Some of the individual technology cost estimates are presented in brief in Section III, and account for both the direct and indirect costs incurred in the manufacturing and dealer industries (for a complete presentation of technology costs, please refer to Chapter 2 of the RIA).  To account for the indirect costs on Class 2b and 3 pickup trucks and vans, the agencies have applied an ICM factor to all of the direct costs to arrive at the estimated technology cost.   The ICM factor used was 1.24 in the short-term (2014 through 2021) to account for differences in the levels of R&D, tooling, and other indirect costs that will be incurred. Once the program has been fully implemented, some of the indirect costs will no longer be attributable to these standards and, as such, a lower ICM factor is applied to direct costs in 2022 and later.  The agencies have also applied ICM factors to Class 4 through 8 trucks and to heavy-duty diesel engine technologies.  Markup factors in these categories range from 1.15 to 1.30 in the short term (2014 through 2021) depending on the complexity of the given technology.  We have modified the manner in which ICMs are applied in that they are no longer applied as a simple multiplicative factor on top of the direct manufacturing costs.  Instead, we have broken out the warranty cost portion of the ICM and apply it in a multiplicative manner then add the non-warranty cost portion of the ICM to that.  The latter portion, that for non-warranty costs, is determined for a given year and held constant rather than decreasing year-over-year.  This new approach, which responds to criticisms from some that the multiplicative approach used in the past essentially double counts learning effects, is discussed in Section VIII.C and is detailed in Chapter 2 of the RIA.  Note that, for the HD diesel engines, the agencies have applied the ICMs to ensure that our estimates are conservative since we have estimated fixed costs separately for technologies applied to these categories -- effectively making the use of markups a double counting of indirect costs.  For the details on the background and the concept behind our use of ICMs to calculate indirect costs, please refer to the report that has been placed in the docket for this final action.
The agencies have also considered the impacts of manufacturer learning on the technology cost estimates by reflecting the phenomenon of volume-based learning curve cost reductions in our modeling using two algorithms depending on where in the learning cycle (i.e., on what portion of the learning curve) we consider a technology to be  -  "steep" portion of the curve for newer technologies and "flat" portion of the curve for mature technologies. The observed phenomenon in the economic literature which supports manufacturer learning cost reductions are based on reductions in costs as production volumes increase, and the economic literature suggests these cost reductions occur indefinitely, though the absolute magnitude of the cost reductions decrease as production volumes increase (with the highest absolute cost reduction occurring with the first doubling of production).   The agencies use the terminology "steep" and "flat" portion of the curve to distinguish among newer technologies and more mature technologies, respectively, and how learning cost reductions are applied in cost analyses.   The steep learning algorithm applies for the early, steep portion of the learning curve and is estimated to result in 20 percent lower costs after two full years of implementation (i.e., a 2016 MY cost would be 20 percent lower than the 2014 and 2015 model year costs for a new technology being implemented in 2014).  The flat learning algorithm applies for the flatter portion of the learning curve and is estimated to result in 3 percent lower costs in each of the five years following first introduction of a mature technology added in response to this final action.  Once two steep learning steps have occurred (for technologies having steep learning applied), flat learning would begin.  For technologies to which flat learning is applied, learning would begin in year 2 at 3 percent per year for 5 years.  Beyond 5 years of flat learning at 3 percent per year, 5 years of flat learning at 2 percent per year, then 5 at 1 percent per year become effective.
Learning impacts have been considered on most but not all of the technologies expected to be used because some of the expected technologies are already used rather widely in the industry and, presumably, learning impacts have already occurred.  The agencies have applied the steep learning algorithm for only a handful of technologies considered to be new or emerging technologies such as energy recovery systems and thermal storage units which might one day be used on big trucks.  For most technologies, the agencies have considered them to be more established and, hence, the agencies have applied the lower flat learning algorithm.  For more discussion of the learning approach and the technologies to which each type of learning has been applied the reader is directed to Chapter 2 of the RIA.
The technology cost estimates discussed in Section III and detailed in Chapter 2 of the RIA are used to build up technology package cost estimates.  For each engine and truck class, a single package for each was developed capable of complying with the final standards and the costs for each package was generated.  The technology packages and package costs are discussed in more detail in Chapter 2 of the RIA.  The compliance cost estimates take into account all credits and trading programs and include costs associated with air conditioning controls.  Table VIII-1 presents the average incremental costs per truck for this final action.  For HD pickup trucks and vans (Class 2b and 3), costs increase as the standards become more stringent in 2014 through 2018.  Following 2018, costs then decrease going forward as learning effects result in decreased costs for individual technologies.  By 2022, the long term ICMs take effect and costs decrease yet again.  For vocational vehicles, cost trends are more difficult to discern as diesel engines begin adding technology in 2014, gasoline engines begin adding technology in 2016, and the trucks themselves begin adding technology in 2014.  With learning effects the costs, in general, decrease each year except for the heavy-duty gasoline engine changes in 2016.  Long term ICMs take effect in 2022 to provide more cost reductions.  For combination tractors, costs generally decrease each year due to learning effects with the exception of 2017 when the engines placed in sleeper cab tractors add turbo compounding.  Following that, learning impacts result in cost reductions and the long term ICMs take effect in 2022 for further cost reductions.  By 2030 and later, cost - per-truck estimates remain constant for all classes.  Regarding the long term ICMs taking effect in 2022, the agencies consider this the point at which some indirect costs decrease or are no longer considered attributable to the program (e.g., warranty costs go down). Costs per truck remain essentially constant thereafter.
Table VIII-1:  Estimated Cost per Truck (2009 dollars)

HD Pickups & Vans
Vocational
Combination
2014
$232
$414
$6,019
2015
$302
$408
$5,871
2016
$591
$457
$5,677
2017
$885
$450
$6,413
2018
$1,473
$419
$6,215
2020
$1,392
$404
$6,004
2030
$1,384
$336
$5,075
2040
$1,384
$329
$5,075
2050
$1,384
$329
$5,075
These costs would, presumably, have some impact on new truck prices, although the agencies make no attempt at determining what the impact of increased costs would be on new truck prices.  Nonetheless, on a percentage basis, the costs shown in Table VIII-1 for 2018 MY trucks (when all final requirements are fully implemented) would be roughly four percent for a typical HD pickup truck or van, less than one percent for a typical vocational vehicle, and roughly six percent for a typical combination truck/tractor using new truck prices of $40,000, $100,000 and $100,000, respectively.  The costs would represent lower or higher percentages of new truck prices for new trucks with higher or lower prices, respectively.  Given the wide range of new truck prices in these categories -- a Class 4 Vocational work truck might be $40,000 when new while a Class 8 refuse truck (i.e., a large vocational vehicle) might be as much as $200,000 when new -- it is very difficult to reflect incremental costs as percentages of new truck prices for all trucks.  What is presented here is the average cost (Table VIII-1) compared with typical new truck prices.
 As noted above, the fixed costs were estimated separately from the hardware costs for HD diesel engines that are placed in vocational vehicles and combination tractors.  Those fixed costs are not included in Table VIII-1.  The agencies have estimated the R&D costs at $6.8 million per manufacturer per year for five years and the new test cell costs (to accommodate measurement of N2O emissions) at $63,087 per manufacturer.  The test cell costs of N2O emissions measurement has been adjusted for the final rulemaking to reflect comments which stated approximately 75 percent of manufacturers would be required to update existing equipment while the other 25 percent would require new equipment.  These costs apply individually for LHD, MHD and HHD engines.  Given the 14 manufacturers impacted by the final standards, 11 of which are estimated to sell both MHD and HHD engines and 3 of which are estimated to sell LHD engines, we have estimated a five year annual R&D cost of $170.3 million dollars (2 x 11 x $6.8 million plus 3 x $7.75 million for each year 2012-2016) and a one-time test cell cost of $1.6 million dollars (2 x 11 x $63,087 plus 3 x $63,087 in 2013).  Estimating annual sales of HD diesel engines at roughly 600,000 units results in roughly $284 per engine per year for five years beginning in 2012 and ending in 2016.  Again, these costs are not reflected in Table VIII-1, but are included in Table VIII-2 as "Other Engineering Costs."
The certification and compliance program costs, for all engine and truck types, are estimated at $6.5 million in the first year dropping to $2.3 million in each year thereafter and continuing indefinitely.  These costs are detailed in the "Draft Supporting Statement for Information Collection Request" which is contained in the docket for this final action.  The costs are higher in the first year due to capital expenses required to comply with new reporting burdens (facility upgrade costs are included in engineering costs as described above).  Estimating annual sales of heavy-duty trucks at roughly 1.5 million units would result in just over $4 per engine/truck in the first year and less than $2 per engine/truck per year thereafter.  These costs are not reflected in Table VIII-1, but are included in Table VIII-2 below as "Compliance Program" costs.
Annual Costs of the HD National Program
The costs presented here represent the incremental costs for newly added technology to comply with the program.  Together with the projected increases in truck sales, the increases in per-truck average costs shown in Table VIII-1, above result in the total annual costs presented in Table VIII-2 below.  Note that the costs presented in Table VIII-2 do not include the savings that will occur as a result of the improvements to fuel consumption.  Those impacts are presented in Section VIII. E.   Note also that the costs presented here represent costs estimated to occur presuming that the final standards will continue in perpetuity.  Any changes to the final standards would be considered as part of a future rulemaking.  In other words, the final standards do not apply only to 2014-2018 model year trucks - they do, in fact, apply to all 2014 and later model year trucks.  We present more detail regarding the 2014-2018 model year trucks in Sections VIII.L and M, where we summarize all monetized costs and benefits.
Table VIII-2: Annual Costs Associated with the Program ($Millions of 2009 dollars)
Year
HD Pickup and Vans
Vocational Vehicles
Combination Tractors
Other
Engineering
Costs
Compliance Program Costs
Annual
Costs
2012
                                                                             $0
                                                                             $0
                                                                             $0
                                                                           $170
                                                                             $0
                                                                           $170
2013
                                                                             $0
                                                                             $0
                                                                             $0
                                                                           $172
                                                                             $0
                                                                           $172
2014
                                                                           $182
                                                                           $233
                                                                         $1,078
                                                                           $170
                                                                           $6.5
                                                                         $1,670
2015
                                                                           $221
                                                                           $216
                                                                           $922
                                                                           $170
                                                                           $2.3
                                                                         $1,532
2016
                                                                           $421
                                                                           $233
                                                                           $820
                                                                           $170
                                                                           $2.3
                                                                         $1,647
2017
                                                                           $627
                                                                           $230
                                                                           $951
                                                                             $0
                                                                           $2.3
                                                                         $1,810
2018
                                                                         $1,055
                                                                           $222
                                                                         $1,000
                                                                             $0
                                                                           $2.3
                                                                         $2,280
2020
                                                                         $1,066
                                                                           $223
                                                                         $1,001
                                                                             $0
                                                                           $2.3
                                                                         $2,292
2030
                                                                         $1,300
                                                                           $234
                                                                         $1,076
                                                                             $0
                                                                           $2.3
                                                                         $2,612
2040
                                                                         $1,450
                                                                           $304
                                                                         $1,372
                                                                             $0
                                                                           $2.3
                                                                         $3,128
2050
                                                                         $1,638
                                                                           $383
                                                                         $1,777
                                                                             $0
                                                                           $2.3
                                                                         $3,800
NPV, 3%
                                                                        $24,155
                                                                         $5,461
                                                                        $24,487
                                                                           $793
                                                                            $52
                                                                        $54,948
NPV, 7%
                                                                        $11,974
                                                                         $2,889
                                                                        $12,855
                                                                           $724
                                                                            $30
                                                                        $28,473
Indirect Cost Multipliers
Markup Factors to Estimate Indirect Costs
For all segments in this analysis, indirect costs are estimated by applying indirect cost multipliers (ICM) to direct cost estimates.  ICMs were calculated by EPA as a basis for estimating the impact on indirect costs of individual vehicle technology changes that would result from regulatory actions.  Separate ICMs were derived for low, medium, and high complexity technologies, thus enabling estimates of indirect costs that reflect the variation in research, overhead, and other indirect costs that can occur among different technologies.  ICMs were also applied in the MY 2012-2016 rulemaking.
Prior to developing the ICM methodology, EPA and NHTSA both applied a retail price equivalent (RPE) factor to estimate indirect costs.  RPEs are estimated by dividing the total revenue of a manufacturer by the direct manufacturing costs.  As such, it includes all forms of indirect costs for a manufacturer and assumes that the ratio applies equally for all technologies.  ICMs are based on RPE estimates that are then modified to reflect only those elements of indirect costs that would be expected to change in response to a regulatory-induced technology change.  For example, warranty costs would be reflected in both RPE and ICM estimates, while marketing costs might only be reflected in an RPE estimate but not an ICM estimate for a particular technology, if the new regulatory-induced technology change is not one expected to be marketed to consumers.  Because ICMs calculated by EPA are for individual technologies, many of which are small in scale, they often reflect a subset of RPE costs; as a result, the RPE is typically higher than an ICM.  This is not always the case, as ICM estimates for complex technologies may reflect higher than average indirect costs, with the resulting ICM larger than the averaged RPE for the industry.

