or understates them for advanced technologies.
---------------------------------------------------------------------------

    \484\ Helfand, Gloria, and Sherwood, Todd. ``Documentation of
the Development of Indirect Cost Multipliers for Three Automotive
Technologies.'' Memorandum, Assessment and Standards Division,
Office of Transportation and Air Quality, U.S. Environmental
Protection Agency, August 2009.
    \485\ NHTSA staff participated in the development of the process
for the second, modified Delphi panel, and reviewed the results as
they were developed, but did not serve on the panel.
    \486\ The results of the RTI report were published in Alex
Rogozhin, Michael Gallaher, Gloria Helfand, and Walter McManus,
``Using Indirect Cost Multipliers to Estimate the Total Cost of
Adding New Technology in the Automobile Industry.'' International
Journal of Production Economics 124 (2010): 360-368.
---------------------------------------------------------------------------

    In the proposal, we requested comment on our ICM factors and
whether it was most appropriate to use ICMs or RPEs. We received no
comment on the issue specifically, other than

basic comments that perhaps our ICM factors were low. In response, for
this final action, we have adjusted our ICM factors such that they are
slightly higher and, importantly, we have changed the way in which the
factors are applied. The first change--increased ICM factors--has been
done as a result of further thought among the EPA and NHTSA team that
the ICM factors presented in the original RTI report \487\ for low and
medium complexity technologies should no longer be used and that we
should rely solely on the modified-Delphi values for these complexity
levels.\488\ For that reason, we have eliminated the averaging of
original RTI values with modified-Delphi values and instead are relying
solely on the modified-Delphi values for low and medium complexity
technologies. The second change--the way the factors are applied--
results in the warranty portion of the indirect costs being applied as
a multiplicative factor (thereby decreasing going forward as direct
manufacturing costs decrease due to learning), and the remainder of the
indirect costs being applied as an additive factor (thereby remaining
constant year-over-year and not being reduced due to learning). This
second change has a comparatively large impact on the resultant
technology costs and, we believe, more appropriately estimates costs
over time. In addition to these changes, a secondary-level change was
also made as part of this ICM recalculation to the light-duty ICMs and,
therefore, to the ICMs used in this analysis for heavy-duty pickups and
vans. That change was to revise upward the RPE level reported in the
original RTI report from an original value of 1.46 to 1.5 to reflect
the long term average RPE. The original RTI study was based on 2008
data. However, an analysis of historical RPE data indicates that,
although there is year to year variation, the average RPE has remained
constant at roughly 1.5. ICMs will be applied to future year's data and
therefore NHTSA and EPA staff believe that it would be appropriate to
base ICMs on the historical average rather than a single year's result.
Therefore, ICMs were adjusted to reflect this average level since the
original value excluded net income. As a result, even the High 1 and
High 2 ICMs used for heavy-duty pickups and vans have also changed.
These changes to our ICMs and the methodology are described in greater
detail in Chapter 2 of the final RIA.
---------------------------------------------------------------------------

    \487\ Rogozhin, Alex, Michael Gallaher, and Walter McManus.
``Automobile Industry Retail Price Equivalent and Indirect Cost
Multipliers.'' Report prepared for EPA by RTI International. EPA
Report EPA-420-R-09-003, February 2009.
    \488\ Helfand, Gloria, and Sherwood, Todd. ``Documentation of
the Development of Indirect Cost Multipliers for Three Automotive
Technologies.'' Memorandum, Assessment and Standards Division,
Office of Transportation and Air Quality, U.S. Environmental
Protection Agency, August 2009.
---------------------------------------------------------------------------

D. Cost per Ton of Emissions Reductions

    The agencies have calculated the cost per ton of GHG reductions
associated with this program on a CO2eq basis using the
above costs and the emissions reductions described in Sections VI and
VII. These values are presented in Table VIII-3 through Table VIII-5
for HD pickups & vans, vocational vehicles and combination trucks/
tractors, respectively. The cost per metric ton of GHG emissions
reductions has been calculated in the years 2020, 2030, 2040, and 2050
using the annual vehicle compliance costs and emission reductions for
each of those years. The value in 2050 represents the long-term cost
per ton of the emissions reduced. The agencies have also calculated the
cost per metric ton of GHG emission reductions including the savings
associated with reduced fuel consumption (presented below in Section
0). This latter calculation does not include the other benefits
associated with this program such as those associated with energy
security benefits as discussed later in Section VIII.I. By including
the fuel savings, the cost per ton is generally less than $0 since the
estimated value of fuel savings outweighs the program costs. The
results for CO2eq costs per ton under the HD National
Program across all regulated categories are shown in Table VIII-6.

               Table VIII-3--Annual Cost per Metric Ton of CO2eq Reduced--HD Pickup Trucks & Vans
                                                 [2009 dollars]
----------------------------------------------------------------------------------------------------------------
                                                                                Cost per ton    Cost per ton
              Year                 Program cost    Fuel savings    CO2eq Reduced   (without fuel    (with fuel
                                                     (pre-tax)                       Savings)        savings)
----------------------------------------------------------------------------------------------------------------
2020............................            $800            $900               3            $240            -$30
2030............................             900           3,000              10              90            -200
2040............................           1,000           4,300              14              70            -240
2050............................           1,200           5,500              16              80            -270
----------------------------------------------------------------------------------------------------------------

                Table VIII-4--Annual Cost per Metric Ton of CO2eq Reduced--Vocational Vehicles a
                                                 [2009 dollars]
----------------------------------------------------------------------------------------------------------------
                                                                                Cost per ton    Cost per ton
              Year                 Program cost    Fuel savings    CO2eq reduced   (without fuel    (with fuel
                                                     (pre-tax)                       savings)        savings)
----------------------------------------------------------------------------------------------------------------
2020............................            $200          $1,100               4             $50           -$210
2030............................             200           2,400               9              20            -250
2040............................             300           3,500              12              30            -270
2050............................             400           4,700              14              30            -310
----------------------------------------------------------------------------------------------------------------
Note:
\a\ The program costs, fuel savings, and CO2eq reductions of the engines installed in vocational vehicles are
  embedded in the vehicle standards and analysis.

               Table VIII-5--Annual Cost per Metric Ton of CO2eq Reduced--Combination Tractors \a\
                                                 [2009 dollars]
----------------------------------------------------------------------------------------------------------------
                                                                                Cost per ton    Cost per ton
              Year                 Program cost    Fuel savings    CO2eq reduced   (without fuel    (with fuel
                                                     (pre-tax)                       savings)        savings)
----------------------------------------------------------------------------------------------------------------
2020............................          $1,000          $7,700              32             $30           -$210
2030............................           1,100          15,300              57              20            -250
2040............................           1,400          20,200              68              20            -280
2050............................           1,800          26,400              78              20            -320
----------------------------------------------------------------------------------------------------------------
Note:
\a\ The program costs, fuel savings, and CO2eq reductions of the engines installed in tractors are embedded in
  the tractor standards and analysis.

                        Table VIII-6--Annual Cost per Metric Ton of CO2eq Reduced--Final
                                                 [2009 dollars]
----------------------------------------------------------------------------------------------------------------
                                                                                Cost per ton    Cost per ton
              Year                 Program cost    Fuel savings    CO2eq reduced   (without fuel    (with fuel
                                                     (pre-tax)                       savings)        savings)
----------------------------------------------------------------------------------------------------------------
2020............................          $2,000          $9,600              39             $50           -$190
2030............................           2,200          20,600              76              30            -240
2040............................           2,700          28,000              94              30            -270
2050............................           3,300          36,500             108              30            -310
----------------------------------------------------------------------------------------------------------------

E. Impacts of Reduction in Fuel Consumption

(1) What are the projected changes in fuel consumption?
    The new CO2 standards will result in significant
improvements in the fuel efficiency of affected trucks. Drivers of
those trucks will see corresponding savings associated with reduced
fuel expenditures. The agencies have estimated the impacts on fuel
consumption for the tailpipe CO2 standards. To do this, fuel
consumption is calculated using both current CO2 emission
levels and the new CO2 standards. The difference between
these estimates represents the net savings from the CO2
standards. Note that the total number of miles that vehicles are driven
each year is different under the control case scenario than in the
reference case due to the ``rebound effect,'' which is discussed in
Section 0. EPA also notes that drivers who drive more than our average
estimates for vehicle miles traveled (VMT) will experience more fuel
savings; drivers who drive less than our average VMT estimates will
experience less fuel savings.
    The expected impacts on fuel consumption are shown in Table VIII-7.
The gallons shown in the tables reflect impacts from the new fuel
consumption and CO2 standards and include increased
consumption resulting from the rebound effect.

        Table VIII-7--Fuel Consumption Reductions of the Program
                            [Million gallons]
------------------------------------------------------------------------
                       Year                          Gasoline    Diesel
------------------------------------------------------------------------
2014..............................................          1        473
2015..............................................          3        846
2016..............................................         14      1,171
2017..............................................         31      1,643
2018..............................................         58      2,123
2020..............................................        114      2,986
2030..............................................        348      5,670
2040..............................................        453      7,046
2050..............................................        522      8,158
------------------------------------------------------------------------

(2) Potential Impacts on Global Fuel Use and Emissions
    EPA's quantified reductions in fuel consumption focus on the gains
from reducing fuel used by heavy-duty vehicles within the United
States. However, as discussed in Section VIII.I, EPA also recognizes
that this regulation will lower the world price of oil (the
``monopsony'' effect). Lowering oil prices could lead to an uptick in
oil consumption globally, leading to a corresponding increase in GHG
emissions in other countries. This global increase in emissions could
slightly offset some of the emission reductions achieved domestically
as a result of the regulation.
(3) What are the monetized fuel savings?
    Using the fuel consumption estimates presented in Table VIII-7, the
agencies can calculate the monetized fuel savings associated with the
final standards. To do this, reduced fuel consumption is multiplied in
each year by the corresponding estimated average fuel price in that
year, using the reference case taken from the AEO 2011. These estimates
do not account for the significant uncertainty in future fuel prices;
the monetized fuel savings will be understated if actual fuel prices
are higher (or overstated if fuel prices are lower) than estimated. AEO
is a standard reference used by NHTSA and EPA and many other government
agencies to estimate the projected price of fuel. This has been done
using both the pre-tax and post-tax fuel prices. Since the post-tax
fuel prices are the prices paid at fuel pumps, the fuel savings
calculated using these prices represent the savings consumers would
see. The pre-tax fuel savings are those savings that society would see.
Assuming no change in fuel tax rates, the difference between these two
columns represents the reduction in fuel tax revenues that will be
received by state and federal governments--about $200 million in 2014
and $3 billion by 2050. These results are shown in Table VIII-8. Note
that in Section VIII.L, the overall benefits and costs of the rules are
presented and, for that reason, only the pre-tax fuel savings are
presented there.

