quality modeling.
---------------------------------------------------------------------------

    \435\ The level of visibility impairment in an area is based on
the light-extinction coefficient and a unitless visibility index,
called a ``deciview'', which is used in the valuation of visibility.
The deciview metric provides a scale for perceived visual changes
over the entire range of conditions, from clear to hazy. Under many
scenic conditions, the average person can generally perceive a
change of one deciview. The higher the deciview value, the worse the
visibility. Thus, an improvement in visibility is a decrease in
deciview value.
---------------------------------------------------------------------------

H. What are the estimated cost, economic, and other impacts of the
program?

    In this section, EPA presents the costs and impacts of EPA's GHG
program. It is important to note that NHTSA's CAFE standards and EPA's
GHG standards will both be in effect, and each will lead to average
fuel economy increases and CO2 emissions reductions. The two
agencies' standards comprise the National Program, and this discussion
of costs and benefits of EPA's GHG standard does not change the fact
that both the CAFE and GHG standards, jointly, are the source of the
benefits and costs of the National Program. These costs and benefits
are appropriately analyzed separately by each agency and should not be
added together.
    This section outlines the basis for assessing the benefits and
costs of the GHG standards and provides estimates of these costs and
benefits. Some of these effects are private, meaning that they affect
consumers and producers directly in their sales, purchases, and use of
vehicles. These private effects include the upfront costs of the
technology, fuel savings, and the benefits of additional driving and
reduced refueling. Other costs and benefits affect people outside the
markets for vehicles and their use; these effects are termed external,
because they affect people in ways other than the effect on the market
for and use of new vehicles and are generally not taken into account by
the purchaser of the vehicle. The external effects include the climate
impacts, the effects on non-GHG pollutants, energy security impacts,
and the effects on traffic, accidents, and noise due to additional
driving. The sum of the private and external benefits and costs is the
net social benefits of the program. There is some debate about the

role of private benefits in assessing the benefits and costs of the
program: If consumers optimize their purchases of fuel economy, with
full information and perfect foresight, in perfectly efficient markets,
it is possible that they have already considered these benefits in
their vehicle purchase decisions. If so, then no net private benefits
would result from the program, because consumers would already buy
vehicles with the amount of fuel economy that is optimal for them;
requiring additional fuel economy would alter both the purchase prices
of new cars and their lifetime streams of operating costs in ways that
will inevitably reduce consumers' well-being. If these conditions do
not hold, then the private benefits and costs would both count toward
the program's benefits. Section III.H.1 discusses this issue more
fully.
    The net benefits of EPA's final program consist of the effects of
the program on:
     The vehicle program costs (costs of complying with the
vehicle CO2 standards, taking into account FFV credits
through 2015, the temporary lead-time alternative allowance standard
program (TLAASP), full car/truck trading, and the A/C credit program,
and other flexibilities built into the final program),
     Fuel savings associated with reduced fuel usage resulting
from the program,
     Greenhouse gas emissions,
     Other pollutants,
     Noise, congestion, accidents,
     Energy security impacts,
     Reduced refueling events
     Increased driving due to the ``rebound'' effect.

EPA also presents the cost-effectiveness of the standards.
    The total monetized benefits (excluding fuel savings) under the
program are projected to be $17.5 to $41.8 billion in 2030, using a 3
percent discount rate applied to the valuation of PM2.5-
related premature mortality and depending on the value used for the
social cost of carbon. The total monetized benefits (excluding fuel
savings) under the program are projected to be $17.4 to $41.7 billion
in 2030, using a 7 percent discount rate applied to the valuation of
PM2.5-related premature mortality and depending on the value
used for the social cost of carbon. These benefits are summarized below
in Table III.H.10-2. The costs of the program in 2030 are estimated to
be approximately $15.8 billion for new vehicle technology less $79.8
billion in savings realized by consumers through fewer fuel
expenditures (calculated using pre-tax fuel prices). These costs are
summarized below in Table III.H.10-1. The estimates developed here use
as a baseline for comparison the fuel economy associated with MY 2011
vehicles. To the extent that greater fuel economy improvements than
those assumed to occur under the baseline may have occurred due to
market forces alone (absent the rule), the analysis overestimates
private and social net benefits.
    EPA has undertaken an analysis of the economy-wide impacts of the
GHG tailpipe standards as an exploratory exercise that EPA believes
could provide additional insights into the potential impacts of the
program.\436\ These results were not a factor regarding the
appropriateness of the GHG tailpipe standards. It is important to note
that the results of this modeling exercise are dependent on the
assumptions associated with how producers will make fuel economy
improvements and how consumers will respond to increases in higher
vehicle costs and improved vehicle fuel economy as a result of the
program. Section III.H.1 discusses the underlying distinctions and
implications of the role of consumer response in economic impacts.
---------------------------------------------------------------------------

    \436\ See Memorandum to Docket, ``Economy-Wide Impacts of
Proposed Greenhouse Gas Tailpipe Standards,'' March 4, 2010. Docket
EPA-HQ-OAR-2009-0472.
---------------------------------------------------------------------------

    Further information on these and other aspects of the economic
impacts of our rule are summarized in the following sections and are
presented in more detail in the RIA for this rulemaking.
1. Conceptual Framework for Evaluating Consumer Impacts
    For this rule, EPA projects significant private gains to consumers
in three major areas: (1) Reductions in spending on fuel, (2) time
saved due to less refueling, and (3) welfare gains from additional
driving that results from the rebound effect. In combination, these
private savings, mostly from fuel savings, appear to outweigh by a
large margin the costs of the program, even without accounting for
externalities.
    Admittedly, these findings pose an economic conundrum. On the one
hand, consumers are expected to gain significantly from the rules, as
the increased cost of fuel efficient cars appears to be far smaller
than the fuel savings. Yet these technologies are readily available;
financially savvy consumers could have sought vehicles with improved
fuel efficiency, and auto makers seeking those customers could have
offered them. Assuming full information, perfect foresight, perfect
competition, and financially rational consumers and producers, standard
economic theory suggests that normal market operations would have
provided the private net gains to consumers, and the only benefits of
the rule would be due to external benefits. If our analysis projects
net private benefits that consumers have not realized in this perfectly
functioning market, then increased fuel economy should be accompanied
by a corresponding loss in consumer welfare. This calculation assumes
that consumers accurately predict and act on all the benefits they will
get from a new vehicle, and that producers market products providing
those benefits. The existence of large private net benefits from this
rule, then, suggests either that the assumptions noted above do not
hold, or that EPA's analysis has missed some factor(s) tied to improved
fuel economy that reduce(s) consumer welfare.
    With respect to the latter, EPA believes the costs of the
technologies developed for this rule take into account the cost needed
to ensure that all vehicle qualities (including performance,
reliability, and size) stay constant, except for fuel economy and
vehicle price. As a result, there would need to be some other changed
qualities that would reduce the benefits consumers receive from their
vehicles. Changing circumstances (e.g., increased demand for horsepower
in response to a drop in fuel prices), and any changes in vehicle
attributes that manufacturers elect to make may result in additional
private impacts to vehicle buyers from requiring increased fuel
economy. Most comments generally supported the cost estimates and the
maintenance of vehicle quality, though two comments expressed concern
over unspecified losses to vehicle quality. Even if there is some such
unidentified loss (which, given existing evidence and modeling
capabilities, is very difficult to quantify), EPA believes that under
realistic assumptions, the private gains from the rule, together with
the social gains (in the form of reduction of externalities), will
continue to substantially outweigh the costs.
    The central conundrum has been referred to as the Energy Paradox in
this setting (and in several others).\437\ In short, the problem is
that consumers appear not to purchase products that are in their
economic self-interest. There are

strong theoretical reasons why this might be so: \438\
---------------------------------------------------------------------------

    \437\ Jaffe, A.B., and Stavins, R.N. (1994). The Energy Paradox
and the Diffusion of Conservation Technology. Resource and Energy
Economics, 16(2), 91-122. Docket EPA-HQ-OAR-2009-0472-11415.
    \438\ For an overview, see id.
---------------------------------------------------------------------------

     Consumers might be myopic and hence undervalue the long-
term.
     Consumers might lack information or a full appreciation of
information even when it is presented.
     Consumers might be especially averse to the short-term
losses associated with the higher prices of energy efficient products
relative to the uncertain future fuel savings, even if the expected
present value of those fuel savings exceeds the cost (the behavioral
phenomenon of ``loss aversion'')
     Even if consumers have relevant knowledge, the benefits of
energy-efficient vehicles might not be sufficiently salient to them at
the time of purchase, and the lack of salience might lead consumers to
neglect an attribute that it would be in their economic interest to
consider.
     In the case of vehicle fuel efficiency, and perhaps as a
result of one or more of the foregoing factors, consumers may have
relatively few choices to purchase vehicles with greater fuel economy
once other characteristics, such as vehicle class, are chosen.\439\
---------------------------------------------------------------------------

    \439\ For instance, the range of fuel economy (combined city and
highway) available among all listed 2010 6-cylinder minivans is 18
to 20 miles per gallon. With a manual-transmission 4-cylinder
minivan, it is possible to get 24 mpg. See http://
www.fueleconomy.gov, which is jointly maintained by the U.S.
Department of Energy and the EPA. For recent but unpublished
evidence, see Allcott, Hunt, and Nathan Wozny, ``Gasoline Prices,
Fuel Economy, and the Energy Paradox'' (2010), available at http://
web.mit.edu/allcott/www/Allcott%20and%20Wozny%202010%20-
%20Gasoline%20Prices,%20Fuel%20Economy,%20and%20the%20Energy%20Parado
x.pdf.
---------------------------------------------------------------------------