There is some level of uncertainty surrounding both the ICM and RPE markup factors.  The ICM estimates used in this final action group all technologies into three broad categories and treat them as if individual technologies within each of the three categories (low, medium, and high complexity) will have the same ratio of indirect costs to direct costs.  This simplification means it is likely that the direct cost for some technologies within a category will be higher and some lower than the estimate for the category in general.  More importantly, the ICM estimates have not been validated through a direct accounting of actual indirect costs for individual technologies.  Rather, the ICM estimates were developed using adjustment factors developed in two separate occasions:  the first, a consensus process, was reported in the RTI report; the second, a modified Delphi method, was conducted separately and reported in an EPA memo.  Both these panels were composed of EPA staff members with previous background in the automobile industry; the memberships of the two panels overlapped but were not the same.  The panels evaluated each element of the industry's RPE estimates and estimated the degree to which those elements would be expected to change in proportion to changes in direct manufacturing costs.  The method and estimates in the RTI report were peer reviewed by three industry experts and subsequently by reviewers for the International Journal of Production Economics.   RPEs themselves are inherently difficult to estimate because the accounting statements of manufacturers do not neatly categorize all cost elements as either direct or indirect costs.  Hence, each researcher developing an RPE estimate must apply a certain amount of judgment to the allocation of the costs.  Moreover, RPEs for heavy- and medium-duty trucks and for engine manufacturers are not as well studied as they are for the light-duty automobile industry.  Since empirical estimates of ICMs are ultimately derived from the same data used to measure RPEs, this affects both measures.  However, the value of RPE has not been measured for specific technologies, or for groups of specific technologies.  Thus applying a single average RPE to any given technology by definition overstates costs for very simple technologies, or understates them for advanced technologies.
In the proposal, we requested comment on our ICM factors and whether it was most appropriate to use ICMs or RPEs.  We received no comment on the issue specifically, other than basic comments that perhaps our ICM factors were low.  In response, for this final action, we have adjusted our ICM factors such that they are slightly higher and, importantly, we have changed the way in which the factors are applied.  The first change -- increased ICM factors -- has been done as a result of further thought among the EPA and NHTSA team that the ICM factors presented in the original RTI report for low and medium complexity technologies should no longer be used and that we should rely solely on the modified-Delphi values for these complexity levels.  For that reason, we have eliminated the averaging of original RTI values with modified-Delphi values and instead are relying solely on the modified-Delphi values for low and medium complexity technologies.  The second change -- the way the factors are applied -- results in the warranty portion of the indirect costs being applied as a multiplicative factor (thereby decreasing going forward as direct manufacturing costs decrease due to learning), and the remainder of the indirect costs being applied as an additive factor (thereby remaining constant year-over-year and not being reduced due to learning).  This second change has a comparatively large impact on the resultant technology costs and, we believe, more appropriately estimates costs over time.  These changes to our ICMs and the methodology are described in greater detail in Chapter 2 of the final RIA.
Cost per Ton of Emissions Reductions
The agencies have calculated the cost per ton of GHG reductions associated with this program on a CO2eq basis using the above costs and the emissions reductions described in Sections VI and VII.  These values are presented in Table VIII-3 through Table VIII-5 for HD pickups & vans, vocational vehicles and combination trucks/tractors, respectively.  The cost per metric ton of GHG emissions reductions has been calculated in the years 2020, 2030, 2040, and 2050 using the annual vehicle compliance costs and emission reductions for each of those years.  The value in 2050 represents the long-term cost per ton of the emissions reduced.  The agencies have also calculated the cost per metric ton of GHG emission reductions including the savings associated with reduced fuel consumption (presented below in Section VIII. E. ).  This latter calculation does not include the other benefits associated with this program such as those associated with energy security benefits as discussed later in Section VIII.I.  By including the fuel savings in the cost estimates, the cost per ton is generally less than $0 since the estimated value of fuel savings outweighs the program costs. The results for CO2eq costs per ton under the HD National Program across all regulated categories are shown in Table VIII-6.
Table VIII-3: Annual Cost per Metric Ton of CO2eq Reduced  -  HD Pickup Trucks & Vans (2009 dollars)
Year
Program Cost
Fuel Savings (post-tax)
CO2eq Reduced
Cost per Ton (without Fuel Savings)
Cost per Ton (with Fuel Savings)
2020
$1,100
$1,000
3
$330
$30
2030
$1,300
$3,300
10
$120
-$190
2040
$1,500
$4,700
14
$110
-$230
2050
$1,600
$5,900
16
$100
-$270

Table VIII-4: Annual Cost per Metric Ton of CO2eq Reduced  -  Vocational Vehicles (2009 dollars)
Year
Program Cost
Fuel Savings (post-tax)
CO2eq Reduced
Cost per Ton (without Fuel Savings)
Cost per Ton (with Fuel Savings)
2020
$200
$1,300
5
$40
-$240
2030
$200
$2,900
9
$20
-$290
2040
$300
$4,200
13
$20
-$310
2050
$400
$5,500
15
$30
-$340

Table VIII-5: Annual Cost per Metric Ton of CO2eq Reduced  -  Combination Tractors (2009 dollars)
Year
Program Cost
Fuel Savings (post-tax)
CO2eq Reduced
Cost per Ton (without Fuel Savings)
Cost per Ton (with Fuel Savings)
2020
$1,000
$8,700
32
$30
-$240
2030
$1,100
$17,000
57
$20
-$280
2040
$1,400
$22,100
68
$20
-$300
2050
$1,800
$28,500
78
$20
-$340

Table VIII-6: Annual Cost per Metric Ton of CO2eq Reduced  -  Final (2009 dollars)
Year
Program Cost
Fuel Savings (post-tax)
CO2eq Reduced
Cost per Ton (without Fuel Savings)
Cost per Ton (with Fuel Savings)
2020
$2,300
$11,000
40
$60
-$220
2030
$2,600
$23,200
76
$30
-$270
2040
$3,100
$31,000
94
$30
-$300
2050
$3,800
$39,900
109
$30
-$330

Impacts of Reduction in Fuel Consumption
What are the Projected Changes in Fuel Consumption
The new CO2 standards will result in significant improvements in the fuel efficiency of affected trucks.  Drivers of those trucks will see corresponding savings associated with reduced fuel expenditures.  The agencies have estimated the impacts on fuel consumption for the tailpipe CO2 standards.  To do this, fuel consumption is calculated using both current CO2 emission levels and the new CO2 standards. The difference between these estimates represents the net savings from the CO2 standards.   Note that the total number of miles that vehicles are driven each year is different under the control case scenario than in the reference case due to the "rebound effect," which is discussed in Section VIII. E. (5) .  EPA also notes that drivers who drive more than our average estimates for vehicle miles traveled (VMT) will experience more fuel savings; drivers who drive less than our average VMT estimates will experience less fuel savings.
The expected impacts on fuel consumption are shown in Table VIII-7.  The gallons shown in the tables reflect impacts from the new fuel consumption and CO2 standards and include increased consumption resulting from the rebound effect.
Table VIII-7: Fuel Consumption Reductions of the Program (Million gallons)
                                                                           Year
                                                                       Gasoline
                                                                         Diesel
                                                                           2014
                                                                              1
                                                                            479
                                                                           2015
                                                                              3
                                                                            856
                                                                           2016
                                                                             15
                                                                          1,186
                                                                           2017
                                                                             32
                                                                          1,662
                                                                           2018
                                                                             60
                                                                          2,146
                                                                           2020
                                                                            117
                                                                          3,017
                                                                           2030
                                                                            355
                                                                          5,729
                                                                           2040
                                                                            461
                                                                          7,123
                                                                           2050
                                                                            531
                                                                          8,248

