***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 1 EO12866_GHG+Fuel Eff Stds for Med - Heavy- Duty Engines+Vehicles 2060 - AP61 FRM RIA_20110 722 _Ch 09 Economic and Social Impacts .docx 

## Chapter 9. **Economic and Social Impacts**  **9.1 Framework** 

**for Benefits and Costs** The net benefits of the HD National Program consist of the effects of the program on:

• the engine and truck program costs ,

• fuel savings associated with reduced fuel usage resulting from the program ,

• greenhouse gas emissions,

• other air pollutants,

• noise, congestion, accidents resulting from truck use ,

• refuel ing savings ,

• energy

security impacts,

• increased driving due to the “rebound” effect. The benefits and costs of this rule are anal yz ed using 3% and 7% discount rates. These rates are intended to represent the social opportunity costs of capital; they

are not necessarily

the discount rates that private decision - makers use. 

### 9.2 **Conceptual Framework for Evaluating Impacts**  This regulation is motivated primarily by the goals of reducing emissions of greenhouse gases and promoting U.S. energy security b y reducing consumption and imports of petroleum based fuels. These motivations involve classic externalities, meaning that private decisions do not incorporate all of the costs associated with these problems; these costs are not borne completely

by the households or businesses whose actions are responsible for them. In the absence of some mechanism to “internalize” these costs

– that is, to transfer their burden to individuals or firms whose decisions impose them

– individuals and firms will consume more petroleum - based fuels than is socially optimal. Externalities are a classic motivation for government intervention in markets. These externalities, as well as effects due to changes in emissions of other pollutants and other impacts, are discussed in Sections VII I.H

– VIII.K.**Regulatory Impact Analysis**

9 - 2

In some cases, these classic externalities are by themselves enough to justify the costs of imposing fuel efficiency

standards. F or some discount rates and some projected social costs of carbon, however, the reductions in these external c osts are less than the costs of new fuel saving technologies needed to meet the standards . (See T able s 9 - 18 and 9 - 1 9 in the RIA .) Nevertheless , th is regulation reduces trucking companies’ fuel costs; according to our estimates, these savings in fuel costs are by themselves sufficient to pay for the technologies over periods of time considerabl y shorter than vehicles’ expected lifetimes under the assumptions used for this anal ys is (e.g., AEO 2011 projected fuel prices). If these estimates are correct, then the entire value of the reductions in external costs represents additional net benefits of the program, beyond those resulting from the fact that the value of fuel savings exceeds the costs of technologies necessary to achieve them. It is often asserted th at there are cost - effective fuel - saving technologies that markets do not take advantage of

. This is commonly

known as the “energy gap” or “energy paradox.” S tandard economic theory suggests that in normally functioning competitive markets, interactions b etween vehicle buyers and producers would lead producers to incorporate all cost effective technology

into the vehicles that they offer, without government intervention. Unlike in the light - duty

vehicle market, the vast majority of vehicles in the medium - and heav y- duty truck market are purchased and operated b y businesses with narrow profit margins, and for which fuel costs represent a substantial operating expense. Even in the presence of uncertainty and imperfect information

– conditions that hold to some degree in every

market

– we generally

expect firms to attempt to minimize their costs in an effort to survive in a competitive marketplace, and therefore to make decisions that are in the best interest of the company and its owners and/or shareholders . In this case, the benefits of the rules would be due exclusively to reducing the economic costs of externalities resulting from fuel production and consumption. However, as discussed below in Section VIII. E, the agencies have estimated that the application of fuel - saving technologies in response to the final standards would , on average, yi eld private returns to truck owners of 140% to 420% (see Table VIII - 21 below). The agencies have also estimated that the application of these technologies would be si gnificantl y lower in the absence of the final standards ( i.e. , under the “no action” regulatory alternative), meaning that truck buyers and operators ignore opportunities to make investments in higher fuel efficiency that appear to offer significant cost s avings . As discussed in the NPRM, there are several possible explanations in the economics literature for why

trucking companies do not adopt technologies that would be expected to increase their profits: there could be a classic market failure in the truc king industry

– market power, externalities, or asy mmetric or incomplete (i.e., missing market) information; there could be institutional or behavioral rigidities in the industry (union rules, standard operating procedures, statutory

requirements, loss aversion, etc.), whereb y participants collective ly do not minimize costs; or the engineering estimates of fuel savings and costs fo r these technologies might overstate their benefits or understate their costs in real - world application s . To try to understand why

trucking companies have not adopted these seemingl y cost effective fuel - saving technologies, the agencies surveyed published literature about the energy paradox, and held discussions with numerous truck market participants. The proposal discussed five c ategories of possible explanations derived from these sources . Collectively , these five***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 3 hy potheses may explain the apparent inconsistency between the engineering anal ys is, which finds a number of cost - effective methods of improving fuel efficiency , and the observation that many of these technologies are not widely adopted. These hypotheses include imperfect information in the original and resale markets, split incentives, uncertainty

about future fuel prices, and adjustment and transactions costs. As the discussion indicated, some of these explanations suggest failures in the private market for fuel saving technology

in addition to the externalities caused by producing and consuming fuel that are the primary

motivation for the rules. Other explanations suggest market - based behaviors that may impl y additional costs of regulating truck fuel efficiency that are not accounted for in this anal ys is. As noted above, an additional explanation

– adverse effects on other vehicle attributes

-- did not elicit supporting information in the public comments. Anecdotal evidence from various segments of the trucking industry suggests that many

of the hy potheses discussed here may play a role in explaining the puzzle of why truck purchasers appear to under- invest in fuel efficiency , although different explanations may apply to different segments, or even different companies. The published literature does not appear to include empirical analy sis or data related to this question. The agencies invite d comment on these explanations, and on any data or information that could be used to investigate the role of any or all of these five hypotheses in explaining this energy paradox as it applies specificall y to trucks . Some comments expressed dissatisfaction about the explanations presented; they argued that these arguments were not sufficient to explain the phenomenon. These comments argued that the truck owners and operators are better judges of the appropriate amount of fuel efficiency than are government agencies; they choose n ot to invest because of warranted skepticism about these technologies. The agencies also request ed comment and information regarding any other hy potheses that could explain the appearance that cost - effective fuel - saving technologies have not been widel y incorporated into trucks . The following discussion summarizes the fuller discussion provided in the NPRM and includes discussion of the comments received. 