    A great deal of work in behavioral economics identifies and
elaborates factors of this sort, which help account for the Energy
Paradox.\440\ This point holds in the context of fuel savings (the main
focus here), but it applies equally to the other private benefits,
including reductions in refueling time and additional driving.\441\ For
example, it might well be questioned whether significant reductions in
refueling time, and corresponding private savings, are fully
internalized when consumers are making purchasing decisions.
---------------------------------------------------------------------------

    \440\ Jaffe, A.B., and Stavins, R.N. (1994). The Energy Paradox
and the Diffusion of Conservation Technology. Resource and Energy
Economics, 16(2), 91-122. Docket EPA-HQ-OAR-2009-0472-11415. See
also Allcott and Wozny, supra note.
    \441\ For example, it might be maintained that, at the time of
purchase, consumers take full account of the time spent refueling
potentially saved by fuel-efficient cars, but it might also be
questioned whether they have adequate information to do so, or
whether that factor is sufficiently salient to play the proper role
in purchasing decisions.
---------------------------------------------------------------------------

    Considerable research findings indicate that the Energy Paradox is
real and significant but the literature has not reached a consensus
about the reasons for its existence. Several researchers have found
evidence suggesting that consumers do not give full or appropriate
weight to fuel economy in purchasing decisions. For example, Sanstad
and Howarth \442\ argue that consumers optimize behavior without full
information by resorting to imprecise but convenient rules of thumb.
Some studies find that a substantial portion of this undervaluation can
be explained by inaccurate assessments of energy savings, or by
uncertainty and irreversibility of energy investments due to
fluctuations in energy prices.\443\ For a number of reasons, consumers
may undervalue future energy savings due to routine mistakes in how
they evaluate these trade-offs. For instance, the calculation of fuel
savings is complex, and consumers may not make it correctly.\444\ The
attribute of fuel economy may be insufficiently salient, leading to a
situation in which consumers pay less than $1 for an expected $1
benefit in terms of discounted gasoline costs.\445\ Larrick and Soll
(2008) find that consumers do not understand how to translate changes
in miles-per-gallon into fuel savings (a concern that EPA is continuing
to attempt to address).\446\ In addition, future fuel price (a major
component of fuel savings) is highly uncertain. Consumer fuel savings
also vary across individuals, who travel different amounts and have
different driving styles. Cost calculations based on the average do not
distinguish between those that may gain or lose as a result of the
policy.\447\ Studies regularly show that fuel economy plays a role in
consumers' vehicle purchases, but modeling that role is still in
development, and there is no consensus that most consumers make fully
informed tradeoffs.\448\
---------------------------------------------------------------------------

    \442\ Sanstad, A., and R. Howarth (1994). `` `Normal' Markets,
Market Imperfections, and Energy Efficiency.'' Energy Policy 22(10):
811-818 (Docket EPA-HQ-OAR-2009-0472-11416).
    \443\ Greene, D., J. German, and M. Delucchi (2009). ``Fuel
Economy: The Case for Market Failure'' in Reducing Climate Impacts
in the Transportation Sector, Sperling, D., and J. Cannon, eds.
Springer Science (Docket EPA-HQ-OAR-2009-0472-11538); Dasgupta, S.,
S. Siddarth, and J. Silva-Risso (2007). ``To Lease or to Buy? A
Structural Model of a Consumer's Vehicle and Contract Choice
Decisions.'' Journal of Marketing Research 44: 490-502 (Docket EPA-
HQ-OAR-2009-0472-11539); Metcalf, G., and D. Rosenthal (1995). ``The
`New' View of Investment Decisions and Public Policy Analysis: An
Application to Green Lights and Cold Refrigerators,'' Journal of
Policy Analysis and Management 14: 517-531 (Docket EPA-HQ-OAR-2009-
0472-11540); Hassett, K., and G. Metcalf (1995), ``Energy Tax
Credits and Residential Conservation Investment: Evidence from Panel
Data,'' Journal of Public Economics 57: 201-217 (Docket EPA-HQ-OAR-
2009-0472-11543); Metcalf, G., and K. Hassett (1999), ``Measuring
the Energy Savings from Home Improvement Investments: Evidence from
Monthly Billing Data,'' The Review of Economics and Statistics
81(3): 516-528 (Docket EPA-HQ-OAR-2009-0472-0051); van Soest D., and
E. Bulte (2001), ``Does the Energy-Efficiency Paradox Exist?
Technological Progress and Uncertainty.'' Environmental and Resource
Economics 18: 101-12 (Docket EPA-HQ-OAR-2009-0472-11542).
    \444\ Turrentine, T. and K. Kurani (2007). ``Car Buyers and Fuel
Economy?'' Energy Policy 35: 1213-1223 (Docket EPA-HQ-OAR-2009-
0472); Larrick, R.P., and J.B. Soll (2008). ``The MPG illusion.''
Science 320: 1593-1594 (Docket EPA-HQ-OAR-2009-0472-0041).
    \445\ Allcott, Hunt, and Nathan Wozny, ``Gasoline Prices, Fuel
Economy, and the Energy Paradox'' (2010), available at http://
web.mit.edu/allcott/www/Allcott%20and%20Wozny%202010%20-
%20Gasoline%20Prices,%20Fuel%20Economy,%20and%20the%20Energy%20Parado
x.pdf (Docket EPA-HQ-OAR-2009-0472-11554).
    \446\ Sanstad, A., and R. Howarth (1994). `` `Normal' Markets,
Market Imperfections, and Energy Efficiency.'' Energy Policy 22(10):
811-818 (Docket EPA-HQ-OAR-2009-0472-11415); Larrick, R. P., and
J.B. Soll (2008). ``The MPG illusion.'' Science 320: 1593-1594
(Docket EPA-HQ-OAR-2009-0472-0043).
    \447\ Hausman J., Joskow P. (1982). ``Evaluating the Costs and
Benefits of Appliance Efficiency Standards.'' American Economic
Review 72: 220-25 (Docket EPA-HQ-OAR-2009-0472-11541).
    \448\ E.g., Goldberg, Pinelopi Koujianou, ``Product
Differentiation and Oligopoly in International Markets: The Case of
the U.S. Automobile Industry,'' Econometrica 63(4) (July 1995): 891-
951 (Docket EPA-HQ-OAR-2009-0472-0021); Goldberg, Pinelopi
Koujianou, ``The Effects of the Corporate Average Fuel Efficiency
Standards in the U.S.,'' Journal of Industrial Economics 46(1)
(March 1998): 1-33 (Docket EPA-HQ-OAR-2009-0472-0017); Busse, Meghan
R., Christopher R. Knittel, and Florian Zettelmeyer (2009). ``Pain
at the Pump: How Gasoline Prices Affect Automobile Purchasing in New
and Used Markets,'' Working paper (accessed 6/30/09), available at
http://www.econ.ucdavis.edu/faculty/knittel/papers/gaspaper_
latest.pdf. (Docket EPA-HQ-OAR-2009-0472-0044).
---------------------------------------------------------------------------

    Some studies find that a substantial portion of the Energy Paradox
can be explained in models of consumer behavior. For instance, one set
of studies finds that accounting for uncertainty in fuel savings over
time due to unanticipated changes in fuel prices goes a long way toward
explaining this paradox. In this case, consumers give up some uncertain
future fuel savings to avoid higher upfront costs.
    A recent review commissioned by EPA supports the finding of great
variability, by looking at one key parameter: The role of fuel economy
in consumers' vehicle purchase decisions.\449\ The review finds no

consensus on the role of fuel economy in consumer purchase decisions.
Of 27 studies, significant numbers of them find that consumers
undervalue, overvalue, or value approximately correctly the fuel
savings that they will receive from improved fuel economy. The
variation in the value of fuel economy in these studies is so high that
it appears to be inappropriate to identify one central estimate from
the literature. Thus, estimating consumer response to higher vehicle
fuel economy is still unsettled science.
---------------------------------------------------------------------------

    \449\ Greene, David L. ``How Consumers Value Fuel Economy: A
Literature Review.'' EPA Report EPA-420-R-10-008, March 2010 (Docket
EPA-HQ-OAR-2009-0472-11575).
---------------------------------------------------------------------------

    If there is a difference between fuel savings and consumers'
willingness to pay for fuel savings, the next question is, which is the
appropriate measure of consumer benefit? Fuel savings measure the
actual monetary value that consumers will receive after purchasing a
vehicle; the willingness to pay for fuel economy measures the value
that, before a purchase, consumers place on additional fuel economy. As
noted, there are a number of reasons that consumers may incorrectly
estimate the benefits that they get from improved fuel economy,
including risk or loss aversion, and poor ability to calculate savings.
Also as noted, fuel economy may not be as salient as other vehicle
characteristics when a consumer is considering vehicles. If these
arguments are valid, then there will be significant gains to consumers
of the government mandating additional fuel economy.
    EPA requested and received a number of comments discussing the role
of the Energy Paradox in consumer vehicle purchase decisions. Ten
commenters, primarily from a number of academic and non-governmental
organizations, argued that there is a gap between the fuel economy that
consumers purchased and the cost-effective amount, due to a number of
market and behavioral phenomena. These include consumers having
inadequate information about future fuel savings relative to up-front
costs; imperfect competition among auto manufacturers; lack of choice
over fuel economy within classes; lack of salience of fuel economy
relative to other vehicle features at the time of vehicle purchase;
consumer use of heuristic decision-making processes or other rules of
thumb, rather than analyzing fuel economy decisions; consumer risk and
loss aversion leading to more attention to up-front costs than future
fuel savings; and consumer emphasis on visible, status-providing
features of vehicles more than on relatively invisible features such as
fuel economy. The RIA, Chapter 8.1.2, includes further discussion of
these phenomena.
    Because of the gap between the fuel economy consumers purchase and
the cost-effective amount, those and additional commenters support
using the full value of fuel savings as a benefit of the rule. A few
asserted, in addition, that auto companies would benefit from offering
vehicles with improved fuel economy. Automakers might underprovide fuel
economy because they believe consumers would not buy it, or that it is
not as salient as price when consumers are buying a vehicle. The
commenters who supported the existence of the gap cite these phenomena
as a basis for regulation of fuel economy. In contrast, two commenters
(the United Auto Workers and one nonprofit research organization)
argued that the market for fuel economy works efficiently; consumers
reveal through their purchase decisions that additional fuel economy is
not important for them. These commenters expressed concern that
regulation to promote more fuel economy would limit consumers' choices
as well as the value of the vehicles to consumers. Yet other commenters
(including some states) noted that the rule protects the existing
variety and choice of vehicles in the market; for this reason, the
value of vehicles to consumers should not suffer as a result of the
rule.
    While acknowledging the diversity of perspectives, EPA continues to
include the full fuel savings as private benefits of the rule. Improved
fuel economy will significantly reduce consumer expenditures on fuel,
thus benefiting consumers. It is true that limitations in modeling
affect our ability to estimate how much of these savings would have
occurred in the absence of the rule. For example, some of the
technologies predicted to be adopted in response to the rule may
already be developing due to shifts in consumer demand for fuel
economy. It is possible that some of these savings would have occurred
in the absence of the rule. To the extent that greater fuel economy
improvements than those assumed to occur under the baseline may have
occurred due to market forces alone (absent the rule), the analysis
overestimates private and social net benefits. In the absence of robust
means to identify the changes in fuel economy that would have occurred
without the rule, we estimate the benefits and costs under the
assumption that the rule will lead to more fuel-efficient vehicles than
would have occurred without the rule. As discussed below, limitations
in modeling also affect our ability to estimate the effects of the rule
on net benefits in the market for vehicles.
    Consumer vehicle choice models estimate what vehicles consumers buy
based on vehicle and consumer characteristics. In principle, such
models could provide a means of understanding both the role of fuel
economy in consumers' purchase decisions and the effects of this rule
on the benefits that consumers will get from vehicles. The NPRM
included a discussion of the wide variation in the structure and
results of these models. Models or model results have not frequently
been systematically compared to each other. When they have, the results
show large variation over, for instance, the value that consumers place
on additional fuel economy. As a result, EPA found that further
assessment needed to be done before adopting a consumer vehicle choice
model. In the NPRM, EPA asked for comment on the state of the art of
consumer vehicle choice modeling and whether it is sufficiently
developed for use in regulatory analysis.
    The responses were varied. Of the six commenters on this issue,
five supported EPA's performing consumer vehicle choice modeling, but
only in general terms; they did not provide recommendations for how to
evaluate the quality of different models or identify a model
appropriate for EPA's purposes. One commenter argued that, if key
differences across models were controlled, then different models would
produce similar results, but there were no suggestions for what choices
to make to control the key differences. One commenter specifically
asked for estimates that quantify losses to consumer welfare. Two
commenters mentioned the importance of taking into account any losses
in vehicle attributes due to increasing fuel economy, but without
specific guidance for how to do so. Some commenters, including some who
supported the use of these models, highlighted some of the models'
potential limitations. Two commenters noted the challenges of modeling
for vehicles that are not yet in the market. Most consumer vehicle
choice models are based on existing vehicle fleets. Future vehicles
will present combinations of vehicle characteristics not previously
seen in markets, such as higher fuel economy and higher price with
other characteristics constant; the existing models may not do well in
predicting consumer responses to these changes. One comment suggested
that the models might be sufficient for predicting changes in consumer
purchase patterns, but not for calculating the welfare gains and losses
to consumers of the changes.
    EPA has not used a consumer vehicle choice model for the final rule
analysis, due to concerns we explained in the

proposal (and discussed in Chapter 8.1 of the RIA), and because no new
information became available to resolve those concerns. It is likely
that variation exists in measuring consumer response to changes in fuel
economy as well as other vehicle characteristics, such as performance.
Thus, there does not appear to be evidence at this time to develop
robust estimates of consumer welfare effects of changes in vehicle
attributes. As noted earlier, EPA's and NHTSA's cost estimates are
based on maintaining these other vehicle attributes. Comments generally
supported the finding that our cost and technology estimates succeeded
in maintaining these other attributes.
    EPA will continue its efforts to review the literature, but, given
the known difficulties, EPA has not conducted an analysis using these
models for this program. These issues are discussed in detail in RIA
Chapter 8.1.2.
    The next issue is the potential for loss in consumer welfare due to
the rule. As mentioned above (and discussed more thoroughly in Section
III.D of this preamble), the technology cost estimates developed here
take into account the costs to hold other vehicle attributes, such as
size and performance, constant. In addition, the analysis assumes that
the full technology costs are passed along to consumers. With these
assumptions, because welfare losses are monetary estimates of how much
consumers would have to be compensated to be made as well off as in the
absence of the change,\450\ the price increase measures the loss to the
consumer.\451\ Assuming that the full technology cost gets passed along
to the consumer as an increase in price, the technology cost thus
measures the welfare loss to the consumer. Increasing fuel economy
would have to lead to other changes in the vehicles that consumers find
undesirable for there to be additional losses not included in the
technology costs.
---------------------------------------------------------------------------

    \450\ This approach describes the economic concept of
compensating variation, a payment of money after a change that would
make a consumer as well off after the change as before it. A related
concept, equivalent variation, estimates the income change that
would be an alternative to the change taking place. The difference
between them is whether the consumer's point of reference is her
welfare before the change (compensating variation) or after the
change (equivalent variation). In practice, these two measures are
typically very close together.
    \451\ Indeed, it is likely to be an overestimate of the loss to
the consumer, because the consumer has choices other than buying the
same vehicle with a higher price; she could choose a different
vehicle, or decide not to buy a new vehicle. The consumer would
choose one of those options only if the alternative involves less
loss than paying the higher price. Thus, the increase in price that
the consumer faces would be the upper bound of loss of consumer
welfare, unless there are other changes to the vehicle due to the
fuel economy improvements that make the vehicle less desirable to
consumers.
---------------------------------------------------------------------------

    At this time EPA has no available methods to estimate potential
additional effects on consumers not included in the technology cost
estimates, e.g., due to changes in vehicles that consumers find
undesirable, shifts in consumer demand for other attributes, and
uncertainties about the long term reliability of new technologies.
Comments on the rule generally supported EPA's analysis of the
technology costs and the assumption that other vehicle characteristics
were not adversely affected. Any consumer welfare loss cannot be
quantified at this time. For reasons stated above, EPA believes that
any such loss is likely far smaller than the private gains, including
fuel savings and reduced refueling time.
    Chapter 8.1 of the RIA discusses in more depth the research on the
Energy Paradox and the state of the art of consumer vehicle choice
modeling.
2. Costs Associated With the Vehicle Program
    In this section, EPA presents our estimate of the costs associated
with the final vehicle program. The presentation here summarizes the
costs associated with the new vehicle technology expected to be added
to meet the new GHG standards, including hardware costs to comply with
the A/C credit program. The analysis summarized here provides our
estimate of incremental costs on a per vehicle basis and on an annual
total basis.
    The presentation here summarizes the outputs of the OMEGA model
that was discussed in some detail in Section III.D of this preamble.
For details behind the analysis such as the OMEGA model inputs and the
estimates of costs associated with individual technologies, the reader
is directed to Chapters 1 and 2 of the RIA, and Chapter 3 of the Joint
TSD. For more detail on the outputs of the OMEGA model and the overall
vehicle program costs summarized here, the reader is directed to
Chapters 4 and 7 of the RIA.
    With respect to the cost estimates for vehicle technologies, EPA
notes that, because these estimates relate to technologies which are in
most cases already available, these cost estimates are technically
robust. Some comments were received that addressed the technology costs
that served as inputs to the OMEGA model as was mentioned in Section
II.E. While those comments did not result in changes to the technology
cost inputs, the technology cost estimates for a select group of
technologies have changed since the NPRM thus changing the vehicle
program costs presented here. These changes, as summarized in Section
II.E and in Chapter 3 of the Joint TSD, were made in response to
updated cost estimates, from the FEV teardown study, available to the
agencies shortly after publication of the NPRM, not in response to
comments. Those cost changes are summarized in Section II.E and in
Chapter 3 of the Joint TSD. EPA believes that we have been conservative
in estimating the vehicle hardware costs associated with this rule.
    With respect to the aggregate cost estimations presented in Section
III.H.2.b, EPA notes that there are a number of areas where the results
of our analysis may be conservative and, in general, EPA believes we
have directionally overestimated the costs of compliance with these new
standards, especially in not accounting for the full range of credit
opportunities available to manufacturers. For example, some cost saving
programs are considered in our analysis, such as full car/truck
trading, while others are not, such as early credit generation and
advanced vehicle technology credits.
a. Vehicle Compliance Costs Associated With the CO2
Standards
    For the technology and vehicle package costs associated with adding
new CO2-reducing technology to vehicles, EPA began with
EPA's 2008 Staff Report and NHTSA's 2011 CAFE FRM both of which
presented costs generated using existing literature, meetings with
manufacturers and parts suppliers, and meetings with other experts in
the field of automotive cost estimation.\452\ EPA has updated some of
those technology costs with new information from our contract with FEV,
through further discussion with NHTSA, and by converting from 2006
dollars to 2007 dollars using the GDP price deflator. The estimated
costs presented here represent the incremental costs associated with
this rule relative to what the future vehicle fleet would be expected
to look like absent this rule. A more detailed description of the
factors considered in our reference case is presented in Section III.D.
---------------------------------------------------------------------------

    \452\ ``EPA Staff Technical Report: Cost and Effectiveness
Estimates of Technologies Used to Reduce Light-Duty Vehicle Carbon
Dioxide Emissions,'' EPA 420-R-08-008; NHTSA 2011 CAFE FRM is at 74
FR 14196; both documents are contained in Docket EPA-HQ-OAR-2009-
0472.
---------------------------------------------------------------------------

    The estimates of vehicle compliance costs cover the years of
implementation of the program--2012 through 2016. EPA has also
estimated compliance costs for the years following implementation so
that we can shed

light on the long term (2022 and later) cost impacts of the
program.\453\ EPA used the year 2022 here because our short-term and
long-term markup factors described shortly below are applied in five
year increments with the 2012 through 2016 implementation span and the
2017 through 2021 span both representing the short-term. Some of the
individual technology cost estimates are presented in brief in Section
III.D, and account for both the direct and indirect costs incurred in
the automobile manufacturing and dealer industries (for a complete
presentation of technology costs, please refer to Chapter 3 of the
Joint TSD). To account for the indirect costs, EPA has applied an
indirect cost markup (ICM) factor to all of our direct costs to arrive
at the estimated technology cost.\454\ The ICM factors used range from
1.11 to 1.64 in the short-term (2012 through 2021), depending on the
complexity of the given technology, to account for differences in the
levels of R&D, tooling, and other indirect costs that will be incurred.
Once the program has been fully implemented, some of the indirect costs
will no longer be attributable to these standards and, as such, a lower
ICM factor is applied to direct costs in years following full
implementation. The ICM factors used range from 1.07 to 1.39 in the
long-term (2022 and later) depending on the complexity of the given
technology.\455\ Note that the short-term ICMs are used in the 2012
through 2016 years of implementation and continue through 2021. EPA
does this since the standards are still being implemented during the
2012 through 2016 model years. Therefore, EPA considers the five year
period following full implementation also to be short-term. Note that,
in general the comments received were supportive of our use of ICMs as
opposed to the more traditional Retail Price Equivalent (RPE).\456\
However, we did receive some comment that we applied inappropriate ICM
factors to some technologies. We have not changed our approach in
response to those comments as explained in greater detail in our
Response to Comments document.
---------------------------------------------------------------------------

    \453\ Note that the assumption made here is that the standards
would continue to apply for years beyond 2016 so that new vehicles
sold in model years 2017 and later would continue to incur costs as
a result of this rule. Those costs are estimated to get lower in
2022 because some of the indirect costs attributable to this rule in
the years prior to 2022 would be eliminated in 2022 and later.
    \454\ Need to add the recent reference for this study by RTI.
Alex Rogozhin et al., Automobile Industry Regail Price Equivalent
and Indirect Cost Multipliers. Prepared for EPA by RTI International
and Transportation Research Institute, University of Michigan. EPA-
420-R-09-003, February 2009 (Docket EPA-HQ-OAR-2009-0472).
    \455\ Gloria Helfand and Todd Sherwood, ``Documentation of the
Development of Indirect Cost Multipliers for Three Automotive
Technologies,'' Office of Transportation and Air Quality, U.S. EPA,
August 2009 (Docket EPA-HQ-OAR-2009-0472).
    \456\ The RPE is based on the historical relationship between
direct costs and consumer prices; it is intended to reflect the
average markup over time required to sustain the industry as a
viable operation. Unlike the RPE approach, the ICM focuses more
narrowly on the changes that are required in direct response to
regulation-induced vehicle design changes which may not directly
influence all of the indirect costs that are incurred in the normal
course of business. For example, an RPE markup captures all indirect
costs including costs such as the retirement benefits of retired
employees. However, the retirement benefits for retired employees
are not expected to change as a result of a new GHG regulation and,
therefore, those indirect costs should not increase in relation to
newly added hardware in response to a regulation.
---------------------------------------------------------------------------

    EPA has also considered the impacts of manufacturer learning on the
technology cost estimates. Consistent with past EPA rulemakings, EPA
has estimated that some costs would decline by 20 percent with each of
the first two doublings of production beginning with the first year of
implementation. These volume-based cost declines, which EPA calls
``volume'' based learning, take place after manufacturers have had the
opportunity to find ways to improve upon their manufacturing processes
or otherwise manufacture these technologies in a more efficient way.
After two 20 percent cost reduction steps, the cost reduction learning
curve flattens out considerably as only minor improvements in
manufacturing techniques and efficiencies remain to be had. By then,
costs decline roughly three percent per year as manufacturers and
suppliers continually strive to reduce costs. These time-based cost
declines, which EPA calls ``time'' based learning, take place at a rate
of three percent per year. EPA has considered learning impacts on most
but not all of the technologies expected to be used because some of the
expected technologies are already used rather widely in the industry
and, presumably, learning impacts have already occurred. EPA has
considered volume-based learning for only a handful of technologies
that EPA considers to be new or emerging technologies such as the
hybrids and electric vehicles. For most technologies, EPA has
considered them to be more established given their current use in the
fleet and, hence, we have applied the lower time based learning. We
have more discussion of our learning approach and the technologies to
which we have applied which type of learning in Chapter 3 of the Joint
TSD.
    The technology cost estimates discussed in Section III.D and
detailed in Chapter 3 of the Joint TSD are used to build up technology
package cost estimates which are then used as inputs to the OMEGA
model. EPA discusses our technology packages and package costs in
Chapter 1 of the RIA. The model determines what level of CO2
improvement is required considering the reference case for each
manufacturer's fleet. The vehicle compliance costs are the outputs of
the model and take into account FFV credits through 2015, TLAAS, full
car/truck trading, and the A/C credit program. Table III.H.2-1 presents
the fleet average incremental vehicle compliance costs for this rule.
As the table indicates, 2012-2016 costs increase every year as the
standards become more stringent. Costs per car and per truck then
remain stable through 2021 while cost per vehicle (car/truck combined)
decline slightly as the fleet mix trends slowly to increasing car
sales. In 2022, costs per car and per truck decline as the long-term
ICM is applied because some indirect costs decrease or are no longer
considered attributable to the program (e.g., warranty costs go down).
Costs per car and per truck remain constant thereafter while the cost
per vehicle declines slightly as the fleet continues to trend toward
cars. By 2030, projections of fleet mix changes become static and the
cost per vehicle remains constant. EPA has a more detailed presentation
of vehicle compliance costs on a manufacturer by manufacturer basis in
Chapter 6 of the RIA.

  Table III.H.2-1--Industry Average Vehicle Compliance Costs Associated
                     With the Tailpipe CO2 Standards
                       [$/vehicle in 2007 dollars]
------------------------------------------------------------------------
                                                              $/vehicle
                                                                (car &
          Calendar year               $/car       $/truck       truck
                                                              combined)
------------------------------------------------------------------------
2012.............................         $342         $314         $331

2013.............................          507          496          503
2014.............................          631          652          639
2015.............................          749          820          774
2016.............................          869        1,098          948
2017.............................          869        1,098          947
2018.............................          869        1,098          945
2019.............................          869        1,098          943
2020.............................          869        1,098          940
2021.............................          869        1,098          939
2022.............................          817        1,032          882
2030.............................          817        1,032          878
2040.............................          817        1,032          875
2050.............................          817        1,032          875
------------------------------------------------------------------------

b. Annual Costs of the Vehicle Program
    The costs presented here represent the incremental costs for newly
added technology to comply with the final program. Together with the
projected increases in car and light-truck sales, the increases in per-
vehicle average costs shown in Table III.H.2-1 above result in the
total annual costs reported in Table III.H.2-2 below. Note that the
costs presented in Table III.H.2-2 do not include the savings that
would occur as a result of the improvements to fuel consumption. Those
impacts are presented in Section III.H.4.

  Table III.H.2-2--Quantified Annual Costs Associated With the Vehicle
                                 Program
                       [$Millions of 2007 dollars]
------------------------------------------------------------------------
                                                            Quantified
                          Year                             annual costs
------------------------------------------------------------------------
2012....................................................          $4,900
2013....................................................           8,000
2014....................................................          10,300
2015....................................................          12,700
2016....................................................          15,600
2020....................................................          15,600
2030....................................................          15,800
2040....................................................          17,400
2050....................................................          19,000
NPV, 3%.................................................         345,900
NPV, 7%.................................................         191,900
------------------------------------------------------------------------

3. Cost per Ton of Emissions Reduced
    EPA has calculated the cost per ton of GHG (CO2-
equivalent, or CO2e) reductions associated with this rule
using the above costs and the emissions reductions described in Section
III.F. More detail on the costs, emission reductions, and the cost per
ton can be found in the RIA and Joint TSD. EPA has calculated the cost
per metric ton of GHG emissions reductions in the years 2020, 2030,
2040, and 2050 using the annual vehicle compliance costs and emission
reductions for each of those years. The value in 2050 represents the
long-term cost per ton of the emissions reduced. EPA has also
calculated the cost per metric ton of GHG emission reductions including
the savings associated with reduced fuel consumption (presented below
in Section III.H.4). This latter calculation does not include the other
benefits associated with this rule such as those associated with
criteria pollutant reductions or energy security benefits as discussed
later in sections III.H.4 through III.H.9. By including the fuel
savings in the cost estimates, the cost per ton is less than $0, since
the estimated value of fuel savings outweighs the vehicle program
costs. With regard to the CH4 and N2O standards,
since these standards will be emissions caps designed to ensure that
manufacturers do not backslide from current levels, EPA has not
estimated costs associated with the standards (since the standards will
not require any change from current practices nor does EPA estimate
they will result in emissions reductions).
    The results for CO2e costs per ton under the rule are
shown in Table III.H.3-1.

                 Table III.H.3-1-- Annual Cost Per Metric Ton of CO2e Reduced, in $2007 Dollars
----------------------------------------------------------------------------------------------------------------
                                                                                Cost per ton
                                      Vehicle      Fuel savings    CO2e reduced    Cost per ton   of the vehicle
              Year                 program cost         \b\          (million     of the vehicle   program with
                                        \a\         ($millions)    metric tons)    program only    fuel savings
                                    ($millions)                                         \a\             \b\
----------------------------------------------------------------------------------------------------------------
2020............................         $15,600        -$35,700             160            $100           -$130
2030............................          15,800         -79,800             310              50            -210
2040............................          17,400        -119,300             400              40            -250
2050............................          19,000        -171,200             510              40            -300
----------------------------------------------------------------------------------------------------------------
\a\ Costs here include vehicle compliance costs and do not include any fuel savings.
\b\ Fuel savings calculated using pre-tax fuel prices.

4. Reduction in Fuel Consumption and Its Impacts
a. What are the projected changes in fuel consumption?
    The new CO2 standards will result in significant
improvements in the fuel efficiency of affected vehicles. Drivers of
those vehicles will see corresponding savings associated with reduced
fuel expenditures. EPA has estimated the impacts on fuel consumption
for both the tailpipe CO2 standards and the A/C credit
program. To do this, fuel consumption is calculated using both current
CO2 emission levels and the new CO2 standards.
The difference between these estimates represents the net savings from
the CO2 standards. Note that the total number of miles that
vehicles are driven each year is different under each of the control
case scenarios than in the reference case due to the ``rebound
effect,'' which is discussed in Section III.H.4.c. EPA also notes that
consumers who drive more than our average estimates for vehicle miles
traveled (VMT) will experience more fuel savings; consumers who drive
less than our average VMT estimates will experience less fuel savings.
    The expected impacts on fuel consumption are shown in Table
III.H.4-1. The gallons shown in the tables reflect impacts from the new
CO2 standards, including the A/C credit program, and include
increased consumption resulting from the rebound effect.

Table III.H.4-1--Fuel Consumption Impacts of the Vehicle Standards and A/
                            C Credit Programs
                            [Million gallons]
------------------------------------------------------------------------
                            Year                                Total
------------------------------------------------------------------------
2012.......................................................          550
2013.......................................................        1,320
2014.......................................................        2,330
2015.......................................................        3,750
2016.......................................................        5,670
2020.......................................................       12,590
2030.......................................................       24,730
2040.......................................................       32,620
2050.......................................................       41,520
------------------------------------------------------------------------

b. What are the monetized fuel savings?
    Using the fuel consumption estimates presented in Section
III.H.4.a, EPA can calculate the monetized fuel savings associated with
the CO2 standards. To do this, we multiply reduced fuel
consumption in each year by the corresponding estimated average fuel
price in that year, using the reference case taken from the AEO 2010
Early Release.\457\ AEO is the government consensus estimate used by
NHTSA and many other government agencies to estimate the projected
price of fuel. EPA has done this calculation using both the pre-tax and
post-tax fuel prices. Since the post-tax fuel prices are what consumers
pay, the fuel savings calculated using these prices represent the
savings consumers will see. The pre-tax fuel savings are those savings
that society will see. These results are shown in Table III.H.4-2. Note
that in Section III.H.10, EPA presents the benefit-cost of the rule
and, for that reason, presents only the pre-tax fuel savings.
---------------------------------------------------------------------------

    \457\ Energy Information Administration. Annual Energy Outlook
2010 Early Release. Supplemental Transportation Tables. December
2009. http://www.eia.doe.gov/oiaf/aeo/supplement/sup_tran.xls.

            Table III.H.4-2--Estimated Monetized Fuel Savings
                       [Millions of 2007 dollars]
------------------------------------------------------------------------
                                           Fuel savings    Fuel savings
              Calendar year                  (pre-tax)      (post-tax)
------------------------------------------------------------------------
2012....................................          $1,137          $1,400
2013....................................           2,923           3,800
2014....................................           5,708           6,900
2015....................................           9,612          11,300
2016....................................          14,816          17,400
2020....................................          35,739          41,100
2030....................................          79,838          89,100
2040....................................         119,324         131,700
2050....................................         171,248         186,300
NPV, 3%.................................       1,545,638       1,723,900
NPV, 7%.................................         672,629         755,700
------------------------------------------------------------------------

    As shown in Table III.H.4-2, EPA is projecting that consumers would
realize very large fuel savings as a result of the standards contained
in this rule. As discussed further in Section III.H.1, it is a
conundrum from an economic perspective that these large fuel savings
have not been provided by automakers and purchased by consumers. A
number of behavioral and market phenomena may lead to this disparity
between the fuel economy that makes financial sense to consumers and
the fuel economy they purchase. Regardless how consumers make their
decisions on how much fuel economy to purchase, EPA expects that, in
the aggregate, they will gain these fuel savings, which will provide
actual money in consumers' pockets. We received considerable comment on
this issue, as discussed in Section III.H.1, and the issue is discussed
further in Chapter 8 of the RIA.
c. VMT Rebound Effect
    The fuel economy rebound effect refers to the fraction of fuel
savings expected to result from an increase in vehicle fuel economy,
particularly one required by higher fuel efficiency standards, that is
offset by additional vehicle use. The increase in vehicle use occurs
because higher fuel economy reduces the fuel cost of driving, which is
typically the largest single component of the monetary cost of
operating a

vehicle, and vehicle owners respond to this reduction in operating
costs by driving slightly more.
    For this rule, EPA is using an estimate of 10% for the rebound
effect. This value is based on the most recent time period analyzed in
the Small and Van Dender 2007 paper,\458\ and falls within the range of
the larger body of historical work on the rebound effect.\459\ Recent
work by David Greene on the rebound effect for light-duty vehicles in
the U.S. further supports the hypothesis that the rebound effect is
decreasing over time.\460\ If we were to use a dynamic estimate of the
future rebound effect, our analysis shows that the rebound effect could
be in the range of 5% or lower.\461\ The rebound effect is also further
discussed in Chapter 4 of the Joint TSD which reviews the relevant
literature and discusses in more depth the reasoning for the rebound
values used here.
---------------------------------------------------------------------------

    \458\ Small, K. and K. Van Dender, 2007a. ``Fuel Efficiency and
Motor Vehicle Travel: The Declining Rebound Effect'', The Energy
Journal, vol. 28, no. 1, pp. 25-51 (Docket EPA-HQ-OAR-2009-0472-
0018).
    \459\ Sorrell, S. and J. Dimitropoulos, 2007. ``UKERC Review of
Evidence for the Rebound Effect, Technical Report 2: Econometric
Studies'', UKERC/WP/TPA/2007/010, UK Energy Research Centre, London,
October (Docket EPA-HQ-OAR-2009-0472-0012).
    \460\ Report by Kenneth A. Small of University of California at
Irvine to EPA, ``The Rebound Effect from Fuel Efficiency Standards:
Measurement and Projection to 2030'', June 12, 2009 (Docket EPA-HQ-
OAR-2009-0472-0002).
    \461\ Revised Report by David Greene of Oak Ridge National
Laboratory to EPA, ``Rebound 2007: Analysis of National Light-Duty
Vehicle Travel Statistics,'' February 9, 2010 (Docket EPA-HQ-OAR-
2009-0472-0220). This paper has been accepted for an upcoming
special issue of Energy Policy, although the publication date has
not yet been determined.
---------------------------------------------------------------------------

    We received several comments on the proposed value of the rebound
effect. The California Air Resources Board (CARB) and the New Jersey
Department of Environmental Protection supported the use of a 10%
rebound effect, although CARB encouraged EPA to consider lowering the
value to 5%. Other commenters, such as the Missouri Department of
Natural Resources, the International Council on Clean Transportation
(ICCT), the Center for Biological Diversity, and the Consumer
Federation of America, recommended using a lower rebound effect. ICCT
specifically recommended that the dynamic rebound effect methodology
utilized by Small & Van Dender was the most appropriate methodology,
which would support a rebound effect of 5% or lower. In contrast, the
National Association of Dealerships asserted that the rebound effect
should be higher (e.g., in the lower range of the 15-30% historical
range), but did not submit any data to support this claim.
    While we appreciate the input provided by commenters, we did not
receive any new data or analysis to justify revising our initial
estimates of the rebound effect at this time. Based on the positive
comments we received, we will continue using the dynamic rebound effect
to help inform our estimate of the rebound effect in future
rulemakings. However, given the relatively new nature of this
analytical approach, we believe the larger body of historical studies
should also be considered when determining the value of the rebound
effect. As we described in the Technical Support Document, the more
recent literature suggests that the rebound effect is 10% or lower,
whereas the larger body of historical studies suggests a higher rebound
effect. Therefore, we will continue to use the 10% rebound effect for
this rulemaking. However, we plan to update our estimate of the rebound
effect in future rulemakings as new data becomes available.
    We also invited comments on whether we should also explore other
alternatives for estimating the rebound effect, such as whether it
would be appropriate to use the price elasticity of demand for gasoline
to guide the choice of a value for the rebound effect. We received only
one comment on this issue from ICCT. In their comments, ICCT stated
that the short run elasticity can provide a useful point of comparison
for rebound effect estimates, but it should not be used to guide the
choice of a value for the rebound effect. Therefore, we have not
incorporated this metric into our analysis.
5. Impacts on U.S. Vehicle Sales and Payback Period
a. Vehicle Sales Impacts
    This analysis compares two effects. On the one hand, the vehicles
will become more expensive, which would, by itself, discourage sales.
On the other hand, the vehicles will have improved fuel economy and
thus lower operating costs. If consumers do not accurately compare the
value of fuel savings with the increased cost of fuel economy
technology in their vehicle purchase decisions, as discussed in
Preamble III.H.1, they will continue to behave in this way after this
rule. If auto makers have accurately gauged how consumers consider fuel
economy when purchasing vehicles and have provided the amount that
consumers want in vehicles, then consumers should not be expected to
want the more fuel-efficient vehicles. After all, auto makers would
have provided as much fuel economy as consumers want. If, on the other
hand, auto makers underestimated consumer demand for fuel economy, as
suggested by some commenters and discussed in Preamble Section III.H.1
and RIA Section 8.1.2, then this rule may lead to production of more
desirable vehicles, and vehicle sales may increase. This assumption
implies that auto makers have missed some profit-making opportunities.
    The methodology EPA used for estimating the impact on vehicle sales
is relatively straightforward, but makes a number of simplifying
assumptions. According to the literature, the price elasticity of
demand for vehicles is commonly estimated to be -1.0.\462\ In other
words, a one percent increase in the price of a vehicle would be
expected to decrease sales by one percent, holding all other factors
constant. For our estimates, EPA calculated the effect of an increase
in vehicle costs due to the GHG standards and assumes that consumers
will face the full increase in costs, not an actual (estimated) change
in vehicle price. (The estimated increases in vehicle cost due to the
rule are discussed in Section III.H.2.) This is a conservative
methodology, since an increase in cost may not pass fully into an
increase in market price in an oligopolistic industry such as the
automotive sector.\463\ EPA also notes that we have not used these
estimated sales impacts in the OMEGA Model.
---------------------------------------------------------------------------

    \462\ Kleit A.N., 1990. ``The Effect of Annual Changes in
Automobile Fuel Economy Standards.'' Journal of Regulatory Economics
2: 151-172 (Docket EPA-HQ-OAR-2009-0472-0015); McCarthy, Patrick S.,
1996. ``Market Price and Income Elasticities of New Vehicle
Demands.'' Review of Economics and Statistics 78: 543-547 (Docket
EPA-HQ-OAR-2009-0472-0016); Goldberg, Pinelopi K., 1998. ``The
Effects of the Corporate Average Fuel Efficiency Standards in the
U.S.,'' Journal of Industrial Economics 46(1): 1-33 (Docket EPA-HQ-
OAR-2009-0472-0017).
    \463\ See, for instance, Gron, Ann, and Deborah Swenson, 2000.
``Cost Pass-Through in the U.S. Automobile Market,'' Review of
Economics and Statistics 82: 316-324 (Docket EPA-HQ-OAR-2009-0472-
0007).
---------------------------------------------------------------------------

    Although EPA uses the one percent price elasticity of demand for
vehicles as the basis for our vehicle sales impact estimates, we
assumed that the consumer would take into account both the higher
vehicle purchasing costs as well as some of the fuel savings benefits
when deciding whether to purchase a new vehicle. Therefore, the
incremental cost increase of a new vehicle would be offset by reduced
fuel expenditures over a certain period of time (i.e., the ``payback
period''). For the purposes of this rulemaking, EPA used a five-year
payback period, which is consistent with the length of a typical new
light-

duty vehicle loan.\464\ The one commenter on this analysis stated that
use of the five-year payback period was reasonable. This approach may
not accurately reflect the role of fuel savings in consumers' purchase
decisions, as the discussion in Section III.H.1 suggests. If consumers
consider fuel savings in a different fashion than modeled here, then
this approach will not accurately reflect the impact of this rule on
vehicle sales.
---------------------------------------------------------------------------

    \464\ As discussed further in Section III.H.1, there is not a
consensus in the literature on how consumers consider fuel economy
in their vehicle purchases. Results are inconsistent, possibly due
to fuel economy not being a major focus of many of the studies, and
possibly due to sensitivity of results to modeling and data used. A
survey by Greene (Greene, David L. ``How Consumers Value Fuel
Economy: A Literature Review.'' EPA Report EPA-420-R-10-008, March
2010 (Docket EPA-HQ-OAR-2009-0472-11575)) finds that estimates in
the literature of the value that consumers place on fuel economy
when buying a vehicle range from negative--consumers would pay to
reduce fuel economy--to more than 1000 times the value of fuel
savings.
---------------------------------------------------------------------------

    This increase in costs has other effects on consumers as well: if
vehicle prices increase, consumers will face higher insurance costs and
sales tax, and additional finance costs if the vehicle is bought on
credit. In addition, the resale value of the vehicles will increase.
EPA received no comments on these adjustments. The only change to these
adjustments between the NPRM and this discussion is an updating of the
interest rate on auto loans. EPA estimates that, with corrections for
these factors, the effect on consumer expenditures of the cost of the
new technology should be 0.914 times the cost of the technology at a 3%
discount rate, and 0.876 times the cost of the technology at a 7%
discount rate. The details of this calculation are in the RIA, Chapter
8.1.
    Once the cost estimates are adjusted for these additional factors,
the fuel cost savings associated with the rule, discussed in Section
III.H.4, are subtracted to get the net effect on consumer expenditures
for a new vehicle. With the assumed elasticity of demand of -1, the
percent change in this ``effective price,'' estimated as the adjusted
increase in cost, is equal to the negative of the percent change in
vehicle purchases. The net effect of this calculation is in Table
III.H.5-1 and Table III.H.5-2. The values have changed slightly from
the NPRM, due to changes in fuel prices and fuel savings, technology
costs, and baseline vehicle sales projections, in addition to the
adjustment in financing costs.
    The estimates provided in Table III.H.5-1 and Table III.H.5-2 are
meant to be illustrative rather than a definitive prediction. When
viewed at the industry-wide level, they give a general indication of
the potential impact on vehicle sales. As shown below, the overall
impact is positive and growing over time for both cars and trucks.
Because the fuel savings associated with this rule are expected to
exceed the technology costs, the effective prices of vehicles (the
adjusted increase in technology cost less the fuel savings over five
years) to consumers will fall, and consumers will buy more new
vehicles. As a result, the lower net cost of the vehicles is projected
to lead to an increase in sales for both cars and trucks.
    As discussed above, this result depends on the assumption that more
fuel efficient vehicles that yield net consumer benefits over five
years would not otherwise be offered on the vehicle market due to
market failures on the part of vehicle manufacturers. If vehicles that
achieve the fuel economy standards prescribed by today's rulemaking
would already be available, but consumers chose not to purchase them,
then this rulemaking would not result in an increase in vehicle sales,
because it does not alter how consumers make decisions about which
vehicles to purchase. In addition, this analysis has not accounted for
a number of factors that might affect consumer vehicle purchases, such
as changing market conditions, changes in vehicle characteristics that
might accompany improvements in fuel economy, or consumers considering
a different ``payback period'' for their fuel economy purchases. If
consumers use a shorter payback period, the sales impacts will be less
positive, possibly negative; if consumers use a higher payback period,
the impacts will be more positive. Also, this is an aggregate analysis;
some individual consumers (those who drive less than estimated here)
will face lower net benefits, while others (who drive more than
estimated here) will have even greater savings. These complications add
considerable uncertainty to our vehicle sales impact analysis.

                         Table III.H.5-1--Vehicle Sales Impacts Using a 3% Discount Rate
----------------------------------------------------------------------------------------------------------------
                                                               Change in                 Change in
                                                               car sales     % Change   truck sales    % Change
----------------------------------------------------------------------------------------------------------------
2012........................................................       67,500          0.7       62,100          1.1
2013........................................................       76,000          0.8      190,200          3.2
2014........................................................      114,000          1.1      254,900          4.3
2015........................................................      222,200          2.1      352,800          6.1
2016........................................................      360,500          3.3      488,000          8.6
----------------------------------------------------------------------------------------------------------------

    Table III.H.5-1 shows the impacts on new vehicle sales using a 3%
discount rate. The fuel savings over five years are always higher than
the technology costs. Although both cars and trucks show very small
effects initially, over time vehicle sales become increasingly
positive, as increased fuel prices make improved fuel economy more
desirable. The increases in sales for trucks are larger than the
increases for trucks (except in 2012) in both absolute numbers and
percentage terms.

                       Table III.H.5-2--New Vehicle Sales Impacts Using a 7% Discount Rate
----------------------------------------------------------------------------------------------------------------
                                                             Change in                  Change in
                                                             car sales     % Change    truck sales    % Change
----------------------------------------------------------------------------------------------------------------
2012......................................................       62,800           0.7       58,300           1
2013......................................................       70,500           0.7       92,300           1.5
2014......................................................      106,100           1        127,700           2.1

2015......................................................      208,400           2        194,200           3.3
2016......................................................      339,400           3.1      280,000           4.9
----------------------------------------------------------------------------------------------------------------

    Table III.H.5-2 shows the impacts on new vehicle sales using a 7%
interest rate. While a 7% interest rate shows slightly lower impacts
than using a 3% discount rate, the results are qualitatively similar to
those using a 3% discount rate. Sales increase for every year. For both
cars and trucks, sales become increasingly positive over time, as
higher fuel prices make improved fuel economy more valuable. The car
market grows more than the truck market in absolute numbers, but less
on a percentage basis.
    The effect of this rule on the use and scrappage of older vehicles
will be related to its effects on new vehicle prices, the fuel
efficiency of new vehicle models, and the total sales of new vehicles.
If the value of fuel savings resulting from improved fuel efficiency to
the typical potential buyer of a new vehicle outweighs the average
increase in new models' prices, sales of new vehicles will rise, while
scrappage rates of used vehicles will increase slightly. This will
cause the ``turnover'' of the vehicle fleet (i.e., the retirement of
used vehicles and their replacement by new models) to accelerate
slightly, thus accentuating the anticipated effect of the rule on
fleet-wide fuel consumption and CO2 emissions. However, if
potential buyers value future fuel savings resulting from the increased
fuel efficiency of new models at less than the increase in their
average selling price, sales of new vehicles will decline, as will the
rate at which used vehicles are retired from service. This effect will
slow the replacement of used vehicles by new models, and thus partly
offset the anticipated effects of this rule on fuel use and emissions.
    Because the agencies are uncertain about how the value of projected
fuel savings from this rule to potential buyers will compare to their
estimates of increases in new vehicle prices, we have not attempted to
estimate explicitly the effects of the rule on scrappage of older
vehicles and the turnover of the vehicle fleet.
    A detailed discussion of the vehicle sales impacts methodology is
provided in the Chapter 8 of EPA's RIA.
b. Consumer Payback Period and Lifetime Savings on New Vehicle
Purchases
    Another factor of interest is the payback period on the purchase of
a new vehicle that complies with the new standards. In other words, how
long would it take for the expected fuel savings to outweigh the
increased cost of a new vehicle? For example, a new 2016 MY vehicle is
estimated to cost $948 more (on average, and relative to the reference
case vehicle) due to the addition of new GHG reducing technology (see
Section III.D.6 for details on this cost estimate). This new technology
will result in lower fuel consumption and, therefore, savings in fuel
expenditures (see Section III.H.10) for details on fuel savings). But
how many months or years would pass before the fuel savings exceed the
upfront cost of $948?
    Table III.H.5-3 provides the answer to this question for a vehicle
purchaser who pays for the new vehicle upfront in cash (we discuss
later in this section the payback period for consumers who finance the
new vehicle purchase with a loan). The table uses annual miles driven
(vehicle miles traveled, or VMT) and survival rates consistent with the
emission and benefits analyses presented in Chapter 4 of the Joint TSD.
The control case includes rebound VMT but the reference case does not,
consistent with other parts of the analysis. Also included are fuel
savings associated with A/C controls (in the control case only). Not
included here are the likely A/C-related maintenance savings as
discussed in Chapter 2 of EPA's RIA. Further, this analysis does not
include other societal impacts such as the value of increased driving,
or noise, congestion and accidents since the focus is meant to be on
those factors consumers think about most while in the showroom
considering a new car purchase. Car/truck fleet weighting is handled as
described in Chapter 1 of the Joint TSD. As can be seen in the table,
it will take under 3 years (2 years and 7 months at a 3% discount rate,
2 years and 9 months at a 7% discount rate) for the cumulative
discounted fuel savings to exceed the upfront increase in vehicle cost.
More detail on this analysis can be found in Chapter 8 of EPA's RIA.

                   Table III.H.5-3--Payback Period on a 2016 MY New Vehicle Purchase via Cash
                                                 [2007 dollars]
----------------------------------------------------------------------------------------------------------------
                                                                                Cumulative      Cumulative
                                                     Increased      Annual fuel     discounted      discounted
                Year of ownership                  vehicle cost     savings \b\    fuel savings    fuel savings
                                                        \a\                            at 3%           at 7%
----------------------------------------------------------------------------------------------------------------
1...............................................          $1,018            $424            $418            $410
2...............................................  ..............            $420            $820            $790
3...............................................  ..............            $414          $1,204          $1,139
4...............................................  ..............            $402          $1,567          $1,457
----------------------------------------------------------------------------------------------------------------
\a\ Increased vehicle cost due to the rule is $948; the value here includes nationwide average sales tax of 5.3%
  and increased insurance premiums of 1.98%; both of these percentages are discussed in Section 8.1.1 of EPA's
  RIA.
\b\ Calculated using AEO 2010 Early Release reference case fuel price including taxes.

    However, most people purchase a new vehicle using credit rather
than paying cash up front. The typical car loan today is a five year,
60 month loan. As of February 9, 2010, the national average interest
rate for a 5 year new car loan was 6.54 percent. If the increased
vehicle cost is spread out over 5 years at 6.54 percent, the analysis
would look like that shown in Table III.H.5-4. As can be seen in this
table, the fuel savings immediately outweigh the

increased payments on the car loan, amounting to $177 in discounted net
savings (3% discount rate) in the first year and similar savings for
the next two years before reduced VMT starts to cause the fuel savings
to fall. Results are similar using a 7% discount rate. This means that
for every month that the average owner is making a payment for the
financing of the average new vehicle their monthly fuel savings would
be greater than the increase in the loan payments. This amounts to a
savings on the order of $9 to $15 per month throughout the duration of
the 5 year loan. Note that in year six when the car loan is paid off,
the net savings equal the fuel savings (as would be the case for the
remaining years of ownership).

                  Table III.H.5-4--Payback Period on a 2016 MY New Vehicle Purchase via Credit
                                                 [2007 dollars]
----------------------------------------------------------------------------------------------------------------
                                                     Increased                        Annual          Annual
                Year of ownership                  vehicle cost     Annual fuel   discounted net  discounted net
                                                        \a\         savings \b\    savings at 3%   savings at 7%
----------------------------------------------------------------------------------------------------------------
1...............................................            $245            $424            $177            $173
2...............................................            $245            $420            $167            $158
3...............................................            $245            $414            $157            $142
4...............................................            $245            $402            $142            $124
5...............................................            $245            $391            $127            $107
6...............................................              $0            $374            $318            $258
----------------------------------------------------------------------------------------------------------------
\a\ This uses the same increased cost as Table III.H.4-3 but spreads it out over 5 years assuming a 5 year car
  loan at 6.54 percent.
\b\ Calculated using AEO 2010 Early Release reference case fuel price including taxes.

    The lifetime fuel savings and net savings can also be calculated
for those who purchase the vehicle using cash and for those who
purchase the vehicle with credit. This calculation applies to the
vehicle owner who retains the vehicle for its entire life and drives
the vehicle each year at the rate equal to the national projected
average. The results are shown in Table III.H.5-5. In either case, the
present value of the lifetime net savings is greater than $3,100 at a
3% discount rate, or $2,300 at a 7% discount rate.

               Table III.H.5-5--Lifetime Discounted Net Savings on a 2016 MY New Vehicle Purchase
                                                 [2007 dollars]
----------------------------------------------------------------------------------------------------------------
                                                                                Lifetime
                                                                     Increased      discounted       Lifetime
                         Purchase option                            discounted     fuel savings   discounted net
                                                                   vehicle cost         \b\           savings
----------------------------------------------------------------------------------------------------------------
                                                3% discount rate
----------------------------------------------------------------------------------------------------------------
Cash............................................................          $1,018          $4,306          $3,303
Credit \a\......................................................           1,140           4,306           3,166
----------------------------------------------------------------------------------------------------------------
                                                7% discount rate
----------------------------------------------------------------------------------------------------------------
Cash............................................................           1,018           3,381           2,396
Credit \a\......................................................           1,040           3,381           2,340
----------------------------------------------------------------------------------------------------------------
\a\ Assumes a 5 year loan at 6.54 percent.
\b\ Fuel savings here were calculated using AEO 2010 Early Release reference case fuel price including taxes.

    Note that throughout this consumer payback discussion, the average
number of vehicle miles traveled per year has been used. Drivers who
drive more miles than the average would incur fuel related savings more
quickly and, therefore, the payback would come sooner. Drivers who
drive fewer miles than the average would incur fuel related savings
more slowly and, therefore, the payback would come later.
6. Benefits of Reducing GHG Emissions
a. Social Cost of Carbon
    In today's final rule, EPA and NHTSA assigned a dollar value to
reductions in CO2 emissions using the marginal dollar value
of climate-related damages resulting from carbon emissions, also
referred to as ``social cost of carbon'' (SCC). The SCC estimates used
in today's rule were recently developed by an interagency process, in
which EPA and NHTSA participated. As part of the interagency group, EPA
and NHTSA have critically evaluated the new SCC estimates and endorse
them for use in these regulatory analyses, for the reasons presented
below. The SCC TSD, Social Cost of Carbon for Regulatory Impact
Analysis Under Executive Order 12866, presents a more detailed
description of the methodology used to generate the new estimates, the
underlying assumptions, and the limitations of the new SCC estimates.
    Under Executive Order 12866, agencies are required, to the extent
permitted by law, ``to assess both the costs and the benefits of the
intended regulation and, recognizing that some costs and benefits are
difficult to quantify, propose or adopt a regulation only upon a
reasoned determination that the benefits of the intended regulation
justify its costs.'' The purpose of the SCC estimates presented here is
to incorporate the social benefits of reducing carbon dioxide
(CO2) emissions from light-duty vehicles into a cost-benefit
analysis of this final rule, which has a small, or ``marginal,'' impact
on cumulative global emissions. The estimates are presented with an
acknowledgement of the many

uncertainties involved and with a clear understanding that they should
be updated over time to reflect increasing knowledge of the science and
economics of climate impacts.
    The interagency process that developed these SCC estimates involved
a group of technical experts from numerous agencies, which met on a
regular basis to consider public comments, explore the technical
literature in relevant fields, and discuss key model inputs and
assumptions. The main objective of this process was to develop a range
of SCC values using a defensible set of input assumptions grounded in
the existing scientific and economic literatures. In this way, key
uncertainties and model differences transparently and consistently
inform the range of SCC estimates used in this rulemaking process.
    The interagency group selected four SCC values for use in
regulatory analyses, which EPA and NHTSA have applied to this final
rule. Three values are based on the average SCC from three integrated
assessment models, at discount rates of 2.5, 3, and 5 percent. The
fourth value, which represents the 95th percentile SCC estimate across
all three models at a 3 percent discount rate, is included to represent
higher-than-expected impacts from temperature change further out in the
tails of the SCC distribution.

            Table III.H.6-1--Social Cost of CO2, 2010--2050a
                            [in 2007 dollars]
------------------------------------------------------------------------
                                             Discount Rate
            Year             -------------------------------------------
                                5% Avg     3% Avg    2.5% Avg   3% 95th
------------------------------------------------------------------------
2010........................          5         21         35         65
2015........................          6         24         38         73
2020........................          7         26         42         81
2025........................          8         30         46         90
2030........................         10         33         50        100
2035........................         11         36         54        110
2040........................         13         39         58        119
2045........................         14         42         62        128
2050........................         16         45         65        136
------------------------------------------------------------------------
\a\ The SCC estimates presented above have been rounded to nearest
  dollar for consistency with the benefits analysis. The SCC TSD
  presents estimates rounded to the nearest tenth of a cent.

i. Monetizing Carbon Dioxide Emissions
    The ``social cost of carbon'' (SCC) is an estimate of the monetized
damages associated with an incremental increase in carbon emissions in
a given year. It is intended to include (but is not limited to) changes
in net agricultural productivity, human health, property damages from
increased flood risk, and the value of ecosystem services. We report
estimates of the social cost of carbon in dollars per metric ton of
carbon dioxide throughout this document.
    When attempting to assess the incremental economic impacts of
carbon dioxide emissions, the analyst faces a number of serious
challenges. A 2009 report from the National Academies of Science points
out that any assessment will suffer from uncertainty, speculation, and
lack of information about (1) future emissions of greenhouse gases, (2)
the effects of past and future emissions on the climate system, (3) the
impact of changes in climate on the physical and biological
environment, and (4) the translation of these environmental impacts
into economic damages.\465\ As a result, any effort to quantify and
monetize the harms associated with climate change will raise serious
questions of science, economics, and ethics and should be viewed as
provisional.
---------------------------------------------------------------------------

    \465\ National Research Council (2009). Hidden Costs of Energy:
Unpriced Consequences of Energy Production and Use. National
Academies Press.
---------------------------------------------------------------------------

    Despite the serious limits of both quantification and monetization,
SCC estimates can be useful in estimating the social benefits of
reducing carbon dioxide emissions. Under Executive Order 12866,
agencies are required, to the extent permitted by law, ``to assess both
the costs and the benefits of the intended regulation and, recognizing
that some costs and benefits are difficult to quantify, propose or
adopt a regulation only upon a reasoned determination that the benefits
of the intended regulation justify its costs.'' EPA and NHTSA have used
the SCC estimates to incorporate social benefits from reducing carbon
dioxide emissions from light-duty vehicles into a cost-benefit analysis
of this final rule, which has a small, or ``marginal,'' impact on
cumulative global emissions. Most Federal regulatory actions can be
expected to have marginal impacts on global emissions.
    For policies that have marginal impacts on global emissions, the
benefits from reduced (or costs from increased) emissions in any future
year can be estimated by multiplying the change in emissions in that
year by the SCC value appropriate for that year. The net present value
of the benefits can then be calculated by multiplying each of these
future benefits by an appropriate discount factor and summing across
all affected years. This approach assumes that the marginal damages
from increased emissions are constant for small departures from the
baseline emissions path, an approximation that is reasonable for
policies that have effects on emissions that are small relative to
cumulative global carbon dioxide emissions. For policies that have a
large (non-marginal) impact on global cumulative emissions, there is a
separate question of whether the SCC is an appropriate tool for
calculating the benefits of reduced emissions; we do not attempt to
answer that question here.
    As noted above, the interagency group convened on a regular basis
to consider public comments, explore the technical literature in
relevant fields, and discuss key inputs and assumptions in order to
generate SCC estimates. In addition to EPA and NHTSA, agencies that
actively participated in the interagency process included the
Departments of Agriculture, Commerce, Energy, and Treasury. This
process was convened by the Council of Economic Advisers and the Office
of Management and Budget, with active participation and regular input
from the Council on Environmental Quality, National Economic Council,
Office of Energy and Climate Change, and Office of Science and
Technology Policy. The main objective of this process was to develop a
range of SCC values using a defensible

set of input assumptions that are grounded in the existing literature.
In this way, key uncertainties and model differences can more
transparently and consistently inform the range of SCC estimates used
in the rulemaking process.
    The interagency group selected four global SCC estimates for use in
regulatory analyses. For 2010, these estimates are $5, $21, $35, and
$65 (in 2007 dollars). The first three estimates are based on the
average SCC across models and socio-economic and emissions scenarios at
the 5, 3, and 2.5 percent discount rates, respectively. The fourth
value is included to represent the higher-than-expected impacts from
temperature change further out in the tails of the SCC distribution.
For this purpose, we use the SCC value for the 95th percentile at a 3
percent discount rate. The central value is the average SCC across
models at the 3 percent discount rate. For purposes of capturing the
uncertainties involved in regulatory impact analysis, we emphasize the
importance and value of considering the full range. These SCC estimates
also grow over time. For instance, the central value increases to $24
per ton of CO2 in 2015 and $26 per ton of CO2 in
2020. See the SCC TSD for the full range of annual SCC estimates from
2010 to 2050.
    These new SCC estimates represent global measures and the center of
our current attention because of the distinctive nature of the climate
change problem. The climate change problem is highly unusual in at
least two respects. First, it involves a global externality: Emissions
of most greenhouse gases contribute to damages around the world even
when they are emitted in the United States. Consequently, to address
the global nature of the problem, the SCC must incorporate the full
(global) damages caused by GHG emissions. Second, climate change
presents a problem that the United States alone cannot solve. Even if
the United States were to reduce its greenhouse gas emissions to zero,
that step would be far from enough to avoid substantial climate change.
Other countries would also need to take action to reduce emissions if
significant changes in the global climate are to be avoided.
    It is important to emphasize that the interagency process is
committed to updating these estimates as the science and economic
understanding of climate change and its impacts on society improves
over time. Specifically, the interagency group has set a preliminary
goal of revisiting the SCC values within two years or at such time as
substantially updated models become available, and to continue to
support research in this area. In the meantime, the interagency group
will continue to explore the issues raised in the SCC TSD and consider
public comments as part of the ongoing interagency process.
ii. Social Cost of Carbon Values Used in Past Regulatory Analyses
    To date, economic analyses for Federal regulations have used a wide
range of values to estimate the benefits associated with reducing
carbon dioxide emissions. In the final model year 2011 CAFE rule, the
Department of Transportation (DOT) used both a ``domestic'' SCC value
of $2 per ton of CO2 and a ``global'' SCC value of $33 per
ton of CO2 for 2007 emission reductions (in 2007 dollars),
increasing both values at 2.4 percent per year. It also included a
sensitivity analysis at $80 per ton of CO2. A domestic SCC
value is meant to reflect the value of damages in the United States
resulting from a unit change in carbon dioxide emissions, while a
global SCC value is meant to reflect the value of damages worldwide.
    A 2008 regulation proposed by DOT assumed a domestic SCC value of
$7 per ton CO2 (in 2006 dollars) for 2011 emission
reductions (with a range of $0-$14 for sensitivity analysis), also
increasing at 2.4 percent per year. A regulation finalized by DOE in
October of 2008 used a domestic SCC range of $0 to $20 per ton
CO2 for 2007 emission reductions (in 2007 dollars). In
addition, EPA's 2008 Advance Notice of Proposed Rulemaking for
Greenhouse Gases identified what it described as ``very preliminary''
SCC estimates subject to revision. EPA's global mean values were $68
and $40 per ton CO2 for discount rates of approximately 2
percent and 3 percent, respectively (in 2006 dollars for 2007
emissions).
    In 2009, an interagency process was initiated to offer a
preliminary assessment of how best to quantify the benefits from
reducing carbon dioxide emissions. To ensure consistency in how
benefits are evaluated across agencies, the Administration sought to
develop a transparent and defensible method, specifically designed for
the rulemaking process, to quantify avoided climate change damages from
reduced CO2 emissions. The interagency group did not
undertake any original analysis. Instead, it combined SCC estimates
from the existing literature to use as interim values until a more
comprehensive analysis could be conducted.
    The outcome of the preliminary assessment by the interagency group
was a set of five interim values: Global SCC estimates for 2007 (in
2006 dollars) of $55, $33, $19, $10, and $5 per ton of CO2.
The $33 and $5 values represented model-weighted means of the published
estimates produced from the most recently available versions of three
integrated assessment models (DICE, PAGE, and FUND) at approximately 3
and 5 percent discount rates.\466\ The $55 and $10 values were derived
by adjusting the published estimates for uncertainty in the discount
rate (using factors developed by Newell and Pizer (2003)) at 3 and 5
percent discount rates, respectively.\467\ The $19 value was chosen as
a central value between the $5 and $33 per ton estimates. All of these
values were assumed to increase at 3 percent annually to represent
growth in incremental damages over time as the magnitude of climate
change increases.
---------------------------------------------------------------------------

    \466\ The DICE (Dynamic Integrated Climate and Economy) model by
William Nordhaus evolved from a series of energy models and was
first presented in 1990 (Nordhaus and Boyer 2000, Nordhaus 2008).