Potential Impacts on Global Fuel Use and Emissions
EPA's quantified reductions in fuel consumption focus on the gains from reducing fuel used by heavy-duty vehicles within the United States. However, as discussed in Section VIII.I, EPA also recognizes that this regulation will lower the world price of oil (the "monopsony" effect).  Lowering oil prices could lead to an uptick in oil consumption globally, leading to a corresponding increase in GHG emissions in other countries.  This global increase in emissions could slightly offset some of the emission reductions achieved domestically as a result of the regulation.
What are the Monetized Fuel Savings?
Using the fuel consumption estimates presented in Table VIII-7, the agencies can calculate the monetized fuel savings associated with the final standards.  To do this, reduced fuel consumption is multiplied in each year by the corresponding estimated average fuel price in that year, using the reference case taken from the AEO 2011.   These estimates do not account for the significant uncertainty in future fuel prices; the monetized fuel savings will be understated if actual fuel prices are higher (or overstated if fuel prices are lower) than estimated.  AEO is a standard reference used by NHTSA and EPA and many other government agencies to estimate the projected price of fuel. This has been done using both the pre-tax and post-tax fuel prices.  Since the post-tax fuel prices are the prices paid at fuel pumps, the fuel savings calculated using these prices represent the savings consumers would see.  The pre-tax fuel savings are those savings that society would see.  These results are shown in Table VIII-8.  Note that in Sections VIII.L and M, the overall benefits and costs of the rules are presented and, for that reason, only the pre-tax fuel savings are presented there.
Table VIII-8: Estimated Monetized Fuel Savings ($Millions of 2009 dollars)
Year
Fuel Savings (pre-tax)
Fuel Savings (post-tax)
2014
$1,200
$1,400
2015
$2,300
$2,600
2016
$3,300
$3,800
2017
$4,800
$5,600
2018
$6,500
$7,500
2020
$9,700
$11,000
2030
$20,900
$23,200
2040
$28,300
$31,000
2050
$36,900
$39,900
NPV, 3%
                                                                       $379,500
                                                                       $419,900
NPV, 7%
                                                                       $168,400
                                                                       $187,400

As shown in Table VIII-8, the agencies are projecting that truck consumers would realize very large fuel savings as a result of the final standards.  As discussed further in the introductory paragraphs of Section VIII, it is a conundrum from an economic perspective that these large fuel savings have not been provided by manufacturers and purchased by consumers of these products.  Unlike in the light-duty vehicle market, the vast majority of vehicles in the medium- and heavy-duty truck market are purchased and operated by businesses; for them, fuel costs may represent substantial operating expenses.  Even in the presence of uncertainty and imperfect information  -  conditions that hold to some degree in every market  -  we generally expect firms to be cost-minimizing to survive in a competitive marketplace and to make decisions that are therefore in the best interest of the company and its owners and/or shareholders.
A number of behavioral and market phenomena may lead to a disconnect between how businesses account for fuel savings in their decisions and the way in which we account for the full stream of fuel savings for these rules, including imperfect information in the original and resale markets, split incentives, uncertainty in future fuel prices, and adjustment or transactions costs (see Section VIII.A for a more detailed discussion).  As discussed below in the context of rebound in Section VIII.E.5, the nature of the explanation for this gap may influence the actual magnitude of the fuel savings.
Payback Period and Lifetime Savings on New Truck Purchases
Another factor of interest is the payback period on the purchase of a new truck that complies with the new standards.  In other words, how long would it take for the expected fuel savings to outweigh the increased cost of a new vehicle?  For example, a new 2018 MY HD pickup truck and van is estimated to cost $1,473 more, a vocational vehicle $419 more, and a combination tractor $6,215 more (all values are on average, and relative to the reference case vehicle) due to the addition of new GHG reducing technology.  This new technology will result in lower fuel consumption and, therefore, savings in fuel expenditures.  But how many months or years would pass before the fuel savings exceed the upfront costs?  Table VIII-9 shows the payback period analysis for HD pickup trucks and vans.  The table shows fuel consumed under the reference case and fuel consumed by a 2018 model year truck under the program, inclusive of fuel consumed due to rebound miles.  The decrease in fuel consumed under the program is then monetized by multiplying by the fuel price reported by AEO (reference case) for 2018 and later.  This value represents the fuel savings expected under the program for a HD pickup or van.  These savings are then discounted each year since future savings are considered to be of less value than current savings.  Shown next are estimated increased costs (costs do not necessarily reflect increased prices which may be higher or lower than costs) for the new truck (refer to Table VIII-1).  The next columns of Table VIII-9 show the period required for the fuel savings to exceed the new truck costs.  As seen in the table, in the third year of ownership, the discounted fuel savings (at both 3% and 7% discount rates) have begun to outweigh the increased cost of the truck.  As shown in the table, the full life savings using 3% discounting would be $5,714 and at 7% discounting would be $4,035.
Costs in this section, and summarized in Section VIII.L, are shown from the greenhouse gas perspective where fuel savings are treated as negative costs, since the primary motivations of this program are U.S. energy security and reductions in GHG emissions.  From that perspective, the benefits of the program are the external effects, and the net effects on truck owners and operators are the costs.  EPA prefers to account for all costs (positive and negative) directly realized by the end user to accurately present the total cost and to differentiate those costs and cost savings from more generally realized societal benefits.  In Section VIII.M, the agencies also present summary tables that show the cost and benefit analysis from the fuel efficiency perspective, where the purpose of a program to regulate fuel efficiency is primarily to save fuel.  From this perspective, fuel savings are counted as benefits that occur over the lifetime of the vehicle as it consumes less fuel, rather than as negative costs that would be experienced either at the time of purchase or over the lifetime of the vehicle.  OMB's Circular A-4, which provides guidance to Federal agencies on the development of regulatory analysis, makes clear that either approach is acceptable.
Table VIII-9:  Payback Period for a 2018 Model Year HD Pickup or Van (2009$)
                               Year of Ownership
                         Reduced Fuel Use (gallons)[b]
                                Fuel Savings[a]
                                Increased Cost
                              Cumulative Savings

                                   Gasoline
                                    Diesel
                                 3% Discount
                                  7% Discount

                                 3% Discount
                                  7% Discount
                                       1
                                                                             67
                                                                            122
                                                                           $627
                                                                           $616
                                                                        -$1,473
                                                                          -$845
                                                                          -$857
                                       2
                                                                             67
                                                                            122
                                                                           $617
                                                                           $583

                                                                          -$228
                                                                          -$274
                                       3
                                                                             66
                                                                            120
                                                                           $600
                                                                           $546

                                                                           $372
                                                                           $272
                                       4
                                                                             64
                                                                            117
                                                                           $570
                                                                           $499

                                                                           $942
                                                                           $771
                                       5
                                                                             62
                                                                            113
                                                                           $544
                                                                           $458

                                                                         $1,486
                                                                         $1,229
                                       6
                                                                             59
                                                                            108
                                                                           $507
                                                                           $411

                                                                         $1,992
                                                                         $1,641
                                       7
                                                                             56
                                                                            102
                                                                           $474
                                                                           $370

                                                                         $2,466
                                                                         $2,010
                                   Full Life
                                                                            894
                                                                          1,617
                                                                         $7,187
                                                                         $5,507
                                                                        -$1,473
                                                                         $5,714
                                                                         $4,035
Notes:
[a] Fuel savings calculated using the AEO 2011 reference case fuel prices through 2035.  Fuel prices beyond 2035 were extrapolated from an average growth rate for the years 2017 to 2035. Gasoline and diesel fuel prices have been weighted by gasoline and diesel fuel reductions estimated for all 2018 MY heavy-duty trucks during their lifetimes.
[b] Gallons under the control case include gallons consumed during rebound driving.
The story is somewhat different for vocational vehicles and combination tractors.  These cases are shown in Table VIII-10 and Table VIII-11, respectively.  Since these trucks travel more miles in a given year, their payback periods are shorter and are expected to occur within the second year of ownership under both the 3% and 7% discounting cases.  As can be seen in Table VIII-10 and Table VIII-11, the lifetime fuel savings are estimated to be considerable with savings of $6,084 (3%) and $4,716 (7%) for the vocational vehicles and $72,875 (3%) and $58,162 (7%) for the combination tractors.
Table VIII-10: Payback Period for a 2018 Model Year Vocational Vehicle (2009$)
                               Year of Ownership
                         Reduced Fuel Use (gallons)[b]
                                Fuel Savings[a]
                                Increased Cost
                              Cumulative Savings

                                   Gasoline
                                    Diesel
                                 3% Discount
                                  7% Discount

                                 3% Discount
                                  7% Discount
                                       1
                                                                             59
                                                                            173
                                                                           $769
                                                                           $754
                                                                          -$419
                                                                           $350
                                                                           $336
                                       2
                                                                             55
                                                                            158
                                                                           $698
                                                                           $660

                                                                         $1,048
                                                                           $995
                                       3
                                                                             51
                                                                            145
                                                                           $633
                                                                           $576

                                                                         $1,681
                                                                         $1,571
                                       4
                                                                             48
                                                                            132
                                                                           $568
                                                                           $498

                                                                         $2,250
                                                                         $2,069
                                       5
                                                                             44
                                                                            119
                                                                           $511
                                                                           $430

                                                                         $2,760
                                                                         $2,499
                                       6
                                                                             40
                                                                            107
                                                                           $447
                                                                           $363

                                                                         $3,208
                                                                         $2,862
                                       7
                                                                             37
                                                                             95
                                                                           $398
                                                                           $311

                                                                         $3,605
                                                                         $3,173
                                   Full Life
                                                                            642
                                                                          1,587
                                                                         $6,503
                                                                         $5,135
                                                                          -$419
                                                                         $6,084
                                                                         $4,716
Notes:
[a] Fuel savings calculated using the AEO 2011 reference case fuel prices through 2035.  Fuel prices beyond 2035 were extrapolated from an average growth rate for the years 2017 to 2035. Gasoline and diesel fuel prices have been weighted by gasoline and diesel fuel reductions estimated for all 2018 MY heavy-duty trucks during their lifetimes.
[b] Gallons under the control case include gallons consumed during rebound driving.
Table VIII-11: Payback Period for a 2018 Model Year Combination Tractor (2009$)
                               Year of Ownership
                         Reduced Fuel Use (gallons)[b]
                                Fuel Savings[a]
                                Increased Cost
                              Cumulative Savings

                                   Gasoline
                                    Diesel
                                 3% Discount
                                  7% Discount

                                 3% Discount
                                  7% Discount
                                       1
                                                                              0
                                                                          3,223
                                                                        $10,736
                                                                        $10,539
                                                                        -$6,215
                                                                         $4,522
                                                                         $4,324
                                       2
                                                                              0
                                                                          2,897
                                                                         $9,619
                                                                         $9,089

                                                                        $14,141
                                                                        $13,413
                                       3
                                                                              0
                                                                          2,619
                                                                         $8,564
                                                                         $7,790

                                                                        $22,705
                                                                        $21,203
                                       4
                                                                              0
                                                                          2,359
                                                                         $7,532
                                                                         $6,595

                                                                        $30,237
                                                                        $27,797
                                       5
                                                                              0
                                                                          2,096
                                                                         $6,626
                                                                         $5,585

                                                                        $36,863
                                                                        $33,382
                                       6
                                                                              0
                                                                          1,842
                                                                         $5,684
                                                                         $4,611

                                                                        $42,546
                                                                        $37,993
                                       7
                                                                              0
                                                                          1,617
                                                                         $4,951
                                                                         $3,867