### 9.2.1 **Information Issues in the Original Sale Markets** One potential hypothesis for why the trucking industry does not adopt what appear to be inexpensive fuel saving technologies is that there is inadequate or unreliable information available about the effectiveness of many fuel - saving technologies for new vehicles. If reliable information on the effectiveness of many new technologies is absent, truck buyers will understandabl y be reluctant to spend additional money to purchase vehicles equipped with unproven technologies. This lack of information can manifest itself in multiple way s. For instance, the probl em may arise purely because collecting reliable information on technologies is costly (also see Section VIII.A. 5 on transaction costs). Moreover, information has aspects of a public good , in that no single firm has the incentive to do the costly experimentation to determine whether or not particular technologies are cost - effective, while all firms benefit from the knowledge that would be gained from that experimentation. Similarly, if multiple firms must conduct the same tests to**Regulatory Impact Analysis**

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get the same information, costs could be reduced by some form of coordination of information gathering. While its effect on information is indirect, we expect the requirement for the use of new technologies included in this program will circumvent these information issues, resulting in their adoption, thus providing more readily available information about their benefits. The agencies appreciate, however, that the diversity of truck uses, driving situations, and driver behavior will lead to variation in the fuel savings that individual trucks or fleets experience from using specific technologies. One commenter noted that the SmartWay program targets semi - truck owners and thus should have the largest impact on that sector, rather than vocational or medium - duty trucks. However, the gap between actual investment in fuel efficiency and the agencies’ estimates of optimal investment is largest for combination tractors. Some of the difference in magnitude is likely to be due to the higher vehicle miles traveled for semi - trucks compared to medium - duty and vocational vehicles: more driving means more fuel savings. Additionally , not even a majority of semi - trucks are owned b y participants in SmartWay ; non - participants are unlikel y to get all the benefits of participants. Other explanatio ns, noted below, are also likely to play a role. This observation may

also suggest some limitations of improved information provision as a means of addressing the “efficiency gap.” 

### 9.2.2 **Information Issues in the Resale Market** In addition to issues in the new vehicle market, a second hypothesis for why trucking companies may

not adopt what appear to be cost - effective technologies to save fuel is that the resale market may

not adequatel y reward the addition of fuel - saving technology to vehicles to ensure their o riginal purchase b y new truck buye rs. This inadequate pay back for users beyond the original owner may

contribute to the short payback period that new purchasers appear to expect. 1 The agencies requested data and information on the extent to which costs o f fuel saving equipment can be recovered in the resale truck market. No data were received. One reviewer disputed this theory

on the basis that people are willing to pay more for better vehicles, new or used. It is not clear, however, whether buyers of used vehicles can tell which are the better vehicles. 2 Some of this unwillingness to pay for fuel - saving technology may be due to the extension of the information problems in the new vehicle market into resale markets. Buyers in the resale market have no more reason to trust information on fuel - saving technologies than buyers in the original market. Because actual fuel efficiency of trucks on the road depends on many factors, including geography and driving styl es or habits, even objective sources such as logs of truck performance for used vehicles may not provide reliable information about the fuel efficiency that potential purchasers of used trucks will experience. A related possibility

is that vehicles will be used for different purposes by their second owners than those for which they were originall y designed, and the fuel - saving technology is therefore of less value.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 5 It is possible, though, that the fuel savings experienced by the secondary

purchasers may not match those experienced b y their original owners if the optimal secondary new use of the vehicle does not earn as many benefits from the technologies. One commenter asks whether the fuel - saving technology

is unvalued because it is unproven or overrated. In that case, the premium for fuel - savin g technology in the secondary

market should accurately reflect its value to potential buyers participating in that market, even if it is lower than its value in the original market, and the market has not failed. Because the information necessary to optim ize use in the secondary market may

not be readil y available or reliable, however, buye rs in the resale market may have less ability than purchasers of new vehicles to identify and gain the advantages of new fuel - saving technologies, and may thus be even l ess likely

to pay a premium for them. For these reasons, purchasers’ willingness to pay for fuel efficiency

technologies may

be even lower in the resale market than in the original equipment market. Even when fuel - saving technologies will provide benefits in the resale markets, purchasers of used vehicles may not be willing to compensate their original owners fully for their remaining value. As a result, the purchasers of original equipment may expect the resale market to provide inadequate appropriate co mpensation for the new technologies, even when those technologies would reduce costs for the new buyers. This information issue may partially explain what appears to be the very short pay back periods required for new technologies in the new vehicle market . 

### 9.2.3 **Split I**  **ncentives in the M**  **edium -**  **and H**  **eavy -**  **D uty**  **T ruck**  **I ndustry**  A third hy

pothesis explaining the energy paradox as applied to trucking involves split incentives. When markets work effectivel y, signals provided by transactions in one market are quickly transmitted to related markets and influence the decisions of buyers and sellers in those related markets. For instance, in a well - functioning market s ys tem, changes in the expected future price of fuel should be transmitted rapidl y to those who purchase tr ucks, who will then reevaluate the amount of fuel - saving technology to purchase for new vehicles. If for some reason a truck purchaser will not be directly responsible for future fuel costs, or the individual who will be responsible for fuel costs does no t decide which truck characteristics to purchase, then those price signals may not be transmitted effectivel y, and incentives can be described as “split.” One place where such a split may occur is between the owners and operators of trucks. Because they are generally responsible for purchasing fuel, truck operators have strong incentives to economize on its use, and are thus likely to support the use of fuel - saving technology . However, the owners of trucks or trailers are often different from operators, and may be more concerned about their longevity or maintenance costs than about their fuel efficiency , when purchasing vehicles. As a result, capital investments by truck owners may

be channeled into equipment that improves vehicles’ durability or reduces their maintenance costs, rather than into fuel - saving technology . If operators can choose freel y am ong the trucks they drive, competition among truck owners to employ operators would encourage owners to invest in fuel - saving technology . However, if truc k owners have more ability to choose among operators, then market signals for improved fuel savings that would normally be transmitted to truck owners may

be muted. Truck fleets that rent their vehicles may provide an example: renters**Regulatory Impact Analysis**

9 - 6

may observe the cos t of renting the truck, but not its fuel efficiency ; if so, then the purchasers will aim for vehicles with lower costs, to lower the cost of the rental. One commenter noted that there are always tradeoffs in an investment decision: a purchaser may

prefe r to invest in other vehicle attributes than fuel efficiency. In an efficient market, however, a purchaser should invest in fuel - saving technology as long as the increase in fuel - saving technology

costs less than the expected fuel savings. This result sh ould hold regardless of the level of investment in other attributes, unless there are constraints on a purchaser’s access to investment capital. Anecdotal information about large truck fleets suggests that, even within a company , the office or department responsible for truck purchas es is often different from that responsible for purchasing fuel. Therefore, the employees who purchase trucks may have strong incentives to lower the ir initial capital cost, but not equally strong incentives to lower operating costs. In addition, the NAS report notes that split incentives can arise between tractor and trailer operators. 3 Trailers affect the fuel efficiency of shipping, but trailer owners do not face strong incentives to coordinate with truck owners. EPA and NHTSA are not regulating trailers in this action. By

itself, information provision may be inadequate to address the potential underinvestment in fuel efficiency resulting from such split incentives. In this setting, regulation may contribute to fuel savings that otherwise may be difficult to achieve. 

