***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 1 

## Chapter 8. **Economic and Other Impacts**  **8.1 Framework** 

**for Benefits and Costs** This Chapter presents the costs, benefits and other economic impacts of the proposed Phase 2 standards. It is important to note that NHTSA’s proposed fuel consumption standards and EPA’s proposed GHG standards would both be in effect, and each would lead to average f uel efficiency

increases and GHG emission reductions. The net benefits of the proposed Phase 2 standards consist of the effects of the program on:  the vehicle program costs (costs of compl ying with the vehicle CO 2 and fuel consumption standards),  changes in fuel expenditure s associated with reduced fuel use resulting from more efficient vehicles and increased fuel use associated with the “rebound” effect, both of which result from the program,  the economic value of reductions in GHGs,  the economic value of reductions in other non - GHG pollutants,  costs associated with increases in noise, congestion, and accidents resulting from increased vehicle use,  savings in drivers’ time from less frequent refueling,  benefits of increased vehicle use associated with the “rebound” effect ,  the economic value of improvements in U.S. energy security . The benefits and costs of th e s e rule s are anal yz ed using 3 percent and 7 percent discount rates, consistent with current OMB guidance. A These rates are intended to represent consumers’ preference for current over future consumption (3 percent), and the real rate of return on private investment (7 percent) which indicates the opportunity cost of capital. However, n either of these rates necessaril y represents the discount rate that individual decision - makers use. The program may also have other economic effects that are not included here. In particular, as discussed in Chapter 2 of the draft RIA, the technology cost estimates developed here take into account the costs to hold other vehicle attributes, such as size and performance, constant. With these assumptions, and because welfare losses represent monetary estimates of how much buyers would have to be compensated to be made as well off as they would have been in the absence of this regulation, B price increases for new vehicles measure the welfare losses to

A The range of Social Cost of Carbon (SCC) values uses several discount rates 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 intergene rational context (where costs and benefits are incurred by different generations). Refer to Section F.1 for more information. B This approach describes the economic concept of compensating variation, a payment of money after a change that wo uld make a con sumer as well off after the change as before it. A related concept, equivalent variation, estimates***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 2 the vehicle buye rs . C If the full technology cost gets passed along to the buye r as an increase in price, the tec hnology

cost thus measures the primary welfare loss of the standards, including impacts on buyers. Increasing fuel efficiency would have to lead to other changes in the vehicles that buyers find undesirable for there to be additional welfare losses that are not included in the technology

costs. As the 2012 - 2016 and 2017 - 2025 light - duty GHG/CAFE rules discussed, if other vehicle attributes are not held constant, then the technology cost estimates do not capture the losses to vehicle buyers associated with these changes. 1 The light - duty rules also discussed other potential issues that could affect the calculation of the welfare impacts of these ty pes of changes, such as aspects of buye rs’ behavior that might affect the demand for technology investments, un certainty in buyers’ investment horizons, and the rate at which truck owners trade off higher vehicle purchase price against future fuel savings. The agencies seek comments, including supporting data and quantitative analy ses, of any additional impacts of the proposed standards on vehicle attributes and performance, or other potential aspects that could positively or negativel y affect the welfare implications of this proposed rulemaking. Where possible, we identify the uncertain aspects of these economic impacts and attempt to quantify

them (e.g., sensitivity

ranges associated with quantified and monetized GHG impacts; range of dollar- per- ton values to monetize non - GHG health benefits; unc ertainty with respect to learning and markups). For HD pickup s and van s , the agencies explicitly analyzed the uncertainty surrounding its estimates of the economic impacts from requiring higher fuel efficiency in Chapter 7 . The agencies have als o examined the sensitivity of our estimates of savings in fuel expenditures to alternative assumptions about future fuel prices; results of this sensitivity

anal ys is can be found in Chapter 8.12 of this draft RIA. NHTSA’s draft EIS also characterizes the uncertainty in economic impacts associated with the HD national program. For other impacts, however, there is in adequate information to inform a thorough, quantitative assessment of uncertainty . EPA and NHTSA continue to work toward developing a comprehensive strategy

for characterizing the aggregate impact of uncertainty in key elements of its analy ses and we will continue to work to refine these uncertainty anal ys es in the future as time and res ources permit. This and other chapters of the draft RIA address Se ction 317 of the Clean Air Act on economic analy sis. Chapter 8.11 addresses Section 321 of the Clean Air Act on employ ment anal ys is. The total monetized benefits and costs of the program are summarized in Section 8.10 for the preferred alternative and in Chapter 9 for all alternatives. the income change that would be an alternative to the change taking place. The difference between them is whether the consumer’s point of reference is her we lfare before the change (compensating variation) or after the change (equivalent variation). In practice, these two measures are typically very close together. C Indeed, it is likely to be an overestimate of the loss to the consumer, because the buye r has choices other than buying the same vehicle with a higher price; she could choose a different vehicle, or decide not to buy a new vehicle. The buyer would choose one of those options only if the alternative involves less loss than paying the higher p rice. Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer welfare, unless there are other changes to the vehicle due to the fuel efficiency improvements that make the vehicle less desirable to consumers.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 3 

### 8.2 **Conceptual Framework for Evaluating Impacts** The HD Phase 2 proposed standards would implement both the 2007 Energy Independence and Security Act requirement that NHTSA establish fuel efficiency standards for medium - and heav y- duty vehicles and the Clean Air Act requirement that EPA adopt technology- based standards to control pollutant emissions from motor vehicl es and engines contributing to air pollution that endangers public health and welfare . NHTSA’s statutory mandate is intended to further the agency ’s long - standing goals of reducing U.S. consumption and imports of petroleum energy to improve the nation’s energy security . From an economics perspective, g overnment actions to improve our nation’s energy securi ty and to protect our nation from the potential threats of climate change address “externalities,” or economic consequences of decisions by individuals and businesses that extend beyond those who make these decisions. For example, u sers of transportation fuels increase the entire U.S. economy’s risk of having to make costly adjustments due to rapid increases in oil prices, but these users generall y do not consider such costs when they decide to consume more fuel . Similarly, consuming transportation fuel also increase s emissions of greenhouse gases and other more localized air pollutants that occur when fuel is refined, distributed, and consumed. Some of these emissions increase the likelihood and severity of potential climate related economic damages , an d others cause economic damages by adverse ly a ffecting human health. The need to address these e xternal costs and other adverse effects provide s a well established economic rationale that supports the statutory direction given to government agencies to es tablish regulatory

programs that reduce the magnitude of these adverse effects at reasonable costs . The proposed Phase 2 standards would require manufacturers of new heavy - duty vehicles , including trailers (HDVs), to improve the fuel efficiency of the pro ducts that they produce . As HDV users purchase and operate these new vehicles, they would consume significantl y less fuel, in turn reducing U.S. petroleum consumption and imports as well as emissions of GHGs and other air pollutants. Thus as a consequenc e of the agencies’ efforts to meet NHTSA statutory

obligations to improve U .S. energy security and EPA’s obligation to issue standards “to regulate emissions of the deleterious pollutant... from motor vehicles” that endangers public health and welfare , 2 the proposed fuel efficiency and GHG emission standards w ould also reduce HDV operators’ outlay s for fuel purchases. These fuel savings are one measure of the proposed rule’s effectiveness in promoting NHTSA’s statuto ry goal of conserving energy , as well as EPA’s obligation to assess the cost of standards under section 202 (a) (1) and (2) of the Clean Air Act . Although the se savings are not th e agenc ies’ primary motivation for adopting higher fuel efficiency standard s, these substantial fuel savings represent significant additional economic benefits of this proposal . Potential savings in fuel costs would appear to offer HDV buyers strong incentives to pay higher prices for vehicles that feature technology or equipment that reduces fuel consumption . These potential savings would also appear to offer HDV manufacturers similarly strong incentives to produce more fuel - efficient vehicles. Economic theory suggests that interactions between vehicle buye rs and sellers in a n ormall y- functioning competitive market would lead***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 4 HDV manufacturers to incorporate all technolog ies that contribute to lower net costs into the vehicles they

offer , and buyers to purchase them willingly . Nevertheless, many readil y available technologies that appear to offer cost - effective increases in HDV fuel efficiency (when evaluated over their expected lifetimes using conventional discount rates) have not been widel y adopted, despite their potential to repay buyers ’ initial investments rapidly . This economic situation is commonly known as the “energy

efficiency

gap” or “energy paradox.” This situation is perhaps more challenging to understand with respect to the heav yduty sector versus the light - duty vehicle sector . Unlike light - duty vehicles

– which are purchased and used mainly

by individuals and households

– the vast majority of HDVs are purchased and operated b y profit - seeking businesses for which fuel costs represent a substantial operating expense. Nevertheless, on the basis of evidence re viewed below, the agencies believe that a significant number of fuel efficiency improving technologies would remain far less widel y adopted in the absence of these proposed standards. Economic research offers several possible explanations for why the pro spect of these apparent savings might not lead HDV manufacturers and buye rs to adopt technologies that would be expected to reduce HDV operating costs . Some of these explanations involve failures of the HDV market for reasons other than the externalities caused b y producing and consuming fuel . These include situations where information about the performance of fuel economy technologies is incomplete, costly to obtain, or available only to one party to a transaction (or “as ymmetrical”), as well as behavior al rigidities in either the HDV manufacturing or HDV operating industries, such as standardized or inflexibly administered operating procedures, or requirements of other regulations on HDVs. Other explanations for the limited use of apparentl y cost - effecti ve technologies that do not involve market failures include HDV operators’ concerns about the performance, reliability, or maintenance requirements of new technology under the demands of everyday

use, uncertainty about the fuel savings they will actually realize, and questions about possible effects on carrying capacity or other aspects of HDVs’ utility . In the HD Phase 1 rulemaking (which, in contrast to these proposed standards, did not apply to trailers), the agencies raised five hy potheses that might explain this energy efficiency gap or paradox:  Imperfect information in the new vehicle market: information available to prospective buyers about the effectiveness of some fuel - saving technologies for new vehicles may be inadequate or unreliable. If relia ble information on their effectiveness in reducing fuel consumption is unavailable or difficult to obtain, HDV buyers will understandabl y be reluctant to pay higher prices to purchase vehicles equipped with unproven technologies.  Imperfect information in the resale market: buye rs in the used vehicle market may not be willing to pay

adequate premiums for more fuel efficient vehicles when they are offered for resale to ensure that buyers of new vehicles can recover the remaining value of their original investment in higher fuel efficiency . The prospect of an inadequate return on their original owners’ investments in higher fuel efficiency may contribute to the short pay back periods that buyers of new vehicles appear to demand. 3***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 5  Principal - agent problems caus ing split incentives: an HDV buye r may not be directl y responsible for its future fuel costs, or the individual who will be responsible for fuel costs may not participate in the HDV purchase decision. In these cases, the signal to invest in higher fuel efficiency normall y provided by savings in fuel costs may not be transmitted effectively to HDV buyers , and the incentives of HDV buye rs and fuel buyers will div erge, or be “split.” The trailers towed b y heav y- duty tractors, which are typicall y not supplied by the tractor manufacturer or seller, present an obvious potential situation of split incentives that was not addressed in the HD Phase 1 rulemaking, but it may

apply in this rulemaking. If there is inadequate pass - through of price signals from trailer users to their buye rs, then low adoption of fuel - s aving technologies may

result.  Uncertainty about future fuel cost savings: HDV buyers may be uncertain about future fuel prices, or about maintenance costs and reliability of some fuel efficiency technologies . Buye rs may react to this uncertainty by implicitly discounting potential future savings at rates above discount rates used in this analy sis . In contrast, the costs of fuel - saving or maintenance - reducing technologies are immediate and thus not subject to discounting . In this situation, potential variability about buye rs’ expected return s on capital investments to achieve higher fuel efficiency may shorten the pay back period

– the time required to repay those investments

– they demand in order to make them.  Adjustment and transactions costs: potential resistance to new technologies

– stemming, for example, from drivers’ reluctance or slowness to adjust to ch anges in the way vehicles operate

– may

slow or inhibit new technology adoption. If a conservative approach to new technologies leads HDV buyers to adopt them slowly , then successful new technologies would be adopted over time without market intervention, but only

with potentially

significant delays in achieving the fuel saving, environmental, and energy security benefits they offer. There also may be costs associated with training drivers to realize potential fuel savings enabled b y new technologies, or with accelerating fleet operators’ scheduled fleet turnover and replacement to hasten their acquisition of vehicles equipped with these technologies. Some of these explanations imply failures in the private market for fuel - saving technology

beyond the e xternalities caused by producing and consuming fuel, while others suggest that complications in valuing or adapting to technologies that reduce fuel consumption may partly explain buyers’ hesitance to purchase more fuel - efficient vehicles. In either case, adopting this proposed rule would provide regulatory certainty and thus generate important economic benefits in addition to reducing externalities. Since the HD Phase 1 rulemaking, new research has provided further insight into potential barriers to adoption of fuel - saving technologies. Several studies utilized focus groups and interviews involving small numbers of participants, who were people with time and inclination to join such studies, rather than selected at random. 4 As a result, the information from these groups is not necessarily representative of the industry as a whole. While these studies cannot provide conclusive evidence about how all HDV buye rs make their decisions, they do describe issues that arise for those that participated.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 6 One comm on theme that emerges from these studies is the inability of HDV buyers to obtain reliable information about the fuel savings, reliability , and maintenance costs of technologies that improve fuel efficiency . In many product markets, such as consumer electronics, credible reviews and tests of product performance are readil y available to potential buyers. In the trucking industry , however, the performance of fuel - saving technology is likely to depend on many firm - specific attributes, including the intensity of HDV use, the ty pical distance and routing of HDV trips , driver characteristics, road conditions, regional geography and traffic patterns. As a result, businesses that operate HDVs have strong preferences for testing fuel - saving technologies “in - house ” because they are concerned that their patterns of vehicle use may lead to different results from those reported in published information. Businesses with less capability to do in - house testing often seek information from peers, yet often remain skeptical of its applicability

due to differences in the nature of their operations. One source of imperfect information is the lack of availability of certain technologies from preferred suppliers. HDV buyers often prefer to have technology or equipment install ed by their favored original equipment manufacturers. However, some technologies may not be available through these preferred sources, or may be available onl y as after- market installations from third parties (Aarnink et al. 2012, Roeth et al. 2013). Alth ough these studies appear to show that information in the new HDV market is often limited or viewed as unreliable, the evidence for imperfect information in the market for used HDVs is mixed. On the one hand, some studies noted that fuel - saving technology is often not valued or demanded in the used vehicle market, because of imperfect information about its benefits, or greater mistrust of its performance among buye rs in the used vehicle market than among buye rs of new vehicles. The lack of demand might al so be due to the intended use of the used HDV, which may

not require or reward the presence of certain fuel - saving technologies. In other cases, however, fuel - saving technology can lead to a premium in the used market, as for instance to meet the more stringent requirements for HDVs operating in California. All of the recent research identifies split incentives, or principal - agent problems, as a potential barrier to technology adoption. These occur when those responsible for investment decisions are different from the main beneficiaries of the technology . For instance, businesses that own and lease trailers to HDV operators may not have an incentive to invest in trailerspecific fuel - saving technology , since they do not collect the savings from the lower fuel costs that result. Vernon and Meier (2012) estimate that 23 percent of trailers may be exposed to this kind of principal - agent problem, although they do not quantify

its financial significance. 5 Split incentives can also exist when the HDV driver is not responsible for paying fuel costs. Some technologies require additional effort, training, or changes in driving behavior to achieve their promised fuel savings; drivers who do not pay for fuel may

be reluctant to undertake those changes, thus reducing the fuel - saving benefits from the perspective of the individual or company

paying for the fuel. For instance, drivers might not consistently deplo y***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 7 boat - tails equipped on trailers to improve vehicle aerodynamics. D Vernon and Meier also calculate that 91 percent of HDV fuel use is subject to this form of principal - agent problem, although they

do not estimate how much it might reduce fuel savings to those who are paying for the fuel. The studies based on focus groups and interviews (Klemick et al. 2013, Aarnink et al. 2012, Roeth et al. 2013) provide mixed evidence on the severity of the split - incentive problem. Focus groups often do identify diverging incentives between drivers and the decision - makers responsible for purchasing vehicles, and economics literature recognizes that this split incentive can be a barrier to adopting new technology . Aarnink et al. (2012) and Roeth et al. (2013) cite examples of split incentives involving trailers and fuel surcharges, although the latter also cites other examples where these same issues do not lead to split incentives. In an effort to minimize problems that can arise form split incentives, many businesses that operate HDVs also train drivers in the use of specific technologies or to modify their driving behav ior in order to improve fuel efficiency , while some also offer financial incentives to their drivers to conserve fuel. All of these options can help to reduce the split incentive problem, although they

may not be effective where it arises from different o wnership of combination tractors and trailers . Uncertainty about future costs for fuel and maintenance, or about the reliability of new technology , also appears to be a significant obstacle that can slow the adoption of fuel - saving technologies. These examples illustrate the problem of uncertain or unreliable information about the actual performance of fuel efficiency technology

discussed above. In addition, businesses that operate HDVs may

be concerned about how reliable new technologies will prove to be on the road, and whether significant additional maintenance costs or equipment malfunctions that result in costly

downtime could occur. Roeth et al. (2013) and Klemick et al. (2013) both document the short pay

back periods that HDV buyers require on their investments -- usually about 2 years -- which may be partl y attributable to these uncertainties. These studies also provide some support for the view that adjustment and transactions costs may impede HDV buyers from investing in higher fuel efficiency . As discussed above, several studies note that HDV buyers are less likely to select new technology when it is not available from their preferred manufacturers. Some technologies are onl y available as aftermarket additions, which can add other costs to adopting them. Some studies also cite driver acceptance of new equipment or technologies as a barrier to their adoption. HDV driver turnover is high in the U.S., and businesses that operate HDVs are concerned about retaining their best drivers. Therefore, they may avoid technologies that require significant new training or adjustments in driver behavior. For some technologies that can be used to meet the proposed standards, such as automatic tire inflation sy stems, training costs are likely to be minimal. Other technologies such as stop - start sy stems, however, may require

D Some bo at - tails are being developed with technology to open them automatically when the trailer reaches a suitable speed, to reduce this problem.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 8 drivers to adjust their expectations about vehicle operation, and it is difficult for the agencies to anticipate how drivers will respond to such changes. E In addition to these factors, the studies considered other possible explanations for HDV buyers’ apparent reluctance or slowness to invest in fuel - saving equipment or technology . Financial constraints

– access to lending sources willing to finance purchases of more expensive vehicles

– do not appear to be a problem for the medium - and large - sized businesses participating in Klemick et al.’s (2013) study. However, Roeth et al. (2013) noted that access to capital can be a significant challenge to smaller or independent businesses , and that price is alway s a concern to buyers. In general, businesses that operate HDVs face a range of competing uses for available capital other than investing in fuel - saving technologies, and may assign higher priority to these other uses, even when investin g in higher fuel efficiency HDVs appears to promise adequate financial returns. Other potentially

important barriers to the adoption of measures that improve fuel efficiency may arise from “network externalities,” where the benefits to new users of a technology

depend on how many

others have already

adopted it. One example where network externalities seem likely

to arise is the market for natural gas - fueled HDVs: the limited availability

of refueling stations may reduce potential buyers’ willingness to purchase natural gas - fueled HDVs, while the small number of such HDVs in - use does not provide sufficient economic incentive to construct more natural gas refueling stations. Some businesses that operate HDVs may also be concerned about the difficulty in lo cating repair facilities or replacement parts, such as single - wide tires, wherever their vehicles operate. When a technology has been widely

adopted, then it is likely

to be serviceable even in remote or rural places, but until it becomes widely available , its earl y adopters may face difficulties with repairs or replacements. By accelerating the widespread adoption of these technologies, the proposed standards may assist in overcoming these difficulties. As discussed previousl y, the lack of availability o f fuel - saving technologies from preferred manufactures can also be a significant barrier to adoption (Roeth et al. 2013). Manufacturers may

be hesitant to offer technologies for which there is not strong demand, especiall y if the technologies require significant research and development expenses and other costs of bringing the technology to a market of uncertain demand. Roeth et al. (2013) also noted that it can take ye ars, and sometimes as much as a decade, for a specific technology

to become available f

rom all manufacturers. Many manufacturers prefer to observe the market and follow other manufacturers rather than be the first to market with a specific technology . The “first - mover disadvantage” has been recognized in other research where the “first - mov er” pay s a higher proportion of the costs of developing technology , but loses the long - term advantage when other businesses follow quickly . 6 In this way , there may

E The distinction between simply requiring drivers (or mechanics) to adjust their expectations and compromises in vehicle performance or utility is subtle. While the former may not impose significant compliance costs in the long run, the latter would represent additional economic costs of complying with the standard.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 9 be barriers to innovation on the supply side that result in lower adoption rates of fuel - ef ficiency technology

than would be optimal. In summary , the agencies recognize that businesses that operate HDVs are under competitive pressure to reduce operating costs, which should compel HDV buyers to identify and rapidly adopt cost - effective fuel - savi ng technologies. Outlays for labor and fuel generall y constitute the two largest shares of HDV operating costs, depending on the price of fuel , distance traveled, type of HDV , and commodity transported (if any), so businesses that operate HDVs face strong incentives to reduce these costs. 7 , 8 However, the short pay

back periods that buye rs of new HDVs appear to require suggest that some combination of uncertainty about future cost savings, transactions costs, and imperfectl y functioning markets impedes this process. M arket s for both new and used HDVs may face these problems , although it is difficult to assess empiricall y the degree to which they actuall y do. Even if the benefits from widespread adoption of fuel - saving technologies exceed their costs, their use may

remain limited or spread slowly because their early

adopters bear a disproportionate share of those costs. In this case, the proposed standards may help to overcome such barriers b y ensuring that these measures would be widely adopted. Providing information about fuel - saving technologies, offering incentives for their adoption, and sharing HDV operators’ real - world experiences with their performance through voluntary programs such as EPA’s SmartWay Transport Partnership should assist in the adoption of new cost - saving technologies. Nevertheless, other barriers that impede the diffusion of new technologies are likely

to remain. Buye rs who are willing to experiment with new technologies expect to find cost savings, but th ose savings may be difficult to verify

or replicate. As noted previously , because benefits from employing these technologies are likel y to vary with the characteristics of individual routes and traffic patterns, buyers of new HDVs may find it difficult to identify

or verify the effects of fuel - saving technologies in their operations. Risk - averse buyers may

also avoid new technologies out of concerns over the possibility of inadequate returns on their investments, or with other possible adverse impacts. Some HDV manufacturers may delay in investing in the development and production of new technologies, instead waiting for other manufacturers to bear the risks of those investments first. Competitive pressures in the HDV freight transport industry can prov ide a strong incentive to reduce fuel consumption and improve environmental performance. However, not every HDV operator has the requisite ability or interest to access and utilize the technical information, or the resources necessary to evaluate this in formation within the context of his or her own operations. As discussed previousl y, whether the technologies available to improve HDVs’ fuel efficiency would be adopted widely in the absence of the program is challenging to assess. To the extent that thes e technologies would be adopted in its absence, neither their costs nor their benefits would be attributed to the program. To account for this possibility , the agencies anal yz ed the proposed standards and the regulatory alternatives against two reference cases , or baselines, as described in Section X . The first c ase uses a baseline that projects some improvement in fuel efficiency for new trailers, but no improvement in fuel efficiency for other vehicle segments in the absence of new***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 10 Phase 2 standards. Th is first case is referred to as the less dy namic baseline, or Alternative 1a. The second case uses a baseline that projects some improvement in vehicle fuel efficiency for tractors, trailers, pickup trucks, and vans but not for vocational vehicles. This second case is referred to as the more dynamic baseline, or Alternative 1b . The agencies will continue to explore reasons for the slow adoption of readily available and apparently

cost - effective technologies for improving fuel efficiency . We also will rev iew any comments we receive on our hy potheses about its causes, as well as data or other information that can inform our understanding of why this situation seems to persist. 

### 8.3 **Analysis of the Rebound Effect** The “rebound effect ” has been defined a number of way s in the literature, and one common definition states that the rebound effect is the increase in demand for an energy service when the cost of the energy service is reduced due to efficiency improvements . 9 , 10 , 11 Inthe context of heavy- duty vehicles (HD Vs ), this can be interpreted as an increase in HDV fuel consumption resulting from more intensive vehicle use in response to increased vehicle fuel efficiency . F Although much of this vehicle use increase is likel y to take the form of increases in the number of miles vehicles are driven, it can also take the form of increases in the loaded weight at which vehicles operate or changes in traffic and road conditions vehicles encounter as operators alter their routes and schedules in response to improved fuel efficiency . Because this more intensive use consumes fuel and generates emissions, it reduces the fuel savings and avoided emissions that would otherwise be expected to result from the increases in fuel e fficiency this rulemaking proposes. Unlike the light - duty vehicle (LDV) rebound effect, the HDV rebound effect has not been extensively

studied. According to a 2010 HDV report published b y the National Research Council of the Natio nal Academ ies (NRC) 12 , it is “not possible to provide a confident measure of the rebound effect,” yet NRC concluded that a HDV rebound effect probably exists and that , “estimates of fuel savings from regulatory standards will be somewhat misestimated if the rebound effect is not considered.” Although we believe the HDV rebound effect needs to be studied in more detail, we have nevertheless attempted to capture its potential effect in our anal ys is of th ese proposed rule s , rather than to await further study. We have elected to do so because the magnitude of th e rebound effect is an important determinant of the actual fuel savings and emission reductions that are likel y to result from adopting stricter fuel efficiency and GHG emission standards. In our anal ys is and discussion below, we focus on one widel y- used metric to estimate the rebound effect associated with all ty pes of more intensive vehicle use, the increase in vehicle miles traveled (VMT) that results from improved fuel efficiency . VMT can often provide a

F We discuss other potential rebound effects in secti on 8.3 .3 , such as the indirect and economy - wide rebound effects. Note also that there is more than one way to measure HDV energy services and vehicle use . The agencies’ analyses use VMT as a measure (as discussed below) ; other potential measures include ton - miles, cube - miles, and fuel consumption.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 11 reasonable approximation for all ty pes of more intensive vehicle use. For simplicity, we refer to this as “the VMT rebound effect” or “VMT rebound” throughout this section, although we acknowledge that i t is an approximation to the rebound effect associated with all ty pes of more intensive vehicle use . The agencies use our VMT rebound estimates to generate VMT inputs that are then entered into the EPA MOVES national emissions inventory model and the Volp e Center’s HD CAFE model . B oth of these models use these inputs along with many others to generate projected emissions and fuel consumption changes resulting from each of the regulatory alternatives analyzed. Using VMT rebound to approximate the fuel cons umption impact from all ty pes of more intensive vehicle use may not be completel y accurate. Many factors other than distance traveled

– for example, a vehicle’s loaded weight

– play a role in determining its fuel consumption, so it is also important to co nsider how changes in these factors are correlated with variation in vehicle miles traveled. Empirical estimates of the effect of weight on HDV fuel consumption vary , but universally

show that loaded weight has some effect on fuel consumption that is independent of distance traveled. Therefore , the product of vehicle payload and miles traveled, which ty picall y is expressed in units of “ ton - miles ” or “ ton - kilometers ”, has also been considered as a metric to approximate the rebound effect. Becau se this metric’s value depend s on both payload and distance , it is important to note that changes in these two variables can have different impact s on HDV fuel consumption. This is because the fuel consumed by HDV freight transport is determined b y several vehicle attributes including engine and accessory efficiencies, aerodynamic characteristics, tire rolling resistance and total vehicle mass

—including payload carried, if any. Other factors such as vehicle route and traffic patterns can also affec t how each of the se vehicle attributes contribute s to the overall fuel consumption of a vehicle. While it seems intuitive that if all of these other conditions remain constant , a vehicle driving the same route and distance twice will consume twice as much fuel as driving that same route once . However, because of the other vehicle attributes, it is less intuitive how a change in vehicle payload would affect vehicle fuel consumption. Because the factors influencing HDV VMT rebound are generally different f

rom those affecting LDV VMT rebound, much of the research on the LDV sector is likely to not appl y to the HDV sector. For example, the owners and operators of LDVs may respond to the costs and benefits associated with changes in their per sonal vehicle’s fuel efficiency very differentl y than a HDV fleet owner or operator would view the costs and benefits (e.g., profits, offering more competitive prices for services) associated with changes in their HDVs’ fuel efficiency . To the extent the response differs, such differences may be smaller for HD pickups and vans, which share some similarities with LDVs . As discussed in the 2010 NRC HD report, one difference from the LDV case is that when calculating the change in HDV costs that causes the rebound effect, it is more important to consider all components of HDV operating costs . The costs of labor and fuel generally

constitute the two largest shares of HDV operating costs, depending on the price of petroleum, distance traveled, type of vehicle , and commodity transported (if any). 13 , 14 Equipment depreciation costs associated with the purchase or lease of an HDV are another significant component of total operating costs (Figure 8 - 1) . Even when HDV purchases involve upfront, one - time pay ments, HDV operators must recover the depreciation in the value***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 12 of their vehicles resulting from their use, so this is likely to be considered as an operating cost they will attempt to pa ss on to final consumers of HDV operator services .

**Figure 8**  **- 1**  **Average Truck Operation Costs** Estimates of the impact of fuel efficiency standards on HDV VMT , and hence fuel consumption, should account for changes in all of these components of HDV operating costs . The higher the net savings in total operating costs is , the higher the expected rebound effect would be. Conversel y, if higher HDV purchase costs outweigh future cost savings and total operating costs increase, HDV costs could rise, which would likely result in a decrease in HDV VMT. In theory, other cost changes resulting from any requirement to achieve higher fuel efficiency , such as changes in maintenance costs or insurance rates, should also be taken into account, although information on these elements of HDV operating costs is extremely limited. In this analysis, the agencies adapt estimates of the VMT rebound effect to project the response of HDV use to the estimated changes in total operating costs that result from the proposed Phase 2 standards.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 13 Since businesses are profit - driven, one would expect their decisions to be based on the costs and benefits of different operating decisions, both in the near- term and long - ter m. Specificall y, one would expect commercial HDV operators to take into account changes in overall operating costs per mile when making decisions about HDV use and setting rates they charge for their services. If demand for those services is sensitive to the rates HDV operators charge, HDV VMT could change in response to the effect of higher fuel efficiency on the rates HDV operators charge . I f demand for HDV services is insensitive to price (e.g., due to lack of good substitutes), however, or if changes in HDV operating costs due to the proposed standards are not passed on to final consumers of HDV operator service s , the proposed standards may have a limited impact on HDV VMT. The following sections describe the factors affecting the magnitude of HDV VMT rebound ; review the econometric and o th er evidence related to HDV VMT rebound ; and summarize h ow we e stimate d the HDV rebound effect for this p roposal . 

### 8.3.1 **Factors Affecting the Magnitude of HDV VMT**  **Rebound** The magnitude and timing of HDV VMT rebound result from the interaction of many different factors. 15 Fuel savings resulting from fuel efficiency standards may cause HDV operators and their customers to change their patterns of HDV use and fuel consumption in a variety of way s. For example, HDV operators may pass on the fuel cost savings to their customers b y decreasing prices for shipping products or providing services, which in turn could stimulate more demand for those products and services (e.g., increases in freight output), and result in higher VMT. As discussed later in this section, HDV VMT rebound estimates determined via other proxy elasticities vary

widely , but in no case has there been an estimate that fully offsets the fuel saved due to efficiency improvements (i.e., no rebound effect greater than or equal to 100 percent). If fuel cost savings are passed on to the HDV operators’ customers (e.g., logistics businesses , manufacturers, retailers , municipalities, utilities consumers ), those customers m ight reorganize their logistics and distribution networks over time to take advantage of lower operating costs. For example, customers might order more frequent shipments or choose products that entail longer shipping distances, while freight carriers might divert some shipments to trucks from other shipping modes such as rail, barge or air . In addition, customers m ight choose to reduce the ir number of warehouses, reduce shipment rates or make smaller but more frequent shipments, all of which could lead to an increase in HDV VMT. Ultimately , fuel cost savings could ripple through the entire economy , thus increasing demand for goods and services shipped by

trucks, and therefore increase HDV VMT due to increased gross domestic product (GDP) . Conversel y, if fuel efficiency standards lead to net increases in the total cost s of HDV operation because fuel cost savings do not fully offset the increase in HDV purchase prices and associated depreciation costs, then the p rice of HDV services could rise . This is likely to spur a decrease in HDV VMT , and perhaps a shift to alternative shipping modes. These effects could also ripple through the economy and affect GDP . Note, however, that we project fuel cost savings will of

fset technology

costs in our analy sis supporting our proposed standards .***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 14 It is also important to note that any increase in VMT on HDVs impacted by our proposed standards may

be offset, to some extent, by a decrease in VMT on older HDVs. This may occur if lower fuel costs resulting from our standards cause multi - vehicle fleet operators to shift VMT to newer, more efficient HDVs in their fleet or cause operators with newer, more efficient HDVs to be more successful at winning contracts than operators with older HDVs . Also, as discussed in Chapter 8 .3.3 of th is Draft RIA, the magnitude of the rebound effect is likely to be influenced by

the extent of any

market failures that affect the demand for more fuel efficient HD Vs , as well as by HDV op erators’ responses to the ir perception of the tradeoff between higher upfront HDV purchase costs versus lower but uncertain future expenditures on fuel. 

### 8.3.2 **Econometric and Other Evidence Related to HDV VMT Rebound** As discussed above, HDV VMT rebound is def ined as the change in HDV VMT that occurs in response to an increase in HDV fuel efficiency . We are not aware of any studies that directly estimate this elasticityG for the U.S. This section discusses econometric anal ys es of other related elasticities th at could potentially be used as a prox y for measuring HDV VMT rebound , as well as other anal ys es that may provide insight into the magnitude of HDV VMT rebound . One of the challenges t o developing robust econometric anal ys es of HDV VMT reboun d in the U.S . is data limitations. For example, the main source of time - series HDV fuel efficiency data in the U.S. is derived from aggregate fuel consumption and HDV VMT data . This may introduce interdependence or “simultaneity” between measures of HD V VMT and HDV fuel efficiency , because estimates of HDV fuel efficiency are derived partl y from HDV VMT . This mutual interdependence makes it difficult to isolate the causal effect of HDV fuel efficiency on HD V VMT and to measure the response of HDV VMT to changes in HDV fuel efficiency . Data on other important determinants of HDV VMT , such as freight shipping rates, shipment sizes , HDV payloads, and congestion levels on key HDV routes is also limited, of questionable reliability , or unavailable. Additionally , data on HDVs and their use is usually onl y available at an aggregate level, making it difficult to evaluate potential differences in determinants of VMT for different types of HDV operations (e.g., long - haul freight vs. regional delivery operations) or vehicle sub - classes (e.g., utility vehicles vs. school buses). Another challenge inherent in using econometric techniques to measure the response of HDV VMT to HDV fuel efficiency is developing model specifications that incorporate the mathematical form and rang e of explanatory variables necessary

to produce reliable estimates of

G E lasticity is the measurement of how responsive an economic variable is to a change in another. For example: price elasticity of demand is a measure used in economics to show the responsiveness, or ela sticity, of the quantity demanded of a good or service to a change in its price. More precisely, it gives the percentage change in quantity demanded in response to a one percent change in price .***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 15 HDV VMT rebound . Many different factors can influence HDV VMT, and the complex relationships among those factors should be considered when measuring the rebound effect. 16 In practice, however, most studies have employed simpl ified models. Many use price variables (e.g., price per gallon of fuel, or fuel cost per mile driven) and some measure of aggregate economic activity , such as GDP . However, some of these studies exclude potentiall y important variables such as the amount of road capacity (which affects travel speeds and may be related to other important characteristics of highway infrastructure), or the price or availability of competing forms of freight transport such as rail or barge (i.e., characteristics of the overall freight transport network). 