                                                                        $47,497
                                                                        $41,860
                                   Full Life
                                                                              0
                                                                         26,148
                                                                        $79,089
                                                                        $64,376
                                                                        -$6,215
                                                                        $72,875
                                                                        $58,162
Notes:
[a] Fuel savings calculated using the AEO 2011 reference case fuel prices through 2035.  Fuel prices beyond 2035 were extrapolated from an average growth rate for the years 2017 to 2035. Gasoline and diesel fuel prices have been weighted by gasoline and diesel fuel reductions estimated for all 2018 MY heavy-duty trucks during their lifetimes.
[b] Gallons under the control case include gallons consumed during rebound driving.
All of these payback analyses include fuel consumed during rebound VMT in the control case but not in the reference case, consistent with other parts of the analysis.  Further, this analysis does not include other societal impacts such as reduced time spent refueling or noise, congestion and accidents since the focus is meant to be on those factors buyers think about most while considering a new truck purchase.  Note also that operators that drive more miles per year than the average would realize greater fuel savings than estimated here, and those that drive fewer miles per year would realize lesser savings.  The same holds true for operators that keep their vehicles longer (i.e., more years) than average in that they would realize greater lifetime fuel savings than operators that keep their vehicles for fewer years than average.  Likewise, should fuel prices be higher than the AEO 2011 reference case, operators will realize greater fuel savings than estimated here while they would realize lesser fuel savings were fuel prices to be lower than the AEO 2011 reference case.
Rebound Effect
The VMT rebound effect refers to the fraction of fuel savings expected to result from an increase in fuel efficiency that is offset by additional vehicle use.  If truck shipping costs decrease as a result of lower fuel costs, an increase in truck VMT may occur.  Unlike the light-duty rebound effect, the heavy-duty (HD) rebound effect has not been extensively studied.  Because the factors influencing the HD rebound effect are generally different from those affecting the light-duty rebound effect, much of the research on the light-duty rebound effect is not likely to apply to the HD sectors.  One of the major differences between the HD rebound effect and the light-duty rebound effect is that HD vehicles are used primarily for business purposes.  Since these businesses are profit driven, decision makers are highly likely to be aware of the costs and benefits of different shipping decisions, both in the near term and long term.  Therefore, shippers are much more likely to take into account changes in the overall operating costs per mile when making shipping decisions that affect VMT.
Another difference from the light-duty case is that, as discussed in the recent NAS Report, when calculating the percentage change in trucking costs to determine the rebound effect, all changes in the operating costs should be considered.  The cost of labor and fuel generally constitute the top two shares of truck operating costs, depending on the price of petroleum,, distance traveled, type of truck, and commodity.  Finally, the equipment costs associated with the purchase or lease of the truck is also a significant component of total operating costs.  Even though vehicle costs are lump-sum purchases, they can be considered operating costs for trucking firms, and these costs are, in many cases, expected to be passed onto the final consumers of shipping services on a variable basis.  This shipping cost increase could help temper the rebound effect relative to the case of light-duty vehicles, in which vehicle costs are not considered an operating cost by vehicle owners.
When calculating the net change in operating costs, both the increase in new vehicle costs and the decrease in fuel costs per mile should be taken into consideration.  The higher the net cost savings, the higher the expected rebound effect.  Conversely, if the upfront vehicle costs outweighed future cost savings and total costs increased, shipping costs would rise, which would likely result in a decrease in truck VMT.  In theory, other changes such as maintenance costs and insurance rates would also be taken into account, although information on these potential cost changes is extremely limited.  In the proposal, we invited comments on the most appropriate methodology for factoring new vehicle purchase or leasing costs into the per-mile operating costs.  We also invited comment or data on how these regulations could affect maintenance, insurance, or other operating costs.  We did not receive any comments on these assumptions.
The following sections describe the factors affecting the rebound effect, different methodologies for estimating the rebound effect, and examples of different estimates of the rebound effect to date.  According to the NAS study, it is "not possible to provide a confident measure of the rebound effect," yet NAS concluded that a rebound effect likely exists and that "estimates of fuel savings from regulatory standards will be somewhat misestimated if the rebound effect is not considered."  While we believe the HD rebound effect needs to be studied in more detail, we have attempted to capture the potential impact of the rebound effect in our analysis.  In the proposal, we solicited data on the rebound effect and input on the most appropriate estimates to use for the rebound effect.  However, we did not receive any new data or substantive comments.  Therefore, for this final action, we continue to use a rebound effect for vocational vehicles of 15%, a rebound effect for HD pickup trucks and vans of 10%, and a rebound effect for combination tractors of 5%.  These VMT impacts are reflected in the estimates of total GHG and other air pollution reductions presented in Chapter 5 of the RIA.
Factors Affecting the Magnitude of the Rebound Effect
The HD vehicle rebound effect is driven by the interaction of several different factors.  In the short-run, decreasing the fuel cost per mile of driving could lead to a decrease in end product prices.  Lower prices could stimulate additional demand for those products, which would then result in an increase in VMT.  In the long run, shippers could reorganize their logistics and distribution networks to take advantage of lower truck shipping costs.  For example, shippers may shift away from other modes of shipping such as rail, barge, or air.  In addition, shippers may also choose to reduce the number of warehouses, reduce load rates, and make smaller, more frequent shipments, all of which could also lead to an increase in HD VMT.  Finally, the benefits of the fuel savings could ripple through the economy, which could in turn increase overall demand for goods and services shipped by trucks, and therefore increase HD VMT.
Conversely, if a fuel efficiency regulation leads to net increases in the cost of trucking because fuel savings do not fully offset the increase in upfront vehicle costs, then the price of trucking services could rise, spurring a decrease in HD VMT and a shift to alternative shipping modes. These effects would also ripple through the economy.
Options for Quantifying the Rebound Effect
As described in the previous section, the fuel efficiency rebound effect for HD vehicles has not been studied as extensively as the rebound effect for light-duty vehicles, and virtually no research has been conducted on the HD pickup truck and van rebound effect.  In the proposal, we discussed four options for quantifying the rebound effect and requested comments.  We did not receive any substantive comments on the described methodologies.
Aggregate Estimates
The aggregate approximation approach quantifies the overall change in truck VMT as a result of a percentage change in freight rates.  It is important to note that most of the aggregate estimates measure the change in freight demanded (tons or ton-miles), rather than a change in fuel consumption or VMT.  The change in tons or ton-miles is more accurately characterized as a freight elasticity.  Therefore, it may not be entirely appropriate to interpret these freight elasticities as measures of the rebound effect, although these terms are sometimes used interchangeably in the literature.  Given these caveats, freight elasticity estimates rely on estimates of aggregate price elasticity of demand for trucking services, given a percentage change in trucking prices, which is generally referred to as an "own-price elasticity."  Estimates of trucking own-price elasticities vary widely from positive 1.72 to negative 7.92), and there is no general consensus on the most appropriate values to use, though a 2004 literature survey found aggregate elasticity estimates generally fall in the range of -0.5 to -1.5.  In other words, given an own-price elasticity of -1.5, a 10% decrease in trucking prices leads to a 15% increase in truck shipping demand.
Another challenge of estimating the rebound effect using freight elasticities is that these values appear to vary substantially based on the demand elasticity measure (e.g., ton or ton-mile), the model specification (e.g., linear functional form or log linear), the length of the trip, and the type of cargo.  In general, elasticity estimates of longer trips tend to be larger than elasticity estimates for shorter trips.  In addition, elasticities tend to be larger for lower-value commodities compared to higher-value commodities.  Although these factors explain some of the differences in estimates, much of the observed variation cannot be explained quantitatively.  For example, a recent study that controlled for these variables only accounted for about half of the observed variation.
Another important variable influencing freight elasticity estimates is whether potential mode shifting is taken into account.  Although the total demand for freight transport is generally determined by economic activity, there is often the choice of shipping freight on modes other than truck.  This is because the United States has extensive rail, waterway and air transport networks in addition to an extensive highway network; these networks closely parallel each other and are often both viable choices for freight transport for long-distance routes within the continent.  If rates go down for one mode, there will be an increase in demand for that mode and some demand will be shifted from other modes.  This "cross-price elasticity" is a measure of the percentage change in demand for shipping by another mode (e.g., rail) given a percentage change in the price of trucking.  Aggregate estimates of cross-price elasticities also vary widely, and there is no general consensus on the most appropriate value to use for analytical purposes.  The NAS report cites values ranging from 0.35 to 0.59.  Other reports provide significantly different cross-price elasticities, ranging from 0.1 to 2.0.
When considering intermodal shift, the most relevant kinds of shipments are those that are competitive between rail and truck modes. These trips generally include long-haul shipments greater than 500 miles, which weigh between 50,000 and 80,000 pounds (the legal road limit in many states).  Special kinds of cargo like coal and short-haul deliveries are of less interest because they are generally not economically transferable between truck and rail modes, and they would not be expected to shift modes except under an extreme price change.  However, the total amount of freight that could potentially be subject to mode shifting has also not been studied extensively.
Sector-Specific Estimates
Given the limited data available regarding the HD rebound effect, the aggregate approach greatly simplifies many of the assumptions associated with calculations of the rebound effect.  In reality, however, responses to changes in fuel efficiency and new vehicle costs will vary significantly based on the commodities affected.  A detailed, sector-specific approach would be expected to more accurately reflect changes in the trucking market in response to the standards in this rule.  For example, input-output tables could be used to determine the trucking cost share of the total delivered price of a commodity.  Using the change in trucking prices described in the aggregate approach, the product-specific demand elasticities could be used to calculate the change in sales and shipments for each product.  The change in shipment increases could then be weighted by the share of the trucking industry total, and then summed to get the total increase in trucking output.  A simplifying assumption could then be made that the increase in output results in an increase in VMT.  To the best of our knowledge, this type of data has not yet been collected.  We did not receive any new information in response to our request for comments in the proposal, therefore we were unable to use this methodology for estimating the rebound effect for this final action.
Econometric Estimates
Similar to the methodology used to estimate the light-duty rebound effect, the HD rebound effect could be modeled econometrically by estimating truck demand as a function of economic activity (e.g., GDP) and different input prices (e.g., vehicle prices, driver wages, and fuel costs per mile).  This type of econometric model could be estimated for either truck VMT or ton-miles as a measure of demand.  The resulting elasticity estimates could then be used to determine the change in trucking demand, given the change in fuel cost and truck prices per mile from these standards.  One of the challenges associated with an econometric analysis is the potential for omitted variable bias, which could either overstate or understate the potential rebound effect if the omitted variable is correlated with the controlled variables.
Other Modeling Approaches
Regulation of the heavy-duty industry has been studied in more detail in Europe, as the European Commission (EC) has considered allowing longer and heavier trucks for freight transport.  Part of the analysis considered by the EC relies on country-specific modeling of changes in the freight sector that would result from changes in regulations.  This approach attempts to explicitly calculate modal shift decisions and impacts on GHG emissions.  Although similar types of analysis have not been conducted extensively in the United States, research is currently underway that explores the potential for intermodal shifting in the United States.  For example, Winebrake and Corbett have developed the Geospatial Intermodal Freight Transportation model, which evaluates the potential for GHG emissions reductions based on mode shifting, given existing limitations of infrastructure and other route characteristics in the United States.    This model connects multiple road, rail, and waterway transportation networks and embeds activity-based calculations in the model.  Within this intermodal network, the model assigns various economic, time-of-delivery, energy, and environmental attributes to real-world goods movement routes.  The model can then calculate different network optimization scenarios, based on changes in prices and policies.  However, more work is needed in this area to determine whether this type of methodology is appropriate for the purposes of capturing the rebound effect.  Therefore, we have not been able to use this methodology for estimating the rebound effect for this final action.
Estimates of the Rebound Effect
The aggregate methodology was used by Cambridge Systematics, Inc. (CSI) to show several examples of the magnitude of the rebound effect.   In their paper commissioned by the NAS in support of the recent HD report, CSI calculated an effective rebound effect for two different technology cost and fuel savings scenarios associated with an example Class 8 truck.  Scenario 1 increased average fuel economy from 5.59 mpg to 6.8 mpg, with an additional cost of $22,930.  Scenario 2 increased the average fuel economy to 9.1 mpg, at an incremental cost of $71,630 per vehicle.  The CSI examples provided estimates using a range of own-price elasticities (-0.5 to -1.5) and cross-price elasticities (0.35 to 0.59) from the literature.   Based on these two scenarios and a number of simplifying assumptions to aid the calculations, CSI found a rebound effect of 11-31% for Scenario 1 and 5-16% for Scenario 2 when the fuel savings from reduced rail usage were not taken into account ("First rebound effect").  When the fuel savings from reduced rail usage were included in the calculations, the overall rebound effect was between 9-13% for Scenario 1 and 3-15% for Scenario 2 ("Second Rebound Effect").  See Table VIII-12.
CSI included a number of caveats associated with these calculations.  Namely, the elasticity estimates derived from the literature are "heavily reliant on factors including the type of demand measures analyzed (vehicle-miles of travel, ton-miles, or tons), analysis geography, trip lengths, markets served, and commodities transported."  Furthermore, the CSI example only focused on Class 8 combination tractors and did not attempt to quantify the potential rebound effect for any other truck classes.  Finally, these scenarios were characterized as "sketches" and were not included in the final NAS report.  In fact, the NAS report asserted that it is "not possible to provide a confident measure of the rebound effect," yet concluded that a rebound effect likely exists and that "estimates of fuel savings from regulatory standards will be somewhat misestimated if the rebound effect is not considered."
Table VIII-12: Range of Rebound Effect Estimates from Cambridge Systematics Aggregate Assessment