             Table VIII-8--Estimated Monetized Fuel Savings
                            [Millions, 2009$]
------------------------------------------------------------------------
                                                    Fuel         Fuel
                     Year                         savings      savings
                                                 (pre-tax)    (post-tax)
------------------------------------------------------------------------
2014..........................................       $1,200       $1,400

2015..........................................        2,200        2,600
2016..........................................        3,300        3,800
2017..........................................        4,800        5,500
2018..........................................        6,400        7,400
2020..........................................        9,600       10,900
2030..........................................       20,600       23,000
2040..........................................       28,000       30,600
2050..........................................       36,500       39,500
NPV, 3%.......................................      375,300      415,300
NPV, 7%.......................................      166,500      185,400
------------------------------------------------------------------------

    As shown in Table VIII-8, the agencies are projecting that truck
consumers would realize very large fuel savings as a result of the
final standards. As discussed further in the introductory paragraphs of
Section VIII, it is a conundrum from an economic perspective that these
large fuel savings have not been provided by manufacturers and
purchased by consumers of these products. Unlike in the light-duty
vehicle market, the vast majority of vehicles in the medium- and heavy-
duty truck market are purchased and operated by businesses; for them,
fuel costs may represent substantial operating expenses. Even in the
presence of uncertainty and imperfect information--conditions that hold
to some degree in every market--we generally expect firms to be cost-
minimizing to survive in a competitive marketplace and to make
decisions that are therefore in the best interest of the company and
its owners and/or shareholders.
    A number of behavioral and market phenomena may lead to a
disconnect between how businesses account for fuel savings in their
decisions and the way in which we account for the full stream of fuel
savings for these rules, including imperfect information in the
original and resale markets, split incentives, uncertainty in future
fuel prices, and adjustment or transactions costs (see Section VIII.A
for a more detailed discussion). As discussed below in the context of
rebound in Section VIII.E.5, the nature of the explanation for this gap
may influence the actual magnitude of the fuel savings.
(4) Payback Period and Lifetime Savings on New Truck Purchases
    Another factor of interest is the payback period on the purchase of
a new truck that complies with the new standards. In other words, how
long would it take for the expected fuel savings to outweigh the
increased cost of a new vehicle? For example, a new 2018 MY HD pickup
truck and van is estimated to cost $1,048 more, a vocational vehicle
$378 more, and a combination tractor $6,215 more (all values are on
average, and relative to the reference case vehicle) due to the
addition of new GHG reducing technology. This new technology will
result in lower fuel consumption and, therefore, savings in fuel
expenditures. But how many months or years would pass before the fuel
savings exceed the upfront costs? Table VIII-9 shows the payback period
analysis for HD pickup trucks and vans. The table shows fuel consumed
under the reference case and fuel consumed by a 2018 model year truck
under the program, inclusive of fuel consumed due to rebound miles. The
decrease in fuel consumed under the program is then monetized by
multiplying by the fuel price reported by AEO (reference case) for 2018
and later. This value represents the fuel savings expected under the
program for a HD pickup or van. These savings are then discounted each
year since future savings are considered to be of less value than
current savings. Shown next are estimated increased costs (costs do not
necessarily reflect increased prices which may be higher or lower than
costs) for the new truck (refer to Table VIII-1). The next columns of
Table VIII-9 show the period required for the fuel savings to exceed
the new truck costs. As seen in the table, in the second year of
ownership, the discounted fuel savings (at both 3 and 7 percent
discount rates) have begun to outweigh the increased cost of the truck.
As shown in the table, the full life savings using 3 percent
discounting would be $6,138 and at 7 percent discounting would be
$4,459.

                                           Table VIII-9--Payback Period for a 2018 Model Year HD Pickup or Van
                                                                         [2009$]
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                   Reduced fuel use          Fuel savings \a\                      Cumulative savings
                                                                     (gallons) \b\      --------------------------  Increased  -------------------------
                      Year of ownership                       --------------------------                               cost
                                                                 Gasoline      Diesel    3% discount  7% discount               3% discount  7% discount
--------------------------------------------------------------------------------------------------------------------------------------------------------
1............................................................           67          122         $627         $616      -$1,048        -$421        -$433
2............................................................           67          122          617          583  ...........          196          151
3............................................................           66          120          600          546  ...........          796          696
4............................................................           64          117          570          499  ...........        1,366        1,196
5............................................................           62          113          544          458  ...........        1,910        1,654
6............................................................           59          108          507          411  ...........        2,417        2,065
7............................................................           56          102          474          370  ...........        2,890        2,435
Full Life....................................................          894        1,617        7,187        5,507       -1,048        6,138        4,459
--------------------------------------------------------------------------------------------------------------------------------------------------------
Notes:
\a\ Fuel savings calculated using the AEO 2011 reference case fuel prices through 2035. Fuel prices beyond 2035 were extrapolated from an average growth
  rate for the years 2017 to 2035. Gasoline and diesel fuel prices have been weighted by gasoline and diesel fuel reductions estimated for all 2018 MY
  heavy-duty trucks during their lifetimes. These estimates assume no changes in fuel tax rates. If fuel taxes are increased to offset lost revenues,
  the post-tax savings will increase.
\b\ Gallons under the control case include gallons consumed during rebound driving.

    The story is somewhat different for vocational vehicles and
combination tractors. These cases are shown in Table VIII-10 and Table
VIII-11, respectively. Since these trucks travel more miles in a given
year, their payback periods are shorter and are expected to occur
within the second year of ownership under both the 3 and 7 percent
discounting cases. As can be seen in Table VIII-10 and Table VIII-11,
the lifetime fuel savings are estimated to be considerable with savings
of $5,494 (3%) and $4,268 (7%) for the vocational vehicles and $72,875
(3%) and $58,162 (7%) for the combination tractors.

                                         Table VIII-10--Payback Period for a 2018 Model Year Vocational Vehicle
                                                                         [2009$]
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                   Reduced fuel use          Fuel savings \a\                      Cumulative savings
                                                                     (gallons) \b\      --------------------------  Increased  -------------------------
                      Year of ownership                       --------------------------                               cost
                                                                 Gasoline      Diesel    3% discount  7% discount               3% discount  7% discount
--------------------------------------------------------------------------------------------------------------------------------------------------------
1............................................................           51          161         $702         $690        -$378         $325         $312
2............................................................           47          146          637          602  ...........          962          914
3............................................................           44          134          576          524  ...........        1,538        1,438
4............................................................           41          122          516          452  ...........        2,054        1,889
5............................................................           38          110          463          390  ...........        2,516        2,279
6............................................................           34           98          404          328  ...........        2,921        2,607
7............................................................           31           87          359          280  ...........        3,279        2,887
Full Life....................................................          550        1,458        5,872        4,646         -378        5,494        4,268
--------------------------------------------------------------------------------------------------------------------------------------------------------
Notes:
\a\ Fuel savings calculated using the AEO 2011 reference case fuel prices through 2035. Fuel prices beyond 2035 were extrapolated from an average growth
  rate for the years 2017 to 2035. Gasoline and diesel fuel prices have been weighted by gasoline and diesel fuel reductions estimated for all 2018 MY
  heavy-duty trucks during their lifetimes. These estimates assume no changes in fuel tax rates. If fuel taxes are increased to offset lost revenues,
  the post-tax savings will increase.
\b\ Gallons under the control case include gallons consumed during rebound driving.

                                         Table VIII-11--Payback Period for a 2018 Model Year Combination Tractor
                                                                         [2009$]
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                   Reduced fuel use          Fuel savings \a\                      Cumulative savings
                                                                     (gallons) \b\      --------------------------  Increased  -------------------------
                      Year of ownership                       --------------------------                               cost
                                                                 Gasoline      Diesel    3% discount  7% discount               3% discount  7% discount
--------------------------------------------------------------------------------------------------------------------------------------------------------
1............................................................            0        3,223      $10,736      $10,539      -$6,215       $4,522       $4,324
2............................................................            0        2,897        9,619        9,089  ...........       14,141       13,413
3............................................................            0        2,619        8,564        7,790  ...........       22,705       21,203
4............................................................            0        2,359        7,532        6,595  ...........       30,237       27,797
5............................................................            0        2,096        6,626        5,585  ...........       36,863       33,382
6............................................................            0        1,842        5,684        4,611  ...........       42,546       37,993
7............................................................            0        1,617        4,951        3,867  ...........       47,497       41,860
Full Life....................................................            0       26,148       79,089       64,376       -6,215       72,875       58,162
--------------------------------------------------------------------------------------------------------------------------------------------------------
Notes:
\a\ Fuel savings calculated using the AEO 2011 reference case fuel prices through 2035. Fuel prices beyond 2035 were extrapolated from an average growth
  rate for the years 2017 to 2035. Gasoline and diesel fuel prices have been weighted by gasoline and diesel fuel reductions estimated for all 2018 MY
  heavy-duty trucks during their lifetimes. These estimates assume no changes in fuel tax rates. If fuel taxes are increased to offset lost revenues,
  the post-tax savings will increase.
\b\ Gallons under the control case include gallons consumed during rebound driving.