### 9.2.4 **Uncertainty A bout Future Cost Savings**  Another hypothesis for the lack of adoption of seemingl y fuel saving technologies may be uncertainty

about future fuel prices or truck maintenance costs . When purchasers have less than perfect foresight about future operating expenses , they may implicitly

discount future savings in those costs due to uncertainty about potential returns from investments that reduce future costs. In contrast, the immediate costs of the fuel - saving or maintenance - reducing technologies are certain and immediate, and thus not subject to discounting . Inthis situation, both the expected return on capital investments in higher fuel efficiency and potential variance about its expected rate may pl ay a role in a firm’s calculation of its pay back period on such investments. In the context of energy efficiency investments for the home, Metcalf and Rosenthal (1995) and Metcalf and Hassett (1995) observe that households weigh known, up - front costs that are essentially

irreversible against an unknown stream of future fuel savings. 4 Notably , in this situation, requiring households to adopt technologies more quickl y may make them worse off by imposing additional risk on them . Greene et al (2009) also find s support for this explanation in the context of light - duty fuel economy decisions: a loss - averse consumer’s expected net present value of increasing the fuel economy of a passenger car can be very close to zero , even if a risk - neutral expected value calculation shows that its buyer can expect significant net benefits from purchasing a more fuel efficient car . 5 Supporting this hy pothesis is a finding b y Dasgupta et. al (2007) that consumers***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 7 are more likely

to lease than buy

a vehicle with higher maintenanc e costs because it provides them with the option to return it before those costs become too high. 6 However, the agencies know of no studies that have estimated the impact of uncertainty on perceived future savings for medium - and heav y- duty vehicles . Purc hasers’ uncertainty

about future fuel prices implies that mandating improvements in fuel efficiency

can reduce the expected utility associated with truck purchases. This is because adopting such regulation requires purchasers to assume a greater level of risk than they would in its absence, even if the future fuel savings predicted by a risk - neutral calculation actually materialize. One commenter expressed support for this argument. Thus the mere existence of uncertainty about future savings in fuel cost s does not by itself assure that regulations requiring improved fuel efficiency

will necessaril y provide economic benefits for truck purchasers and operators. On the other hand, because risk aversion reduces expected returns for businesses, competitive pressures can reduce risk aversion: risk - neutral companies can make higher average profits over time. Thus, significant risk aversion is unlikely to survive competitive pressures. 

### 9.2.5 **Adjustment and Transactions Costs** Another hypothesis is that transactions co sts of changing to new technologies (how easily drivers will adapt to the changes, e.g.) may slow or prevent their adoption. Because of the diversity in the trucking industry , truck owners and fleets may like to see how a new technology works in the field , when applied to their specific operations, before they adopt it. One commenter expressed support for this argument. If a conservative approach to new technologies leads truck buye rs to adopt new technologies slowly , then successful new technologies are likel y to be adopted over time without market intervention, but with potentially significant delay s in achieving fuel saving, environment, and energy security benefits. In addition, there may

be costs associated with training drivers to realize the 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 new fuel - saving technologies. Here, again, there may be no market failure; requiring the widespread use of these technologies may

impose adjustment and transactions costs not included in this analysis. As in the discussion of the role of risk, these adjustment and transactions costs are ty pically immediate and undiscounted, while their benefits are future and uncertain; risk or loss aversion may further discourage companies from adopting new technologies. To the extent that there may be transactions costs associated with the new technologies, then regulation gives all new truck purchasers a level playing field, because it will require all of them to adjust on approximately the same time schedule. If experience with the new technologies serves to reduce uncertainty and risk, the industry as a whole may become more accepting of new technologies. This could increase demand for future new technologies and induce additional benefits in the legacy fleet through complementary efforts such as SmartWay .**Regulatory Impact Analysis**

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### 9.2.6 **Additional Hypotheses** In the public comments, two additional ideas were raised for the lack of adoption of what appears to be cost - effective fuel - saving technology . The first suggestion is that tighter diesel emissions standards caused fuel efficiency for diesel trucks to decline in the past decade. Because engine manufacturers would have to invest heavily (both financially

and with personnel) in emissions reduction technologies, they would not invest in fuel efficiency . The costs associated with the decline in fuel efficiency due to the emissions regulations were accounted for in that rul emaking. A second suggestion is that a truck may be a “positional good”

– that is, a good whose value depends on how it compares to the goods owned b y others. If trucks confer status on their owners or operators, and if that status depends on easily obs ervable characteristics, then owners may invest disproportionately in status - granting characteristics rather than less visible characteristics, such as fuel efficiency . Because status depends on comparisons to others, an “arms race” may

develop in which all parties spend additional money on visible characteristics but may not manage to make themselves better off. In this case, regulation may improve welfare: b y increasing the requirements for non - positional fuel efficiency , regulation could reduce expenditures made purel y for competition rather than actual increase in welfare. In a competitive business, cost reduction provides a major opportunity cost to investing in status rather than in fuel - saving technology ; thus, this argument may play less of a rol e in the heav yduty market than in the consumer market for vehicles. Both these hypotheses leave open the question, though, why additional investments were not made in fuel efficiency if they would provide rapid pay back. Truck purchasers should, in principle, be willing to buy additional fuel - saving technology as long as it is cost - effective, regardless of other vehicle attributes. Limited access to capital, if it is a problem in this sector, might provide some reason for the “crowding out” of the purchas e of fuel - saving technology . 

### 9.2.7 **Summary** On the one hand, commercial vehicle operators are under competitive pressure to reduce operating costs, and thus their purchasers would be expected to pursue and rapidly adopt cost effective fuel - saving technologies. O n the other hand, the short pay back period required b y buyers of new trucks is a sy mptom that suggests some combination of uncertainty about future cost savings, transactions costs, and imperfectly functioning markets. In addition, widespread use of tract or- trailer combinations introduces the possibility that owners of trailers may have weaker incentives than truck owners or operators to adopt fuel - saving technology f or their trailers. The market for medium - and heav y- duty trucks may face these problems, both in the new vehicle market and in the resale market. Provision of information about fuel - saving technologies through voluntary programs such as SmartWay will assist in the adoption of new cost - saving technologies, but diffusion of new technologies can still be obstructed. Those who are willing to experiment with new technologies expect to find cost savings, but those may be difficult to prove. As noted above, because individual results of new technologies vary , new truck purchasers may find it difficult to identify or verify the effects of fuel - saving technologies. Those who are risk- averse are likel y to avoid***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 9 new technologies out of concerns over the possibility of inadequate returns on the investment, or with other adverse impacts. Competitive pres sures in the freight transport industry can provide a strong incentive to reduce fuel consumption and improve environmental performance. However, not every driver or trucking fleet operating today has the requisite ability

or interest to access the techni cal information, some of which is already provided by SmartWay, nor the resources necessary to evaluate this information within the context of his or her own freight operation. It is unclear, as discussed above, whether some or many of the technologies would be adopted in the absence of the program. To the extent that they would have been adopted, the costs and the benefits attributed to those technologies may not in fact be due to the program and may therefore be overstated. Both baselines used project s ubstantially less adoption than the agencies consider to be cost - effective. The agencies will continue to explore reasons for this slow adoption of cost - effective technologies.