Scenario 1
(6.8 mpg, $22,930)
Scenario 2
(9.1 mpg, $71,630)
"First Rebound Effect" (increase in truck VMT resulting from decrease in operating costs)
11-31%
5-16%
"Second Rebound Effect" (net fuel savings when decreases from rail are taken into account)
9-13 %
3-15%
As an alternative, using the econometric approach, NHTSA has estimated the rebound effect in the short run and long run for single unit (Class 4-7) and (Class 8) combination tractors.  As shown in Table VIII-13:, the estimates for the long-run rebound effect are larger than the estimates in the short run, which is consistent with the theory that shippers have more flexibility to change their behavior (e.g., restructure contracts or logistics) when they are given more time.  In addition, the estimates derived from the national data also showed larger rebound effects compared to the state data.  One possible explanation for the difference in the estimates is that the national rebound estimates are capturing some of the impacts of changes in economic activity.  Historically, large increases in fuel prices are highly correlated with economic downturns, and there may not be enough variation in the national data to differentiate the impact of fuel price changes from changes in economic activity.  In contrast, some states may see an increase in output when energy prices increase (e.g., large oil producing states such as Texas and Alaska); therefore, the state data may be more accurately isolating the individual impact of fuel price changes.
Table VIII-13: Range of Rebound Effect Estimates from NHTSA Econometric Analysis
Truck Type
National Data
State Data