### 8.3.2.1 **Fuel Price and Fuel Cost Elasticities** This sub - section reviews econometric anal ys es of the change in HDV use (measured in VMT, ton - mile, or fuel consumption) in response to change s in fuel price ($/gallon) or fuel cost ($/mile or $/ton - mile). The studies presented below attempt to estimate these elasticities in the HDV sector using varying approaches and data sources. Gatel y (1990) employed an econometric anal ys is of U.S. data for the years 1966

– 1988 to examine the relationship between HDV VMT and average fuel cost per mile, real Gross National Product ( GNP ) , and variables capturing the effects of fuel shortages in 1974 and 1979. 17 The study f ound no statistically

significant relationship between HDV VMT and fuel cost per mile. Gately ’s estimates of the elasticity of HD V VMT with respect to fuel cost per mile were - 0.035 with and - 0.029 without the fuel shortage variables , but both estimates h ad large standard errors . However, Gatel y’ s study was beset by

numerous statistical problems, which raise serious questions about the reliability of its results. H More recentl y, Matos and Silva (2011) anal yz ed road freight transportation sector data for the ye ars 1987

– 2006 in Portugal to identify the determinant s of demand for HDV freight transportation . 18 Using a reduced - form equation relating HDV use (measured in ton - km) to economic activity

(GDP) and the energy

cost of HDV use (measured in fuel co st per ton - km carried) , these authors estimated the elasticity of HDV ton - km with respect to energy costs to be

- 0.24 1 . An important strength of Matos and Silva’s study is that it also estimated this same elasticity using a procedure that accounted for the effect of potential mutual causality between HDV ton - km and energy

costs , and arrived at an identical value. Differences between HDV use and the level of highway service in Portugal and in the U.S. might limit the applic ability of Matos and Silva’s result to the U.S. The volume and mix of commodities could differ between the two nations , as could the levels of congestion on their respective highway

networks, transport distances, the extent of intermodal competition, and the

H The most important of these problems

– similar historical time trends in the model’s dependent variable and the measures used to explain its historical variation

– can lead to “spurious regressions,” or the appearance of behavioral relationships that are simply artifacts of the similarity (or correlation) in historical trends among the model’s variables.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 1 6 characteristics of HDVs themselves. HDVs also operate over a more limited highway network in Portugal than in the United States. Unfortunately , it is difficult to anticipate how these differences might cause Matos and Silva’s elasticity estimate s to differ from what we might find in the U.S. Finally , their anal ys is focused on HDV freight transport and did not consider non freight uses of HDVs , which somewhat limits its usefulness in th e anal ys is of this proposed rulemaking . De Borger and Mulalic (20 12) examined the determinants of fuel use in the Denmark HDV freight transport sector for the ye ars 1980

– 2007. The authors developed a sy stem of equation s that capture linkages among the demand for HDV freight transport , HDV fleet characteristics, and H DV fuel consumption. 19 As De Borger and Mulalic state, “we precisely define and estimate a rebound effect of improvements in fuel efficiency in the trucking industry : behavioral adjustments in the industry imply that an exogenous improvement in fuel efficiency reduces fuel use less than proportionatel y. Our best estimate of this effect is approximately 10 percent in the short run and 17 percent in the long run, so that a 1 percent improvement in fuel efficiency reduces fuel use by

0.90 percent (short - run) to 0.83 percent (long - run). ” While De Borger and Mulalic capture a number of important responses that contribute to the rebound effect , some caution is appropriate when using their results to estimate the VMT rebound effect for this proposal. Like the Matos and Silva study, this study examined HDV activity in another country , Denmark, which has a less - developed highway sy stem, lower levels of freight railroad service than the U .S., and is also likely to have a different composition of freight shipping activity . Although the effect of some of these differences i s unclear, greater competition from rail shipping in the U.S. and the resulting potential for lower trucking costs to divert some rail freight to truck could cause the VMT rebound effect to be larger in the U.S. than De Borger and Mulalic’s estimate for Denmark. On the other hand, if freight networks are denser and commodity ty pes are more homogenous in Denmark than the U .S. , then shippers may have wider freight trucking options . If this is the case, shippers in Denmark might be more sensitive to changes in freight costs, which could cause the rebound effect in Denmark to be larger than the U.S. Like the Matos and Silva study, this analy sis also focuses on freight trucking and does not consider non - freight HDVs (e.g. vocational vehicles). We have been unable to identify adequate data to empl oy De Borger and Mulalic’s model for the U.S. (mainl y because time - series data o n freight carriage b y trucks, driver wages, and vehicle prices in the U.S. are limited). The Volpe National Transportation Sy stems Center previousl y has developed a series of travel forecasting models for the Federal Highway Administration (FHWA). 20 Work conducted by the Volpe Center during 2009 - 2011 to develop the original version of FHWA’s forecasting model was presented in the Regulatory Impact Analy sis for the HD GHG Phase 1 rule ( see Ta ble 9 - 2 in that document: “Range of Rebound Effect Estimates from NHTSA Econometric Analy sis” ). 21 Inthe anal ys is for the Phase 1 rule, Volpe estimated both state - level and national aggregate models to forecast HDV single unit and combination truck VMT that included fuel***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 17 cost per mile as an explanatory variable. This analy sis used data from 1970

– 20 08 for its n ational aggregate model, and data for the 50 individual states from 199 4

– 20 08 for its state level model. I , J Volpe anal ys ts tested a large number of different specifications for its national and state level models that incorporated the effects of factors such a s aggregate economic activity and its composition, the volume of U.S. exports and imports, and factors a ffecting the cost of producing trucking services (e.g., driver wage rates, truck purchase prices, and fuel costs), and the extent and capacity of the U.S. and states’ highway networks. Table 8 - 1 summarizes Volpe’s Phase 1 estimates of the elasticity of truck VMT with respect to fuel cost per mile. K As it indicates, these estimates vary

widel y, and the estimates based on state - level and national data differ substantially . **Table 8**  **- 1**  **Summary of Volpe Center Estimates of Elasticity of Truck VMT with Respect to Fuel Cost per**  **Mile** Truck T ype 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% Volpe staff conducted additional analy sis of the models that yielded the estimates of the elasticity of truck VMT with respect to fuel cost per mile reported in Table 8 - 1 , using updated information on fuel costs and other variables appearing in these models, together with revised historical data on truck VMT provided b y DOT’s Federal Highway Administration. The newl yavailable data, statistical procedures emplo yed in conducting this additional analysis, and its results are summarize d in materials that can be found in the docket for this rulemaking. This new Volpe anal ys is was not available at the time the agencies selected the values of the rebound effect for this proposal, but the agencies will consider this work and any other new work that becomes available in the final rule .

I Com bination trucks are defined as “all [Class 7/8] trucks designed to be used in combination with one or more trailers with a gross vehicle weight rating over 26,000 lbs.” (AFDC, 2014; ORNL, 2013c). Single - unit trucks are defined as “single frame trucks that have 2

– axles and at least 6 tires or a gross vehicle weight rating exceeding 10,000 lbs.” (FHWA, 2013). J The national - level and functional class VMT forecasting models utilize aggregate time - series data for the nation as a whole, so that only a single measure of each variable is available during each time period (i.e., year). In contrast, the state - level VMT models have an additional data dimension, since both their dependent variable (VMT) and most explanatory

variables have 51 separate observations available for each time period (one for each of the 50 states as well as Washington, DC). In this context, the states represent a “cross - section,” and a continuous annual sequence of these cross - sections is avail able. K One draw

back of the fuel cost measure employed in Volpe’s models is that it is based on estimates of fuel economy derived from truck VMT and fuel consumption, which introduces the potential for mutual causality (or “simultaneity”) between VMT and the fuel cost measure and makes the effect of the latter difficult to isolate. This may cause their estimates of the sensitivity of truck VMT to fuel costs to be inaccurate, although the direction of any resulting bias is difficult to anticipate.***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 18 Finally , EPA has contracted with Energy and Environmental Research Associates (EERA), LLC to anal yz e the HDV rebound effect for regulatory assessment purposes. Excerpts of EERA ’s initial report to EPA are included in the docket and contain detailed qualitative discussions of the rebound effect as well as data sources that could be used in quantitative anal ys is. 23 EERA also conducted follow - on quantitative anal ys es focused on estimating the impact of fuel prices on VMT and fuel consumption . W e have included a working paper in the docket on this work . 24 Note that EERA’s working paper was not availab le at the time the agencies conducted the analy sis of the rebound effect for this proposal, but the agencies will consider this work and any other work in the final rule. There are reasons to be cautious about interpreting the elasticities from the studies reviewed in this section as a measure of VMT rebound resulting from our proposed standards. For example, vehicle capacity and loaded weight can vary dy namically in the HDV sector

– possibly in response to changes in fuel price and fuel efficiency

– and data on these measures are limited. This makes it difficult to confidentl y infer a direct relationship between trucking output (e.g., ton - miles carried) and VMT assuming a constant average payload. In addition, fuel cost per mile

– calculated b y multiplying fuel price per g allon by fuel efficiency in gallons per mile

– and fuel price may be imprecise prox ies for an improvement in fuel efficiency , because the response of VMT to these variables may differ. For example, if truck operators are more attentive to variation in fuel prices than to changes in fuel efficiency , then fuel price or fuel cost elasticities may overstate the true magnitude of the rebound effect. Similarly, there is some evidence in the literature that demand for crude petroleum and refined fuels is more responsive to increases than to decreases in their prices, although this research is not specific to the HDV sector . 25 Since improved fuel efficiency ty pically causes fuel costs for HDVs to fall (and assuming fuel costs ar e not fully offset by increases in vehicle purchase prices), fuel price or cost elasticities derived from historical periods when fuel prices were increasing or fuel efficiency was declining may

also overstate the magnitude of the rebound effect . An additional unknown is that HDV operators may factor fuel prices and fuel costs into their decision - making about rates to charge for their service differentl y from the way they incorporate initial vehicle purchase costs. Despite these limitations, elasticities with respect to fuel price and fuel cost can provide some insight into the magnitude of the HDV VMT rebound effect. 

### 8.3.2.2 **Freight Price Elasticities** Freight price elasticities measure the percent change in demand for freight in response to a percent change in freight prices, controlling for other variables that may influence freight demand such as GDP, the extent that goods are traded internationall y, and road supply and capacity . This type of elasticity is only applicable to the HDV subcategory of freight trucks (i.e., combination tractors and vocational vehicles that transport freight). One desirable attribute of***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review *****

8 - 19 such measures for purposes of this anal ys is is that they show the response of freight trucking activity to changes to trucking rates, including changes that result from fuel cost savings as well as increases in HDV technology costs. M Freight price elasticities, however, are imperfect proxi es for the rebound effect in freight trucks for a number of reasons. 26 For example, in order to appl y these elasticities we must assume that our proposed rule’s impact on fuel and vehicle costs is fully reflected in freight rates. This may not be the case if truck operators adjust their profit margins or other operational practices (e.g., loading practices, truck driver’s wages) instead of freight rates. Itis not well understood how trucking firms respond to different ty pes of cost changes (e.g., changes to fuel costs versus labor costs). Freight price elasticity e stimates in the literature ty pically measure freight activity in tons or ton - miles, rather than VMT . As discussed in the previous section, average truck capacity and payload in the HDV sector varies dy namically

– possibly

in response to changes in fuel price and fuel efficiency

– and data on these measures are limited. This makes it difficult to confidently infer a direct relationship between ton - miles and VMT by assuming a constant average payload . In ferring a direct relationship between tons and VMT is even less straightforward. Additionall y , there are significant limitations on national freight rate and freight truck ton - mile data in the U.S., making it difficult to confidently measure the impact of a change in freight rates on ton miles. 27

M Note however that a percent change in freight activity in response to a percent change in freight rates should theoretically be larger than a percent change in freight activity in response to a percent change in fuel efficiency because fuel efficiency only imp acts a portion of freight operating costs (e.g., fuel and vehicle costs, but not likely driver wages or highway tolls).***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review ** **

8 - 20 **Table 8**  **- 2**  **An Illustration of the Impact o**  **f Various Factors on t**  **he Elasticity of Dem and**  **f or HDV Freight Services With Respect**  **t o the Price**  **o f Those**  **Services FACTOR OF**  **VARIABILITY (1)**  **SOURCE(2) LEAST**  **ELASTIC (3)**  **FACTORS OF**

**VARIABILITY FOR LEAST ELASTIC**  **VALUE (4) MOST**  **ELASTIC (5)**  **FACTORS OF**

**VARIABILITY FOR MOST ELASTIC**  **VALUE (6) REGION**  **(7) DEMAND** 

**MEASURE (8) COMMODITY** 

**(9) Commodity**  **shipped** Abdelwahab (1998) - 0.75 Construction - 1.40 Textile products US A Mode choice Varies

Friedlaender & Spady (1980) - 1.00 Food Products - 3.55 Electrical machinery USA Ton - miles Varies Oum et al (1990) - 0.41 Metallic products - 1.07 Fuel oil Canada Ton - miles Varies Winston (1981) - 0.14 Lumber, wood and Furniture - 2.96 Transport Equipment USA Tons Varies Friedlaender & Spady (1980) - 0.15 Wood/wood products - 5.06 Electrical machinery Southern USA Ton - miles Varies Friedlaender and Spady (1981) - 0.59 Petroleum products - 1.72 Wood USA tonne - km Various Campisi and Gastaldi (1996) - 0.27 Petroleum products - 1.37 Minerals Italy tonnes Varies Li et al. (2011) - 1.09 Other - 1.30 Nature resource USA, Italy & India tonne - km Varies Bonilla (2008) - 0.43 Oil and Coal - 1.75 Building materials Denmark tonne - km Varies **Distance shipped**  Beuthe et al. (2001) - 1.06 <300 km - 1.31 >300 km Belgium tonne - km Aggregate

Winston ( 1981 ) - 0.34 <900 miles (average) - 1.56 >900 miles (average) USA tons Varies Christidis and Leduc (2009) - 0.21 < 800 km - 1.15 > 1500 km EU Tons All **Competing mode**  Rich et al (2011) - 0.08 All O - D pairs - 0.11 O - D Pairs w/alternatives Scandinavia tonne - km Agricultural products **Demand Measure**  Beuthe et al. (2001) - 0.58 Tonnes - 1.06 tonne - km Belgium -- Aggregate

Li et al. (2011) - 1.02 Tonnes - 1.30 tonne - km USA, Italy & India -- Natural Resources **Region** Abdelwahab (1998) - 0.80 USA - 2.18 Southwestern USA Various U.S. Mode Choice Metal products

Friedlaender & Spady (1980) - 1.66 Mountain - Pacific USA - 5.06 Southern USA Various U.S. Ton - miles Electrical Machinery Li et al. (2011) - 0.86 Canada - 1.96 Australia Various tonne - km Natural Resources **Model Form** Oum et al. (1992) - 0.69 Translog 1.34 Log - linear Various All Aggregate

S ource: J.J. Winebrake et al./Energy Policy 48 (2012) 252 - 259***** E.O. 12866 Review –**  **REVISED –**  **Do Not Cite, Quote, or Release During Review ** **

8 - 21 This Page Intentionally Left BlankF inally , freight price elasticity estimates in the literature vary significantl y based on commodity

ty pe, length of haul, region, availability of alternative modes (discussed further in Section 8. 3. 2. 3 below), and functional form of the model (i.e., log - linear, linear, translog) making it difficult to confidently appl y any single estimate reported in the literature to nationwide freight activity ( Table 8 - 2 ) . For example, elasticity estimates for longer trips tend to be larger in magnitude than those for shorter trips, while demand to ship bulk commodities tends to be less elastic than for non - bulk commodities. Although these factors explain some of the difference s among reported estimates, much of the observed variation cannot be explained quantitatively . For example, one study that controlled for mode, commodity class, demand elasticity

measure (i.e., tons or ton - miles), model estimation form, country , and temporal nature of data only accounted for about half of the observed variation. 28 

### 8.3.2.3 **Mode Shift Case Study** Although the total demand for freight transport is generall y determined b y economic activity , there is often the choice of shipping freight on modes other than HDVs . This is because the United States has extensive rail, waterway , pipeline, and air transport networks in addition to an extensive highway

network; these networks often closel y parallel each other and are often viable choices for freight transport for many long - distance shipping routes within the continental U.S. I f rates for one mode decline, demand for that mode is likely to increase, and some of this new demand could represent shifts from other modes. N The “cross - price elasticity of demand,” which measures the percentage change in demand for shipping by another mode ( e.g. , rail) given a percentage change in the price of HDV freight transport services , provides a measure of the importance of such mode shifting. Aggregate estimates of cross - pric e elasticities vary widel y29 , and there is no general consensus on the most appropriate value to use for anal ytical purposes. When considering intermodal shift, one of the most relevant kinds of shipments are those that are competitive between rail and HD V modes. These trips generall y 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 generall y not economically transferable between HDV and rail modes, so they would not be expected to shift modes except under an extreme price change. However, to the best of our knowledge, the total amount of freight that could potentially be s ubject to mode shifting has not been studied extensively . In order to explore the potential for HD V fuel efficiency standards to produce economic conditions that favor a mode shift from rail to HDVs , EPA commissioned GIFT Solutions, LLC to perform case s tudies on the HD GHG Phase 1 rule using a number of data sources, including the Commodity

Flow Survey , interviews with trucking firms, and the Geospatial Intermodal

N Rail lines in parts of the U.S. are thought to be currently oversubscribed. If that is the case, and new freight demand is already be ing satisfied by trucks, then this would limit the potential for intermodal freight shifts between trucks and rail as the result of this proposed rule.Freight Transportation (GIFT) model developed by Winebrake and Corbett, which includes info rmation on infrastructure and other route characteristics in the U.S. 30 , 31 A central assumption in the case studies was that economic conditions would favor a shift from rail to HDVs if either the price per ton - mile to ship a commodity by HDV , or the price to ship a given quantity

of a commodity

by HDV , became lower relative to rail transport options post - regulation. The results of the case studies indicate that the HD Phase 1 rule would not seem to create obvious economic conditions that lead to a mode shift from rail to truck, but there are a number of limitations and caveats to this analy sis, which are discussed in the final report to EPA by GIFT. 32 , 33 For example, even if tr ucking did not become less expensive than rail post regulation, a relative decrease in the truck versus rail rates might be enough to produce a shift, given that other factors could influence shippers’ decisions on modal choice. The study did not, however , consider these other factors such as time - of - delivery and modal capacity. As another example, the anal ys is assumes all fuel cost savings and incremental vehicle costs from the HD Phase 1 rule would be passed on to shippers via changes in freight rates, even though the anal ys is found some evidence that this might not occur (in two cases, the charges for shipping a truckload over a given route and distance were the same despite differences in payloads that should have been reflected in their fuel costs). Given these limitations, more work is needed in this area to explore the potential for mode shift in response to HD fuel efficiency standards. 

### 8.3.2.4 **Case Study Using Freight Price Elasticities** Cambridge S ys tematics, Inc. (CSI) employed a case study approach using freight price elasticity estimates in the literature to show several examples of the magnitude of the HDV rebound effect. 38 In their unpublished paper commissioned by the National Research Council of the National Academ ies in support of its 2010 HDV report, CSI estimated the effect on HDV VMT from a net decrease in operating costs associated with fuel efficiency improvements, using two different technology

cost and fuel savings scenarios for Class 8 combination tractors. Scenario 1 increased average fuel efficiency of the tractor from 5.59 miles per gallon to 6.8 miles per gallon, with an additional cost of $22,930 for purchas ing the improved tractor. Scenario 2 increased the average fuel efficiency to 9.1 miles per gallon, at an incremental cost of $71,630 per tractor. Both of these scenarios were based on the technologies and targets from a report authored b y the Northeast States Center for a Clean Air Future (NESCCAF) and International Council on Clean Transportation (I CCT). 39 The CSIestimates were based on a range of direct (or “own - price”) freight elasticities ( - 0.5 to - 1.5) 40 and cross - price freight elasticities (0.35 to 0.59) 41 obtained from the literature. 42 In their calculations, CSI assumed 142,706 million miles of tractor VMT and 1,852 billion ton miles were affected. The tractor VMT was based on the Bureau of Transportation Statistics’ (BTS) estimate of highway mil es for combination tractors in 2006, and the rail ton - miles were based on the BTS estimate of total railroad miles during 2006. This assumption is likely to overstate the rebound effect, since not all freight shipments occur on routes where tractors and rail service shipments compete directl y. Nevertheless, this assumption appears to be reasonable in the absence of more detailed information on the percentage of total miles and ton - miles that are subject to potential mode shifting.For CSI’s calculation s, all costs except fuel costs and vehicle costs were taken from a

2008 ATRIstudy . 43 Itis not clear from the report how the new vehicle costs were incorporated into CSI’s calculations of per- mile tractor operating costs. For example, neither the ATRI report nor the CSIreport discusses assumptions about depreciation, useful lifetimes of tractors , and the opportunity

cost of capital . Based on these two scenarios, CSIestimated the change in tractor VMT in response to a net decrease in operating costs (i .e., accounting for fuel cost and changes in tractor purchase costs) associated with fuel efficiency improvement of 11 - 31 percent for Scenario 1 and 5 - 16 percent for Scenario 2, without accounting for any fuel savings from reduced rail service. When the f uel savings from reduced rail usage were included in the calculations, they estimated the change in tractor VMT in response to a net decrease in operating costs associated with fuel efficiency improvement would be 9 - 30 percent for Scenario 1, and 3 - 15 perc ent for Scenario 2. Note that these estimates reflect changes to tractor VMT with respect to total operating costs, so they

should theoreticall y be larger than a percent change in tractor VMT with respect to a percent change in fuel efficiency because fu el efficiency

onl y impacts a portion of truck operating costs (e.g., fuel and vehicle costs, but not likely driver wages or highway tolls). CSIincluded caveats associated with these calculations. For example, their report states that freight price elasti city estimates derived from the literature are “heavil y reliant on factors including the type of demand measures anal yz ed (vehicle - miles of travel, ton - miles, or tons), geography, trip lengths, markets served, and commodities transported.” These factors c an increase variability in the results. Also, estimates in CSI’s study have the limitation of using freight price elasticities to estimate the HDV rebound effect discussed previously in Section IV .D.2.b. 

### 8.3.2.5 **Simulation Model Study Using Freight Price Elasticities**  Guerrero (2014) constructs a freight simulation model of the California trucking sector to measure the impact of fuel saving investments and fleet management on GHG emissions. 44 Rather than es timating these impacts using econometric anal ys is of raw data, the study uses values from the existing literature. Guerrero determines that “...improving the performance of trucking also increases the number of trips demanded because the market price also decreases. This ‘rebound’ effect offsets around 40 - 5 0 percent of these vehicle efficiency emission reductions, with 9 - 14 percent of the effect coming from increased pavement deterioration and 31 - 36 percent coming from increased fuel combustion.” Note that to the extent that trip lengths also vary in respon se to improvements in HDV fuel efficiency , changes in the number of HDV trips may not exactl y reflect changes in the total number of miles the vehicles are operated . However, these findings are based on freight price elasticities, which

– as we discus s in Section IV.D.2.b and in the context of the CSIstudy above

– have significant limitations. The study also simulates only one state’s freight network (California), which may not be a good representation of national activity .

### 8.3.3 **How the Agencies Estimate**  **d the HDV Rebound Effect for this Proposal** 

**8.3.3.1 Values Used in the Phase 1 Analysis**  At the time the agencies conducted their anal ys is of the Phase 1 fuel efficiency and GHG emissions standards, the only evidence on the HD V rebound effect were the previousl y described studies from CSI and the Volpe Center. P On the basis of this evidence, the agencies chose rebound effects of 15 percent for vocational vehicles and 5 percent for combination tractors , both of which were toward the lower end of the range of values f ro m these studies. The agencies found no evidence on the rebound effect for HD pickup trucks and vans, but concluded it would be inappropriate to use the values selected for vocational vehicles or combination tractors for those vehicles. Because the usage patterns of HD pickup trucks and vans can more closel y resemble those of larg e light - duty veh icles, the agencies used the 10 percent rebound effect we had employed in our most recent light - duty rulemaking to anal yz e the Phase 1 standards for 2b/3 vehicles. 

### 8.3.3.2 **How the Agencies Analyzed VMT Rebound in this Proposal**  After considering the new evidence that has become available since the HD Phase 1 final rule , the agencies elected to continue using the rebound effect estimates we used previousl y in the HD Phase 1 rule in our anal ys is of Phase 2 proposed standards. In arriving at this decision, the agencies considered the shortcomings and limitati ons of the newl y- available studies described previously , particularly the limited applicability

of the two published studies using data from European nations to the U.S. context. After weighing these attributes of the more recent studies, the agencies con cluded that we had insufficient evidence to justify revising the rebound effect values that were used in the Phase 1 anal ys is . In our assessment , we do not differentiate between short - run and long - run rebound effects, although these effects may differ. The vocational and combination truck estimates are based on the Volpe Center anal ys is presented in the HD Phase 1 rule and the case study from CSI . As with th e HD Phase 1 rule, we did not find any literature specifically

examining the HD pickup and truck sector. Since these vehicles are used for very different purposes than combination tractors and vocational vehicles , and they are more similar in use to large light - duty vehicles, we have chosen the light - duty rebound effect of 10 percent used in the final rule establishing fuel economy and GHG standards for MYs 2017 - 2025 light - duty vehicles in our anal ys is of HD pickup trucks and vans. While for this proposal , the agencies have selected to use these rebound effect values, we acknowledge the literature shows a wide range of rebound effect estimates. Therefore, w e will review and consider revising these estimates in the final rule, taking into consideration all available data and analy sis, including submissions from public commenters and new research on the rebound effect .

P The Gately study was also available, however, the agencies were not aware of the work at the time.It should be noted that the rebound estimates we have selected for our analy sis represent

the VMT impact from our proposed standards with re spect to changes in the fuel cost per mile driven. As described previously , the HDV rebound effect should ideally be a measure of the change in fuel consumed with respect to the change in overall operating costs due to a change in HDV fuel efficiency . Su ch a measure would incorporate all impacts from our proposal, including those from incremental increases in vehicle prices that reflect costs for improving their fuel efficiency . Therefore, VMT rebound estimates with respect to fuel costs per mile must be “scaled” to appl y to total operating costs, b y dividing them b y the fraction of total operating costs accounted for b y fuel . The agencies scaled the VMT rebound calculations to total operating costs using the most recent information from the American Transportation Research Institute (ATRI). 45 ATRI estimates that the average motor carrier cost per mile is $1.633 for 2012. Other elements of the total costs are listed below in Table 8 - 3 . **Table 8**  **- 3**  **Elements of the Operating Costs per Mile OPERATING COST PER M**  **ILE ATRI**  Fuel Cost $0.641 New Vehicle Cost $0.174 Maintenance & Repair Cost $0.138 All Other (labor, insurance, etc.) $0.680 Total Motor Carrier Costs $1.633 The agencies made simplifying assumptions in the VMT rebound anal ys is for this proposal, similar to the approach taken during the development of the HD GHG Phase 1 final rule . However, f

or the HD Phase 2 final rulemaking, we plan to use a more comprehensive approach. Due to timing constraints during the development of this proposal, the agencies did not have the technology

package costs for each of the alternatives prior to the need to conduct the inventory

anal ys is , except for the pickup truck and van category in analy sis Method A . Therefore, the same "overall" VMT rebound values were used for Alternatives 2 through 5 (as discussed in Chapter 8. 3 .3 of this Draft RIA and analyzed in Chapter 6 of the Draft RIA) , despite the fact that each alternative results in a different change in incremental technology and fuel costs . For the final rulemaking, we plan to determine VMT rebound separatel y for each HDV catego ry and for each alternative. Tables 64 through 66 in Chapter 7 of the Draft RIA present VMT rebound for each HDV sector that we estimated for the preferred alternative. These VMT impacts are reflected in the estimates of total fuel savings and reductions in emissions of GHG and other air pollutants presented in Section VI and VII of this preamble for all categories . For the purposes of this proposal, we made several additional simplifying assumptions when appl ying the overall rebound effect to e ach class of truck. For example, we assumed that per mile vehicle costs were based on the new vehicle cost ( e.g. , $125,000 for the reference case Class 8 combination tractor, $40,000 for the reference case HD pickups, and $70,000 for the vocational vehicl es) 46 divided by the total lifetime number of expected vehicle miles ( e.g. , 1.53 million miles for a Class 8 combination tractor, 265,869 miles for 2b/3 trucks, and 306,457 miles for vocational vehicles). 47 We recognize that this calculation implicitly as sumes that truck depreciation is strictly

a function of usage, and that it does not take into account the opportunitycost of alternative uses of capital. As a result, the new vehicle cost per mile assumptions used in

these calculations represent a smaller percentage of total operating costs compared to the ATRI and CSIexamples. The proposal assumes an “average” incremental technology cost for the alternatives, as shown in Table 8 - 4 . The technology cost of the combination tractor category is based on the HD GHG Phase 1 technology package cost, plus $2,368 for the trailer technology cost. 48 The technology

cost for HD pickup and vans is also based on the HD GHG Phase 1 technology package cost. 49 The agencies developed a unique vocational vehicle technology cost estimate because the HD GHG Phase 1 onl y represents the impact of tire and engine technologies.

**Table 8**  **- 4**  **Technology Costs Used to Determine the Rebound Effect of Each Alternative**

**VEHICLE CATEGORY TECHNOLOGY COST**  Combination Tractors $8,372 HD Pickup & Vans $985 Vocational Vehicles $1,000 The fuel costs per mile in the anal ys is were calculated using EIA’s Annual Energy Outlook 2014’s projections for diesel fuel price. 50 The average fuel economy for each category was determined using MOVES2014. The combination tractor- trail er fuel economy used was 6.03 mpg, the vocational vehicle category was 9.84 mpg, and the HD pickup category was 13 mpg. The technology

effectiveness of the alternatives in the proposal was assumed to be 20 percent for combination tractors and 15 percent f or HD pickups and vocational vehicles. The operating costs calculated based on all of these inputs are shown below in Table 8 - 5 .

**Table 8**  **- 5**  **Operating Costs for the Reference and Alternatives OPERATING COST PER M**  **ILE REFERENCE CASE**  **ALTERNATIVES** Fuel Cost $0.620 $0.517 New Vehicle Cost $0.082 $0.087 Maintenance & Repair Cost $0.138 $0.138 All Other (labor, insurance, etc.) $0.680 $0.680 Total Motor Carrier Costs $1.520 $1.422 Other simplifying assumptions include the use of an average cost rather than a marginal cost. Some trucking firms may use a marginal cost to determine whether to increase the ir fuel usage, however we do not have any data on when firms might use a marginal cost calculation rather than an average cost calculation. Although using a marginal cost might be more appropriate for calculating the rebound effect, we do not have a metho dology for calculating the marginal cost. 51 In the costs and benefits summarized in preamble Section IX . K , we have not explicitly taken into account any potential fuel savings or GHG emission reductions from the rail, air or water- borne shipping sector s due to mode shifting because estimates of this effect seem too speculative at this time . Likewise we have not taken into account any fuel savings or GHG emissions reductions from the potential shift in VMT from older HDVs to newer, more efficientHDVs. As discussed in the preamble at Section IX. E , w e have found limited evidence of the

impact of HDV fuel efficiency standards on mode shifting and no evidence on shifting activity away from older HDVs to newer HDVs . In addition, we have not attempted to capture the extent of how current market failures might impact the rebound effect. The direction and magnitude of the rebound effect in the HD truck 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 cost - effective fuel saving technologies. As a result, the rebound effect would actuall y be negative and truck VMT would decrease as a result of these regulations. However, if firms are not optimizing their behavior today due to facto rs such as lack of reliable information (see preamble Section IX .A or RIA Chapter 8.2 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 ful l expected lifetime of the trucks. If those costs are spread over a shorter period, as the expected short pay

back period implies, then those purchase costs will inhibit reduction of freight rates, and to the extent that they do so the rebound effect will be proportionally smaller. As discussed in more detail in preamble Section IX.A and RIA Chapter 8.2 , 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 origin al purchaser cannot full y 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 s hipping 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 is not the same entity

that purchases the fuel, than there would theoreticall y 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. Note that while we focus on the VMT rebound effect in our anal ys is of this proposed rule, there are at least two other ty pes of rebound effects discussed in the economics literature. In addition to VMT rebound effects, there are “indirect” rebound effects, which refers to the purchase of other goods or services (that consume energy) with the costs savings from energy efficiency improvements; and “economy- wide” rebound effects, which refers to the increased demand for energy

throughout the economy in response to the reduced market price of energy that happens as a result of energy efficiency

i mprovements.Research on indirect and economy- wide rebound effects is nascent, and we have not

identified any that attempts to quantify indirect or economy- wide rebound effects for HD Vs . In particular, the agencies are not aware of any data to indicate that the magnitude of indirect or economy- wide rebound effects, if any , would be significant for this proposed rule. R Therefore, we rel y the same analy sis of vehicle miles traveled to estimate the rebound e ffect in this proposal that we did for the HD Phas e 1 rule, where we attempt ed to quantify onl y rebound effects from our rule that impact HD V VMT. We will review any comments received a s well as any new work in this area that helps to assess and quantify different rebound effects that could result from i mprovements in HD V efficiency , including different ty pes of more intensive truck usage that affect fuel consumption but not VMT such as loaded weight, truck routing , and scheduling. In order to test the effect of alternative assumptions about the rebound effect, NHTSA examined the sensitivity

of its estimates of benefits and costs of the Phase 2 Preferred Alternative for HD pickups and vans to alternative assumptions about the rebound effect. While the main anal ys is for pickups and vans assumes a 10 percent rebound effect, the sensitivity anal ys is estimates the benefits and costs of the proposed standards under the assumptions of 5, 15, and 20 percent rebound effects. Alternative valu es of the rebound effect change the estimates of benefits and costs from the proposed standards in three way s. First, higher values of the rebound effect increase the amount of additional VMT that results from improved fuel efficiency ; this increases cost s associated with additional congestion, accidents, and noise, thus increasing total costs associated with the proposed standards. Conversel y, smaller values of the rebound effect reduce costs from additional congestion, accidents, and noise, so they redu ce total costs of the proposed standards. Second, l arger increases in VMT associated with higher values of the rebound effect reduce the value of fuel savings and related benefits (such as reductions in GHG emissions) by progressivel y larger amounts, whil e smaller values of the rebound effect cause smaller reductions in these benefits. At the same time, however, a higher rebound effect generates larger benefits from increased vehicle use, while a smaller rebound effect reduces these benefits compared to the base case. Thus the impact of alternative values of the rebound effect on total benefits from the proposed standards depends on the exact magnitudes of these latter two effects. On balance, these three effects can cause net benefits to increase or decrease for alternative values of the rebound effect.