    All of these payback analyses include fuel consumed during rebound
VMT in the control case but not in the reference case, consistent with
other parts of the analysis. Further, this analysis does not include
other societal impacts such as reduced time spent refueling or noise,
congestion and accidents since the focus is meant to be on those
factors buyers think about most while considering a new truck purchase.
Note also that operators that drive more miles per year than the
average would realize greater fuel savings than estimated here, and
those that drive fewer miles per year would realize lesser savings. The
same holds true for operators that keep their vehicles longer (i.e.,
more years) than average in that they would realize greater lifetime
fuel savings than operators that keep their vehicles for fewer years
than average. Likewise, should fuel prices be higher than the AEO 2011
reference case, operators will realize greater fuel savings than
estimated here while they would realize lesser fuel savings were fuel
prices to be lower than the AEO 2011 reference case.
(5) Rebound Effect
    The VMT rebound effect refers to the fraction of fuel savings
expected to result from an increase in fuel efficiency that is offset
by additional vehicle use. If truck shipping costs decrease as a result
of lower fuel costs, an increase in truck VMT may occur. Unlike the
light-duty rebound effect, the heavy-duty (HD) rebound effect has not
been extensively studied. Because the factors influencing the HD
rebound effect are generally different from those affecting the light-
duty rebound effect, much of the research on the light-duty rebound
effect is not likely to apply to the HD sectors. One of the major
differences between the HD rebound effect and the light-duty rebound
effect is that HD vehicles are used primarily for business purposes.
Since these businesses are profit driven, decision makers are highly
likely to be aware of the costs and benefits of different shipping
decisions, both in the near term and long term. Therefore, shippers are
much more likely to take into account changes in the overall operating
costs per mile when making shipping decisions that affect VMT.
    Another difference from the light-duty case is that, as discussed
in the recent NAS Report,\489\ when calculating the percentage change
in trucking costs to determine the rebound effect, all changes in the
operating costs should be considered. The cost of labor and fuel
generally constitute the top two shares of truck operating costs,
depending on the price of petroleum,\490\ distance traveled, type of
truck, and

commodity.\491\ Finally, the equipment costs associated with the
purchase or lease of the truck is also a significant component of total
operating costs. Even though vehicle costs are lump-sum purchases, they
can be considered operating costs for trucking firms, and these costs
are, in many cases, expected to be passed onto the final consumers of
shipping services on a variable basis. This shipping cost increase
could help temper the rebound effect relative to the case of light-duty
vehicles, in which vehicle costs are not considered an operating cost
by vehicle owners.
---------------------------------------------------------------------------

    \489\ See NAS Report, Note 197.
    \490\ American Transportation Research Institute, An Analysis of
the Operational Costs of Trucking, December 2008 (Docket ID: EPA-HQ-
OAR-2010-0162-0007).
    \491\ Transport Canada, Operating Cost of Trucks, 2005. See
http://www.tc.gc.ca/eng/policy/report-acg-operatingcost2005-2005-e-2-1727.htm, accessed on July 16, 2010 (Docket ID: EPA-HQ-OAR-2010-
0162-0006). See also ATRI, 2008.
---------------------------------------------------------------------------

    When calculating the net change in operating costs, both the
increase in new vehicle costs and the decrease in fuel costs per mile
should be taken into consideration. The higher the net cost savings,
the higher the expected rebound effect. Conversely, if the upfront
vehicle costs outweighed future cost savings and total costs increased,
shipping costs would rise, which would likely result in a decrease in
truck VMT. In theory, other changes such as maintenance costs and
insurance rates would also be taken into account, although information
on these potential cost changes is extremely limited. In the proposal,
we invited comments on the most appropriate methodology for factoring
new vehicle purchase or leasing costs into the per-mile operating
costs. We also invited comment or data on how these regulations could
affect maintenance, insurance, or other operating costs. We did not
receive any comments on these assumptions.
    The following sections describe the factors affecting the rebound
effect, different methodologies for estimating the rebound effect, and
examples of different estimates of the rebound effect to date.
According to the NAS study, it is ``not possible to provide a confident
measure of the rebound effect,'' yet NAS concluded that a rebound
effect likely exists and that ``estimates of fuel savings from
regulatory standards will be somewhat misestimated if the rebound
effect is not considered.'' While we believe the HD rebound effect
needs to be studied in more detail, we have attempted to capture the
potential impact of the rebound effect in our analysis. In the
proposal, we solicited data on the rebound effect and input on the most
appropriate estimates to use for the rebound effect. However, we did
not receive any new data or substantive comments. Therefore, for this
final action, we continue to use a rebound effect for vocational
vehicles of 15 percent, a rebound effect for HD pickup trucks and vans
of 10 percent, and a rebound effect for combination tractors of 5
percent. These VMT impacts are reflected in the estimates of total GHG
and other air pollution reductions presented in Chapter 5 of the RIA.
(a) Factors Affecting the Magnitude of the Rebound Effect
    The HD vehicle rebound effect is driven by the interaction of
several different factors. In the short-run, decreasing the fuel cost
per mile of driving could lead to a decrease in end product prices.
Lower prices could stimulate additional demand for those products,
which would then result in an increase in VMT. In the long run,
shippers could reorganize their logistics and distribution networks to
take advantage of lower truck shipping costs. For example, shippers may
shift away from other modes of shipping such as rail, barge, or air. In
addition, shippers may also choose to reduce the number of warehouses,
reduce load rates, and make smaller, more frequent shipments, all of
which could also lead to an increase in HD VMT. Finally, the benefits
of the fuel savings could ripple through the economy, which could in
turn increase overall demand for goods and services shipped by trucks,
and therefore increase HD VMT.
    Conversely, if a fuel efficiency regulation leads to net increases
in the cost of trucking because fuel savings do not fully offset the
increase in upfront vehicle costs, then the price of trucking services
could rise, spurring a decrease in HD VMT and a shift to alternative
shipping modes. These effects would also ripple through the economy.
(b) Options for Quantifying the Rebound Effect
    As described in the previous section, the fuel efficiency rebound
effect for HD vehicles has not been studied as extensively as the
rebound effect for light-duty vehicles, and virtually no research has
been conducted on the HD pickup truck and van rebound effect. In the
proposal, we discussed four options for quantifying the rebound effect
and requested comments. We did not receive any substantive comments on
the described methodologies.
(i) Aggregate Estimates
    The aggregate approximation approach quantifies the overall change
in truck VMT as a result of a percentage change in freight rates. It is
important to note that most of the aggregate estimates measure the
change in freight demanded (tons or ton-miles), rather than a change in
fuel consumption or VMT. The change in tons or ton-miles is more
accurately characterized as a freight elasticity. Therefore, it may not
be entirely appropriate to interpret these freight elasticities as
measures of the rebound effect, although these terms are sometimes used
interchangeably in the literature.\492\ Given these caveats, freight
elasticity estimates rely on estimates of aggregate price elasticity of
demand for trucking services, given a percentage change in trucking
prices, which is generally referred to as an ``own-price elasticity.''
Estimates of trucking own-price elasticities vary widely from positive
1.72 to negative 7.92), and there is no general consensus on the most
appropriate values to use, though a 2004 literature survey found
aggregate elasticity estimates generally fall in the range of -0.5 to -
1.5.\493\ In other words, given an own-price elasticity of -1.5, a 10
percent decrease in trucking prices leads to a 15 percent increase in
truck shipping demand.
---------------------------------------------------------------------------

    \492\ Memo from Energy and Environmental Research Associates,
LLC Regarding HDV Rebound Effect, dated June 8, 2011.
    \493\ Graham and Glaister, ``Road Traffic Demand Elasticity
Estimates: A Review,'' Transport Reviews Volume 24, 3, pp. 261-274,
2004 (Docket ID: EPA-HQ-OAR-2010-0162-0005).
---------------------------------------------------------------------------

    Another challenge of estimating the rebound effect using freight
elasticities is that these values appear to vary substantially based on
the demand elasticity measure (e.g., ton or ton-mile), the model
specification (e.g., linear functional form or log linear), the length
of the trip, and the type of cargo. In general, elasticity estimates of
longer trips tend to be larger than elasticity estimates for shorter
trips. In addition, elasticities tend to be larger for lower-value
commodities compared to higher-value commodities. Although these
factors explain some of the differences in estimates, much of the
observed variation cannot be explained quantitatively. For example, a
recent study that controlled for these variables only accounted for
about half of the observed variation.\494\
---------------------------------------------------------------------------

    \494\ Li, Z., D.A. Hensher, and J.M. Rose, Identifying sources
of systematic variation in direct price elasticities from revealed
preference studies of inter-city freight demand. Transport Policy,
2011.
---------------------------------------------------------------------------

    Another important variable influencing freight elasticity estimates
is whether potential mode shifting is taken into account. Although the
total demand for freight transport is generally determined by economic
activity, there is often the choice of shipping freight on modes other
than truck. This is because the United States has extensive rail,
waterway and air transport networks in addition to an extensive highway
network; these networks closely parallel

each other and are often both viable choices for freight transport for
long-distance routes within the continent. If rates go down for one
mode, there will be an increase in demand for that mode and some demand
will be shifted from other modes. This ``cross-price elasticity'' is a
measure of the percentage change in demand for shipping by another mode
(e.g., rail) given a percentage change in the price of trucking.
Aggregate estimates of cross-price elasticities also vary widely, and
there is no general consensus on the most appropriate value to use for
analytical purposes. The NAS report cites values ranging from 0.35 to
0.59.\495\ Other reports provide significantly different cross-price
elasticities, ranging from 0.1 \496\ to 2.0.\497\
---------------------------------------------------------------------------

    \495\ See 2010 NAS Report, Note 197. See also 2009 Cambridge
Systematics, Inc., Draft Final Paper commissioned by the NAS in
support of the medium-duty and heavy-duty report. Assessment of Fuel
Economy Technologies for Medium and Heavy-duty Vehicles:
Commissioned Paper on Indirect Costs and Alternative Approaches
Docket ID: EPA-HQ-OAR-2010-0162-0009).
    \496\ Friedlaender, A. and Spady, R. (1980) A derived demand
function for freight transportation, Review of Economics and
Statistics, 62, pp. 432-441 (Docket ID: EPA-HQ-OAR-2010-0162-0004).
    \497\ Christidis and Leduc, ``Longer and Heavier Vehicles for
freight transport,'' European Commission Joint Research Center's
Institute for Prospective Technology Studies, 2009 (Docket ID: EPA-
HQ-OAR-2010-0162-0010).
---------------------------------------------------------------------------