Short Run
Long Run
Short Run
Long Run
Single Unit
13-22%
28-45%
3-8%
12-21%
Combination
N/A
12-14%
N/A
4-5%
As discussed throughout this section, there are multiple methodologies for quantifying the rebound effect, and these different methodologies produce a large range of potential values of the rebound effect.  However, for the purposes of quantifying the rebound effect for this program, we have used a rebound effect with respect to changes in fuel costs per mile on the lower range of the long-run estimates.  Given the fact that the long-run state estimates are generally more consistent with the aggregate estimates, for this program we have chosen a rebound effect for vocational vehicles (single unit trucks) of 15% that is within the range of estimates from both methodologies.  Similarly, we have chosen a rebound effect for combination tractors of 5%.
To date, no estimates of the HD pickup truck and van rebound effect have been cited in the literature.  Since these vehicles are used for very different purposes than heavy-duty vehicles, it does not necessarily seem appropriate to apply one of the heavy-duty estimates to the HD pickup trucks and vans.  These vehicles are more similar in use to large light-duty vehicles, so for the purposes of our analysis, we have chosen to apply the light-duty rebound effect of 10% to this class of vehicles.
For the purposes of this program, we have not taken into account any potential fuel savings or GHG emission reductions from the rail sector due to mode shifting.  We requested comments on this assumption in the proposal, but we did not receive any new data or input.
Furthermore, we have made a number of simplifying assumptions in our calculations, which are discussed in more detail in the RIA.  Specifically, we have not attempted to capture how current market failures might impact the rebound effect.  The direction and magnitude of the rebound effect in the HD market are expected to vary depending on the existence and types of market failures affecting the fuel efficiency of the trucking fleet.  If firms are already accurately accounting for the costs and benefits of these technologies and fuel savings, then these regulations would increase their net costs, because trucks would already include all the cost-effective technologies.  As a result, the rebound effect would actually be negative and truck VMT would decrease as a result of these final regulations.  However, if firms are not optimizing their behavior today due to factors such as lack of reliable information (see Section VIII.A. for further discussion), it is more likely that truck VMT would increase.  If firms recognize their lower net costs as a result of these regulations and pass those costs along to their customers, then the rebound effect would increase truck VMT.  This response assumes that trucking rates include both truck purchase costs and fuel costs, and that the truck purchase costs included in the rates spread those costs over the full expected lifetime of the trucks.  If those costs are spread over a shorter period, as the expected short payback period implies, then those purchase costs will inhibit reduction of freight rates, and the rebound effect will be smaller.
As discussed in more detail in Section VIII.A, if there are market failures such as split incentives, estimating the rebound effect may depend on the nature of the failures.  For example, if the original purchaser cannot fully recoup the higher upfront costs through fuel savings before selling the vehicle nor pass those costs onto the resale buyer, the firm would be expected to raise shipping rates.  A firm purchasing the truck second-hand might lower shipping rates if the firm recognizes the cost savings after operating the vehicle, leading to an increase in VMT.  Similarly, if there are split incentives and the vehicle buyer isn't the same entity that purchases the fuel, than there would theoretically be a positive rebound effect.  In this scenario, fuel savings would lower the net costs to the fuel purchaser, which would result in a larger increase in truck VMT.
If all of these scenarios occur in the marketplace, the net effect will depend on the extent and magnitude of their relative effects, which are also likely to vary across truck classes (for instance, split incentives may be a much larger problem for Class 7 and 8 tractors than they are for HD pickup trucks).  Additional details on the rebound effect are included in the RIA.
Class Shifting and Fleet Turnover Impacts
The agencies considered two additional potential indirect costs, benefits, effects, and externalities which may lead to unintended consequences of the program to improve the fuel efficiency and reduce GHG emissions from HD trucks.  The next sections cover the agencies' qualitative discussions on potential class shifting and fleet turnover effects.
Class Shifting
Heavy-duty vehicles are typically configured and purchased to perform a function.  For example, a concrete mixer truck is purchased to transport concrete, a combination tractor is purchased to move freight with the use of a trailer, and a Class 3 pickup truck could be purchased by a landscape company to pull a trailer carrying lawnmowers.  The purchaser makes decisions based on many attributes of the vehicle, including the gross vehicle weight rating of the vehicle which in part determines the amount of freight or equipment that can be carried. If the final HD National Program impacts either the performance of the vehicle or the marginal cost of the vehicle relative to the other vehicle classes, then consumers may choose to purchase a different vehicle, resulting in the unintended consequence of increased fuel consumption and GHG emissions in-use.
The agencies, along with the NAS panel, found that there is little or no literature which evaluates class shifting between trucks.  NHTSA and EPA qualitatively evaluated the final rules in light of potential class shifting.  The agencies looked at four potential cases of shifting: - from light-duty pickup trucks to heavy-duty pickup trucks; from sleeper cabs to day cabs; from combination tractors to vocational vehicles; and within vocational vehicles.
Light-duty pickup trucks, those with a GVWR of less than 8,500 pounds, are currently regulated under the existing CAFE program and will meet GHG emissions standards beginning in 2012.  The increased stringency of the 2012-2016 light-duty GHG and CAFE rule has led some to speculate that vehicle consumers may choose to purchase heavy-duty pickup trucks that are currently unregulated if the cost of the light-duty regulation is high relative to the cost to buy the larger heavy-duty pickup trucks.  Since fuel consumption and GHG emissions rise significantly with vehicle mass, a shift from light-duty trucks to heavy-duty trucks would likely lead to higher fuel consumption and GHG emissions, an untended consequence of the regulations.  Given the significant price premium of a heavy-duty truck (often five to ten thousand dollars more than a light-duty pickup), we believe that such a class shift would be unlikely even absent this program.  With these final regulations, any incentive for such a class shift is significantly diminished. The final regulations for the HD pickup trucks, and similarly for vans, are based on similar technologies and therefore reflect a similar expected increase in cost when compared to the light-duty GHG regulation.  Hence, the combination of the two regulations provides little incentive for a shift from light-duty trucks to HD trucks.  To the extent that our final regulation of heavy-duty pickups and vans could conceivably encourage a class shift towards lighter pickups, this unintended consequence would in fact be expected to lead to lower fuel consumption and GHG emissions as the smaller light-duty pickups are significantly more efficient than heavy-duty pickup trucks.
The projected cost increases for this final action differ significantly between Class 8 day cabs and Class 8 sleeper cabs, reflecting our expectation that compliance with the final standards will lead truck consumers to specify sleeper cabs equipped with APUs while day cab consumers will not.  Since Class 8 day cab and sleeper cab trucks perform essentially the same function when hauling a trailer, this raises the possibility that the higher cost for an APU equipped sleeper cab could lead to a shift from sleeper cab to day cab trucks.  We do not believe that such an intended consequence will occur for the following reasons.  The addition of a sleeper berth to a tractor cab is not a consumer-selectable attribute in quite the same way as other vehicle features.  The sleeper cab provides a utility that long-distance trucking fleets need to conduct their operations -- an on-board sleeping berth that lets a driver comply with federally-mandated rest periods, as required by the Department of Transportation Federal Motor Carrier Safety Administration's hours-of-service regulations.   The cost of sleeper trucks is already higher than the cost of day cabs, yet the fleets that need this utility purchase them.  A day cab simply cannot provide this utility.  The need for this utility would not be changed even if the marginal costs to reduce greenhouse gas emissions from sleeper cabs exceed the marginal costs to reduce greenhouse gas emissions from day cabs.  A trucking fleet could decide to put its drivers in hotels in lieu of using sleeper berths, and switch to day cabs.  However, this is unlikely to occur in any great number, since the added cost for the hotel stays would far overwhelm differences in the marginal cost between day and sleeper cabs.  Even if some fleets do opt to buy hotel rooms and switch to day cabs, they would be highly unlikely to purchase a day cab that was aerodynamically worse than the sleeper cab they replaced, since the need for features optimized for long-distance hauling would not have changed.  So in practice, there would likely be little difference to the environment for any switching that might occur.  Further, while our projected costs assume the purchase of an APU for compliance, in fact our regulatory structure would allow compliance using a near zero cost software utility that eliminates tractor idling after five minutes.  Using this compliance approach, the cost difference between a Class 8 sleeper cab and day cab due to our final regulations is small.  We are providing this alternative compliance approach reflecting that some sleeper cabs are used in team driving situations where one driver sleeps while the other drives.  In that situation, an APU is unnecessary since the tractor is continually being driven when occupied.  When it is parked, it will automatically eliminate any additional idling through the shutdown software.  If trucking companies choose this option, then costs based on purchase of APUs may overestimate the costs of this program to this sector.
Class shifting from combination tractors to vocational vehicles may occur if a customer deems the additional marginal cost of tractors due to the regulation to be greater than the utility provided by the tractor.  The agencies initially considered this issue when deciding whether to include Class 7 tractors with the Class 8 tractors or regulate them as vocational vehicles.  The agencies' evaluation of the combined vehicle weight rating of the Class 7 shows that if these vehicles were treated significantly differently from the Class 8 tractors, then they could be easily substituted for Class 8 tractors.  Therefore, the agencies are finalizing to include both classes in the tractor category.  The agencies believe that a shift from tractors to vocational vehicles would be limited because of the ability of tractors to pick up and drop off trailers at locations which cannot be done by vocational vehicles.
The agencies do not envision that the final regulatory program will cause class shifting within the vocational class.  The marginal cost difference due to the regulation of vocational vehicles is minimal.  The cost of LRR tires on a per tire basis is the same for all vocational vehicles so the only difference in marginal cost of the vehicles is due to the number of axles.  The agencies believe that the utility gained from the additional load carrying capability of the additional axle will outweigh the additional cost for heavier vehicles.
In conclusion, NHTSA and EPA believe that the final regulatory structure for HD trucks does not significantly change the current competitive and market factors that determine purchaser preferences among truck types.  Furthermore, even if a small amount of shifting does occur, any resulting GHG impacts are likely to be negligible because any vehicle class that sees an uptick in sales is also being regulated for fuel efficiency.  Therefore, the agencies did not include an impact of class shifting on the vehicle populations used to assess the benefits of the program.
Fleet Turnover Effect
A regulation that increases the cost to purchase and/or operate trucks could impact whether a consumer decides to purchase a new truck and the timing of that purchase.  The term pre-buy refers to the idea that truck purchases may occur earlier than otherwise planned to avoid the additional costs associated with a new regulatory requirement.  Slower fleet turnover, or low-buys, may occur when owners opt to keep their existing truck rather than purchase a new truck due to the incremental cost of the regulation.
The NAS panel discusses the topics associated with HD truck fleet turnover.  NAS noted that there is some empirical evidence of pre-buy behavior in response to the 2004 and 2007 heavy-duty engine emission standards, with larger impacts occurring in response to higher costs.  However, those regulations increased upfront costs to firms without any offsetting future cost savings from reduced fuel purchases. In summary, NAS stated that
      ...during periods of stable or growing demand in the freight sector, pre-buy behavior may have significant impact on purchase patterns, especially for larger fleets with better access to capital and financing.  Under these same conditions, smaller operators may simply elect to keep their current equipment on the road longer, all the more likely given continued improvements in diesel engine durability over time.  On the other hand, to the extent that fuel economy improvements can offset incremental purchase costs, these impacts will be lessened.  Nevertheless, when it comes to efficiency investments, most heavy-duty fleet operators require relatively quick payback periods, on the order of two to three years.
The final regulations are projected to return fuel savings to the truck owners that offset the cost of the regulation within a few years for vocational vehicles and Class 7 and 8 tractors, the categories where the potential for prebuy and delayed fleet turnover are concerns.  In the case of vocational vehicles, the added cost is small enough that it is unlikely to have a substantial effect on purchasing behavior.  In the case of Class 7 and 8 trucks, the effects of the regulation on purchasing behavior will depend on the nature of the market failures and the extent to which firms consider the projected future fuel savings in their purchasing decisions.
If trucking firms account for the rapid payback, they are unlikely to strategically accelerate or delay their purchase plans at additional cost in capital to avoid a regulation that will lower their overall operating costs.  As discussed in Section VIII.A, this scenario may occur if this final program reduces uncertainty about fuel-saving technologies.  More reliable information about ways to reduce fuel consumption allows truck purchasers to evaluate better the benefits and costs of additional fuel savings, primarily in the original vehicle market, but possibly in the resale market as well.
Other market failures may leave open the possibility of some pre-buy or delayed purchasing behavior.  Firms may not consider the full value of the future fuel savings for several reasons.  For instance, truck purchasers may not want to invest in fuel efficiency because of uncertainty about fuel prices.  Another explanation is that the resale market may not fully recognize the value of fuel savings, due to lack of trust of new technologies or changes in the uses of the vehicles.  Lack of coordination (also called split incentives -- see Section VIII.A) between truck purchasers (who emphasize the up-front costs of the trucks) and truck operators, who would like the fuel savings, can also lead to pre-buy or delayed purchasing behavior.  If these market failures prevent firms from fully internalizing fuel savings when deciding on vehicle purchases, then pre-buy and delayed purchase could occur and could result in a slight decrease in the GHG benefits of the regulation.
Thus, whether pre-buy or delayed purchase is likely to play a significant role in the truck market depends on the specific behaviors of purchasers in that market.  Without additional information about which scenario is more likely to be prevalent, the agencies are not projecting a change in fleet turnover characteristics due to this regulation.
Benefits of Reducing CO2 Emissions
Social Cost of Carbon
EPA has assigned a dollar value to reductions in CO2 emissions using recent estimates of the social cost of carbon (SCC).  The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year.  It is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services due to climate change.  The SCC estimates used in this analysis were developed through an interagency process that included EPA, DOT/NHTSA, and other executive branch entities, and concluded in February 2010.  We first used these SCC estimates in the benefits analysis for the final joint EPA/DOT rule to establish MY 2012-2016 light-duty vehicle GHG emission standards and CAFE standards; see that rule's preamble for discussion about application of the SCC.  The SCC Technical Support Document (SCC TSD) provides a complete discussion of the methods used to develop these SCC estimates.
The interagency group selected four SCC values for use in regulatory analyses, which we have applied in this analysis: $5, $22, $36, and $67 per metric ton of CO2 emissions in 2010, in 2009 dollars.[,]  The first three values are based on the average SCC from three integrated assessment models, at discount rates of 5, 3, and 2.5 percent, respectively.  SCCs at several discount rates are included because the literature shows that the SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all three models at a 3 percent discount rate.  It is included to represent higher-than-expected impacts from temperature change further out in the tails of the SCC distribution. Low probability, high impact events are incorporated into all of the SCC values through explicit consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity.  Treating climate sensitivity probabilistically results in more high temperature outcomes, which in turn lead to higher projections of damages.
The SCC increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change.  Note that the interagency group estimated the growth rate of the SCC directly using the three integrated assessment models rather than assuming a constant annual growth rate.  This helps to ensure that the estimates are internally consistent with other modeling assumptions.  Table VIII-14 presents the SCC estimates used in this analysis.
When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analyst faces a number of serious challenges.  A recent report from the National Academies of Science points out that any assessment will suffer from uncertainty, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate system, (3) the impact of changes in climate on the physical and biological environment, and (4) the translation of these environmental impacts into economic damages.  As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science, economics, and ethics and should be viewed as provisional.
The interagency group noted a number of limitations to the SCC analysis, including the incomplete way in which the integrated assessment models capture catastrophic and non-catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion.  The limited amount of research linking climate impacts to economic damages makes the interagency modeling exercise even more difficult.  The interagency group hopes that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory analysis by the Federal government will continue to evolve with improvements in modeling. Additional details on these limitations are discussed in the SCC TSD.
We received several comments regarding the SCC estimates used to analyze the proposed standards.  In particular, these commenters discussed the incomplete treatment of impacts as well as discount rate selection.  EPA has reviewed these comments in detail and responded to them in the EPA Response to Comments Document for the Joint Rulemaking.  As noted in that document, the U.S. government intends to revise these estimates, taking into account new research findings that were not included in the first round, and has  set a preliminary goal of revisiting the SCC values in the next few years or at such time as substantially updated models become available, and to continue to support research in this area.  The EPA Response to Comments Document for the Joint Rulemaking discusses ongoing research in greater detail.
Applying the global SCC estimates, shown in Table VIII-14, to the estimated domestic reductions in CO2 emissions under this final program, we estimate the dollar value of the climate related benefits for each analysis year.  For internal consistency, the annual benefits are discounted back to net present value terms using the same discount rate as each SCC estimate (i.e., 5%, 3%, and 2.5%) rather than 3% and 7%.  These estimates are provided in Table VIII-15.
Table VIII-14: Social Cost of CO2, 2012  -  2050[a] (in 2009 dollars)
                                     Year
                          Discount Rate and Statistic

                                  5% Average
                                  3% Average
                                 2.5% Average
                              3%
95th Percentile
                                     2012
                                                                         $5.28
                                                                        $23.06
                                                                        $37.53
                                                                        $70.14
                                     2015
                                                                         $5.93
                                                                        $24.58
                                                                        $39.57
                                                                        $75.03
                                     2020
                                                                         $7.01
                                                                        $27.10
                                                                        $42.98
                                                                        $83.17
                                     2025
                                                                         $8.53
                                                                        $30.43
                                                                        $47.28
                                                                        $93.11
                                     2030
                                                                        $10.05
                                                                        $33.75
                                                                        $51.58
                                                                       $103.06
                                     2035
                                                                        $11.57
                                                                        $37.08
                                                                        $55.88
                                                                       $113.00
                                     2040
                                                                        $13.09
                                                                        $40.40
                                                                        $60.19
                                                                       $122.95
                                     2045
                                                                        $14.63
                                                                        $43.34
                                                                        $63.59
                                                                       $131.66
                                     2050
                                                                        $16.18
                                                                        $46.27
                                                                        $66.99
                                                                       $140.37
Note:
[a] The SCC values are dollar-year and emissions-year specific.
Table VIII-15:  Monetized CO2 Benefits of Vehicle Program, CO2 Emissions[a] (Million 2009$)
YEAR
CO2 EMISSIONS REDUCTION  (MMT)
                                   BENEFITS

Avg SCC at 5% ($5-$16)[a]
Avg SCC at 3% ($23-$46)[a]
Avg SCC at 2.5% ($38-$67)[a]
95[th] percentile SCC at 3% ($70-$140)[a]
2020
38.0
                                                                          $266
                                                                        $1,030
                                                                        $1,634
                                                                        $3,161
2030
73.7
                                                                          $741
                                                                        $2,489
                                                                        $3,803
                                                                        $7,599
2040
91.2
                                                                        $1,194
                                                                        $3,684
                                                                        $5,489
                                                                       $11,213
2050
104.9
                                                                        $1,698
                                                                        $4,856
                                                                        $7,030
                                                                       $14,730
Net Present Value[b]

                                                                         $9,129
                                                                        $46,499
                                                                        $78,763
                                                                       $141,738
Notes:
a Except for the last row (net present value), the SCC values are dollar-year and emissions-year specific.
[b] Net present value of reduced CO2 emissions is calculated differently from other benefits.  The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency.  Refer to the SCC TSD for more detail.