R One entity sought reconsideration of the Phase 1 rule on the grounds that indirect rebound effects had not been considered by the agencies and could negate all of the benefits of the standard s. This assertion rested on an unsupported affidavit lacking any peer review or other indicia of objectivity. This affidavit cited only one published study. The study cited did not deal with vehicle efficiency, has methodological limitations (many of the m acknowledged), and otherwise was not pertinent. EPA and NHTSA thus declined to reconsider the Phase 1 rule based on these speculative assertions. See generally 77 FR 51703 - 04 (Aug. 27, 2012) and 77 FR 51502 - 03 (Aug. 24, 2012).**Table 8**  **- 6**  **Sensitivity of Preferred Alternative Impacts under Different Assumptions about Rebound Effect for Pickups and Vans, using 3% Discount Rate**  HD PIC KUPS AND VANS REBOUND EFFECT Main Analysis Sensitivity Cases Using

Alternative Rebound Assumptions 10 % 5 % 15 % 20 % Fuel Reductions (Billion Gallons) 7.8 8.2 7.5 7.1 GHG Reductions (MMT CO 2 eq) 94.1 95.7 87.2 83.0 Total Costs ($ billion) 5.5 5.0 6.5 7.2 Total Benefits ($ billion) 23.5 23.0 22.9 22.8 Net Benefits ($ billion) 18.0 18.0 16.4 15.5 Table 8 - 6 summarizes the impact of these alternative assumptions on fuel and GHG emissions savings, total costs, total benefits, and net benefits. As it indicates, using a 5 percent value for the rebo und effect reduces benefits and costs of the proposed standards by identical amounts, leaving net benefits unaffected. V alues of the rebound effect above 10 percent increase costs and reduce benefits from their values in the main anal ys is, thus reducing n et benefits of the proposed standards. Nevertheless, the preferred alternative has significant net benefits under each alternative assumption about the magnitude of the rebound effect for HD pickups and vans. Thus, these alternative values of the rebound effect would not have affected the agencies’ selection of the preferred alternative, as that selection is based on NHTSA’s assessment of the maximum feasible fuel efficiency standards and EPA’s selection of appropriate GHG standards . 

### 8.4 **Impact on Class Shifti ng, Fleet Turnover, and Sales**  The agencies considered two additional potential indirect effects which may lead to unintended consequences of the pro gram 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. 

### 8.4.1 **Class S**  **hifting** Heav y- duty vehicles are ty picall y 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 b y a landscape company to pull a trailer carrying lawnmowers. The purchaser makes decisions based on many attributes of the vehicle, including the gross vehicle weight rating of the vehicle, which in part determines the amount of freight or equipment that can be carried. If the proposed Phase 2 standards impact either the performance of the vehicle or the marginal cost of the vehicle relative to the other vehicle classes, then consumers may choose to purchase a different vehicle, resulting in the unintended consequence of increased fuel consumption and GHG emissions in - use.The agencies, along with the NAS panel, found that there is little or no literature which

evaluates class shifting between trucks. 52 NHTSA and EPA qualitatively evaluated the proposed rules in light of potential class shifting. The agencies looked at four potential cases of shifting: - from light - duty pickup trucks to heavy- duty pickup trucks; from sleeper cabs to day cabs; from combination tractors to vocational vehicles; and within vocational vehicles. Li ght - duty pickup trucks, those with a GVWR of less than 8,500 pounds, are currently regulated under the e xisting GHG/CAFE Phase 1 program and will meet GHG/CAFE Phase 2 emission standards beginning in 2017. The increased stringency of the light - duty 2017 - 2025 MY vehicle rule has led some to speculate that vehicle consumers may choose to purchase heav y- duty pickup trucks that are currentl y regulated under the HD Phase 1 program if the cost of the light - duty regulation is high relative to the cost to buy the larger heavy- duty pickup trucks. Since fuel consumption and GHG emissions ri se 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 heav y- duty truck ( often five to ten thousand dollars more than a light - duty pickup), we believe that such a class shift would be unlikely even absent this program. These proposed rules would continue to diminish any

incentive for such a class shift because they would narro w the GHG and fuel efficiency

performance gap between light - duty and heav y- duty

pickup trucks. The proposed regulations for the HD pickup trucks, and similarly for vans, are based on similar technologies and therefore reflect a similar expected increase in cost when compared to the light duty GHG regulation. Hence, the combination of the two regulations provides little incentive for a shift from light - duty trucks to HD trucks. To the extent that our proposed regulation of heavyduty pickups and vans coul d conceivabl y encourage a class shift towards lighter pickups, this unintended consequence would in fact be expected to lead to lower fuel consumption and GHG emissions as the smaller light - duty pickups have significantly better fuel economy ratings than h eav y- duty pickup trucks. The projected cost increases for this proposed action differ between Class 8 day cabs and Class 8 sleeper cabs, reflecting our expectation that compliance with the proposed standards would lead truck consumers to specify sleeper ca

bs equipped with APUs while day cab consumers would not. Since Class 8 day cab and sleeper cab trucks perform essentially the same function when hauling a trailer, this raises the possibility that the higher cost for an APU equipped sleeper cab could lead to a shift from sleeper cab to day cab trucks. We do not believe that such an intended consequence would occur for the following reasons. The addition of a sleeper berth to a tractor cab is not a consumer - selectable attribute in quite the same way as other vehicle features. The sleeper cab provides a utility that long - distance trucking fleets need to conduct their operations -- an on - board sleeping berth that lets a driver comply with federall ymandated rest periods, as required b y 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. 53 A day

cab simpl y cannot provide this uti lity with a single driver . The need for this utility would not be changedeven if the additional costs to reduce greenhouse gas emissions from sleeper cabs exceed th os e

reducing greenhouse gas emissions from day cabs. S A trucking fleet could instead decide to put its drivers in hotels in lieu of using sleeper berths, and switch to day

cabs. However, this is unlikely to occur in any

great number, since the added cost for the hotel stay s 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 highl y unlikel y 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 likel y 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 proposed regulations is s mall. We are proposing this alternative compliance approach reflecting that some sleeper cabs are used in team driving situations where one driver sleeps while the other drives. In that situation, an APU is unnecessary since the tractor is continually being driven when occupied. When it is parked, it would automatically eliminate any

additional idling through the shutdown software. If trucking businesses choose this option, then costs based on purchase of APUs may overestimate the costs of this program to this sector. Class shifting from combination tractors to vocational vehicles may occur if a customer deems the additional marginal cost of tractors due to the regulation to be greater than the utility provided b y the tractor. The agencies initially considered this issue when deciding whether to include Class 7 tractors with the Class 8 tractors or regulate them as vocational vehicles. The agencies’ evaluation of the combined vehicle weight rating of the Class 7 shows that if these vehicles were treated significantly differently

from the Class 8 tractors, then they could be easily substituted for Class 8 tractors. Therefore, the agencies are proposing to continue to include both classes in the tractor category . The agencies believe that a shif t 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 b y vocational vehicles. The agencies do not envision that the proposed regulatory program would ca

use class shifting within the vocational vehicle class. The marginal cost difference due to the regulation of vocational vehicles is minimal. The cost of LRR tires on a per tire basis is the same for all vocational vehicles so the only difference in marg inal cost of the vehicles is due to the number of axles. The agencies believe that the utility gained from the additional load carrying capability of the additional axle would outweigh the additional cost for heavier vehicles. T

S The average marginal co st difference between sleeper cabs and day cabs in the proposal is roughly $2,500. T The proposed rule projects the difference in costs between the HHD and MHD vocational vehicle technologies is approximately $30.In conclusion, NHTSA and E PA believe that the proposed regulatory structure for HD trucks would not significantly change the current competitive and market factors that determine purchaser preferences among truck ty pes. Furthermore, even if a small amount of shifting would occur, any resulting GHG impacts would likely to be negligible because any vehicle class that sees an uptick in sales is also being regulated for fuel efficiency . Therefore, the agencies did not include an impact of class shifting on the vehicle populations used to assess the benefits of the proposed program. 

### 8.4.2 **Fleet Turnover and Sales**  **Effect s**  A regulation that affects the cost to purchase and/or operate trucks could affect whether a consumer decides to purchase a new truck and the timing of that purchase. The term pre - buy refers to the idea that truck purchases may occur earlier than otherwise planned to avoid the additional costs associated with a new regulatory requirement. Slower fleet turnover, or low buy s, may

occur when owners opt to keep their existing truck rather than purchase a new truck due to the incremental cost of the regulation. The 2010 NAS HD Report discussed the topics associated with HD truck fleet turnover. NAS noted that there is some empirical evidence of pre - buy behavior in response to the 2004 and 2007 heav y- duty engine emission standards, with larger impacts occurring in response to higher costs. 54 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 conditio ns, 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 i ncremental purchase costs, these impacts will be lessened. Nevertheless, when it comes to efficiency investments, most heav y- duty fleet operators require relativel y quick payback periods, on the order of two to three years. 55 The proposed regulations are projected to return fuel savings to the truck owners that offset the cost of the regulation within a few years. The effects of the regulation on purchasing behavior and sales will depend on the nature of the market failures and the extent to which firms consider the projected future fuel savings in their purchasing decisions. If trucking firms account for the rapid pay back, they

are unlikel y to strategically accelerate or delay

their purchase plans at additional cost in capit al to avoid a regulation that will lower their overall operating costs. As discussed in Chapter 8.2 , this scenario may occur if this proposed program reduces uncer tainty about fuel - saving technologies. More reliable information about way s to reduce fuel consumption allows truck purchasers to evaluate better the benefits and costs of additional fuel savings, primarily in the original vehicle market, but possibly in the resale market as well. In addition, the proposed standards are expected to lead manufacturers to install more fuel - saving technologies and promote their purchase; the increased availability

and promotion may

encourage sales.Other market failures may leave open the possibility of some pre - buy

or delayed purchasing behavior. Firms may not consider the full value of the future fuel savings for several reasons. For instance, truck purchasers may not want to invest in fuel efficiency because of uncertainty about fuel prices. Another explanation is that the resale market may not full y 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

— s ee Chapter 8.2 ) between truck purchasers (who may emphasize the up - front costs of the trucks) and truck operators, who would like the fuel savings, can also lead to pre - buy or delayed purchasing behavior. If these market failures prevent firms from fully internalizing fuel savings when deciding on vehicle purchases, then pre - buy and delayed purchase could occur and could result in a slight decrease in the GHG benefits of the regulation. Thus, whether pre - buy

or delayed purchase is likely to play a significant role in the truck market depends on the specific behaviors of purchasers in that market. Without additional information about which scenario is more likel y t o be prevalent, the a gencies are not projecting a change in fleet turnover characteristics due to this regulation. Whether vehicle sales appear to be affected b y the HD Phase 1 standards could provide some insight into the impacts of the prop osed standards. At the time of this NPRM, sales data are not yet available for 2014 model year , the first year of the Phase 1 standards. In addition, any trends in sales are likely

to be affected b y m acroeconomic conditions, which have been recovering since 2009 - 2010. As a result, it is unlikely to be possible, even when vehicle sales data are av ailable, to separate the effects of the existing standards from other confounding factors. 

### 8.5 **Monetized GHG Impacts 8.5.1**  **Monetized CO 2**  

### Impacts **- Social Cost of Carbon**  We estimate the global social benefits of CO 2 emission reductions expected from the Proposed HD Phase 2 program using the social cost of carbon (SC - CO 2 ) estimates presented in the 2013 Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (2013 SCC TSD). We refer to thes e estimates, which were developed b y the U.S ., as “SC - CO 2 estimates.” The SC - CO 2 is a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 emissions in a given ye ar. It includes a wide range of anticipated climate impacts, such as net changes in agricultural productivity

and human health, property damage from increased flood risk, and changes in energy

s ys tem costs, such as reduced costs for heating and increased costs for air conditioning. It is used in regulatory impact analy ses to quantify the benefits of reducing CO 2 emissions, or the disbenefit from increasing emissions . The SC - CO 2 estimates used in th is analy sis were developed over many years, based on the best science available, and with input from the public. EPA and other federal agencies have considered the extensive public comments on ways to improve SC - CO 2 estimation received via the notice and c omment period that was part of numerous rulemakings since 2006. In addition, OMB’s Office of Information and Regulatory Affairs sought public comment on the approach used to develop the SC - CO 2 estimates. The comment period ended on February 26, 2014, andOMB is reviewing the comments received. An interagency process that included EPA and other executive branch entities used three integrated assessment models (IAMs) to develop SC - CO 2 estimates and selected four global values for use in regulatory anal ys es. The SC - CO 2 estimates were first released in February 2010 and updated in 2013 using new versions of each IAM. The SC - CO 2 estimates represent global measures because of the distinctive nature of the climate change problem. The climate change problem i s highly unusual in at least two respects. First, emissions of most GHGs contribute to damages around the world even when they are emitted in the United States. The SC - CO 2 must therefore incorporate the full (global) damages caused b y GHG emissions in ord er to address the global nature of the problem. Second, climate change presents a problem that the United States alone cannot solve. The US now operates in a global, highl y interconnected economy such that impacts on the other side of the world now affec t our economy . Climate damages in other countries can affect the U.S. economy; climate exacerbated conflict can require military expenditures by

the U.S. All of this means that the true cost of climate change to U.S. is much larger than impacts that simp ly occur in the U.S. A global number is the economically appropriate reference point for collective actions to reduce climate change.A key

objective in the development of the SC - CO 2 estimates was to enable a consistent exploration of three IAMs (DI CE, FUND, and PAGE) while respecting the different approaches to quantifying damages taken by the key modelers in the field. The selection of the three input parameters (equilibrium climate sensitivity, reference socioeconomic scenarios, discount rate) was based on an extensive review of the literature. Specificall y, a probability distribution for climate sensitivity

was specified as an input into all three models. In addition, the interagency group used a range of scenarios for the socio - economic parameters an d a range of values for the discount rate. All other model features were left unchanged, rel ying on the model developers’ best estimates and judgments. In DICE, these parameters are handled deterministically and represented b y fixed constants; in PAGE, m ost parameters are represented by probability distributions. FUND was also run in a mode in which parameters were treated probabilistically . The use of three models and these input parameters allowed for exploration of important uncertainties in the way

cl imate damages are estimated, including equilibrium climate sensitivity, reference socioeconomic and emission trajectories, and discount rate. As stated in the 2010 SCC TSD, however, key uncertainties remain as the existing models are imperfect and incompl ete. See the 2010 SCC TSD for a complete discussion of the methods used to develop the estimates and the key uncertainties, and the 2013 SCC TSD for the updated estimates. Notably , the 2013 process did not revisit the 2010 interagency modeling decisions ( e.g., with regard to the equilibrium climate sensitivity, reference case socioeconomic and emission scenarios, or discount rates). Rather, improvements in the way damages are modeled are confined to those that have been incorporated into the latest versio ns of the models by the developers themselves and used for anal ys es in peer- reviewed publications. The model updates that are relevant to the SC - CO 2 estimates include: an explicit representation of sea level rise damages in the DICE and PAGE models; updat ed adaptation assumptions, revisions to ensure damages are constrained by GDP, updated regional scaling of damages, and a revised treatment of potentially

abrupt shifts in climate damages in the PAGE model; an updated carbon cycle in the DI CE model; and up dated damage functions for sea level rise impacts, the agricultural sector, and reduced space heating requirements, as well as changes to the transient response oftemperature to the buildup of GHG concentrations and the inclusion of indirect effects of

me thane emissions in the FUND model. The 2013 SCC TSD provides complete details. When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analy st faces a number of serious challenges. A report from the National Academies o f Science (NRC, 2009) 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 sy stem, (3) the impact of ch anges in climate on the phy sical 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 questio ns of science, economics, and ethics and should be viewed as provisional. The 2010 SCC TSD noted a number of limitations to the SC - CO 2 analysis, including the incomplete way

in which the IAMs capture catastrophic and non - catastrophic impacts, their incomp lete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Current IAMs do not assign value to all of the important phy sical, ecological, and economic i mpacts of climate change recognized in the climate change literature due to a lack of precise information on the nature of damages and because the science incorporated into these models understandably lags behind the most recent research. The limited amount of research linking climate impacts to economic damages makes the modeling exercise even more difficult. These individual limitations do not all work in the same direction in terms of their influence on the SC - CO 2 estimates, though taken together they suggest that the SC - CO 2 estimates are likely conservative. In particular, the IP CC Fourth Assessment Report (2007) concluded that “It is very likel y that [SC - CO 2 estimates] underestimate the damage costs because they cannot include many

non - quantifiable impacts.” Nonetheless, these estimates and the discussion of their limitations represent the best available information about the social benefits of CO 2 reductions to inform benefit - cost anal ys is. The new versions of the models used to estimate the values presented below offer some improvements in these areas, although further work is warranted. Accordingl y, EPA and other agencies continue to engage in research on modeling and valuation of climate impacts with the goal to improve these estimates. The EPA and other federal agencies have considered the extensive public comments on way s to improve SC - CO 2 estimation recei ved via the notice and comment periods that were part of numerous rulemakings. In addition, OMB’s Office of In formation and Regulatory Affairs sought public comment on the approach used to develop the SC - CO 2 estimates (78 FR 70586; November 26, 2013). Th e comment period ended on February 26, 2014, and OMB is reviewing the comments received. OMB also responded in January 2014 to concerns submitted in a Request for Correction on the SCC TSDs. U

U OMB’s 1/24/14 response to the petition i s available at https://www.whitehouse.gov/sites/default/files/omb/inforeg/ssc - rfc - under - iqa - response.pdfThe four SC - CO 2 estimates, updated in 2013, are as follows: $13, $46, $69, and $140 per

metric ton of CO 2 emissions in the year 2020 (2012$). V Table 8 - 7 presents the SC - CO 2 estimates in selected ye ars, rounded to two significant digits. The first three values are based on the average SC - CO 2 from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively . SC - CO 2 estimates for several discount rates are included because the literature shows that the SC - CO 2 is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context (where costs and benefits are incurred b y different generations). The fourth value is the 95th percentile of the SC - CO 2 from all three models at a 3 percent discount rate. It is included to represent higher- than - expected impacts from temperature change further out in the tails of the SC - CO 2 distribution (representing less likely , but potentially catastro phic, outcomes). The SC - CO 2 increases over time because future emissions are expected to produce larger incremental damages as economies grow and phy sical and economic sy stems become more stressed in response to greater climate change.

**Table 8**  **- 7**  **Social Cost of CO 2**  **, 201 2**  **– 2050**  **a (in**  **2012$ per Metric Ton )**  **CALENDAR YEAR**  **DISCOUNT RATE AND ST ATISTIC** 

**5% Average 3% Average**  **2.5% Average 3%**  **95 th**  **percentile 201**  **2** $12 $37 $58 $100 **2015** $12 $40 $61 $120 **2020** $13 $46 $69 $140 **2025** $15 $51 $74 $150 **2030** $17 $56 $81 $170 **2035** $20 $60 $86 $190 **2040** $23 $66 $93 $210 **2045** $26 $71 $99 $220 **2050** $28 $77 $100 $240 Note: a The SC - C O 2 values are dollar - year and emissions - year specific and have been rounded to two significant digits. Unrounded numbers from the 2013 SCC TSD we re adjusted to 2012$ and used to calculate the CO 2 benefits . Applying the global SC - C O 2 estimates, shown in Table 8 - 7 , to the estimated reductions in domestic CO 2 emissions for the proposed program, we estimate the dollar value of the climate related benefits for each anal ys is year. In order to calculate the doll ar value for emission reductions, the SC - C O 2 estimate for each emissions year would be applied to changes in CO 2 emissions for that year, and then discounted back to the anal ys is year using the same discount rate used to estimate the SC - C O 2 . For internal consistency , the annual benefits are discounted back to net present value terms using the same discount rate as each SC - C O 2 estimate (i.e. 5 percent, 3 percent, and 2.5 percent) rather than the discount rates of 3 percent and 7 percent used

V The SC - CO 2 values have been rounded to two significant digits. Unrounded numbers from the 2013 SCC TSD were adjusted to 2012$ and used to calculate the CO 2 benefits.to derive the net present value of other streams of costs and benefits of the proposed rule. W The

SC - C O 2 estimates are presented in and the associated CO 2 benefit estimates for each calendar ye ar are shown in Table 8 - 8 . **Table 8**  **- 8**  **Annual Upstream and Downstream** **CO 2**  **Benefits and Net Present Values**  **for the Given SC -**  **C O**  **2 Value**  **for the Preferred Alternative Relative to the Less Dynam ic Baseline and using Method B**  **, a** **,b (Millions**  **of 2012$ )**  CALEND AR YEAR 5%

(AVERAGE SC - CO 2

= $12 IN 2012) 3 %

(AVERAGE SC - CO 2

= $37 IN 2012) 2.5 %

(AVERAGE SC - CO 2

= $58 IN 2012) 3%

(95 TH PERCENTILE = $105 IN 2012) 2018 $13 $43 $65 $130 2019 $26 $91 $130 $270 2020 $40 $140 $210 $420 2021 $92 $330 $500 $1,000 2022 $170 $590 $880 $1,800 2023 $250 $860 $1,300 $2,600 2024 $400 $1,300 $1,900 $4,000 2025 $540 $1,800 $2,600 $5,500 2026 $720 $2,300 $3,400 $7,000 2027 $890 $2,900 $4,200 $8,900 2028 $1,100 $3,500 $5,100 $11,000 2029 $1,300 $4,200 $5,900 $13,000 2030 $1,500 $4,800 $6,900 $15,000 2035 $2,500 $7,400 $11,000 $23,000 2040 $3,300 $9,700 $14,000 $30,000 2050 $5,000 $14,000 $19,000 $42,000 NPVb $22,000 $100,000 $160,000 $320,000 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b The SC - C O 2 values are dollar - year and emissions - year specific . Note that discounted values of reduced GHG emissions are calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SC - C O 2 at 5, 3, and 2.5 percent) is used to calculate disco unted values of SC - C O 2 for internal consistency. Refer to SCC TSD for more detail. We also conducted a separate anal ys is of the CO 2 benefits over the model year lifetimes of vehicles sold in the regulatory timeframe . In contrast to the calendar ye ar anal ys is, the model ye ar lifetime anal ys is shows the impacts of the program on each of these MY fleets over the course of their lifetime s . Full details of the inputs to this analy sis can be found in RIA chapter 5. The CO 2 b enefits in the context of this MY lifetime anal ys is are shown in Table 8 - 9 for each of the four different social cost of carbon values. The CO 2 benefits shown for each model ye ar represent the net present value of the benefits in each year in the model year life discounted back

W See more discussion on the appropriate discounting of climate benefits using SC - C O 2 in the 2010 SC C TSD. Other benefits and costs of proposed regulations unrelated to CO 2 emissions are discounted at the 3% and 7% rates specified in OMB guidance for regulatory analysis.to the first year of the model year. The same discount rate used to discount the value of damages

from future emissions (SC - CO 2 at 5, 3, and 2.5 percent) is used to calculate the net present value of SCC for internal consistency. **Table 8**  **- 9**  **Discounted Model Year Lifetim e**  **Upstream & Downstream** **CO 2**  **Benefits for the Given SC -**  **CO 2**  **Value for the Preferred Alternative Relative to the Less Dynam ic Baseline and using Method B**  **(M illions of**  **2012$) a,b**  MODEL YEAR 5%

(AVERAGE SC - CO 2 = $12 IN 2012) 3 %

(AVERAGE SC - CO 2 = $37 IN 2012) 2.5 %

(AVERAGE SC - CO 2 = $58 IN 2012) 3%

(95 TH PERCENTILE = $105 IN 2012) 2018 $93 $380 $580 $1,100 2019 $90 $370 $570 $1,100 2020 $87 $360 $560 $1,100 2021 $520 $2,200 $3,400 $6,600 2022 $540 $2,300 $3,500 $6,900 2023 $550 $2,300 $3,600 $7,200 2024 $870 $3,700 $5,800 $11,000 2025 $900 $3,900 $6,100 $12,000 2026 $920 $4,000 $6,300 $12,000 2027 $1,100 $4,800 $7,600 $15,000 2028 $1,100 $4,800 $7,600 $15,000 2029 $1,100 $4,900 $7,700 $15,000 Sum $7,800 $34,000 $53,000 $100,000 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b The SC - CO 2 values are dollar - year and emissions - year specific . Note that discounted values of reduced GHG emissions are calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SC - CO 2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC - CO 2 for internal consistency. Refer to SCC TSD for more detail. 

### 8.5.2 **Sensitivity Analysis -**  **Monetized Non -**  **CO 2**  

### Impacts One limitation of the primary benefits anal ys is is that it does not include the valuation of non - CO 2 GHG impacts (CH 4 , N 2 O, HFC - 134a). Specificall y, the IWG did not estimate the social costs of non - CO 2 GHG emissions using an approach analogous to the one used to estimate the SC - CO 2 . However, EPA recognizes that non - CO 2 GHG impacts a ssociated with this rulemaking (e.g., net reductions in CH 4 , N 2 O, HFC - 134a) would provide benefits to society. To understand the potential implication of omitting these benefits, EPA has conducted sensitivity anal ys is using two approaches: 1) an approximation approach based on global warming potential (GWP) gas comparison metrics that has been used in previous rulemakings, and 2) a set of recentl y published SC - CH 4 and SC -N 2 O estimates that are consistent with the modeling assumptions underlying the SC - C O 2 estimates (Marten et al. 2014). This section describes both approaches and presents estimates of the non - CO 2 benefits of the proposed rulemaking. Other unquantified non - CO 2 benefits are discussed in this section as well.

### 8.5.2.1 **Non -**  **CO 2**  

### GHG Benefits Based on **the GWP Approximation Approach**

In the absence of directly modeled estimates, o ne potential method for approximating the value of marginal non - CO 2 GHG emission reductions is to convert non - CO 2 emissions reductions to CO 2 - equivalents that may then be valued using the SC - C O 2 . Conversion to CO 2 - equivalents is ty

pically based on the global warming potentials ( GWPs ) for the non - CO 2 gas es . This approach, henceforth referred to as the “GWP approach,” has been used in sensitivity anal ys es to estimate the non - CO 2 benefits in previous EPA rulemakings (see US EPA 2012, 2013). 56 EPA has not presented these estimates in a main benefit - cost anal ys is due to the limitations associated with using the GWP approach to value changes in non - CO 2 GHG emissions, and considered the GWP approach as an interim method of analysis until social cost estimates for non - CO 2 GHGs, consistent with the SC - C O 2 estimates, were developed. The GWP is a simple, transparent, and well - established metric for assessing the relative impacts of non - CO 2 emissions compared to CO 2 on a purel y phys ical basis. However, as discussed both in the 2010 SCC TSD and previous rulemakings (e.g., US EPA 2012, 2013), the GWP approximation approach to measuring non - CO 2 GHG benefits has several well documented limitat ions (e.g., Reill y and Richards 1993 57 ; Schmalensee 1993 58 ; Fankhauser 1994 59 ; Marten and Newbold 2012 60 ). Gas comparison metrics, such as the GWP, are designed to measure the impact of non - CO 2 GHG emissions relative to CO 2 at a specific point along the pathw ay from emissions to monetized damages (depicted in Figure 8 - 2 ), and this point may differ across measures. The GWP measures the cumulative radiative forcing from a perturbation of a non - CO 2 GHG relative to a perturbation of CO 2 over a fixed time horizon. The GWP and other gas comparison metrics are not ideally suited for use in benefit - cost analyses to approximate the social cost of non - CO 2 GHGs because they i gnore important nonlinear relationships beyond radiative forcing in the chain between emissions and damages . These can become relevant because gases have different lifetimes and the SC - C O 2 takes into account the fact that marginal dama ges from an increase in temperature are a function of existing temperature levels. Another limitation of gas comparison metrics for this purpose is that some environmental and socioeconomic impacts are not linked to all of the gases under consideration an d will therefore be incorrectly allocated. For example, the economic impacts associated with increased agricultural productivity due to higher atmospheric CO 2 concentrations included in the SC - CO 2 would be incorrectly allocated to CH 4 emissions with the G WP - based valuation approach.

**Figure 8**  **- 2**  **Path from**

**GHG Emissions to Monetized Damages (Source: Marten et al., 2014)** Furthermore, the assumptions made in estimating the GWP are not consistent with the assumptions underlying SC - C O 2 estimates in general, and the SC - C O 2 estimates more specificall y. For example the 100 year time horizon usually used in estimating the GWP is less than the 300 year horizon used in developing the SC - C O 2 estimates. The GWP approach also treats all impacts within the time horizon equally, independent of the time at which they occur. This is inconsistent with the role of discounting in economic analysis, which accounts for a basicpreference for earlier o ver later gains in utility , the small but positive probability of a large global catastrophe (e.g., large asteroid collision, super volcanic eruption, pandemic), and expectations regarding future levels of economic growth. In the case of CH 4 , which has a relativel y short lifetime compared to CO 2 , the temporal independence of the GWP could lead the GWP approach to underestimate the SC - CH 4 with a larger downward bias under higher discount rates (Marten and Newbold 2012). X Similar to the approach used in th e RIA of the Final Rulemaking for 2017 - 2025 Light Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards (US EPA, 2013), EPA applies the GWP approach to estimate the benefits associated with reductions of CH 4 , N 2 O and H FCs in each calendar ye ar. Under the GWP Approach, EPA converted CH 4 , N 2 O, and HFC - 134a to CO 2 equivalents using the AR4 100 - ye ar GWP for each gas: CH 4 (25), N 2 O (298), and HFC - 134a (1,430). 61 These CO 2 - equivalent emission reductions are multiplied by

the SC - C O 2 estimate corresponding to each ye ar of emission reductions . As with the calculation of annual benefits of CO 2 emission reductions, the annual benefits of non CO 2 emission reductions based on the GWP approach are discounted back to net present value terms using the same discount rate as each SC - CO 2 estimate. The estimated non - CO 2 GHG benefits using the GWP approach are presented in Table 8 - 10 through Table 8 - 11 . The total net present value of the GHG benefits for this proposed rulemaking would increase b y about $ 760 m illion to $ 1 1 billion (2012$) , depending on discount rate, or roughl y 3 percent if these non - CO 2 estimates were included.

X We note that the truncation of the time period in the GWP calculation could lead to an overestimate of SC - CH 4 for near term perturbation years in cases where the SC - CO 2 is based on a sufficiently low or steeply declining discount rate.**Table 8**  **- 10**  **Annual Upstream and Downstream**  **CH 4**  **GHG Benefits**  **and Net Present Values for the Given SC**  ****

**C O**  **2 Value**  **for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using the GWP**  **Approach (Millions of 2012$**  **) a,b**  CALENDA R YEAR 5%

(AVERAGE SC - CO 2

= $12 IN 2012) 3 %

(AVERAGE SC - CO 2

= $37 IN 2012) 2.5 %

(AVERAGE SC - CO 2

= $58 IN 2012) 3%

(95 TH PERCENTILE = $105 IN 2012) 2018 $0.3 $1.1 $1.6 $3.2 2019 $0.6 $2.2 $3.3 $6.6 2020 $1.0 $3.5 $5.2 $10 2021 $3.1 $11 $17 $33 2022 $6.0 $20 $30 $62 2023 $8.8 $30 $45 $93 2024 $14 $46 $68 $140 2025 $19 $62 $91 $190 2026 $25 $79 $120 $240 2027 $30 $99 $140 $300 2028 $36 $120 $170 $360 2029 $43 $140 $200 $420 2030 $49 $160 $230 $480 2035 $82 $240 $350 $760 2040 $110 $320 $440 $990 2050 $160 $440 $600 $1,400 NPVb $730 $3,400 $5,400 $11,000 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b The SC - CO 2 values are dollar - year and emissions - year specific . Note that discounted values of reduced GHG emissions are calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SC - CO 2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC - CO 2 for internal consistency. Refer to SCC TSD for more detail.**Table 8**  **- 11**  **Annual Upstream and Downstream**  **N 2**  **O GHG Benefits**  **and Net Present Values for the Given SC**  ****

**CO 2**  **Value for the Preferred Alternative Relative to the Less Dyna**  **mic Baseline and using Method B, using the GWP**  **Approach (Millions of 2012$**  **) a,b**  CALENDA R YEAR 5%

(AVERAGE SC - CO 2

= $12 IN 2012) 3 %

(AVERAGE SC - CO 2

= $37 IN 2012) 2.5 %

(AVERAGE SC - CO 2

= $58 IN 2012) 3%

(95 TH PERCENTILE = $105 IN 2012) 2018 $0.0 $0.0 $0.1 $0.2 2019 $0.0 $0.1 $0.2 $0.3 2020 $0.0 $0.2 $0.2 $0.5 2021 $0.1 $0.4 $0.5 $1.1 2022 $0.2 $0.6 $1.0 $1.9 2023 $0.3 $0.9 $1.4 $2.8 2024 $0.4 $1.4 $2.1 $4.4 2025 $0.6 $2.0 $2.9 $6.0 2026 $0.8 $2.6 $3.7 $7.8 2027 $1.0 $3.2 $4.7 $10 2028 $1.2 $3.9 $5.7 $12 2029 $1.5 $4.6 $6.6 $14 2030 $1.6 $5.3 $7.7 $16 2035 $2.8 $8.3 $12 $26 2040 $3.8 $11 $15 $34 2050 $5.6 $15 $21 $47 NPVb $25 $120 $180 $360 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b The SC - CO 2 values are dollar - year and emissions - year specific . Note that discounted values of reduced GHG emissions are calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SC - CO 2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC - CO 2 for internal consistency. Refer to SCC TSD for more detail.**Table 8**  **- 12**  **Annual Upstream and Downstream**  **HFC -**  **134a GHG Benefits**  **and Net Present Values for the** 

**Given SC -**  **CO 2**  **Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method**  **B, using the GWP Approach**  **(M illions of 2012$**  **) a,b**  CALENDA R YEAR 5%

(AVERAGE SC - CO 2

= $12 IN 2012) 3 %

(AVERAGE SC - CO 2

= $37 IN 2012) 2.5 %

(AVERAGE SC - CO 2

= $58 IN 2012) 3%

(95 TH PERCENTILE = $105 IN 2012) 2018 $0.0 $0.0 $0.0 $0.0 2019 $0.0 $0.0 $0.0 $0.0 2020 $0.0 $0.0 $0.0 $0.0 2021 $0.2 $0.8 $1.3 $2.6 2022 $0.5 $1.7 $2.6 $5.3 2023 $0.8 $2.7 $4.0 $8.1 2024 $1.1 $3.7 $5.4 $11 2025 $1.4 $4.7 $6.9 $14 2026 $1.8 $5.9 $8.6 $18 2027 $2.2 $7.1 $10 $22 2028 $2.5 $8.3 $12 $25 2029 $3.0 $10 $14 $29 2030 $3.4 $11 $16 $34 2035 $5.2 $15 $22 $48 2040 $6.1 $18 $25 $56 2050 $8.4 $23 $31 $71 NPVb $44 $200 $320 $630 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b The SC - CO 2 values are dollar - year and emissions - year specific . Note that discounted values of reduced GHG emissions are calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SC - CO 2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC - CO 2 for internal consistency. Refer to SCC TSD for more detail. 

### 8.5.2.2 **Non -**  **CO 2**  

### GHG Benefits Based on Directly Modeled Estimates Several researchers have directly estimated the social cost of non - CO 2 emissions using integrated assessment models (IAMs), though the number of such estimates is small compared to the large number of SC - CO 2 estimates available in the literature. As discussed in previous RIAs (e.g., EPA 2012), there is considerable variatio n among these published estimates in the models and input assumptions they employ . These studies differ in the emission perturbation year, employ a wide range of constant and variable discount rate specifications, and consider a range of baseline socioeco nomic and emissions scenarios that have been developed over the last 20 ye ars. However, none of the other published estimates of the social cost of non - CO 2 GHG are consistent with the SC - CO 2 estimates, and most are likely underestimates due to changes in the underlying science since their publication. Recently , a paper by Marten et al. (2014) provide d the first set of published SC - CH 4 and SC -N 2 O estimates that are consistent with the modeling assumptions underlying the SC - CO 2 . 62 Specificall y, the estimatio n approach of Marten et al. (2014) used the same set of three IAMs, five socioeconomic - emissions scenarios, equilibrium climate sensitivity distribution, and threeconstant discount rates used to develop the SC - CO 2 estimates. Marten et al. also used the s ame

aggregation method as the SC - CO 2 to distill the 45 distribution of the SC - CH 4 and SC -N 2 O produced for each emissions year into four estimates: the mean across all models and scenarios using a 2.5 percent, 3 percent, and 5 percent discount rate, and the 95 th percentile of the pooled estimates from all models and scenarios using a 3 percent discount rate. Marten et al. used lifetimes and radiative efficiencies for CH 4 and N 2 O based on the IPCC AR4 values. The authors also adjusted the CH 4 radiative efficiency for CH 4 to account for additional radiative effects due to increases in tropospheric ozone and stratospheric water vapor resulting from methane emissions, using the same adjustment used by in IPCC AR4 for calculating GWP values. Usin g this approach, Marten et al. (2014) f inds that the GWP approach provides conservative estimate s for the benefits of marginal reductions in CH 4 and N 2 O emissions. The resulting SC - CH 4 and SC -N 2 O estimates are presented in Table 8 - 13 . More detailed results and a comparison to other published estimates can be found in Marten et al. (2014). The tables do not include HFC - 134a because EPA is unaware of analogous estim ates.

**Table 8**  **- 13**  **Social Cost of CH 4**  **and N 2**  **O, 2012 –**  **2050 a**  **[2012$ per m etric ton]** 

**(Source: Marten et al. (2014)) YEAR**  **SC -**  **CH 4**  **SC -**  **N 2**  **O**

**5% Average**  **3%**

**Average 2.5%** 

**Average 3%** 

**95 th**  **percentile 5%** 

**Average 3%** 

**Average 2.5%** 

**Average 3%** 

**95 th**  **percentile 201**  **2** $440 $1000 $1400 $2800 $4000 $14000 $2 0 000 $37000 **2015** 500 1200 1500 3100 4400 15000 22000 39000 **2020** 590 1300 1700 3500 5200 16000 24000 44000 **2025** 710 1500 19000 4100 6000 18000 27000 50000 **2030** 840 1700 2300 4600 7000 20000 29000 55000 **2035** 990 2000 2500 5400 8100 23000 32000 61000 **2040** 1200 2300 2800 6000 9300 25000 35000 67000 **2045** 1300 2500 3100 6800 11000 27000 38000 73000 **2050** 1500 2700 3300 7400 12000 29000 41000 80000 Note: a The values are emissions - ye ar specific and have been rounded to two significant digits , as shown in Marten et al. (2014) . These rounded numbers were used to calculate the GHG benefits . The application of directly modeled estimates from Marten et al. (2014) to benefit - cost anal ys is of a regulatory

action is analogous to the use of the SC - CO 2 estimates. Specifically , the SC - CH 4 and SC -N2 O e stimates in Table 8 - 13 are used to monetize the benefits of changes in CH 4 and N 2 O emissions expected as a result of the proposed rulemaking. Forecast changes in CH 4 and N 2 O emissions in a given year resulting from the regulatory action are multiplied by the SC - CH 4 and SC -N2 O estimate for that year , respectively . To obtain a present value estimate, the monetized stream of future non - CO 2 benefits are discounted back to the analy sis year using the same discount rate used to estimate the social cost of the non - CO 2 GHG emission changes . The CH 4 and N 2 O benefits based on Marten et al. (2014) are presented for each calendar ye ar in Table 8 - 14 . Including these benefits would increase the total net present value of the GHG benefits for this proposed rulemaking b y about $ 1.5 billion to $ 12 billion (2012$) , or roughl y 4 percent to 7 percent, depending on discount rate.**Table 8**  **- 14**  **Annual Upstream and Downstream**  **CH 4**  **GHG Benefits**  **and Net Present Values for the Given SC**  ****

**C H**  **4 Value**  **for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using the** 

**Directly Modeled Approach , Calendar Year**  **Analysis (Mi** **llions of 2012$ )**  **a,b** CALENDAR YEAR 5%

(AVERAGE SC - CH 4

= $440 IN 2012) 3 %

(AVERAGE SC - CH 4

= $1000 IN 2012) 2.5 %

(AVERAGE SC - CH 4

= $1400 IN 2012) 3%

(95 TH PERCENTILE = $2800 IN 2012) 2018 $0.6 $1.3 $1.6 $3.3 2019 $1.1 $2.6 $3.4 $6.8 2020 $1.8 $3.9 $5.2 $10 2021 $5.8 $13 $17 $35 2022 $11 $24 $31 $65 2023 $17 $35 $49 $97 2024 $26 $56 $72 $150 2025 $35 $74 $95 $200 2026 $46 $99 $130 $260 2027 $57 $120 $150 $320 2028 $69 $140 $190 $390 2029 $82 $170 $220 $460 2030 $95 $190 $260 $520 2035 $160 $330 $400 $870 2040 $230 $430 $540 $1,200 2050 $350 $620 $770 $1,700 NPVb $1,500 $4,600 $6,400 $12,000 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b The SC - C H 4 values are dollar - year and emissions - year specific . Note that discounted values of reduced GHG emissions are calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SC - CH 4 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC - CH 4 for internal consistency.**Table 8**  **- 15**  **Annual Upstream and Downstream**  **N 2**  **O GHG Benefits**  **and Net Present Values for the Given SC**  ****

**N 2**  **O Value**  **for the Preferred Alternative Relative to the Less Dyna mic Baseline and using Method B, using** 

**the Directly Modeled Approach , Calendar Year**  **Analysis (M**  **illions of 2012$ )**  **a,b** CALENDAR YEAR 5%

(AVERAGE SC -N 2 O

= $4000 IN 2012) 3 %

(AVERAGE SC -N 2 O = $14000 IN 2012) 2.5 %

(AVERAGE SC -N 2 O = $20000 IN 2012) 3%

(95 TH PERCENTILE = $37000 IN 2012) 2018 $0.0 $0.1 $0.1 $0.2 2019 $0.0 $0.1 $0.2 $0.3 2020 $0.1 $0.2 $0.3 $0.5 2021 $0.1 $0.4 $0.6 $1.2 2022 $0.3 $0.8 $1.1 $2.1 2023 $0.4 $1.1 $1.7 $3.1 2024 $0.6 $1.8 $2.5 $4.7 2025 $0.8 $2.4 $3.5 $6.5 2026 $1.0 $3.0 $4.5 $8.4 2027 $1.3 $4.0 $5.8 $11 2028 $1.6 $4.8 $6.9 $13 2029 $1.9 $5.8 $8.2 $15 2030 $2.2 $6.5 $9.3 $18 2035 $3.7 $10 $15 $28 2040 $5.2 $14 $19 $37 2050 $7.9 $20 $27 $53 NPVb $34 $150 $230 $400 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b The SC -N 2 O values are dollar - year and emissions - year specific . Note that discounted values of reduced GHG emissions are calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (N 2 O at 5, 3, and 2.5 percent) is used to calculate discounted values of N 2 O for internal consistency. As illustrated above, compared to the use of directly modeled estimates the GWP - based approximation approach underestimates the climate benefits of the CH 4 emission reductions by 12 percent to 52 percent, and the climate benefits of N 2 O reductions b y 10 percent to 26 percent , depending on the discount rate assumption. 

### 8.5.2.3 **Additional non -**  **CO 2**  

### GHGs Co **- B**  **enefits** In determining the relative social costs of the different gases, the Marten et al. (2014) anal ys is accounts for differences in lifetime and radiative efficiency between the non - CO 2 GHGs and CO 2 . The anal ys is also accounts for radiative forcing resulting from methane’s effects on tropospheric ozone and stratospheric water vapor, and for at least some of the fertilization effects of elevated carbon dioxide concentrations. However, there exist several other differences between these gases that have not yet been captured in this analysis, namel y the non - radiative effects of methane - driven elevated tropospheric ozone levels on human health, agriculture, and ecos ys tems, and the effects of carbon dioxide on ocean acidification. Inclusion of these additional non - radiative effects would potentially change both the absolute and relative value of the various gases.Of these effects, the human health effect of elevated tropospheric ozone levels resulting

from methane emissions is the closest to being monetized in a way that would be comparable to the SCC. Premature ozone - related cardiopulmonary deaths resulting from global increases in tropospheric ozone concentrations produced b y the methane oxidation process have been the focus of a number of studies over the past decade (e.g., West et al. 2006 63 ). Recent studies have produced an estimate of a monetized benefit of methane emissions reductions, with results on the order of $1000 per metric ton of CH 4 emissions reduced (Anenberg et al. 2012 64 ; Shindell et al. 2012 65 ), an estimate similar in magnitude to the climate ben efits of CH 4 reductions estimated by the Marten et al. or GWP methods. However, though EPA is continuing to monitor this area of research as it evolves, EPA is not applying them for benefit estimates at this time. 

### 8.6 **Quantified and Monetized Non -**  **GHG Healt h and Environmental Impacts**  This section anal yz es the economic benefits from reductions in health and environmental impacts resulting from non - GHG emission reductions that can be expected to occur as a result of the proposed Phase 2 standards. CO 2 emissions are predominantl y the b yp roduct of fossil fuel combustion processes that also produce criteria and hazardous air pollutant emissions. The vehicles that are subject to the proposed standards are also significant sources of mobile source air pollution such as direct PM, NO X , VOCs and air toxics. The proposed standards would affect exhaust emissions of these pollutants from vehicles and would also affect emissions from upstream sources that occur during the refining and distribution of fuel. Chan ges in ambient concentrations of ozone, PM 2.5 , and air toxics that would result from the proposed standards are expected to affect human health by reducing premature deaths and other serious human health effects, as well as other important improvements in public health and welfare. It is important to quantify the health and environmental impacts associated with the proposed standards because a failure to adequately consider these ancillary impacts could lead to an incorrect assessment of their costs and b enefits. Moreover, the health and other impacts of exposure to criteria air pollutants and airborne toxics tend to occur in the near term, while most effects from reduced climate change are likely to occur onl y over a time frame of several decades or long er. EPA ty picall y quantifies and monetizes the health and environmental impacts related to both PM and ozone in its regulatory impact anal ys es (RIAs) when possible. However, EPA was unable to do so in time for this proposal. EPA attempts to make emissi ons and air quality modeling decisions earl y in the anal ytical process so that we can complete the photochemical air quality modeling and use that data to inform the health and environmental impacts anal ys is. Time constraints precluded the a gency from completing this work in time for the proposal. Instead, EPA has applied PM - related benefits per- ton values to its estimated emission reductions as an interim approach to estimating the PM - related benefits of the proposal. 66 , Y EPA also

Y See also: http://www.epa.gov/airqual ity/benmap/sabpt.html . The current values available on the webpage have been updated since the publication of the Fann et al., 2012 paper. For more information regarding the updated values, see: http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf (accessed September 9, 2014).characterizes the health and environmental impacts that will be quantified and monetized for the

final rulemaking. This section is split into two sub - sections: the first presents the benefits - per - ton values used to monetize the benefits from reducing population exposure to PM associated with the propos ed standards ; the second explains what PM - and ozone - related health and environmental impacts EPA will quantify and monetize in the analy sis for the final rule. EPA bases its anal ys es on peer- reviewed studies of air quality and h ealth and welfare effects and peer- reviewed studies of the monetary

values of public health and welfare improvements, and is generall y consistent with benefits anal ys es performed for the analy sis of the final Tier 3 Vehicle Rule, 67 the final 2012 PM NAAQS Revision, 68 and the final 2017 - 2025 Light Duty Vehicle GHG Rule. 69 Though EPA is characterizing the changes in emissions associated with toxic pollutants, we are not able to quantify or monetize the human health effects associated with air toxic pollutants for either the proposal or the final rule analy ses (see Chapter 8.6.2.3 for more information). Please refer to Chapter 5 for more information about the air toxics emissions impacts associated with the proposed standards. 

### 8.6.1 **Economic Value of Reductions in Criteria Pollutants**  As described in Chapter 5 , the proposed standards would reduce emissions of several criteria and toxic pollutants and their precursors. In this analy sis, EPA estimates the economic value of the human health benefits associated with the resulting reductions in PM 2.5 exposure. Due to anal ytical limitations with the benefit per - ton method, this analy sis does not estimate benefits resulting from reductions in population exposure to other criteria pollutants such as ozone. Z Furthermore, th e benefits per - ton method, like all air quality impact anal ys es, does not monetize all of the potential health and welfare effects associated with reduced concentrations of PM 2.5 . This analy sis uses estimates of the benefits from reducing the incidence of the specific PM 2.5 - related health impacts described below. These estimates, which are expressed per ton of PM 2.5 - related emissions eliminated by the proposed rule, represent the total monetized value of human health benefits ( including reduction in both p remature mortality and premature morbidity) from reducing each ton of directl y emitted PM 2.5 , or its precursors (SO 2 and NO x ), from a specified source. Ideall y, the human health benefits would be estimated based on changes in ambient PM 2.5 as determined b y full - scale air quality modeling. However, the length of time needed to prepare the necessary emissions inventories, in addition to the processing time associated with the modeling itself, has precluded us from performing air quality modeling for this proposal. We will conduct this modeling for the final rule.

Z The air quality modeling that underlies the PM - related benefit per ton values also produced estimates of ozone levels attributable to each sector. However, the complex non - linear chemistry governing ozone formation prevented EPA from developing a complementary array of ozone benefit per ton values. This limitation notwithstanding, we anticipate that the ozone - related benefits associated with reducing emissions of NO X and VOC could be substantial.The dollar- per- ton estimates used in this analy sis are provided in Table 8 - 16 . As the

table indicates, these values differ among pollutants, and also depend on their original source, because emissions from different sources can result in different degrees of population exposure and resulting health impacts. Inthe summary of costs and benefits, Chapter 8.10 , EPA presents the monetized value of PM - related improvements associated with the proposal. **Table 8**  **- 16**  **Benefits -**  **per -**  **ton Va lues (thousands, 2012$**  **) a**  YEARC ON - ROAD MOBILE SOURCES UPSTREAM SOURCES D

Direct PM 2.5 SO 2 NO X Direct PM 2.5 SO 2 NO X

Estimated Using a 3 Percent Discount Rate b 2016 $380 - $850 $20 - $45 $7.7 - $18 $330 - $750 $69 - $160 $6.8 - $16 2020 $400 - $910 $22 - $49 $8.1 - $18 $350 - $790 $75 - $170 $7.4 - $17 2025 $440 - $1,000 $24 - $55 $8.8 - $20 $390 - $870 $83 - $190 $8.1 - $18 2030 $480 - $1,100 $27 - $61 $9.6 - $22 $420 - $950 $91 - $200 $8.7 - $20 Estimated Using a 7 Percent Discount Rate b 2016 $340 - $770 $18 - $41 $6.9 - $16 $290 - $670 $63 - $140 $6.2 - $14 2020 $370 - $820 $20 - $44 $7.4 - $17 $320 - $720 $67 - $150 $6.6 - $15 2025 $400 - $910 $22 - $49 $8.0 - $18 $350 - $790 $75 - $170 $7.3 - $17 2030 $430 - $980 $24 - $55 $8.6 - $20 $380 - $850 $81 - $180 $7.9 - $18 Notes: a The benefit -per - ton estimates presented in this table are based on a range of premature mortality estimates derived from the ACS study (Krewski et al., 2009) and the Six - Cities study (Lepeule et al., 2012). b The benefit -per - ton estimates presented in thi s table assume either a 3 percent or 7 percent discount rate in the valuation of premature mortality to account for a twenty - year segmented cessation lag. c Benefit -per - ton values were estimated for the years 2016, 2020, 2025 and 2030. We hold values co nstant for intervening years (e.g., the 2016 values are assumed to apply to years 2017 - 2019; 2020 values for years 2021 - 2024; 2030 values for years 2031 and beyond). d We assume for the purpose of this analysis that “upstream emissions” are most closely associated with refinery sector benefit per - ton values. The majority of upstream emission reductions associated with the NPRM are related to domestic onsite refinery em issions and domestic crude production. While upstream emissions also include storage and transport sources, as well as upstream refinery sources, we have chosen to simply apply the refinery values. Full - scale air quality modeling, and the associated bene fits analysis, will include upstream emissions from all sources in the FRM. The benefit per- ton technique has been used in previous analy ses, including EPA’s 2017 - 2025 Light - Duty Vehicle Greenhouse Gas Rule, 70 the Reciprocating Internal Combustion Engine r ules, 71 , 72 and the Residential Wood Heaters NSPS. 73 Table 8 - 17 shows the quantified PM 2.5 - related co - benefits captured in those benefit per- ton estimates , as well as unquantified effects the benefits per - ton estimates are unable to capture . **Table 8**  **- 17**  **Hum an Health and Welfare Effects of PM**  **2.5 POLLUTANT**  **QUANTIFIED AND MONET IZ** **ED IN PRIMARY ESTIMATES**  **UNQUANTIFIED EFFECTS CHANGES IN:**  PM 2.5 Adult premature mortality

Ac ute bronchitis Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarction) Lo wer and upper respiratory illness Minor restricted - activity days Chronic and subchronic bronchitis cases Strokes and cerebrovascular disease Lo w birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non - asthma respiratory emergency room visits VisibilityWork loss days Asthma exacerbations (asthmatic population) Infant mortality Household soiling Consistent with the cost - benefit anal ys is that accompanied the 2012 PM NAAQS revision, the benefits estimates utilize the concentration - response functions as report ed in the epidemiology

literature. AA , 74 To calculate the total monetized impacts associated with quantified health impacts, EPA applies values derived from a number of sources. For premature mortality , EPA applies a value of a statistical life (VSL) derived from the mortality valuation literature. For certain health impacts, such as respiratory- related ailments, EPA applies willingness - to - pay estimates derived from the valuation literature. For the remaining health impacts, EPA applies values derived from current cost - of - illness and/or wage estimates. Readers interested in reviewing the complete methodology for creating the benefit - perton estimates used in this anal ys is can consult EPA’s “Technical Support Document: Estimating the Benefit per Ton of Reducing PM 2.5 Precursors from 17 Sectors.” BB Readers can also refer to Fann et al. (2012) 75 for a detailed description of the benefit - per- ton methodology . As described in the documentation for the benefit per- ton estimates cited above, national per- ton estimates were developed for select ed pollutant/source category combinations. The perton values calculated therefore appl y only to tons reduced from those specific pollutant/source combinations (e.g., NO 2 emitted from on - road mobile sources; direct PM emitted from electricity generating units). Our estimate of PM 2.5 benefits is therefore based on the total direct PM 2.5 and PM - related precursor emissions controlled by sector and multiplied by

each per- ton value. As Table 8 - 17 indicates, EPA projects that the per - ton values for reducing emissions of non - GHG pollutants from both vehicle use and upstream sources such as fuel refineries . CC These projected increases reflect rising income levels, which increase affected individuals’ willingness to pay

for reduced exposure to health threats from air pollution. DD They also reflect future population growth and increased life expectancy , which expands the size of the population

AA Although we summarize the main issues in this chapter, we encourage interested readers to see the benefits chapter of the RIA that accompanied the PM NAA QS for a more detailed description of recent changes to the quantification and monetization of PM benefits. Note that the cost -benefit analysis was prepared solely for purposes of fulfilling analysis requirements under Executive Order 12866 and was not co nsidered, or otherwise played any part, in the decision to revise the PM NAAQS. BB For more information regarding the updated values, see: http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf (accessed September 9, 2014). CC As we discuss in the emissions chapter (Chapter 5), the rule would yield emission reductions from upstream refining and fuel distribution due to decreased petroleum consumption. DD The issue is discussed in more detail in the 2012 PM NAAQS RIA, Section 5.6 .8. See U.S. Environmental Protection Agency. (2012). Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter , Health and Environmental Impacts Division, Office of Air Quality Planning and S tandards, EPA - 452 - R - 12 - 005, December 2012. Available on the internet: http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdfexposed to air pollution in both urban and rural areas, especiall y among older age groups with

the highest mortality risk. EE The benefit - per- ton estimates are subject to a number of assumptions and uncertainties:  The ben efit - per- ton estimates used here reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions associated with the derivation of those estimates (see the TSD describing the calculation of the nation al benefit - per - ton estimates). 76 , FF Consequently , these estimates may

not reflect local variability in population density , meteorology , exposure, baseline health incidence rates, or other local factors associated with the current anal ys is. Therefore, use of these benefit - per- ton values to estimate non GHG benefits may

lead to higher or lower benefit estimates than if these benefits were calculated based on direct air quality modeling. EPA will conduct full - scale air quality

modeling for the final rulemaking in an effort to capture this variability .  This analy sis assumes that all fine particles, regardless of their chemical composition, are equall y potent in causing premature mortality . This is an important assumption, because PM 2.5 produced via transported precursors emitted from stationary

sources may

differ significantly

from direct PM 2.5 released from diesel engines and other industrial sources. The PM I SA, which was twice reviewed b y SAB - CASAC, concluded that “many constituents of PM 2.5 can be linked with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closel y related to specific outcomes”. 77 PM composition and the size distribution of those particles vary within and between areas due to source characteristics. Any specific location could have higher or lower contributions of certain PM species and other pollutants than the national average, meaning potential regional differences in health impact of gi ven control strategies. Depending on the toxicity of each PM species reduced b y the proposed standards, assuming equal toxicity could over or underestimate benefits.  This analy sis assumes that the health impact function for fine particles is linear within the range of ambient concentrations under consideration. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM 2.5, including regions that are in attainment with the fine particle standard . The direction of bias that assuming linear - no threshold model or alternative model introduces depends upon the “true” functional from of the relationship and the specific assumptions and data in a particular anal ys is. For

EE For more information about EPA’s population projections, please refer to the following: http://www.epa.gov/air/benmap/models/BenM APManualAppendicesAugust2010.pdf (See Appendix K) FF See also: http://www.epa.gov/airquality/benmap/sabpt.html . The current values available on the webpage have been updated since the publication of the Fann et al., 2012 paper. For more information regarding the updated values, see: http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf (accessed September 9, 2014).example, if the true function identi fies a threshold below which health effects do not occur, benefits may

be overestimated if a substantial portion of those benefits were estimated to occur below that threshold. Alternatel y, if a substantial portion of the benefits occurred above that thres hold, the benefits may be underestimated because an assumed linear no - threshold function may not reflect the steeper slope above that threshold to account for all health effects occurring above that threshold.  There are several health benefit categories that EPA was unable to quantify due to limitations associated with using benefits - per - ton estimates, several of which could be substantial. Because the NO X and VOC emission reductions associated with this proposal are also precursors to ozone, reductions in NO X and VOC would also reduce ozone formation and the health effects associated with ozone exposure. Unfortunatel y, ozone - related benefits - per- ton estimates do not exist due to issues associated with the complexity of the atmospheric air chemistry and nonlinearities associated with ozone formation. The PM - related benefits - per- ton estimates also do not include any human welfare or ecological benefits. Please refer to Chapter 8.6.2 for a description of the agency ’s plan to quantify and monetize the PM - and ozone - related health impacts for the FRM and a description of the unquantified co - pollutant benefits associated with this rulemaking.  There are many uncertainties associated with the health impact functions that underlie the benefits - per - ton estimates. These include: within - study variability (the precision with which a given study estimates the relationship between air quality changes and health effects); across - study variation (different published studies of the same pollutant/health effect relationship ty picall y do not report identical findings and in some instances the differences are substantial); the application of concentration - response functions nationwide (does not account for any relationship between region and health effect, to the extent that such a relationship exists); extrapolation of impact functions across population (we assumed that certain health impact functions applied to age ranges broader than that considered in the original epidemiological study); and various uncertainties in the c oncentration - response function, including causality and thresholds. These uncertainties may

under- or over- estimate benefits.  EPA has investigated methods to characterize uncertainty in the relationship between PM 2.5 exposure and premature mortality. EPA ’s final PM 2.5 NAAQS anal ys is provides a more complete picture about the overall uncertainty in PM 2.5 benefits estimates. For more information, please consult the PM 2.5 NAAQS RIA. 78  The benefit - per- ton unit values used in this anal ys is incorporate projecti ons of key variables, including atmospheric conditions, source level emissions, population, health baselines , incomes, and technology . These projections introduce some uncertainties to the benefit per ton estimates. As mentioned above, emissions changes and benefits - per- ton estimates alone are not a good indication of local or regional air quality and health impacts, as there may be localized impacts associated with the proposed rulemaking. Additionally , the atmospheric chemistryrelated to ambient conce ntrations of PM 2.5 , ozone and air toxics is very complex. Full - scale

photochemical modeling is therefore necessary to provide the needed spatial and temporal detail to more completel y and accurately estimate the changes in ambient levels of these pollutants and their associated health and welfare impacts. As discussed above, timing constraints precluded EPA from conducting a full - scale photochemical air quality modeling anal ysis in time for the NPRM. For the final rule, however, a national - scale air qual ity modeling analy sis will be performed to anal yz e the impacts of the standards on PM 2.5 , ozone, and selected air toxics. The benefits anal ys is plan for the final rulemaking is discussed in the next section. 

### 8.6.2 **Human Health and Environmental Benefits for the Final Rule**  **8.6.2.1 Human Health and Environmental Impacts**  As noted above , t o model the ozone and PM air quality benefits of the final standards , EPA plans to use the Community Multiscale Air Quality

(CMAQ) model (see Chapter 6 for a description of the CMAQ model ). The modeled ambient air quality data will serve as an input to the Environmental Benefits Mapping and Analy sis Program (BenMAP). GG BenMAP is a computer program developed by EPA that integrates a number of the modeling elements used in previous RIAs (e.g., interpolation functions, population projections, health impact functions, valuation functions, analy sis and pooling methods) to translate modeled air concentration estimates into health effects incidence estimates and monetized benefits estimates. Table 8 - 18 lists the PM - and ozone - related b enefits categories we will use to quantify the non - GHG incidence impacts associated with the final standard s . Table 8 - 18 also lists non - GHG related endpoints we are currentl y unable to quantify and/or monetize. **Table 8**  **- 18**  **Estim ated Quantified and Unquantified Health Effects**  BENEFITS CATEGORY SPECIFIC EFFECT EFFECT HAS BEEN QUANTIFIED EFFECT HAS

BEEN MONETIZED MORE

INFORMATION **Improved Human Health** Reduced incidence of premature mo rtality and mo rbidity from exposure to PM 2.5 Adult premature mortality based on

cohort study estimates and expert elicitation estimates (age >25 or age >30)   PM NAAQS RIA, Section 5.6 Infant mortality (age <1)   PM NAAQS RIA, Section 5.6 Non - fatal heart attacks (age > 18)   PM NAAQS RIA, Section 5.6 Hospital admissions

—

respiratory (all ages)   PM NAAQS RIA, Section 5.6 Hospital admissions

—cardiovascular (age >20)   PM NAAQS RIA, Section 5.6

GG Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ecas/ benmodels.html.BENEFITS

CATEGORY SPECIFIC EFFECT EFFECT HAS BEEN QUANTIFIED EFFECT HAS

BEEN MONETIZED MORE

INFORMATION Emergency department visits for asthma (all ages)   PM NAAQS RIA, Section 5.6 Ac ute bronchitis (age 8

– 12)   PM NAAQS RIA, Section 5.6 Lo wer respiratory symptoms (age 7

– 14)   PM NAAQS RIA, Section 5.6 Upper respiratory symptoms (asthmatics age 9

– 11)   PM NAAQS RIA, Section 5.6 Asthma exacerbation (asthmatics age 6

– 18)   PM NAAQS RIA, Section 5.6 Lost work days (age 18

– 65)   PM NAAQS RIA, Section 5.6 Minor restricted - activity days (age 18

– 65)   PM NAAQS RIA, Section 5.6 Chronic Bronchitis (age >26)

— — PM NAAQS RIA, Section 5.6 c Emergency department visits for cardiovascular effects (all ages) — — PM NAAQS RIA, Section 5.6 c Strokes and cerebrovascular disease (age 50

– 79) — — PM NAAQS RIA, Section 5.6 c Other cardiovascular effects (e.g., other ages) — — PM ISA a Other respiratory effects (e.g., pulmonary function, non - asthma ER visits, non -bronchitis chronic diseases, other ages and populations) — — PM ISA a Reproductive and developmental effects (e.g., low

birth weight, pre term births, etc.) — — PM ISA a , b Cancer, mutagenicity, and genotoxicity effects — — PM ISA a , b Reduced incidence of premature mo rtality and mo rbidity from exposure to ozone Premature mortality based on short term study estimates (all ages)   Ozone ISA Premature mortality based on long term study estimates (age 30

– 99) — — Ozone ISA c Hospital admissions

—

respiratory causes (age > 65)   Ozone ISA Hospital admissions

—

respiratory causes (age <2)   Ozone ISA Emergency department visits for asthma (all ages)   Ozone ISA Minor restricted - activity days (age 18

– 65)   Ozone ISA School absence days (age 5

– 17)   Ozone ISA Decreased outdoor worker productivity (age 18

– 65)   Ozone ISA Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA a Cardiovascular and nervous system effects — — Ozone ISA bBENEFITS

CATEGORY SPECIFIC EFFECT EFFECT HAS BEEN QUANTIFIED EFFECT HAS

BEEN MONETIZED MORE

INFORMATION Reproductive and developmental effects — — Ozone ISA b Reduced incidence of mo rbidity from exposure to air toxics 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) Irrit ation of the eyes, skin, and respiratory tract (acetaldehyde) Upper respiratory tract irritation and congestion (acrolein) — — IRIS a,b

Notes: a We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods. b We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association. c We assess these benefits qualitatively due to time and resource limit ations for this analysis. Table 8 - 19 lists the specific PM - and ozone - related health effect exposure - response functions we will use to quantify

the non - GHG incidence impacts associated with the final standard s . **Table 8**  **- 19**  **Health Impact Functions Used in BenMAP to Estimate Impacts of PM 2.5**  **and Ozone Reductions ENDPOINT**  **POLLUTANT STUDY**  **STUDY POPULATION Prem**  **ature Mortality** Premature mortality

– daily time series O 3 Multi - city Bell et al (2004) (NMMAPS study) 79

– Non accidental Huang et al (2005) 80

- Cardiopulmonary Schwartz (2005) 81

– Non - accidental Meta - analyses: Bell et al (2005) 82

– All cause Ito et al (2005) 83

– Non - accidental All ages**ENDPOINT POLLUTANT**  **STUDY STUDY POPULATION**  Levy et al (2005) 84

– All cause Premature mortality

— cohort study, all - cause PM 2.5 Krewski et al. (200 9 ) 85 L epeule et al. (20 12 ) 86 >29 years >25 years Premature mortality, total exposures PM 2.5 Expert Elicitation (IEc, 2006) 87 >24 years Premature mortality

— all - cause PM 2.5 Woodruff et al. (1997) 88 Infant (<1 year) **Chronic Illness** Nonfatal heart attacks PM 2.5 Peters et al. (2001) 89 Pooled estimate: Pope et al. (2006) 90 Sullivan et al. (2005) 91 Zanobetti et al. (2009) 92 Zanobetti and Schwartz (2006) 93 Adults (>18 years)

**Hospital Admissions** Respiratory O 3 Pooled estimate: Schwartz (1995) - ICD 460 - 519 (all resp) 94 Schwartz (1994a; 1994b) - ICD 480 - 486 (pneumonia) 95 , 96 Moolgavkar et al. (1997) - ICD 480 - 487 (pneumonia) 97 Schwartz (1994b) - ICD 491 - 492, 494 - 496 (COPD) Moolgavkar et al. (1997)

– ICD 490 - 496 (COPD) >64 years

Burnett et al. (2001) 98 <2 years PM 2.5 Pooled estimate:

Zanobetti et al. (2009)

—ICD 460 - 519 (All respiratory) Kloog et al. (2012)

—ICD 460 - 519 (All Respiratory) 99 >64 years

PM 2.5 Moolgavkar (2000)

—ICD 490

–496 (Chronic

lung disease) 100 18

– 64 years PM 2.5 Pooled estimate:

Babin et al. (2007)

—ICD 493 (asthma) 101 Sheppard (2003)

—ICD 493 (asthma) 102 < 18 years Cardiovascular PM 2.5 Pooled estimate: Zanobetti et al. (2009)

—ICD 390 - 459 (all cardiovascular) Peng et al. (2009)

—ICD 426 - 427; 428; 430 - 438; 410 - 414; 429; 440 - 449 (Cardio - , cerebro and peripheral vascular disease) 103 Peng et al. (2008)

—ICD 426 - 427; 428; 43 0 - 438; 410 - 414; 429; 440 - 449 (Cardio - , cerebro and peripheral vascular disease) 104 Bell et al. (2008)

—ICD 426 - 427; 428; 430 - 438; 410 - 414; 429; 440 - 449 (Cardio - , cerebro and peripheral vascular disease) 105 >64 years

PM 2.5 Moolgavkar (2000)

—ICD 390 - 429 (all

cardiovascular) 20

– 64 years Asthma - related ER visits O 3 Pooled estimate: Peel et al (2005) 106 Wilson et al (2005) 107 All ages All ages**ENDPOINT POLLUTANT**  **STUDY STUDY POPULATION** 

Asthma - related ER visits (cont ’d) PM 2.5 Pooled estimate: Mar et al. (2010) 108 Slaughter et al. (2005) 109 Glad et al. (2012) 110 All ages **Other Health Endpoints** Ac ute bronchitis PM 2.5 Dockery et al. (1996) 111 8

– 12 years Upper respiratory symptoms PM 2.5 Pope et al. (1991) 112 Asthmatics, 9

– 11 years Lo wer respiratory symptoms PM 2.5 Schwartz and Neas (2000) 113 7

– 14 years Asthma exacerbations PM 2.5 Pooled estimate: Ostro et al. (2001) 114 (cough, wheeze and shortness of breath) Mar et al. (2004) (cough, shortness of breath) 6

– 18 years a Work loss days PM 2.5 Ostro (1987) 115 18

– 65 years School absence days O 3 Pooled estimate: Gilliland et al. (2001) 116 Chen et al. (2000) 117 5

– 17 years b Minor Restricted Activity Days (MRADs) O 3 Ostro and Rothschild (1989) 118 18

– 65 years PM 2.5 Ostro and Rothschild (1989) 18

– 65 years Notes: a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (2004) study. Based on advice from the SAB - HES, we extended the applied population to 6 - 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. EPA - S AB (2004) and NRC (2002). b Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on advice from the National Research Council and EPA SAB - HES, we have calculated reductions in school absences for all school - aged children based on the biological similarity between children aged 5 to 17. 

### 8.6.2.2 **Monetized Estimates of Impacts of Reductions in Co**  **- Pollutants**  Table 8 - 20 presents the monetary values we will apply

to changes in the incidence of health and welfare effects associated with reductions in non - GHG pollutants that will occur when these GHG control strategies are finalized .**Table 8**  **- 20**  **Valuation Metrics Used in BenMAP to Estimate Monetary Co -**  **Benefits**

ENDPOINT VALUATION METHOD **VALUATION (2011**  **$)** Premature mortality Assumed Mean VSL $ 8,300,000 Myocardial Infarctions, Nonfatal Medical Costs Over 5 Years. Varies by age and discount rate. Russell (1998) 119 --- Medical Costs Over 5 Years. Varies by age and discount rate. Wittels (1990) 120 --- Hospital Admissions Respiratory, Age 65+ COI: Medical Costs + Wage Lost $ 37,000 Respiratory, Ages 0 - 2 COI: Medical Costs $ 13,000 Chronic Obstructive Pulmonary Disease (COPD) COI: Medical Costs + Wage Lost $ 22,000 Asthma COI: Medical Costs + Wage Lost $ 17,000 Cardiovascular CO I: Medical Costs + Wage Lost (18 - 64) $ 43,000 COI: Medical Costs + Wage Lost (65 - 99) $ 42,000 ER Visits, Asthma COI: Average of Smith et al. (1997) 121 $ 440 and Standford et al. (1999) 122 Other Health Endpoints Ac ute Bronchitis WTP: 6 Day Illness, CV Studies $ 470 Upper Respiratory Symptoms WTP: 1 Day, CV Studies $ 32 Lo we r Respiratory Symptoms WTP: 1 Day, CV Studies $ 21 Asthma Exacerbation WTP: Bad Asthma Day, Rowe and Chestnut (1986) 123 $ 57 Work Loss Days Median Daily Wage, County - Specific (median = $150) --- Minor Restricted Activity Days WTP: 1 Day, CV Studies $ 66 School Absence Days Median Daily Wage, Women 25+ $ 98 Source: Dollar amounts for each valuation method were extracted from BenMAP and adjusted to year 2011 dollars (from year 2000 dollars) using the Consumer Price Urban Index (CPI - U). For endpoints valued using measures of VSL, WTP, or are wage -based, we use the CPI - U for “all items”: 224.939 (2011) and 172.2 (2000). For endpoints valued using a Cost - of

- Illness measure, we use the CPI - U for “medical care”: 375.613 (2009) and 260.8 (2000). 

### 8.6.2.3 **Other Unquantified Health and Environmental Impacts** In addition to the co - pollutant health and environmental impacts we plan to quantify for the anal ys is of the HD GHG standard s , there are a number of other health and human welfare endpoints that we will not be able to quantify because of current limitations in the methods or available data. These impacts are associated with emissions of air toxics (including benzene, 1,3 - butadiene, formaldehyde, acetaldehyde, acrolein, and ethanol), ambient ozone, and ambient PM 2.5 exposures. For example, we have not quantified a number of known or suspected health effects linked with ozone and PM for which a ppropriate health impact functions are not available or which do not provide easily interpretable outcomes (i.e., changes in heart rate variability). In addition, we are currently unable to quantify a number of known welfare effects, including reduced aci d and particulate deposition damage to cultural monuments and other materials, and environmental benefits due to reductions of impacts of eutrophication in coastal areas. Although there will be impacts associated with air toxic pollutant emission changes that result from th is action , we do not attempt to monetize those impacts. This is primarily becausecurrentl y 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 currentl y available for assessment at the national scale are those used in the National - Scale Air Toxics Assessment (NATA). EPA ’s Science Advisory Board specificall y commented in the ir review of the 1996 NATA that these tools were not yet ready for use in a national - scale benefits anal ys is, because they did not consider the full distribution of exposure and risk, or address sub - chronic health effects. 124 While EPA has since improved th e se tools, there remain critical limitations for estimating incidence and assessing benefits of reducing mobile source air toxics. As part of the second prospective anal ys is of the benefits and costs of the Clean Air Act , 125 EPA conducted a case study anal ys is 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 Anal ys is 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 .” 126 EPA continues to work to address these limitations; however, we will not have the methods and tools av ailable for national - scale application in time for the analy sis of the final action . HH 

### 8.7 **Additional Impacts 8.7.1**  **Cost of Noise, Congestion**  **, and Accidents** Section 8.3 discusses the likely sign of the rebound effect. If net operating costs of the vehicle decline, then we expect a positive rebound effect. Increased vehicle use associated with a positive rebound effect also contributes to increased traffic congestion, motor vehicle accidents, and highway

noise. Depending on how the additional travel is distributed throughout the day and on where it takes place, additional vehicle use can contribute to traffic congestion and delay s by

increasing traffic volumes on facilities that are already heavily traveled during peak periods. These added delay s impose higher cos ts on drivers and other vehicle occupants in the form of increased travel time and operating expenses. Because drivers do not take these added costs into account in deciding when and where to travel, they must be accounted for separately as a cost of the added driving associated with the rebound effect.

HH In April, 2009, EPA hosted a workshop on estimating the benefits or reducing h azardous air pollutants. This workshop built upon the work accom plished in the June 2000 Science Advisory Board/EPA Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on approaches to esti mating human health benefits from reductions in air toxics exposure, but no consensus was reached on methods that could be implemented in the near term for a broad selection of air toxics. Please visit http://epa.gov/air/toxicair/2009workshop.html for mor e information about the workshop and its associated materials.EPA and NHTSA rel y on estimates of congestion, accident, and noise costs caused by

pickup trucks and vans, single unit trucks, buses, and combination tractors developed b y the Federal Highway

Administration to estimate the increased external costs caused by added driving due to the rebound effect. 127 The FHWA estimates are intended to measure the increases in costs from added congestion, property damages and injuries in traffic accidents, and noise levels cau sed b y various classes of trucks that are borne b y persons other than their drivers (or “marginal” external costs). EPA and NHTSA employed estimates from this source previously in the anal ys is accompanying the light - duty 2012 - 2016 vehicle rulemaking. The agencies continue to find them appropriate for this anal ys is after reviewing the procedures used by FHWA to develop them and considering other available estimates of these values. FHWA’s congestion cost estimates for trucks, which are weighted averages based on the estimated fractions of peak and off - peak freeway

travel for each class of trucks, already account for the fact that trucks make up a smaller fraction of peak period traffic on congested roads because they

try to avoid peak periods when possibl e. FHWA’s congestion cost estimates focus on freeway s because non - freeway effects are less serious due to lower traffic volumes and opportunities to re - route around the congestion. The agencies, however, applied the congestion cost to the overall VMT inc rease, though the fraction of VMT on each road ty pe used in MOVES range from 27 to 29 percent of the vehicle miles on freeway s for vocational vehicles and 53 percent for combination tractors. The results of this analy sis potentiall y overestimate the conge stions costs associated with increased truck use, and thus lead to a conservative estimate of benefits. EPA and NHTSA estimated the costs of additional vocational vehicle travel using a weighted average of 15 percent of the FHWA estimate for bus costs and 85 percent of the FHWA estimate for single unit truck costs to reflect the make - up of this segment. The low, mid, and high cost estimates from FHWA updated to 2012 dollars are included in Table 8 - 21 . **Table 8**  **- 21**  **Low -**  **Mid -**  **High Cost Estimates (2012$/m ile)**  **NOISE High**  **Middle Low**  Pickup Truck, Van $0.002 $0.001 $0.000 Vocational Vehicle $0.024 $0.009 $0.003 Com bination Tractor $0.054 $0.021 $0.006 **Accidents High**  **Middle Low**  Pickup Truck, Van $0.086 $0.028 $0.015 Vocational Vehicle $0.050 $0.017 $0.008 Com bination Tractor $0.073 $0.023 $0.011 **Congestion High**  **Middle Low**  Pickup Truck, Van $0.151 $0.051 $0.014 Vocational Vehicle $0.344 $0.117 $0.031 Com bination Tractor $0.331 $0.113 $0.030 The agencies are using FHWA’s “Middle” estimates for marginal congestion, accident, and noise costs caused by increased travel from trucks . 128 This approach is consis tent with the methodology

used in the HD GHG Phase 1 rule and both LD GHG rules. These costs aremultiplied by the annual increases in vehicle miles travelled from the rebound effect to yield the estimated increases in congestion, accident, and noise externality costs during each future year. The results are shown in Table 8 - 22 through Table 8 - 24 . **Table 8**  **- 22**  **Annual Costs & Net Present Values Associated with Increased Noise, Accidents and Congestion for the Preferred Alternative Relative to the Less Dynamic Baseline**  **and using Method B (M**  **illions of 2012$) a**  CALENDAR YEAR HD PICKUP AND

VANS VOCATIONAL TRACTOR/TRAILER SUM 2018 $0 $0 $0 $0 2019 $0 $0 $0 $0 2020 $0 $0 $0 $0 2021 $9 $26 $82 $117 2022 $18 $51 $103 $172 2023 $27 $76 $123 $226 2024 $35 $102 $141 $279 2025 $44 $127 $159 $330 2026 $52 $151 $177 $379 2027 $59 $173 $192 $425 2028 $66 $194 $207 $467 2029 $72 $213 $221 $506 2030 $78 $230 $234 $542 2035 $100 $294 $282 $676 2040 $111 $330 $317 $758 2050 $126 $375 $370 $871 NPV , 3% $1,335 $3,934 $4,066 $9,334 NPV, 7% $593 $1,743 $1,867 $4,202 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1**Table 8**  **- 23**  **Discounted Model Year Lifetim e Costs Associated with Increased Noise, Accidents and** 

**Congestion for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (3% discount**  **rate, Millions of 2012$) a**  MODEL YEAR HD PICKUP AND

VANS VOCATIONAL TRACTOR/TRAILER SUM 2018 $0 $0 $132 $132 2019 $0 $0 $146 $146 2020 $0 $0 $162 $162 2021 $71 $203 $176 $450 2022 $69 $197 $173 $438 2023 $66 $191 $169 $427 2024 $65 $190 $168 $424 2025 $64 $189 $169 $422 2026 $64 $188 $168 $420 2027 $63 $185 $167 $415 2028 $61 $182 $166 $409 2029 $60 $178 $164 $402 Sum $583 $1,704 $1,959 $4,247 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 **Table 8**  **- 24**  **Discounted Model Year Lifetim e Costs Associated with Increased Noise, Accidents and**  **Congestion for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (7%**  **discount rate, Millions of 2012$) a**  MODEL YEAR HD PICKUP AND

VANS VOCATIONAL TRACTOR/TRAILER SUM 2018 $0 $0 $85 $85 2019 $0 $0 $94 $94 2020 $0 $0 $103 $103 2021 $45 $129 $110 $284 2022 $42 $120 $104 $266 2023 $39 $112 $98 $250 2024 $37 $108 $94 $239 2025 $35 $103 $91 $229 2026 $33 $98 $88 $219 2027 $32 $93 $84 $209 2028 $30 $88 $80 $198 2029 $28 $83 $76 $187 Sum $320 $935 $1,106 $2,362 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 

### 8.7.2 **Benefits of Reduced Refueling Time** Reducing the fuel consumption of heavy- duty trucks will either increase their driving range before they

require refueling , or lead truck manufacturers to offer, and truck purchasers to buy , smaller fuel tanks . Keeping the fuel tank the same size will allow truck operators to reduc ethe frequency with which drivers ty

picall y refuel their vehicles, b y extending the upper limit o n the distance they

can travel before requiring refueling. Alternatively , if truck purchasers and manufacturers respond to improved fuel economy by reducing the size of fuel tanks , the smaller tank will require less time to fill during each refueling stop . Because refueling time represents a time cost of truck operation, these time savings should be incorporated into truck purchasers’ decisions about how much fuel - saving technology they purchase as part of their choices of new vehicles. The savings calculated here thus raise the same questions discussed in preamble Section IX VIII.A and RIA Chapter 8.2: does the apparent existence of these savings reflect failures in the market for fuel economy , or does it reflect costs that are not addressed in this anal ys is? The response to these questions could vary across truck segment. No direct estimates of the value of extended vehicle range or reduced fuel tank size are readil y available . Instead, this analy sis calculate s the reduction in the annual amount of time a driver of each type of truck would spend filling it s fuel tank; this reduced time could result either from fewer refueling events, if new trucks’ fuel tanks stay the same size, or from less time spent filling the tank during each refueling stop, if new trucks’ fuels tank are made proportionatel y smaller. As discussed in Section 8.3 in this RIA, the average number of miles each type of vehicle is driven annually would likel y increase under the regulation, as truck operators respond to lower fuel expenditures (the “rebound effect”). The estimates of refueling time with the proposal in effect allow for this increase in truck use. However, the estimate of the rebound effect does not account for any reduction in net operating costs from lower refueling time. Because the rebound effect should measure the change in VMT with respect to the net change in overall operating costs, refueling time costs would ideally factor into this calculation. The effect of this omission is expected to be minor because refueling time savings are generall y small relative to the valu e of reduced fuel expenditures. The savings in refueling time are calculated as the total amount of time the driver of a ty pical truck in each class would save each year as a consequence of pumping less fuel into the vehicle’s tank. The calculation also includes a fixed time per refill event of 3.5 minutes which would not occur as frequently due to the fewer number of refills. The calculation uses the reduced number of gallons consumed by truck type and divides that value b y the tank volume and refill amount to get the number of refills, then multiplies that by the time per refill to determine the number of hours saved in a given year. The calculation then applies DOT - recommended values of travel time savings to convert the resulting time savings to th eir economic value. The input metrics used in the anal ys is are included in Table 8 - 25 . The equation for the calculation is shown below:

= − × + × $ h The annual impacts associated with reduced refueling time are shown in Table 8 - 26 and the MY lifetime impacts are shown in Table 8 - 27 a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1Table 8 - 27 and Table 8 - 28 . **Table 8**  **- 25**  **Inputs to Calculate Refueling Time Savings** HD PICKUP AND VAN VOCATIONAL VEHICLE TRACTOR Fuel Dispensing Rate (gallon/minute) 129 10 10 20 Refueling fixed time (minutes/refill) 130 3.5 3.5 3.5 Tank volume (gallons) a 30 40 200 Refill amount (%volume/refill) a 60% 75% 75% Resultant time/refill (minutes/refill) 5.3 6.5 11.0 Wage rate (2012$/hr) 131 ,b $27.22 31.01 28.56 Notes: a HD pickup and van values based on a NHTSA survey, other are estimated. b A wa ge growth rate of 1.2% has been assumed for future years.

**Table 8**  **- 26**  **Annual Refueling Benefits and Net Present Values for the Preferred Alternative Relative to the**  **Less Dyna mic Baseline and using Method B**  **(Dollar Values in Millions of 2012$) a**  CALENDAR YEAR HD PICKUP AND VANS VOCATIONAL TRACTOR/TRAILER SUM OF BENEFITS Hours saved (thousands) Benefits Hours saved (thousands) Benefits Hours saved (thousands) Benefits 2018 0 $0 0 $0 90 $3 $3 2019 0 $0 0 $0 183 $6 $6 2020 0 $0 0 $0 278 $9 $9 2021 51 $2 122 $4 598 $19 $25 2022 192 $6 244 $9 999 $32 $47 2023 419 $13 365 $13 1,401 $46 $72 2024 732 $23 620 $22 2,046 $67 $113 2025 1,130 $36 874 $32 2,691 $90 $157 2026 1,611 $52 1,124 $41 3,327 $112 $205 2027 2,164 $70 1,558 $58 4,043 $138 $266 2028 2,699 $89 1,979 $74 4,731 $164 $327 2029 3,211 $107 2,387 $91 5,387 $188 $386 2030 3,694 $125 2,778 $107 6,011 $213 $444 2035 5,595 $200 4,343 $177 8,526 $320 $698 2040 6,695 $254 5,286 $229 10,205 $407 $890 2050 7,788 $334 6,260 $305 12,365 $556 $1,195 NPV , 3% $2,627 $2,347 $4,436 $9,410 NPV, 7% $1,067 $950 $1,851 $3,868 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1**Table 8**  **- 27**  **Discounted Model Year Lifetim e Refueling Benefits at 3% for the Preferred Alternative Relative**  **to the Less Dynamic Baseline and using Method B (Millions of 2012$) a**  MODEL YEAR HD PICKUP

AND VANS VOCATIONAL TRACTOR/ TRAILER SUM 2018 $0 $0 $23 $23 2019 $0 $0 $22 $22 2020 $0 $0 $21 $21 2021 $13 $36 $114 $163 2022 $35 $36 $113 $184 2023 $56 $35 $112 $203 2024 $77 $72 $176 $325 2025 $97 $73 $179 $349 2026 $118 $73 $181 $372 2027 $136 $123 $207 $466 2028 $135 $122 $207 $465 2029 $134 $121 $208 $463 Sum $801 $691 $1,563 $3,055 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 **Table 8**  **- 28**  **Discounted Model Year Lifetim e Refueling Benefits at 7%**  **for the Preferred Alternative Relative**

**to the Less Dynamic Baseline and using Method B (Millions of 2012$) a**  MODEL YEAR HD PICKUP

AND VANS VOCATIONAL TRACTOR/ TRAILER SUM 2018 $0 $0 $16 $16 2019 $0 $0 $15 $15 2020 $0 $0 $14 $14 2021 $8 $23 $71 $101 2022 $21 $21 $67 $110 2023 $33 $20 $64 $117 2024 $43 $40 $97 $181 2025 $52 $39 $95 $187 2026 $61 $38 $93 $191 2027 $68 $61 $102 $231 2028 $65 $59 $98 $222 2029 $62 $56 $95 $213 Sum $413 $357 $827 $1,597 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 

### 8.7.3 **Benefits of Increased Travel Associated with Rebound Driving**  The increase in travel associated with the rebound effect produces additional benefits to vehicle owners and operators, which reflect the value of the adde d (or more desirable) social and economic opportunities that become accessible with additional travel. The anal ys is estimates the economic benefits from increased rebound - effect driving as the sum of fuel expenditures incurred plus the vehicle owner/operator surplus from the additional accessibility it provides. Asevidenced b y the fact that vehicles make more frequent or longer trips when the cost of driving

declines, the benefits from this added travel exceed added expenditures for the fuel consumed. Note that the amount by

which the benefits from this increased driving exceed its increased fuel costs measures the net benefits from the additional travel, usuall y referred to as increased consumer surplus or, in this case, increased owner/operator surplus . The equation for the calculation of the total travel benefit is shown below:

= ( ) $ + 1 2 ( ) $

− $ The agencies’ anal ys is estimates the economic value of the increased owner/operator surplus provided by

added driving using the conventional approximation, which is one half of the product of the decline in vehicle operating costs per vehicle - mile and the resulting increase in the annual number of miles driven. Because it depends on the extent of improvement in fuel economy, the value of benefits from increased vehicle use changes b y model year and varies among alternative standards. Under even those alternatives that would impose the highest standards, however, the magnitude of the surplus from additional vehicle use represents a small fraction of this benefit. The benefits are shown in Table 8 - 29 through Table 8 - 31 . **Table 8**  **- 29**  **Annual Value of Increased Travel and Net Present Values at 3% and 7% Discount Rates for the**  **Preferred Alternative Relative to the Less Dynam** **ic Baseline and using Method B (M**  **illions of 2012$) a**  CALENDAR YEAR HD PICKUP AND

VANS VOCATIONAL TRACTOR/TRAILER SUM 2018 $0 $0 $0 $0 2019 $0 $0 $0 $0 2020 $0 $0 $0 $0 2021 $32 $68 $345 $445 2022 $64 $138 $434 $636 2023 $96 $208 $517 $821 2024 $127 $279 $595 $1,001 2025 $157 $349 $672 $1,179 2026 $186 $416 $744 $1,346 2027 $213 $481 $813 $1,506 2028 $237 $538 $872 $1,647 2029 $261 $593 $929 $1,783 2030 $282 $644 $983 $1,909 2035 $372 $855 $1,217 $2,445 2040 $437 $1,008 $1,428 $2,873 2050 $489 $1,141 $1,656 $3,286 NPV , 3% $5,021 $11,518 $17,700 $34,240 NPV, 7% $2,209 $5,045 $8,062 $15,316 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1**Table 8**  **- 30**  **Discounted Model Year Lifetim e Value of Increased Travel for the Preferred Alternative Relative**  **to the Less Dynamic Baseline and using Method B (3% discount rate, Millions of 2012$)**  **a** MODEL YEAR HD PICKUP AND

VANS VOCATIONAL TRACTOR/TRAILER SUM 2018 $0 $0 $554 $554 2019 $0 $0 $618 $618 2020 $0 $0 $686 $686 2021 $252 $548 $711 $1,510 2022 $244 $539 $706 $1,488 2023 $236 $529 $698 $1,463 2024 $232 $522 $680 $1,434 2025 $229 $524 $689 $1,442 2026 $227 $525 $695 $1,447 2027 $223 $511 $688 $1,421 2028 $220 $507 $688 $1,415 2029 $218 $502 $686 $1,406 Sum $2,080 $4,706 $8,098 $14,884 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1

**Table 8**  **- 31**  **Discounted Model Year Lifetim e Value of Increased Travel for the Preferred Alternative Relative** 

**to the Less Dynamic Baseline and using Method B (7% discount rate, Millions of 2012$) a**  MODEL YEAR HD PICKUP AND

VANS VOCATIONAL TRACTOR/TRAILER SUM 2018 $0 $0 $353 $353 2019 $0 $0 $390 $390 2020 $0 $0 $429 $429 2021 $158 $343 $441 $942 2022 $148 $325 $422 $894 2023 $138 $307 $402 $847 2024 $130 $292 $377 $799 2025 $124 $282 $368 $774 2026 $118 $272 $358 $748 2027 $111 $255 $341 $708 2028 $106 $244 $328 $678 2029 $101 $232 $315 $649 Sum $1,134 $2,553 $4,524 $8,211 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 **8.8 The Effect**  **of Safety Standards and Voluntary Safety Improvements on Vehicle**  **Weight** Safety standards developed by NHTSA in previous rulemakings may make compliance with the fuel efficiency

and CO 2 emissions standards more difficult or may reduce the projected benefits of the program. The primary way that safety

regulations can impact fuel efficiency andCO 2 emissions is through increased vehicle weight, which reduces the fuel efficiency (and thus

increases the CO 2 emissions) of the vehicle. Using MY 2010 as a baseline, this section discusses the effects of other government regulations on MY s 2014 - 2016 medium and heavy- duty vehicle fuel efficiency and CO 2 emissions . At this time, no known safety standards wi ll affect new models in MY 2017 or 2018. NHTSA’s estimates are based on cost and weight tear- down studies of a few vehicles and cannot possibly cover all the variations in the manufacturers’ fleets. NHTSA also requested, and various manufacturers provide d, confidential estimates of increases in weight resulting from safety improvements. Those increases are shown in subsequent tables. We have broken down our anal ys is of the impact of safety standards that might affect the MY s 2014 - 20 16 fleets into three parts: 1) those NHTSA final rules with known effective dates, 2) proposed rules or soon - to - be proposed rules b y NHTSA with or without final effective dates, and 3) currentl y voluntary safety improvements planned b y the manufacturers. 

### 8.8.1 **Weight Impacts of Required Safety Standards**  NHTSA has undertaken several rulemakings in which several standards would become effective for medium - and heav y- duty (MD/HD) vehicles between MY 2014 and MY 2016. We will examine the potential impact on MD/HD vehicle weights fo r MY s 2014 - 2016 using MY 2010 as a baseline.

1. FMVSS 119, Heav y Truck Tires Endurance and High Speed Tests 2. FMVSS 121, Air Brake Sy stems Stopping Distance 3. FMVSS 214, Motor Coach Lap/Shoulder Belts 4. MD/HD Vehicle Electronic Stability Control Sy stems 

### 8.8.1.1 **FMVSS 11 9, Heavy Truck Tires Endurance and High Speed Tests**  NHTSA tentatively

determined that the FMVSS No. 119 performance tests developed in 1973 should be updated to reflect the increased operational speeds and duration of truck tires in commercial service. A Notice of Proposed Rulemaking (NPRM) was issued December 7, 2010 (75 FR 60036). It proposed to increase significantly the stringency

of the endurance test and to add a new high speed test. The data in the large truck crash causation study (LTCCS) that preceded that NPRM found that J and Lload range tires were having proportionately more problems than the other sizes and the agency ’s test results indicate that H, J , and Lload range tires are more likel y to fail the proposed requirements among the targete d F, G, H, J and Lload range tires. 132 To address these problems, the H and J load range tires could potentially use improved rubber compounds, which would add no weight to the tires, to reduce heat retention and improve the durability of the tires. The L load range tires, in contrast, appear to need to use high tensile strength steel chords in the tire bead, carcass and belt areas, which would enable a weight reduction with no strength penalties. Thus , if the update to FMVSS No. 119 was finalized, we anticipate no change in weight for H and J load range tires and a small reduction in weight for L load range tires. This proposal could become a final rule with an effective date of MY 2016.

### 8.8.1.2 **FMVSS No. 121, Airbrake Systems Stopping Distance**

FMVSS No. 121 co ntains performance and equipment requirements for braking s ys tems on vehicles with air brake sy stems. The most recent major final rule affecting FMVSS No. 121 was published on July

27, 2009 , and became effective on November 24, 2009 (MY 2009). The final rule requires the vast majority of new heav y truck tractors (approximately 99 percent of the fleet) to achieve a 30 percent reduction in stopping distance compared to currentl y required levels. Three - ax l e tractors with a gross vehicle weight rating (GVWR) of 59,600 pounds or less must meet the reduced stopping distance requirements by August 1, 2011 (MY 2011) , while two axle tractors and tractors with a GVWR above 59,600 pounds must meet the reduced stopping distance requirements b y the later date of Augus t 1, 2013 (MY 2013). NHTSA determined that there are several brake s ystems that can meet the requirements established in the final rule , including installation of larger S - cam drum brakes or disc brake s ys tems at all positions, or hy brid disc and larger rear S - cam drum brake s ys tems. According to data provided by a manufacturer (Bendix) in response to the NPRM , the heaviest drum brakes weigh more than the lightest disc brakes , while the heaviest disc brakes weigh more than the lightest drum brakes. For a three - axle tractor equipped with all disc brakes, then, the total weight could increase b y 212 pounds or could decrease b y 134 pounds compared t o an all - drum - braked tractor , depending on which disc or drum brakes are used for comparison. The improved brakes may add a small amount of weight to the affected vehicle s for MY s 2014 - 2016 , resulting in a slight increase in fuel consumption. 

### 8.8.1.3 **FMVSS No. 208, Motor coach Lap/Shoulder Belts**  NHTSA is proposing lap/shoulder belts for all motorcoach seats. About 2,000 motorcoaches are sold per year in the United States. Based on preliminary results from the agency ’s cost/weight teardown studies of motor coac h seats, 133 NHTSA estimate s that the weight added b y 3 - point lap/shoulder belts ranges from 5.96 to 9.95 pounds per 2 - person seat. This is the weight only

of the seat belt assembly itself , and does not include changing the design of the seat, reinforcing th e floor, walls or other areas of the motor coach. Few current production motor coaches have been installed with lap/shoulder belts on their seats, and the number of vehicles with these belts already installed could be negligible. Assuming a 54 passenger motor coach, the added weight for the 3 - point lap/shoulder belt assembl y would be in the range of 161 to 269 pounds (27 * (5.96 to 9.95)) per vehicle. This proposal could become a final rule with an effective date of MY 2016. 

### 8.8.2 **Electronic Stability Contr ol Systems (ESC) for Medium**  **- and Heavy**  **- Duty**  **(MD/HD) Vehicles** The purpose of an ESC system for MD/HD vehicles is to reduce crashes caused by rollover or b y directional loss - of - control. ESC monitors a vehicle’s rollover threshold and lateral stability using vehicle speed, wheel speed, steering wheel angle, lateral acceleration, side slip and yaw rate data and upon sensing an impending rollover or loss of directional control situation automatically

reduces engine throttle and applies braking forces to individ ual wheels or sets of wheel to slow the vehicle down and regain directional control. ESC is not currentl y required in MD/HD vehicles , but could be proposed to be required in the se vehicles by NHTSA. FMVSSNo. 105, Hydraulic and electric brake s ys tems, re quires multipurpose passenger vehicles, trucks

and buses with a GVWR greater than 4,536 kg (10,000 pounds) to be equipped with an antilock brake s ys tem (ABS). All MD/HD vehicles hav ing a GVWR of more than 10,000 pounds, are required to have ABS installed by that standard. In addition to the existing ABS functionality, ESC requires sensors including a yaw rate sensor, lateral acceleration sensor, steering angle sensor and brake pressure sensor along with a brake solenoid valve. According to data provided b y Meritor WABCO, the weight of an ESC sy stem for the model 4S4M tractor is estimated to be around 55. 5 pounds, and the weight of the ABS only is estimated to be 45.5 pounds. Thus, we estimate the added weight for the ESC for the vehicle to be 10 (55. 5

– 4 5.5) pounds. 

### 8.8.3 **Summary –**  **Overview of Anticipated Weight Increases** Table 8 - 32 s ummarizes estimates made by NHTSA regarding the weight added by the above discussed standards or likely rulemakings. NHTSA estimates that weight additions required b y final rules and likely NHTSA regulations effective in MY 2016 compared to the MY 2010 fleet will increase motor coach vehicle weight b y 171 - 279 pounds and will increase other heav y- duty truck weights by 10 pounds. **Table 8**  **- 32**  **W eight Additions Due to Final Rules or Likely NHTSA Regulations: Comparing MY 2016 to the**  **MY 2010 Baseline Fleet** STANDARD NUMBER ADDED WEIGHT IN POUNDS MD/HD VEHICLE ADDED WEIGHT IN KILOGRAMS MD/HD VEHICLE 119 0 0 121 0 a 0 a 208 Motor coaches only 161 - 269 73 - 122 MD/HD Vehicle Electronic Stability Control Systems 10 4.5 Total Motor coaches 171 - 279 77.5 - 126.5 Total All other MD/HD vehicles 10 4.5 Note: NHTSA’s final rule on Air Brakes, docket NHTSA - 2009 - 0083, dated July 27, 2009, concluded that a small amount of weight would be added to the brake systems but a weight value was not provided. 

### 8.8.4 **Effects of Vehicle Mass Reduction on Safety**  NHTSA and EPA have been considering the effect of vehicle weight on vehicle safety for the past several years in the context of our joint rulemaking for light - duty vehicle CAFE and GHG standards, consistent with NHTSA’s long - standing consideration of safety effects in setting CAFE standards. Combining all modes of impact, the latest anal ys is by NHTSA for the MYs 201 2 - 2016 final rule 134 found that reducing the weight of the heavier light trucks (LT > 3,870) had a positive overall effect on safety , reducing societal fatalities.In the context of the current rulemaking for HD fuel consumption and GHG standards,

one woul d expect that reducing the weight of medium - duty trucks similarl y would, if anything, have a positive impact on safety. However, given the large difference in weight between light duty vehicles and medium - duty trucks, and even larger difference between li ght - duty vehicles and heav y- duty vehicles with loads, the agencies believe that the impact of weight reductions of medium - and heav y- duty trucks would not have a noticeable impact on safety for any of these classes of vehicles. However, the agencies recognize that it is important to conduct further study and research into the interaction of mass, size and safety to assist future rulemakings, and we expect that the collaborative interagency work currentl y on - going to address this issue for the light - duty veh icle context may

also be able to inform our evaluation of safety effects for the final HD vehicle rule. We intend to continue monitoring this issue going forward, and may take steps in a future rulemaking if it appears that the MD/HD fuel efficiency and GHG standards have unforeseen safety consequences. The American Chemistry Council stated in comments to the agencies that plastics and plastic composite materials provide a new way to lighten vehicles while maintaining passenger safety. They

added that properties of plastics including strength to weight ratio, energy absorption, and flexible design make these materials well suited for the manufacture of medium - and heav y- duty vehicles. They submitted supporting analy ses with their comments. The National School Transportation Association stated that adde d structural integrity requirements increase weight of school buses, and thus decrease fuel economy . They asked that if there are safety and fuel economy trade - offs manufacturers should be able to receive a waiver from the regulation requirements. Since no weight reduction is required for school buses

– or any other vocational vehicle

– the agencies do not believe this is an issue with the current regulation. 

### 8.9 **Petroleum, Energy and National Security impact 8.9.1**  

### Energy **Security Impacts**  The Phase 2 standards are designed to require improvements in the fuel efficiency of medium - and heav y- duty vehicles and, thereb y, reduce fuel consumption and GHG emissions. In turn, the Phase 2 standards help to reduce U.S. petroleum imports. A reduction of U.S. petroleum imports reduces both financial and strategic risks caused b y potential sudden disruptions in the supply

of imported petroleum to the U.S. This reduction in risk increases U.S. energy security. This section summarizes the agency’s estimates of U.S. oil import reductions and energy

security benefits of the proposed Phase 2 standards . Additional discussion of this issue can be found in Section IX .H of the preamble. The U.S., as a large oil importer and oil consumer, is economicall y vulnerable to outcomes in a vo latile global oil market that relies on oil supplies from potentially unstable sources. Much of the world’s oil and gas supplies are located in countries facing social, economic, and demographic challenges , thus making them vulnerable to potential localinstability . In 2010 , just over 4 0 percent of world oil supply came from OPEC (e.g., Organization of Petroleum Exporting Countries) nations and the Annual Energy Outlook 2014 (Earl y Release) projects that this share will rise steadily . In the AEO 20 14 (Early Release) projections , OPEC nations supply over 44 percent by

2040. II Approximately 31 percent of global suppl y is from Middle East and North African countries alone , a share that is expected to grow. JJ Measured in terms of the share of world oil resources or the share of global oil export supply , rather than oil production, the concentration of global petroleum resources in OPEC nations is even larger. As another measure of concentration, of the 137 countries/principalities that export either crude or product, the top 12 have recentl y accounted for over 55 percent of exports. 135 Eight of these countries are members of OPEC, and a ninth is Russia. KK Ina market where even a 1 - 2 percent supply loss raises prices noticeabl y, and where a 10 percent suppl y loss could lead to an unprecedented price shock, this regional concentration is of concern. LL Historically , the countries of the Middl e East have been the source of eight of the ten major world oil disruptions , with the ninth originating in Venezuela, an OPEC country , and the tenth being Hurricanes Katrina and Rita . MM One impact of the Proposed Phase 2 HD National Program is that it promotes more efficient use of transportation fuels in the U.S . The result is that it reduces U.S. oil consumption and imp orts, which reduces both financial and strategic risks associated with a potential disruption in supply

or a spike in the cost of a particular energy source. This reduction in risks increases U.S. energy

security . For this rule, an “oil premium” approach is utilized that identif ies those energy security

related economic costs which are not reflected in the market price of oil, and which are expected to change in response to an incremental change in the level of U.S. oil imports.

II The agencies used the AEO 2014 (Early Release) since this version of AEO was available at the time that fuel savings from the rule were being estimated. The AEO 2014 (Early Release) and the AEO 2014 have very similar energy market and economic projections. For example, world oil prices are the same between the two forecasts. JJ Middle East and North African oil supply share reaches 36 percent in 2040 in the AEO (Early Release) Reference Case. KK The other three are Norway, Canada, and the EU, an exporter of product. LL For example, the 2005 Hurricanes Katrina/Rita and the 2011 Libyan conflict both led to a 1.8 percent reduction in global crude supply. While the price impact of the latter is not easily distinguished given the rapidly rising post recession prices, the former event was associated with a 10 - 15 percent world oil price increase. There are a range of smaller events with smaller but noticeable impacts. Somewhat larger events, such as the 2002/3 Venezuelan Strike and the War in Iraq, corresponded to about a 2.9 percent sustained loss of supply, and was associated with a 28 percent world oil price increase. (Compiled from EIA oil price data, IEA2012 [IEA Response System for Oil Supply Emergencies ] (http://www.iea.org/publications/freepublications/publication/EPPD_Brochure_English_2012_02.pdf ) See table on P. 11. and Hamilton 2011 "Historical Oil Shocks,"(http://econweb.ucsd.edu/~jhamilto/oil_history.pdf ) in *Routledge Handbook of Major Events in Economic History*, pp. 239 - 265, edited by Randall E. Parker and Robert Whaples, New

York: Routledge Taylor and Francis Group, 2013). MM The events IEA categorized as oil supply disruptions all had a gross peak oil supply loss of at least 1.5 million barrels a day as a result of wars, revolutions, embargoes or strikes involving major oil exporting nations or from major storm events or disasters (like the double Hurricane Katrina/Rita) affecting oil producing/processing regions. IEA 2011 “IEA Response System for Oil Supply Emergencies.”**8.9.2 Impact on U.S.**  **P etroleum**  **I mports** 

U.S. energy

security is broadly defined as the continued availability of energy

sources at an acceptable price. Most discussion of U.S. energy security revolves around the topic of the economic costs of U.S. dependence on oil imports. However, it is not imports alone, but both imports and consumption of petroleum from all sources, and their role in economic activity , that expose the U.S. to risk from price shocks in the world oil price. The relative significance of petroleum consumption and import levels for the macroeconomic disturbances that follow from oil price shocks is not fully understood. Recognizing that changing petroleum consumption will change U.S. imports, this assessment of oil costs focuses on those incremental social costs that fo llow from the resulting changes in imports, employing the usual oil import premium measure. The agencies will review any comments we receive on how to incorporate the impacts of changes in oil consumption, rather than imports exclusively, into our energy security anal ys is. The U.S.’s energy

security problem is that the U.S. relies on imported oil from potentially unstable sources. In addition, oil exporters have the ability to raise the price of oil by exerting monopoly

power through the formation of a cartel, the Organization of Petroleum Exporting Countries (OPEC). These factors contribute to the vulnerability of the U.S. economy to episodic oil supply shocks and price spikes. In 2012, U.S. net expenditures for imports of crude oil and petroleum p roducts were $290 billion, and total consumption expenditure was $634 billion (in 2012$) ( see Figure 8 - 3 ). 136 Import costs have declined since 2011 but total oil expen ditures (domestic and imported) remain near historical highs, at roughl y triple the real oil costs experienced b y the U.S. from 1986 to 2002.**Figure 8**  **- 3**  **U.S. Expenditures on Crude Oil from** **1970 through 2015**  **137**

The agencies used EPA’s MOVES model to estimate the reductions in U.S. fuel consumption due to this proposed rule for vocational vehicles and tractors . For HD pickups and vans, the agencies used both DOT’s CAFE model and EPA’s MOVES model to estimate the fuel consumption impacts. ( Detailed explanations of the MOVES and CAFE models can be found in Chapters 5 and 10 of the draft RIA. See IX.C of the preamble for estimates of reduced fuel consumption from the proposed rule). Based on a detailed anal ys is of differences in U.S. fuel consumption, petroleum imports, and imports of petroleum products, the agencies estimate that approximately

90 percent of the reduction in fuel consumption resulting from adopting improved GHG emissions standards and fuel ef ficiency standards is likely

to be reflected in reduced U.S. imports of crude oil and net imported petroleum products. NN Thus, on balance, each gallon of fuel saved as a consequence of the HD GHG and fuel efficiency standards is anticipated to

NN We looked at changes in crude oil imports and net petroleum products in the Reference Case in comparison to two cases from the AEO 2014. The two cases are the Low Demand and Low VMT cases. See the spreadsheet “I mp acts on Fuel Demands and ImportsJan9.xlsx” comparing the AEO 2014 Reference Case to the Low Demand Case. See the spreadsheet “Impact of Fuel Demand and Impacts J anuary20VMT.xlsl” for a comparison of AEO 2014 Reference Case and the Low VMT Case. We also considered a paper entitled “Effect of a U.S. Demand Reduction on Imports and Domestic Supply Levels” by Paul Leiby, 4/16/2013. This paper suggests that “Given a particular reduction in oil demand stemming from a policy or significant technology change, the fraction of oil use savings that shows up as reduced U.S. imports, rather than reduced U.S. supply, is actually quite close to 90 percent, and probably close to 9 5 percent”.

**0 100**  **200 300**  **400 500**  **600 700**  **800**

**1970 1975**  **1980 1985**  **1990 1995**  **2000 2005**  **2010 2015** **$ B** **i l** **l /** **y r** **, 2** **0 1** **2 $** 

**Ye ar** 



### U.S. Expenditur**es on Crude Oil**

**Domestic Imported** reduce total U.S. imports of petroleum by 0.90 gallons. OO Based upon the fuel savings estimated by the MOVES/CAFE models and the 90 percent oil import factor, the reduction in U.S. oil imports from this rule are estimated for the ye ars 2020, 2025, 2030, 2040, and 2050 (in millions of barrels per day

(MMBD)) in Table 8 - 27 below. For comparison purposes, Table 8 - 27 also shows U.S. imports of crude oil in 2020, 2025, 2030 and 2040 as projected by DOE in the Annual Energy

Outlook 2014 (Earl y Release) Reference Case. U.S. Gross Domestic Product (GDP) is projected to grow by roughly 59 percent between 2020

– 2040 in the AEO 2014 (Earl y Release) projections. **Table 8**  **- 33**  **Projected U.S. Imports of Crude Oil and U.S. Oil Import Re ductions in 2020, 2025, 2030, 2040**  **and 2050 for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (M**  **illions of barrels per day (MMBD)) a**  YEAR U.S. OIL

IMPORTS REDUCTIONS FROM

PROPOSED HD RULE 2020 4.94 0.01 2025 5.07 0.16 2030 5.38 0.37 2040 6.0 0.65 2050 X 0.78 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 X

– The AEO 2014 (Early Release) only projects energy market and economic trends through 2040. 

### 8.9.3 **Methodology U**  **sed to E**  **stimate U.S. E**  **nergy S**  **ecurity B**  **enefits** In order to understand the energy security implications of reducing U.S. oil imports, EPA has worked w ith Oak Ridge National Laboratory (ORNL), which has developed approaches for evaluating the social costs and energy security implications of oil use. The energy security estimates provide d below are based upon a methodology developed in a peer- reviewed study entitled, “ The Energy Security Benefits of Reduced Oil Use, 2006- 2015 , ” completed in March 2008. Th is ORNLstudy

is an update d version of the approach used for estimating the energy

OO The NHTSA analysis uses a slightly different value that was estimated using unique runs of the National Energy Modeling System (NEMS ) that forms the foundation of the Annual Energy Outlook. NHTSA ran a version of NEMS from 2012 (w

hich would have been used in the 2013 AEO) and computed the change in imports of petroleum products with and without the Phase 1 MDHD program to estimate the relationship between changes in fuel consumption and oil imports. The analysis found that reducing gasoline consum ption by 1 gallon reduces im ports of refined gasoline by 0.06 gallons and domestic refining from imported crude by 0.94 gallons. Similarly, one gallon of diesel saved by the Phase 1 rule was estimated to reduce imports of refined diesel by 0.26 gallo ns and domestic refining of im

ported crude by 0.74 gallons. The agencies will update this analysis for the Final Rule using the model associated with AEO2014, modeling the Phase 2 Preferred Alternative explicitly.security benefits of U.S. oil import reductions developed in a 1997 ORNL Report . 138 For EPA

and NHTSA rulemakings, the ORNLmethodology is updated periodicall y t o account for forecasts of future energy market and economic trends reported in the U.S. Energy Information Administration’s Annual Energy Outlook . As part of th e process for developing the ORNL energy security estimates, EPA sponsored an independent , expert peer review of th e 2008 ORNL study . 139 Inaddition, EPA worked with ORNL to address comments raised in the peer review and to develop estimates of the energy

s ecurity benefits associated with a reduction in U.S. oil imports. Inresponse to peer reviewer comments, ORNLmodified its model by changing several key

parameters involving OPEC supply

behavior, the responsiveness of oil demand and suppl y to a change in the world oil price, and the responsiveness of U.S. economic output to a change in the world oil price. When conducting this analy sis, OR NL considered the full cost of imp orting petroleum into the U.S. The f

ull economic cost is defined to include two comp onents in addition to the purchase price of petroleum itself. These are: (1) the higher costs for oil imports resulting from the effect of U.S. demand on the world oil price ( i.e . , the “ demand” or “monopsony” costs); and (2) the risk of reductions in U.S. economic output and disruption to the U.S. economy caused b y sudden disruptions in the supply of imported oil to the U.S. ( i.e . , macroeconomic disruption/adjustment costs). The literature on the energy security for the last two decades has routinel y combined the monopsony

and the macroeconomic disruption components when calculating the total value of the energy

security premium. However, in the context of using a global value for the Social Cost of Carbon (SCC) the question arises: how should the energy security premium be used when some benefits from the rule, such as the benefits of reducing greenhouse gas emissions, are calculated from a global perspective ? Monopsony benefits represent avoided pay ments by U.S. consumers to oil producers that result f rom a decrease in the world oil price as the U.S. decreases its demand for oil. Although there is clearl y an overall benefit to the U.S. when considered from a domestic perspective, the decrease in price due to decreased demand in the U.S. also represents a loss to oil producing countries , one of which is the United States . Given the redistributive nature of this monopsony effect from a global perspective , and the fact that an increasing fraction of it represents a transfer from U.S. consumers and producers , it is excluded in the energy security benefits calculations for this p roposed rule . In contrast, the other portion of the energy security premium, the avoided U.S. macroeconomic disruption and adjustment cost that arises from reductions in U.S . petroleum imports, does not have offsetting impacts outside of the U.S., and, thus, is included in the energy security benefits estimated for this proposed rule . To summarize, the agencies have included only the avoided macroeconomic disruption portion of the energy security benefits to estimate the monetary

value of the total energy

security benefits of this proposed rule .For this rulemaking , ORNLupdated the energy security premium s by

incorporating the most recent oil price forecast and energy mark et trends , particularl y regional oil supplies and demands, from the AEO 2014 (Earl y Release) into its model. 140 Table 8 - 28 provides estimates for energy

security premium s for the years 2020, 20 25, 2030 and 20 40 , PP as well as a breakdown of the components of the energy security premium s for each year. The components of the energy security premium s and their values are discussed below.

**Table 8**  **- 34**  **Energy Security Prem ium**  **s in 2020, 2025, 2030 and 2040 (20012$/Barrel)** * YEAR (RANGE) MONOPSONY

(RANGE) AVOIDED MACROECONOMI C

DISRUPTION/ADJUSTMEN T

COSTS (RANGE) TO TAL MID - POINT (RANGE) 2020 $4.91

(1.63

– 9.15) $6.35 (3.07

– 10.15) $11.25 (6.67

– 16.53) 2025 $5.46

(1.81

– 10.47) $7.29 (3.57

– 11.67) $12.75 (7.58

– 18.65) 2030 $6.04

(2.00

– 11.67) $8.39 (4.12

– 13.41) $14.43 (8.54

– 21.13) 2040 $7.17

(2.32

– 14.03) $10.74 (5.36

– 17.22) $17.91 (10.52

– 26.14) Note: *T op values in each cell are the midpoints, the values in parentheses are the 90 percent confidence intervals. **8.9.3.1 Effect of Oil Use on**  **the Long**  **- Run Oil Price**  The first component of the full economic costs of importing petroleum into the U.S. follows from the effect of U.S. import demand on the world oil price over the long - run. B ecause the U.S. is a sufficientl y large purchaser of global oil supplies , its purchases can affect the world oil price. This monopsony power means that increases in U.S. petroleum demand can cause the world price of crude oil to rise, and conversel y , that reduced U.S. petroleum demand can reduce the world price of crude oil. Thus , one benefit of decreasing U.S. oil purchases , due to improvements in the fuel efficiency of medium - and heav y- duty vehicles is the potential decrease in the crude oil price paid for all crude oil purchased. The demand or monopsony effect can be readily

illustrated with an example. If the U.S. imports 10 million barrels per day at a world oil price of $1 00 per bar rel, its total daily

bill for oil imports is one b illion dollars . If a 10 percent decrease in U.S. imports to 9 million barrels per day causes the world oil price to drop to $ 99 per barrel, the daily U.S. oil import bill drops to $ 891 million (9 million barrels times $ 99 per barrel). While the world oil price only declines $1, the resulting decrease in oil purchase pay ments of $ 109 million per day ( one billion dollars minus $ 891 million) is equivalent to an incremental benefit of $ 109 per barrel of oil imports reduced, or $ 10 more than the newl y - decreased world price of $ 99 per barrel. This additional $ 10 per barrel “import cost premium” represents the incremental external benefits to the U.S. for avoided

PP AEO 2014 (Early Release) f o recasts energy market trends and values only to 2040. The post - 2040 energy security premium values are assumed to be equal to the 2040 estimate.import costs beyond the pric e paid for oil purchases. This additional benefit from import

reduction arises onl y to the extent that a reduction in U.S. oil imports affects the world oil price. ORNLestimates this component of the energy security benefit in 2020 to be $4.91/barrel, w ith a range of $ 1.6 3/barrel to $ 9. 1 5 /barrel of imported oil reduced. A variety of oil market and economic factors have contributed to lowering the estimated monopsony

premium compared to monopsony premiums cited in recent EPA/NHTSA rulemakings. Three prin cipal factors contribute to lowering the monopsony premium: lower world oil prices, lower U.S. oil imports and less responsiveness of world oil prices to changes in U.S. oil demand. For example, between 2012 (using the AEO 2012 (Early Release)) and 2014 (using the AEO 2014 (Early Release)), there has been a general downward revision in world oil price projections in the near term (e.g. 19 percent in 2020) and a sharp reduction in projected U.S. oil imports in the near term, due to increased U.S. supply (i. e. , a 41 percent reduction in U.S. oil imports by

2017 and a 36 percent reduction in 2020) . Over the longer term , oil ’s share of total U.S. imports is projected to gradually increase after 2020 but still remain 27 percent below the AEO 2012 (Early Release ) projected level in 2035 . Another factor influencing the monopsony premium is that U.S. demand on the global oil market is projected to decline, suggesting diminished overall influence and some reduction in the influence of U.S. oil demand on the world price of oil. Outside of the U.S., projected OPEC supply remains roughly

steady as a share of world oil supply compared to the AEO 2012 (Earl y Release). OPEC’s share of world oil supply outside of the U.S. actually increases slightl y. Since OPEC supply

is estimated to be more price sensitive than non - OPEC supply , this means that under AEO 2014 (Earl y Release) world oil supply is slightl y more responsive to changes in U.S. oil demand. Together, these factors suggest that changes in U.S. oil import reductio ns have a somewhat smaller effect on the long - run world oil price than changes based the 2012 estimates. These changes in oil price and import levels lower the monopsony portion of energy security premium since this portion of the security premium is relat ed to the change in total U.S. oil import cost s that is achieved b y a marginal reduction in U.S oil imports. Since both the price and the quantity

of oil imports are lower, the monopsony premium component is 46

– 57 percent lower over the years 2017

–2025 than the estimates based upon the AEO 2012 (Earl y Release) projections. There is disagreement in the literature about the magnitude of the monopsony component, and its relevance for policy analy sis. Brown and Huntington (2013) 141 , for example, argue that the United States’ refusal to exercise its market power to reduce the world oil price does not represent a proper externality , and that the monopsony component should not be considered in calculations of the energy security externality . However, they als o note in their earlier discussion paper (Brown and Huntington 2010) 142 that this is a departure from the traditional energy

security literature, which includes sustained wealth transfers associated with stable but higher- price oil markets. On the other han d, Greene (2010) 143 and others in prior literature (e.g., Toman 1993) 144 have emphasized that the monopsony cost component is policyrelevant because the world oil market is non - competitive and strongl y influenced b y cartelized and government - controlled supply decisions. Thus, while sometimes couched as an externality, Greene notes that the monopsony component is best viewed as stemming from a completel ydifferent market failure than an externality (Ledyard 2008) 145 , yet still implying marginal social costs to i mporters. There is also a question about the ability of gradual, long - term reductions, such as those resulting from this proposed rule, to reduce the world oil price in the presence of OPEC’s monopoly

power. OPEC is currentl y the world’s marginal petroleum supplier, and could conceivabl y respond to gradual reductions in U.S. demand with gradual reductions in supply over the course of several years as the fuel savings resulting from this rule grow. However, if OPEC opts for a long - term strategy to preserve its market share, rather than maintain a particular price level (as they

have done recently

in response to increasing U.S. petroleum production), reduced demand would create downward pressure on the global price. The Oak Ridge anal ys is assumes that OPEC does respond to demand reductions over the long run, but there is still a price effect in the model. Under the mid - case behavioral assumption used in the premium calculations, OPEC responds by graduall y reducing suppl y to maintain market share (consistent with the long - term self

- interested strategy

suggested by

Gatel y (2004, 2007)). 146 It is important to note that the decrease in global petroleum prices resulting from this rulemaking could spur increased consumption of petroleum in other sectors and countries, leading to a modest uptick in GHG emissions outside of the United States. This increase in global fuel consumption could offset some portion of the GHG reduction benefits associated with this proposed rule. The agency have not quantified this increa se in global GHG emissions. We will review any comments we receive , as well as data sources and methodologies for how global rebound effects may be quantified . 

### 8.9.3.2 **Macroeconomic Disruption Adjustment Costs** The second component of the oil import premium, “av oided macroeconomic disruption/adjustment costs ,” arises from the effect of oil imports on the expected cost of suppl y disruptions and accompanying price increases. A sudden increase in oil prices triggered b y a disruption in world oil supplies has two main effects: (1) it increases the costs of oil imports in the short - run, and (2) it can lead to macroeconomic contraction, dislocation and Gross Domestic Product (GDP) losses. For example, ORNLestimates the combine value of these two factors to be $ 6. 34 /barrel when U.S. oil imports are reduced in 2020, with a range from $ 3.0 7 /barrel to $ 10. 15 /barrel of imported oil reduced. There are two main effects of macroeconomic disruption/adjustment costs. The first is the aggregate effect of the short - run price incr ease from an oil shock. The oil price shock results in a combination of real resource shortages, costly short - run shifts in energy supply , behavioral and demand adjustments by energy users, and other response costs. Unlike pure transfers, the root cause of the disruption price increase is a real resource supply reduction due, for example, to disaster or war. Regions where supplies are disrupted, such as the U.S., suffer high costs. Businesses’ and households’ emergency responses to supply

disruptions an d rapid price increases consume real economic resources. When households and businesses make decisions related to their oil consumption, such as whether to invest in fuel - saving technologies or use futures markets, they are unlikely

to account for the effect of their petroleum consumption on the magnitude of costs that supply interruptionsand accompanying price shocks impose on others. As a consequence, the U.S. economy as a whole will not make sufficient use of these mechanisms to insulate itself fro m the real costs of rapid increases in energy

prices and outlay s that usually accompany oil supply

interruptions. Th erefore, the ORNL estimate of avoided macroeconomic disruption/adjustment costs that the agencies use to value energy security benefits inc ludes the increased oil import costs stemming from oil price shocks that are unanticipated and not internalized by advance actions of U.S. consumers and businesses. This aggregate output effect will last as long as the oil price is elevated. It depends on the extent and duration of any disruption in the world supply of oil, since these factors determine the magnitude of the resulting increases in prices for petroleum products, as well as how rapidly

these prices return to their pre - disruption level. The se cond main effect of macroeconomic disruption/adjustment costs is the macroeconomic losses due to “allocative” losses. These are the costs of temporary dislocation and underutilization of available resources due to the oil shock, such as labor unemploy ment and idle plant capacity. Because supply disruptions and resulting price increases occur suddenl y, empirical evidence shows they impose additional costs on businesses and households that must adjust their use of petroleum and other productive factors more rapidl y than if the same price increase had occurred gradually . Dislocational effects include the unemploy ment of workers and other resources during the time needed for their intersectoral or interregional reallocation, and pauses in capital investment d ue to uncertainty . These adjustments temporarily reduce the level of economic output that can be achieved even below the “potential” output level that would ultimately

be reached once the economy’s adaptation to higher petroleum prices is complete. The additional costs imposed on businesses and households for making these adjustments reflect their limited ability

to adjust prices, output levels, and their use of energy , labor and other inputs quickly and smoothl y in response to rapid changes in prices for petroleum products. Since future disruptions in foreign oil supplies are an uncertain prospect, each of the disruption cost components must be weighted b y the probability that the supply

of petroleum to the U.S. will actually

be disrupted. Thus, the “exp ected value” of these costs

– the product of the probability

that a suppl y disruption will occur and the sum of costs from reduced economic output and the economy’ s abrupt adjustment to sharply higher petroleum prices

– is the relevant measure of their magnitude. Further, when assessing the energy security value of a policy to reduce oil use, it is only

the change in the expected costs of disruption that results from the policy that is relevant. The expected costs of disruption may change from lowering th e normal ( i.e. , pre - disruption) level of domestic petroleum use and imports, from any induced alteration in the likelihood or size of disruption, or from altering the short - run flexibility ( e.g. , elasticity) of petroleum use. With updated oil market and ec onomic factors, the avoided macroeconomic disruption component of the energy

security premiums is slightly lower in comparison to avoided macroeconomic disruption premiums used in previous rulemakings. There are several reasons why the avoided macroeconom ic disruption premiums change only moderately . One reason is that the macroeco nomic sensitivity to oil price shocks is assumed unchanged in recent years since U.S. oil consumption levels and the value share of oil in the U.S. economy remain at high levels . For example, Figure 8 - 4 below shows that under AEO 2014 (Earl y Release), projected U.S. real annual oil expenditures continue to rise after 2015 to over $800 billi on (2012$) by 2030. The value share of oil use in the U.S. economy remains between three and four percent,well above the levels observed from 1985 to 2005. A second factor is that oil disruption risks are

little changed . The two factors influencing dis ruption risks are the probability of global suppl y interruptions and the world oil supply share from OPEC. Both factors are not significantl y different from previous forecasts of oil market trends. Factors that contribute to moderatel y lowering the avoided m acro economic disruption c omponent are lower projected GDP, moderatel y lower oil prices and slightly smaller price increases during prospective shocks. For example, oil price levels are 5

– 19 percent lower over the 2020

– 2035 period, and the likely increase in oil prices in the event of an oil shock are somewhat smaller, given small increases in the responsiveness of oil supply to changes in the world price of oil. Overall, the avoided macroeconomic disruption component estimates for the oil security

premium s are 2

– 19 percent lower over the period from 2020 - 2035 based upon different projected oil market and economic trends in the AEO 2014 (Early Release) compared to the AEO 2012 (Earl y Release). The energy

security costs estimated here follow the oil security premium framework, which is well established in the energy economics literature. The oil import premium gained attention as a guiding concept for energy policy around the time of the second and third major post - war oil shocks (Boh i and Montgomery 198 2 , EMF 1982) . 147 Plummer (198 2 ) 148 provided valuable discussion of many of the key

issues related to the oil import premium as well as the analogous oil stockpiling premium. Bohi and Montgomery ( 1982) 149 detailed the theoretical foundation s of the oil import premium established many of the critical analy

tic relationships through their thoughtful anal ys is. Hogan (1981 ) 150 and Broadman and Hogan (1986, 1988) 151 revised and extended the established anal ytical framework to estimate optimal oil import premia with a more detailed accounting of macroeconomic effects. Since the original work on energy security was undertaken in the 1980’s, there have been several reviews on this topic. For example, Leib y, Jones, Curlee and Lee (1997) 152 provided an extended review of the literature and issues regarding the estimation of the premium. Parry and Darmstadter (2004) 153 also provided an overview of extant oil security premium estimates and estimated of some premium components. The recent econ omics literature on whether oil shocks are a threat to economic stability that they once were is mixed. Some of the current literature asserts that the macroeconomic component of the energy

security externality is small. For example, the National Researc h Council (2009) argue d that the non - environmental externalities associated with dependence on foreign oil are small, and potentially trivial. 154 Analyses b y Nordhaus (2007) and Blanchard and Gali (2010) question the impact of more recent oil price shocks o n the economy . 155 They were motivated by

attempts to explain why

the economy

actuall y expanded immediatel y after the last shocks, and why there was no evidence of higher energy prices being passed on through higher wage inflation. Using different methodolo gies, they conclude that the economy has largel y gotten over its concern with dramatic swings in oil prices. One reason, according to Nordhaus, is that monetary policy has become more accommodating to the price impacts of oil shocks. Another is that consumers have simpl y decided that such movements are temporary , and have noted that price impacts are not passed on as inflation in other parts of the economy. He also notes that real changes to productivity due tooil price increases are incredibly modest , QQ and that the general direction of the economy matters a great deal regarding how the economy responds to a shock. Estimates of the impact of a price shock on aggregate demand are insignificantl y different from zero. Blanchard and Gali (2010) contend that improvements in monetary policy (as noted above), more flexible labor markets, and lessening of energy intensity in the economy, combined with an absence of concurrent shocks, all contributed to lessen the impact of oil shocks after 1980. They

find “... the effects of oil price shocks have changed over time, with steadily smaller effects on prices and wages, as well as on output and employ ment.” 156 In a comment at the chapter’s end, this work is summarized as follows: “The message of this chapter is thus opti mistic in that it suggests a transformation in U.S. institutions has inoculated the economy against the responses that we saw in the past.” At the same time, the implications of the “Shale Oil Revolution” are now being felt in the international markets, wi th current prices at four year lows. Analysts generally attribute this result in part to the significant increase in supply resulting from U . S . production, which has put liquid petroleum production on par with Saudi Arabia. The price decline is also attributed to the sustained reductions in U.S. consumption and global demand growth from fuel efficiency policies and high oil prices. The resulting decrease in foreign imports, down to about one - third of domestic consumption (from 60 percent in 2005, for example 157 ), effectively permits U . S . supply to act as a buffer against artificial or other supply restrictions (the latter due to conflict or natural disaster, for example). However, other papers suggest that oil shocks , particularly sudden supply

shocks, remain a concern. Both Blanchard and Gali’s and Nordh aus work were based on data and anal ys is through 2006, ending with a period of strong global economic growth and growing global oil demand. The Nordhaus work particularly stressed the effects of the price increase from 2002 - 2006 that were comparatively gradual (about half the growth rate of the 1973 event and one - third that of the 1990 event). The Nordhaus study emphasizes the robustness of the U.S. economy during a time period through 2006. This time period was just before rapid further increases in the price of oil and other commodities with oil prices more - than - doubling to over $130/barrel by mid - 2008, only

to drop after the onset of the largest recession since the Great Depression. Hamilton (2012) reviewed the empirical literature on oil shocks and suggested that the results are mixed, noting that some work (e.g. Rasmussen and Roitman (2011) finds less evidence for economic effects of oil shocks, or declining effects of shocks (Blanchard and Gali 2010), while other work continues to find evidence regarding the economic importance of oil

QQ In fact, “... energy -price changes have no effect on multifactor productivity and very little effect on labor p roductivity.” Page 19. He calculates the productivity effect of a doubling of oil prices as a decrease of 0.11 percent for one year and 0.04 percent a year for ten years. Page 5. (The doubling reflects the historical experience of the post - war shocks, as described in Table 7.1 in Blanchard and Gali, p. 380.)shocks. For example, Baumeister and Peersman (2011) found that an oil price increase of a

given size seems to have a decreasing e ff ect over time, but noted that the declining price elasticity of demand meant that a given phys ical disruption had a bigger e ff ect on price and turned out to have a similar e ff ect on o utput as in the earlier data.” 158 Hamilton observes that “a negative effect of oil prices on real output has also been reported for a number of other countries, particularl y when nonlinear functional forms have been employed” (citing as recent examples Kim 2012, Engemann, Kliesen, and Owyang 2011 and Daniel, et. al. 2011). Alternativel y, rather than a declining e ffect, Ramey and Vine (2010) found “remarkable stability in the response of aggregate real variables to oil shocks once we account for the extra co sts imposed on the economy in the 1970s by price controls and a complex system of entitlements that led to some rationing and shortages.” 159 Some of the recent literature on oil price shocks has emphasized that economic impacts depend on the nature of the oi l shock, with differences between price increases caused by sudden supply loss and those caused by rapidl y growing demand. Most recent analyses of oil price shocks have confirmed that “ demand - driven ” oil price shocks have greater effects on oil prices and tend to have positive effects on the economy while “ supply- driven ” oil shocks still have negati ve economic impacts (Baumeister , Peersman and Robays, 2010). A recent paper by Kilian and Vigfusson (2014) , for example, assigned a more prominent role to the effects of price increases that are unusual, in the sense of being beyond range of recent experience. Kilian and Vigfussen also conclude that the difference in response to oil shocks may well stem from the different effects of demand - and supply- based pri ce increases: “One explanation is that oil price shocks are associated with a range of oil demand and oil supply shocks, some of which stimulate the U.S. economy in the short run and some of which slow down U.S. growth (see Kilian 2009a). How recessionary the response to an oil price shock is thus depends on the average composition of oil demand and oil supply shocks over the sample period.” The general conclusion that oil supply- driven shocks reduce economic output is also reached in a recently publishe d paper b y Cashin et al. (2014) for 38 countries from 1979 - 2011. “The results indicate that the economic consequences of a suppl y- driven oil - price shock are very different from those of an oil - demand shock driven by global economic activity, and vary for oil importing countries compared to energy exporters,” and “oil importers [including the U.S.] ty picall y face a long - lived fall in economic activity in response to a suppl y - driven surge in oil prices” but almost all countries see an increase in real output for an oil - demand disturbance. Note that the energy

security premium calculation in this analy sis is based on price shocks from potential future supply

events only . Despite continuing uncertainty about oil market behavior and outcomes and the sensitivity of the U.S. economy to oil shocks, it is generall y agreed that it is beneficial to reduce petroleum fuel consumption from an energy security standpoint. Reducing fuel consumption reduces the amount of domestic economic activity associated with a commodity whose price depends on volatile international markets. Also, reducing U.S. oil import levels reduces the likelihood and significance of suppl y disruptions.**Figure 8**  **- 4**  **Projected and Historical U.S. Expenditures, and Expenditure Share, on Crude Oil** 160



### 8.9.3.3 **Cost of Existing U.S. Energy Security Policies** The last often - identified component of the full economic costs of U.S. oil import s are the costs to the U.S. taxpayers of existing U.S. energy security policies. The two primary examples are maintaining the Strategic Petroleum Reserve (SPR) and maintaining a military presence to help secure a stable oil supply from potentiall y vulnerable regions of the world. The SPR is the larg est stockpile of government - owned emergency crude oil in the world. Establish ed in the aftermath of the 1973/ 74 oil embargo, the SPR provid es the U.S. with a response option should a disruption in commercial oil supplies threaten the U.S. eco nomy . It als o allows the U.S. to meet part of its International Energy Agency obligation to maintain emergency oil stocks, and it provides a national defense fuel reserve. While the costs for building and maintaining the SPR are more clearl y related to U.S. oil use and imports , historically these costs have not varied in response to changes in U.S. oil import levels. Thus, while the effect of the SPR in moderating price shocks is factored into the ORNLanal ys is, the cost of maintaining the SPR is excluded. U.S. milit ary

costs are excluded from the anal ys is performed b y ORNL because their attribution to particular missions or activities is difficult, and because it is not clear that these outlay s would decline in response to incremental reductions in U.S. oil imports. Most military forces serve a broad range of security and foreign policy

objectives. Attempts to attribute some share of U.S. military

costs to oil imports are further challenged b y the need to estimate how those costs might vary

with incremental variatio ns in U.S. oil imports. **0% 1%**  **2% 3%**  **4% 5%**  **6% 7%** 

**0 100**  **200 300**  **400 500**  **600 700**  **800 900** 

**1970 1975**  **1980 1985**  **1990 1995**  **2000 2005**  **2010 2015**  **2020 2025**  **2030 E** **x p** **e n** **d i** **t u** **r e** **a s** **S h** **a r** **e o** **f G** **D P** **$ B** **i l** **l /** **y r** **, 2** **0 1** **2 $** 

**Ye ar** 



### Projected **and Historical U.S. Expenditures, and Expenditur**  **e Shar e, on Crude**  **Oil Domestic**  **Imported US Oil Expenditures as Share of GDP** 

### 8.9.4 **Energy S**  **ecurity B**  **enefits of this Program** 

Using the ORNL “oil premium” methodology , updating world oil price values and energy trends using AEO 2014 (Earl y Release) and using the estimated fuel savings from the proposed rule estimated from the MOVES/CAFE models, the agencies has calculated the energy security benefits of this proposed rule for different classes for medium - and heav y- duty vehicles for the various ye ars up to 2050. RR Since the agencies are taking a global perspective with respect to valuing greenhouse gas benefits from the rule, onl y the avoided macroeconomic adjustment/disruption portion of the energy security premium is used in the energy security benefits estimates present below. These results are shown below in Table 8 - 35 . Table 8 - 36 and Table 8 - 37 show discounted model year lifetime energy security benefits for different classes of heav y- duty vehicles using a three and seven percent discount rate. **Table 8**  **- 35**  **Annual U.S. Energy Security Benefits and Net Present Values at 3% and 7% Discount Rates for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (in**  **Millions of**  **2012$) a** 

CALENDAR YEAR HD

PI CKUP & VANS VOCATIONAL TRACTOR/ TRAILER SUM

2018 $0 $0 $10 $10 2019 $0 $0 $20 $20 2020 $0 $0 $31 $31 2021 $3 $6 $69 $77 2022 $10 $12 $118 $140 2023 $23 $19 $170 $211 2024 $41 $33 $255 $328 2025 $65 $47 $344 $456 2026 $95 $63 $438 $596 2027 $131 $91 $548 $770 2028 $167 $120 $660 $947 2029 $204 $150 $772 $1,126 2030 $241 $180 $884 $1,306 2035 $413 $322 $1,421 $2,156 2040 $556 $443 $1,922 $2,920 2050 $647 $522 $2,328 $3,498 NPV, 3% $5,356 $4,209 $19,383 $28,947 NPV, 7% $2,163 $1,689 $8,005 $11,857 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1

RR In order to determine the energy security benefits beyond 2040, we use the 2040 energy security premium multiplied by the estimate fuel savings from the proposed rule. Since the AEO 2014 (Early Rele ase) only goes to 2040, we only calculate energy security premiums to 2040.**Table 8**  **- 36**  **Discounted Model Year Lifetim e Energy Security Benefits at a 3% Discount Rate for the** 

**Preferred Alternative Relative to the Less Dynam ic Baseline and using Method B (Millions of 2012$)**  **a**

MODEL YEAR HD PICKUP

AND VANS VOCATIONAL TRACTOR/ TRAILER SUM 2018 $0 $0 $86 $86 2019 $0 $0 $85 $85 2020 $0 $0 $84 $84 2021 $24 $55 $455 $534 2022 $66 $54 $458 $579 2023 $107 $54 $460 $621 2024 $148 $116 $732 $996 2025 $190 $118 $751 $1,060 2026 $233 $120 $768 $1,121 2027 $272 $216 $887 $1,375 2028 $273 $217 $898 $1,388 2029 $272 $217 $907 $1,397 Sum $1,587 $1,168 $6,571 $9,325 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 **Table 8**  **- 37**  **Discounted Model Year Lifetim e Energy Security Benefits due to the Preferred Alternative at a**  **7% Discount Rates for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B using the MOVES Analysis of HD Pickups and Vans a**  **nd Relative to the Less Dynamic Baseline (Millions of 2012$)**  **a** MODEL YEAR HD PICKUP

AND VANS VOCATIONAL TRACTOR/ TRAILER SUM 2018 $0 $0 $60 $60 2019 $0 $0 $56 $56 2020 $0 $0 $53 $53 2021 $15 $34 $277 $326 2022 $40 $32 $269 $341 2023 $62 $31 $260 $353 2024 $82 $64 $400 $546 2025 $102 $63 $395 $560 2026 $120 $62 $390 $571 2027 $135 $107 $434 $676 2028 $130 $104 $423 $657 2029 $125 $100 $412 $637 Sum $810 $597 $3,430 $4,837 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 

### 8.10 **Summary of Benefits and Costs** This section presents the costs, benefits, and other economic impacts of the proposed Phase 2 standards. It is important to note that NHTSA’s proposed fuel consumption standardsand EPA’s proposed GHG standards would both be in effect, and would jointly lead to increased fuel efficiency

and reductions in GHG and non - GHG emissions. The individual categories of benefits and costs presented in the tables below include:

• the vehicle program costs (costs of compl ying with the vehicle CO2 and fuel consumption standards), • changes in fuel expenditures associated with reduced fuel use b y more efficient vehicles and increased fuel u s e associated with the “rebound” effect, both of which result from the program, • the global economic value of reductions in GHGs, • the economic value of reductions in non - GHG pollutants, • costs associated with increases in noise, congestion, and accidents resulting from increased vehicle use, • savings in drivers’ time from less frequent refueling, • benefits of increased vehicle use associated with the “rebound” effect, and • the economic value of improvements in U.S. energy security impacts. For a discussion of the cost of ownership and the agencies’ pay back analy sis of vehicles covered b y this proposal , please see Chapter 7 of this draft RIA. The agencies conducted coordinated and complementary anal ys es using two anal ytical methods referred to as Method A and Method B. For an explanation of these methods, please see Section I.D for the preamble. And a s discussed in preamble Section X.A.1 , the agencies present estimates of benefits and costs that are measured against two different assumptions about improvements in fuel efficiency that might occur in the absence of the Phase 2 standards. The first case (Alternative 1a) uses a baseline that projects very little improvement in new vehicles in the absence of new Phase 2 standards, and the second (Alternative 1b) uses a more dy namic baseline that projects more significant improvements in vehicle fuel efficiency . Table 8 - 38 shows benefits and costs for the proposed standards from the perspective of a program designed to improve the nation’s energy security and conserve energy by improving fuel efficiency . From this viewpoint, technology costs occur when the vehicle is purchased. Fuel savings are counted as benefits that occur over the lifetimes of the vehicles produced during the model years subject to the Phase 2 standards as they consume less fuel. The table shows that benefits far outweigh the costs, and the preferred alternative is anticipated to result in large net benefits to the U.S economy .**Table 8**  **- 38**  **Lifetim e Benefits & Costs of the Preferred Alternative for Model Years 2018**  **- 2029 Vehicles**  **Using Analysis Method A (Billions of 2012$ discounted at 3% and 7%) CATEGORY**  **BASELINE 1A BASELINE 1B**  **3% 7%**  **3% 7%**  Vehicle Program: Technology and Indirect Costs, Normal Profit on Additional Investments 25.4 17.1 25.0 16.8 Additional Routine Maintenance 1.1 0.6 1.0 0.6 Congestion, Accidents, and Noise from Increased Vehicle Use 4.7 2.8 4.5 2.6 **Total Costs 31.1**  **20.5 30.5**  **20.0** Fuel Savings (valued at pre -tax prices) 175.1 94.2 165.1 89.2 Savings from Less Frequent Refueling 3.1 1.6 2.9 1.5 Economic Benefits from Additional Vehicle Use 15.1 8.4 14.7 8.2 Reduced Climate Damages from GHG Emissions a 34.9 34.9 32.9 32.9 Reduced Health Damages from Non GHG Emissions 38.8 20.7 37.2 20.0 Increased U.S. Energy Security 8.9 4.7 8.1 4.3 **Total Benefits 276**  **165 261**  **156 Net Benefits**  **245 144**  **231 136**  Note: a Benefits and net benefits use the 3 percent average global SCC value applied only to CO2 emissions; GHG reductions include CO2, CH4, N2O and HFC reductions, and include benefits to other nations as well as the U.S. See Draft RIA Chapter 8.5 and Preamble Section IX.G for further discussion. Table 8 - 39 , Table 8 - 40 and Table 8 - 41 report benefits and cost from the perspective of reducing GHG. Table 8 - 39 shows the annual impacts and net benefits of the preferred alternative for selected future ye ars , together with the net present values of cumulative annual impacts from 2018 through 2050 , discounted at 3 percent and 7 percent rates. Table 8 - 40 and Table 8 - 41 sho w the discounted lifetime costs and benefits for each model year affected by the Phase 2 standards at 3 percent and 7 percent discount rates , respectivel y .**Table 8**  **- 39**  **Annual Benefits & Costs**  **and Net Present Values for the Preferred Alternative Relative to the Less Dyna**  **mic Baseline and using Method B**

**( B**  **illions of 20 12$**  **) a,b,c**  2018 202 1 2024 2030 2035 2040 2050 NPV, 3% NPV,

7% Vehicle program - $0.1 - $2.4 - $3.7 - $5.4 - $5.9 - $6.3 - $7.0 - $86.8 - $41.1 Maintenance $0.0 $0.0 - $0.1 - $0.1 - $0.1 - $0.1 - $0.1 - $1.8 - $0.9 Pre - tax Fuel $0.2 $1.7 $6.9 $24.0 $37.2 $47.8 $57.5 $495.6 $206.7 Energy security $0.0 $0.1 $0.3 $1.3 $2.2 $2.9 $3.5 $28.9 $11.9 Accidents/ Congestion/ Noise $0.0 - $0.1 - $0.3 - $0.5 - $0.7 - $0.8 - $0.9 - $9.3 - $4.2 Refueling $0.0 $0.0 $0.1 $0.4 $0.7 $0.9 $1.2 $9.4 $3.9 Travel value $0.0 $0.4 $1.0 $1.9 $2.4 $2.9 $3.3 $34.2 $15.3 Non - GHG $0.0 to

$0.1 $0.4 to

$0.9 $1.0 to

$2.4 $3.3 to

$8.3 $4.8 to

$12.1 $5.7 to

$14.3 $7.0 to

$17.5 $69.7 to

$157.0 $26.6 to

$60.4 SCC SC - CO 2 ; 5% avg $0.0 $0.1 $0.4 $1.5 $2.5 $3.3 $5.0 $22.1 $22.1 SC - CO 2 ; 3% avg $0.0 $0.3 $1.3 $4.8 $7.4 $9.7 $13.6 $103.1 $103.1 SC - CO 2 ; 2.5% avg $0.1 $0.5 $1.9 $6.9 $10.6 $13.7 $18.5 $164.1 $164.1 SC - CO 2 ; 3% 95th $0.1 $1.0 $4.0 $14.6 $23.2 $30.3 $42.0 $320.5 $320.5 Net benefits d SC - CO 2 ; 5% avg $0.2 $0.4 $6.4 $28.8 $46.8 $60.6 $74.6 $605.8 $257.1 SC - CO 2 ; 3% avg $0.2 $0.7 $7.3 $32.1 $51.7 $66.9 $83.2 $686.8 $338.1 SC - CO 2 ; 2.5% avg $0.2 $0.8 $7.9 $34.2 $54.9 $70.9 $88.2 $747.8 $399.1 SC - CO 2 ; 3% 95th $0.3 $1.3 $10.0 $41.9 $67.5 $87.6 $111.7 $904.1 $555.5 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b Note that net present value of reduced CO 2 emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SC - C O 2 at 5, 3, 2.5 percent) is used to calculate net present value of SC - C O 2 for internal consistency. Refer to the SCC TSD for more detail. c Section 8.5 of the draft RIA notes that SC - C O 2 increases over time. Corresponding to the years in this table (2020 - 2050), the SC - C O 2 estimates range as follows: for Average SC - C O 2 at 5%: $7 - $16; for Average SC - C O 2 at 3% : $27 - $46; for Average SC - C O 2 at 2.5%: $43 - $67; and for 95 th percentile SC - C O 2 at 3%: $83 - $140. Section VIII.F also presents these SC - C O 2 estimates. d Net impacts are the summation of results within columns of the table with the exception that the net impacts at each SC - CO 2 value include only the SC - CO 2 im pacts at that value. The table shows the benefits of reduced CO 2 emissions

—and consequentl y the annual quantified benefits ( i.e. , total benefits)

—for each of four SC - CO 2 values estimated by the interagency

working group. As discussed in Section 8.5, there are some limitations to the SC -CO 2 analy sis, 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. In addition, these monetized GHG benefits exclude the value of reductions in non - CO 2 GHG emissions (CH 4 , N 2 O, HFC) expected under this program. Although EPA has not monetized the benefits of reductions in non - CO 2 GHGs in this Section 8.10 , the value of these reductions should not be interpreted as zero. The reader is referred to Section 8.5.2 of this draft RIA to see the value of those monetized benefits. Also, note that the net reductions in non - CO 2 GHGs will contribute to this rule’s climate benefits, as explained in Section III.F of the preamble. The agencies have also conducted a separate analy sis of the total benefits over the model ye ar lifetimes of 2018 through 2029 model year vehicles . In contrast to the calendar year anal ys is presented in Table 8 - 39 , the model year lifetime anal ys is shows the impacts of the program on vehicles produced during each of the affected model years over the course of their expected lifetime s . The net societal benefits over the full life times of vehicles produced during each of the model years are shown in Table 8 - 40 and Table 8 - 41 at both 3 percent and 7 percent discount rate s , respectively .**Table 8**  **- 40**  **Discounted Model Year Lifetim e Impacts for the Preferred Alterna**  **tive Relative to the Less Dynam** **ic Baseline and using Method B (**  **B illions of 20**  **12$ ; 3% Discount Rate)**  **a,b ,c**  201 8 201 9 20 20 20 21 20 22 2023 2024 2025 2026 2027 2028 2029 SUM Vehicle Program - $0.1 - $0.1 - $0.1 - $2.0 - $1.9 - $1.9 - $2.8 - $2.7 - $2.7 - $3.7 - $3.6 - $3.5 - $25.1 Maintenance - $0.1 $0.0 $0.0 - $0.1 - $0.1 - $0.1 - $0.1 - $0.1 - $0.1 - $0.1 - $0.1 - $0.1 - $1.1 Pre - tax Fuel $1.9 $1.9 $1.8 $11.1 $11.5 $11.9 $18.9 $19.6 $20.2 $24.1 $24.1 $24.1 $171.1 Energy Security $0.1 $0.1 $0.1 $0.5 $0.6 $0.6 $1.0 $1.1 $1.1 $1.4 $1.4 $1.4 $9.3 Accidents, Noise, Congestion - $0.1 - $0.1 - $0.2 - $0.4 - $0.4 - $0.4 - $0.4 - $0.4 - $0.4 - $0.4 - $0.4 - $0.4 - $4.2 Refueling $0.0 $0.0 $0.0 $0.2 $0.2 $0.2 $0.3 $0.3 $0.4 $0.5 $0.5 $0.5 $3.1 Travel value $0.6 $0.6 $0.7 $1.5 $1.5 $1.5 $1.4 $1.4 $1.4 $1.4 $1.4 $1.4 $14.9 Non - GHG $0.2

to $0.5 $0.2

to $0.4 $0.2

to $0.4 $2.0

to $4.5 $2.0

to $4.5 $2.0

to $4.5 $2.9

to $6.6 $3.0

to $6.8 $2.6

to $5.9 $3.1

to $6.9 $3.1

to $6.9 $3.1

to $7.0 $24.4

to $55.0 SCC SC - CO 2 ; 5% avg $0.1 $0.1 $0.1 $0.5 $0.5 $0.5 $0.9 $0.9 $0.9 $1.1 $1.1 $1.1 $7.8 SC - CO 2 ; 3% avg $0.4 $0.4 $0.4 $2.2 $2.3 $2.3 $3.7 $3.9 $4.0 $4.8 $4.8 $4.9 $34.0 SC - CO 2 ; 2.5% avg $0.6 $0.6 $0.6 $3.4 $3.5 $3.6 $5.8 $6.1 $6.3 $7.6 $7.6 $7.7 $53.4 SC - CO 2 ; 3% 95th $1.1 $1.1 $1.1 $6.6 $6.9 $7.2 $11.5 $12.0 $12.4 $14.9 $15.0 $15.1 $105.0 Net benefits d SC - CO 2 ; 5% avg $2.8 $2.7 $2.7 $14.6 $15.1 $15.5 $23.9 $25.0 $25.1 $29.2 $29.4 $29.4 $215.5 SC - CO 2 ; 3% avg $3.0 $3.0 $3.0 $16.2 $16.8 $17.3 $26.8 $28.0 $28.2 $33.0 $33.1 $33.2 $241.7 SC - CO 2 ; 2.5% avg $3.2 $3.2 $3.2 $17.4 $18.1 $18.6 $28.9 $30.2 $30.5 $35.7 $35.9 $36.0 $261.1 SC - CO 2 ; 3% 95th $3.8 $3.8 $3.7 $20.7 $21.5 $22.1 $34.5 $36.0 $36.6 $43.1 $43.3 $43.5 $312.7 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b The monetized GHG benefits presented in this analysi s exclude the value of changes in non - CO 2 GHG emissions expected under this program (see draft RIA Chapter 8. 5). Although EPA has not monetized changes in non - CO 2 GHGs, the value of any increases or reductions should not be interpreted as zero. c Note th at net present value of reduced CO 2 emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SC - CO 2 at 5, 3, 2.5 percent) is used to calculate net present value of SC - CO 2 for internal consistency. Refer to the SCC TSD for more detail. d Net impacts are the summation of results within columns of the table with the exception that the net impacts at each SC - CO 2 value include only the SC - CO 2 im pacts at that value.**Table 8**  **- 41**  **Discounted Model Year Lifetim e Impacts for the Preferred Alternative Relative to the Less**  **Dynam ic Baseline and using Method B**  **( B**  **illions of 20 12$**  **; 7**  **% Discount Rate) a,b**  **,c** 201 8 201 9 20 20 20 21 20 22 2023 2024 2025 2026 2027 2028 2029 SUM Vehicle Program - $0.1 - $0.1 - $0.1 - $1.6 - $1.4 - $1.4 - $1.9 - $1.8 - $1.7 - $2.3 - $2.1 - $2.0 - $16.6 Maintenance $0.0 $0.0 $0.0 - $0.1 - $0.1 - $0.1 - $0.1 - $0.1 - $0.1 - $0.1 - $0.1 $0.0 - $0.6 Pre - tax Fuel $1.4 $1.3 $1.2 $6.9 $6.9 $6.8 $10.5 $10.4 $10.4 $11.9 $11.5 $11.0 $90.1 Energy Security $0.1 $0.1 $0.1 $0.3 $0.3 $0.4 $0.5 $0.6 $0.6 $0.7 $0.7 $0.6 $4.8 Accidents, Noise, Congestion - $0.1 - $0.1 - $0.1 - $0.3 - $0.3 - $0.2 - $0.2 - $0.2 - $0.2 - $0.2 - $0.2 - $0.2 - $2.4 Refueling $0.0 $0.0 $0.0 $0.1 $0.1 $0.1 $0.2 $0.2 $0.2 $0.2 $0.2 $0.2 $1.6 Travel value $0.4 $0.4 $0.4 $0.9 $0.9 $0.8 $0.8 $0.8 $0.7 $0.7 $0.7 $0.6 $8.2 Non - GHG $0.1

to $0.3 $0.1

to $0.3 $0.1

to $0.3 $1.1

to $2.5 $1.1

to $2.4 $1.0

to $2.3 $1.4

to $3.3 $1.4

to $3.2 $1.2

to $2.7 $1.3

to $3.0 $1.3

to $2.9 $1.3

to $2.8 $11.5

to $26.0 SCC SC - CO 2 ; 5% avg $0.1 $0.1 $0.1 $0.5 $0.5 $0.5 $0.9 $0.9 $0.9 $1.1 $1.1 $1.1 $7.8 SC - CO 2 ; 3% avg $0.4 $0.4 $0.4 $2.2 $2.3 $2.3 $3.7 $3.9 $4.0 $4.8 $4.8 $4.9 $34.0 SC - CO 2 ; 2.5% avg $0.6 $0.6 $0.6 $3.4 $3.5 $3.6 $5.8 $6.1 $6.3 $7.6 $7.6 $7.7 $53.4 SC - CO 2 ; 3% 95th $1.1 $1.1 $1.1 $6.6 $6.9 $7.2 $11.5 $12.0 $12.4 $14.9 $15.0 $15.1 $105.0 Net benefits d SC - CO 2 ; 5% avg $1.9 $1.8 $1.7 $8.7 $8.7 $8.7 $13.0 $13.1 $12.7 $14.3 $13.8 $13.4 $111.8 SC - CO 2 ; 3% avg $2.2 $2.1 $2.0 $10.3 $10.4 $10.5 $15.8 $16.1 $15.8 $18.0 $17.6 $17.2 $138.0 SC - CO 2 ; 2.5% avg $2.4 $2.3 $2.2 $11.5 $11.7 $11.8 $17.9 $18.3 $18.1 $20.7 $20.4 $20.0 $157.4 SC - CO 2 ; 3% 95th $2.9 $2.8 $2.8 $14.8 $15.1 $15.3 $23.6 $24.2 $24.2 $28.1 $27.8 $27.4 $209.0 Notes: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1 b The monetized GHG benefits presented in this analysis exclude the value of changes in non - CO 2 GHG emissions expected under this program (see draft RIA Chapter 8. 5). Although EPA has not monetized changes in non - CO 2 GHGs, the value of any increases or reductions should not be interpreted as zero. c Note that net present value of reduced CO 2 emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SC - CO 2 at 5, 3, 2.5 percent) is used to calculate net present value of SC - CO 2 for internal consistency. Refer to the SCC TSD for more detail. d Net impacts are the summation of results within columns of the table with the exception that th e net impacts at each SC - CO 2 value include only the SC - CO 2 im pacts at that value.

### 8.11 **Employment Impacts** 

**8.11.1 Introduction**  Executive Order 13563 (January 18, 2011) directs federal agencies to consider regulatory impacts on, among other criteria, job creation. 161 According to the Executive Order “ Our regulatory

s ys tem must protect public health, welfare, safety , and our environment while promoting economic growth, innovation, competitiveness, and job creation . It must be based on the best available science. ” Anal ys is of employ ment impacts of a regulation is not part of a standard benefit - cost analy sis (except to the extent that labor costs contribute to costs) . Employ ment impacts of federal rules are of general interest, however, and have been particularly so, hi storically , in the auto sector during periods of challenging labor market conditions . For this reason, we are describing the connections of these proposed standards t o employ ment in the regulated sector, the motor vehicle manufacturing sector , as well as the motor vehicle body and trailer and motor vehicle parts manufacturing sector s . The overall effect of the proposed rule s on motor vehicle sector employ ment depends on the relative magnitude of output and substitution effects, described be low. Because we do not have quantitative estimates of the output effect, and only a partial estimate of the substitution effect, we cannot reach a quantitative estimate of the overall emplo ym ent effects of the proposed rule s on motor vehicle sector employ ment or even whether the total effect will be positive or negative. According to the U.S. Bureau of Labor Statistics, in 201 4 , about 850 ,000 people in the U.S. were employed in the Motor Vehicle and Parts Manufacturing Sector (NAI CS 3361, 3362, and 336 3) , 162 the directly regulated sector. The employ ment effects of th e s e proposed rule s are expected to expand beyond the regulated sector. Though some of the parts used to achieve the proposed standards are likely to be built by motor vehicle manufac turers (including trailer manufacturers ) themselves, the motor vehicle parts manufacturing sector also play s a significant role in providing those parts, and will also be affected b y changes in vehicle sales. Changes in truck sales, discussed in Chapter 8.4.2 , could also affect emplo ym ent for truck and trailer vendors . As discussed in Chapter 7.2 , this proposed rule is expected to reduce the amount of fuel these vehicles use, and thus affect the petroleum refinery and suppl y industries as well . Finall y, since the net reduction in cost associated with th e s e proposed rule s is expected to lead to lower transportation and shipping costs, in a competitive market a substantial portion of those cost saving s will be passed along to consumers, who then will have additional discretionary income (how much of the cost is passed along to consumers depends on market structure and the relative price elasticities). The proposed rule s are not expected to have any notable inflationary or recessionary

effect. The employ

ment effects of environmental regulation are difficult to disentangle from other economic changes and business decisions that affect emplo ym ent, over time and across regions and industries. In light of these difficulties, we lean on economic theory to provide a constructive framework for approaching these assessments and for better understanding the inherent complexities in such assessments. Neoclassical microeconomic theory describes how profit - maximizing firms adjust their use of productive inputs in response to changes in their economic conditions. 163 Berman and Bui (2001, pp. 274 - 75) model two components that drivechanges in firm - level lab or demand: output effects and substitution effects. 164 , TT Regulation can

affect the profit - maximizing quantity of output by changing the marginal cost of production. If regulation causes marginal cost to increase, it will place upward pressure on output prices, leading to a decrease in the quantity demanded, and resulting in a decrease in production. The output effect describes how, holding labor intensity constant, a decrease in production causes a decrease in labor demand. As noted by

Berman and Bui, although many assume that regulation increases marginal cost, it need not be the case. A regulation could induce a firm to upgrade to less polluting and more efficient equipment that lowers marginal production costs, or it may induce use of technologies that may prove popular with buyers or provide positive network externalities (see Chapter 8.2 for discussion of this effect). In such a case, output could increase. Th e substitution effect describes how, holding output constant, regulation affects labor intensity of production. Although increased environmental regulation may increase use of pollution control equipment and energy to operate that equipment, the impact on labor demand is ambiguous. For example, equipment inspection requirements, specialized waste handling, or pollu tion technologies that alter the production process may affect the number of workers necessary to produce a unit of output. Berman and Bui (2001) model the substitution effect as the effect of regulation on pollution control equipment and expenditures req uired by the regulation and the corresponding change in labor intensity

of production. In summary, as output and substitution effects may be positive or negative, theory alone cannot predict the direction of the net effect of regulation on labor demand at the level of the regulated firm. Operating within the bounds of standard economic theo ry , empirical estimation of net employ

ment effects on regulated firms is possible when data and methods of sufficient detail and quality are available. The literature, however, illustrates difficulties with empirical estimation. For example, studies sometimes rely on confidential plant - level employment data from the U.S. Census Bureau, possibly combined with pollution abatement expenditure data that are too dated to be reliably informative. In addition, the most commonly used empirical methods do not permit estimation of net effects. The conceptual framework described thus far focused on regulatory effects on plant- level decisions within a regulated industry. Employ ment impacts at an individual plant do not necessarily represent impacts for the se ctor as a whole. The approach must be modified when applied at the industry level. At the industry level, labor demand is more responsive if: (1) the price elasticity of demand for the product is high, (2) other factors of production can be easily substituted for labor, (3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total production costs. 165 For example, if all firms in an industry are faced with the same regulatory compliance costs and product demand is inelastic, then industry output may not change much, and output of individual firms may

change slightly . 166 In this case, the output effect may be small, while the substitution

TT Berman and Bui also discuss a third component, the impact of regulation on factor prices, but conclude that this effect is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and Shih (2002) use a ve ry similar model, but they break the employment effect into three parts: 1) a demand effect; 2) a cost effect; and 3) a factor - shift effect.effect depends on input substitutability. Suppose, for example, that new equipment for fuel efficiency

improvements requires labor to install and operate. In this case, the substitution effect may be positive, and with a small output effect, the total effect may be positive. As with potential effects for an individual firm, theory cannot determine the sign or magnitude of industry- level regulatory effects on labor demand. Determining these signs and magnitudes requires additional sector- specific empirical study . For environmental rules, much of the data needed for these empirical st udies is not publicly available, would require significant time and resources in order to access confidential U.S. Census data for research, and also would not be necessary for other components of a typical RIA. In additi on to changes to labor demand in the regulated industry, net employment impacts encompass changes in other related sectors. For example, the proposed standards are expected to increase demand for fuel - saving technologies. This increased demand may increa se revenue and employ ment in the firms providing th e s e technolog ies . At the same time, the regulated industry is purchasing the equipment, and these costs may impact labor demand at regulated firms. Therefore, it is important to consider the net effect of compliance actions on employment across multiple sectors or industries. If the U.S. economy

is at full employ

ment, even a large - scale environmental regulation is unlikely to have a noticeable impact on aggregate net employment. UU Instead, labor would primarily be reallocated from one productive use to another, and net national employment effects from environmental regulation would be small and transitory (e.g., as workers move from one job to another) . 167 Affected sectors may experience transitory effects as workers change jobs. Some workers may retrain or relocate in anticipation of new requirements or require time to search for new jobs, while shortages in some sectors or regions could bid up wages to attract workers. These adjustment costs can lead to local labor disruptions. Although the net change in the national workforce is expected to be small, localized reductions in employment may adversely impact individuals and communities just as localized increases may have positive impacts. If the economy is operating at less than full employment, economic theory does not clearly indicate the direction or magnitude of the net impact of environmental regulation on employment; it could cause either a short - run net increase or short - run net decrease . 168 An important research question is how to accommodate unemployment as a structural feature in economic models. This feature may

be important in assessing large - scale regulatory impacts on employment. 169 Environmental regulation may also affect labor suppl y. In particular, pollution and other environmental risks may impact labor productivity or employees’ ability to work. 170 While the theoretical framework for analyzing labor supply effects is analogous to that for labor demand, it is more difficult to study empirically. There is a small emerging literature described in the next section that uses detailed labor and environmental data to assess these impacts.

UU Full employment is a conceptual target for the economy where everyone who wants to work and is available to do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero.To summarize, economic theory provides a framework for analyzing the impacts of

environmental regulation on employment. The net employ ment effect incorporates expected employ ment changes (both positive and negative) in the regulat ed sector and elsewhere. Labor demand impacts for regulated firms, and also for the regulated industry, can be decomposed into output and substitution effects which may be either negative or positive. Estimation of net employ ment effects for regulated se ctors is possible when data of sufficient detail and quality are available. Finally, economic theory suggests that labor supply effects are also possible. In the next section, we discuss the empirical literature. 

### 8.11.1.1 **Current State of Knowledge Based on the Pe er**  **- Reviewed Literature**  In the labor economics literature there is an extensive body of peer- reviewed empirical work anal yz ing various aspects of labor demand, relying on the above theoretical framework. 171 This work focuses primarily on the effects of employ

ment policies, e.g. labor taxes, minimum wage, etc. 172 Incontrast, the peer - reviewed empirical literature specificall y estimating employ ment effects of environmental regulations is very limited. S everal empirical studies , including Berman and Bui (2001) , 173 Morgenstern , Pizer and Shih (2002) , 174 Gray et al (2014), 175 and Ferris, Shadbegian and Wolverton (2014) 176 suggest that net employ ment impacts may be zero or slightl y positive but small even in the regulated sector. Other research suggests that more highl y regulated counties may generate fewer jobs than less regulated ones. 177 However, since these latter studies compare more regulated to less regulated counties , they overstate the net national impact of regulation to the extent that regulation causes plants t o locate in one area of the country rather than another. Li st et al . (2003) 178 find some evidence that this ty pe of geographic relocation may be occurring. Overall , the peer- reviewed literature does not contain evidence that environmental regulation has a large impact on net employ ment (either negative or positive) in the long run across the whole economy. Analy tic challenges make it very difficult to accurately

produce net employment estimates for the whole economy that would appropriately capture the way in which costs, compliance spending, and environmental benefits propagate through the macro - economy. Quantitative estimates are further complicated b y the fact that macroeconomic models often have very little sectoral detail and usually assume that the ec onomy is at full employ ment. EPA is currentl y in the process of seeking input from an independent expert panel on modeling economy- wide impacts, including emplo ym ent effects. For more information, see: https://federalregister.gov/a/2014 - 02471 . 

### 8.11.2 **Employment Impacts in the**  **Motor Vehicle and Parts Manufacturing Sector**  This chapter describes changes in employ ment in the motor vehicle, trailer, and parts (hence, motor vehicle) manufacturing sectors due to th e s e proposed rule s . We focus on the motor vehicle manufacturing sector because it is directly regulated, and because it is likely to bear a substantial share of changes in employ ment due to th e s e proposed rule s . We include discussion of effects on the parts manufacturing sector, because the motor vehicle manufacturing sector can either produce parts internall y or buy them from an external supplier, and we do not have estimates of the likely breakdown of effort between the two sectors.We follow the theoretical structure of Berman and Bui 179 of the impacts of regulation in

employ ment in the regulated sectors. In Berman and Bui’s (2001, p. 274 - 75) theoretical model, as described above, the change in a firm’s labor demand arising from a change in regulation is decomposed into two main components: output and substitution effects. VV As the output and substitution effects may

be both positive, both negative , or some combination, standard neoclassical theory

alone does not point to a definitive net effect of regulation on labor demand at regulated firms. Following the Berman and Bui framework for the impacts of regulation on employ ment in the regulated sector, we consider two effects for the motor vehicle sector: the output effect and the substitution effect.



### 8.11.2.1 **The Output Effect** If truck or trailer sales increase, then more people will be required to assemble trucks , trailers, and their components. If truck or trailer sales decrease, employ ment associated with these activities will decrease. The effects of this proposed rulemaking on HD vehicle sales thu s depend on the perceived desirability of the new vehicles. On one hand, this proposed rulemaking will increase truck and trailer costs; by itself, this effect would reduce truck and trailer sales. In addition, while decreases in truck performance would also decrease sales , this program is not expected to have any negative effect on truck performance . On the other hand, this proposed rulemaking will reduce the fuel costs of operating the trucks; by itself, this effect would increase truck sales, especial ly if potential buyers have an expectation of higher fuel prices. The agencies have not made an estimate of the potential change in truck or trailer sales. However , as discussed in Chapter 8.3 , the agencies have estimated an increase in vehicle miles traveled ( i.e. , VMT rebound) due to the reduced operating costs of trucks meeting these proposed standards. Since increased VMT is most likely to be met with more drivers and more trucks, our projection of VMT rebound is suggestive of an increase in vehicle sales and truck driver employ ment (recognizing that these increases may be partiall y offset by a decrease in manufacturing and sales for equipment of other modes of transportation such as rail cars or barges). 

### 8.11.2.2 **The Substitution Effect** The output effect, above, measures the effect due to new truck and trailer sales only . The substitution effect includes the impacts due to the changes in technologies needed for vehi cles to meet the proposed standards , separate from the effect on output (that is, as though holding output constant) . This effect includes both changes in employ ment due to incorporation of abatement

VV The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect is unlikely to be important for larg e com petitive factor markets, such as labor and capital. Morgenstern, Pizer and Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) the demand effect; 2) the cost effect; and 3) the factor - shift effect. See Morg enstern, Richard D., William A. Pizer, and Jhih - Shyang Shih. “Jobs Versus the Environment: An Industry - Level Perspective.” Journal of Environmental Economics and Management 43 (2002): 412 - 436 (Docket EPA - HQ - OAR) .technologies and overall changes in the labor intensity of manufacturing. We present estimates

for this effect to provide a sense of the order of magnitude of expected impacts on employ ment, which we expect to be small in the automotive sector, and to repeat that regulations may have positive as well as negative effects on employ ment. One way to estimate th i s effect, given the cost estimates for compl ying with the proposed rule , is to use the ratio of workers to each $1 million of expenditures in that sector. The use of these ratios has both advantages and l imitations. It is often possible to estimate these ratios for quite specific sectors of the economy: for instance, it is possible to estimate the average number of workers in the motor vehicle body and trailer manufacturing sector per $1 million spent in the sector, rather than use the ratio from another, more aggregated sector, such as motor vehicle manufacturing. As a result, it is not necessary to extrapolate employ

ment ratios from possibly unrelated sectors. On the other hand, these estimates are av erages for the sectors, covering all the activities in those sectors; they may not be representative of the labor required when expenditures are required on specific activities, or when manufacturing processes change sufficiently

that labor intensity

chang es . For instance, the ratio for the motor vehicle manufacturing sector represents the ratio for all vehicle manufacturing, not just for emissions reductions associated with compliance activities . In addition, these estimates do not include changes in sectors that supply these sectors, such as steel or electronics producers. They thus may best be viewed as the effects on employ ment in the motor vehicle sector due to the changes in expenditures i n that sector, rather than as an assessment of all employ ment changes due to these changes in expenditures. In addition, this approach estimates the effects of increased expenditures while holding constant the labor intensity of manufacturing; it does not take into account changes in labor intensity due to changes in the nature of production. This latter effect could either increase or decrease the employ ment impacts estimated here. WW Some of the costs of th e s e proposed rule s will be spent directl y in the motor vehicle manufacturing sector, but it is also likely that some of the costs will be spent in the motor vehicle body and trailer and motor vehicle parts manufacturing sector s . The anal ysis here draws on estimates of workers per $1 million of e xpenditures for each of these sectors. There are several public sources for estimates of employ ment per $1 million expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employ ment Requirements Matrix (ERM), 180 which provides direct estimates of the employ ment per $1 million in sales of goods in 202 sectors. The values considered here are for Motor Vehicle Manufacturing (NAICS 3361) , Motor V ehicle B ody and T railer M anufacturing (NAICS 3362), and Motor Vehicle Parts Manufacturing (NAICS 3363) f or 2012 . The Census Bureau provides the Annual Survey of Manufacturer s 181 (ASM ) , a subset of the Economic Census , based on a sample of establishments; though the Census itself is more

WW As noted above, Morgenstern et al. (200 2) separate the effect of holding output constant into two effects: the cost effect, which holds labor intensity constant, and the factor shift effect, which estimates those changes in labor intensity.complete, it is conducted only every 5 years, while the ASM is annual. Both include more sectoral detail than the BLS ERM: for instance, while the ERM includes the Motor Vehicle Manufacturing sector, the ASM and Economic Census have detail at the 6 - digit NAICS code level (e.g., light truck and utility vehicle manufacturing). While the ERM provides direct estimates of employees/$1 million in expenditures, the ASM and Economic Census sep arately provide number of employees and value of shipments; the direct employ ment estimates here are the ratio of those values. At this time, the Economic Census values for 2012 (the most recent ye ar) are not fully

available; we therefore do not report th em, and instead provide the 2011 ASM results (the most recent available). The values reported are for Motor Vehicle Manufacturing (NAICS 3361), Light Truck and Utility Vehicle Manufacturing (NAICS 33611 2 ), Heav y Duty Truck Manufacturing (33612), Motor Veh icle Body and Trailer Manufacturing (3362), and Motor Vehicle Parts Manufacturing (NAICS 3363) . The values used here are adjusted to remove the employ

ment effects of imports through use of a ratio of domestic production to domestic sales of 0. 78 . XX Table 8 - 42 provides the values, either given (BLS) or calculated (ASM) for employ ment per $1 million of expenditures, all adjusted to 2012 dollars using the Bureau of Economic Analy sis’s Implicit GDP Price Deflators. Although the ASM appears to provide slightly higher values than the ERM, the different data sources provide similar patterns for the estimates for the sectors. Body and trailer manufacturing and parts manufacturing appear to be more labor intensive than vehicle manufacturing; light truck and utility vehicle manufacturing appears to be less, and heav y duty truck manufacturing appears to be more, labor- intensive than motor vehicle manufacturing as a whole . **Table 8**  **- 42**  **E mp**  **loym ent per $1 Million Expenditure**  **s (2012$) in the Motor Vehicle Manufacturing Sector a**  SOURCE SECTOR RATIO OF WORKERS PER $1 MILLION EXPENDITURE S RATIO OF WORKERS

PER $1 MILLION EXPENDITURES, ADJUSTED FOR DOMESTIC VS. FOREIGN PRODUCTION BLS ERM Motor vehicle mfg (3361) 0.460 0.355 BLS ERM Motor vehicle body & trailer mfg (3362) 1.450 1.153 BLS ERM Motor vehicle parts mfg (3363) 1.950 1.590 ASM Motor vehicle mfg (3361) 0.538 0.414 ASM Light truck & utility vehicle mfg

(336112) 0.443 0.341 ASM Heavy duty truck mfg (33612) 0.832 0.641 ASM Motor vehicle body & trailer mfg (3362) 2.797 2.156 ASM Motor Vehicle Parts Mfg (3363) 1.635 1.260

XX To estimate the proportion of domestic production af fected by the change in sales, we use data from Ward’s Automotive Group for total truck production in the U.S. compared to total truck sales in the U.S. For the period 2004 - 2013, the proportion is 78 percent (Docket EPA - HQ - OAR - ), ranging from 68 percent ( 2009) to 83 percent (2012) over that time .Note: a BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix, 2012 values. ASM refers to the U.S. Census Bureau’s Annual Survey of Manufactures, 2011 values. Over time, the amount of labor needed in the motor vehicle industry has changed: automation and improved methods have led to significant productivity increases. The BLS ERM, for instance, provided estimates that, in 1993, 1. 52 workers in the Motor Vehicle Manufacturing sector were needed per $1 million, but only 0.53 worker s by

2012 (in 2005$) . 182 Because the ERM is available annuall y for 1993 - 2012, we used these data to estimate productivity

improvements over time. We regressed logged ERM values on a ye ar trend for the Motor Vehicle Manufacturing, Motor Vehicle Body and Trailer Manufacturing, and Motor Vehicle Parts Manufacturing sectors. We used this approach because the coefficient describing the relationship between time and productivity is a direct measure o f the average percent change in productivity

per ye ar. The results suggest a 5.1 percent per ye ar productivity improvement in the Motor Vehicle Manufacturing Sector, and a 4.7 percent per ye ar improvement in the Motor Vehicle Parts Manufacturing Sector. The Motor Vehicle Body and Trailer Manufacturing Sector results were more complex: the workers/$1 million values before 2009 are substantially higher (averaging 5.90 in 2005$) than those in 2009 and after (averaging 2.45 in 2005$); we used dummy variables to account for this shift, and estimate productivity gains of 0.4 percent per ye ar before 2009, and 22 percent after. This dramatic difference may suggest taking care when relying on the data for this sector . As discussed further below, we onl y report m aximum and minimum employ

ment impacts, and the Motor Vehicle Body and Trailer Manufacturing estimates provide the minimum values; they may therefore create greater uncertainty about the lower bound of the substitution - effect employ ment. We then used the re gression results to project the number of workers per $1 million through 20 2 7 . We calculated separate sets of projections (adjusted to 201 2 $) for both the BL S ERM data as well as the ASM for all three sectors discussed above. The BLS ERM projections were calculated directl y from the fitted regression equations since the regressions themselves used ERM data . F or the ASM projections , we used the ERM’s ratio of the projected value in each future ye ar to the projected value in 2011 (the base ye ar for the ASM ) to determine how many workers will be needed per $1 million of 2012$. Inother words, we apply the projected productivity

growth estimated using the ERM data to the A S M numbers. Finally , to simplify

the presentation and give a range of estimates , we c ompared the projected emplo ym ent among the 3 sectors for the ERM and ASM, and we provide onl y the maximum and minimum employ ment effects estimated for the ERM and the ASM . We provide the range rather than a point estimate because of the inherent difficulties in estimating employ ment impacts; the range gives an estimate of the expected magnitude. The details of the calculations may

be found in the docket. The ERM estimate in the Motor Vehicle Parts Manufacturing Sector are consistently the maximum values . The ERM estimate in the Motor Vehicle Body and Trailer Manufacturing Sector are the minimum values for all years but 2018 - 201 9 , where the ASM value for the Li ght Truck and Utility Vehicle Manufacturing Sector (336112) provide the minimum values . Chapter 7 of the draft RIA discusses the vehicle cost estimates developed for th e s e proposed rule s . The final step in estimating employ ment impacts is to multiply costs (in $millions) by workers per $1 million in costs, to estimate employ ment impacts in the regulated and parts manufacturing sectors. Increased costs of vehicles and parts would, by itself, and holding labor intensity constant, be expected to increase emplo ym ent between 20 18 and 202 7 from none to a few thousand jobs each ye ar. While we estimate employment impacts, measured in job - ye ars, beginning with program implementation , some of these employ ment gains may

occur earlier as motor vehicle manufacturers and parts suppliers hire staff in anticipation of compliance with the standard s . A job - ye ar is a way

to calculate the amount of work needed to complete a specific task. For example, a job - ye ar is one year of work for one person. **Table 8**  **- 43**  **Em ploym**  **ent Effects due to Increased Costs of Vehicles and Parts (Substitution Effect)**  **, in J**  **ob**   ** years** YEAR COSTS

(MILLIONS OF 2012$) MINIMUM EMPLOYMENT

DUE TO SUBSTITUTION EFFECT (ERM ESTIMATE S, EXPENDITURES IN THE PARTS SECTORa) MAXIMUM EMPLOYMENT DUE TO SUBSTITUTION EFFE CT (ERM ESTIMATES, EXPENDITURES IN THE BODY AND TRAILER MFG

SECTOR) 2018 $ 116 0 100 2019 $ 113 0 100 2020 $ 112 0 100 2021 $ 2,173 300 2,300 2022 $ 2,161 300 2,200 2023 $ 2,224 200 2,100 2024 $ 3,455 300 3,200 2025 $ 3,647 200 3,200 2026 $ 3,736 200 3,100 2027 $ 5,309 200 4,200 Note: a For 2018 and 2019, the minimum employment effects are associated with the ASM’s Light Truck and Utility Vehicle Manufacturing sector.



### 8.11.2.3 **Summary of Employment Effects in the Motor Vehicle**  **Sector** The overall effect of the se proposed rule s on motor vehicle sector emplo ym ent depends on the relative magnitude of the output effect and the substitution effect. Because we do not have quantitative estimates of the output effec t, and only a partial estimate of the substitution effect, we cannot reach a quantitative estimate of the overall emplo ym ent effects of the se proposed rule s on motor vehicle sector employ ment or even whether the total effect will be positive or neg ative. The proposed standards are not expected to provide incentives for manufacturers to shift employ ment between domestic and foreign production. This is because the proposed standards will apply

to vehicles sold in the U.S. regardless of where they are produced. If foreign manufacturers already have increased expertise in satisfying the requirements of the standards , there may be some initial incentive for foreign production, but the opportunity for domestic manufacturers to sell in other markets might increase. To the extent that the requirements of th e s e proposed rule s might lead to installation and use of technologies that other countries mayseek now or in the future, developing this capacity for domestic produ ction now may provide some additional ability

to serve those markets. Some vehicle parts are made in - house and would be included directly in the regulated sector . Others are made by independent suppliers and are n ot directl y regulated, but they will be affected b y the rules as well. The parts manufacturing sector will be involved primaril y in providing “add - on” parts, or components for replacement parts built internally . If demand for these parts increase s due to the increased use of these parts , employ ment effects in this sector are expected to be positive. If the demand effect in the regulated sectors is significantly negative enough, it is possible that demand for other parts may decrease. As noted, the agenc ies do not predict a direction for the demand effect. 

### 8.11.3 **Employment Impacts in Other Affected Sectors 8.11.3.1**  **Transport and Shipping Sectors** Although not directl y regulated by these proposed rule s, employ ment effects in the transport and shipping sector are likely to result from these regulations. If the overall cost of shipping a ton of freight decreases because of increased fuel efficiency (taking into account the increase in upfront purchasing costs), in a perfectly competitive industry these costs savings , depending on the relative elasticities of supply and demand, will be passed along to customers. With lower prices, demand for shipping would lead to an increase in demand for truck shipping services (consistent with the VMT rebound effect anal ys is) and therefor e an increase in employ ment in the truck shipping sector . In addition, if the relative cost of shipping freight via trucks becomes cheaper than shipping b y other modes ( e.g. , rail or barge), then employ ment in the truck transport industry is likel y to increase. If the trucking industry is more labor intensive than other modes, we would expect this effect to lead to an overall increase in employ ment in the transport and shipp ing sectors. 183 , 184 Such a shift would, however, be at the expense of employ ment in the sectors that are losing business to trucking. The first effect

– a gain due to lower shipping costs

– is likely to lead to a net increase in employ ment. The second effec t, due to mode - shifting, may

increase employ

ment in trucking, but decrease employ ment in other shipping sectors (e.g., rail or barge) , with the net effects dependent on the labor- intensity of the sectors and the volumes. 

### 8.11.3.2 **Fuel Suppliers** In addition to the effects on the trucking industry and related truck parts sector, these proposed rule s will result in reductions in fuel use that lower GHG emissions. Fuel saving, principally

reductions in liquid fuels such as diesel and gasoline, will affect employ ment in the fuel suppliers industry

sectors, principall y the Petroleum Refinery sector. Chapter 7.2 of this draft RIA provides estimates of the effects of these proposed standards on e xpected fuel consumption. While reduced fuel consumption represent s savings forpurchasers of fuel, it also represents a loss in value of output for the petroleum refinery industry , which will result in reduced sectoral employ ment. Because this sector is material - intensive, the employ ment effect is not expected to be large. YY 

### 8.11.3.3 **Fuel Savings** As a result of this proposed rulemaking, it is anticipated that trucking firms will experience fuel savings. Fuel savings lower the costs of transportation goods and services. In a competitive market, some of the fuel saving s that initially accrue to trucking firms are likel y to be passed along as lower transportation costs that, in turn, could result in lower prices for final goods and services. Some of the savings might also be retained b y firms for investments or for distribution s to firm owners. Again, how much accrues to customers versus firm owners will depend on the relative elasticities of supply and demand . Regardless , the savings will accrue to some segment of consumers: either owners of trucking firms or the g eneral public, and the effect will be increased spending b y consumers in other sectors of the economy, creating jobs in a diverse set of sectors, including retail and service industries. As described in Chapter 7.2 , the value of fuel savings from this proposed rulemaking is projected to be $ 15 . 1 billion (20 12 $) in 20 2 7 , according to Table 7 - 19 . If all those savings are spent, the fuel savings will stimulate increased employ ment in the economy through those expenditures. If the fuel savings accrue primarily to firm owners, they

may either reinvest the money or take it as profit. Reinvesting the money in firm operations c ould increase employ ment directly . If they

take the money

as profit, to the extent that these owners are wealthier than the general pub lic, they

may spend less of the savings, and the resulting employment impacts would be smaller than if the savings went to the public. Thus, while fuel savings are expected to decrease employ

ment in the refinery

sector, they

are expected to increase emplo ym ent through increased consumer expenditures. 

### 8.11.4 **Summary of Employment Impacts**  The primary

employ ment effects of th e s e rule s are expected to be found through out several key

sectors: truck and engine manufacturers, the trucking industry , truck parts manufacturing, fuel production, and consumers. Th e s e rule s initially take effect in model year 20 18 , a time period sufficiently far in the future that the unemploy ment rate at that time is unknowable . In an economy with full employ

ment, the primary

employ ment effect of a rulemaking is likel y to be to move employment from one sector to another, rather than to increase or decrease employ ment. For that reason, we focus our partial quantitative analysis on employ ment in the regulated sector, to examine the impacts on that sector directly . We discuss the likely direction of other impacts in the regulated sector as well as in other directly related sectors, but we do not quantify those impacts, because they are more difficult to quantify with rea sonable accuracy , particularly so far into the future.

YY In the 2012 BLS ERM cited above, the Petroleum and Coal Products Manufacturing sector has a ratio of workers per $1 million of 0.242, lower than all but two of the 181 sectors with non - zero employment per $1 million.For the regulated sector, we have not quantified the output effect. The substitution effect

is associated with potential increase d employ ment from none to a few thousand job s per ye ar between 2018 and 202 7 , depending on the share of employ ment impacts in the affected sector s (Motor Vehicle Manufacturing, Motor Vehicle Body and Trailer Manufacturing, and Motor Vehicle Parts Manufacturing) . These estimates do not include potential changes, either greater or less, in labor intensity

of production. As mentioned above, some of these job gains may occur earlier as auto manufacturers and parts suppliers hire staff to prepare to comply with the standard. Lower prices for shipping are expected to lead to an increase in demand for truck shipping services and, therefore, an increase in employ ment in that sector , though this effect may be offset somewhat b y changes in employment in other shipping sectors. Reduced fuel production implies less employ ment in the fuel provision sectors. Finall y, any net cost savings would be expected to be passed along to some segment of consumers: either the general public or the owners of trucking firms, who are expected then to increase employ ment through their expenditures . Under conditions of full employ ment, any changes in employ

ment levels in the regulated sector due to this program are mostly expected to be offset b y changes in employment in other sectors. 

### 8.12 **Oil Price Sensitivity Analysis**  **using Method B** In this section, EPA presents a sensitivity anal ys is examining the impact on net benefits using AEO’s “low oil price” and “high oil price” cases. The sensitivity analy sis is based on the preferred alternative relative to the less dy namic baseline as the “primary” case using Method B . F uel price changes were not used as an input to technology application rates (i.e., a constant $/vehicle has been used throughout this sensitivity anal ys is). The primary analy sis (presented earlier in this chapter) uses the AEO reference case o il prices. The primary case and both high and low oil price case $/gallon values are shown in Table 8 - 44 . **Table 8**  **- 44**  **AEO Fuel Prices in the Low Oil Price Case, our Prim ary Analysis Case, and the AEO High Oil**  **Price Case (2012$)** YEAR RETAIL UNTAXED

Diesel Gasoline Diesel Gasoline Lo w Primary High Lo w Primary High Lo w Primary High Lo w Primary High 2018 $2.94 $3.53 $4.89 $2.54 $3.02 $4.10 $2.48 $3.07 $4.43 $2.13 $2.61 $3.68 2019 $2.95 $3.61 $4.94 $2.54 $3.03 $4.13 $2.49 $3.15 $4.49 $2.13 $2.62 $3.71 2020 $2.96 $3.67 $4.99 $2.55 $3.08 $4.16 $2.51 $3.22 $4.54 $2.15 $2.67 $3.74 2021 $3.00 $3.74 $5.03 $2.59 $3.12 $4.17 $2.55 $3.29 $4.59 $2.19 $2.71 $3.76 2022 $3.01 $3.82 $5.08 $2.59 $3.17 $4.18 $2.56 $3.37 $4.64 $2.19 $2.77 $3.77 2023 $3.01 $3.87 $5.10 $2.57 $3.22 $4.19 $2.57 $3.43 $4.67 $2.18 $2.82 $3.78 2024 $3.02 $3.92 $5.14 $2.56 $3.26 $4.21 $2.58 $3.48 $4.70 $2.16 $2.86 $3.80 2025 $3.03 $3.98 $5.17 $2.55 $3.29 $4.23 $2.59 $3.54 $4.74 $2.16 $2.89 $3.82 2026 $3.04 $4.02 $5.24 $2.55 $3.32 $4.26 $2.60 $3.59 $4.81 $2.16 $2.92 $3.86 2027 $3.03 $4.08 $5.30 $2.54 $3.36 $4.31 $2.60 $3.65 $4.88 $2.15 $2.96 $3.91 2028 $3.03 $4.12 $5.39 $2.54 $3.37 $4.38 $2.61 $3.69 $4.97 $2.16 $2.98 $3.98 2029 $3.04 $4.16 $5.47 $2.54 $3.40 $4.43 $2.61 $3.74 $5.06 $2.16 $3.01 $4.03 2030 $3.06 $4.20 $5.51 $2.55 $3.43 $4.45 $2.64 $3.79 $5.09 $2.17 $3.04 $4.06 2031 $3.06 $4.25 $5.58 $2.55 $3.46 $4.51 $2.64 $3.84 $5.17 $2.17 $3.08 $4.122032 $3.06 $4.30 $5.66 $2.56 $3.50 $4.57 $2.64 $3.89 $5.25 $2.18 $3.12 $4.18

2033 $3.07 $4.36 $5.72 $2.57 $3.54 $4.62 $2.66 $3.95 $5.32 $2.20 $3.16 $4.24 2034 $3.08 $4.43 $5.77 $2.58 $3.61 $4.66 $2.67 $4.02 $5.37 $2.21 $3.24 $4.28 2035 $3.08 $4.47 $5.83 $2.58 $3.65 $4.71 $2.67 $4.06 $5.44 $2.22 $3.27 $4.33 2036 $3.09 $4.51 $5.89 $2.59 $3.69 $4.77 $2.68 $4.11 $5.50 $2.23 $3.32 $4.39 2037 $3.09 $4.54 $5.96 $2.59 $3.73 $4.81 $2.69 $4.15 $5.57 $2.23 $3.36 $4.44 2038 $3.10 $4.58 $6.03 $2.60 $3.77 $4.87 $2.70 $4.19 $5.65 $2.24 $3.40 $4.49 2039 $3.11 $4.65 $6.12 $2.61 $3.83 $4.95 $2.71 $4.26 $5.74 $2.25 $3.47 $4.57 2040 $3.11 $4.73 $6.23 $2.61 $3.90 $5.04 $2.72 $4.34 $5.85 $2.26 $3.54 $4.67 Note: Our Primary

case values are the AEO reference case values and are taken from AEO2014 Early Release; other values from AEO2014. The impacts of using the low and high oil price cases on our estimated fuel savings and net benefits are sho wn in Table 8 - 45 . **Table 8**  **- 45**  **MY2018 -**  **2029 Lifetim e**  **Sensitivity on Net Benefits using AEO2014 Low and High Oil Price Cases for the Preferred Alternative Relativ**  **e to the Less Dynamic Baseline and using Method B (**  **Billions of 2012$)**  **a** LOW OIL PRICE CASE PRIMARY

CASE HIGH OIL

PRICE CASE Vehicle program - $25 - $25 - $25 Maintenance - $1.1 - $1.1 - $1.1 Fuel $117 $171 $230 Benefits $93 $97 $101 Net benefits $184 $242 $305 Note: a For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline , 1b, please see Preamble Section X.A.1

### References 1 Environmental Protecti on Agency and Department of Transportation, “Light - Duty Vehicle Greenhouse Gas Emissions Standards and Corporate Average Fuel Economy Standards; Final Rule,” Federal Register 75(88) (May 7, 2010), especially Sections III.H.1 (pp. 25510 - 25513) and IV.G.6 (pp. 25651 - 25657); Environmental Protection Agency and Department of Transportation, ”2017 and Later Model Year Light - Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule,” Federal Register 77(199) (October 15, 2012) , especially Sections III.H.1 (pp. 62913 - 62919) and IV.G.5.a (pp. 63102 - 63104). 2 State of Massachusetts v. EPA , 549 U.S. at 533. 3 Committee to Assess Fuel Economy Technologies for Medium - and Heavy - Duty Vehicles; National Research Council; Transportation Research Board (2010). “Technologies and Approaches to Reducing the Fuel Consumption of Medium - and Heavy - Duty Vehicles,” (hereaft er, “NAS 2010”). Washington, D.C. The National Academies Press. Available electronically from the National Academies Press Website at http://www.nap.edu/catalog.php?record_id=12845 (accessed September 10, 2010). 4 Klemick, Heather, Elizabeth Kopits, Keith Sargent, and Ann Wolverton (2014). “Heavy - Duty Trucking and the Energy Efficiency Paradox.” US EPA NCEE Working Paper Series. Working Paper 14 - 02; Roeth, Mike, Dave Kircher, Joel Smith, and Rob Swim (2013). “Barriers to the Increased Adoption of Fuel Eff iciency Technologies in the North American On - Road Freight Sector.” NACFE report for the International Council on Clean Transportation; Aarnink, Sanne, Jasper Faber, and Eelco den Boer (2012). “Market Barriers to Increased Efficiency in the European On - road Freight Sector.” CE Delft report for the International Council on Clean Transportation. 5 Vernon, David and Alan Meier (2012). “Identification and quantification of principal - agent problems affecting energy effic iency investments and use decisions in the trucking industry.” Energy Policy, 49(C), pp. 266 - 273 6 Blumstein, Carl and Margaret Taylor (2013). “Rethinking the Energy - Efficiency Gap: Producers, Intermediaries, and Innovation,” Energy Institute at Haas Worki ng Paper 243, University of California at Berkeley ; Tirole, Jean (1998). The Theory of Industrial Organization. Cambridge, MA: MIT Press, pp.400, 402. This first - mo ver disadvantage must large enough to overcome the incentive normally offered by the pot ential to for first movers to earn unusually high (but temporary) profit levels. 7 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking , September 2013). 8 Transport Canada, Operating Cost of Trucks, 2005. See http://www.tc.gc.ca/eng/policy/report - acg operatingcost2005 - 2 005 - e - 2 - 1727.htm , accessed on July 16, 2010. 9 Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J., Froman, S., 2012. Estimating the direct rebound effect for on - road freight transportation. Energy Policy 48, 252 - 259. 10 Greene, D.L., Kahn, J.R., Gibson, R.C., 1999, “Fuel economy rebound effect for U.S. household vehicles”, The Energy Journal, 20. 11 For a discussion of the wide range of definitions found in the literature, see Appendix D: Discrepancy in Rebound Effect Definitions, in EERA (2014), “Research to Inform Analysis of the Heavy - Duty vehicle Rebound Effect”, Excerpts of Draft Final Report of Phase 1 under EPA contract EP - C - 13 - 025. (Docket ID: EPA - HQ - OAR - 2014 - 0827). See also Greening, L.A., Greene, D.L., Difiglio, C., 2000, “Energy efficiency and consumption

— the rebound effect

— a survey”, Energy Policy, 28, 389 - 401. 12 Committee to Assess Fuel Economy Technologies for Medium - and Heavy - Duty Vehicles; National Research Council; Transportation Research Board (2010). “Technologies and Approaches to Reducing the Fuel Consumption of Medium - and Heavy - Duty Vehicles,” Was hington, D.C. The National Academies Press. Available electronically from the National Academies Press Website at http://www.nap.edu/catalog.php?record_id=12845 (last accessed September 10, 2010). 13 American Transportation Research Institute, An Analysis o f the Operational Costs of Trucking , September 2013 . 14 Transport Canada, Operating Cost of Trucks, 2005. See http://www.tc.gc .ca/eng/policy/report - acg operatingcost2005 - 2005 - e - 2 - 1727.htm , accessed on July 16, 2010 . 15 These factors are discussed more fully in a report to EPA from EERA , which illustrates in a series of diagrams the complex system of decisions and decision - makers that could influence the magnitude and timing of the rebound effect. See Sections 2.2.2, 2.2.3, 2.2.4, and 2.3 in EERA (2014), “Research to Inform Analysis of the Heavy - Duty Vehicle Rebound Effect”, Excerpts of Draft Final Report of Phase 1 under EPA contract EP - C - 13 - 025. 16 A useful framework for understanding how various responses interact to determine the rebound effect is presented in Section 2 and Appendix B of De Borger, B. and Mulalic, I. (2012), “The determinants of fuel use in the trucking industry

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–2020 . EPA - COUNCIL - 08 - 001. July. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$File/EPA - COUNCIL 08 - 001 - unsigned.pdf>. 127 These estimates were developed by FHWA for use in its 1997 Federal Highway Cost Allocation Study ; see http://www.fhwa.dot.gov/policy/hcas/final/index.htm (last accessed July 21, 2010). 128 See Federal Highway Administration, 1997 Federal Highway Cost Allocation Study , http://www.fhwa.dot.gov/policy/hcas/final/index.htm, Tables V - 22, V - 23, and V - 24 (last accessed July 8, 2012). 129 Passenger vehicle fuel dispensing rate per EPA regulations, last vie wed on August 4, 2010 at http://www.epa.gov

/oms/regs/ld

- hwy/evap/spitback.txt 130 Survey data results on refueling times for light - duty trucks derived from “Evaluation of the Effectiveness of TPMS in Proper Tire Pressure Maintenance”, November 2012. Report No. DOT HS 811 681. http://www nrd.nhtsa.do t.gov/Pubs/811681.pdf . 131 U.S. Department of Transportation, Valuation of Travel Guidance, July 9, 2014, at page 14 . 132 “Preliminary Regulatory Impact Analysis, FMVSS No. 119, New Pneumatic Tires for Motor Vehicles with a GVWR of More Than 4,536 kg (10,000 p ounds), June 2010. 133 Cost and Weight Analysis of Two Motorcoach Seating Systems: One With and One Without Three - Point Lap/Shoulder Belt Restraints, Ludtke and Associates, July 2010. 134 “Final Regulatory Impact Analysis, Corporate Average Fuel Economy for M Y 2012 - MY 2016 Passenger Cars and Light Trucks”, NHTSA, March 2010, (Docket No. NHTSA - 2009 - 0059 - 0344.1). 135 Based on data from the CIA, combining various recent years, https://www.cia.gov/library/publications/the - world factbook/rankorder/2242rank.html 136 See EIA Annual Energy Review , various editions. For data 2011 - 2013, and projected data: EIA Annual Energy Outlook (AEO) 2014 (Reference Case). See Table 11, file "aeotab_11.xls" 137 For historical data: EIA Annual Energy Review, various editions. For data 2011 - 2013, and projected data: EIA Annual Energy Outlook (AEO) 2014 (Re ference Case). See Table 11, file "aeotab_11.xls" 138 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and Costs , ORNL - 6851, Oak Ridge National Laboratory, November, 1997. 139 Peer Review

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– 1621. doi:10.1016/j.enpol.2009.01.041 144 Reassessing the Oil Security Premium. RFF Discussion Paper Series, (RFF DP 10 - 05). doi:RFF DP 10 - 05 145 Ledyard, J ohn O. "Market Failure." The New Palgrave Dictionary of Economics. Second Edition. Eds. Steven N. Durlauf and Lawrence E. Blume. Palgrave Macmillan, 2008 146 Gately, Dermot 2004. “OPEC's Incentives for Faster Output Growth” , The Energy Journal ,25(2):75 - 96; G ately, Dermot 2007. "What Oil Export Levels Should We Expect From OPEC?" , The Energy Journal, 28(2):151 - 173. 147 Bohi, Douglas R. And W. David Montgomery 1982. “Social Cost of Imported and U.S. Import Policy”, Annual Revie w of

Energy , 7:37 - 60 . Energy Modelin g Forum, 1981. World Oil, EMF Report 6 (Stanford University Press: Stanford 39 CA. https//emf.stanford.edu/publications/emf - 6 - world - oil. 148 Plummer, James L. (Ed.) 1982. Energy Vulnerability, “Basic Concepts, Assumptions and Numercial Results”, pp. 13 - 36, (Cambridge MA: Ballinger Publishing Co.) 149 Bohi, Douglas R. And W. David Montgomery 1982 “Social Cost of Imported and U.S. Import Policy”, Annual Revie w of

Energy , 7:37 - 60 . 150 Hogan, William W., 1981. “Import Management and Oil Emergencies”, Chapter 9 in De ese, 5 David and Joseph Nye, eds. Energy and Security. Cambridge, MA: Ballinger Publishing Co. 151 Broadman, H. G. 1986. “The Social Cost of Imported Oil,” Energy Policy 14(3):242 - 252. Broadman H.G. and W.W. Hogan, 1988. “Is an Oil Import Tariff Justified? An American Debate: The Numbers Say ‘Yes’ ” , The Energy Journal 9: 7 - 29. 152 Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and Costs, ORNL - 6851, Oak Ridge National Laboratory, November 1, 1997. 153 P arry, Ian W.H. and Joel Darmstadter 2004. “The Costs of U.S. Oil Dependency,” Resources for the Future, November 17, 2004 (also published as NCEP Technical Appendix Chapter 1: Enhancing Oil Security, the National Commission on Energy Policy 2004 Ending the Energy Stalemate - A Bipartisan Strategy to Meet America’s Energy Challenges.) 154 National Research Council, 2009. Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use. National Academy of Science, Washington, DC. 155 See, William Nordh aus, “Who’s Afraid of a Big Bad Oil Shock?,” available at http://aida.econ.yale.edu/~nordhaus/homepage/Big_Bad_Oil_Shock_Meeting.pdf , and Olivier Blanchard and Jordi Gali, “ The macroeconomic Effects of Oil price Shocks: Why are the 2000s so different from the 1970s? ” , pp. 373 - 421, in The International Dimensions of Monetary Policy , Jordi Gali and Mark Gertler, editors, University of Chicago Press, February 2010, available at http://www.nber.org/chapters/c0517.pdf 156 Blanchard and Gali, p. 414. 157 See, Oil price Drops on Oversupply, http://www.oil -price.net/en/articles/oil -price - drops - on - oversupply.php , 10/6/2014. 158 Hamilton, J. D. (2012). Oil Prices , Exhaustible Resources , and Economic Growth. In Handbook of Energy and Climate Change. Retrieved from http://econweb.ucsd.edu/~jhamilto/handbook_climate.pdf 159 Ramey, V. A., & Vine, D. J. (2010). “ Oil, Automobiles, and the U.S. Economy: How Much have Things Really Changed? ”, National Bureau of Economic Research Working Papers, WP 16067(June). Retrieved from http://www.nber.org/papers/w16067.pdf 160 Historical data are from EIA Annual Energy Review, various editions. For data since 2011 and projected data: source is EIA Annual Energy Outlook (AEO) 2014 (Ref erence Case). See Table 11, file "aeotab_11.xlsx" and Table 20 (Macroeconomic Indicators,” (file “aeotab_20.xlsx”). 161 http://www. whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf. 162 U.S. Department of Labor, Bureau of Labor Statis tics. “ Automotive Industry; Employment, Earnings, and Hours ” , http://www.bls.gov/iag/tgs/iagauto.htm , accessed 8/18/14. 163 Layard, P.R.G., and A. A. Walters (1978), Microeconomic Theory (McGraw - Hill, Inc.), Chapter 9 (Docket EPA HQ - OAR - 2011 - 0135) . 164 Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: Evidence from the South Coast Air Basin.” Journal of Public Economics 79 (2): 265 - 295 (Docket EPA - HQ - OAR- 2011 - 0135) .165 Ehrenberg, Ronald G., and Robert S. Smith (2000), Modern Labor Economics: Theory and Public Policy

(Addison Wesley Longman, Inc.), , p. 108. 166 This discussion draws from Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: Evidence from the South Coast Air Basin.” Journal of Public E conomics 79(2): 265 - 295 (Docket EPA - HQ - OAR), p. 293. 167 Arrow et al. (1996). “Benefit - Cost Analysis in Environmental, Health, and Safety Regulation: A Statement of Principles.” American Enterprise Institute, The Annapolis Center, and Resources for the Future. See discussion on bottom of p. 6. In practice, distributional impacts on individual workers can be important, as discussed later in this section. 168 Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis of EPA’s Transport Rule.” White paper commissioned by Excelon Corporation, March 2011. 169 Klaiber, H. Allen, and V. Kerry Smith (2012). “Developing General Equilibrium Benefit Analyses for Social Programs: An Introduction and Example.” Journal of Benefit - Cost Analys is 3(2). 170 Graff Zivin, J., and M. Neidell (2012). “The Impact of Pollution on Worker Productivity.” American Economic Revie w 102: 3652 - 3673). 171 Hamermesh (1993), Labor Demand (Princeton, NJ: Princeton University Press), Chapter 2 (Docket E PA - HQ OAR - 2011 - 0135) . 172 Ehrenberg, Ronald G., and Robert S. Smith (2000), Modern Labor Economics: Theory and Public Policy (Addison Wesley Longman, Inc.), Chapter 4 (Docket EPA - HQ - OAR - ) ,. 173 Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: Evidence from the South Coast Air Basin.” Journal of Public Economics 79 (2): 265 - 295 (Docket EPA - HQ - OAR- 2011 - 0135) . 174 Morgenstern, Richard D., William A. Pizer, and Jhih - Shyang Shih. “Jobs Versus the Environment: An Industry Level Perspective.” Journal of Environmental Economics and Management 43 (2002): 412 - 436 (Docket EPA - HQ OAR - 2011 - 0135 - 0057) . 175 Gray, Wayne B., Ronald J. Shadbegian, Chunbei Wang, and Merve Meral. “Do EPA Regulations Affect Labor Demand? Evidence from the Pulp and Paper Industry.” Journal of Environmental Economics and Management: 68, 2014, 188 - 202. 176 Ferris, Ann, Ronald J. Shadbegian and Ann Wolverton. “The Effect of Environmental Regulation on Power Secto r Employment: Phase I of the Title IV SO2 Trading Program.” Journal of the Association of Environmental and Resource Economists. (Forthcoming 2014). 177 Greenstone, M. (2002). “The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures,” Journal of Political Economy 110(6): 1175 - 1219 (Docket EPA - HQ - OAR - 2011 - 0135) ; Walker, Reed. (2011).“Environmental Regulation and Labor Reallocation." American Economic Review: Pap ers and Proceedings 101(3): 442 - 447 (Docket EPA - HQ - OAR 2011 - 0135) . 178 List, J. A., D. L. Millimet, P. G. Fredriksson, and W. W. McHone (2003). “Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity Score Matching Esti mator.” The Review of Economics and Statistics 85 (4): 944 - 952 (Docket EPA - HQ - OAR - 2011 - 0135) . 179 Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: Evidence from the South Coast Air Basin.” Journal of Public Economics 79 (2): 265 - 2 95 (Docket EPA - HQ - OAR - 2011 - 0135) . 180 http://www.bls.gov/emp/ep_data_emp_requirements.htm. 181 http://www.census.gov/manufacturing/asm/index.html. 182 http://www.bls.gov/emp/ep_data_emp_requirements.htm; this analysis used data for sectors 81 (Motor Vehicle Manufacturing), 82 (Motor Vehicle Body and Trailer Manufacturing), and 83 (Motor Vehicle Parts Manufacturing) from “ Chain - weighted (2005 dollars) real domestic employment requirements tables.” 183 American Transportation Research Institute, “An Analysis of the Operational Costs of Trucking: 2011 Update.” See http://www.atri - online.org/research/results/Op_Costs_2011_Update_one_page_summary.pdf . 184 Association of American Railroads, “All Inclusive Index and Rail Adjustment Factor.” June 3, 2011. See http://www.aar.org/~/media/aar/RailCostIndexes/AAR - RCAF - 2011 - Q3.ashx