    When considering intermodal shift, the most relevant kinds of
shipments are those that are competitive between rail and truck modes.
These trips generally include long-haul shipments greater than 500
miles, which weigh between 50,000 and 80,000 pounds (the legal road
limit in many states). Special kinds of cargo like coal and short-haul
deliveries are of less interest because they are generally not
economically transferable between truck and rail modes, and they would
not be expected to shift modes except under an extreme price change.
However, the total amount of freight that could potentially be subject
to mode shifting has also not been studied extensively.
(ii) Sector-Specific Estimates
    Given the limited data available regarding the HD rebound effect,
the aggregate approach greatly simplifies many of the assumptions
associated with calculations of the rebound effect. In reality,
however, responses to changes in fuel efficiency and new vehicle costs
will vary significantly based on the commodities affected. A detailed,
sector-specific approach would be expected to more accurately reflect
changes in the trucking market in response to the standards in this
program. For example, input-output tables could be used to determine
the trucking cost share of the total delivered price of a commodity.
Using the change in trucking prices described in the aggregate
approach, the product-specific demand elasticities could be used to
calculate the change in sales and shipments for each product. The
change in shipment increases could then be weighted by the share of the
trucking industry total, and then summed to get the total increase in
trucking output. A simplifying assumption could then be made that the
increase in output results in an increase in VMT. To the best of our
knowledge, this type of data has not yet been collected. We did not
receive any new information in response to our request for comments in
the proposal, therefore we were unable to use this methodology for
estimating the rebound effect for this final action.
(iii) Econometric Estimates
    Similar to the methodology used to estimate the light-duty rebound
effect, the HD rebound effect could be modeled econometrically by
estimating truck demand as a function of economic activity (e.g., GDP)
and different input prices (e.g., vehicle prices, driver wages, and
fuel costs per mile). This type of econometric model could be estimated
for either truck VMT or ton-miles as a measure of demand. The resulting
elasticity estimates could then be used to determine the change in
trucking demand, given the change in fuel cost and truck prices per
mile from these standards. One of the challenges associated with an
econometric analysis is the potential for omitted variable bias, which
could either overstate or understate the potential rebound effect if
the omitted variable is correlated with the controlled variables.
(iv) Other Modeling Approaches
    Regulation of the heavy-duty industry has been studied in more
detail in Europe, as the European Commission (EC) has considered
allowing longer and heavier trucks for freight transport. Part of the
analysis considered by the EC relies on country-specific modeling of
changes in the freight sector that would result from changes in
regulations.\498\ This approach attempts to explicitly calculate modal
shift decisions and impacts on GHG emissions. Although similar types of
analysis have not been conducted extensively in the United States,
research is currently underway that explores the potential for
intermodal shifting in the United States. For example, Winebrake and
Corbett have developed the Geospatial Intermodal Freight Transportation
model, which evaluates the potential for GHG emissions reductions based
on mode shifting, given existing limitations of infrastructure and
other route characteristics in the United States.\499\ This model
connects multiple road, rail, and waterway transportation networks and
embeds activity-based calculations in the model. Within this intermodal
network, the model assigns various economic, time-of-delivery, energy,
and environmental attributes to real-world goods movement routes. The
model can then calculate different network optimization scenarios,
based on changes in prices and policies.\500\ However, more work is
needed in this area to determine whether this type of methodology is
appropriate for the purposes of capturing the rebound effect.
Therefore, we have not been able to use this methodology for estimating
the rebound effect for this final action.
---------------------------------------------------------------------------

    \498\ Christidis and Leduc, ``Longer and Heavier Vehicles for
freight transport,'' European Commission Joint Research Center's
Institute for Prospective Technology Studies, 2009.
    \499\ Winebrake, James and Corbett, James J. (2010). ``Improving
the Energy Efficiency and Environmental Performance of Goods
Movement,'' in Sperling, Daniel and James S. Cannon (2010) Climate
and Transportation Solutions: Findings from the 2009 Asilomar
Conference on Transportation and Energy Policy. See http://www.its.ucdavis.edu/events/2009book/Chapter13.pdf (Docket ID: EPA-
HQ-OAR-2010-0162-0011)
    \500\ Winebrake, J. J.; Corbett, J. J.; Falzarano, A.; Hawker,
J. S.; Korfmacher, K.; Ketha, S.; Zilora, S., Assessing Energy,
Environmental, and Economic Tradeoffs in Intermodal Freight
Transportation, Journal of the Air & Waste Management Association,
58(8), 2008 (Docket ID: EPA-HQ-OAR-2010-0162-0008).
---------------------------------------------------------------------------

(c) Estimates of the Rebound Effect
    The aggregate methodology was used by Cambridge Systematics, Inc.
(CSI) to show several examples of the magnitude of the rebound
effect.\501\ In their paper commissioned by the NAS in support of the
recent HD report, CSI calculated an effective rebound effect for two
different technology cost and fuel savings scenarios associated with an
example Class 8 truck. Scenario 1 increased average fuel economy from
5.59 mpg to 6.8 mpg, with an additional cost of $22,930. Scenario 2
increased the average fuel economy to 9.1 mpg, at an incremental cost
of $71,630 per vehicle. The CSI examples provided estimates using a
range of own-price elasticities (-0.5 to -1.5) and cross-price
elasticities (0.35 to 0.59) from the literature. Based on these two
scenarios and a number of simplifying assumptions to aid the
calculations, CSI found a rebound effect of 11-31 percent for Scenario
1 and 5-16 percent for

Scenario 2 when the fuel savings from reduced rail usage were not taken
into account (``First rebound effect''). When the fuel savings from
reduced rail usage were included in the calculations, the overall
rebound effect was between 9-13 percent for Scenario 1 and 3-15 percent
for Scenario 2 (``Second Rebound Effect''). See Table VIII-12.
---------------------------------------------------------------------------

    \501\ Cambridge Systematics, Inc., 2009.
---------------------------------------------------------------------------

    CSI included a number of caveats associated with these
calculations. Namely, the elasticity estimates derived from the
literature are ``heavily reliant on factors including the type of
demand measures analyzed (vehicle-miles of travel, ton-miles, or tons),
analysis geography, trip lengths, markets served, and commodities
transported.'' Furthermore, the CSI example only focused on Class 8
combination tractors and did not attempt to quantify the potential
rebound effect for any other truck classes. Finally, these scenarios
were characterized as ``sketches'' and were not included in the final
NAS report. In fact, the NAS report asserted that it is ``not possible
to provide a confident measure of the rebound effect,'' yet concluded
that a rebound effect likely exists and that ``estimates of fuel
savings from regulatory standards will be somewhat misestimated if the
rebound effect is not considered.''

     Table VIII-12--Range of Rebound Effect Estimates From Cambridge
                    Systematics Aggregate Assessment
------------------------------------------------------------------------
                                            Scenario 1      Scenario 2
                                             (6.8 mpg,       (9.1 mpg,
                                             $22,930)        $71,630)
------------------------------------------------------------------------
``First Rebound Effect'' (increase in             11-31%           5-16%
 truck VMT resulting from decrease in
 operating costs).......................
``Second Rebound Effect'' (net fuel                9-13%           3-15%
 savings when decreases from rail are
 taken into account)....................
------------------------------------------------------------------------

    As an alternative, using the econometric approach, NHTSA has
estimated the rebound effect in the short run and long run for single
unit (Class 4-7) and (Class 8) combination tractors. As shown in Table
VIII-13, the estimates for the long-run rebound effect are larger than
the estimates in the short run, which is consistent with the theory
that shippers have more flexibility to change their behavior (e.g.,
restructure contracts or logistics) when they are given more time. In
addition, the estimates derived from the national data also showed
larger rebound effects compared to the state data.\502\ One possible
explanation for the difference in the estimates is that the national
rebound estimates are capturing some of the impacts of changes in
economic activity. Historically, large increases in fuel prices are
highly correlated with economic downturns, and there may not be enough
variation in the national data to differentiate the impact of fuel
price changes from changes in economic activity. In contrast, some
states may see an increase in output when energy prices increase (e.g.,
large oil producing states such as Texas and Alaska); therefore, the
state data may be more accurately isolating the individual impact of
fuel price changes.
---------------------------------------------------------------------------

    \502\ NHTSA's estimates of the rebound effect are derived from
econometric analysis of national and state VMT data reported in
Federal Highway Administration, Highway Statistics, various
editions, Tables VM-1 and VM-4. Specifically, the estimates of the
rebound effect reported in Table VIII-10 are ranges of the estimated
short-run and long-run elasticities of annual VMT by single-unit and
combination trucks with respect to fuel cost per mile driven. (Fuel
cost per mile driven during each year is equal to average fuel price
per gallon during that year divided by average fuel economy of the
truck fleet during that same year.) These estimates are derived from
time-series regression of annual national aggregate VMT for the
period 1970-2008 on measures of nationwide economic activity,
including aggregate GDP, the value of durable and nondurable goods
production, and the volume of U.S. exports and imports of goods, and
variables affecting the price of trucking services (driver wage
rates, truck purchase prices, and fuel costs), and from regression
of VMT for each individual state over the period 1994-2008 on
similar variables measured at the state level.

 Table VIII-13--Range of Rebound Effect Estimates From NHTSA Econometric
                                Analysis
------------------------------------------------------------------------
                         National data                 State data
  Truck type   ---------------------------------------------------------
                   Short run       Long run        Short run    Long run
------------------------------------------------------------------------
 Single Unit          13-22%          28-45%            3-8%    12-21%
 Combination             N/A          12-14%             N/A    4-5%
------------------------------------------------------------------------

    As discussed throughout this section, there are multiple
methodologies for quantifying the rebound effect, and these different
methodologies produce a large range of potential values of the rebound
effect. However, for the purposes of quantifying the rebound effect for
this program, we have used a rebound effect with respect to changes in
fuel costs per mile on the lower range of the long-run estimates. Given
the fact that the long-run state estimates are generally more
consistent with the aggregate estimates, for this program we have
chosen a rebound effect for vocational vehicles (single unit trucks) of
15 percent that is within the range of estimates from both
methodologies. Similarly, we have chosen a rebound effect for
combination tractors of 5 percent.
    To date, no estimates of the HD pickup truck and van rebound effect
have been cited in the literature. Since these vehicles are used for
very different purposes than heavy-duty vehicles, it does not
necessarily seem appropriate to apply one of the heavy-duty estimates
to the HD pickup trucks and vans. These vehicles are more similar in
use to large light-duty vehicles, so for the purposes of our analysis,
we have chosen to apply the light-duty rebound effect of 10 percent to
this class of vehicles.
    For the purposes of this program, we have not taken into account
any potential fuel savings or GHG emission reductions from the rail
sector due to mode shifting. We requested comments on this assumption
in the proposal, but we did not receive any new data or input.
    Furthermore, we have made a number of simplifying assumptions in
our calculations, which are discussed in more detail in the RIA.
Specifically, we have not attempted to capture how current market
failures might impact the rebound effect. The direction and magnitude
of the rebound effect in the HD market are expected to vary depending
on the existence and types of market failures affecting the fuel
efficiency of the trucking fleet. If firms

are already accurately accounting for the costs and benefits of these
technologies and fuel savings, then these regulations would increase
their net costs, because trucks would already include all the cost-
effective technologies. As a result, the rebound effect would actually
be negative and truck VMT would decrease as a result of these final
regulations. However, if firms are not optimizing their behavior today
due to factors such as lack of reliable information (see Section
VIII.A. for further discussion), it is more likely that truck VMT would
increase. If firms recognize their lower net costs as a result of these
regulations and pass those costs along to their customers, then the
rebound effect would increase truck VMT. This response assumes that
trucking rates include both truck purchase costs and fuel costs, and
that the truck purchase costs included in the rates spread those costs
over the full expected lifetime of the trucks. If those costs are
spread over a shorter period, as the expected short payback period
implies, then those purchase costs will inhibit reduction of freight
rates, and the rebound effect will be smaller.
    As discussed in more detail in Section VIII.A, if there are market
failures such as split incentives, estimating the rebound effect may
depend on the nature of the failures. For example, if the original
purchaser cannot fully recoup the higher upfront costs through fuel
savings before selling the vehicle nor pass those costs onto the resale
buyer, the firm would be expected to raise shipping rates. A firm
purchasing the truck second-hand might lower shipping rates if the firm
recognizes the cost savings after operating the vehicle, leading to an
increase in VMT. Similarly, if there are split incentives and the
vehicle buyer isn't the same entity that purchases the fuel, than there
would theoretically be a positive rebound effect. In this scenario,
fuel savings would lower the net costs to the fuel purchaser, which
would result in a larger increase in truck VMT.
    If all of these scenarios occur in the marketplace, the net effect
will depend on the extent and magnitude of their relative effects,
which are also likely to vary across truck classes (for instance, split
incentives may be a much larger problem for Class 7 and 8 tractors than
they are for HD pickup trucks). Additional details on the rebound
effect are included in the RIA.