Non-GHG Health and Environmental Impacts
This section presents EPA's analysis of the non-GHG health and environmental impacts that can be expected to occur as a result of the HD National Program.  GHG emissions are predominantly the byproduct of fossil fuel combustion processes that also produce criteria and hazardous air pollutants.  The vehicles that are subject to the standards are also significant sources of mobile source air pollution such as direct PM, NOX, VOCs and air toxics.  The standards will affect exhaust emissions of these pollutants from vehicles.  They will also affect emissions from upstream sources related to changes in fuel consumption.  Changes in ambient ozone, PM2.5, and air toxics that will result from the standards are expected to affect human health in the form of premature deaths and other serious human health effects, as well as other important public health and welfare effects.
As many commenters noted, it is important to quantify the health and environmental impacts associated with the final rules because a failure to adequately consider these ancillary co-pollutant impacts could lead to an incorrect assessment of their net costs and benefits.  Moreover, co-pollutant impacts tend to accrue in the near term, while any effects from reduced climate change mostly accrue over a time frame of several decades or longer.
This section is organized as follows: the first presents the PM- and ozone-related health and environmental impacts associated with final program in calendar year (CY) 2030; the second discusses the related co-benefits associated with the model year (MY) analysis of the program.
Quantified and Monetized Non-GHG Human Health Benefits of the 2030 Calendar Year Analysis
This analysis reflects the impact of the HD National Program in 2030 compared to a future-year reference scenario without the program in place.  Overall, we estimate that the final rules will lead to a net decrease in PM2.5-related health impacts. See Section VII.D of this preamble for more information about the air quality modeling results.  While the PM-related air quality impacts are relatively small, the decrease in population-weighted national average PM2.5 exposure results in a net decrease in adverse PM-related human health impacts (the decrease in national population-weighted annual average PM2.5 is 0.004 μg/m[3]).
The air quality modeling also projects decreases in ozone concentrations in many areas.  While the ozone-related impacts are relatively small, the decrease in population-weighted national average ozone exposure results in a net decrease in ozone-related health impacts (population-weighted maximum 8-hour average ozone decreases by 0.0122 ppb).
We base our analysis of the program's impact on human health in 2030 on peer-reviewed studies of air quality and human health effects.[,]  These methods are described in more detail in the RIA that accompanies this action.  Our benefits methods are also consistent with recent rulemaking analyses such as the final Transport Rule, the final 2012-2016 MY Light-Duty Vehicle Rule, and the final Portland Cement National Emissions Standards for Hazardous Air Pollutants (NESHAP) RIA.  To model the ozone and PM air quality impacts of this final action, we used the Community Multiscale Air Quality (CMAQ) model (see Chapter 8.2.2 of the RIA that accompanies this preamble).  The modeled ambient air quality data serves as an input to the Environmental Benefits Mapping and Analysis Program version 4.0 (BenMAP).  BenMAP is a computer program developed by the U.S. EPA that integrates a number of the modeling elements used in previous analyses (e.g., interpolation functions, population projections, health impact functions, valuation functions, analysis and pooling methods) to translate modeled air concentration estimates into health effects incidence estimates and monetized benefits estimates.
The range of total monetized ozone- and PM-related health impacts is presented in Table VIII-16 .  We present total benefits based on the PM- and ozone-related premature mortality function used.  The benefits ranges therefore reflect the addition of each estimate of ozone-related premature mortality (each with its own row in Table VIII-16) to estimates of PM-related premature mortality.  These estimates represent EPA's preferred approach to characterizing a best estimate of benefits.  As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods used to estimate air quality benefits evolve to reflect the agency's most current interpretation of the scientific and economic literature.

Table VIII-16: Estimated 2030 Monetized PM-and Ozone-Related Health Benefits[a]
2030 Total Ozone and PM Benefits  -  PM Mortality Derived from American Cancer Society Analysis and Six-Cities Analysis[a]
                      Premature Ozone Mortality Function
                                   Reference
                                Total Benefits
                   (Millions, 2009$, 3% Discount Rate)[b,c]
                                Total Benefits
                    (Millions, 2009$, 7% Discount Rate) b,c
Multi-city analyses
Bell et al., 2004
Total: $110 - $200
PM: $67 - $160
Ozone: $42
Total: $100 - $190
PM: $60 - $140
Ozone: $42

Huang et al., 2005
Total: $120 - $220
PM: $67 - $160
Ozone: $56
Total: $120 - $200
PM: $60 - $140
Ozone: $56

Schwartz, 2005
Total: $130 - $220
PM: $67 - $160
Ozone: $63
Total: $120 - $210
PM: $60 - $140
Ozone: $63
Meta-analyses
Bell et al., 2005
Total: $200 - $290
PM: $67 - $160
Ozone: $130
Total: $190 - $270
PM: $60 - $140
Ozone: $130

Ito et al., 2005
Total: $240 - $330
PM: $67 - $160
Ozone: $170
Total: $230 - $320
PM: $60 - $140
Ozone: $170

Levy et al., 2005
Total: $250 - $340
PM: $67 - $160
Ozone: $180
Total: $240 - $330
PM: $60 - $140
Ozone: $180
Notes:
a Total includes premature mortality-related and morbidity-related ozone and PM2.5 benefits.  Range was developed by adding the estimate from the ozone premature mortality function to the estimate of PM2.5-related premature mortality derived from either the ACS study (Pope et al., 2002) or the Six-Cities study (Laden et al., 2006).
b Note that total benefits presented here do not include a number of unquantified benefits categories.  A detailed listing of unquantified health and welfare effects is provided in Table VIII-17.
c Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA's Guidelines for Preparing Economic Analyses and OMB Circular A-4.  Results are rounded to two significant digits for ease of presentation and computation.
The benefits in Table VIII-16 include all of the human health impacts we are able to quantify and monetize at this time.  However, the full complement of human health and welfare effects associated with PM and ozone remain unquantified because of current limitations in methods or available data.  We have not quantified a number of known or suspected health effects linked with ozone and PM for which appropriate health impact functions are not available or which do not provide easily interpretable outcomes (e.g., changes in heart rate variability).  Additionally, we are unable to quantify a number of known welfare effects, including reduced acid and particulate deposition damage to cultural monuments and other materials, and environmental benefits due to reductions of impacts of eutrophication in coastal areas.  These are listed in Table VIII-17.  As a result, the health benefits quantified in this section are likely underestimates of the total benefits attributable to this final action.

Table VIII-17: Unquantified and Non-Monetized Potential Effects
Pollutant/Effects
Effects Not Included in Analysis - Changes in:
Ozone Healtha
Chronic respiratory damage[b]
Premature aging of the lungs[b]
Non-asthma respiratory emergency room visits
Exposure to UVb (+/-)e
Ozone Welfare
Yields for
-commercial forests
-some fruits and vegetables
-non-commercial crops
Damage to urban ornamental plants
Impacts on recreational demand from damaged forest aesthetics
Ecosystem functions
Exposure to UVb (+/-)[e]
PM Healthc
Premature mortality - short term exposuresd
Low birth weight
Pulmonary function
Chronic respiratory diseases other than chronic bronchitis
Non-asthma respiratory emergency room visits
Exposure to UVb (+/-)[e]
PM Welfare
Residential and recreational visibility in non-Class I areas
Soiling and materials damage
Damage to ecosystem functions
Exposure to UVb (+/-)[e]
Nitrogen and Sulfate Deposition Welfare
Commercial forests due to acidic sulfate and nitrate deposition
Commercial freshwater fishing due to acidic deposition
Recreation in terrestrial ecosystems due to acidic deposition
Existence values for currently healthy ecosystems
Commercial fishing, agriculture, and forests due to nitrogen deposition
Recreation in estuarine ecosystems due to nitrogen deposition
Ecosystem functions
Passive fertilization
CO Health
Behavioral effects
HC/Toxics Healthf
Cancer (benzene, 1,3-butadiene, formaldehyde, acetaldehyde)
Anemia (benzene)
Disruption of production of blood components (benzene)
Reduction in the number of blood platelets (benzene)
Excessive bone marrow formation (benzene)
Depression of lymphocyte counts (benzene)
Reproductive and developmental effects (1,3-butadiene)
Irritation of eyes and mucus membranes (formaldehyde)
Respiratory irritation (formaldehyde)
Asthma attacks in asthmatics (formaldehyde)
Asthma-like symptoms in non-asthmatics (formaldehyde)
Irritation of the eyes, skin, and respiratory tract (acetaldehyde)
Upper respiratory tract irritation and congestion (acrolein)
HC/Toxics Welfare
Direct toxic effects to animals
Bioaccumulation in the food chain
Damage to ecosystem function
Odor
Notes:
a The public health impact of biological responses such as increased airway responsiveness to stimuli, inflammation in the lung, acute inflammation and respiratory cell damage, and increased susceptibility to respiratory infection are likely partially represented by our quantified endpoints.
[b] The public health impact of effects such as chronic respiratory damage and premature aging of the lungs may be partially represented by quantified endpoints such as hospital admissions or premature mortality, but a number of other related health impacts, such as doctor visits and decreased athletic performance, remain unquantified.
c In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health effects including morphological changes and altered host defense mechanisms.  The public health impact of these biological responses may be partly represented by our quantified endpoints.
d While some of the effects of short-term exposures are likely to be captured in the estimates, there may be premature mortality due to short-term exposure to PM not captured in the cohort studies used in this analysis.  However, the PM mortality results derived from the expert elicitation do take into account premature mortality effects of short term exposures.
e May result in benefits or disbenefits.
f Many of the key hydrocarbons related to this action are also hazardous air pollutants listed in the CAA.
While there will be impacts associated with air toxic pollutant emission changes that result from this final action, we do not attempt to monetize those impacts.  This is primarily because currently available tools and methods to assess air toxics risk from mobile sources at the national scale are not adequate for extrapolation to incidence estimations or benefits assessment.  The best suite of tools and methods currently available for assessment at the national scale are those used in the National-Scale Air Toxics Assessment (NATA).  The EPA Science Advisory Board specifically commented in their review of the 1996 NATA that these tools were not yet ready for use in a national-scale benefits analysis, because they did not consider the full distribution of exposure and risk, or address sub-chronic health effects.  While EPA has since improved these tools, there remain critical limitations for estimating incidence and assessing benefits of reducing mobile source air toxics.
As part of the second prospective analysis of the benefits and costs of the Clean Air Act, EPA conducted a case study analysis of the health effects associated with reducing exposure to benzene in Houston from implementation of the Clean Air Act. While reviewing the draft report, EPA's Advisory Council on Clean Air Compliance Analysis concluded that "the challenges for assessing progress in health improvement as a result of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of exposure-response functions, uncertainties in emissions inventories and background levels, the difficulty of extrapolating risk estimates to low doses and the challenges of tracking health progress for diseases, such as cancer, that have long latency periods." EPA continues to work to address these limitations; however, we did not have the methods and tools available for national-scale application in time for the analysis of the final action.
EPA is also unaware of specific information identifying any effects on listed endangered species from the small fluctuations in pollutant concentrations associated with this program (see Section VII.D).  Furthermore, our current modeling tools are not designed to trace fluctuations in ambient concentration levels to potential impacts on particular endangered species.
 	Quantified Human Health Impacts
Table VIII-18 and Table VIII-19 present the annual PM2.5 and ozone health impacts, respectively, in the 48 contiguous U.S. states associated with the HD National Program for 2030.  For each endpoint presented in Table VIII-18 and Table VIII-19, we provide both the mean estimate and the 90% confidence interval.
Using EPA's preferred estimates, based on the American Cancer Society (ACS) and Six-Cities studies and no threshold assumption in the model of mortality, we estimate that the final rules will result in between 7 and 17 cases of avoided PM2.5-related premature mortalities annually in 2030.  As a sensitivity analysis, when the range of expert opinion is used, we estimate between 2 and 23 fewer premature mortalities in 2030 (see Table 8-14 in the RIA that accompanies this action).  For ozone-related premature mortality in 2030, we estimate a range of between 5 to 18 fewer premature mortalities.
Table VIII-18: Estimated PM2.5-Related Health Impacts[a]
Health Effect
2030 Annual Reduction in Incidence
(5[th]% - 95[th]%ile)
Premature Mortality  -  Derived from epidemiology literature[b]
  Adult, age 30+, ACS Cohort Study (Pope et al., 2002)

