principal-agent problem, although they do not estimate how much it
might reduce fuel savings to those who are paying for the fuel.
---------------------------------------------------------------------------

    \628\ Some boat-tails are being developed with technology to
open them automatically when the trailer reaches a suitable speed,
to reduce this problem.
---------------------------------------------------------------------------

    The studies based on focus groups and interviews (Klemick et al.
2013, Aarnink et al. 2012, Roeth et al. 2013) provide mixed evidence on
the severity of the split-incentive problem. Focus groups often do
identify diverging incentives between drivers and the decision-makers
responsible for purchasing vehicles, and economics literature
recognizes that this split incentive can be a barrier to adopting new
technology. Aarnink et al. (2012) and Roeth et al. (2013) cite examples
of split incentives involving trailers and fuel surcharges, although
the latter also cites other examples where these same issues do not
lead to split incentives.
    In an effort to minimize problems that can arise from split
incentives, many businesses that operate HDVs also train drivers in the
use of specific technologies or to modify their driving behavior in
order to improve fuel efficiency, while some also offer financial
incentives to their drivers to conserve fuel. All of these options can
help to reduce the split incentive problem, although they may not be
effective where it arises from different ownership of combination
tractors and trailers.
    Uncertainty about future costs for fuel and maintenance, or about
the reliability of new technology, also appears to be a significant
obstacle that can slow the adoption of fuel-saving technologies. These
examples illustrate the problem of uncertain or unreliable information
about the actual performance of fuel efficiency technology discussed
above. In addition, businesses that operate HDVs may be concerned about
how reliable new technologies will prove to be on the road, and whether
significant additional maintenance costs or equipment malfunctions that
result in costly downtime could occur. Roeth et al. (2013) and Klemick
et al. (2013) both document the short payback periods that HDV buyers
require on their investments--usually about 2 years--which may be
partly attributable to these uncertainties.
    These studies also provide some support for the view that
adjustment and transactions costs may impede HDV buyers from investing
in higher fuel efficiency. As discussed above, several studies note
that HDV buyers are less likely to select new technology when it is not
available from their preferred manufacturers. Some technologies are
only available as after-market additions, which can add other costs to
adopting them.
    Some studies also cite driver acceptance of new equipment or
technologies as a barrier to their adoption. HDV driver turnover is
high in the U.S., and businesses that operate HDVs are concerned about
retaining their best drivers. Therefore, they may avoid technologies
that require significant new training or adjustments in driver
behavior. For some technologies that can be used to meet the proposed
standards, such as automatic tire inflation systems, training costs are
likely to be minimal. Other technologies such as stop-start systems,
however, may require drivers to adjust their expectations about vehicle
operation, and it is difficult for the agencies to anticipate how
drivers will respond to such changes.\629\
---------------------------------------------------------------------------

    \629\ The distinction between simply requiring drivers (or
mechanics) to adjust their expectations and compromises in vehicle
performance or utility is subtle. While the former may not impose
significant compliance costs in the long run, the latter would
represent additional economic costs of complying with the standard.
---------------------------------------------------------------------------

    In addition to these factors, the studies considered other possible
explanations for HDV buyers' apparent reluctance or slowness to invest
in fuel-saving equipment or technology. Financial constraints--access
to lending sources willing to finance purchases of more expensive
vehicles--do not appear to be a problem for the medium- and large-sized
businesses participating in Klemick et al.'s (2013) study. However,
Roeth et al. (2013) noted that access to capital can be a significant
challenge to smaller or independent businesses, and that price is
always a concern to buyers. In general, businesses that operate HDVs
face a range of competing uses for available capital other than
investing in fuel-saving technologies, and may assign higher priority
to these other uses, even when investing in higher fuel efficiency HDVs
appears to promise adequate financial returns.
    Other potentially important barriers to the adoption of measures
that improve fuel efficiency may arise from ``network externalities,''
where the benefits to new users of a technology depend on how many
others have already adopted it. One example where network externalities
seem likely to arise is the market for natural gas-fueled HDVs: The
limited availability of refueling stations may reduce potential buyers'
willingness to purchase natural gas-fueled HDVs, while the small number
of such HDVs in-use does not provide sufficient economic incentive to
construct more natural gas refueling stations.
    Some businesses that operate HDVs may also be concerned about the
difficulty in locating repair facilities or replacement parts, such as
single-wide tires, wherever their vehicles operate. When a technology
has been widely adopted, then it is likely to be serviceable even in
remote or rural places, but until it becomes widely available, its
early adopters may face difficulties with repairs or replacements. By
accelerating the widespread adoption of these technologies, the
proposed standards may assist in overcoming these difficulties.
    As discussed previously, the lack of availability of fuel-saving
technologies from preferred manufactures can also be a significant
barrier to adoption (Roeth et al. 2013). Manufacturers may be hesitant
to offer technologies for which there is not strong demand, especially
if the technologies require significant research and development
expenses and other costs of bringing the technology to a market of
uncertain demand.
    Roeth et al. (2013) also noted that it can take years, and
sometimes as much as a decade, for a specific technology to become
available from all manufacturers. Many manufacturers prefer to observe
the market and follow other manufacturers rather than be the first to
market with a specific technology. The ``first-mover disadvantage'' has
been recognized in other research where the ``first-mover'' pays a
higher proportion of the costs of developing technology, but loses the
long-term advantage when other businesses follow quickly.\630\ In this
way, there may be barriers to innovation on the supply side that result
in lower adoption rates of fuel-efficiency technology than would be
optimal.
---------------------------------------------------------------------------

    \630\ Blumstein, Carl and Margaret Taylor (2013). ``Rethinking
the Energy-Efficiency Gap: Producers, Intermediaries, and
Innovation,'' Energy Institute at Haas Working Paper 243, University
of California at Berkeley; Tirole, Jean (1998). The Theory of
Industrial Organization. Cambridge, MA: MIT Press, pp.400, 402. This
first-mover disadvantage must large enough to overcome the incentive
normally offered by the potential to for first movers to earn
unusually high (but temporary) profit levels.
---------------------------------------------------------------------------

    In summary, the agencies recognize that businesses that operate
HDVs are under competitive pressure to reduce operating costs, which
should compel

HDV buyers to identify and rapidly adopt cost-effective fuel-saving
technologies. Outlays for labor and fuel generally constitute the two
largest shares of HDV operating costs, depending on the price of fuel,
distance traveled, type of HDV, and commodity transported (if any), so
businesses that operate HDVs face strong incentives to reduce these
costs.631 632
---------------------------------------------------------------------------

    \631\ American Transportation Research Institute, An Analysis of
the Operational Costs of Trucking, September 2013 (Docket ID: EPA-
HQ-OAR-2014-0827).
    \632\ 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-2014-
0827).
---------------------------------------------------------------------------

    However, the short payback periods that buyers of new HDVs appear
to require suggest that some combination of uncertainty about future
cost savings, transactions costs, and imperfectly functioning markets
impedes this process. Markets for both new and used HDVs may face these
problems, although it is difficult to assess empirically the degree to
which they actually do. Even if the benefits from widespread adoption
of fuel-saving technologies exceed their costs, their use may remain
limited or spread slowly because their early adopters bear a
disproportionate share of those costs. In this case, the proposed
standards may help to overcome such barriers by ensuring that these
measures would be widely adopted.
    Providing information about fuel-saving technologies, offering
incentives for their adoption, and sharing HDV operators' real-world
experiences with their performance through voluntary programs such as
EPA's SmartWay Transport Partnership should assist in the adoption of
new cost-saving technologies. Nevertheless, other barriers that impede
the diffusion of new technologies are likely to remain. Buyers who are
willing to experiment with new technologies expect to find cost
savings, but those savings may be difficult to verify or replicate. As
noted previously, because benefits from employing these technologies
are likely to vary with the characteristics of individual routes and
traffic patterns, buyers of new HDVs may find it difficult to identify
or verify the effects of fuel-saving technologies in their operations.
Risk-averse buyers may also avoid new technologies out of concerns over
the possibility of inadequate returns on their investments, or with
other possible adverse impacts.
    Some HDV manufacturers may delay in investing in the development
and production of new technologies, instead waiting for other
manufacturers to bear the risks of those investments first. Competitive
pressures in the HDV freight transport industry can provide a strong
incentive to reduce fuel consumption and improve environmental
performance. However, not every HDV operator has the requisite ability
or interest to access and utilize the technical information, or the
resources necessary to evaluate this information within the context of
his or her own operations.
    As discussed previously, whether the technologies available to
improve HDVs' fuel efficiency would be adopted widely in the absence of
the program is challenging to assess. To the extent that these
technologies would be adopted in its absence, neither their costs nor
their benefits would be attributed to the program. To account for this
possibility, the agencies analyzed the proposed standards and the
regulatory alternatives against two reference cases, or baselines, as
described in Section X.
    The first case uses a baseline that projects some improvement in
fuel efficiency for new trailers, but no improvement in fuel efficiency
for other vehicle segments in the absence of new Phase 2 standards.
This first case is referred to as the less dynamic baseline, or
Alternative 1a. The second case uses a baseline that projects some
improvement in vehicle fuel efficiency for tractors, trailers, pickup
trucks, and vans but not for vocational vehicles. This second case is
referred to as the more dynamic baseline, or Alternative 1b.
    The agencies will continue to explore reasons for the slow adoption
of readily available and apparently cost-effective technologies for
improving fuel efficiency. We also seek comments on our hypotheses
about its causes, as well as data or other information that can inform
our understanding of why this situation seems to persist.

B. Vehicle-Related Costs Associated With the Program

(1) Technology Cost Methodology
(a) Direct Manufacturing Costs
    The direct manufacturing costs (DMCs) used throughout this analysis
are derived from several sources. Many of the tractor, vocational and
trailer DMCs can be sourced to the Phase 1 rule which, in turn, were
sourced largely from a contracted study by ICF International for
EPA.\633\ We have updated those costs by converting them to 2012
dollars, as described in Section IX.B.1.e below, and by continuing the
learning effects described in the Phase 1 rule and in Section IX.B.1.c
below. The new tractor, vocational and trailer costs can be sourced to
a more recent study conducted by Tetra Tech under contract to
NHTSA.\634\ The cost methodology used by Tetra Tech was to estimate
retail costs and work backward from there to derive a DMC for each
technology. The agencies did not agree with the approach used by Tetra
Tech to move from retail cost to DMC as the approach was to simply
divide retail costs by 2 and use the result as a DMC. Our research,
discussed below, suggests that a divisor of 2 is too high. Therefore,
where we have used a Tetra Tech derived retail estimate, we have
divided by our researched markups to arrive at many of the DMCs used in
this analysis. In this way, the agencies have used an approach
consistent with past GHG/CAFE/fuel consumption rules by dividing
estimated retail prices by our estimated retail price equivalent (RPE)
markups to derive an appropriate DMC for each technology. We describe
our RPEs in Section IX.B.1.b, below.
---------------------------------------------------------------------------

    \633\ ICF International. Investigation of Costs for Strategies
to Reduce Greenhouse Gas Emissions for Heavy-Duty On-Road Vehicles.
July 2010.
    \634\ Schubert, R., Chan, M., Law, K. (2015). Commercial Medium-
and Heavy-Duty (MD/HD) Truck Fuel Efficiency Cost Study. Washington,
DC: National Highway Traffic Safety Administration.
---------------------------------------------------------------------------

    For HD pickups and vans, we have relied primarily on the Phase 1
rule and the recent light-duty 2017-2025 model year rule since most
technologies expected on these vehicles are, in effect, the same as
those used on light-duty pickups. Many of those technology DMCs are
based on cost teardown studies which the agencies consider to be the
most robust method of cost estimation. However, because most of the HD
versions of those technologies are expected to be more costly than
their light-duty counterparts, we have scaled upward most of the light-
duty DMCs for this analysis. We have also used some costs developed
under contract to NHTSA by Tetra Tech.\635\
---------------------------------------------------------------------------

    \635\ Schubert, R., Chan, M., Law, K. (2015). Commercial Medium-
and Heavy-Duty (MD/HD) Truck Fuel Efficiency Cost Study. Washington,
DC: National Highway Traffic Safety Administration.
---------------------------------------------------------------------------

    Importantly, in our methodology, all technologies are treated as
being sourced from a supplier rather than being developed and produced
in-house. As a result, some portion of the total indirect costs of
making a technology or system--those costs incurred by the supplier for
research, development, transportation, marketing etc.--are contained in
the sales price to the engine and/or vehicle/trailer manufacturer
(i.e., the original equipment manufacturer (OEM)). That

sale price paid by the OEM to the supplier is the DMC we estimate.
    We present the details--sources, DMC values, scaling from light-
duty values, markups, learning effects, adoption rates--behind all our
costs in Chapter 2 of the draft RIA.
(b) Indirect Costs
    To produce a unit of output, engine and truck manufacturers incur
direct and indirect costs. Direct costs include cost of materials and
labor costs. Indirect costs are all the costs associated with producing
the unit of output that are not direct costs--for example, they may be
related to production (such as research and development [R&D]),
corporate operations (such as salaries, pensions, and health care costs
for corporate staff), or selling (such as transportation, dealer
support, and marketing). Indirect costs are generally recovered by
allocating a share of the costs to each unit of good sold. Although it
is possible to account for direct costs allocated to each unit of good
sold, it is more challenging to account for indirect costs allocated to
a unit of goods sold. To make a cost analysis process more feasible,
markup factors, which relate total indirect costs to total direct
costs, have been developed. These factors are often referred to as
retail price equivalent (RPE) multipliers.
    While the agencies have traditionally used RPE multipliers to
estimate indirect costs, in recent GHG/CAFE/fuel consumption rules RPEs
have been replaced in the primary analysis with indirect cost
multipliers (ICMs). ICMs differ from RPEs in that they attempt to
estimate not all indirect costs incurred to bring a product to point of
sale, but only those indirect costs that change as a result of a
government action or regulatory requirement. As such, some indirect
costs, notably health and retirement benefits of retired employees,
among other indirect costs, would not be expected to change due to a
government action and, therefore, the portion of the RPE that covered
those costs does not change.
    Further, the ICM is not a ``one-size-fits-all'' markup as is the
traditional RPE. With ICMs, higher complexity technologies like
hybridization or moving from a manual to automatic transmission may
require higher indirect costs--more research and development, more
integration work, etc.--suggesting a higher markup. Conversely, lower
complexity technologies like reducing friction or adding passive aero
features may require fewer indirect costs thereby suggesting a lower
markup.
    Notably, ICMs are also not a simple multiplier as are traditional
RPEs. The ICM is broken into two parts--warranty related and non-
warranty related costs. The warranty related portion of the ICM is
relatively small while the non-warranty portion represents typically
over 95 percent of indirect costs. These two portions are applied to
different DMC values to arrive at total costs (TC). The warranty
portion of the markup is applied to a DMC that decreases year-over-year
due to learning effects (described below in Section IX.B.1.c).\636\ As
learning effects decrease the DMC with production volumes, it makes
sense that warranty costs would decrease since those parts replaced
under warranty should be less costly. In contrast, the non-warranty
portion of the markup is applied to a static DMC year-over-year
resulting in static indirect costs. This is logical since the
production plants and transportation networks and general overhead
required to build parts, market them, deliver them and integrate them
into vehicles do not necessarily decrease in cost year-over-year.
Because the warranty and non-warranty portions of the ICM are applied
differently, one cannot compare the markup itself to the RPE to
determine which markup would result in higher indirect cost estimates,
at least in the time periods typically considered in our rules (four to
ten years).
---------------------------------------------------------------------------

    \636\ We note that the labor portion of warranty repairs does
not decrease due to learning. However, we do not have data to
separate this portion and so we apply learning to the entire
warranty cost. Because warranty costs are a small portion of overall
indirect costs, this has only a minor impact on the analysis.
---------------------------------------------------------------------------

    The agencies are concerned that some potential costs associated
with this rulemaking may not be adequately captured by our ICMs. ICMs
are estimated based on a few specific technologies and these
technologies may not be representative of the changes actually made to
meet the proposed requirements. Specifically, we may not have
adequately estimated the costs for accelerated R&D or potential
reliability issues with advanced technologies required by Alternative
4. There is a great deal of uncertainty regarding these costs, and this
makes estimates for this alternative of particular concern. We request
comment on that aspect of our estimates and on all aspects of our
indirect cost estimation approach.
    We provide more details on our ICM approach and the markups used
for each technology in Chapter 2.12 of the draft RIA.
(c) Learning Effects on Direct and Indirect Costs
    For some of the technologies considered in this analysis,
manufacturer learning effects would be expected to play a role in the
actual end costs. The ``learning curve'' or ``experience curve''
describes the reduction in unit production costs as a function of
accumulated production volume. In theory, the cost behavior it
describes applies to cumulative production volume measured at the level
of an individual manufacturer, although it is often assumed--as both
agencies have done in past regulatory analyses--to apply at the
industry-wide level, particularly in industries that utilize many
common technologies and component supply sources. Both agencies believe
there are indeed many factors that cause costs to decrease over time.
Research in the costs of manufacturing has consistently shown that, as
manufacturers gain experience in production, they are able to apply
innovations to simplify machining and assembly operations, use lower
cost materials, and reduce the number or complexity of component parts.
All of these factors allow manufacturers to lower the per-unit cost of
production (i.e., the manufacturing learning curve).\637\
---------------------------------------------------------------------------

    \637\ See ``Learning Curves in Manufacturing'', L. Argote and D.
Epple, Science, Volume 247; ``Toward Cost Buy down Via Learning-by-
Doing for Environmental Energy Technologies, R. Williams, Princeton
University, Workshop on Learning-by-Doing in Energy Technologies,
June 2003; ``Industry Learning Environmental and the Heterogeneity
of Firm Performance, N. Balasubramanian and M. Lieberman, UCLA
Anderson School of Management, December 2006, Discussion Papers,
Center for Economic Studies, Washington DC.
---------------------------------------------------------------------------

    In this analysis, the agencies are using the same approach to
learning as done in past GHG/CAFE/fuel consumption rules. In short,
learning effects result in rapid cost reductions in the early years
following introduction of a new technology. The agencies have estimated
those cost reductions as resulting in 20 percent lower costs for every
doubling of production volume. As production volumes increase, learning
rates continue at the same pace but flatten asymptotically due to the
nature of the persistent doubling of production required to realize
that cost reduction. As such, the cost reductions flatten out as
production volumes continue to increase. Consistent with the Phase 1
rule, we refer to these two distinct portions of the ``learning cost
reduction curve'' or ``learning curve'' as the steeper and flatter
portions of the curve. On that steep portion of the curve, costs are
estimated to decrease by

20 percent for each double of production or, by proxy, in the third and
then fifth year of production following introduction. On the flat
portion of the curve, costs are estimated to decrease by 3 percent per
year for 5 years, then 2 percent per year for 5 years, then 1 percent
per year for 5 years. Also consistent with the Phase 1 rule, the
majority of the technologies we expect would be adopted are considered
to be on the flat portion of the learning curve meaning that the 20
percent cost reductions are rarely applied. The agencies request
comment on this approach to estimating these effects, and request that
commenters provide data and forward-looking information to support any
alternative methods or specific estimates.
    We provide more details on the concept of learning-by-doing and the
learning effects applied in this analysis in Chapter 2 of the draft
RIA.
(d) Technology Adoption Rates and Developing Package Costs
    Determining the stringency of the proposed standards involves a
balancing of relevant factors--chiefly technology feasibility and
effectiveness, costs, and lead time. For vocational vehicles, tractors
and trailers, the agencies have projected a technology path to achieve
the proposed standards reflecting an application rate of those
technologies the agencies consider to be available at reasonable cost
in the lead times provided. The agencies do not expect each of the
technologies for which costs have been developed to be employed by all
trucks and trailers across the board. Further, many of today's vehicles
are already equipped with some of the technologies and/or are expected
to adopt them by MY2018 to comply with the HD Phase 1 standards.
Estimated adoption rates in both the reference and control cases are
necessary for each vehicle/trailer category. The adoption rates for
most technologies are zero in the reference case; however, for some
technologies--notably aero and tire technologies--the adoption rate is
not zero in the reference case. These reference and control case
adoption rates are then applied to the technology costs with the result
being a package cost for each vehicle/trailer category.
    For HD pickups and vans, the CAFE model determines the technology
adoption rates that most cost effectively meet the standards being
proposed. Similar to vocational vehicles, tractors and trailers,
package costs are rarely if ever a simple sum of all the technology
costs since each technology would be expected to be adopted at
different rates. The methods for estimating technology adoption rates
and resultant costs (and other impacts) for HD pickups and vans are
discussed above in Section 6.
    We provide details of expected adoption rates in Chapter 2 of the
draft RIA. We present package costs both in Sections III through VI of
this preamble and in more detail in Chapter 2 of the draft RIA.
(e) Conversion of Technology Costs to 2012 U.S. Dollars
    As noted above in Section IX.B.1, the agencies are using technology
costs from many different sources. These sources, having been published
in different years, present costs in different year dollars (i.e., 2009
dollars or 2010 dollars). For this analysis, the agencies sought to
have all costs in terms of 2012 dollars to be consistent with the
dollars used by AEO in its 2014 Annual Energy Outlook.\638\ The
agencies have used the GDP Implicit Price Deflator for Gross Domestic
Product as the converter, with the actual factors used as shown in
Table IX-1.\639\
---------------------------------------------------------------------------

    \638\ U.S. Energy Information Administration, Annual Energy
Outlook 2014, Early Release; Report Number DOE/EIA-0383ER (2014),
December 16, 2013.
    \639\ Bureau of Economic Analysis, Table 1.1.9 Implicit Price
Deflators for Gross Domestic Product; as revised on March 27, 2014.

                                   Table IX-1--Implicit Price Deflators and Conversion Factors for Conversion to 2012$
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                     2006       2007       2008       2009       2010       2011       2012       2013
--------------------------------------------------------------------------------------------------------------------------------------------------------
Price index for GDP.............................................     94.818     97.335     99.236        100    101.211    103.199    105.002    106.588
Factor applied for 2012$........................................      1.107      1.079      1.058      1.050      1.037      1.017      1.000      0.985
--------------------------------------------------------------------------------------------------------------------------------------------------------

(2) Compliance Program Costs
    The agencies have also estimated additional and/or new compliance
costs associated with the proposed standards. Normally, compliance
program costs would be considered part of the indirect costs and,
therefore, would be accounted for via the markup applied to direct
manufacturing costs. However, since the agencies are proposing new
compliance elements that were not present during development of the
indirect cost markups used in this analysis, additional compliance
program costs are being accounted for via a separate ``line-item.'' New
research and development costs (see below) are being handled in the
same way.
    The new compliance program elements included in this proposal are
new powertrain testing within the vocational vehicle program, and an
all-new compliance program where none has existed to date within the
trailer program. Note that for HD pickups and vans, HD engines,
vocational vehicles and tractors, the Phase 1 rule included analogous
compliance program costs meant to account for costs incurred in the
all-new compliance program placed on the regulated firms by that rule.
Compliance program costs cover costs associated with any necessary
compliance testing and reporting to the agencies and differ somewhat by
alternative since, for example, more manufacturers are expected to
conduct powertrain testing under alternative 4 than under alternative
3, etc. The details behind the estimated compliance program costs are
provided in Chapter 7 of the draft RIA. We request comment on our
estimated compliance costs.
(3) Research and Development Costs
    Much like the compliance program costs described above, we have
estimated additional HDD engine, vocational vehicle and tractor R&D
associated with the proposed standards that is not accounted for via
the indirect cost markups used for these segments. Much like the Phase
1 rule, EPA is estimating these additional R&D costs will occur over a
4-year timeframe as the proposed standards come into force and industry
works on means to comply. After that period, the additional R&D costs
go to $0 as R&D expenditures return to their normal levels and R&D
costs are accounted for via the ICMs--and the RPEs behind them--used
for these segments. Note that, due to the accelerated implementation of
some technologies, alternative 4 has higher R&D costs than does
alternative 3. The details behind the estimated R&D costs are provided
in Chapter 7 of the draft RIA. We request comment on our estimated R&D
costs.

(4) Summary of Costs of the Proposed Vehicle Programs
    The agencies have estimated the costs of the proposed vehicle
standards on an annual basis for the years 2018 through 2050, and have
also estimated costs for the full model year lifetimes of MY2018
through MY2029 vehicles. Table IX-2 shows the annual costs of the
proposed standards along with net present values using both 3 percent
and 7 percent discount rates. Table IX-3 shows the discounted model
year lifetime costs of the proposed standards at both 3 percent and 7
percent discount rates along with sums across applicable model years.

 Table IX-2--Annual Costs of the Preferred Alternative and Net Present Values at 3% and 7% Discount Rates Using
                               Method B and Relative to the Less Dynamic Baseline
                                             [$Millions of 2012$] a
----------------------------------------------------------------------------------------------------------------
                  Calendar year                   New technology    Compliance          R&D             Sum
----------------------------------------------------------------------------------------------------------------
2018............................................             116               0               0             116
2019............................................             113               0               0             113
2020............................................             112               0               0             112
2021............................................           2,173              18             240           2,432
2022............................................           2,161               6             240           2,407
2023............................................           2,224               6             240           2,470
2024............................................           3,455               6             240           3,701
2025............................................           3,647               6               0           3,653
2026............................................           3,736               6               0           3,742
2027............................................           5,309               6               0           5,315
2028............................................           5,334               6               0           5,340
2029............................................           5,376               6               0           5,381
2030............................................           5,399               6               0           5,405
2035............................................           5,856               6               0           5,862
2040............................................           6,316               6               0           6,322
2050............................................           6,987               6               0           6,992
NPV, 3%.........................................          85,926             104             759          86,789
NPV, 7%.........................................          40,516              56             561          41,133
----------------------------------------------------------------------------------------------------------------
Note:
\a\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less
  dynamic baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1.

                                 Table IX-3--Discounted MY Lifetime Costs of the Preferred Alternative Using Method B and Relative to the Less Dynamic Baseline
                                                                                [$Millions of 2012$] a
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                Discounted at 3%                                                Discounted at 7%
                           Model year                            -------------------------------------------------------------------------------------------------------------------------------
                                                                  New technology    Compliance          R&D             Sum       New technology    Compliance          R&D             Sum
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
2018............................................................             104               0               0             104              91               0               0              91
2019............................................................              99               0               0              99              84               0               0              84
2020............................................................              95               0               0              95              77               0               0              77
2021............................................................           1,794              15             198           2,007           1,401              12             155           1,567
2022............................................................           1,731               5             193           1,928           1,302               3             145           1,450
2023............................................................           1,730               4             187           1,921           1,252               3             135           1,390
2024............................................................           2,610               4             181           2,795           1,818               3             126           1,947
2025............................................................           2,674               4               0           2,678           1,793               3               0           1,796
2026............................................................           2,660               4               0           2,664           1,717               3               0           1,719
2027............................................................           3,670               4               0           3,673           2,280               2               0           2,283
2028............................................................           3,580               4               0           3,583           2,141               2               0           2,143
2029............................................................           3,502               4               0           3,506           2,017               2               0           2,019
Sum.............................................................          24,248              48             759          25,055          15,973              33             561          16,568
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Note:
\a\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please see Section X.A.1.

    New technology costs begin in MY2018 as trailers begin to add new
technology. Compliance costs begin with the new standards with capital
cost expenditure in that year for building and upgrading test
facilities to conduct the proposed powertrain testing in the vocational
program. Research and development costs begin in 2021 and last for 4
years as engine, tractor and vocational vehicle manufacturers conduct
research and development testing to integrate new technologies into
their engines and vehicles. We request comment on all aspects of our
technology costs, both individual technology costs and package costs,
as detailed in Chapter 2 of the draft RIA.

C. Changes in Fuel Consumption and Expenditures

(1) Changes in Fuel Consumption
    The new GHG and fuel consumption standards would result in
significant improvements in the fuel efficiency of affected vehicles,
and drivers of those vehicles would see corresponding savings
associated with reduced fuel expenditures. The agencies have estimated
the impacts on fuel consumption for the proposed standards. Details
behind how these changes in fuel consumption were calculated are
presented in Section VII of this preamble and in Chapter 5 of the draft
RIA. The total number of miles that vehicles are driven each year is
different under the regulatory alternatives than in the reference case
due to the ``rebound effect'' (discussed below in Section IX.E), so the
changes in fuel consumption associated with each alternative are not
strictly proportional to differences in the fuel economy levels they
require.
    The expected annual impacts on fuel consumption are shown in Table
IX-4. Table IX-5 shows the MY lifetime changes in fuel consumption. The
gallons shown in these tables as reductions in fuel consumption reflect
reductions due to the proposed standards and include any increased
consumption resulting from the rebound effect (discussed below in
Section IX.E).

        Table IX-4--Annual Fuel Consumption Reductions Due to the Preferred Alternative Using Method B and Relative to the Less Dynamic Baseline
                                                                [Millions of gallons] \a\
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                             Gasoline                                         Diesel
                                                         -----------------------------------------------------------------------------------------------
                      Calendar year                                            Fuel                                            Fuel
                                                          Reference case    consumption    %  Reduction   Reference case    consumption    %  Reduction
                                                                             reduction                                       reduction
--------------------------------------------------------------------------------------------------------------------------------------------------------
2018....................................................           6,781               0               0          45,999              74               0
2019....................................................           6,799               0               0          46,362             150               0
2020....................................................           6,832               0               0          46,768             227               0
2021....................................................           6,884              10               0          47,236             523               1
2022....................................................           6,944              29               0          47,761             894               2
2023....................................................           7,005              57               1          48,309           1,276               3
2024....................................................           7,054              99               1          48,807           1,895               4
2025....................................................           7,113             151               2          49,400           2,523               5
2026....................................................           7,169             210               3          49,967           3,152               6
2027....................................................           7,221             291               4          50,420           3,890               8
2028....................................................           7,273             369               5          50,821           4,600               9
2029....................................................           7,332             445               6          51,262           5,278              10
2030....................................................           7,396             516               7          51,792           5,924              11
2035....................................................           7,732             801              10          54,602           8,517              16
2040....................................................           8,075             968              12          58,082          10,209              18
2050....................................................           8,806           1,127              13          65,937          12,310              19
--------------------------------------------------------------------------------------------------------------------------------------------------------
Note:
\a\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic
  baseline, 1b, please see Section X.A.1.

  Table IX-5--Model Year Lifetime Fuel Consumption Reductions Due to the Preferred Alternative Using Method B and Relative to the Less Dynamic Baseline
                                                                [Millions of Gallons] \a\
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                             Gasoline                                         Diesel
                                                         -----------------------------------------------------------------------------------------------
                       Model year                                              Fuel                                            Fuel
                                                             Reference      consumption    %  Reduction      Reference      consumption    %  Reduction
                                                                             reduction                                       reduction
--------------------------------------------------------------------------------------------------------------------------------------------------------
2018....................................................               0               0               0          33,384             754               2
2019....................................................               0               0               0          33,922             745               2
2020....................................................               0               0               0          34,575             738               2
2021....................................................           7,128             113               2          47,792           4,424               9
2022....................................................           7,118             216               3          48,112           4,568               9
2023....................................................           7,106             317               4          48,366           4,703              10
2024....................................................           7,225             493               7          49,577           7,628              15
2025....................................................           7,376             602               8          51,050           7,967              16
2026....................................................           7,535             714               9          52,420           8,289              16
2027....................................................           7,628             982              13          53,532           9,984              19
2028....................................................           7,711             992              13          54,524          10,181              19
2029....................................................           7,769             999              13          55,421          10,360              19
                                                         -----------------------------------------------------------------------------------------------

Sum.....................................................          66,596           5,430               8         562,673          70,342              13
--------------------------------------------------------------------------------------------------------------------------------------------------------
Note:
\a\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic
  baseline, 1b, please see Section X.A.1.

(2) Fuel Savings
    We have also estimated the changes in fuel expenditures, or the
fuel savings, using fuel prices estimated in the Energy and Information
Administration's 2014 Annual Energy Outlook.\640\ As the AEO fuel price
projections go through 2040 and not beyond, fuel prices beyond 2040
were set equal to the 2040 values. These estimates do not account for
the significant uncertainty in future fuel prices; the monetized fuel
savings would be understated if actual fuel prices are higher (or
overstated if fuel prices are lower) than estimated. The Annual Energy
Outlook (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 changes fuel
purchasers would see. The pre-tax fuel savings measure the value to
society of the resources saved when less fuel is refined and consumed.
Assuming no change in fuel tax rates, the difference between these two
columns represents the reduction in fuel tax revenues that would be
received by state and federal governments, or about $240 million in
2021 and $5.2 billion by 2050 as shown in Table IX-6 where annual
changes in monetized fuel savings are shown along with net present
values using 3 percent and 7 percent discount rates. Table IX-7 Table
IX-8 show the discounted model year lifetime fuel savings using 3
percent and 7 percent discount rates, respectively.
---------------------------------------------------------------------------

    \640\ U.S. Energy Information Administration, Annual Energy
Outlook 2014, Early Release; Report Number DOE/EIA-0383ER (2014),
December 16, 2013.

Table IX-6--Annual Fuel Savings and Net Present Values at 3% and 7% Discount Rates Using Method B for the Preferred Alternative and Relative to the Less
                                                                    Dynamic Baseline
                                                                [$Millions of 2012$] \a\
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                       Fuel savings--retail                            Fuel savings--untaxed
              Calendar year              ------------------------------------------------------------------------------------------------    Change in
                                             Gasoline         Diesel            Sum          Gasoline         Diesel            Sum          transfer
--------------------------------------------------------------------------------------------------------------------------------------------------------
2018....................................              $0            $261            $261              $0            $227            $227             $34
2019....................................               0             540             540               0             472             472              68
2020....................................               0             834             834               0             731             731             103
2021....................................              31           1,958           1,989              27           1,723           1,750             239
2022....................................              92           3,413           3,505              80           3,015           3,095             410
2023....................................             183           4,936           5,119             160           4,372           4,532             587
2024....................................             324           7,426           7,750             285           6,594           6,879             871
2025....................................             496          10,035          10,531             436           8,937           9,372           1,158
2026....................................             695          12,683          13,378             613          11,321          11,934           1,445
2027....................................             976          15,883          16,859             861          14,215          15,076           1,782
2028....................................           1,243          18,938          20,181           1,099          16,980          18,079           2,102
2029....................................           1,511          21,974          23,485           1,338          19,745          21,083           2,402
2030....................................           1,770          24,905          26,675           1,571          22,422          23,993           2,682
2035....................................           2,921          38,047          40,968           2,621          34,621          37,242           3,726
2040....................................           3,778          48,300          52,078           3,427          44,357          47,783           4,295
2050....................................           4,397          58,241          62,638           3,988          53,486          57,474           5,164
NPV, 3%.................................          37,319         506,971         544,290          33,603         461,992         495,595          48,695
NPR, 7%.................................          15,211         212,373         227,584          13,663         192,984         206,646          20,937
--------------------------------------------------------------------------------------------------------------------------------------------------------
Note:
\a\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic
  baseline, 1b, please see Section X.A.1.

 Table IX-7--Discounted Model Year Lifetime Fuel Savings, 3% Discount Rate Using Method B for the Preferred Alternative and Relative to the Less Dynamic
                                                                        Baseline
                                                                [$Millions of 2012$] \a\
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                       Fuel savings--retail                            Fuel savings--untaxed
               Model year                ------------------------------------------------------------------------------------------------    Change in
                                             Gasoline         Diesel            Sum          Gasoline         Diesel            Sum          transfer
--------------------------------------------------------------------------------------------------------------------------------------------------------
2018....................................              $0          $2,183          $2,183              $0          $1,937          $1,937            $246
2019....................................               0           2,123           2,123               0           1,890           1,890             234
2020....................................               0           2,066           2,066               0           1,844           1,844             222
2021....................................             258          12,178          12,436             228          10,898          11,126           1,310
2022....................................             487          12,369          12,856             431          11,094          11,525           1,331
2023....................................             700          12,513          13,212             620          11,247          11,867           1,346
2024....................................           1,067          19,934          21,001             947          17,953          18,901           2,100
2025....................................           1,277          20,435          21,712           1,136          18,441          19,577           2,135
2026....................................           1,484          20,858          22,342           1,323          18,858          20,180           2,161
2027....................................           2,001          24,642          26,643           1,787          22,319          24,106           2,537
2028....................................           1,981          24,610          26,592           1,772          22,329          24,101           2,491
2029....................................           1,957          24,536          26,493           1,754          22,298          24,052           2,441
Sum.....................................          11,211         178,448         189,659           9,997         161,107         171,105          18,554
--------------------------------------------------------------------------------------------------------------------------------------------------------
Note:
\a\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic
  baseline, 1b, please see Section X.A.1.

 Table IX-8--Discounted Model Year Lifetime Fuel Savings, 7% Discount Rate Using Method B for the Preferred Alternative and Relative to the Less Dynamic
                                                                        Baseline
                                                                 [Millions of 2012] \a\
--------------------------------------------------------------------------------------------------------------------------------------------------------
                                                       Fuel savings--retail                            Fuel savings--untaxed
               Model year                ------------------------------------------------------------------------------------------------    Change in
                                             Gasoline         Diesel            Sum          Gasoline         Diesel            Sum          transfer
--------------------------------------------------------------------------------------------------------------------------------------------------------
2018....................................              $0          $1,529          $1,529              $0          $1,352          $1,352            $176
2019....................................               0           1,428           1,428               0           1,267           1,267             161
2020....................................               0           1,331           1,331               0           1,185           1,185             146
2021....................................             163           7,538           7,701             143           6,731           6,874             827
2022....................................             295           7,383           7,678             260           6,608           6,869             810
2023....................................             408           7,200           7,607             361           6,458           6,819             789
2024....................................             599          11,055          11,654             531           9,938          10,469           1,186
2025....................................             690          10,917          11,607             613           9,834          10,447           1,160
2026....................................             772          10,734          11,505             687           9,688          10,374           1,131
2027....................................           1,003          12,215          13,218             894          11,046          11,940           1,278
2028....................................             956          11,741          12,697             854          10,636          11,490           1,206
2029....................................             909          11,269          12,179             814          10,228          11,041           1,137
Sum.....................................           5,794          94,339         100,134           5,157          84,971          90,128          10,005
--------------------------------------------------------------------------------------------------------------------------------------------------------
Note:
\a\ For an explanation of analytical Methods A and B, please see Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic
  baseline, 1b, please see Section X.A.1.

D. Maintenance Expenditures

    The agencies expect minimal increases in maintenance costs under
the proposed standards, having estimated increased maintenance costs
associated only with installation of lower rolling resistance tires. We
expect that, when replaced, the lower rolling resistance tires would be
replaced by equivalent performing tires throughout the vehicle
lifetime. As such, the incremental increases in costs for lower rolling
resistance tires would be incurred throughout the vehicle lifetime at
intervals consistent with current tire replacement intervals. Those
intervals are difficult to quantify given the variety of vehicles and
operating modes within the HD industry. We detail the inputs used to
estimate maintenance impacts in Chapter 7.3.3 of the draft RIA. We
request comment on all aspects of the maintenance estimates.
Specifically, for electrified vehicles (mild/strong hybrids) which are
expected in alternatives 3 and 4 and in each vehicle category, we have
not estimated any increased maintenance costs. We have heard from at
least one source \641\ that strong hybrid maintenance can be higher in
some ways, including possible battery replacement, but may also be much
lower for some vehicle systems like brakes and general engine wear.
Given the uncertainty, we have not estimated maintenance costs
specifically for these electrified vehicles but request comment so that
we might be able to include potential costs in the final rule. We also
request comment on any other maintenance costs that should be
considered along with supporting data.
---------------------------------------------------------------------------

    \641\ Allison Transmission's Responses to EPA's Hybrid
Questions, November 6, 2014.
---------------------------------------------------------------------------

    Table IX-9 shows the annual increased maintenance costs of the
preferred alternative along with net present values using both 3
percent and 7 percent discount rates. Table IX-10 shows the discounted
model year lifetime increased maintenance costs of the preferred
alternative at both 3 percent and 7 percent discount rates along with
sums across applicable model years.

 Table IX-9--Annual Maintenance Expenditure Increase Due to the Proposal
  and Net Present Values at 3% and 7% Discount Rates Using Method B and
                  Relative to the Less Dynamic Baseline
                        [$Millions of 2012$] \a\
------------------------------------------------------------------------
                                                            Maintenance
                      Calendar year                         expenditure
                                                             increase
------------------------------------------------------------------------
2018....................................................              $6
2019....................................................              11
2020....................................................              16
2021....................................................              28
2022....................................................              39
2023....................................................              50
2024....................................................              64
2025....................................................              78
2026....................................................              90
2027....................................................             104
2028....................................................             116
2029....................................................             127
2030....................................................             127
2035....................................................             127
2040....................................................             127
2050....................................................             127
NPV, 3%.................................................           1,796
NPV, 7%.................................................             860
------------------------------------------------------------------------
Note:
\a\ For an explanation of analytical Methods A and B, please see Section
  I.D; for an explanation of the less dynamic baseline, 1a, and more
  dynamic baseline, 1b, please see Section X.A.1.

Table IX-10--Discounted MY Lifetime Maintenance Expenditure Increase due
to the Proposal using Method B and Relative to the Less Dynamic Baseline
                        [$Millions of 2012$] \a\
------------------------------------------------------------------------
                                            3% Discount     7% Discount
               Model year                      rate            rate
------------------------------------------------------------------------
2018....................................              51              36
2019....................................              49              33
2020....................................              47              31
2021....................................              90              57
2022....................................              89              54
2023....................................              89              52
2024....................................             112              63
2025....................................             113              61
2026....................................             102              53
2027....................................             116              58
2028....................................             111              54
2029....................................             101              47
                                         -------------------------------
    Sum.................................           1,071             600
------------------------------------------------------------------------
Note:
\a\ For an explanation of analytical Methods A and B, please see Section
  I.D; for an explanation of the less dynamic baseline, 1a, and more
  dynamic baseline, 1b, please see Section X.A.1.

E. Analysis of the Rebound Effect

    The ``rebound effect'' has been defined a number of ways in the
literature, and one common definition states that the rebound effect is
the increase in demand for an energy service when the cost of the
energy service is reduced due to efficiency improvements.\642\ \643\
\644\ In the context of heavy-duty vehicles (HDVs), this can be
interpreted as an increase in HDV fuel consumption resulting from more
intensive vehicle use in response to increased vehicle fuel
efficiency.\645\ Although much of this vehicle use increase is likely
to take the form of increases in the number of miles vehicles are
driven, it can also take the form of increases in the loaded weight at
which vehicles operate or changes in traffic and road conditions
vehicles encounter as operators alter their routes and schedules in
response to improved fuel efficiency. Because this more intensive use
consumes fuel and generates emissions, it reduces the fuel savings and
avoided emissions that would otherwise be expected to result from the
increases in fuel efficiency this rulemaking proposes.
---------------------------------------------------------------------------

    \642\ Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J.,
Froman, S., 2012. Estimating the direct rebound effect for on-road
freight transportation. Energy Policy 48, 252-259.
    \643\ Greene, D.L., Kahn, J.R., Gibson, R.C., 1999, ``Fuel
economy rebound effect for U.S. household vehicles'', The Energy
Journal, 20.
    \644\ For a discussion of the wide range of definitions found in
the literature, see Appendix D: Discrepancy in Rebound Effect
Definitions, in EERA (2014), ``Research to Inform Analysis of the
Heavy-Duty vehicle Rebound Effect'', Excerpts of Draft Final Report
of Phase 1 under EPA contract EP-C-13-025. (Docket ID: EPA-HQ-OAR-
2014-0827). See also Greening, L.A., Greene, D.L., Difiglio, C.,
2000, ``Energy efficiency and consumption--the rebound effect--a
survey'', Energy Policy, 28, 389-401.
    \645\ We discuss other potential rebound effects in section
IX.D.3, such as the indirect and economy-wide rebound effects. Note
also that there is more than one way to measure HDV energy services
and vehicle use. The agencies' analyses use VMT as a measure (as
discussed below); other potential measures include ton-miles, cube-
miles, and fuel consumption.
---------------------------------------------------------------------------

    Unlike the light-duty vehicle (LDV) rebound effect, the HDV rebound
effect has not been extensively studied. According to a 2010 HDV report
published by the National Research Council of the National Academies
(NRC),\646\ it is ``not possible to provide

a confident measure of the rebound effect,'' yet NRC concluded that a
HDV rebound effect probably exists and that, ``estimates of fuel
savings from regulatory standards will be somewhat misestimated if the
rebound effect is not considered.'' Although we believe the HDV rebound
effect needs to be studied in more detail, we have nevertheless
attempted to capture its potential effect in our analysis of these
proposed rules, rather than to await further study. We have elected to
do so because the magnitude of the rebound effect is an important
determinant of the actual fuel savings and emission reductions that are
likely to result from adopting stricter fuel efficiency and GHG
emission standards.
---------------------------------------------------------------------------

    \646\ Committee to Assess Fuel Economy Technologies for Medium-
and Heavy-Duty Vehicles; National Research Council; Transportation
Research Board (2010). ``Technologies and Approaches to Reducing the
Fuel Consumption of Medium- and Heavy-Duty Vehicles,'' Washington,
DC. The National Academies Press. Available electronically from the
National Academies Press Web site at http://www.nap.edu/catalog.php?record_id=12845 (last accessed September 10, 2010).
---------------------------------------------------------------------------

    In our analysis and discussion below, we focus on one widely-used
metric to estimate the rebound effect associated with all types of more
intensive vehicle use, the increase in vehicle miles traveled (VMT)
that results from improved fuel efficiency. VMT can often provide a
reasonable approximation for all types of more intensive vehicle use.
For simplicity, we refer to this as ``the VMT rebound effect'' or ``VMT
rebound'' throughout this section, although we acknowledge that it is
an approximation to the rebound effect associated with all types of
more intensive vehicle use. The agencies use our VMT rebound estimates
to generate VMT inputs that are then entered into the EPA MOVES
national emissions inventory model and the Volpe Center's HD CAFE
model. Both of these models use these inputs along with many others to
generate projected emissions and fuel consumption changes resulting
from each of the regulatory alternatives analyzed.
    Using VMT rebound to approximate the fuel consumption impact from
all types of more intensive vehicle use may not be completely accurate.
Many factors other than distance traveled--for example, a vehicle's
loaded weight--play a role in determining its fuel consumption, so it
is also important to consider how changes in these factors are
correlated with variation in vehicle miles traveled. Empirical
estimates of the effect of weight on HDV fuel consumption vary, but
universally show that loaded weight has some effect on fuel consumption
that is independent of distance traveled. Therefore, the product of
vehicle payload and miles traveled, which typically is expressed in
units of ``ton-miles'' or ``ton-kilometers'', has also been considered
as a metric to approximate the rebound effect. Because this metric's
value depends on both payload and distance, it is important to note
that changes in these two variables can have different impacts on HDV
fuel consumption. This is because the fuel consumed by HDV freight
transport is determined by several vehicle attributes including engine
and accessory efficiencies, aerodynamic characteristics, tire rolling
resistance and total vehicle mass--including payload carried, if any.
    Other factors such as vehicle route and traffic patterns can also
affect how each of these vehicle attributes contributes to the overall
fuel consumption of a vehicle. While it seems intuitive that if all of
these other conditions remain constant, a vehicle driving the same
route and distance twice will consume twice as much fuel as driving
that same route once. However, because of the other vehicle attributes,
it is less intuitive how a change in vehicle payload would affect
vehicle fuel consumption. We request comment on how the agencies should
consider the relationship between changes in vehicle miles traveled,
changes in vehicle ton-miles achieved, and overall fuel consumption
when considering how best to measure the rebound effect.
    Because the factors influencing HDV VMT rebound are generally
different from those affecting LDV VMT rebound, much of the research on
the LDV sector is likely to not apply to the HDV sector. For example,
the owners and operators of LDVs may respond to the costs and benefits
associated with changes in their personal vehicle's fuel efficiency
very differently than a HDV fleet owner or operator would view the
costs and benefits (e.g., profits, offering more competitive prices for
services) associated with changes in their HDVs' fuel efficiency. To
the extent the response differs, such differences may be smaller for HD
pickups and vans, which share some similarities with LDVs. As discussed
in the 2010 NRC HD report, one difference from the LDV case is that
when calculating the change in HDV costs that causes the rebound
effect, it is more important to consider all components of HDV
operating costs. The costs of labor and fuel generally constitute the
two largest shares of HDV operating costs, depending on the price of
petroleum, distance traveled, type of vehicle, and commodity
transported (if any).647 648 Equipment depreciation costs
associated with the purchase or lease of an HDV are another significant
component of total operating costs. Even when HDV purchases involve
upfront, one-time payments, HDV operators must recover the depreciation
in the value of their vehicles resulting from their use, so this is
likely to be considered as an operating cost they will attempt to pass
on to final consumers of HDV operator services.
---------------------------------------------------------------------------

    \647\ American Transportation Research Institute, An Analysis of
the Operational Costs of Trucking, September 2013.
    \648\ 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.
---------------------------------------------------------------------------

    Estimates of the impact of fuel efficiency standards on HDV VMT,
and hence fuel consumption, should account for changes in all of these
components of HDV operating costs. The higher the net savings in total
operating costs is, the higher the expected rebound effect would be.
Conversely, if higher HDV purchase costs outweigh future cost savings
and total operating costs increase, HDV costs could rise, which would
likely result in a decrease in HDV VMT. In theory, other cost changes
resulting from any requirement to achieve higher fuel efficiency, such
as changes in maintenance costs or insurance rates, should also be
taken into account, although information on these elements of HDV
operating costs is extremely limited. In this analysis, the agencies
adapt estimates of the VMT rebound effect to project the response of
HDV use to the estimated changes in total operating costs that result
from the proposed Phase 2 standards. We seek comment and data on how
our proposed standards could impact these and other types of HDV
operating costs, as well as on our procedure for adapting the VMT
rebound effect to estimate the response of HDV use to changes in total
operating costs.
    Since businesses are profit-driven, one would expect their
decisions to be based on the costs and benefits of different operating
decisions, both in the near-term and long-term. Specifically, one would
expect commercial HDV operators to take into account changes in overall
operating costs per mile when making decisions about HDV use and
setting rates they charge for their services. If demand for those
services is sensitive to the rates HDV operators charge, HDV VMT could
change in response to the effect of higher fuel efficiency on the rates
HDV operators charge. If demand for HDV services is insensitive to
price (e.g., due to lack of good substitutes), however, or if changes
in HDV operating costs due to the proposed standards are not

passed on to final consumers of HDV operator services, the proposed
standards may have a limited impact on HDV VMT.
    The following sections describe the factors affecting the magnitude
of HDV VMT rebound; review the econometric and other evidence related
to HDV VMT rebound; and summarize how we estimated the HDV rebound
effect for this proposal.
(1) Factors Affecting the Magnitude of HDV VMT Rebound
    The magnitude and timing of HDV VMT rebound result from the
interaction of many different factors.\649\ Fuel savings resulting from
fuel efficiency standards may cause HDV operators and their customers
to change their patterns of HDV use and fuel consumption in a variety
of ways. For example, HDV operators may pass on the fuel cost savings
to their customers by decreasing prices for shipping products or
providing services, which in turn could stimulate more demand for those
products and services (e.g., increases in freight output), and result
in higher VMT. As discussed later in this section, HDV VMT rebound
estimates determined via other proxy elasticities vary widely, but in
no case has there been an estimate that fully offsets the fuel saved
due to efficiency improvements (i.e., no rebound effect greater than or
equal to 100 percent).
---------------------------------------------------------------------------

    \649\ These factors are discussed more fully in a report to EPA
from EERA, which illustrates in a series of diagrams the complex
system of decisions and decision-makers that could influence the
magnitude and timing of the rebound effect. See Sections 2.2.2,
2.2.3, 2.2.4, and 2.3 in EERA (2014), ``Research to Inform Analysis
of the Heavy-Duty Vehicle Rebound Effect'', Excerpts of Draft Final
Report of Phase 1 under EPA contract EP-C-13-025.
---------------------------------------------------------------------------

    If fuel cost savings are passed on to the HDV operators' customers
(e.g., logistics businesses, manufacturers, retailers, municipalities,
utilities consumers), those customers might reorganize their logistics
and distribution networks over time to take advantage of lower
operating costs. For example, customers might order more frequent
shipments or choose products that entail longer shipping distances,
while freight carriers might divert some shipments to trucks from other
shipping modes such as rail, barge or air. In addition, customers might
choose to reduce their number of warehouses, reduce shipment rates or
make smaller but more frequent shipments, all of which could lead to an
increase in HDV VMT. Ultimately, fuel cost savings could ripple through
the entire economy, thus increasing demand for goods and services
shipped by trucks, and therefore increase HDV VMT due to increased
gross domestic product (GDP).
    Conversely, if fuel efficiency standards lead to net increases in
the total costs of HDV operation because fuel cost savings do not fully
offset the increase in HDV purchase prices and associated depreciation
costs, then the price of HDV services could rise. This is likely to
spur a decrease in HDV VMT, and perhaps a shift to alternative shipping
modes. These effects could also ripple through the economy and affect
GDP. Note, however, that we project fuel cost savings will offset
technology costs in our analysis supporting our proposed standards.
    It is also important to note that any increase in HDV VMT resulting
from our proposed standards may be offset, to some extent, by a
decrease in VMT by older HDVs. This may occur if lower fuel costs
resulting from our standards cause multi-vehicle fleet operators to
shift VMT to newer, more efficient HDVs in their fleet or cause
operators with newer, more efficient HDVs to be more successful at
winning contracts than operators with older HDVs.
    Also, as discussed in Chapter 8.3.3 of the Draft RIA, the magnitude
of the rebound effect is likely to be influenced by the extent of any
market failures that affect the demand for more fuel efficient HDVs, as
well as by HDV operators' responses to their perception of the tradeoff
between higher upfront HDV purchase costs versus lower but uncertain
future expenditures on fuel.
(2) Econometric and Other Evidence Related to HDV VMT Rebound
    As discussed above, HDV VMT rebound is defined as the change in HDV
VMT that occurs in response to an increase in HDV fuel efficiency. We
are not aware of any studies that directly estimate this elasticity
\650\ for the U.S. This section discusses econometric analyses of other
related elasticities that could potentially be used as a proxy for
measuring HDV VMT rebound, as well as other analyses that may provide
insight into the magnitude of HDV VMT rebound. We seek comment on the
applicability of the findings from these analyses, as well as
additional data and research on the topic of HDV VMT rebound.
---------------------------------------------------------------------------

    \650\ Elasticity is the measurement of how responsive an
economic variable is to a change in another. For example: price
elasticity of demand is a measure used in economics to show the
responsiveness, or elasticity, of the quantity demanded of a good or
service to a change in its price. More precisely, it gives the
percentage change in quantity demanded in response to a one percent
change in price.
---------------------------------------------------------------------------

    One of the challenges to developing robust econometric analyses of
HDV VMT rebound in the U.S. is data limitations. For example, the main
source of time-series HDV fuel efficiency data in the U.S. is derived
from aggregate fuel consumption and HDV VMT data. This may introduce
interdependence or ``simultaneity'' between measures of HDV VMT and HDV
fuel efficiency, because estimates of HDV fuel efficiency are derived
partly from HDV VMT. This mutual interdependence makes it difficult to
isolate the causal effect of HDV fuel efficiency on HDV VMT and to
measure the response of HDV VMT to changes in HDV fuel efficiency.
    Data on other important determinants of HDV VMT, such as freight
shipping rates, shipment sizes, HDV payloads, and congestion levels on
key HDV routes is also limited, of questionable reliability, or
unavailable. Additionally, data on HDVs and their use is usually only
available at an aggregate level, making it difficult to evaluate
potential differences in determinants of VMT for different types of HDV
operations (e.g., long-haul freight vs. regional delivery operations)
or vehicle sub-classes (e.g., utility vehicles vs. school buses).
    Another challenge inherent in using econometric techniques to
measure the response of HDV VMT to HDV fuel efficiency is developing
model specifications that incorporate the mathematical form and range
of explanatory variables necessary to produce reliable estimates of HDV
VMT rebound. Many different factors can influence HDV VMT, and the
complex relationships among those factors should be considered when
measuring the rebound effect.\651\
---------------------------------------------------------------------------

    \651\ A useful framework for understanding how various responses
interact to determine the rebound effect is presented in Section 2
and Appendix B of De Borger, B. and Mulalic, I. (2012), ``The
determinants of fuel use in the trucking industry--volume, fleet
characteristics and the rebound effect'', Transportation Policy,
Volume 24, pp. 284-295. See also Section 3.4 of EERA (2014),
``Research to Inform Analysis of the Heavy-Duty vehicle Rebound
Effect'', Excerpts of Draft Final Report of Phase 1 under EPA
contract EP-C-13-025.
---------------------------------------------------------------------------

    In practice, however, most studies have employed simplified models.
Many use price variables (e.g., price per gallon of fuel, or fuel cost
per mile driven) and some measure of aggregate economic activity, such
as GDP. However, some of these studies exclude potentially important
variables such as the amount of road capacity (which affects travel
speeds and may be related to other important characteristics of highway
infrastructure), or the price or availability of competing forms of
freight transport such as rail or barge (i.e., characteristics of the
overall freight transport network).

(a) Fuel Price and Fuel Cost Elasticities
    This sub-section reviews econometric analyses of the change in HDV
use (measured in VMT, ton-mile, or fuel consumption) in response to
changes in fuel price ($/gallon) or fuel cost ($/mile or $/ton-mile).
The studies presented below attempt to estimate these elasticities in
the HDV sector using varying approaches and data sources.
    Gately (1990) employed an econometric analysis of U.S. data for the
years 1966-1988 to examine the relationship between HDV VMT and average
fuel cost per mile, real Gross National Product (GNP), and variables
capturing the effects of fuel shortages in 1974 and 1979.\652\ The
study found no statistically significant relationship between HDV VMT
and fuel cost per mile. Gately's estimates of the elasticity of HDV VMT
with respect to fuel cost per mile were -0.035 with and -0.029 without
the fuel shortage variables, but both estimates had large standard
errors. However, Gately's study was beset by numerous statistical
problems, which raise serious questions about the reliability of its
results.\653\
---------------------------------------------------------------------------

    \652\ Gately, D., The U.S. Demand for Highway Travel and Motor
Fuels, The Energy Journal, Volume 11, No. 3, July 1990, pp.59-73.
    \653\ The most important of these problems--similar historical
time trends in the model's dependent variable and the measures used
to explain its historical variation--can lead to ``spurious
regressions,'' or the appearance of behavioral relationships that
are simply artifacts of the similarity (or correlation) in
historical trends among the model's variables.
---------------------------------------------------------------------------

    More recently, Matos and Silva (2011) analyzed road freight
transportation sector data for the years 1987-2006 in Portugal to
identify the determinants of demand for HDV freight
transportation.\654\ Using a reduced-form equation relating HDV use
(measured in ton-km) to economic activity (GDP) and the energy cost of
HDV use (measured in fuel cost per ton-km carried), these authors
estimated the elasticity of HDV ton-km with respect to energy costs to
be -0.241. An important strength of Matos and Silva's study is that it
also estimated this same elasticity using a procedure that accounted
for the effect of potential mutual causality between HDV ton-km and
energy costs, and arrived at an identical value.
---------------------------------------------------------------------------

    \654\ Matos, F.J.F., and Silva, F.J.F., ``The Rebound Effect on
Road Freight Transport: Empirical Evidence from Portugal,'' Energy
Policy, 39, 2011, pp. 2833-2841.
---------------------------------------------------------------------------

    Differences between HDV use and the level of highway service in
Portugal and in the U.S. might limit the applicability of Matos and
Silva's result to the U.S. The volume and mix of commodities could
differ between the two nations, as could the levels of congestion on
their respective highway networks, transport distances, the extent of
intermodal competition, and the characteristics of HDVs themselves.
HDVs also operate over a more limited highway network in Portugal than
in the United States. Unfortunately, it is difficult to anticipate how
these differences might cause Matos and Silva's elasticity estimates to
differ from what we might find in the U.S. Finally, their analysis
focused on HDV freight transport and did not consider non-freight uses
of HDVs, which somewhat limits its usefulness in the analysis of this
proposed rulemaking.
    De Borger and Mulalic (2012) examined the determinants of fuel use
in the Denmark HDV freight transport sector for the years 1980-2007.
The authors developed a system of equations that capture linkages among
the demand for HDV freight transport, HDV fleet characteristics, and
HDV fuel consumption.\655\ As De Borger and Mulalic state, ``we
precisely define and estimate a rebound effect of improvements in fuel
efficiency in the trucking industry: Behavioral adjustments in the
industry imply that an exogenous improvement in fuel efficiency reduces
fuel use less than proportionately. Our best estimate of this effect is
approximately 10 percent in the short run and 17 percent in the long
run, so that a 1 percent improvement in fuel efficiency reduces fuel
use by 0.90 percent (short-run) to 0.83 percent (long-run).''
---------------------------------------------------------------------------

    \655\ De Borger, B. and Mulalic, I., ``The determinates of fuel
use in the trucking industry--volume, fleet characteristics and the
rebound effect'', Transportation Policy, Volume 24, November 2012,
pp. 284-295.
---------------------------------------------------------------------------

    While De Borger and Mulalic capture a number of important responses
that contribute to the rebound effect, some caution is appropriate when
using their results to estimate the VMT rebound effect for this
proposal. Like the Matos and Silva study, this study examined HDV
activity in another country, Denmark, which has a less-developed
highway system, lower levels of freight railroad service than the U.S.,
and is also likely to have a different composition of freight shipping
activity. Although the effect of some of these differences is unclear,
greater competition from rail shipping in the U.S. and the resulting
potential for lower trucking costs to divert some rail freight to truck
could cause the VMT rebound effect to be larger in the U.S. than De
Borger and Mulalic's estimate for Denmark.
    On the other hand, if freight networks are denser and commodity
types are more homogenous in Denmark than the U.S., then shippers may
have wider freight trucking options. If this is the case, shippers in
Denmark might be more sensitive to changes in freight costs, which
could cause the rebound effect in Denmark to be larger than the U.S.
Like the Matos and Silva study, this analysis also focuses on freight
trucking and does not consider non-freight HDVs (e.g. vocational
vehicles). We have been unable to identify adequate data to employ De
Borger and Mulalic's model for the U.S. (mainly because time-series
data on freight carriage by trucks, driver wages, and vehicle prices in
the U.S. are limited).
    The Volpe National Transportation Systems Center previously has
developed a series of travel forecasting models for the Federal Highway
Administration (FHWA).\656\ Work conducted by the Volpe Center during
2009-2011 to develop the original version of FHWA's forecasting model
was presented in the Regulatory Impact Analysis for the HD GHG Phase 1
rule (see Table 9-2 in that document, which is reproduced below as
Table IX-11).\657\ In the analysis for the Phase 1 rule, Volpe
estimated both state-level and national aggregate models to forecast
HDV single unit and combination truck VMT that included fuel cost per
mile as an explanatory variable. This analysis used data from 1970-2008
for its national aggregate model, and data for the 50 individual states
from 1994-2008 for its state-level model.658 659
---------------------------------------------------------------------------

    \656\ FHWA Travel Analysis Framework Development of VMT
Forecasting Models for Use by the Federal Highway Administration May
12, 2014 http://www.fhwa.dot.gov/policyinformation/tables/vmt/vmt_model_dev.pdf. Volpe's work was advised by a panel of
approximately 20 experts in the measurement, analysis, and
forecasting of travel, including academic researchers,
transportation consultants, and members of local, state, and federal
government transportation agencies. It was also summarized in the
paper ``Developing a Multi-Level Vehicle Miles of Travel Forecasting
Model,'' November, 2011, which was presented to the Transportation
Research Board's 91st Annual Meeting in January, 2012.
    \657\ EPA/NHTSA, August 2011. Chapter 9.3.3, Final Rulemaking to
Establish Greenhouse gas Emission Standards & Fuel Efficiency
Standards for Medium-and Heavy-Duty Engines and Vehicles, Regulatory
Impact Analysis. EPA-420-R-11-901. (http://www.epa.gov/otaq/climate/documents/420r11901.pdf).
    \658\ Combination trucks are defined as ``all [Class 7/8] trucks
designed to be used in combination with one or more trailers with a
gross vehicle weight rating over 26,000 lbs.'' (AFDC, 2014; ORNL,
2013c). Single-unit trucks are defined as ``single frame trucks that
have 2-axles and at least 6 tires or a gross vehicle weight rating
exceeding 10,000 lbs.'' (FHWA, 2013).
    \659\ The national-level and functional class VMT forecasting
models utilize aggregate time-series data for the nation as a whole,
so that only a single measure of each variable is available during
each time period (i.e., year). In contrast, the state-level VMT
models have an additional data dimension, since both their dependent
variable (VMT) and most explanatory variables have 51 separate
observations available for each time period (one for each of the 50
states as well as Washington, DC). In this context, the states
represent a ``cross-section,'' and a continuous annual sequence of
these cross-sections is available.

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

    Volpe analysts tested a large number of different specifications
for its national and state level models that incorporated the effects
of factors such as aggregate economic activity and its composition, the
volume of U.S. exports and imports, and factors affecting the cost of
producing trucking services (e.g., driver wage rates, truck purchase
prices, and fuel costs), and the extent and capacity of the U.S. and
states' highway networks.
    Table IX-11 summarizes Volpe's Phase 1 estimates of the elasticity
of truck VMT with respect to fuel cost per mile.\660\ As it indicates,
these estimates vary widely, and the estimates based on state-level and
national data differ substantially.
---------------------------------------------------------------------------

    \660\ One drawback of the fuel cost measure employed in Volpe's
models is that it is based on estimates of fuel economy derived from
truck VMT and fuel consumption, which introduces the potential for
mutual causality (or ``simultaneity'') between VMT and the fuel cost
measure and makes the effect of the latter difficult to isolate.
This may cause their estimates of the sensitivity of truck VMT to
fuel costs to be inaccurate, although the direction of any resulting
bias is difficult to anticipate.

  Table IX-11--Summary of Volpe Center Estimates of Elasticity of Truck VMT With Respect to Fuel Cost per Mile
----------------------------------------------------------------------------------------------------------------
                                                          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%
----------------------------------------------------------------------------------------------------------------

    Volpe staff conducted additional analysis of the models that
yielded the estimates of the elasticity of truck VMT with respect to
fuel cost per mile reported in Table IX-11, using updated information
on fuel costs and other variables appearing in these models, together
with revised historical data on truck VMT provided by DOT's Federal
Highway Administration. The newly-available data, statistical
procedures employed in conducting this additional analysis, and its
results are summarized in materials that can be found in the docket for
this rulemaking. This new Volpe analysis was not available at the time
the agencies selected the values of the rebound effect for this
proposal, but the agencies will consider this work and any other work
in the analysis supporting the final rule.
    Finally, EPA has contracted with Energy and Environmental Research
Associates (EERA), LLC to analyze the HDV rebound effect for regulatory
assessment purposes. Excerpts of EERA's initial report to EPA are
included in the docket and contain detailed qualitative discussions of
the rebound effect as well as data sources that could be used in
quantitative analysis.\661\ EERA also conducted follow-on quantitative
analyses focused on estimating the impact of fuel prices on VMT and
fuel consumption. We have included a working paper in the docket on
this work, and we seek comment on this work.\662\ Note that EERA's
working paper was not available at the time the agencies conducted the
analysis of the rebound effect for this proposal, but the agencies will
consider this work and any other work in the analysis supporting the
final rule.
---------------------------------------------------------------------------

    \661\ EERA (2014), ``Research to Inform Analysis of the Heavy-
Duty vehicle Rebound Effect'', Excerpts of Draft Final Report of
Phase 1 under EPA contract EP-C-13-025.
    \662\ EERA (2015), ``Working Paper on Fuel Price Elasticities
for Heavy Duty Vehicles'', Draft Final Report of Phase 2 under EPA
contract EP-C-11-046.
---------------------------------------------------------------------------

    There are reasons to be cautious about interpreting the
elasticities from the studies reviewed in this section as a measure of
VMT rebound resulting from our proposed standards. For example, vehicle
capacity and loaded weight can vary dynamically in the HDV sector--
possibly in response to changes in fuel price and fuel efficiency--and
data on these measures are limited. This makes it difficult to
confidently infer a direct relationship between trucking output (e.g.,
ton-miles carried) and VMT assuming a constant average payload.
    In addition, fuel cost per mile--calculated by multiplying fuel
price per gallon by fuel efficiency in gallons per mile--and fuel price
may be imprecise proxies for an improvement in fuel efficiency, because
the response of VMT to these variables may differ. For example, if
truck operators are more attentive to variation in fuel prices than to
changes in fuel efficiency, then fuel price or fuel cost elasticities
may overstate the true magnitude of the rebound effect.
    Similarly, there is some evidence in the literature that demand for
crude petroleum and refined fuels is more responsive to increases than
to decreases in their prices, although this research is not specific to
the HDV sector.\663\ Since improved fuel efficiency typically causes
fuel costs for HDVs to fall (and assuming fuel costs are not fully
offset by increases in vehicle purchase prices), fuel price or cost
elasticities derived from historical periods when fuel prices were
increasing or fuel efficiency was declining may also overstate the
magnitude of the rebound effect. An additional unknown is that HDV
operators may factor fuel prices and fuel costs into their decision-
making about rates to charge for their service differently from the way
they incorporate initial vehicle purchase costs.
---------------------------------------------------------------------------

    \663\ Gately, D. 1993. The Imperfect Price-Reversibility of
World Oil Demand. The Energy Journal, International Association for
Energy Economics, vol. 14 (4), pp. 163-182; Dargay, J.M., Gately, D.
1997. The demand for transportation fuels: Imperfect price-
reversibility? Transportation Research Part B 31(1); and Sentenac-
Chemin, E., 2012. Is the price effect on fuel consumption symmetric?
Some evidence from an empirical study. Energy Policy, vol. 41, pp.
59-65.
---------------------------------------------------------------------------

    Despite these limitations, elasticities with respect to fuel price
and fuel cost can provide some insight into the magnitude of the HDV
VMT rebound effect. The agencies request comment on all of the studies
presented in this section.
(b) Freight Price Elasticities
    Freight price elasticities measure the percent change in demand for
freight in response to a percent change in freight prices, controlling
for other variables that may influence freight demand such as GDP, the
extent that goods are traded internationally, and road supply and
capacity. This type of elasticity is only applicable to the HDV
subcategory of freight trucks (i.e., combination tractors and
vocational vehicles that transport freight). One desirable attribute of
such measures for purposes of this analysis is that they show the
response of freight

trucking activity to changes to trucking rates, including changes that
result from fuel cost savings as well as increases in HDV technology
costs.\664\
---------------------------------------------------------------------------

    \664\ Note however that a percent change in freight activity in
response to a percent change in freight rates should theoretically
be larger than a percent change in freight activity in response to a
percent change in fuel efficiency because fuel efficiency only
impacts a portion of freight operating costs (e.g., fuel and vehicle
costs, but not likely driver wages or highway tolls).
---------------------------------------------------------------------------

    Freight price elasticities, however, are imperfect proxies for the
rebound effect in freight trucks for a number of reasons.\665\ For
example, in order to apply these elasticities we must assume that our
proposed rule's impact on fuel and vehicle costs is fully reflected in
freight rates. This may not be the case if truck operators adjust their
profit margins or other operational practices (e.g., loading practices,
truck driver's wages) instead of freight rates. It is not well
understood how trucking firms respond to different types of cost
changes (e.g., changes to fuel costs versus labor costs).
---------------------------------------------------------------------------

    \665\ Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J.,
Froman, S., 2012. Estimating the direct rebound effect for on-road
freight transportation. Energy Policy 48, 252-259.
---------------------------------------------------------------------------

    Freight price elasticity estimates in the literature typically
measure freight activity in tons or ton-miles, rather than VMT. As
discussed in the previous section, average truck capacity and payload
in the HDV sector varies dynamically--possibly in response to changes
in fuel price and fuel efficiency--and data on these measures are
limited. This makes it difficult to confidently infer a direct
relationship between ton-miles and VMT by assuming a constant average
payload. Inferring a direct relationship between tons and VMT is even
less straightforward. Additionally, there are significant limitations
on national freight rate and freight truck ton-mile data in the U.S.,
making it difficult to confidently measure the impact of a change in
freight rates on ton-miles.\666\
---------------------------------------------------------------------------

    \666\ See, for example, Appendix E in EERA (2014), ``Research to
Inform Analysis of the Heavy-Duty Vehicle Rebound Effect'', Draft
Final Report of Phase 1 under EPA contract EP-C-13-025.
---------------------------------------------------------------------------

    Finally, freight price elasticity estimates in the literature vary
significantly based on commodity type, length of haul, region,
availability of alternative modes (discussed further in Section
IX.E.b.iii below), and functional form of the model (i.e., log-linear,
linear, translog) making it difficult to confidently apply any single
estimate reported in the literature to nationwide freight activity. For
example, elasticity estimates for longer trips tend to be larger in
magnitude than those for shorter trips, while demand to ship bulk
commodities tends to be less elastic than for non-bulk commodities.
    Although these factors explain some of the differences among
reported estimates, much of the observed variation cannot be explained
quantitatively. For example, one study that controlled for mode,
commodity class, demand elasticity measure (i.e., tons or ton-miles),
model estimation form, country, and temporal nature of data only
accounted for about half of the observed variation.\667\
---------------------------------------------------------------------------

    \667\ 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.
---------------------------------------------------------------------------

(c) Mode Shift Case Study
    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 HDVs. This is because the United States has
extensive rail, waterway, pipeline, and air transport networks in
addition to an extensive highway network; these networks often closely
parallel each other and are often viable choices for freight transport
for many long-distance shipping routes within the continental U.S. If
rates for one mode decline, demand for that mode is likely to increase,
and some of this new demand could represent shifts from other
modes.\668\ The ``cross-price elasticity of demand,'' which measures
the percentage change in demand for shipping by another mode (e.g.,
rail) given a percentage change in the price of HDV freight transport
services, provides a measure of the importance of such mode shifting.
Aggregate estimates of cross-price elasticities vary widely,\669\ and
there is no general consensus on the most appropriate value to use for
analytical purposes.
---------------------------------------------------------------------------

    \668\ Rail lines in parts of the U.S. are thought to be
currently oversubscribed. If that is the case, and new freight
demand is already being satisfied by trucks, then this would limit
the potential for intermodal freight shifts between trucks and rail
as the result of this proposed rule.
    \669\ Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J.,
Froman, S., 2012. Estimating the direct rebound effect for on-road
freight transportation. Energy Policy 48, 252-259.
---------------------------------------------------------------------------

    When considering intermodal shift, one of the most relevant kinds
of shipments are those that are competitive between rail and HDV modes.
These trips generally include long-haul shipments greater than 500
miles, which weigh between 50,000 and 80,000 lbs (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 HDV and rail modes, so they would not
be expected to shift modes except under an extreme price change.
However, to the best of our knowledge, the total amount of freight that
could potentially be subject to mode shifting has not been studied
extensively.
    In order to explore the potential for HDV fuel efficiency standards
to produce economic conditions that favor a mode shift from rail to
HDVs, EPA commissioned GIFT Solutions, LLC to perform case studies on
the HD GHG Phase 1 rule using a number of data sources, including the
Commodity Flow Survey, interviews with trucking firms, and the
Geospatial Intermodal Freight Transportation (GIFT) model developed by
Winebrake and Corbett, which includes information on infrastructure and
other route characteristics in the U.S.670 671
---------------------------------------------------------------------------

    \670\ Winebrake, James and James J. Corbett (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.
    \671\ 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).
---------------------------------------------------------------------------

    A central assumption in the case studies was that economic
conditions would favor a shift from rail to HDVs if either the price
per ton-mile to ship a commodity by HDV, or the price to ship a given
quantity of a commodity by HDV, became lower relative to rail transport
options post-regulation. The results of the case studies indicate that
the HD Phase 1 rule would not seem to create obvious economic
conditions that lead to a mode shift from rail to truck, but there are
a number of limitations and caveats to this analysis, which are
discussed in the final report to EPA by GIFT.672 673 For
example, even if trucking did not become less expensive than rail post-
regulation, a relative decrease in the truck versus rail rates might be
enough to produce a shift, given that other factors could influence
shippers' decisions on modal choice. The study did not, however,
consider these other factors such as time-of-delivery and modal
capacity. As another example, the analysis assumes all fuel cost
savings and incremental vehicle

costs from the HD Phase 1 rule would be passed on to shippers via
changes in freight rates, even though the analysis found some evidence
that this might not occur (in two cases, the charges for shipping a
truckload over a given route and distance were the same despite
differences in payloads that should have been reflected in their fuel
costs). Given these limitations, more work is needed in this area to
explore the potential for mode shift in response to HD fuel efficiency
standards.
---------------------------------------------------------------------------

    \672\ See GIFT Solutions, LLC, ``Potential for Mode Shift due to
Heavy Duty Vehicle Fuel Efficiency Improvements''. February, 2012.
    \673\ Winebrake, James, J. Corbett, J. Silberman, E. Erin, & B.
Comer, 2012. Potential for Mode Shift due to Heavy Duty Vehicle Fuel
Efficiency Improvements: A Case Study Approach. GIFT Solutions, LLC.
---------------------------------------------------------------------------

(d) Case Study Using Freight Price Elasticities
    Cambridge Systematics, Inc. (CSI) employed a case study approach
using freight price elasticity estimates in the literature to show
several examples of the magnitude of the HDV rebound effect.\674\ In
their unpublished paper commissioned by the National Research Council
of the National Academies in support of its 2010 HDV report, CSI
estimated the effect on HDV VMT from a net decrease in operating costs
associated with fuel efficiency improvements, using two different
technology cost and fuel savings scenarios for Class 8 combination
tractors. Scenario 1 increased average fuel efficiency of the tractor
from 5.59 miles per gallon to 6.8 miles per gallon, with an additional
cost of $22,930 for purchasing the improved tractor. Scenario 2
increased the average fuel efficiency to 9.1 miles per gallon, at an
incremental cost of $71,630 per tractor. Both of these scenarios were
based on the technologies and targets from a report authored by the
Northeast States Center for a Clean Air Future (NESCCAF) and
International Council on Clean Transportation (ICCT).\675\
---------------------------------------------------------------------------

    \674\ Cambridge Systematics, Inc., Assessment of Fuel Economy
Technologies for Medium and Heavy Duty Vehicles: Commissioned Paper
on Indirect Costs and Alternative Approaches, 2009.
    \675\ Northeast States Center for a Clean Air Future, Southeast
Research Institute, TIAX, LLC., and International Council on Clean
Transportation, Reducing Heavy-Duty Long Haul Truck Fuel Consumption
and CO2 Emissions, September 2009. See http://www.nescaum.org/documents/heavy-duty-truck-ghg_report_final-200910.pdf.
---------------------------------------------------------------------------

    The CSI estimates were based on a range of direct (or ``own-
price'') freight elasticities (-0.5 to -1.5) \676\ and cross-price
freight elasticities (0.35 to 0.59) \677\ obtained from the
literature.\678\ In their calculations, CSI assumed 142,706 million
miles of tractor VMT and 1,852 billion ton-miles were affected. The
tractor VMT was based on the Bureau of Transportation Statistics' (BTS)
estimate of highway miles for combination tractors in 2006, and the
rail ton-miles were based on the BTS estimate of total railroad miles
during 2006. This assumption is likely to overstate the rebound effect,
since not all freight shipments occur on routes where tractors and rail
service shipments compete directly. Nevertheless, this assumption
appears to be reasonable in the absence of more detailed information on
the percentage of total miles and ton-miles that are subject to
potential mode shifting.
---------------------------------------------------------------------------

    \676\ Graham and Glaister, ``Road Traffic Demand Elasticity
Estimates: A Review,'' Transport Reviews Volume 24, 3, pp. 261-274,
2004.
    \677\ Based upon a study for the National Cooperative Highway
Research Program by Cambridge Systematics, Inc., Characteristics and
Changes in Freight Transportation Demand: A Guidebook for Planners
and Policy Analysts Phase II Report, National Cooperative Highway
Research Program Project 8-30, June 1995.
    \678\ The own (i.e., self) price elasticity provides a measure
for describing how the volume of truck shipping (demand) changes
with its price while the cross-price elasticity provides a measure
for describing how the volume of rail shipping changes with truck
price. In general, an elasticity describes the percent change in one
variable (e.g. demand for trucking) in response to a percent-change
in another (e.g. price of truck operations).
---------------------------------------------------------------------------

    For CSI's calculations, all costs except fuel costs and vehicle
costs were taken from a 2008 ATRI study.\679\ It is not clear from the
report how the new vehicle costs were incorporated into CSI's
calculations of per-mile tractor operating costs. For example, neither
the ATRI report nor the CSI report discusses assumptions about
depreciation, useful lifetimes of tractors, and the opportunity cost of
capital.
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    \679\ American Transportation Research Institute, ``An Analysis
of the Operational Costs of Trucking'', October 2008.
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    Based on these two scenarios, CSI estimated the change in tractor
VMT in response to a net decrease in operating costs (i.e., accounting
for fuel cost and changes in tractor purchase costs) associated with
fuel efficiency improvement of 11-31 percent for Scenario 1 and 5-16
percent for Scenario 2, without accounting for any fuel savings from
reduced rail service. When the fuel savings from reduced rail usage
were included in the calculations, they estimated the change in tractor
VMT in response to a net decrease in operating costs associated with
fuel efficiency improvement would be 9-30 percent for Scenario 1, and
3-15 percent for Scenario 2.
    Note that these estimates reflect changes to tractor VMT with
respect to total operating costs, so they should theoretically be
larger than a percent change in tractor VMT with respect to a percent
change in fuel efficiency because fuel efficiency only impacts a
portion of truck operating costs (e.g., fuel and vehicle costs, but not
likely driver wages or highway tolls).
    CSI included caveats associated with these calculations. For
example, their report states that freight price 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), geography, trip lengths, markets served, and
commodities transported.'' These factors can increase variability in
the results. Also, estimates in CSI's study have the limitation of
using freight price elasticities to estimate the HDV rebound effect
discussed previously in Section IV.D.2.b.
(e) Simulation Model Study Using Freight Price Elasticities
    Guerrero (2014) constructs a freight simulation model of the
California trucking sector to measure the impact of fuel saving
investments and fleet management on GHG emissions.\680\ Rather than
estimating these impacts using econometric analysis of raw data, the
study uses values from the existing literature. Guerrero determines
that ``. . . improving the performance of trucking also increases the
number of trips demanded because the market price also decreases. This
`rebound' effect offsets around 40-50 percent of these vehicle
efficiency emission reductions, with 9-14 percent of the effect coming
from increased pavement deterioration and 31-36 percent coming from
increased fuel combustion.'' Note that to the extent that trip lengths
also vary in response to improvements in HDV fuel efficiency, changes
in the number of HDV trips may not exactly reflect changes in the total
number of miles the vehicles are operated.
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    \680\ Guerrero, Sebastian. Modeling fuel saving investments and
fleet management in the trucking industry: The impact of shipment
performance on GHG emissions. Transportation Research Part E, May
2014.
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    However, these findings are based on freight price elasticities,
which--as we discuss in Section IV.D.2.b and in the context of the CSI
study above--have significant limitations. The study also simulates
only one state's freight network (California), which may not be a good
representation of national activity.
(3) How the Agencies Estimated the HDV Rebound Effect for This Proposal
(a) Values Used in the Phase 1 Analysis
    At the time the agencies conducted their analysis of the Phase 1
fuel efficiency and GHG emissions standards, the only evidence on the
HDV rebound effect were the previously

described studies from CSI and the Volpe Center.\681\ The agencies
determined that this evidence did not lend itself to a specific
quantitative value for use in the analysis. Rather, based on a
qualitative assessment of this evidence informed by the agencies' best
professional judgement, the agencies chose rebound effects of 15
percent for vocational vehicles and 5 percent for combination tractors,
both of which were toward the lower end of the range of values from
these studies. The agencies found no evidence on the rebound effect for
HD pickup trucks and vans, but concluded it would be inappropriate to
use the values selected for vocational vehicles or combination tractors
for those vehicles. Because the usage patterns of HD pickup trucks and
vans can more closely resemble those of large light-duty vehicles, the
agencies used our judgement to select the 10 percent rebound effect we
had employed in our most recent light-duty rulemaking to analyze the
Phase 1 standards for 2b/3 vehicles.
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    \681\ The Gately study was also available, however, the agencies
were not aware of the work at the time.
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(b) How the Agencies Analyzed VMT Rebound in This Proposal
    After considering the new evidence that has become available since
the HD Phase 1 final rule, the agencies elected to continue using the
rebound effect estimates we used previously in the HD Phase 1 rule in
our analysis of Phase 2 proposed standards. In arriving at this
decision, the agencies considered the shortcomings and limitations of
the newly-available studies described previously, particularly the
limited applicability of the two published studies using data from
European nations to the U.S. context. After weighing these attributes
of the more recent studies, the agencies concluded that we had
insufficient evidence to justify revising the rebound effect values
that were used in the Phase 1 analysis.
    In our assessment, we do not differentiate between short-run and
long-run rebound effects, although these effects may differ. The
vocational and combination truck estimates are based on the Volpe
Center analysis presented in the HD Phase 1 rule and the case study
from CSI. As with the HD Phase 1 rule, we did not find any literature
specifically examining the HD pickup and truck sector. Since these
vehicles are used for very different purposes than combination tractors
and vocational vehicles, and they are more similar in use to large
light-duty vehicles, we have chosen the light-duty rebound effect of 10
percent used in the final rule establishing fuel economy and GHG
standards for MYs 2017-2025 light-duty vehicles in our analysis of HD
pickup trucks and vans.
    While for this proposal, the agencies have selected to use these
rebound effect values of 5 percent for combination tractors, 10 percent
for heavy duty pickup trucks and vans and 15 percent for vocational
vehicles, we acknowledge the literature shows a wide range of rebound
effect estimates. Therefore, we will review and consider revising these
estimates in the final rule, taking into consideration all available
data and analysis, including submissions from public commenters and new
research on the rebound effect.
    It should be noted that the rebound estimates we have selected for
our analysis represent the VMT impact from our proposed standards with
respect to changes in the fuel cost per mile driven. As described
previously, the HDV rebound effect should ideally be a measure of the
change in fuel consumed with respect to the change in overall operating
costs due to a change in HDV fuel efficiency. Such a measure would
incorporate all impacts from our proposal, including those from
incremental increases in vehicle prices that reflect costs for
improving their fuel efficiency. Therefore, VMT rebound estimates with
respect to fuel costs per mile must be ``scaled'' to apply to total
operating costs, by dividing them by the fraction of total operating
costs accounted for by fuel.
    The agencies made simplifying assumptions in the VMT rebound
analysis for this proposal, similar to the approach taken during the
development of the HD GHG Phase 1 final rule. However, for the HD Phase
2 final rulemaking, we plan to use a more comprehensive approach. Due
to timing constraints during the development of this proposal, the
agencies did not have the technology package costs for each of the
alternatives prior to the need to conduct the inventory analysis,
except for the pickup truck and van category in analysis Method A.
Therefore, the same ``overall'' VMT rebound values were used for
Alternatives 2 through 5 (as discussed in Chapter 8.3.3 of the Draft