F. Class Shifting and Fleet Turnover Impacts

    The agencies considered two additional potential indirect costs,
benefits, effects, and externalities 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 final HD National Program impacts 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.\503\
NHTSA and EPA qualitatively evaluated the final 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.
---------------------------------------------------------------------------

    \503\ See 2010 NAS Report, Note 197, page 152.
---------------------------------------------------------------------------

    Light-duty pickup trucks, those with a GVWR of less than 8,500
pounds, are currently regulated under the existing CAFE program and
will meet GHG emissions standards beginning in 2012. The increased
stringency of the light-duty 2012-2016 MY vehicle rule has led some to
speculate that vehicle consumers may choose to purchase heavy-duty
pickup trucks that are currently unregulated 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. With these
final regulations, any incentive for such a class shift is
significantly diminished. The final 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 final 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 are significantly more efficient than heavy-duty
pickup trucks.
    The projected cost increases for this final action differ
significantly between Class 8 day cabs and Class 8 sleeper cabs,
reflecting our expectation that compliance with the final standards
will lead truck consumers to specify sleeper cabs equipped with APUs
while day cab consumers will 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 will 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.\504\ A day cab simply
cannot provide this utility. The need for this utility would not be
changed even if the marginal costs to reduce greenhouse gas emissions
from sleeper cabs exceed the marginal costs to reduce greenhouse gas
emissions from day

cabs.\505\ A trucking fleet could 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 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 final regulations
is small. We are providing 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 will automatically eliminate any
additional idling through the shutdown software. If trucking companies
choose this option, then costs based on purchase of APUs may
overestimate the costs of this program to this sector.
---------------------------------------------------------------------------

    \504\ 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-2010-0162-
0044.
    \505\ The average marginal cost difference between sleeper cabs
and day cabs in the proposal is nearly $6,000.
---------------------------------------------------------------------------

    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 finalizing 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 final regulatory program will
cause class shifting within the vocational 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
will outweigh the additional cost for heavier vehicles.\506\
---------------------------------------------------------------------------

    \506\ The final 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 final regulatory
structure for HD trucks does not significantly change the current
competitive and market factors that determine purchaser preferences
among truck types. Furthermore, even if a small amount of shifting does
occur, any resulting GHG impacts are 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 program.
(2) Fleet Turnover Effect
    A regulation that increases the cost to purchase and/or operate
trucks could impact 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 NAS panel discusses 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.\507\ However, those regulations increased upfront costs to firms
without any offsetting future cost savings from reduced fuel purchases.
In summary, NAS stated that
---------------------------------------------------------------------------

    \507\ See NAS Report, Note 197, 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.\508\
---------------------------------------------------------------------------

    \508\ See NAS Report, Note 197, page 151.

    The final regulations are projected to return fuel savings to the
truck owners that offset the cost of the regulation within a few years
for vocational vehicles and Class 7 and 8 tractors, the categories
where the potential for prebuy and delayed fleet turnover are concerns.
In the case of vocational vehicles, the added cost is small enough that
it is unlikely to have a substantial effect on purchasing behavior. In
the case of Class 7 and 8 trucks, the effects of the regulation on
purchasing behavior 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 VIII.A, this scenario may
occur if this final 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.
    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 VIII.A)
between truck purchasers (who 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.

G. Benefits of Reducing CO2 Emissions

(1) Social Cost of Carbon
    EPA has assigned a dollar value to reductions in CO2
emissions using recent estimates of the social cost of carbon (SCC).
The SCC is an estimate of the monetized damages associated with an
incremental increase in carbon emissions in a given year. It is
intended to include (but is not limited to) changes in net agricultural
productivity, human health, property damages from increased flood risk,
and the value of ecosystem services due to climate change. The SCC
estimates used in this analysis were developed through an interagency
process that included EPA, DOT/NHTSA, and other executive branch
entities, and concluded in February 2010. We first used these SCC
estimates in the benefits analysis for the light-duty 2012-2016 MY
vehicle rule; see that rule's preamble for a discussion of application
of the SCC.\509\ The SCC Technical Support Document (SCC TSD) provides
a complete discussion of the methods used to develop these SCC
estimates.\510\
---------------------------------------------------------------------------

    \509\ See 2010 Light-Duty Final Rule, Note 5, docket ID EPA-HQ-
OAR-2009-0472-11424.
    \510\ 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 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://epa.gov/otaq/climate/regulations.htm.
---------------------------------------------------------------------------

    The interagency group selected four SCC values for use in
regulatory analyses, which we have applied in this analysis: $5, $22,
$36, and $67 per metric ton of CO2 emissions in 2010, in
2009 dollars.511 512 The first three values are based on the
average SCC from three integrated assessment models, at discount rates
of 5, 3, and 2.5 percent, respectively. SCCs at several discount rates
are included because the literature shows that the SCC is quite
sensitive to assumptions about the discount rate, and because no
consensus exists on the appropriate rate to use in an intergenerational
context. The fourth value is the 95th percentile of the SCC 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 SCC distribution. Low probability, high impact events are
incorporated into all of the SCC values through explicit consideration
of their effects in two of the three models as well as the use of a
probability density function for equilibrium climate sensitivity.
Treating climate sensitivity probabilistically results in more high
temperature outcomes, which in turn lead to higher projections of
damages.
---------------------------------------------------------------------------

    \511\ The interagency group decided that these estimates apply
only to CO2 emissions. Given that warming profiles and
impacts other than temperature change (e.g., ocean acidification)
vary across GHGs, the group concluded ``transforming gases into
CO2-equivalents using GWP, and then multiplying the
carbon-equivalents by the SCC, would not result in accurate
estimates of the social costs of non-CO2 gases'' (SCC
TSD, pg 13).
    \512\ The SCC estimates were converted from 2007 dollars to 2008
dollars using a GDP price deflator (1.021) and again to 2009 dollars
using a GDP price deflator (1.009) obtained from the Bureau of
Economic Analysis, National Income and Product Accounts Table 1.1.4,
Prices Indexes for Gross Domestic Product.
---------------------------------------------------------------------------

    The SCC increases over time because future emissions are expected
to produce larger incremental damages as physical and economic systems
become more stressed in response to greater climatic change. Note that
the interagency group estimated the growth rate of the SCC directly
using the three integrated assessment models rather than assuming a
constant annual growth rate. This helps to ensure that the estimates
are internally consistent with other modeling assumptions. Table VIII-
14 presents the SCC estimates used in this analysis.
    When attempting to assess the incremental economic impacts of
carbon dioxide emissions, the analyst faces a number of serious
challenges. A recent report from the National Academies of Science
points out that any assessment will suffer from uncertainty,
speculation, and lack of information about (1) future emissions of
greenhouse gases, (2) the effects of past and future emissions on the
climate system, (3) the impact of changes in climate on the physical
and biological environment, and (4) the translation of these
environmental impacts into economic damages.\513\ As a result, any
effort to quantify and monetize the harms associated with climate
change will raise serious questions of science, economics, and ethics
and should be viewed as provisional.
---------------------------------------------------------------------------

    \513\ National Research Council (2009). Hidden Costs of Energy:
Unpriced Consequences of Energy Production and Use. National
Academies Press. See docket ID EPA-HQ-OAR-2009-0472-11486.
---------------------------------------------------------------------------

    The interagency group noted a number of limitations to the SCC
analysis, including the incomplete way in which the integrated
assessment models 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. The limited amount of research
linking climate impacts to economic damages makes the interagency
modeling exercise even more difficult. The interagency group hopes that
over time researchers and modelers will work to fill these gaps and
that the SCC estimates used for regulatory analysis by the Federal
government will continue to evolve with improvements in modeling.
Additional details on these limitations are discussed in the SCC TSD.
    We received several comments regarding the SCC estimates used to
analyze the proposed standards. In particular, these commenters
discussed the incomplete treatment of impacts as well as discount rate
selection. EPA has reviewed these comments in detail and responded to
them in the EPA Response to Comments Document for the Joint Rulemaking.
As noted in that document, the U.S. government intends to revise these
estimates, taking into account new research findings that were not
included in the first round, and has set a preliminary goal of
revisiting the SCC values in the next few years or at such time as
substantially updated models become available, and to continue to
support research in this area. The EPA Response to Comments Document
for the Joint Rulemaking discusses ongoing research in greater detail.
    Applying the global SCC estimates, shown in Table VIII-14, to the
estimated domestic reductions in CO2 emissions under this
final program, we estimate the dollar value of the climate related
benefits for each analysis year. For internal consistency, the annual
benefits are discounted back to net present value terms using the same
discount rate as each SCC estimate (i.e., 5%, 3%, and 2.5%) rather than
3% and 7%.\514\ These estimates are provided in Table VIII-15.
---------------------------------------------------------------------------

    \514\ It is possible that other benefits or costs of final
regulations unrelated to CO2 emissions will be discounted
at rates that differ from those used to develop the SCC estimates.

                                 Table VIII-14--Social Cost of CO2, 2012--2050 a
                                        [in 2009 dollars per metric ton]
----------------------------------------------------------------------------------------------------------------
                                                                          Discount rate and statistic
                                                             ---------------------------------------------------
                            Year                                                            2.5%       3% 95th
                                                              5%  Average  3%  Average    Average     percentile
----------------------------------------------------------------------------------------------------------------
2012........................................................        $5.28       $23.06       $37.53       $70.14
2015........................................................         5.93        24.58        39.57        75.03
2020........................................................         7.01        27.10        42.98        83.17
2025........................................................         8.53        30.43        47.28        93.11
2030........................................................        10.05        33.75        51.58       103.06
2035........................................................        11.57        37.08        55.88       113.00
2040........................................................        13.09        40.40        60.19       122.95
2045........................................................        14.63        43.34        63.59       131.66
2050........................................................        16.18        46.27        66.99       140.37
----------------------------------------------------------------------------------------------------------------
Note:
\a\ The SCC values are dollar-year and emissions-year specific.

                    Table VIII-15--Monetized CO2 Benefits of Vehicle Program, CO2 Emissions a
                                                [Millions, 2009$]
----------------------------------------------------------------------------------------------------------------
                                                                                Benefits
                                                             ---------------------------------------------------
                                                     CO2                                                 95th
                      Year                        Emissions    Avg SCC at   Avg SCC at   Avg SCC at   percentile
                                                  reduction   5% ($5-$16)    3% ($23-    2.5% ($38-   SCC at 3%
                                                    (MMT)          a          $46) a       $67) a     ($70-$140)
                                                                                a
----------------------------------------------------------------------------------------------------------------
2020...........................................         37.7         $264       $1,021       $1,619       $3,133
2030...........................................         73.1          734        2,467        3,770        7,532
2040...........................................         90.3        1,182        3,650        5,437       11,108
2050...........................................        103.9        1,682        4,810        6,963       14,590
                                                ----------------------------------------------------------------
    Net Present Valueb.........................  ...........        9,045       46,070       78,037      140,432
----------------------------------------------------------------------------------------------------------------
Notes:
\a\ Except for the last row (net present value), the SCC values are dollar-year and emissions-year specific.
\b\ Net present value of reduced CO2 emissions is calculated differently from other benefits. The same discount
  rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to
  calculate net present value of SCC for internal consistency. Refer to the SCC TSD for more detail.

H. Non-GHG Health and Environmental Impacts

    This section presents EPA's analysis of the non-GHG health and
environmental impacts that can be expected to occur as a result of the
HD National Program. GHG emissions are predominantly the byproduct of
fossil fuel combustion processes that also produce criteria and
hazardous air pollutants. The vehicles that are subject to the
standards are also significant sources of mobile source air pollution
such as direct PM, NOX, VOCs and air toxics. The standards
will affect exhaust emissions of these pollutants from vehicles. They
will also affect emissions from upstream sources related to changes in
fuel consumption. Changes in ambient ozone, PM2.5, and air
toxics that will result from the standards are expected to affect human
health in the form of premature deaths and other serious human health
effects, as well as other important public health and welfare effects.
    As many commenters noted, it is important to quantify the health
and environmental impacts associated with the final rules because a
failure to adequately consider these ancillary co-pollutant impacts
could lead to an incorrect assessment of their net costs and benefits.
Moreover, co-pollutant impacts tend to accrue in the near term, while
any effects from reduced climate change mostly accrue over a time frame
of several decades or longer.
    This section is organized as follows: the first presents the PM-
and ozone-related health and environmental impacts associated with the
final program in calendar year (CY) 2030; the second discusses the
related co-benefits associated with the model year (MY) analysis of the
program.\515\
---------------------------------------------------------------------------

    \515\ EPA typically analyzes rule impacts (emissions, air
quality, costs and benefits) in the year in which they occur; for
this analysis, we selected 2030 as a representative future year. We
refer to this analysis as the ``Calendar Year'' (CY) analysis. EPA
also conducted a separate analysis of the impacts over the model
year lifetimes of the 2012 through 2016 model year vehicles. We
refer to this analysis as the ``Model Year'' (MY) analysis. In
contrast to the CY analysis, the MY lifetime analysis shows the
lifetime impacts of the program on each of these MY fleets over the
course of its lifetime.
---------------------------------------------------------------------------

(1) Quantified and Monetized Non-GHG Human Health Benefits of the 2030
Calendar Year Analysis
    This analysis reflects the impact of the HD National Program in
2030 compared to a future-year reference scenario without the program
in place.\516\ Overall, we estimate that the final rules will lead to a
net decrease in PM2.5-related health impacts. See Section
VII.D of this preamble for more

information about the air quality modeling results. While the PM-
related air quality impacts are relatively small, the decrease in
population-weighted national average PM2.5 exposure results
in a net decrease in adverse PM-related human health impacts (the
decrease in national population-weighted annual average
PM2.5 is 0.005 [mu]g/m\3\).
---------------------------------------------------------------------------

    \516\ The future-year reference scenario to which the program
impacts are compared in this section assumes no future gains in mpg
(a ``flat'' scenario). For the final rulemaking, the agencies have
also conducted a sensitivity analysis relative to the baseline
assumptions. The alternative baseline assumes annual mpg
projections, in the absence of the program, which were developed by
the U.S. Energy Information Administration (EIA) for the Annual
Energy Outlook (AEO). A description of the alternative baseline can
be found in RIA Chapter 6. Due to time and resource constraints, EPA
was unable to conduct full-scale photochemical air quality modeling
to reflect the final rule impacts relative to this alternative
baseline.
---------------------------------------------------------------------------

    The air quality modeling also projects decreases in ozone
concentrations in many areas. While the ozone-related impacts are
relatively small, the decrease in population-weighted national average
ozone exposure results in a net decrease in ozone-related health
impacts (population-weighted maximum 8-hour average ozone decreases by
0.164 ppb).
    We base our analysis of the program's impact on human health in
2030 on peer-reviewed studies of air quality and human health
effects.517 518 These methods are described in more detail
in the RIA that accompanies this action. Our benefits methods are also
consistent with recent rulemaking analyses such as the final Transport
Rule,\519\ the light-duty 2012-2016 MY vehicle rule,\520\ and the final
Portland Cement National Emissions Standards for Hazardous Air
Pollutants (NESHAP) RIA.\521\ To model the ozone and PM air quality
impacts of this final action, we used the Community Multiscale Air
Quality (CMAQ) model (see Chapter 8.2.2 of the RIA that accompanies
this preamble). The modeled ambient air quality data serves as an input
to the Environmental Benefits Mapping and Analysis Program version 4.0
(BenMAP).\522\ BenMAP is a computer program developed by the U.S. EPA
that integrates a number of the modeling elements used in previous
analyses (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.
---------------------------------------------------------------------------

    \517\ U.S. Environmental Protection Agency. (2006). Final
Regulatory Impact Analysis (RIA) for the Proposed National Ambient
Air Quality Standards for Particulate Matter. Prepared by: Office of
Air and Radiation. Retrieved March 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html
    \518\ U.S. Environmental Protection Agency. (2008). Final Ozone
NAAQS Regulatory Impact Analysis. Prepared by: Office of Air and
Radiation, Office of Air Quality Planning and Standards. Retrieved
March 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html.
    \519\ Final Federal Implementation Plans to Reduce Interstate
Transport of Fine Particulate Matter and Ozone. Signed July 6, 2011.
Available at http://epa.gov/airtransport/.
    \520\ U.S. Environmental Protection Agency. (2010). Regulatory
Impact Analysis: Final Rulemaking to Establish Light-Duty Vehicle
Greenhouse Gas Emission Standards and Corporate Average Fuel Economy
Standards, EPA-420-R-10-009, April 2010. Available on the Internet:
http://www.epa.gov/otaq/climate/regulations/420r10009.pdf.
    \521\ U.S. Environmental Protection Agency (U.S. EPA). 2010.
Regulatory Impact Analysis: National Emission Standards for
Hazardous Air Pollutants from the Portland Cement Manufacturing
Industry. Office of Air Quality Planning and Standards, Research
Triangle Park, NC. Augues. Available on the Internet at http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementfinalria.pdf. EPA-
HQ-OAR-2009-0472-0241.
    \522\ Information on BenMAP, including downloads of the
software, can be found at http://www.epa.gov/ttn/ecas/benmodels.html.
---------------------------------------------------------------------------

    The range of total monetized ozone- and PM-related health impacts
is presented in Table VIII-16. We present total benefits based on the
PM- and ozone-related premature mortality function used. The benefits
ranges therefore reflect the addition of each estimate of ozone-related
premature mortality (each with its own row in Table VIII-16) to
estimates of PM-related premature mortality. These estimates represent
EPA's preferred approach to characterizing a best estimate of benefits.
As is the nature of Regulatory Impact Analyses (RIAs), the assumptions
and methods used to estimate air quality benefits evolve to reflect the
agency's most current interpretation of the scientific and economic
literature.

                 Table VIII-16--Estimated 2030 Monetized PM- and Ozone-Related Health Benefits a
----------------------------------------------------------------------------------------------------------------
   2030 Total ozone and PM benefits--PM mortality derived from American Cancer Society analysis and Six-Cities
                                                   Analysis a
-----------------------------------------------------------------------------------------------------------------
                                                                     Total benefits           Total Benefits
  Premature ozone mortality function          Reference           (billions, 2009$, 3%     (billions, 2009$, 7%
                                                                   discount rate) b,c       discount rate) b,c
----------------------------------------------------------------------------------------------------------------
Multi-city analyses..................  Bell et al., 2004......  Total: $1.3-$2.4.......  Total: $1.2-$2.2.
                                                                PM: $0.74-$1.8.........  PM: $0.67-$1.6.
                                                                Ozone: $0.55...........  Ozone: $0.55.
                                       Huang et al., 2005.....  Total: $1.6-$2.7.......  Total: $1.6-$2.5.
                                                                PM: $0.74-$1.8.........  PM: $0.67-$1.6
                                                                Ozone: $0.91...........  Ozone: $0.91.
                                       Schwartz, 2005.........  Total: $1.6-$2.6.......  Total: $1.5-$2.5.
                                                                PM: $0.74-$1.8.........  PM: $0.67-$1.6.
                                                                Ozone: $0.83...........  Ozone: $0.83.
Meta-analyses........................  Bell et al., 2005......  Total: $2.4-$3.5.......  Total: $2.4-$3.3.
                                                                PM: $0.74-$1.8.........  PM: $0.67-$1.6.
                                                                Ozone: $1.7............  Ozone: $1.7.
                                       Ito et al., 2005.......  Total: $3.1-$4.2.......  Total: $3.0-$4.0.
                                                                PM: $0.74-$1.8.........  PM: $0.67-$1.6.
                                                                Ozone: $2.4............  Ozone: $2.4.
                                       Levy et al., 2005......  Total: $3.1-$4.2.......  Total: $3.1-$4.0.
                                                                PM: $0.74-$1.8.........  PM: $0.67-$1.6.
                                                                Ozone: $2.4............  Ozone: $2.4.
----------------------------------------------------------------------------------------------------------------
Notes:

\a\ Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits. Range was
  developed by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-related
  premature mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et
  al., 2006).
\b\ Note that total benefits presented here do not include a number of unquantified benefits categories. A
  detailed listing of unquantified health and welfare effects is provided in Table VIII-17.
\c\ Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA's Guidelines for
  Preparing Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for ease of
  presentation and computation.

    The benefits in Table VIII-16 include all of the human health
impacts we are able to quantify and monetize at this time. However, the
full complement of human health and welfare effects associated with PM
and ozone remain unquantified because of current limitations in methods
or available data. We have not quantified a number of known or
suspected health effects linked with ozone and PM for which appropriate
health impact functions are not available or which do not provide
easily interpretable outcomes (e.g., changes in heart rate
variability). Additionally, we are unable to quantify a number of known
welfare effects, including reduced acid and particulate deposition
damage to cultural monuments and other materials, and environmental
benefits due to reductions of impacts of eutrophication in coastal
areas. These are listed in Table VIII-17. As a result, the health
benefits quantified in this section are likely underestimates of the
total benefits attributable to this final action.

     Table VIII-17--Unquantified and Non-Monetized Potential Effects
------------------------------------------------------------------------
                                       Effects not included in analysis--
          Pollutant/effects                       Changes in:
------------------------------------------------------------------------
Ozone Health \a\.....................  Chronic respiratory damage \b\.
                                       Premature aging of the lungs \b\.
                                       Non-asthma respiratory emergency
                                        room visits.
                                       Exposure to UVb (+/-) \e\.
Ozone Welfare........................  Yields for:
                                       --commercial forests.
                                       --some fruits and vegetables.
                                       --non-commercial crops.
                                       Damage to urban ornamental
                                        plants.
                                       Impacts on recreational demand
                                        from damaged forest aesthetics.
                                       Ecosystem functions.
                                       Exposure to UVb (+/-) \e\.
PM Health \c\........................  Premature mortality--short term
                                        exposures.\d\
                                       Low birth weight.
                                       Pulmonary function.
                                       Chronic respiratory diseases
                                        other than chronic bronchitis.
                                       Non-asthma respiratory emergency
                                        room visits.
                                       Exposure to UVb (+/-) \e\.
PM Welfare...........................  Residential and recreational
                                        visibility in non-Class I areas.
                                       Soiling and materials damage.
                                       Damage to ecosystem functions.
                                       Exposure to UVb (+/-) \e\.
Nitrogen and Sulfate Deposition        Commercial forests due to acidic
 Welfare.                               sulfate and nitrate deposition.
                                       Commercial freshwater fishing due
                                        to acidic deposition.
                                       Recreation in terrestrial
                                        ecosystems due to acidic
                                        deposition.
                                       Existence values for currently
                                        healthy ecosystems.
                                       Commercial fishing, agriculture,
                                        and forests due to nitrogen
                                        deposition.
                                       Recreation in estuarine
                                        ecosystems due to nitrogen
                                        deposition.
                                       Ecosystem functions.
                                       Passive fertilization.
CO Health............................  Behavioral effects.
HC/Toxics Health \f\.................  Cancer (benzene, 1,3-butadiene,
                                        formaldehyde, acetaldehyde).
                                       Anemia (benzene).
                                       Disruption of production of blood
                                        components (benzene).
                                       Reduction in the number of blood
                                        platelets (benzene).
                                       Excessive bone marrow formation
                                        (benzene).
                                       Depression of lymphocyte counts
                                        (benzene).
                                       Reproductive and developmental
                                        effects (1,3-butadiene).
                                       Irritation of eyes and mucus
                                        membranes (formaldehyde).
                                       Respiratory irritation
                                        (formaldehyde).
                                       Asthma attacks in asthmatics
                                        (formaldehyde).
                                       Asthma-like symptoms in non-
                                        asthmatics (formaldehyde).
                                       Irritation of the eyes, skin, and
                                        respiratory tract
                                        (acetaldehyde).
                                       Upper respiratory tract
                                        irritation and congestion
                                        (acrolein).
HC/Toxics Welfare....................  Direct toxic effects to animals.
                                       Bioaccumulation in the food
                                        chain.
                                       Damage to ecosystem function.
                                       Odor.
------------------------------------------------------------------------
Notes:
\a\ The public health impact of biological responses such as increased
  airway responsiveness to stimuli, inflammation in the lung, acute
  inflammation and respiratory cell damage, and increased susceptibility
  to respiratory infection are likely partially represented by our
  quantified endpoints.
\b\ The public health impact of effects such as chronic respiratory
  damage and premature aging of the lungs may be partially represented
  by quantified endpoints such as hospital admissions or premature
  mortality, but a number of other related health impacts, such as
  doctor visits and decreased athletic performance, remain unquantified.
\c\ In addition to primary economic endpoints, there are a number of
  biological responses that have been associated with PM health effects
  including morphological changes and altered host defense mechanisms.
  The public health impact of these biological responses may be partly
  represented by our quantified endpoints.
\d\ While some of the effects of short-term exposures are likely to be
  captured in the estimates, there may be premature mortality due to
  short-term exposure to PM not captured in the cohort studies used in
  this analysis. However, the PM mortality results derived from the
  expert elicitation do take into account premature mortality effects of
  short term exposures.

\e\ May result in benefits or disbenefits.
\f\ Many of the key hydrocarbons related to this action are also
  hazardous air pollutants listed in the CAA.

    While there will be impacts associated with air toxic pollutant
emission changes that result from this final action, we do 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). The
EPA 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.\523\ While EPA has since improved these tools, there remain
critical limitations for estimating incidence and assessing benefits of
reducing mobile source air toxics.
---------------------------------------------------------------------------

    \523\ 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.
---------------------------------------------------------------------------

    As part of the second prospective analysis of the benefits and
costs of the Clean Air Act,\524\ EPA conducted a case study analysis of
the health effects associated with reducing exposure to benzene in
Houston from implementation of the Clean Air Act. While reviewing the
draft report, EPA's Advisory Council on Clean Air Compliance Analysis
concluded that ``the challenges for assessing progress in health
improvement as a result of reductions in emissions of hazardous air
pollutants (HAPs) are daunting...due to a lack of exposure-response
functions, uncertainties in emissions inventories and background
levels, the difficulty of extrapolating risk estimates to low doses and
the challenges of tracking health progress for diseases, such as
cancer, that have long latency periods.'' \525\ EPA continues to work
to address these limitations; however, we did not have the methods and
tools available for national-scale application in time for the analysis
of the final action.\526\
---------------------------------------------------------------------------

    \524\ U.S. Environmental Protection Agency (U.S. EPA). 2011. The
Benefits and Costs of the Clean Air Act from 1990 to 2020. Office of
Air and Radiation, Washington, DC. March. Available on the Internet
at http://www.epa.gov/air/sect812/feb11/fullreport.pdf.
    \525\ U.S. Environmental Protection Agency--Science Advisory
Board (U.S. EPA-SAB). 2008. Benefits of Reducing Benzene Emissions
in Houston, 1990-2020. EPA-COUNCIL-08-001. July. Available at http:/
/yosemite.epa.gov/sab/sabproduct.nsf/
D4D7EC9DAEDA8A548525748600728A83/$File/EPA-COUNCIL-08-001-
unsigned.pdf.
    \526\ In April 2009, EPA hosted a workshop on estimating the
benefits or reducing hazardous air pollutants. This workshop built
upon the work accomplished in the June 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.
---------------------------------------------------------------------------

    EPA is also unaware of specific information identifying any effects
on listed endangered species from the small fluctuations in pollutant
concentrations associated with this program (see Section VII.D).
Furthermore, our current modeling tools are not designed to trace
fluctuations in ambient concentration levels to potential impacts on
particular endangered species.
(a) Quantified Human Health Impacts
    Table VIII-18 and Table VIII-19 present the annual PM2.5
and ozone health impacts, respectively, in the 48 contiguous U.S.
states associated with the HD National Program for 2030. For each
endpoint presented in Table VIII-18 and Table VIII-19, we provide both
the mean estimate and the 90 percent confidence interval.
    Using EPA's preferred estimates, based on the American Cancer
Society (ACS) and Six-Cities studies and no threshold assumption in the
model of mortality, we estimate that the final rules will result in
between 78 and 200 cases of avoided PM2.5-related premature
mortalities annually in 2030. As a sensitivity analysis, when the range
of expert opinion is used, we estimate between 26 and 260 fewer
premature mortalities in 2030 (see Table 8-14 in the RIA that
accompanies this action). For ozone-related premature mortality in
2030, we estimate a range of between 54 to 240 fewer premature
mortalities.

         Table VIII-18--Estimated PM2.5-Related Health Impacts a
------------------------------------------------------------------------
                                                            2030 Annual
                                                           reduction in
                      Health effect                       incidence (5th-
                                                               95th
                                                            percentile)
------------------------------------------------------------------------
Premature Mortality--Derived from epidemiology
 literature \b\
    Adult, age 30+, ACS Cohort Study (Pope et al., 2002)     78 (30-130)
    Adult, age 25+, Six-Cities Study (Laden et al.,        200 (110-290)
     2006)..............................................
    Infant, age <1 year (Woodruff et al., 1997).........         0 (0-1)
Chronic bronchitis (adult, age 26 and over).............      53 (10-97)
Non-fatal myocardial infarction (adult, age 18 and over)    150 (54-240)
Hospital admissions-respiratory (all ages) \c\..........      20 (10-30)
Hospital admissions-cardiovascular (adults, age >18) \d\      45 (32-52)
Emergency room visits for asthma (age 18 years and           81 (48-120)
 younger)...............................................
Acute bronchitis, (children, age 8-12)..................     130 (0-270)
Lower respiratory symptoms (children, age 7-14).........     1,600 (750-
                                                                  2,400)
Upper respiratory symptoms (asthmatic children, age 9-       1,200 (370-
 18)....................................................          2,000)
Asthma exacerbation (asthmatic children, age 6-18)......     1,400 (160-
                                                                  4,000)
Work loss days..........................................   9,700 (8,500-
                                                                 11,000)
Minor restricted activity days (adults age 18-65).......  57,000 (48,000-
                                                                 66,000)
------------------------------------------------------------------------
Notes:
\a\ Incidence is rounded to two significant digits. Estimates represent
  incidence within the 48 contiguous United States.
\b\ PM-related adult mortality based upon the American Cancer Society
  (ACS) Cohort Study (Pope et al., 2002) and the Six-Cities Study (Laden
  et al., 2006). Note that these are two alternative estimates of adult
  mortality and should not be summed. PM-related infant mortality based
  upon a study by Woodruff, Grillo, and Schoendorf, (1997).\527\
\c\ Respiratory hospital admissions for PM include admissions for
  chronic obstructive pulmonary disease (COPD), pneumonia and asthma.
\d\ Cardiovascular hospital admissions for PM include total
  cardiovascular and subcategories for ischemic heart disease,
  dysrhythmias, and heart failure.

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

    \527\ Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997.
``The Relationship Between Selected Causes of Postneonatal Infant
Mortality and Particulate Air Pollution in the United States.''
Environmental Health Perspectives 105(6):608-612.

         Table VIII-19--Estimated Ozone-Related Health Impacts a
------------------------------------------------------------------------
                                                            2030 Annual
                                                           reduction in
                      Health effect                          incidence
                                                             (5th-95th
                                                            percentile)
------------------------------------------------------------------------
Premature Mortality, All ages \b\ Multi-City Analyses:
    Bell et al. (2004)--Non-accidental..................      54 (23-84)
    Huang et al. (2005)--Cardiopulmonary................     90 (43-140)
    Schwartz (2005)--Non-accidental.....................     82 (34-130)
Meta-analyses:
    Bell et al. (2005)--All cause.......................    170 (96-250)
    Ito et al. (2005)--Non-accidental...................   240 (160-320)
    Levy et al. (2005)--All cause.......................   240 (180-310)
Hospital admissions--respiratory causes (adult, 65 and      510 (69-870)
 older) \c\.............................................
Hospital admissions--respiratory causes (children, under   320 (160-470)
 2).....................................................
Emergency room visit for asthma (all ages)..............     230 (0-630)
Minor restricted activity days (adults, age 18-65)......         300,000
                                                          (150,000-450,0
                                                                     00)
School absence days.....................................         120,000
                                                          (52,000-170,00
                                                                       0
------------------------------------------------------------------------
Notes:
\a\ Incidence is rounded to two significant digits. Estimates represent
  incidence within the 48 contiguous U.S.
\b\ Estimates of ozone-related premature mortality are based upon
  incidence estimates derived from several alternative studies: Bell et
  al. (2004); Huang et al. (2005); Schwartz (2005); Bell et al. (2005);
  Ito et al. (2005); Levy et al. (2005). The estimates of ozone-related
  premature mortality should therefore not be summed.
\c\ Respiratory hospital admissions for ozone include admissions for all
  respiratory causes and subcategories for COPD and pneumonia.

(b) Monetized Benefits
    Table VIII-20 presents the estimated monetary value of changes in
the incidence of ozone and PM2.5-related health effects. All
monetized estimates are stated in 2009$. These estimates account for
growth in real gross domestic product (GDP) per capita between the
present and 2030. Our estimate of total monetized benefits in 2030 for
the program, using the ACS and Six-Cities PM mortality studies and the
range of ozone mortality assumptions, is between $1.3 and $4.2 billion,
assuming a 3 percent discount rate, or between $1.2 and $4.0 billion,
assuming a 7 percent discount rate.

   Table VIII-20--Estimated Monetary Value of Changes in Incidence of
                   Health and Welfare Effects in 2030
                          [Millions, 2009$] a b
------------------------------------------------------------------------
   PM2.5-Related health effect           (5th and 95th Percentile)
------------------------------------------------------------------------
Premature Mortality--Derived from
 Epidemiology Studies:c d
    Adult, age 30+--ACS study
     (Pope et al., 2002):
        3% discount rate.........  $680 ($87-$1,800)
        7% discount rate.........  $620 ($79-$1,600)
    Adult, age 25+--Six-Cities
     study (Laden et al., 2006):
        3% discount rate.........  $1,800 ($250-$4,300)
        7% discount rate.........  $1,600 ($220-$3,900)
    Infant Mortality, <1 year-     $2.5 ($0-$9.4)
     (Woodruff et al. 1997).
Chronic bronchitis (adults, 26     $29 ($2.4-$96)
 and over).
Non-fatal acute myocardial
 infarctions:
    3% discount rate.............  $16 ($3.7-$38)
    7% discount rate.............  $16 ($3.4-$38)
Hospital admissions for            $0.31 ($0.15-$0.45)
 respiratory causes.
Hospital admissions for            $1.3 ($0.83-$1.8)
 cardiovascular causes.
Emergency room visits for asthma.  $0.03 ($0.02-$0.05)
Acute bronchitis (children, age 8- $0.01 ($0-$0.03)
 12).
Lower respiratory symptoms         $0.03 ($0.01-$0.06)
 (children, 7-14).
Upper respiratory symptoms         $0.04 ($0.01-$0.08)
 (asthma, 9-11).
Asthma exacerbations.............  $0.08 ($0.009-$0.23)
Work loss days...................  $1.6 ($1.4-$1.8)
Minor restricted-activity days     $3.6 ($2.1-$5.2)
 (MRADs).
------------------------------------------------------------------------
                       Ozone-related Health Effect
------------------------------------------------------------------------
Premature Mortality, All ages--
 Derived from Multi-city
 analyses:
    Bell et al., 2004............  $520 ($69-$1,300)
    Huang et al., 2005...........  $880 ($120-$2,200)
    Schwartz, 2005...............  $800 ($100-$2,000)
Premature Mortality, All ages--
 Derived from Meta-analyses:
    Bell et al., 2005............  $1,700 ($240-$4,100)
    Ito et al., 2005.............  $2,300 ($350-$5,500)
    Levy et al., 2005............  $2,400 ($350-$5,500)
Hospital admissions--respiratory   $13 ($1.7-$22)
 causes (adult, 65 and older).
Hospital admissions--respiratory   $3.4 ($1.8-$5.0)
 causes (children, under 2).
Emergency room visit for asthma    $0.09 ($0-$0.23)
 (all ages).
Minor restricted activity days     $19 ($8.6-$32)
 (adults, age 18-65).

School absence days..............  $11 ($5.0-$16)
------------------------------------------------------------------------
Notes:
\a\ Monetary benefits are rounded to two significant digits for ease of
  presentation and computation. PM and ozone benefits are nationwide.
\b\ Monetary benefits adjusted to account for growth in real GDP per
  capita between 1990 and the analysis year (2030).
\c\ Valuation assumes discounting over the SAB recommended 20 year
  segmented lag structure. Results reflect the use of 3 percent and 7
  percent discount rates consistent with EPA and OMB guidelines for
  preparing economic analyses.

(c) What are the limitations of the benefits analysis?
    Every benefit-cost analysis examining the potential effects of a
change in environmental protection requirements is limited to some
extent by data gaps, limitations in model capabilities (such as
geographic coverage), and uncertainties in the underlying scientific
and economic studies used to configure the benefit and cost models.
Limitations of the scientific literature often result in the inability
to estimate quantitative changes in health and environmental effects,
such as potential decreases in premature mortality associated with
decreased exposure to carbon monoxide. Deficiencies in the economics
literature often result in the inability to assign economic values even
to those health and environmental outcomes which can be quantified.
These general uncertainties in the underlying scientific and economics
literature, which can lead to valuations that are higher or lower, are
discussed in detail in the RIA and its supporting references. Key
uncertainties that have a bearing on the results of the benefit-cost
analysis of the final rules include the following:
     The exclusion of potentially significant and unquantified
benefit categories (such as health, odor, and ecological benefits of
reduction in air toxics, ozone, and PM);
     Errors in measurement and projection for variables such as
population growth;
     Uncertainties in the estimation of future year emissions
inventories and air quality;
     Uncertainty in the estimated relationships of health and
welfare effects to changes in pollutant concentrations including the
shape of the C-R function, the size of the effect estimates, and the
relative toxicity of the many components of the PM mixture;
     Uncertainties in exposure estimation; and
     Uncertainties associated with the effect of potential
future actions to limit emissions.
    As Table VIII-20 indicates, total benefits are driven primarily by
the reduction in premature mortalities each year. Some key assumptions
underlying the premature mortality estimates include the following,
which may also contribute to uncertainty:
     Inhalation of fine particles is causally associated with
premature death at concentrations near those experienced by most
Americans on a daily basis. Although biological mechanisms for this
effect have not yet been completely established, the weight of the
available epidemiological, toxicological, and experimental evidence
supports an assumption of causality. The impacts of including a
probabilistic representation of causality were explored in the expert
elicitation-based results of the PM NAAQS RIA.
     All fine particles, regardless of their chemical
composition, are equally potent in causing premature mortality. This is
an important assumption, because PM produced via transported precursors
emitted from heavy-duty engines may differ significantly from PM
precursors released from electric generating units and other industrial
sources. However, no clear scientific grounds exist for supporting
differential effects estimates by particle type.
     The C-R function for fine particles is approximately
linear within the range of ambient concentrations under consideration.
Thus, the estimates include health benefits from reducing fine
particles in areas with varied concentrations of PM, including both
regions that may be in attainment with PM2.5 standards and
those that are at risk of not meeting the standards.
     There is uncertainty in the magnitude of the association
between ozone and premature mortality. The range of ozone benefits
associated with the coordinated strategy is estimated based on the risk
of several sources of ozone-related mortality effect estimates. In a
report on the estimation of ozone-related premature mortality published
by the National Research Council, a panel of experts and reviewers
concluded that short-term exposure to ambient ozone is likely to
contribute to premature deaths and that ozone-related mortality should
be included in estimates of the health benefits of reducing ozone
exposure.\528\ EPA has requested advice from the National Academy of
Sciences on how best to quantify uncertainty in the relationship
between ozone exposure and premature mortality in the context of
quantifying benefits.
---------------------------------------------------------------------------

    \528\ National Research Council (NRC), 2008. Estimating
Mortality Risk Reduction and Economic Benefits from Controlling
Ozone Air Pollution. The National Academies Press: Washington, DC.
---------------------------------------------------------------------------

    Despite the uncertainties described above, we believe this analysis
provides a conservative estimate of the estimated non-GHG health and
environmental benefits of the standards in future years because of the
exclusion of potentially significant benefit categories that are not
quantifiable at this time. Acknowledging benefits omissions and
uncertainties, we present a best estimate of the total benefits based
on our interpretation of the best available scientific literature and
methods supported by EPA's technical peer review panel, the Science
Advisory Board's Health Effects Subcommittee (SAB-HES). The National
Academies of Science (NRC, 2002) has also reviewed EPA's methodology
for analyzing the health benefits of measures taken to reduce air
pollution. EPA addressed many of these comments in the analysis of the
final PM NAAQS.529 530 This analysis incorporates this work
to the extent possible.
---------------------------------------------------------------------------

    \529\ National Research Council (NRC). 2002. Estimating the
Public Health Benefits of Proposed Air Pollution Regulations. The
National Academies Press: Washington, DC.
    \530\ U.S. Environmental Protection Agency. October 2006. Final
Regulatory Impact Analysis (RIA) for the Proposed National Ambient
Air Quality Standards for Particulate Matter. Prepared by: Office of
Air and Radiation. Available at http://www.epa.gov/ttn/ecas/ria.html.
---------------------------------------------------------------------------

(2) Non-GHG Human Health Benefits of the Model Year (MY) Analysis
    As described in Section VII, the final standards will reduce
emissions of several criteria and toxic pollutants and precursors. EPA
typically analyzes rule