  Adult, age 25+, Six-Cities Study (Laden et al., 2006)

  Infant, age <1 year (Woodruff et al., 1997)

7
(2  -  12)
17
(7  -  27)
0
(0  -  0)
Chronic bronchitis (adult, age 26 and over)
9
(0  -  18)
Non-fatal myocardial infarction (adult, age 18 and over)
10
(2  -  17)
Hospital admissions - respiratory (all ages)[c]
2
(1  -  3)
Hospital admissions - cardiovascular (adults, age >18)[d]
4
(2  -  4)
Emergency room visits for asthma (age 18 years and younger)
12
(6  -  17)
Acute bronchitis, (children, age 8-12)
25
(0  -  55)
Lower respiratory symptoms (children, age 7-14)
320
(130  -  510)
Upper respiratory symptoms (asthmatic children, age 9-18)
240
(49  -  430)
Asthma exacerbation (asthmatic children, age 6-18)
530
(1  -  1,500)
Work loss days
1,900
(1,700  -  2,200)
Minor restricted activity days (adults age 18-65)
11,000
(9,300  -  13,000)
     Notes:
     [a] Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous United States.
     [b] PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002) and the Six-Cities Study (Laden et al., 2006).  Note that these are two alternative estimates of adult mortality and should not be summed.  PM-related infant mortality based upon a study by Woodruff, Grillo, and Schoendorf, (1997).
     [c] Respiratory hospital admissions for PM include admissions for chronic obstructive pulmonary disease (COPD), pneumonia and asthma.
     d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart disease, dysrhythmias, and heart failure.
Table VIII-19: Estimated Ozone-Related Health Impacts[a]
Health Effect
2030 Annual Reduction in Incidence
(5th% - 95th%ile)
Premature Mortality, All ages[b]
Multi-City Analyses
  Bell et al. (2004)  -  Non-accidental

  Huang et al. (2005)  -  Cardiopulmonary

  Schwartz (2005)  -  Non-accidental

Meta-analyses:
  Bell et al. (2005)  -  All cause

  Ito et al. (2005)  -  Non-accidental

  Levy et al. (2005)  -  All cause

4
(2  -  6)
5
(2  -  8)
6
(2  -  9)

13
(7  -  18)
17
(11  -  23)
18
(13  -  23)
Hospital admissions- respiratory causes (adult, 65 and older)c
24
(3  -  44)
Hospital admissions -respiratory causes (children, under 2)
36
(19  -  53)
Emergency room visit for asthma (all ages)
31
(0  -  81)
Minor restricted activity days (adults, age 18-65)
49,000
(25,000  -  74,000)
School absence days
20,000
(8,700  -  28,000
        Notes:
        [a] Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous U.S.
        [b] Estimates of ozone-related premature mortality are based upon incidence estimates derived from several alternative studies: Bell et al. (2004); Huang et al. (2005); Schwartz (2005) ; Bell et al. (2005); Ito et al. (2005); Levy et al. (2005).  The estimates of ozone-related premature mortality should therefore not be summed.
        [c] Respiratory hospital admissions for ozone include admissions for all respiratory causes and subcategories for COPD and pneumonia.
	Monetized Benefits
Table VIII-20 presents the estimated monetary value of changes in the incidence of ozone and PM2.5-related health effects.  All monetized estimates are stated in 2009$.  These estimates account for growth in real gross domestic product (GDP) per capita between the present and 2030.  Our estimate of total monetized benefits in 2030 for the program, using the ACS and Six-Cities PM mortality studies and the range of ozone mortality assumptions, is between $110 and $340  million, assuming a 3 percent discount rate, or between $100 and $330 million, assuming a 7 percent discount rate.
Table VIII-20: Estimated Monetary Value of Changes in Incidence of Health and Welfare Effects in 2030 (Millions of 2009$) [a][,b]
PM2.5-Related Health Effect
(5[th] and 95[th] %ile)
Premature Mortality  -  Derived from Epidemiology Studiesc,d,

Adult, age 30+ - ACS study
(Pope et al., 2002)
          3% discount rate

          7% discount rate

$59
($6.2 - $160)
$53
($5.6 - $140)

Adult, age 25+ - Six-Cities study (Laden et al., 2006)
          3% discount rate

          7% discount rate

$150
($20 - $380)
$140
($18 - $350)

Infant Mortality, <1 year  -  (Woodruff et al. 1997)
$0.6
($0 - $2.3)
Chronic bronchitis (adults, 26 and over)
$4.8
($0.08 - $16)
Non-fatal acute myocardial infarctions
          3% discount rate

          7% discount rate

$1.1
($0.18 - $2.8)
$0.86
($0.13 - $2.1)
Hospital admissions for respiratory causes
$0.03
($0.01 - $0.05)
Hospital admissions for cardiovascular causes
$0.07
($0.02 - $0.12)
Emergency room visits for asthma
$0.005
($0.002 - $0.007)
Acute bronchitis (children, age 8 - 12)
$0.01
($0 - $0.03)
Lower respiratory symptoms (children, 7 - 14)
$0.007
($0.002 - $0.013)
Upper respiratory symptoms (asthma, 9 - 11)
$0.008
($0.002 - $0.018)
Asthma exacerbations
$0.03
($0 - $0.09)
Work loss days
$0.31
($0.26 - $0.36)
Minor restricted-activity days (MRADs)
$0.77
($0.45 - $1.1)
Ozone-related Health Effect
Premature Mortality, All ages  -  Derived from Multi-city analyses
Bell et al., 2004
$37
($4.9 - $93)

Huang et al., 2005
$50
($6.8 - $130)

Schwartz, 2005
$58
($7.5 - $150)
Premature Mortality, All ages  -  Derived from Meta-analyses
Bell et al., 2005
$120
($18 - $300)

Ito et al., 2005
$170
($24 - $390)

Levy et al., 2005
$180
($26 - $410)
Hospital admissions- respiratory causes (adult, 65 and older)
$0.62
($0.08 - $1.1)
Hospital admissions- respiratory causes (children, under 2)
$0.38
($0.20 - $0.57)
Emergency room visit for asthma (all ages)
$0.01
($0 - $0.03)
Minor restricted activity days (adults, age 18-65)
$3.3
($1.5 - $5.6)
School absence days
$1.9
($0.83 - $2.6)
    Notes:
    a Monetary benefits are rounded to two significant digits for ease of presentation and computation.  PM and ozone benefits are nationwide.
    b Monetary benefits adjusted to account for growth in real GDP per capita between 1990 and the analysis year (2030).
    c Valuation assumes discounting over the SAB recommended 20 year segmented lag structure.  Results reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses.
	What Are the Limitations of the Benefits Analysis?
Every benefit-cost analysis examining the potential effects of a change in environmental protection requirements is limited to some extent by data gaps, limitations in model capabilities (such as geographic coverage), and uncertainties in the underlying scientific and economic studies used to configure the benefit and cost models.  Limitations of the scientific literature often result in the inability to estimate quantitative changes in health and environmental effects, such as potential decreases in premature mortality associated with decreased exposure to carbon monoxide.  Deficiencies in the economics literature often result in the inability to assign economic values even to those health and environmental outcomes which can be quantified. These general uncertainties in the underlying scientific and economics literature, which can lead to valuations that are higher or lower, are discussed in detail in the RIA and its supporting references.  Key uncertainties that have a bearing on the results of the benefit-cost analysis of the final rules include the following:
The exclusion of potentially significant and unquantified benefit categories (such as health, odor, and ecological benefits of reduction in air toxics, ozone, and PM);
Errors in measurement and projection for variables such as population growth;
Uncertainties in the estimation of future year emissions inventories and air quality;
Uncertainty in the estimated relationships of health and welfare effects to changes in pollutant concentrations including the shape of the C-R function, the size of the effect estimates, and the relative toxicity of the many components of the PM mixture;
Uncertainties in exposure estimation; and
Uncertainties associated with the effect of potential future actions to limit emissions.
As Table VIII-20 indicates, total benefits are driven primarily by the reduction in premature mortalities each year.  Some key assumptions underlying the premature mortality estimates include the following, which may also contribute to uncertainty: