RIA and analyzed in Chapter 6 of the Draft RIA), despite the fact that
each alternative results in a different change in incremental
technology and fuel costs. For the final rulemaking, we plan to
determine VMT rebound separately for each HDV category and for each
alternative. Tables 64 through 66 in Chapter 7 of the Draft RIA present
VMT rebound for each HDV sector that we estimated for the preferred
alternative. These VMT impacts are reflected in the estimates of total
fuel savings and reductions in emissions of GHG and other air
pollutants presented in Section VI and VII of this preamble for all
categories.
    Section 9.3.3 in the draft RIA provides more details on our
assessment of HDV VMT rebound. We invite comment on our approach, the
rebound estimates, and the related assumptions we made. In particular,
we invite comment on the most appropriate methodology for factoring new
vehicle purchase or leasing costs into the per-mile operating costs.
For the purposes of this proposal, we have not taken into account any
potential fuel savings or GHG emission reductions from the rail sector
due to mode shift because estimates of this effect seem too speculative
at this time. We invite comment on this assumption, as well as
suggestions on alternative modeling frameworks that could be used to
assess mode shifting implications of our proposed regulations.
Similarly, we have not taken into account any fuel savings or GHG
emissions reductions from the potential shift in VMT from older HDVs to
newer, more efficient HDVs because we have found no evidence of this
potential effect from fuel efficiency standards. We invite comment on
suggested modeling frameworks or data that could be used to assess the
potential for activity to shift from older to newer, more efficient
HDVs in response to our proposed standards.
    Note that while we focus on the VMT rebound effect in our analysis
of this proposed rule, there are at least two other types of rebound
effects discussed in the economics literature. In addition to VMT
rebound effects, there are ``indirect'' rebound effects, which refers
to the purchase of other goods or services (that consume energy) with
the costs savings from energy efficiency improvements; and ``economy-
wide'' rebound effects, which refers to the increased demand for energy
throughout the economy in response to the reduced market price of
energy that happens as a result of energy efficiency improvements.
    Research on indirect and economy-wide rebound effects is nascent,
and we have not identified any that attempts to quantify indirect or
economy-wide rebound effects for HDVs. In particular, the agencies are
not aware of any data to indicate that the magnitude of indirect or
economy-wide rebound effects, if any, would be significant for this
proposed rule.\682\ Therefore, we rely

the same analysis of vehicle miles traveled to estimate the rebound
effect in this proposal that we did for the HD Phase 1 rule, where we
attempted to quantify only rebound effects from our rule that impact
HDV VMT. We welcome comments and any new work in this area that helps
to assess and quantify different rebound effects that could result from
improvements in HDV efficiency, including different types of more
intensive truck usage that affect fuel consumption but not VMT such as
loaded weight, truck routing, and scheduling.
---------------------------------------------------------------------------

    \682\ One entity sought reconsideration of the Phase 1 rule on
the grounds that indirect rebound effects had not been considered by
the agencies and could negate all of the benefits of the standards.
This assertion rested on an unsupported affidavit lacking any peer
review or other indicia of objectivity. This affidavit cited only
one published study. The study cited did not deal with vehicle
efficiency, has methodological limitations (many of them
acknowledged), and otherwise was not pertinent. EPA and NHTSA thus
declined to reconsider the Phase 1 rule based on these speculative
assertions. See generally 77 FR 51703-51704, August 27, 2012 and 77
FR 51502-51503, August 24, 2012.
---------------------------------------------------------------------------

    In order to test the effect of alternative assumptions about the
rebound effect, NHTSA examined the sensitivity of its estimates of
benefits and costs of the Phase 2 Preferred Alternative for HD pickups
and vans to alternative assumptions about the rebound effect. While the
main analysis for pickups and vans assumes a 10 percent rebound effect,
the sensitivity analysis estimates the benefits and costs of the
proposed standards under the assumptions of 5, 15, and 20 percent
rebound effects.
    Alternative values of the rebound effect change the estimates of
benefits and costs from the proposed standards in three ways. First,
higher values of the rebound effect increase the amount of additional
VMT that results from improved fuel efficiency; this increases costs
associated with additional congestion, accidents, and noise, thus
increasing total costs associated with the proposed standards.
Conversely, smaller values of the rebound effect reduce costs from
additional congestion, accidents, and noise, so they reduce total costs
of the proposed standards. Larger increases in VMT associated with
higher values of the rebound effect reduce the value of fuel savings
and related benefits (such as reductions in GHG emissions) by
progressively larger amounts, while smaller values of the rebound
effect cause smaller reductions in these benefits. At the same time,
however, a higher rebound effect generates larger benefits from
increased vehicle use, while a smaller rebound effect reduces these
benefits. Thus the impact of alternative values of the rebound effect
on total benefits from the proposed standards depends on the exact
magnitudes of these latter two effects. On balance, these three effects
can cause net benefits to increase or decrease for alternative values
of the rebound effect.

 Table IX-12--Sensitivity of Preferred Alternative Impacts Under Different Assumptions About Rebound Effect for
                                    Pickups and Vans, Using 3% Discount Rate
----------------------------------------------------------------------------------------------------------------
                                                                          Rebound effect
                                                 ---------------------------------------------------------------
                                                           Main analysis              Sensitivity cases using
               HD pickups and vans               --------------------------------       alternative rebound
                                                                                assumptions
                                                        10%             5%       -------------------------------
                                                                                15%             20%
----------------------------------------------------------------------------------------------------------------
Fuel Reductions (Billion Gallons)...............             7.8             8.2             7.5             7.1
GHG Reductions (MMT CO2 eq).....................            94.1            95.7            87.2            83.0
Total Costs ($ billion).........................             5.5             5.0             6.5             7.2
Total Benefits ($ billion)......................            23.5            23.0            22.9            22.8
Net Benefits ($ billion)........................            18.0            18.0            16.4            15.5
----------------------------------------------------------------------------------------------------------------

    Table IX-12 summarizes the impact of these alternative assumptions
on fuel and GHG emissions savings, total costs, total benefits, and net
benefits. As it indicates, using a 5 percent value for the rebound
effect reduces benefits and costs of the proposed standards by
identical amounts, leaving net benefits unaffected. As the table also
shows, rebound effects of 15 percent and 20 percent increase costs and
reduce benefits compared to their values in the main analysis, thus
reducing net benefits of the proposed standards. Nevertheless, the
preferred alternative has significant net benefits under each
alternative assumption about the magnitude of the rebound effect for HD
pickups and vans. Thus, these alternative values of the rebound effect
would not have affected the agencies' selection of the preferred
alternative, as that selection is based on NHTSA's assessment of the
maximum feasible fuel efficiency standards and EPA's selection of
appropriate GHG standards to address energy security and the
environment.

F. Impact on Class Shifting, Fleet Turnover, and Sales

    The agencies considered two additional potential indirect effects
which may lead to unintended consequences of the program to improve the
fuel efficiency and reduce GHG emissions from HD trucks. The next
sections cover the agencies' qualitative discussions on potential class
shifting and fleet turnover effects.
(1) Class Shifting
    Heavy-duty vehicles are typically configured and purchased to
perform a function. For example, a concrete mixer truck is purchased to
transport concrete, a combination tractor is purchased to move freight
with the use of a trailer, and a Class 3 pickup truck could be
purchased by a landscape company to pull a trailer carrying lawnmowers.
The purchaser makes decisions based on many attributes of the vehicle,
including the gross vehicle weight rating of the vehicle, which in part
determines the amount of freight or equipment that can be carried. If
the proposed Phase 2 standards impact either the performance of the
vehicle or the marginal cost of the vehicle relative to the other
vehicle classes, then consumers may choose to purchase a different
vehicle, resulting in the unintended consequence of increased fuel
consumption and GHG emissions in-use.
    The agencies, along with the NAS panel, found that there is little
or no literature which evaluates class shifting between trucks.\683\
NHTSA and EPA qualitatively evaluated the proposed rules in light of
potential class shifting. The agencies looked at four potential cases
of shifting:--From light-duty pickup trucks to heavy-duty pickup
trucks; from sleeper cabs to day cabs;

from combination tractors to vocational vehicles; and within vocational
vehicles.
---------------------------------------------------------------------------

    \683\ See 2010 NAS Report, page 152.
---------------------------------------------------------------------------

    Light-duty pickup trucks, those with a GVWR of less than 8,500 lbs,
are currently regulated under the existing GHG/CAFE Phase 1 program and
will meet GHG/CAFE Phase 2 emission standards beginning in 2017. The
increased stringency of the light-duty 2017-2025 MY vehicle rule has
led some to speculate that vehicle consumers may choose to purchase
heavy-duty pickup trucks that are currently regulated under the HD
Phase 1 program if the cost of the light-duty regulation is high
relative to the cost to buy the larger heavy-duty pickup trucks. Since
fuel consumption and GHG emissions rise significantly with vehicle
mass, a shift from light-duty trucks to heavy-duty trucks would likely
lead to higher fuel consumption and GHG emissions, an untended
consequence of the regulations. Given the significant price premium of
a heavy-duty truck (often five to ten thousand dollars more than a
light-duty pickup), we believe that such a class shift would be
unlikely even absent this program. These proposed rules would continue
to diminish any incentive for such a class shift because they would
narrow the GHG and fuel efficiency performance gap between light-duty
and heavy-duty pickup trucks. The proposed regulations for the HD
pickup trucks, and similarly for vans, are based on similar
technologies and therefore reflect a similar expected increase in cost
when compared to the light-duty GHG regulation. Hence, the combination
of the two regulations provides little incentive for a shift from
light-duty trucks to HD trucks. To the extent that our proposed
regulation of heavy-duty pickups and vans could conceivably encourage a
class shift towards lighter pickups, this unintended consequence would
in fact be expected to lead to lower fuel consumption and GHG emissions
as the smaller light-duty pickups have significantly better fuel
economy ratings than heavy-duty pickup trucks.
    The projected cost increases for this proposed action differ
between Class 8 day cabs and Class 8 sleeper cabs, reflecting our
expectation that compliance with the proposed standards would lead
truck consumers to specify sleeper cabs equipped with APUs while day
cab consumers would not. Since Class 8 day cab and sleeper cab trucks
perform essentially the same function when hauling a trailer, this
raises the possibility that the higher cost for an APU equipped sleeper
cab could lead to a shift from sleeper cab to day cab trucks. We do not
believe that such an intended consequence would occur for the following
reasons. The addition of a sleeper berth to a tractor cab is not a
consumer-selectable attribute in quite the same way as other vehicle
features. The sleeper cab provides a utility that long-distance
trucking fleets need to conduct their operations--an on-board sleeping
berth that lets a driver comply with federally-mandated rest periods,
as required by the Department of Transportation Federal Motor Carrier
Safety Administration's hours-of-service regulations. The cost of
sleeper trucks is already higher than the cost of day cabs, yet the
fleets that need this utility purchase them.\684\ A day cab simply
cannot provide this utility with a single driver. The need for this
utility would not be changed even if the additional costs to reduce
greenhouse gas emissions from sleeper cabs exceed those for reducing
greenhouse gas emissions from day cabs.\685\
---------------------------------------------------------------------------

    \684\ A baseline tractor price of a new day cab is $89,500
versus $113,000 for a new sleeper cab based on information gathered
by ICF in the ``Investigation of Costs for Strategies to Reduce
Greenhouse Gas Emissions for Heavy-Duty On-Road Vehicles'', July
2010. Page 3. Docket Identification Number EPA-HQ-OAR-2014--0827.
    \685\ The average marginal cost difference between sleeper cabs
and day cabs in the proposal is roughly $2,500.
---------------------------------------------------------------------------

    A trucking fleet could instead decide to put its drivers in hotels
in lieu of using sleeper berths, and switch to day cabs. However, this
is unlikely to occur in any great number, since the added cost for the
hotel stays would far overwhelm differences in the marginal cost
between day and sleeper cabs. Even if some fleets do opt to buy hotel
rooms and switch to day cabs, they would be highly unlikely to purchase
a day cab that was aerodynamically worse than the sleeper cab they
replaced, since the need for features optimized for long-distance
hauling would not have changed. So in practice, there would likely be
little difference to the environment for any switching that might
occur. Further, while our projected costs assume the purchase of an APU
for compliance, in fact our proposed regulatory structure would allow
compliance using a near zero cost software utility that eliminates
tractor idling after five minutes. Using this compliance approach, the
cost difference between a Class 8 sleeper cab and day cab due to our
proposed regulations is small. We are proposing this alternative
compliance approach reflecting that some sleeper cabs are used in team
driving situations where one driver sleeps while the other drives. In
that situation, an APU is unnecessary since the tractor is continually
being driven when occupied. When it is parked, it would automatically
eliminate any additional idling through the shutdown software. If
trucking businesses choose this option, then costs based on purchase of
APUs may overestimate the costs of this program to this sector.
    Class shifting from combination tractors to vocational vehicles may
occur if a customer deems the additional marginal cost of tractors due
to the regulation to be greater than the utility provided by the
tractor. The agencies initially considered this issue when deciding
whether to include Class 7 tractors with the Class 8 tractors or
regulate them as vocational vehicles. The agencies' evaluation of the
combined vehicle weight rating of the Class 7 shows that if these
vehicles were treated significantly differently from the Class 8
tractors, then they could be easily substituted for Class 8 tractors.
Therefore, the agencies are proposing to continue to include both
classes in the tractor category. The agencies believe that a shift from
tractors to vocational vehicles would be limited because of the ability
of tractors to pick up and drop off trailers at locations which cannot
be done by vocational vehicles.
    The agencies do not envision that the proposed regulatory program
would cause class shifting within the vocational vehicle class. The
marginal cost difference due to the regulation of vocational vehicles
is minimal. The cost of LRR tires on a per tire basis is the same for
all vocational vehicles so the only difference in marginal cost of the
vehicles is due to the number of axles. The agencies believe that the
utility gained from the additional load carrying capability of the
additional axle would outweigh the additional cost for heavier
vehicles.\686\
---------------------------------------------------------------------------

    \686\ The proposed rule projects the difference in costs between
the HHD and MHD vocational vehicle technologies is approximately
$30.
---------------------------------------------------------------------------

    In conclusion, NHTSA and EPA believe that the proposed regulatory
structure for HD trucks would not significantly change the current
competitive and market factors that determine purchaser preferences
among truck types. Furthermore, even if a small amount of shifting
would occur, any resulting GHG impacts would likely to be negligible
because any vehicle class that sees an uptick in sales is also being
regulated for fuel efficiency. Therefore, the agencies did not include
an impact of class shifting on the vehicle populations used to assess
the benefits of the proposed program.

(2) Fleet Turnover and Sales Effects
    A regulation that affects the cost to purchase and/or operate
trucks could affect whether a consumer decides to purchase a new truck
and the timing of that purchase. The term pre-buy refers to the idea
that truck purchases may occur earlier than otherwise planned to avoid
the additional costs associated with a new regulatory requirement.
Slower fleet turnover, or low-buys, may occur when owners opt to keep
their existing truck rather than purchase a new truck due to the
incremental cost of the regulation.
    The 2010 NAS HD Report discussed the topics associated with HD
truck fleet turnover. NAS noted that there is some empirical evidence
of pre-buy behavior in response to the 2004 and 2007 heavy-duty engine
emission standards, with larger impacts occurring in response to higher
costs.\687\ However, those regulations increased upfront costs to firms
without any offsetting future cost savings from reduced fuel purchases.
In summary, NAS stated that:
---------------------------------------------------------------------------

    \687\ Committee to Assess Fuel Economy Technologies for Medium-
and Heavy-Duty Vehicles; National Research Council; Transportation
Research Board (2010). ``Technologies and Approaches to Reducing the
Fuel Consumption of Medium- and Heavy-Duty Vehicles,'' (hereafter,
``NAS Report''). Washington, DC, the National Academies Press.
Available electronically from the National Academies Press Web site
at http://www.nap.edu/catalog.php?record_id=12845. pp. 150-151.

. . . during periods of stable or growing demand in the freight
sector, pre-buy behavior may have significant impact on purchase
patterns, especially for larger fleets with better access to capital
and financing. Under these same conditions, smaller operators may
simply elect to keep their current equipment on the road longer, all
the more likely given continued improvements in diesel engine
durability over time. On the other hand, to the extent that fuel
economy improvements can offset incremental purchase costs, these
impacts will be lessened. Nevertheless, when it comes to efficiency
investments, most heavy-duty fleet operators require relatively
quick payback periods, on the order of two to three years.\688\
---------------------------------------------------------------------------

    \688\ See NAS Report, Note 687, page 151.

    The proposed regulations are projected to return fuel savings to
the truck owners that offset the cost of the regulation within a few
years. The effects of the regulation on purchasing behavior and sales
will depend on the nature of the market failures and the extent to
which firms consider the projected future fuel savings in their
purchasing decisions.
    If trucking firms account for the rapid payback, they are unlikely
to strategically accelerate or delay their purchase plans at additional
cost in capital to avoid a regulation that will lower their overall
operating costs. As discussed in Section IX. A. this scenario may occur
if this proposed program reduces uncertainty about fuel-saving
technologies. More reliable information about ways to reduce fuel
consumption allows truck purchasers to evaluate better the benefits and
costs of additional fuel savings, primarily in the original vehicle
market, but possibly in the resale market as well. In addition, the
proposed standards are expected to lead manufacturers to install more
fuel-saving technologies and promote their purchase; the increased
availability and promotion may encourage sales.
    Other market failures may leave open the possibility of some pre-
buy or delayed purchasing behavior. Firms may not consider the full
value of the future fuel savings for several reasons. For instance,
truck purchasers may not want to invest in fuel efficiency because of
uncertainty about fuel prices. Another explanation is that the resale
market may not fully recognize the value of fuel savings, due to lack
of trust of new technologies or changes in the uses of the vehicles.
Lack of coordination (also called split incentives--see Section IX. A.)
between truck purchasers (who may emphasize the up-front costs of the
trucks) and truck operators, who would like the fuel savings, can also
lead to pre-buy or delayed purchasing behavior. If these market
failures prevent firms from fully internalizing fuel savings when
deciding on vehicle purchases, then pre-buy and delayed purchase could
occur and could result in a slight decrease in the GHG benefits of the
regulation.
    Thus, whether pre-buy or delayed purchase is likely to play a
significant role in the truck market depends on the specific behaviors
of purchasers in that market. Without additional information about
which scenario is more likely to be prevalent, the agencies are not
projecting a change in fleet turnover characteristics due to this
regulation.
    Whether vehicle sales appear to be affected by the HD Phase 1
standards could provide some insight into the impacts of the proposed
standards. At the time of this proposed rule, sales data are not yet
available for 2014 model year, the first year of the Phase 1 standards.
In addition, any trends in sales are likely to be affected by
macroeconomic conditions, which have been recovering since 2009-2010.
As a result, it is unlikely to be possible, even when vehicle sales
data are available, to separate the effects of the existing standards
from other confounding factors.

G. Monetized GHG Impacts

(1) Monetized CO2 Impacts--The Social Cost of Carbon (SC-
CO2)
    We estimate the global social benefits of CO2 emission
reductions expected from the proposed heavy-duty GHG and fuel
efficiency standards using the social cost of carbon (SC-
CO2) estimates presented in the 2013 Technical Support
Document: Technical Update of the Social Cost of Carbon for Regulatory
Impact Analysis Under Executive Order 12866 (2013 SCC TSD).\689\ (The
SC-CO2 estimates are presented in Table IX-11). We refer to
these estimates, which were developed by the U.S. government, as ``SC-
CO2 estimates.'' The SC-CO2 is a metric that
estimates the monetary value of impacts associated with marginal
changes in CO2 emissions in a given year. It includes a wide
range of anticipated climate impacts, such as net changes in
agricultural productivity and human health, property damage from
increased flood risk, and changes in energy system costs, such as
reduced costs for heating and increased costs for air conditioning. It
is used in regulatory impact analyses to quantify the benefits of
reducing CO2 emissions, or the disbenefit from increasing
emissions.
---------------------------------------------------------------------------

    \689\ Docket ID EPA-HQ-OAR-2014-0827, Technical Support
Document: Technical Update of the Social Cost of Carbon for
Regulatory Impact Analysis Under Executive Order 12866, Interagency
Working Group on Social Cost of Carbon, with participation by
Council of Economic Advisers, Council on Environmental Quality,
Department of Agriculture, Department of Commerce, Department of
Energy, Department of Transportation, Environmental Protection
Agency, National Economic Council, Office of Energy and Climate
Change, Office of Management and Budget, Office of Science and
Technology Policy, and Department of Treasury (May 2013, Revised
November 2013). Available at: http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-social-cost-of-carbon-for-regulator-impact-analysis.pdf.
---------------------------------------------------------------------------

    The SC-CO2 estimates used in this analysis were
developed over many years, using the best science available, and with
input from the public. Specifically, an interagency working group (IWG)
that included EPA, DOT, and other executive branch agencies and offices
used three integrated assessment models (IAMs) to develop the SC-
CO2 estimates and recommended four global values for use in
regulatory analyses. The SC-CO2 estimates were first
released in February 2010 \690\ and

updated in 2013 using new versions of each IAM. These estimates were
published in the 2013 SCC TSD. The 2013 update did not revisit the 2010
modeling decisions (e.g., with regard to the discount rate, reference
case socioeconomic and emission scenarios or equilibrium climate
sensitivity). Rather, improvements in the way damages are modeled are
confined to those that have been incorporated into the latest versions
of the models by the developers themselves and used for analyses in
peer-reviewed publications. The 2010 SCC Technical Support Document
(2010 SCC TSD) provides a complete discussion of the methods used to
develop these estimates and the 2013 SCC TSD presents and discusses the
updated estimates.
---------------------------------------------------------------------------

    \690\ Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support
Document: Social Cost of Carbon for Regulatory Impact Analysis Under
Executive Order 12866, Interagency Working Group on Social Cost of
Carbon, with participation by the Council of Economic Advisers,
Council on Environmental Quality, Department of Agriculture,
Department of Commerce, Department of Energy, Department of
Transportation, Environmental Protection Agency, National Economic
Council, Office of Energy and Climate Change, Office of Management
and Budget, Office of Science and Technology Policy, and Department
of Treasury (February 2010). Also available at: http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf.
---------------------------------------------------------------------------

    The 2010 SCC TSD noted a number of limitations to the SC-
CO2 analysis, including the incomplete way in which the IAMs
capture catastrophic and non-catastrophic impacts, their incomplete
treatment of adaptation and technological change, uncertainty in the
extrapolation of damages to high temperatures, and assumptions
regarding risk aversion. Current IAMs do not assign value to all of the
important physical, ecological, and economic impacts of climate change
recognized in the climate change literature due to a lack of precise
information on the nature of damages and because the science
incorporated into these models understandably lags behind the most
recent research. Nonetheless, these estimates and the discussion of
their limitations represent the best available information about the
social benefits of CO2 reductions to inform benefit-cost
analysis; see RIA of this rule and the SCC TSDs for additional details.
The new versions of the models used to estimate the values presented
below offer some improvements in these areas, although further work is
warranted.
    Accordingly, EPA and other agencies continue to engage in research
on modeling and valuation of climate impacts with the goal to improve
these estimates. The EPA and other federal agencies have considered the
extensive public comments on ways to improve SC-CO2
estimation received via the notice and comment periods that were part
of numerous rulemakings. In addition, OMB's Office of Information and
Regulatory Affairs sought public comment on the approach used to
develop the SC-CO2 estimates (78 FR 70586, November 26,
2013). The comment period ended on February 26, 2014, and OMB is
reviewing the comments received. OMB also responded in January 2014 to
concerns submitted in a Request for Correction on the SCC TSDs.\691\
---------------------------------------------------------------------------

    \691\ OMB's 1/24/14 response to the petition is available at
https://www.whitehouse.gov/sites/default/files/omb/inforeg/ssc-rfc-under-iqa-response.pdf.
---------------------------------------------------------------------------

    The four global SC-CO2 estimates, updated in 2013, are
as follows: $13, $46, $68, and $140 per metric ton of CO2
emissions in the year 2020 (2012$).\692\ The first three values are
based on the average SC-CO2 from the three IAMs, at discount
rates of 5, 3, and 2.5 percent, respectively. SC-CO2
estimates for several discount rates are included because the
literature shows that the SC-CO2 is quite sensitive to
assumptions about the discount rate, and because no consensus exists on
the appropriate rate to use in an intergenerational context (where
costs and benefits are incurred by different generations). The fourth
value is the 95th percentile of the SC-CO2 from all three
models at a 3 percent discount rate. It is included to represent
higher-than-expected impacts from temperature change further out in the
tails of the SC-CO2 distribution (representing less likely,
but potentially catastrophic, outcomes). The SC-CO2
increases over time because future emissions are expected to produce
larger incremental damages as economies grow and physical and economic
systems become more stressed in response to greater climate change. The
SC-CO2 values are presented in Table IX-11.
---------------------------------------------------------------------------

    \692\ The 2013 SCC TSD presents the SC-CO2 estimates
in $2007. These estimates were adjusted to 2012$ using the GDP
Implicit Price Deflator. Bureau of Economic Analysis, Table 1.1.9
Implicit Price Deflators for Gross Domestic Product; last revised on
March 27, 2014.
---------------------------------------------------------------------------

    Applying the global SC-CO2 estimates, shown in Table IX-
13, to the estimated reductions in domestic CO2 emissions
for the proposed program, yields estimates of the dollar value of the
climate related benefits for each analysis year. These estimates are
then discounted back to the analysis year using the same discount rate
used to estimate the SC-CO2. For internal consistency, the
annual benefits are discounted back to net present value terms using
the same discount rate as each SC-CO2 estimate (i.e. 5
percent, 3 percent, and 2.5 percent) rather than the discount rates of
3 percent and 7 percent used to derive the net present value of other
streams of costs and benefits of the proposed rule.\693\ The SC-
CO2 benefit estimates for each calendar year are shown in
Table IX-14. The SC-CO2 benefit estimates for each model
year are shown in Table IX-15.
---------------------------------------------------------------------------

    \693\ See more discussion on the appropriate discounting of
climate benefits using SC-CO2 in the 2010 SCC TSD. Other
benefits and costs of proposed regulations unrelated to
CO2 emissions are discounted at the 3% and 7% rates
specified in OMB guidance for regulatory analysis.

                                Table IX-13--Social Cost of CO\2\, 2012-2050 \a\
                                            (in 2012$ per metric ton)
----------------------------------------------------------------------------------------------------------------
                                                                                3%, 95th
            Calendar year                 5% Average         3% Average        2.5% Average        Percentile
----------------------------------------------------------------------------------------------------------------
2012................................                $12                $37                $58               $100
2015................................                 12                 40                 61                120
2020................................                 13                 46                 69                140
2025................................                 15                 51                 74                150
2030................................                 17                 56                 81                170
2035................................                 20                 60                 86                190
2040................................                 23                 66                 93                210
2045................................                 26                 71                 99                220
2050................................                 28                 77                100                240
----------------------------------------------------------------------------------------------------------------
Note:
\a\ The SC-CO2values are dollar-year and emissions-year specific and have been rounded to two significant
  digits. Unrounded numbers from the 2013 SCC TSD were used to calculate the CO2 benefits.

   Table IX-14--Upstream and Downstream Annual CO2 Benefits for the Given SC-CO2 Value \a\ Using Method B and
                                      Relative to the Less Dynamic Baseline
                                             [millions of 2012$] \b\
----------------------------------------------------------------------------------------------------------------
                                                                                3%, 95th
            Calendar year                 5% Average         3% Average        2.5% Average        Percentile
----------------------------------------------------------------------------------------------------------------
2018................................                $13                $43                $65               $130
2019................................                 26                 91                130                270
2020................................                 40                140                210                420
2021................................                 92                330                500              1,000
2022................................                170                590                880              1,800
2023................................                250                860              1,300              2,600
2024................................                400              1,300              1,900              4,000
2025................................                540              1,800              2,600              5,500
2026................................                720              2,300              3,400              7,000
2027................................                890              2,900              4,200              8,900
2028................................              1,100              3,500              5,100             11,000
2029................................              1,300              4,200              5,900             13,000
2030................................              1,500              4,800              6,900             15,000
2035................................              2,500              7,400             11,000             23,000
2040................................              3,300              9,700             14,000             30,000
2050................................              5,000             14,000             19,000             42,000
NPV.................................             22,000            100,000            160,000            320,000
----------------------------------------------------------------------------------------------------------------
Notes:
\a\ The SC-CO2 values are dollar-year and emissions-year specific.
\b\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less
  dynamic baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1.

   Table IX-15--Upstream and Downstream Discounted Model Year Lifetime CO2 Benefits for the Given SC-CO2 Value
                            Using Method B and Relative to the Less Dynamic Baseline
                                             [millions of 2012$] a b
----------------------------------------------------------------------------------------------------------------
                                                                                3%, 95th
             Model year                   5% Average         3% Average        2.5% Average        Percentile
----------------------------------------------------------------------------------------------------------------
2018................................                $93               $380               $580             $1,100
2019................................                 90                370                570              1,100
2020................................                 87                360                560              1,100
2021................................                520              2,200              3,400              6,600
2022................................                540              2,300              3,500              6,900
2023................................                550              2,300              3,600              7,200
2024................................                870              3,700              5,800             11,000
2025................................                900              3,900              6,100             12,000
2026................................                920              4,000              6,300             12,000
2027................................              1,100              4,800              7,600             15,000
2028................................              1,100              4,800              7,600             15,000
2029................................              1,100              4,900              7,700             15,000
Sum.................................              7,800             34,000             53,000            100,000
----------------------------------------------------------------------------------------------------------------
Notes:
\a\ The SC-CO2 values are dollar-year and emissions-year specific.
\b\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less
  dynamic baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1.

(2) Sensitivity Analysis--Monetized Non-CO2 GHG Impacts
    One limitation of the primary benefits analysis is that it does not
include the valuation of non-CO2 GHG impacts (e.g.,
CH4, N2O, HFC-134a). Specifically, the 2010 and
2013 SCC TSDs do not include estimates of the social costs of non-
CO2 GHG emissions using an approach analogous to the one
used to estimate the SC-CO2. However, EPA recognizes that
non-CO2 GHG impacts associated with this rulemaking (e.g.,
net reductions in CH4,N2O, and HFC-134a) would
provide additional benefits to society. To understand the potential
implication of omitting these benefits, EPA has conducted sensitivity
analysis using two approaches: (1) An approximation approach based on
the global warming potentials (GWP) of non-CO2 GHGs, which
has been used in previous rulemakings, and (2) a set of recently
published SC-CH4 and SC-N2O estimates that are
consistent with the modeling assumptions underlying the SC-
CO2 estimates (Marten et al. 2014). This section presents
estimates of the non-CO2 benefits of the proposed rulemaking
using both approaches. Other unquantified non-CO2 benefits
are discussed in this section as well. Additional details are provided
in the RIA of these rules.
    Currently, EPA is undertaking a peer review of the application of
the Marten et al. (2014) non-CO2 social cost estimates in
regulatory analysis. Pending a favorable peer review, EPA plans to
include monetized benefits of CH4 and N2O
emission reductions in the main benefit-cost analysis of the RIA for
the final rule, using the directly modeled estimates of SC-
CH4 and SC-N2O from Marten et al. EPA seeks
comments on the use of directly modeled estimates for the social cost
of non-CO2 GHGs.

(a) Non-CO2 GHG Benefits Based on the GWP Approximation
Approach
    In the absence of directly modeled estimates, one potential method
for approximating the value of marginal non-CO2 GHG emission
reductions is to convert non-CO2 emissions reductions to
CO2-equivalents that may then be valued using the SC-
CO2. Conversion to CO2-equivalents is typically
based on the global warming potentials (GWPs) for the non-
CO2 gases. This approach, henceforth referred to as the
``GWP approach,'' has been used in sensitivity analyses to estimate the
non-CO2 benefits in previous EPA rulemakings (see U.S. EPA
2012, 2013).\694\ EPA has not presented these estimates in a main
benefit-cost analysis due to the limitations associated with using the
GWP approach to value changes in non-CO2 GHG emissions, and
considered the GWP approach as an interim method of analysis until
social cost estimates for non-CO2 GHGs, consistent with the
SC-CO2 estimates, were developed.
---------------------------------------------------------------------------

    \694\ U.S. EPA. (2012). ``Regulatory impact analysis supporting
the 2012 U.S. Environmental Protection Agency final new source
performance standards and amendments to the national emission
standards for hazardous air pollutants for the oil and natural gas
industry.'' Retrieved from http://www.epa.gov/ttn/ecas/regdata/RIAs/oil_natural_gas_final_neshap_nsps_ria.pdf.
---------------------------------------------------------------------------

    The GWP is a simple, transparent, and well-established metric for
assessing the relative impacts of non-CO2 emissions compared
to CO2 on a purely physical basis. However, as discussed
both in the 2010 SCC TSD and previous rulemakings (e.g., U.S. EPA 2012,
2013), the GWP approximation approach to measuring non-CO2
GHG benefits has several well-documented limitations. These metrics are
not ideally suited for use in benefit-cost analyses to approximate the
social cost of non-CO2 GHGs because the approach would
assume all subsequent linkages leading to damages are linear in
radiative forcing, which would be inconsistent with the most recent
scientific literature. Detailed discussion of limitations of the GWP
approach can be found in the RIA.
    Similar to the approach used in the RIA of the Final Rulemaking for
2017-2025 Light-Duty Vehicle Greenhouse Gas Emission Standards and
Corporate Average Fuel Economy Standards (U.S. EPA, 2013), EPA applies
the GWP approach to estimate the benefits associated with reductions of
CH4, N2O and HFCs in each calendar year. Under
the GWP Approach, EPA converted CH4, N2O and HFC-
134a to CO2 equivalents using the AR4 100-year GWP for each
gas: CH4 (25), N2O (298), and HFC-134a
(1,430).\695\ These CO2-equivalent emission reductions are
multiplied by the SC-CO2 estimate corresponding to each year
of emission reductions. As with the calculation of annual benefits of
CO2 emission reductions, the annual benefits of non-
CO2 emission reductions based on the GWP approach are
discounted back to net present value terms using the same discount rate
as each SC-CO2 estimate. The estimated non-CO2
GHG benefits using the GWP approach are presented in Table IX-16
through Table IX-18. The total net present value of the GHG benefits
for this proposed rulemaking would increase by about $760 million to
$11 billion (2012$), depending on discount rate, or roughly 3 percent
if these non-CO2 estimates were included.
---------------------------------------------------------------------------

    \695\ Source: Table 2.14 (Errata). Lifetimes, radiative
efficiencies and direct (except for CH4) GWPs relative to
CO2. IPCC Fourth Assessment Report ``Climate Change 2007:
Working Group I: The Physical Science Basis.''

 Table IX-16--Annual Upstream and Downstream CH4 Benefits for the Given SC-CO2 Value Using Method B and Relative
                            to the Less Dynamic Baseline, Using the GWP Approach a b
                                            [$Millions of 2012$] \b\
----------------------------------------------------------------------------------------------------------------
                                                                          CH4
                                     ---------------------------------------------------------------------------
            Calendar year                                                                           3%, 95th
                                          5% Average         3% Average        2.5% Average        Percentile
----------------------------------------------------------------------------------------------------------------
2018................................               $0.3               $1.1               $1.6               $3.2
2019................................                0.6                2.2                3.3                6.6
2020................................                1.0                3.5                5.2                 10
2021................................                3.1                 11                 17                 33
2022................................                6.0                 20                 30                 62
2023................................                8.8                 30                 45                 93
2024................................                 14                 46                 68                140
2025................................                 19                 62                 91                190
2026................................                 25                 79                120                240
2027................................                 30                 99                140                300
2028................................                 36                120                170                360
2029................................                 43                140                200                420
2030................................                 49                160                230                480
2035................................                 82                240                350                760
2040................................                110                320                440                990
2050................................                160                440                600              1,400
NPV.................................                730              3,400              5,400             11,000
----------------------------------------------------------------------------------------------------------------
Notes:
\a\ The SC-CO2 values are dollar-year and emissions-year specific
\b\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less
  dynamic baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1.

 Table IX-17--Annual Upstream and Downstream N2O Benefits for the Given SC-CO2 Value Using Method B and Relative
                          to the Less Dynamic Baseline, Using the GWP Approach \a\ \b\
                                            [$Millions of 2012$] \b\
----------------------------------------------------------------------------------------------------------------
                                                                          N2O
                                     ---------------------------------------------------------------------------
            Calendar year                                                                           3%, 95th
                                          5% Average         3% Average        2.5% Average        Percentile
----------------------------------------------------------------------------------------------------------------
2018................................               $0.0               $0.0               $0.1               $0.2
2019................................                0.0                0.1                0.2                0.3
2020................................                0.0                0.2                0.2                0.5
2021................................                0.1                0.4                0.5                1.1
2022................................                0.2                0.6                1.0                1.9
2023................................                0.3                0.9                1.4                2.8
2024................................                0.4                1.4                2.1                4.4
2025................................                0.6                2.0                2.9                6.0
2026................................                0.8                2.6                3.7                7.8
2027................................                1.0                3.2                4.7                 10
2028................................                1.2                3.9                5.7                 12
2029................................                1.5                4.6                6.6                 14
2030................................                1.6                5.3                7.7                 16
2035................................                2.8                8.3                 12                 26
2040................................                3.8                 11                 15                 34
2050................................                5.6                 15                 21                 47
NPV.................................                 25                120                180                360
----------------------------------------------------------------------------------------------------------------
Notes:
\a\ The SC-CO2 values are dollar-year and emissions-year specific.
\b\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less
  dynamic baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1.

   Table IX-18--Annual Upstream and Downstream HFC-134a Benefits for the Given SC-CO2 Value Using Method B and
                      Relative to the Less Dynamic Baseline, Using the GWP Approach \a\ \b\
                                            [$Millions of 2012$] \b\
----------------------------------------------------------------------------------------------------------------
                                                                       HFC-134a
                                     ---------------------------------------------------------------------------
            Calendar year                                                                           3%, 95th
                                          5% Average         3% Average        2.5% Average        Percentile
----------------------------------------------------------------------------------------------------------------
2018................................               $0.0               $0.0               $0.0               $0.0
2019................................                0.0                0.0                0.0                0.0
2020................................                0.0                0.0                0.0                0.0
2021................................                0.2                0.8                1.3                2.6
2022................................                0.5                1.7                2.6                5.3
2023................................                0.8                2.7                4.0                8.1
2024................................                1.1                3.7                5.4                 11
2025................................                1.4                4.7                6.9                 14
2026................................                1.8                5.9                8.6                 18
2027................................                2.2                7.1                 10                 22
2028................................                2.5                8.3                 12                 25
2029................................                3.0                 10                 14                 29
2030................................                3.4                 11                 16                 34
2035................................                5.2                 15                 22                 48
2040................................                6.1                 18                 25                 56
2050................................                8.4                 23                 31                 71
NPV.................................                 44                200                320                630
----------------------------------------------------------------------------------------------------------------
Notes:
\a\ The SC-CO2 values are dollar-year and emissions-year specific.
\b\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less
  dynamic baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1.

(b) Non-CO2 GHG Benefits Based on Directly Modeled Estimates
    Several researchers have directly estimated the social cost of non-
CO2 emissions using integrated assessment models (IAMs),
though the number of such estimates is small compared to the large
number of SC-CO2 estimates available in the literature. As
discussed in previous RIAs (e.g., EPA 2012), there is considerable
variation among these published estimates in the models and input
assumptions they employ. These studies differ in the emission
perturbation year, employ a wide range of constant and variable
discount rate specifications, and consider a range of baseline
socioeconomic and emissions scenarios that have been developed over the
last 20 years. However, none of the other published estimates of the
social cost of non-CO2 GHG are consistent with the SC-
CO2 estimates, and most are likely underestimates due to
changes in the underlying science since their publication.
    Recently, a paper by Marten et al. (2014) provided the first set of
published SC-CH4 and SC-N2O

estimates that are consistent with the modeling assumptions underlying
the SC-CO2.\696\ Specifically, the estimation approach of
Marten et al. (2014) used the same set of three IAMs, five
socioeconomic-emissions scenarios, equilibrium climate sensitivity
distribution, three constant discount rates, and aggregation approach
used to develop the SC-CO2 estimates.
---------------------------------------------------------------------------

    \696\ Marten, A.L., E.A. Kopits, C.W. Griffiths, S.C. Newbold &
A. Wolverton (2014). Incremental CH4 and N2O
mitigation benefits consistent with the U.S. Government's SC-
CO2 estimates, Climate Policy, DOI: 10.1080/
14693062.2014.912981.
---------------------------------------------------------------------------

    The resulting SC-CH4 and SC-N2O estimates are
presented in Table IX-19. More detailed discussion of their
methodology, results and a comparison to other published estimates can
be found in the RIA and in Marten et al. (2014). The tables do not
include HFC-134a because EPA is unaware of analogous estimates.

                                    Table IX-19--Social Cost of CH4 and N2O, 2012-2050 \a\ [in 2012$ per metric ton]
                                                              [Source: Marten et al., 2014]
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                        SC-CH4                                              SC-N2O
                                                 -------------------------------------------------------------------------------------------------------
                      Year                                                      2.5%       3% 95th                                  2.5%       3%  95th
                                                   5% Average   3% Average    Average     percentile   5% Average  3%  Average    Average     percentile
--------------------------------------------------------------------------------------------------------------------------------------------------------
2012............................................         $440       $1,000       $1,400       $2,800       $4,000      $14,000      $20,000      $37,000
2015............................................          500        1,200        1,500        3,100        4,400       15,000       22,000       39,000
2020............................................          590        1,300        1,700        3,500        5,200       16,000       24,000       44,000
2025............................................          710        1,500       19,000        4,100        6,000       18,000       27,000       50,000
2030............................................          840        1,700        2,300        4,600        7,000       20,000       29,000       55,000
2035............................................          990        2,000        2,500        5,400        8,100       23,000       32,000       61,000
2040............................................        1,200        2,300        2,800        6,000        9,300       25,000       35,000       67,000
2045............................................        1,300        2,500        3,100        6,800       11,000       27,000       38,000       73,000
2050............................................        1,500        2,700        3,300        7,400       12,000       29,000       41,000       80,000
--------------------------------------------------------------------------------------------------------------------------------------------------------
Note:
\a\ The values are emissions-year specific and have been rounded to two significant digits. Unrounded numbers were used to calculate the GHG benefits.

    The application of directly modeled estimates from Marten et al.
(2014) to benefit-cost analysis of a regulatory action is analogous to
the use of the SC-CO2 estimates. Specifically, the SC-
CH4 and SC-N2O estimates in Table IX-19 are used
to monetize the benefits of changes in CH4 and
N2O emissions expected as a result of the proposed
rulemaking. Forecast changes in CH4 and N2O
emissions in a given year resulting from the regulatory action are
multiplied by the SC-CH4 and SC-N2O estimate for
that year, respectively. To obtain a present value estimate, the
monetized stream of future non-CO2 benefits are discounted
back to the analysis year using the same discount rate used to estimate
the social cost of the non-CO2 GHG emission changes.
    The CH4 and N2O benefits based on Marten et
al. (2014) are presented for each calendar year in Table IX-20.
Including these benefits would increase the total net present value of
the GHG benefits for this proposed rulemaking by about $1.5 billion to
$12 billion (2012$), or roughly 4 to 7 percent, depending on discount
rate.

     Table IX-20--Annual Upstream and Downstream non-CO2 GHG Benefits for the Given SC-Non-CO2 Value Using Method B and Relative to the Less Dynamic
                                                  Baseline, Using the Directly Modeled Approach \a\ \b\
                                                                 [Millions of 2012$] \c\
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                          CH4                                                 N2O
                                                 -------------------------------------------------------------------------------------------------------
                  Calendar year                                                 2.5%       3% 95th                                  2.5%       3%  95th
                                                   5% Average   3% Average    Average     percentile  5%  Average  3%  Average    Average     percentile
--------------------------------------------------------------------------------------------------------------------------------------------------------
2018............................................         $0.6         $1.3         $1.6         $3.3         $0.0         $0.1         $0.1         $0.2
2019............................................          1.1          2.6          3.4          6.8          0.0          0.1          0.2          0.3
2020............................................          1.8          3.9          5.2           10          0.1          0.2          0.3          0.5
2021............................................          5.8           13           17           35          0.1          0.4          0.6          1.2
2022............................................           11           24           31           65          0.3          0.8          1.1          2.1
2023............................................           17           35           49           97          0.4          1.1          1.7          3.1
2024............................................           26           56           72          150          0.6          1.8          2.5          4.7
2025............................................           35           74           95          200          0.8          2.4          3.5          6.5
2026............................................           46           99          130          260          1.0          3.0          4.5          8.4
2027............................................           57          120          150          320          1.3          4.0          5.8           11
2028............................................           69          140          190          390          1.6          4.8          6.9           13
2029............................................           82          170          220          460          1.9          5.8          8.2           15
2030............................................           95          190          260          520          2.2          6.5          9.3           18
2035............................................          160          330          400          870          3.7           10           15           28
2040............................................          230          430          540        1,200          5.2           14           19           37
2050............................................          350          620          770        1,700          7.9           20           27           53
NPV.............................................        1,500        4,600        6,400       12,000           34          150          230          400
--------------------------------------------------------------------------------------------------------------------------------------------------------
Notes:

\a\ The SC-CH4 and SC-N2O values are dollar-year and emissions-year specific.
\b\ Note that net present discounted values of reduced GHG emissions is are calculated differently than other benefits. The same discount rate used to
  discount the value of damages from future emissions (SC-CH4 and SC-N2O at 5, 3, and 2.5 percent) is used to calculate net present value discounted
  values of SC-CH4 and SC-N2O for internal consistency. Refer to SCC TSD for more detail.
\c\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic
  baseline, 1b, please see Section X.A.1.

    As illustrated above, compared to the use of directly modeled
estimates the GWP-based approximation approach underestimates the
climate benefits of the CH4 emission reductions by 12
percent to 52 percent and the climate benefits of N2O
reductions by 10 percent to 26 percent, depending on the discount rate
assumption.
(c) Additional Non-CO2 GHGs Co-Benefits
    In determining the relative social costs of the different gases,
the Marten et al. (2014) analysis accounts for differences in lifetime
and radiative efficiency between the non-CO2 GHGs and
CO2. The analysis also accounts for radiative forcing
resulting from methane's effects on tropospheric ozone and
stratospheric water vapor, and for at least some of the fertilization
effects of elevated carbon dioxide concentrations. However, there exist
several other differences between these gases that have not yet been
captured in this analysis, namely the non-radiative effects of methane-
driven elevated tropospheric ozone levels on human health, agriculture,
and ecosystems, and the effects of carbon dioxide on ocean
acidification. Inclusion of these additional non-radiative effects
would potentially change both the absolute and relative value of the
various gases.
    Of these effects, the human health effect of elevated tropospheric
ozone levels resulting from methane emissions is the closest to being
monetized in a way that would be comparable to the SCC. Premature
ozone-related cardiopulmonary deaths resulting from global increases in
tropospheric ozone concentrations produced by the methane oxidation
process have been the focus of a number of studies over the past decade
(e.g., West et al. 2006 \697\ ). Recent studies have produced an
estimate of a monetized benefit of methane emissions reductions, with
results on the order of $1,000 per metric ton of CH4
emissions reduced (Anenberg et al. 2012 \698\; Shindell et al. 2012
\699\), an estimate similar in magnitude to the climate benefits of
CH4 reductions estimated by the Marten et al. or GWP
methods. However, though EPA is continuing to monitor this area of
research as it evolves, EPA is not applying them for benefit estimates
at this time.
---------------------------------------------------------------------------

    \697\ West JJ, Fiore AM, Horowitz LW, Mauzerall DL (2006) Global
health benefits of mitigating ozone pollution with methane emission
controls. Proc Natl Acad Sci USA 103(11):3988-3993. doi:10.1073/
pnas.0600201103.
    \698\ Anenberg SC, Schwartz J, Shindell D, Amann M, Faluvegi G,
Klimont Z, . . ., Vignati E (2012) Global air quality and health co-
benefits of mitigating near-term climate change through methane and
black carbon emission controls. Environ Health Perspect 120(6):831.
doi:10.1289/ehp.1104301.
    \699\ Shindell D, Kuylenstierna JCI, Vignati E, van Dingenen R,
Amann M, Klimont Z, . . . , Fowler D (2012) Simultaneously
Mitigating Near-Term Climate Change and Improving Human Health and
Food Security. Science 335 (6065):183-189. doi:10.1126/
science.1210026.
---------------------------------------------------------------------------

H. Monetized Non-GHG Health Impacts

    This section analyzes the economic benefits from reductions in
health and environmental impacts resulting from non-GHG emission
reductions that can be expected to occur as a result of the proposed
Phase 2 standards. CO2 emissions are predominantly the
byproduct of fossil fuel combustion processes that also produce
criteria and hazardous air pollutant emissions. The vehicles that are
subject to the proposed standards are also significant sources of
mobile source air pollution such as direct PM, NOX, VOCs and
air toxics. The proposed standards would affect exhaust emissions of
these pollutants from vehicles and would also affect emissions from
upstream sources that occur during the refining and distribution of
fuel. Changes in ambient concentrations of ozone, PM2.5, and
air toxics that would result from the proposed standards are expected
to affect human health by reducing premature deaths and other serious
human health effects, as well as other important improvements in public
health and welfare.
    It is important to quantify the health and environmental impacts
associated with the proposed standards because a failure to adequately
consider these ancillary impacts could lead to an incorrect assessment
of their costs and benefits. Moreover, the health and other impacts of
exposure to criteria air pollutants and airborne toxics tend to occur
in the near term, while most effects from reduced climate change are
likely to occur only over a time frame of several decades or longer.
    Although EPA typically quantifies and monetizes the health and
environmental impacts related to both PM and ozone in its regulatory
impact analyses (RIAs), it was unable to do so in time for this
proposal. Instead, EPA has applied PM-related ``benefits per-ton''
values to its estimated emission reductions as an interim approach to
estimating the PM-related benefits of the proposal. 700 701
EPA also characterizes the health and environmental impacts that will
be quantified and monetized for the final rulemaking.
---------------------------------------------------------------------------

    \700\ Fann, N., Baker, K.R., and Fulcher, C.M. (2012).
Characterizing the PM2.5-related health benefits of emission
reductions for 17 industrial, area and mobile emission sectors
across the U.S., Environment International, 49, 241-151, published
online September 28, 2012.
    \701\ See also: http://www.epa.gov/airquality/benmap/sabpt.html.
The current values available on the Web page have been updated since
the publication of the Fann et al., 2012 paper. For more information
regarding the updated values, see: http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf (accessed
September 9, 2014).
---------------------------------------------------------------------------

    This section is split into two sub-sections: the first presents the
benefits-per-ton values used to monetize the benefits from reducing
population exposure to PM associated with the proposed standards; the
second explains what PM- and ozone-related health and environmental
impacts EPA will quantify and monetize in the analysis for the final
rule. EPA bases its analyses on peer-reviewed studies of air quality
and health and welfare effects and peer-reviewed studies of the
monetary values of public health and welfare improvements, and is
generally consistent with benefits analyses performed for the analysis
of the final Tier 3 Vehicle Rule,\702\ the final 2012 p.m. NAAQS
Revision,\703\ and the final

2017-2025 Light Duty Vehicle GHG Rule.\704\
---------------------------------------------------------------------------

    \702\ U.S. Environmental Protection Agency. (2014). Control of
Air Pollution from Motor Vehicles: Tier 3 Motor Vehicle Emission and
Fuel Standards Final Rule: Regulatory Impact Analysis, Assessment
and Standards Division, Office of Transportation and Air Quality,
EPA-420-R-14-005, March 2014. Available on the Internet: http://www.epa.gov/otaq/documents/tier3/420r14005.pdf.
    \703\ U.S. Environmental Protection Agency. (2012). Regulatory
Impact Analysis for the Final Revisions to the National Ambient Air
Quality Standards for Particulate Matter, Health and Environmental
Impacts Division, Office of Air Quality Planning and Standards, EPA-
452-R-12-005, December 2012. Available on the Internet: http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf.
    \704\ U.S. Environmental Protection Agency (U.S. EPA). (2012).
Regulatory Impact Analysis: Final Rulemaking for 2017-2025 Light-
Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average
Fuel Economy Standards, Assessment and Standards Division, Office of
Transportation and Air Quality, EPA-420-R-12-016, August 2012.
Available on the Internet at: http://www.epa.gov/otaq/climate/documents/420r12016.pdf.
---------------------------------------------------------------------------

    Though EPA is characterizing the changes in emissions associated
with toxic pollutants, we are not able to quantify or monetize the
human health effects associated with air toxic pollutants for either
the proposal or the final rule analyses (see Section VIII.G.1.b.iii for
more information). Please refer to Section VIII for more information
about the air toxics emissions impacts associated with the proposed
standards.
(1) Economic Value of Reductions in Criteria Pollutants
    As described in Section VIII, the proposed standards would reduce
emissions of several criteria and toxic pollutants and their
precursors. In this analysis, EPA estimates the economic value of the
human health benefits associated with the resulting reductions in
PM2.5 exposure. Due to analytical limitations with the
benefit per ton method, this analysis does not estimate benefits
resulting from reductions in population exposure to other criteria
pollutants such as ozone.\705\ Furthermore, the benefits per-ton
method, like all air quality impact analyses, does not monetize all of
the potential health and welfare effects associated with reduced
concentrations of PM2.5.
---------------------------------------------------------------------------

    \705\ The air quality modeling that underlies the PM-related
benefit per ton values also produced estimates of ozone levels
attributable to each sector. However, the complex non-linear
chemistry governing ozone formation prevented EPA from developing a
complementary array of ozone benefit per ton values. This limitation
notwithstanding, we anticipate that the ozone-related benefits
associated with reducing emissions of NOX and VOC could
be substantial.
---------------------------------------------------------------------------

    This analysis uses estimates of the benefits from reducing the
incidence of the specific PM2.5-related health impacts
described below. These estimates, which are expressed per ton of
PM2.5-related emissions eliminated by the proposed rules,
represent the monetized value of human health benefits (including
reductions in both premature mortality and premature morbidity) from
reducing each ton of directly emitted PM2.5 or its
precursors (SO2 and NOX), from a specified
source. Ideally, the human health benefits would be estimated based on
changes in ambient PM2.5 as determined by full-scale air
quality modeling. However, the length of time needed to prepare the
necessary emissions inventories, in addition to the processing time
associated with the modeling itself, has precluded us from performing
air quality modeling for this proposal. We will conduct this modeling
for the final rule.
    The dollar-per-ton estimates used in this analysis are provided in
Table IX-21. As the table indicates, these values differ among
pollutants, and also depend on their original source, because emissions
from different sources can result in different degrees of population
exposure and resulting health impacts. In the summary of costs and
benefits, Section IX.K of this preamble, EPA presents the monetized
value of PM-related improvements associated with the proposal.

                                                          Table IX-21--Benefits-per-Ton Values
                                                                 [Thousands, 2012$] \a\
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                      On-road mobile sources                           Upstream sources \d\
                        Year \c\                         -----------------------------------------------------------------------------------------------
                                                           Direct PM2.5         SO2             NOX        Direct PM2.5         SO2             NOX
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                      Estimated Using a 3 Percent Discount Rate \b\
--------------------------------------------------------------------------------------------------------------------------------------------------------
2016....................................................       $380-$850         $20-$45        $7.7-$18       $330-$750        $69-$160        $6.8-$16
2020....................................................         400-910           22-49          8.1-18         350-790          75-170          7.4-17
2025....................................................       440-1,000           24-55          8.8-20         390-870          83-190          8.1-18
2030....................................................       480-1,100           27-61          9.6-22         420-950          91-200          8.7-20
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                      Estimated Using a 7 Percent Discount Rate \b\
--------------------------------------------------------------------------------------------------------------------------------------------------------
2016....................................................       $340-$770         $18-$41        $6.9-$16       $290-$670        $63-$140        $6.2-$14
2020....................................................         370-820           20-44          7.4-17         320-720          67-150          6.6-15
2025....................................................         400-910           22-49          8.0-18         350-790          75-170          7.3-17
2030....................................................         430-980           24-55          8.6-20         380-850          81-180          7.9-18
--------------------------------------------------------------------------------------------------------------------------------------------------------
Notes:
\a\ The benefit-per-ton estimates presented in this table are based on a range of premature mortality estimates derived from the ACS study (Krewski et
  al., 2009) and the Six-Cities study (Lepeule et al., 2012). See Chapter VIII of the RIA for a description of these studies.
\b\ The benefit-per-ton estimates presented in this table assume either a 3 percent or 7 percent discount rate in the valuation of premature mortality
  to account for a twenty-year segmented premature mortality cessation lag.
\c\ Benefit-per-ton values were estimated for the years 2016, 2020, 2025 and 2030. We hold values constant for intervening years (e.g., the 2016 values
  are assumed to apply to years 2017-2019; 2020 values for years 2021-2024; 2030 values for years 2031 and beyond).
\d\ We assume for the purpose of this analysis that total ``upstream emissions'' are most appropriately monetized using the refinery sector benefit per-
  ton values. The majority of upstream emission reductions associated with the proposed rule are related to domestic onsite refinery emissions and
  domestic crude production. While total upstream emissions also include storage and transport sources, as well as sources upstream from the refinery,
  we have chosen to simply apply the refinery values. Full-scale air quality modeling, and the associated benefits analysis, will include upstream
  emissions from all sources in the FRM.

    The benefit-per-ton technique has been used in previous analyses,
including EPA's 2017-2025 Light-Duty Vehicle Greenhouse Gas Rule,\706\
the Reciprocating Internal Combustion Engine rules,707 708
and the Residential

Wood Heaters NSPS.\709\ Table IX-22 shows the quantified
PM2.5-related co-benefits captured in those benefit per-ton
estimates, as well as unquantified effects the benefit per-ton
estimates are unable to capture.
---------------------------------------------------------------------------

    \706\ U.S. Environmental Protection Agency (U.S. EPA). (2012).
Regulatory Impact Analysis: Final Rulemaking for 2017-2025 Light-
Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average
Fuel Economy Standards, Assessment and Standards Division, Office of
Transportation and Air Quality, EPA-420-R-12-016, August 2012.
Available on the Internet at: http://www.epa.gov/otaq/climate/documents/420r12016.pdf.
    \707\ U.S. Environmental Protection Agency (U.S. EPA). (2013).
Regulatory Impact Analysis for the Reconsideration of the Existing
Stationary Compression Ignition (CI) Engines NESHAP, Office of Air
Quality Planning and Standards, Research Triangle Park, NC. January.
EPA-452/R-13-001. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/RICE_NESHAPreconsideration_Compression_Ignition_Engines_RIA_final2013_EPA.pdf.
    \708\ U.S. Environmental Protection Agency (U.S. EPA). (2013).
Regulatory Impact Analysis for Reconsideration of Existing
Stationary Spark Ignition (SI) RICE NESHAP, Office of Air Quality
Planning and Standards, Research Triangle Park, NC. January. EPA-
452/R-13-002. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/NESHAP_RICE_Spark_Ignition_RIA_finalreconsideration2013_EPA.pdf>
.
    \709\ U.S. Environmental Protection Agency (U.S. EPA). (2015).
Regulatory Impact Analysis for Residential Wood Heaters NSPS
Revision. Office of Air Quality Planning and Standards, Research
Triangle Park, NC. February. EPA-452/R-15-001. Available at <http://www2.epa.gov/sites/production/files/2015-02/documents/20150204-residential-wood-heaters-ria.pdf>.

         Table IX-22--Human Health and Welfare Effects of PM2.5
------------------------------------------------------------------------
                      Quantified and monetized    Unquantified effects
  Pollutant/ effect     in primary estimates           Changes in:
------------------------------------------------------------------------
PM2.5...............  Adult premature           Chronic and subchronic
                       mortality.                bronchitis cases.
                      Acute bronchitis........  Strokes and
                                                 cerebrovascular
                                                 disease.
                      Hospital admissions:      Low birth weight.
                       Respiratory and          Pulmonary function.
                       cardiovascular.
                      Emergency room visits     Chronic respiratory
                       for asthma.               diseases other than
                                                 chronic bronchitis.
                      Nonfatal heart attacks    Non-asthma respiratory
                       (myocardial infarction).  emergency room visits.
                      Lower and upper           Visibility.
                       respiratory illness.
                      Minor restricted-         Household soiling.
                       activity days.
                      Work loss days..........
                      Asthma exacerbations
                       (asthmatic population).
                      Infant mortality........
------------------------------------------------------------------------

    A more detailed description of the benefit-per-ton estimates is
provided in Chapter VIII of the Draft RIA that accompanies this
rulemaking. Readers interested in reviewing the complete methodology
for creating the benefit-per-ton estimates used in this analysis can
consult EPA's ``Technical Support Document: Estimating the Benefit per
Ton of Reducing PM2.5 Precursors from 17 Sectors.'' \710\
Readers can also refer to Fann et al. (2012) \711\ for a detailed
description of the benefit-per-ton methodology.
---------------------------------------------------------------------------

    \710\ For more information regarding the updated values, see:
http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf (accessed September 9,
2014).
    \711\ Fann, N., Baker, K.R., and Fulcher, C.M. (2012).
Characterizing the PM2.5-related health benefits of emission
reductions for 17 industrial, area and mobile emission sectors
across the U.S., Environment International, 49, 241-151, published
online September 28, 2012.
---------------------------------------------------------------------------

    As Table IX-20 indicates, EPA projects that the per-ton values for
reducing emissions of non-GHG pollutants from both vehicle use and
upstream sources such as fuel refineries will increase over time.\712\
These projected increases reflect rising income levels, which increase
affected individuals' willingness to pay for reduced exposure to health
threats from air pollution.\713\ They also reflect future population
growth and increased life expectancy, which expands the size of the
population exposed to air pollution in both urban and rural areas,
especially among older age groups with the highest mortality risk.\714\
---------------------------------------------------------------------------

    \712\ As we discuss in the emissions chapter of the DRIA
(Chapter V), the rule would yield emission reductions from upstream
refining and fuel distribution due to decreased petroleum
consumption.
    \713\ The issue is discussed in more detail in the 2012 p.m.
NAAQS RIA, Section 5.6.8. See U.S. Environmental Protection Agency.
(2012). Regulatory Impact Analysis for the Final Revisions to the
National Ambient Air Quality Standards for Particulate Matter,
Health and Environmental Impacts Division, Office of Air Quality
Planning and Standards, EPA-452-R-12-005, December 2012. Available
on the internet: http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf.
    \714\ For more information about EPA's population projections,
please refer to the following: http://www.epa.gov/air/benmap/models/BenMAPManualAppendicesAugust2010.pdf (See Appendix K).
---------------------------------------------------------------------------

(2) Human Health and Environmental Benefits for the Final Rule
(a) Human Health and Environmental Impacts
    To model the ozone and PM air quality benefits of the final rule,
EPA will use the Community Multiscale Air Quality (CMAQ) model (see
Section VIII for a description of the CMAQ model). The modeled ambient
air quality data will serve as an input to the Environmental Benefits
Mapping and Analysis Program--Community Edition (BenMAP CE).\715\
BenMAP CE is a computer program developed by EPA that integrates a
number of the modeling elements used in previous RIAs (e.g.,
interpolation functions, population projections, health impact
functions, valuation functions, analysis and pooling methods) to
translate modeled air concentration estimates into health effects
incidence estimates and monetized benefits estimates.
---------------------------------------------------------------------------

    \715\ Information on BenMAP, including downloads of the
software, can be found at http://www.epa.gov/air/benmap/.
---------------------------------------------------------------------------

    Chapter VIII in the DRIA that accompanies this proposal lists the
co-pollutant health effect concentration-response functions EPA will
use to quantify the non-GHG incidence impacts associated with the
proposed heavy-duty vehicle standards. These include PM- and ozone-
related premature mortality, nonfatal heart attacks, hospital
admissions (respiratory and cardiovascular), emergency room visits,
acute bronchitis, minor restricted activity days, and days of work and
school lost.
(b) Monetized Impacts
    To calculate the total monetized impacts associated with quantified
health impacts, EPA applies values derived from a number of sources.
For premature mortality, EPA applies a value of a statistical life
(VSL) derived from the mortality valuation literature. For certain
health impacts, such as a number of respiratory-related ailments, EPA
applies willingness-to-pay estimates derived from the valuation
literature. For the remaining health impacts, EPA applies values
derived from current cost-of-illness and/or wage estimates. Chapter
VIII in the DRIA that accompanies this proposal presents the monetary
values EPA will apply to changes in the incidence of health and welfare
effects associated with reductions in non-GHG pollutants that will
occur when these GHG control strategies are finalized.

(c) Other Unquantified Health and Environmental Impacts
    In addition to the co-pollutant health and environmental impacts
EPA will quantify for the analysis of the final standard, there are a
number of other health and human welfare endpoints that EPA will not be
able to quantify or monetize because of current limitations in the
methods or available data. These impacts are associated with emissions
of air toxics (including benzene, 1,3-butadiene, formaldehyde,
acetaldehyde, acrolein, naphthalene and ethanol), ambient ozone, and
ambient PM2.5 exposures. Chapter VIII of the DRIA lists
these unquantified health and environmental impacts.
    While there will be impacts associated with air toxic pollutant
emission changes that result from the final standard, EPA will not
attempt to monetize those impacts. This is primarily because currently
available tools and methods to assess air toxics risk from mobile
sources at the national scale are not adequate for extrapolation to
incidence estimations or benefits assessment. The best suite of tools
and methods currently available for assessment at the national scale
are those used in the National-Scale Air Toxics Assessment (NATA).
EPA's Science Advisory Board specifically commented in their review of
the 1996 NATA that these tools were not yet ready for use in a
national-scale benefits analysis, because they did not consider the
full distribution of exposure and risk, or address sub-chronic health
effects.\716\ While EPA has since improved the tools, there remain
critical limitations for estimating incidence and assessing benefits of
reducing mobile source air toxics.\717\ EPA continues to work to
address these limitations; however, EPA does not anticipate having
methods and tools available for national-scale application in time for
the analysis of the final rules.\718\
---------------------------------------------------------------------------

    \716\ Science Advisory Board. 2001. NATA--Evaluating the
National-Scale Air Toxics Assessment for 1996--an SAB Advisory.
http://www.epa.gov/ttn/atw/sab/sabrev.html.
    \717\ Examples include gaps in toxicological data, uncertainties
in extrapolating results from high-dose animal experiments to
estimate human effects at lower does, limited ambient and personal
exposure monitoring data, and insufficient economic research to
support valuation of the health impacts often associated with
exposure to individual air toxics. See Gwinn et al., 2011. Meeting
Report: Estimating the Benefits of Reducing Hazardous Air
Pollutants--Summary of 2009 Workshop and Future Considerations.
Environ Health Perspect. Jan 2011; 119(1): 125-130.
    \718\ In April, 2009, EPA hosted a workshop on estimating the
benefits of reducing hazardous air pollutants. This workshop built
upon the work accomplished in the June 2000 in an earlier (2000)
Science Advisory Board/EPA Workshop on the Benefits of Reductions in
Exposure to Hazardous Air Pollutants, which generated thoughtful
discussion on approaches to estimating human health benefits from
reductions in air toxics exposure, but no consensus was reached on
methods that could be implemented in the near term for a broad
selection of air toxics. Please visit http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its
associated materials.
---------------------------------------------------------------------------

I. Energy Security Impacts

    The Phase 2 standards are designed to require improvements in the
fuel efficiency of medium- and heavy-duty vehicles and, thereby, reduce
fuel consumption and GHG emissions. In turn, the Phase 2 standards help
to reduce U.S. petroleum imports. A reduction of U.S. petroleum imports
reduces both financial and strategic risks caused by potential sudden
disruptions in the supply of imported petroleum to the U.S., thus
increasing U.S. energy security. This section summarizes the agency's
estimates of U.S. oil import reductions and energy security benefits of
the proposed Phase 2 standards. Additional discussion of this issue can
be found in Chapter 8 of the draft RIA.
(1) Implications of Reduced Petroleum Use on U.S. Imports
    U.S. energy security is broadly defined as the continued
availability of energy sources at an acceptable price. Most discussion
of U.S. energy security revolves around the topic of the economic costs
of U.S. dependence on oil imports. However, it is not imports alone,
but both imports and consumption of petroleum from all sources and
their role in economic activity, that expose the U.S. to risk from
price shocks in the world oil price. The relative significance of
petroleum consumption and import levels for the macroeconomic
disturbances that follow from oil price shocks is not fully understood.
Recognizing that changing petroleum consumption will change U.S.
imports, this assessment of oil costs focuses on those incremental
social costs that follow from the resulting changes in imports,
employing the usual oil import premium measure. The agencies request
comment on how to incorporate the impact of changes in oil consumption,
rather than imports exclusively, into our energy security analysis.
    While the U.S. has reduced its consumption and increased its
production of oil in recent years, it still relies on oil from
potentially unstable sources. In addition, oil exporters with a large
share of global production have the ability to raise the price of oil
by exerting the monopoly power associated with a cartel, the
Organization of Petroleum Exporting Countries (OPEC), to restrict oil
supply relative to demand. These factors contribute to the
vulnerability of the U.S. economy to episodic oil supply shocks and
price spikes. In 2012, U.S. net expenditures for imports of crude oil
and petroleum products were $290 billion and expenditures on both
imported oil and domestic petroleum and refined products totaled $634
billion (see Figure IX-1).\719\ Import costs have declined since 2011
but total oil expenditures (domestic and imported) remain near
historical highs, at roughly triple the inflation-adjusted levels
experienced by the U.S. from 1986 to 2002.
---------------------------------------------------------------------------

    \719\ See EIA Annual Energy Review, various editions. For data
2011-2013, and projected data: EIA Annual Energy Outlook (AEO) 2014
(Reference Case). See Table 11, file ``aeotab_11.xls.''
---------------------------------------------------------------------------

    In 2010, just over 40 percent of world oil supply came from OPEC
nations and the AEO 2014 (Early Release) \720\ projects that this share
will rise gradually to over 45 percent by 2040. Approximately 31
percent of global supply is from Middle East and North African
countries alone, a share that is also expected to grow. Measured in
terms of the share of world oil resources or the share of global oil
export supply, rather than oil production, the concentration of global
petroleum resources in OPEC nations is even larger. As another measure
of concentration, of the 137 countries/principalities that export
either crude or refined products, the top 12 have recently accounted
for over 55 percent of exports.\721\ Eight of these countries are
members of OPEC, and a ninth is Russia.\722\ In a market where even a
1-2 percent supply loss can raise prices noticeably, and where a 10
percent supply loss could lead to an unprecedented price shock, this
regional concentration is of concern.\723\

Historically, the countries of the Middle East have been the source of
eight of the ten major world oil disruptions,\724\ with the ninth
originating in Venezuela, an OPEC country, and the tenth being
Hurricanes Katrina and Rita.
---------------------------------------------------------------------------

    \720\ The agencies used the AEO 2014 (Early Release) since this
version of AEO was available at the time that fuel savings from the
rule were being estimated. The AEO 2014 (Early Release) and the AEO
2014 have very similar energy market and economic projections. For
example, world oil prices are the same between the two forecasts.
    \721\ Based on data from the CIA, combining various recent
years, https://www.cia.gov/library/publications/the-world-factbook/rankorder/2242rank.html.
    \722\ The other three are Norway, Canada, and the EU, an
exporter of product.
    \723\ For example, the 2005 Hurricanes Katrina/Rita and the 2011
Libyan conflict both led to a 1.8 percent reduction in global crude
supply. While the price impact of the latter is not easily
distinguished given the rapidly rising post-recession prices, the
former event was associated with a 10-15 percent world oil price
increase. There are a range of smaller events with smaller but
noticeable impacts. Somewhat larger events, such as the 2002/3
Venezuelan Strike and the War in Iraq, corresponded to about a 2.9
percent sustained loss of supply, and was associated with a 28
percent world oil price increase.
    \724\ IEA 2011 ``IEA Response System for Oil Supply
Emergencies.''
[GRAPHIC] [TIFF OMITTED] TP13JY15.017

    The agencies used EPA's MOVES model to estimate the reductions in
U.S. fuel consumption due to this proposed rule for vocational vehicles
and tractors. For HD pickups and vans, the agencies used both DOT's
CAFE model and EPA's MOVES model to estimate the fuel consumption
impacts. (Detailed explanations of the MOVES and CAFE models can be
found in Chapters 5 and 10 of the draft RIA. See IX.C of the preamble
for estimates of reduced fuel consumption from the proposed rule).
Based on a detailed analysis of differences in U.S. fuel consumption,
petroleum imports, and imports of petroleum products, the agencies
estimate that approximately 90 percent of the reduction in fuel
consumption resulting from adopting improved GHG emission standards and
fuel efficiency standards is likely to be reflected in reduced U.S.
imports of crude oil and net imported petroleum products.\726\ Thus, on
balance, each gallon of fuel saved as a consequence of the HD GHG and
fuel efficiency standards is anticipated to reduce total U.S. imports
of petroleum by 0.90 gallons.\727\ Based upon the fuel savings
estimated by the MOVES/CAFE models and the 90 percent oil import
factor, the reduction in U.S. oil imports from these proposed rules are
estimated for the years 2020, 2025, 2030, 2040, and 2050 (in millions
of barrels per day (MMBD)) in Table IX-25 below. For comparison
purposes, Table IX-25 also shows U.S. imports of crude oil in 2020,
2025, 2030 and 2040 as projected by DOE in the Annual Energy Outlook
2014 (Early Release) Reference Case. U.S. Gross Domestic

Product (GDP) is projected to grow by roughly 59 percent over the same
time frame (e.g., from 2020 to 2040) in the same AEO projections.
---------------------------------------------------------------------------

    \725\ For historical data: EIA Annual Energy Review, various
editions. For data 2011-2013, and projected data: EIA Annual Energy
Outlook (AEO) 2014 (Reference Case). See Table 11, file
``aeotab_11.xls''.
    \726\ We looked at changes in crude oil imports and net
petroleum products in the Reference Case in comparison to two cases
from the AEO 2014. The two cases are the Low (i.e., Economic Growth)
Demand and Low VMT cases. See the spreadsheet ``Impacts on Fuel
Demands and ImportsJan9.xlsx'' comparing the AEO 2014 Reference Case
to the Low Demand Case. See the spreadsheet ``Impact of Fuel Demand
and Impacts January20VMT.xlsl'' for a comparison of AEO 2014
Reference Case and the Low VMT Case. We also considered a paper
entitled ``Effect of a U.S. Demand Reduction on Imports and Domestic
Supply Levels'' by Paul Leiby, 4/16/2013. This paper suggests that
``Given a particular reduction in oil demand stemming from a policy
or significant technology change, the fraction of oil use savings
that shows up as reduced U.S. imports, rather than reduced U.S.
supply, is actually quite close to 90 percent, and probably close to
95 percent''.
    \727\ The NHTSA analysis uses a slightly different value that
was estimated using unique runs of the National Energy Modeling
System (NEMS) that forms the foundation of the Annual Energy
Outlook. NHTSA ran a version of NEMS from 2012 (which would have
been used in the 2013 AEO) and computed the change in imports of
petroleum products with and without the Phase 1 MDHD program to
estimate the relationship between changes in fuel consumption and
oil imports. The analysis found that reducing gasoline consumption
by 1 gallon reduces imports of refined gasoline by 0.06 gallons and
domestic refining from imported crude by 0.94 gallons. Similarly,
one gallon of diesel saved by the Phase 1 rule was estimated to
reduce imports of refined diesel by 0.26 gallons and domestic
refining of imported crude by 0.74 gallons. The agencies will update
this analysis for the Final Rule using the model associated with
AEO2014, modeling the Phase 2 Preferred Alternative explicitly.

  Table IX-23--Projected U.S. Imports of Crude Oil and U.S. Oil Import
 Reductions Resulting From the Proposed Phase 2 Heavy-Duty Vehicle Rule
  in 2020, 2025, 2030, 2040 and 2050 Using Method B and Relative to the
                          Less Dynamic Baseline
                 [Millions of barrels per day (MMBD)] a
------------------------------------------------------------------------
                                                            Reductions
                  Year                       U.S. oil      from proposed
                                              imports         HD rule
------------------------------------------------------------------------
2020....................................            4.93            0.01
2025....................................            5.04            0.16
2030....................................            5.35            0.37
2040....................................            5.92            0.65
2050....................................               *            0.78
------------------------------------------------------------------------
Notes:
* The AEO 2014 (Early Release) only projects energy market and economic
  trends through 2040.
\a\ For an explanation of analytical Methods A and B, please see Section
  I.D; for an explanation of the less dynamic baseline, 1a, and more
  dynamic baseline, 1b, please see Section X.A.1.

(2) Energy Security Implications
    In order to understand the energy security implications of reducing
U.S. oil imports, EPA has worked with Oak Ridge National Laboratory
(ORNL), which has developed approaches for evaluating the social costs
and energy security implications of oil use. The energy security
estimates provided below are based upon a methodology developed in a
peer-reviewed study entitled, ``The Energy Security Benefits of Reduced
Oil Use, 2006-2015,'' completed in March 2008. This ORNL study is an
updated version of the approach used for estimating the energy security
benefits of U.S. oil import reductions developed in a 1997 ORNL
Report.\728\ For EPA and NHTSA rulemakings, the ORNL methodology is
updated periodically to account for forecasts of future energy market
and economic trends reported in the U.S. Energy Information
Administration's Annual Energy Outlook.
---------------------------------------------------------------------------

    \728\ Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and
Russell Lee, Oil Imports: An Assessment of Benefits and Costs, ORNL-
6851, Oak Ridge National Laboratory, November, 1997.
---------------------------------------------------------------------------

    When conducting this analysis, ORNL considered the full cost of
importing petroleum into the U.S. The full economic cost is defined to
include two components in addition to the purchase price of petroleum
itself. These are: (1) The higher costs for oil imports resulting from
the effect of U.S. demand on the world oil price (i.e., the ``demand''
or ``monopsony'' costs); and (2) the risk of reductions in U.S.
economic output and disruption to the U.S. economy caused by sudden
disruptions in the supply of imported oil to the U.S. (i.e.,
macroeconomic disruption/adjustment costs).
    The literature on the energy security for the last two decades has
routinely combined the monopsony and the macroeconomic disruption
components when calculating the total value of the energy security
premium. However, in the context of using a global value for the Social
Cost of Carbon (SCC) the question arises: How should the energy
security premium be used when some benefits from the rule, such as the
benefits of reducing greenhouse gas emissions, are calculated from a
global perspective? Monopsony benefits represent avoided payments by
U.S. consumers to oil producers that result from a decrease in the
world oil price as the U.S. decreases its demand for oil. Although
there is clearly an overall benefit to the U.S. when considered from a
domestic perspective, the decrease in price due to decreased demand in
the U.S. also represents a loss to oil producing countries, one of
which is the United States. Given the redistributive nature of this
monopsony effect from a global perspective, and the fact that an
increasing fraction of it represents a transfer between U.S. consumers
and producers, it is excluded in the energy security benefits
calculations for these proposed rules.
    In contrast, the other portion of the energy security premium, the
avoided U.S. macroeconomic disruption and adjustment cost that arises
from reductions in U.S. petroleum imports, does not have offsetting
impacts outside of the U.S., and, thus, is included in the energy
security benefits estimated for these proposed rules. To summarize, the
agencies have included only the avoided macroeconomic disruption
portion of the energy security benefits to estimate the monetary value
of the total energy security benefits of these proposed rules.
    For this rulemaking, ORNL updated the energy security premiums by
incorporating the most recent oil price forecast and energy market
trends, particularly regional oil supplies and demands, from the AEO
2014 (Early Release) into its model.\729\ ORNL developed energy
security premium estimates for a number of different years. Table IX-24
provides estimates for energy security premiums for the years 2020,
2025, 2030 and 2040,\730\ as well as a breakdown of the components of
the energy security premiums for each year. The components of the
energy security premiums and their values are discussed below.
---------------------------------------------------------------------------

    \729\ Leiby, P., Factors Influencing Estimate of Energy Security
Premium for Heavy-Duty Phase 2 Proposed Rule, 11/1/2014, Oak Ridge
National Laboratory.
    \730\ AEO 2014 (Early Release) forecasts energy market trends
and values only to 2040. The post-2040 energy security premium
values are assumed to be equal to the 2040 estimate.

                       Table IX-24--Energy Security Premiums in 2020, 2025, 2030 and 2040
                                                [2012$/Barrel] *
----------------------------------------------------------------------------------------------------------------
                                                                                Avoided
                                                                              macroeconomic
                      Year (range)                       Monopsony (range)     disruption/      Total mid-point
                                                                             adjustment costs       (range)
                                                                                (range)
----------------------------------------------------------------------------------------------------------------
2020...................................................              $4.91              $6.35             $11.25
                                                               (1.63-9.15)       (3.07-10.15)       (6.67-16.53)
2025...................................................              $5.46              $7.29             $12.75
                                                              (1.81-10.47)       (3.57-11.67)       (7.58-18.65)
2030...................................................              $6.04              $8.39             $14.43
                                                              (2.00-11.67)       (4.12-13.41)       (8.54-21.13)
2040...................................................              $7.17             $10.74             $17.91
                                                              (2.32-14.03)       (5.36-17.22)            -26.14)
----------------------------------------------------------------------------------------------------------------
Note:
* Top values in each cell are the midpoints, the values in parentheses are the 90 percent confidence intervals.

(a) Effect of Oil Use on the Long-Run Oil Price
    The first component of the full economic costs of importing
petroleum into the U.S. follows from the effect of U.S. import demand
on the world oil price over the long-run. Because the U.S. is a
sufficiently large purchaser of global oil supplies, its purchases can
affect the world oil price. This monopsony power means that increases
in U.S. petroleum demand can cause the world price of crude oil to
rise, and conversely, that reduced U.S. petroleum demand can reduce the
world price of crude oil. Thus, one benefit of decreasing U.S. oil
purchases, due to improvements in the fuel efficiency of medium- and
heavy-duty vehicles, is the potential decrease in the crude oil price
paid for all crude oil purchased.
    A variety of oil market and economic factors have contributed to
lowering the estimated monopsony premium compared to monopsony premiums
cited in recent EPA/NHTSA rulemakings. Three principal factors
contribute to lowering the monopsony premium: Lower world oil prices,
lower U.S. oil imports and less responsiveness of world oil prices to
changes in U.S. oil demand. For example, between 2012 (using the AEO
2012 (Early Release)) and 2014 (using the AEO 2014 (Early Release)),
there has been a general downward revision in world oil price
projections in the near term (e.g. 19 percent in 2020) and a sharp
reduction in projected U.S. oil imports in the near term, due to
increased U.S. supply (i.e., a 41 percent reduction in U.S. oil imports
by 2017 and a 36 percent reduction in 2020). Over the longer term,
oil's share of total U.S. imports is projected to gradually increase
after 2020 but still remain 27 percent below the AEO2012 (Early
Release) projected level in 2035.
    Another factor influencing the monopsony premium is that U.S.
demand on the global oil market is projected to decline, suggesting
diminished overall influence and some reduction in the influence of
U.S. oil demand on the world price of oil. Outside of the U.S.,
projected OPEC supply remains roughly steady as a share of world oil
supply compared to the AEO2012 (Early Release). OPEC's share of world
oil supply outside of the U.S. actually increases slightly. Since OPEC
supply is estimated to be more price sensitive than non-OPEC supply,
this means that under AEO2014 (Early Release) world oil supply is
slightly more responsive to changes in U.S. oil demand. Together, these
factors suggest that changes in U.S. oil import reductions have a
somewhat smaller effect on the long-run world oil price than changes
based on 2012 estimates.
    These changes in oil price and import levels lower the monopsony
portion of energy security premium since this portion of the security
premium is related to the change in total U.S. oil import costs that is
achieved by a marginal reduction in U.S oil imports. Since both the
price and the quantity of oil imports are lower, the monopsony premium
component is 46-57 percent lower over the years 2017-2025 than the
estimates based upon the AEO 2012 (Early Release) projections.
    There is disagreement in the literature about the magnitude of the
monopsony component, and its relevance for policy analysis. Brown and
Huntington (2013),\731\ for example, argue that the United States'
refusal to exercise its market power to reduce the world oil price does
not represent a proper externality, and that the monopsony component
should not be considered in calculations of the energy security
externality. However, they also note in their earlier discussion paper
(Brown and Huntington 2010) \732\ that this is a departure from the
traditional energy security literature, which includes sustained wealth
transfers associated with stable but higher-price oil markets. On the
other hand, Greene (2010) \733\ and others in prior literature (e.g.,
Toman 1993) \734\ have emphasized that the monopsony cost component is
policy-relevant because the world oil market is non-competitive and
strongly influenced by cartelized and government-controlled supply
decisions. Thus, while sometimes couched as an externality, Greene
notes that the monopsony component is best viewed as stemming from a
completely different market failure than an externality (Ledyard
2008),\735\ yet still implying marginal social costs to importers.
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    \731\ Brown, Stephen P.A. and Hillard G. Huntington. 2013.
Assessing the U.S. Oil Security Premium. Energy Economics, vol. 38,
pp 118-127.
    \732\ Reassessing the Oil Security Premium. RFF Discussion Paper
Series, (RFF DP 10-05). doi: RFF DP 10-05
    \733\ Greene, D.L. 2010. Measuring energy security: Can the
United States achieve oil independence? Energy Policy, 38(4), 1614-
1621. doi:10.1016/j.enpol.2009.01.041.
    \734\ Reassessing the Oil Security Premium. RFF Discussion Paper
Series, (RFF DP 10-05). doi:RFF DP 10-05.
    \735\ Ledyard, John O. ``Market Failure.'' The New Palgrave
Dictionary of Economics. Second Edition. Eds. Steven N. Durlauf and
Lawrence E. Blume. Palgrave Macmillan, 2008.
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    There is also a question about the ability of gradual, long-term
reductions, such as those resulting from this proposed rule, to reduce
the world oil price in the presence of OPEC's monopoly power. OPEC is
currently the world's marginal petroleum supplier, and could
conceivably respond to gradual reductions in U.S. demand with gradual
reductions in supply over the course of several years as the fuel

savings resulting from this rule grow. However, if OPEC opts for a
long-term strategy to preserve its market share, rather than maintain a
particular price level (as they have done recently in response to
increasing U.S. petroleum production), reduced demand would create
downward pressure on the global price. The Oak Ridge analysis assumes
that OPEC does respond to demand reductions over the long run, but
there is still a price effect in the model. Under the mid-case
behavioral assumption used in the premium calculations, OPEC responds
by gradually reducing supply to maintain market share (consistent with
the long-term self-interested strategy suggested by Gately (2004,
2007)).\736\
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    \736\ Gately, Dermot 2004. ``OPEC's Incentives for Faster Output
Growth'', The Energy Journal, 25 (2):75-96; Gately, Dermot 2007.
``What Oil Export Levels Should We Expect From OPEC?'', The Energy
Journal, 28(2):151-173.
---------------------------------------------------------------------------

    It is important to note that the decrease in global petroleum
prices resulting from this rulemaking could spur increased consumption
of petroleum in other sectors and countries, leading to a modest uptick
in GHG emissions outside of the United States. This increase in global
fuel consumption could offset some portion of the GHG reduction
benefits associated with these proposed rules. The agencies have not
quantified this increase in global GHG emissions. We request comments,
data sources and methodologies for how global rebound effects may be
quantified.
(b) Macroeconomic Disruption Adjustment Costs
    The second component of the oil import premium, ``avoided
macroeconomic disruption/adjustment costs'', arises from the effect of
oil imports on the expected cost of supply disruptions and accompanying
price increases. A sudden increase in oil prices triggered by a
disruption in world oil supplies has two main effects: (1) It increases
the costs of oil imports in the short-run and (2) it can lead to
macroeconomic contraction, dislocation and Gross Domestic Product (GDP)
losses. For example, ORNL estimates the combined value of these two
factors to be $6.34/barrel when U.S. oil imports are reduced in 2020,
with a range from $3.07/barrel to $10.15/barrel of imported oil
reduced.
    Since future disruptions in foreign oil supplies are an uncertain
prospect, each of the disruption cost components must be weighted by
the probability that the supply of petroleum to the U.S. will actually
be disrupted. Thus, the ``expected value'' of these costs--the product
of the probability that a supply disruption will occur and the sum of
costs from reduced economic output and the economy's abrupt adjustment
to sharply higher petroleum prices--is the relevant measure of their
magnitude. Further, when assessing the energy security value of a
policy to reduce oil use, it is only the change in the expected costs
of disruption that results from the policy that is relevant. The
expected costs of disruption may change from lowering the normal (i.e.,
pre-disruption) level of domestic petroleum use and imports, from any
induced alteration in the likelihood or size of disruption, or from
altering the short-run flexibility (e.g., elasticity) of petroleum use.
    With updated oil market and economic factors, the avoided
macroeconomic disruption component of the energy security premiums is
slightly lower in comparison to avoided macroeconomic disruption
premiums used in previous rulemakings. Factors that contribute to
moderately lowering the avoided macroeconomic disruption component are
lower projected GDP, moderately lower oil prices and slightly smaller
price increases during prospective shocks. For example, oil price
levels are 5-19 percent lower over the 2020-2035 period, and the likely
increase in oil prices in the event of an oil shock are somewhat
smaller, given small increases in the responsiveness of oil supply to
changes in the world price of oil. Overall, the avoided macroeconomic
disruption component estimates for the oil security premiums are 2-19
percent lower over the period from 2020-2035 based upon different
projected oil market and economic trends in the AEO2014 (Early Release)
compared to the AEO2012 (Early Release).
    There are several reasons why the avoided macroeconomic disruption
premiums change only moderately. One reason is that the macroeconomic
sensitivity to oil price shocks is assumed unchanged in recent years
since U.S. oil consumption levels and the value share of oil in the
U.S. economy remain at high levels. For example, Figure IX-2 below
shows that under AEO2014 (Early Release), projected U.S. real annual
oil expenditures continue to rise after 2015 to over $800 billion
(2012$) by 2030. The value share of oil use in the U.S. economy remains
between three and four percent, well above the levels observed from
1985 to 2005. A second factor is that oil disruption risks are little
changed. The two factors influencing disruption risks are the
probability of global supply interruptions and the world oil supply
share from OPEC. Both factors are not significantly different from
previous forecasts of oil market trends.
    The energy security costs estimated here follow the oil security
premium framework, which is well established in the energy economics
literature. The oil import premium gained attention as a guiding
concept for energy policy around the time of the second and third major
post-war oil shocks (Bohi and Montgomery 1982, EMF 1982).\737\ Plummer
(1982) \738\ provided valuable discussion of many of the key issues
related to the oil import premium as well as the analogous oil
stockpiling premium. Bohi and Montgomery (1982) \739\ detailed the
theoretical foundations of the oil import premium established many of
the critical analytic relationships through their thoughtful analysis.
Hogan (1981) \740\ and Broadman and Hogan (1986, 1988)\741\ revised and
extended the established analytical framework to estimate optimal oil
import premia with a more detailed accounting of macroeconomic effects.
---------------------------------------------------------------------------

    \737\Bohi, Douglas R. And W. David Montgomery 1982. Social Cost
of Imported and Import Policy, Annual Review of Energy, 7:37-60.
Energy Modeling Forum, 1981. World Oil, EMF Report 6 (Stanford
University Press: Stanford 39 CA. https//emf.stanford.edu/publications/emf-6-world-oil.
    \738\ Plummer, James L. (Ed.) 1982. Energy Vulnerability,
``Basic Concepts, Assumptions and Numerical Results'', pp. 13-36,
(Cambridge MA: Ballinger Publishing Co.)
    \739\ Bohi, Douglas R. And W. David Montgomery 1982. Social Cost
of Imported and U.S. Import Policy, Annual Review of Energy, 7:37-
60.
    \740\ Hogan, William W., 1981. ``Import Management and Oil
Emergencies'', Chapter 9 in Deese, 5 David and Joseph Nye, eds.
Energy and Security. Cambridge, MA: Ballinger Publishing Co.
    \741\Broadman, H.G. 1986. ``The Social Cost of Imported Oil,''
Energy Policy 14(3):242-252. Broadman H.G. and W.W. Hogan, 1988.
``Is an Oil Import Tariff Justified? An American Debate: The Numbers
Say `Yes'.'' The Energy Journal 9: 7-29.
---------------------------------------------------------------------------

    Since the original work on energy security was undertaken in the
1980's, there have been several reviews on this topic. For example,
Leiby, Jones, Curlee and Lee (1997) \742\ provided an extended review
of the literature and issues regarding the estimation of the premium.
Parry and Darmstadter (2004) \743\ also provided an overview of extant
oil security premium estimates

and estimated of some premium components.
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    \742\ Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and
Russell Lee, Oil Imports: An Assessment of Benefits and Costs, ORNL-
6851, Oak Ridge National Laboratory, November 1, 1997.
    \743\ Parry, Ian W.H. and Joel Darmstadter 2004. ``The Costs of
U.S. Oil Dependency,'' Resources for the Future, November 17, 2004
(also published as NCEP Technical Appendix Chapter 1: Enhancing Oil
Security, the National Commission on Energy Policy 2004 Ending the
Energy Stalemate--A Bipartisan Strategy to Meet America's Energy
Challenges.)
---------------------------------------------------------------------------

    The recent economics literature on whether oil shocks are a threat
to economic stability that they once were is mixed. Some of the current
literature asserts that the macroeconomic component of the energy
security externality is small. For example, the National Research
Council (2009) argued that the non-environmental externalities
associated with dependence on foreign oil are small, and potentially
trivial.\744\ Analyses by Nordhaus (2007) and Blanchard and Gali (2010)
question the impact of more recent oil price shocks on the