### 9.3 **Rebound Effect** The VMT rebound effect refers to the fraction of fuel savings expected to result from an increase in fuel efficiency that is offset by

additional vehicle use. If truck shipping costs decrease as a result of lower fuel costs, an increase in truck VMT may occur. Unlike the light duty rebound effect, the heavy- duty (HD) rebound effect has not been extensively studied. Because the factors influencing the HD rebound effect are generally different from those affecting the light - duty rebound effect, much of the research on the light - duty sector is not likely to apply to the HD se ctors. O ne of the major differences between the HD rebound effect and the light - duty rebound effect is that HD vehicles are used primarily for business purposes. Since these businesses are profit driven, decision makers are highl y likely to be aware of the costs and benefits of different operating and shipping decisions, both in the near- term and long - term. Therefore, both truck operators and shippers are likely to take into account changes in the overall operating costs per mile when making operating an d shipping decisions that affect truck usage . Another difference from the light - duty case is that, a s discussed in the recent NAS Report, when calculating the change in trucking costs that causes the rebound effect, all components of truck operating cost s should be considered. The cost of labor and fuel generally constitute the two largest share s of truck operating costs , depending on the price of petroleum, distance traveled, type of truck, and commodity . 8 9 See Figure 9 - 1. In addition , the equipment d epreciation costs associated with the purchase or leas e of the truck is also a significant component of total operating costs. Even though vehicle purchases are lump - sum costs , they are likely to be considered as operating costs by trucking firms, and the se costs are, in many cases, expected to be passed onto the final consumers of shipping services. By partially offsetting the reduction in fuel costs resulting from higher fuel efficiency , higher vehicle purchase or lease prices could thus help temper the magnitude of the fuel economy rebound effect relative to that**Regulatory Impact Analysis**

9 - 10

for light - duty vehicles, in which vehicle depreciation costs may not be considered an operating cost by vehicle owners . When calculating the net change in operating costs, both the increase in new vehicle costs and the decrease in fuel costs per mile should be taken into consideration. The higher the net cost savings, the higher the expected rebound effect. Conversel y, if the upfront vehicle costs outweighed future cost savings and total cost s increased, shipping costs would rise, which would likely result in a decrease in truck VMT. In theory, other cost changes resulting from any requirement to achieve higher fuel economy, such as changes in maintenance costs or insurance rates , sh ould also be taken into account, although information on potential changes in these elements of truck operating cost s is extremel y limited.

$0.634

$0.062 $0.206 $0.092 $0.060 $0.030 $0.024 $0.019 $0.441 $0.126 $0.036 **Co st**  **P er**  **Mile S**  **um mary**  **Sour ce:**  **A**

**TR I, 2**  **008** Fuel - Oil Costs*( $/m ile)

Fuel Ta xes ($/m ile) Truck/Tra iler Lease or Pu rchase Paym ents ($/m ile) Repa iran d Ma in tena nce ( $/m ile ) Truck Insura nce Pr em ium s ($/m ile) Ti res ($/m ile) Li censing an d Overweight - Oversize Pe rm its ($/m ile) Tol ls ($/m ile) Driver Pay ($/m ile ) Driver Be nef its ($/m ile ) Driver Bo nus Paym ents ($/m ile ) Total Cost Pe r Mile : $ 1. 73 * Base d on $4. 79/ gallo n di es el i n 2008 **Figure 9**  **- 1**  **: Cost Per Mile Summary** The following sections describe the factors affecting the rebound effect, different methodologies for estimating the rebound effect, and examples of different estimates of the***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 11 rebound effect to date. According to the NAS study , it is “not possible to provide a confident measure of the rebound effect , ” yet NAS concluded that a rebound effect likely exists and that “estimates of fuel savings from regulatory standards will be somewhat misestimated if the rebound effect is not considered.” While we believe the HD rebound effect needs to be studie d in more detail, we have attempted to capture the potential impact of the rebound effect in our anal ys is. For this rule , we have used a rebound effect for single unit trucks of 15%, a rebound effect for medium - duty (2b and 3) trucks of 10%, and a rebound effect for combination tractors of 5%. That is, we assume that for every 1% decrease in operating costs for trucks ($/mile), we anticipate a 0.15%, 0.10%, and 0.05% increase in VMT for single unit trucks, MD trucks, and combination tractors, respectivel y . For this rule, we do not discern between short - run and long run rebound effects, although these effects may differ, as discussed below. These VMT impacts are reflected in the estimates of total GHG and other air pollution reductions presented in Chapter 5 of the RIA . 

### 9.3.1 **Factors Affecting the Magnitude of the Rebound Effect** The HD rebound effect is driven by the interaction of several different factors. In the short - run, decreasing the fuel cost per mile of operating trucks could lead to a decrease in d elivered prices for product s shipped by truck . Lower delivered prices could stimulate additional demand for those products, which would then result in an increase in truck usage and VMT. In the long - run, shippers could reorganize their logistics and dis tribution networks to take advantage of lower truck shipping costs. For example, shippers may shift away

from other modes of shipping such as rail, barge, or air. In addition, shippers may also choose to reduce the number of warehouses, reduce load rates , and make smaller, more frequent shipments, all of which could also lead to an increase in HD VMT. Finally , the benefits of the fuel savings could ripple through the economy , which could in turn increase overall demand for goods and services shipped by

trucks , and therefore increase HD VMT. Conversel y, if a fuel efficiency regulation leads to net increases in the cost of trucking because fuel savings do not fully offset the increase in upfront vehicle costs, then the price of trucking services could ris e, spurring a decrease in HD VMT and a shift to alternative shipping modes . These effects would also ripple through the economy. As discussed in Section 8 of the preamble, the magnitude of the rebound effect is likely to be determined b y the extent of market failures that affect demand for fuel economy in HD fleets, such as split incentives and imperfect information, as well as rational firm responses to the tradeoff between higher certain upfront vehicle costs and lower but uncertain future expenditures on fuel. 

### 9.3.2 **Options for Quantifying the Rebound Effect**  As described in the previous section, the fuel economy rebound effect for heavy- duty vehicles has not been studied as extensively as the rebound effect for light - duty vehicles, and virtually no research h as been conducted on the medium - duty truck rebound effect. In this rule , we discuss four options for quantifying the rebound effect.**Regulatory Impact Analysis**

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### 9.3.2.1 **Aggregate Estimates** The aggregate approximation approach quantifies the overall change in truck VMT as a result of a p ercentage change in freight rates . It is important to note that most of the aggregate estimates measure the change in freight demanded (tons or ton - miles), rather than a change in fuel consumption or VMT. The change in ton s or ton - miles is more accuratel y characterized as a freight elasticity. Therefore, it may not be entirely appropriate to interpret these freight elasticities as measure s of the rebound effect, although these terms are sometimes used interchangeabl y in the literature. 10 Given these cave ats, freight elasticity estimates rel y on estimates of aggregate price elasticity of demand for trucking services, given a percentage change in trucking prices, which is generally referred to as an “own price elasticity .” Estimates of trucking own - price e lasticities vary widely (from positive 1.72 to negative 7.92) , though a 2004 literature survey

found aggregate elasticity estimates generall y fall in the range of - 0.5 to - 1.5 . 11 See Figure 9 - 2. In other words, given an own price elasticity of - 1.5, a 10% decrease in trucking prices leads to a 15% increase in truck shipping demand. Another challenge of estimating the rebound effect using f reight elasticities is that these values appear to v ary substantially based on the demand elasticity measure (e.g., t on or ton mile), the model specification (e.g., linear functional form or log linear), the length of the trip , and the ty pe of cargo. Ingeneral, elasticity estimates of longer trips tend to be larger than elasticity estimates for shorter trips. In addition, elasticities tend to be larger for lower- value commodities compared to higher - value commodities. Although these factors explain some of the differences in estimates , much of the observed variation cannot be explained quantitatively . For example, a r ecent study that controlled for these variables onl y accounted for about half of the observed variation . 12 Another important variable influencing freight elasticity estimates is whether potential mode shifting is taken into account. Although the total demand for freight transport is generally determined b y economic activity , there is often the choice of shipping freight on modes other than truck . This is because the United States has extensive rail, waterway and air transport networks in addition to an e xtensive highway network ; these networks often closel y paralle l each other and are often viable choices for freight transport for many long - distance routes within the continent. If rates go down for one mode, there will be an increase in demand for that mode and some demand will be shifted from other modes. This “cross - price elasticity” is a measure of the percentage change in demand for shipping b y another mode (e.g., rail) given a percentage change in the price of trucking. Aggregate estimates of cross - price elasticities also vary widel y, and there is no general consensus on the most appropriate value to use for analy tical purposes. The NAS report cites values ranging from 0.35 to 0.59. 13 Other reports provide significantly different cross - price ela sticities, ranging from 0.1 14 to 2.0 . 15***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 13 **Auth or/Date**  **Eva lua** **tio n**  **ba se**  **d on**  **... Least** 

**El astic**  **De scription**  **of Leas**  **t Ela** **stic Valu**  **e Mo**  **st**

**El ast**  **ic Des** **criptio n**  **of M**  **ost E**  **las tic**  **Value Reg** **io n**  **Dem an**  **d Un**  **its of**  **Measurement Co**  **mm odi**  **ty** Abdelwah ab( 1998) Co mmo dity -0.75 Cons tructi on m at erial s - 1.40 Textil e p roducts U.S . IC C Offici al Prob ab ility of

mo de cho ic e Va ries Sp ad y & Fr ie dl aen der (1980) Co mmo dity -0.15 Woo d an d wo od products -5.06 Electri cal Ma chi nery U.S . IC C So uthern ton - mi les Va ries Sp ad y & Fr ie dl aen der (1980) Co mmo dity - 1.00 Food P ro ducts -3.55 Electri cal Ma chi nery U.S . IC C Average ton - mi les Va ries Oum ( 1980) Co mmo dity -0.41 Me tall ic p roducts - 1.07 Fu elo il except gasol in e Ca na da ton - mi les Va ries Wi nston (1 981) Co mmo dity -0.14 Lumber, woo d an d Furni tu re - 2.96 Tr an sp ort Eq ui pm ent USA tons Va ries Li e t al . (2 011) Co mmo dity - 1.09 Ot her (Com mo di ty Cat ego ry) - 1.30 Natu ral R eso urces USA, Ital y & Indi a tonne - km Va ries Ca mp is i an d Ga st ald i (1996) Co mmo dity -0.27 Pe tro le um p ro ducts - 1.37 Mi nerals Italy tonnes Va riou s Fr ie dl ae nd er an d Sp ady (1981) Co mmo dity -0.59 Pe tro le um p ro ducts - 1.72 Wo od USA tonne - km Va riou s Bo ni ll a (2008) Co mmo dity -0.43 Oi l an d Coa l - 1.75 Bu il di ng m at erial s Denm ark tonne - km Va ries Ri ch e t a l (2 011) Co mpe titio n -0.08 Al l O - D pa irs -0.11 O - D Pai rs w here com peti ng mo des a va il ab le Sc an di na vi a tonne - km Ag ricultura l produ ct s Beu the et a l. ( 2001) De ma nd me as ur e -0.58 tonnes - 1.06 to nne - km Be lg iu m tonne, tonne - km Ag gr egate Li e t al . (2 011) De ma nd me as ur e - 1.02 tonnes - 1.30 to nne - km USA, Ital y & Indi a tonne, tonne - km Na tura l Re so urces Beu the et a l. ( 2001) Di st an ce - 1.06 <3 00 k m - 1.31 >300 k m Be lg iu m tonne - km Ag gr egate Wi nston (1 981) Di st an ce -0.34 <9 00 m iles (a ve rage) - 1.56 >900 m il es (av erage) USA tons Va ries Ch ri st id is a nd L educ (2009) Di st an ce -0.21 < 800 k m - 1.15 > 1500 km EU tons Al l Oum ( 1992) Mod el f or m -0.69 Tr an sl og - 1.34 Lo g- li near Va ri ou s ton, ton - mi le s Ag gr egate Gr ah aman d Gl ai ste r (2004) Mod el for m -0.048 Li near - 1.34 Lo g- li near Va ri ou s Va ri ous Va riou s Li e t al . (2 011) Pa nelvs. CS -0.93 Pa nel Transl og - 1.30 CSTra ns lo g USA, Ital y & Indi a tonne - km Na tura l Re so urces Abdelwah ab( 1998) Regi on -0.80 U. S. IC C Offici al - 2.18 U.S . IC C So uthweste rn Va ri ou s U. S. Prob ab ility of mo de cho ic e Meta l p roducts Sp ad y & Fr ie dl aen der (1980) Regi on - 1.66 US ICC Mou nta in Pa cifi c -5.06 U.S . IC C So uthern Va ri ou s U. S. ton - mi les El ectrica l Ma chi nery Li e t al . (2 011) Regi on -0.86 Can ad a - 1.96 Aus tral ia Va ri ou s tonne - km Na tura l Re so urces Gr ah aman d Gl ai ste r (2004) Allel asti city 1.72 Least ela sti c en d of ra ng e -7.92 Mo stel as tic end of ran ge Va ri ou s Va ri ous Va riou s **Figure 9**  **- 2**  **: Elasticity of Demand Estimates for Truck Freight Showing Low/High Elasticity Values Based on**  **Various Study Ele ments**  When considering intermodal shift , on e of the most relevant kinds of shipments are those that are competitive between rail and truck 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 truck and rail modes, and they would not be expected to shift modes except under an extreme price change . However, the total amount of freight that could potentially be subject to mode shifting has also not been studied extensively . 

### 9.3.2.2 **Sector -**  **Specific Estimates** Given the limited data available regarding the HD rebound effect, the aggregate approach greatl y s implifies many

of the assumptions associated with calculations of the rebound effect. In**Regulatory Impact Analysis**

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reality , however, responses to changes in fuel efficiency and new vehicle costs will vary significantl y based on the commodities affected. A detailed, sector specifi c approach, would be expected to more accurately reflect changes in the trucking market in response to the standards in this rule . For example, input - output tables could be used to determine the trucking cost share of the total delivered price of a commodity . Using the change in trucking prices described in the aggregate approach, the product - specific demand elasticities could be used to calculate the change in sales and shipments for each product. The change in shipment increases could then be weighted by

the share of the trucking industry total, and then summed to get the total increase in trucking output. A simplifying assumption could then be made that the increase in output results in an increase in VMT. To the best of our knowledge, this ty pe of d etailed data has not yet been collected, therefore we were unable to use this methodology for estimating the rebound effect for this rule. 

### 9.3.2.3 **Econometric Estimates** Similar to the methodology used to estimate the light - duty rebound effect, the HD rebound effect could be modeled econometricall y by estimating truck demand as a function o f economic activity

(e.g., GDP) and different input prices (e.g., vehicle prices, driver wages, and fuel costs per mile). This ty pe of econometric model could be estimated f or either truck VMT or ton - miles as a measure of demand. The resulting elasticity estimates could then be used to determine the change in trucking demand, given the change in fuel cost and truck prices per mile from these standards. One of the challenges associated with an econometric anal ys is is the potential for omitted variable bias, which could either overstate or understate the potential rebound effect if the omitted variable is correlated with the controlled variables. 

### 9.3.2.4 **Other Modeling Approach**  **es** Regulation of the heavy- duty vehicle industry has been studied in more detail in Europe, as the European Commission (EC) has considered allowing longer and heavier trucks for freight transport. Part of the analy sis considered by the EC relies on country- speci fic modeling of changes in the freight sector that would result from changes in regulations. 16 This approach attempts to explicitly

calculate modal shift decisions and impacts on GHG emissions. Although similar ty pes of anal ys is have not been conducted extensively in the U.S., research is currently underway that explores the potential for intermodal shifting in the U.S. For example, Winebrake and Corbett have developed the Geospatial Intermodal Freight Transportation (GIFT) model, which evaluates the pote ntial for GHG emissions reductions based on mode shifting, given existing limitations of infrastructure and other route characteristics in the U.S. 17 This model connects multiple road, rail, and waterway transportation networks and embeds activity- based c alculations in the model. Within this intermodal network, the model assigns various economic, time - of - delivery , energy , and environmental attributes to real - world goods movement routes. The model can then calculate different network optimization scenario s, based on changes in prices and policies. 18 However, more work is needed in this area to determine whether this type of methodology

is appropriate for the purposes of capturing the rebound effect.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

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### 9.3.3 **Estimates of the Rebound Effect**  The aggregate methodolo gy was used by

Cambridge S ys tematics, Inc. (CSI) to show several examples of the magnitude of the rebound effect. 19 Intheir paper commissioned by the NAS in support of the HD report, CSI calculated an effective rebound effect for two different technology cost and fuel savings scenarios associated with an example Class 8 combination tractor . Scenario 1 increased average fuel economy from 5.59 mpg to 6.8 mpg, with an additional cost of $22,930. Scenario 2 increased the average fuel economy to 9.1 mpg, at an incremental cost of $71,630 per vehicle. Both of these scenarios were based on the technologies and targets from a recent Northeast States Center for a Clean Air Future (NESCCAF) and International Council on Clean Transportation (I CCT) report . 20 The CSI examples provided estimates using a range of own price elasticities ( - 0.5 to - 1.5) and cross - price elasticities (0.35 to 0.59) from the literature. For these calculations, CSIassumed 142,706 million miles of truck VMT and 1,852 billion ton - miles were af fected. The truck VMT was based on the Bureau of Transportation Statistics (BTS) highway miles for combination tractors in 2006, and the rail ton miles were based on the 2006 BTS total railroad miles. This assumption may overstate the potential rebound e ffect, since not all highway miles and rail ton miles are in direct competition. However, 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 mod e shifting. For CSI’s calculations, all costs except fuel costs and vehicle costs were taken from the 2008 ATRIstudy . It is not clear from the report how the new vehicle costs were incorporated into the per mile operating costs calculations. For examp le, in both the ATRIreport and the CSI report, assumptions about depreciation , useful life, and the opportunity cost of capital are not explicitly discussed. Based on these two scenarios, CSIfound a rebound effect of 11 - 31% for Scenario 1 and 5 - 16% for Scenario 2 when the fuel savings from reduced rail usage were not taken into account (“First rebound effect”). When the fuel savings from reduced rail usage were included in the calculations, the overall rebound effect was between 9 - 13% for Scenario 1 an d 3 - 15% for Scenario 2 (“Second Rebound Effect”). See Table 9 - 1 . CSIincluded a number of caveats associated with these calculations. Namely , the elasticity estimates derived from the literature are “heavil y reliant on factors including the type of dem and measures analyzed (vehicle - miles of travel, ton - miles, or tons), geography , trip lengths, markets served, and commodities transported .” Furthermore, the CSIexample only focused on Class 8 trucks and did not attempt to quantify the potential rebound e ffect for any other truck classes. Finally , these scenarios were characterized as “sketches” and were not included in the final NAS report. In fact, the NAS report asserted that it is “not possible to provide a confident measure of the rebound effect”, yet concluded that a rebound effect likely exists and that “estimates of fuel savings from regulatory standards will be somewhat misestimated if the rebound effect is not considered.”**Regulatory Impact Analysis**

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**Table 9**  **- 1**  **Range of Rebound Effect Estimates from Cambridge Systematics Aggregate Assessment**  Scenario 1 (6.8 mpg, $22,930) Scenari o 2 (9.1 mpg, $71,630) “First Rebound Effect” (increase in truck VMT resulting from decrease in operating costs) 11 - 31% 5 - 16% “Second Rebound Effect” (net fuel savings when decreases from rail are taken into account) 9 - 13 % 3 - 15% As an alternative, using the econometric approach, NHTSA has estimated the rebound effect in the short - run and long run for single unit (Class 4 - 7) and combination (Class 8) trucks . As shown in Table 9 - 2 , the estimates for the long - run rebound effect are larger than the estimat es in the short run, which is consistent with the theory that shippers have more flexibility to change their behavior (e.g., restructure contracts or logistics) when they are given more time. In addition, the estimates derived from the national data also showed larger rebound effects compared to the state data. 21 One possible explanation for the difference in the estimates is that the national rebound estimates are capturing some of the impacts of changes in economic activity. Historically , large increa ses in fuel prices are highl y correlated with economic downturns, and there may not be enough variation in the national data to differentiate the impact of fuel price changes from changes in economic activity . In contrast, some states may see an increase in output when energy prices increase (e.g., large oil producing states such as Texas and Alaska), therefore the state data may

be more accurately

isolating the impact of fuel price changes from that of changes in economic activity . It is important to not e that these estimates of the rebound effect reflect the partial effects of fuel prices and fuel economy changes on truck usage, but not the effect of truck prices. Therefore, these estimates do not take into account the partially offsetting impacts of in creases in new vehicle costs that are likel y to result from regulations requiring higher fuel economy. For example, if the increase in new vehicle prices associated with increased fuel economy offset half of the resulting savings in fuel costs, then the e ffective rebound effect would be half of the value shown in Table 9 - 2.

**Table 9**  **- 2**  **Range of Rebound Effect Estimates from NHTSA Econometric Analysis**  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% As discussed thro ughout this section, there are multiple methodologies for quantifying the rebound effect, and these different methodologies produce a large range of potential values of the rebound effect. However, for the purposes of quantifying the rebound effect for th is program , we have used a rebound effect with respect to changes in fuel costs per mile on the***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 17 lower range of the long - run estimates. Given the fact that the long - run state econometric estimates are generally

more consistent with the aggregate estimates, for this program we have chosen a rebound effect for vocational vehicles of 15% that is within the range of estimates from both methodologies. Similarly , we have chosen a rebound effect for combination tractors of 5%. To date, no estimates of the HD p ickup truck and van ( Class 2b and 3) rebound effect have been cited in the literature. Since these vehicles are used for very different purposes than heav y- duty vehicles, it does not necessarily seem appropriate to apply one of the heavy- duty estimates to the HD pickup trucks and vans . These vehicles are more similar in use to large light - duty vehicles, so for the purposes of our analy sis, we have chosen to apply the light - duty rebound effect of 10% used in the recent final rule establishing fuel economy and GHG standards for MYs 2012 - 2016 light - duty vehicles to this class of vehicles. 

### 9.3.4 **Application of the Rebound Effect**  **to VMT Estimates**  It should be noted that the NHTSA econometric analy sis attempts to isolate the rebound effect with respect to changes in the fuel cost per mile driven. As described previously , the rebound effect should be a measure of the change in VMT with respect to the change in overall operating costs. Therefore, NHTSA’s rebound estimates with respect to fuel costs per mile must be “scaled” to appl y to total op erating costs. For example, we assumed the elasticity of C lass 8 truck use with respect to fuel cost per mile driven is - 0.05 (which corresponds to a 5% fuel economy rebound effect), and that fuel costs average 4 5 % of total tr uck operating costs ; therefore , the elasticity

of truck use with respect to total operating costs is - 0.05/0.4 5 = - 0.11 . This calculation would correspond to an “overall” rebound effect value

– that is, a rebound effect with respect to total truck operating costs

– of - 11%. In other words, cutting fuel costs per mile by 10% would correspond to only a 4. 5 % decline in total truck operating costs, so the elasticity of truck use with respect to total operating costs would have to be 2.3 times (100%/4 5 %) larg er than the elasticity

of truck use with respect to fuel cost alone, in order to produce the same response in truck VMT (4 .5 %* - 0.11 = 10%* - 0.05). We conducted similar calculations for 2b/3 trucks assuming fuel costs are on average 2 1 % of total operating costs, and for vocational vehicles assuming fuel costs are on average 2 5 % of total operating costs. Furthermore, due to timing constraints we assumed an “average” incremental technology cost including indirect costs, as shown in Table 9 - 3 , based on the estimates developed for the NPRM, which differ slightly from the values included in RIA Chapter 7 . **Table 9**  **- 3**  **: Technology Costs Used to Determ ine the Rebound Effect of Each Alternative**  Vehicle Category Alternative 2 Alternative 3 (Preferred Case) Alternative 4 Alternative 5 Combination Tractors $3,909 $5,901 $10,048 $17,040**Regulatory Impact Analysis**

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HD Pickup Trucks and Vans $918 $1,411 $2,283 $6,783 Vocational Vehicles $253 $359 $1,959 $13,692

For the purposes of this rulemaking , we made several additional simplifying assumptions when appl ying the overall rebound ef fect to each class of truck. For example , we assumed that per mile vehicle costs were based on the new vehicle cost (e.g., $100,000 for the reference case Class 8 combination tractor) divided b y the total lifetime number of expected vehicle miles (e.g., 1 .26 million miles for a Class 8 combination tractor , 288,000 miles for 2b/3 trucks, and 334,000 miles for vocational vehicles ). We recognize that this calculation implicitly assumes that truck depreciation is strictly

a function of usage , and that it does not take into account the opportunity cost 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. F urthermore, this assumption implies that the new vehicle buyer and the used vehicle buye r value fuel economy in the same way. Although anecdotal evidence suggests fuel efficiency

may not be taken into account in the used vehicle market , this phenomenon is not well understood and therefore not taken into account in this analy sis . Other simplifying assumptions include the use of an average cost rather than a marginal cost . Some shippers may use a marginal cost to determine whether to increase their fuel us age, however we do not have any data on when shippers 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 methodology for calculating the marginal cost . 22 In the costs and benefits summarized in Chapter 9.5 , we have not 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. How ever, we have provided CSI ’s example calculations in Table 9 - 1. The rebound effect values used in the cost and benefit anal ys is fall within the range of the “second rebound effect” identified in the CSIanaly sis , which does account for offsetting savings from reduced rail shipping . In addition, we have not attempted to capture how current market failures might impact the rebound effect. The direction and magnitude of the rebound effect in the HD truck market are expected to vary

depending on the existen ce and ty pes of market failures affecting the fuel economy of the trucking fleet. If firms are already accuratel y 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 factors such as lack of reliable information (see p reamble Section VIII.A. for further discussion), it is more likely that***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 19 truck VMT would increase. If firms recognize their lower net costs as a result of these regulations and pass those costs alo ng to their customers, then the rebound effect would increase truck VMT. This response assumes that trucking rates include both truck purchase costs and fuel costs, and that the truck purchase costs included in the rates spread those costs over the full e xpected 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 proportionall y smaller. As discussed in more detail in p reamble Section VIII.A, if there are market failures such as split incentives, estimating the rebound effect may depend on the nature of the failures. For example, if the original purchaser cannot f ully recoup the higher upfront costs through fuel savings before selling the vehicle nor pass those costs onto the resale buyer, the firm would be expected to raise shipping rates. A firm purchasing the truck second - hand might lower shipping rates if the firm recognizes the cost savings after operating the vehicle, leading to an increase in VMT. Similarly, if there are split incentives and the vehicle buyer is n o t 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. If all of these scenarios occur in the marketplace, the ir consequences for the rebound effect will depend o n the extent and magnitude of their relative effects, which are also likel y to vary across truck classes (for instance, split incentives may be a much larger problem for C lass 7 and 8 combination tractor than they are for heav y- duty

pickup trucks). 

### 9.4 **Mone tized CO**  **2** 

### Impacts We assigned a dollar value to reductions in carbon dioxide (CO 2 ) emissions using recent estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emi ssions in a given year. Itis intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecos ys tem services due to climate change. The SCC estimates use d in this analy sis were developed through an interagency process that included EPA, DOT/NHTSA, and other executive branch entities, and concluded in February 2010. We first used these SCC estimates in the benefits anal ys is for the final joint EPA/DOT Rulemaking to establish Light - Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; see th at rule’s preamble for discussion about application of the SCC (75 FR 25324; 5/7/10). The SCC Technical Support Document (SCC TSD ) provides a complete discussion of the methods used to develop these SCC estimates. 23 The interagency

group selected four SCC values for use in regulatory anal yses, which we have applied in this anal ysis: $5, $22, $36, and $6 7 per metric ton of CO 2 emis sions in 2010, in 2009 dollars. 24 , 25 The first three values are based on the average SCC from three integrated assessment models, at discount rates of 5, 3, and 2.5 percent, respectively . SCCs at several discount rates are included because the literature shows that the SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to**Regulatory Impact Analysis**

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use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all three models at a 3 percent discount rate. It is included to represent higher- than - expected impacts from temperature change further out in the tails of the SCC distribution. L ow probability , high impact events are incorporated into all of the SCC values through explicit consid eration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity . Treating climate sensitivity probabilistically

results in more high temperature outcomes, which in turn lead to higher projections of damages. The SCC increases over time because future emissions are expected to produce larger incremental damages as phy sical and economic sy stems become more stressed in response to greater climatic change. Note that the interagency group estimated the growth rate of the SCC directly using the three integrated assessment models rather than assuming a constant annual growth rate. This helps to ensure that the estimates are internally consistent with other modeling assumptions. Table VI II.G.1 - 1 presents the SCC estimates used in this analy sis. When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analy st faces a number of serious challenges. A recent report from the National Academies of 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 changes in climate on the phy sical and biological environment, and (4) the translation of these environmental impacts into economic damages. 26 As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science , economics, and ethics and should be viewed as provisional. The interagency

group noted a number of limitations to the SCC analysis, including the incomplete way

in which the integrated assessment models capture catastrophic and non catastrophic impacts , their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of research linking climate impacts to economic damages make s the interagency

modeling exercise even more difficult. The interagency group hopes that over time researchers and modelers will work to fill these gaps and that the SCC estimates used for regulatory

anal ys is by

the Federal government will continue to ev olve with improvements in modeling. Additional details on these limitations are discussed in the SCC TSD. In light of these limitations, the interagency group has committed to updating the current estimates as the science and economic understanding of clim ate change and its impacts on society improves over time. Specificall y, the interagency group has set a preliminary goal of revisiting the SCC values in the next few years or at such time as substantially updated models become available, and to continue to support research in this area. Applying the global SCC estimates, shown in Table 9 - 4 , to the estimated reductions in domestic CO 2 emissions for the final program, we estimate the dollar value of the climate related benefits for each anal ys is year. For internal consistency , the annual benefits are discounted back***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 21 to net present value terms using the same discount rate as each SCC estimate (i.e. 5%, 3%, and 2.5%) rather than 3% and 7%. A The SCC estimates are presented in and the associated CO 2 benefit estimates for each calendar year are shown in Table 9 - 5 .

**Table 9**  **- 4**  **: Social Cost of CO**  **2 , 201**  **2 –**  **2050 a**  **(in 200 9**  **dollars per Metric Ton**  **) Year**  **Discount Rate and Statistic**

**5% Average 3% Average**  **2.5% Average 3%**  **95 th**  **percentile 201**  **2** $ 5 . 2 8 4 $2 3 . 06 $3 7 . 53 $ 70 . 14 **2015** $5. 93 $24. 5 8 $39. 57 $74. 0 3 **2020** $ 7.01 $2 7 . 10 $42. 9 8 $8 3 . 17 **2025** $8. 5 3 $30. 43 $4 7 . 2 8 $9 3 . 11 **2030** $ 10 . 0 5 $33. 75 $51. 58 $10 3 . 06 **2035** $11. 57 $3 7 . 08 $55. 88 $11 3 . 00 **2040** $1 3 . 0 9 $40. 4 0 $ 60 . 19 $12 2 . 95 **2045** $14. 63 $4 3 . 3 4 $ 63. 59 $13 1 . 66 **2050** $16. 18 $4 6 . 27 $66. 99 $1 40 . 37

a The SCC values are dollar - year and emissions - year specific . **Table 9**  **- 5**  **: Upstream** **and Downstream CO**  **2 Benefits for the Given SCC Value, Calendar Year Analysis**  **a (M**  **illions of 2009 dollars)**  YEAR 5%

(AVERAGE SCC = $5 IN 2012) 3 %

(AVERAGE SCC = $23 IN 2012) 2.5 %

(AVERAGE SCC = $38 IN 2012) 3%

(95 TH PERCENTILE = $70 IN 2012) 2012 $0 $0 $0 $0 2013 $0 $0 $0 $0 2014 $33 $141 $227 $429 2015 $63 $262 $422 $800 2016 $93 $379 $608 $1,157 2017 $134 $539 $862 $1,648 2018 $179 $709 $1,131 $2,172 2019 $221 $867 $1,379 $2,659 2020 $264 $1,021 $1,619 $3,133 202 1 $310 $1,177 $1,858 $3,609 2022 $357 $1,331 $2,093 $4,080 2023 $404 $1,484 $2,324 $4,547 2024 $451 $1,634 $2,548 $5,002 2025 $499 $1,781 $2,767 $5,450 2026 $546 $1,924 $2,978 $5,884 2027 $593 $2,061 $3,180 $6,302 2028 $640 $2,197 $3,378 $6,712 2029 $686 $2,3 31 $3,573 $7,119

A It is possible that other benefits or costs of proposed regulations unrelated to CO 2 emissions will be discounted at rates that differ from those used to develop the SCC estimates.**Regulatory Impact Analysis**

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2030 $734 $2,467 $3,770 $7,532 2031 $780 $2,592 $3,949 $7,910 2032 $823 $2,710 $4,117 $8,268 2033 $866 $2,826 $4,282 $8,619 2034 $911 $2,944 $4,449 $8,975 2035 $955 $3,061 $4,614 $9,330 2036 $1,000 $3,179 $4,779 $9,685 2037 $1,045 $3,296 $4,944 $10,040 2038 $1,091 $3,414 $5,109 $10,395 2039 $1,136 $3,532 $5,273 $10,751 2040 $1,182 $3,650 $5,437 $11,108 2041 $1,229 $3,759 $5,583 $11,437 2042 $1,276 $3,870 $5,729 $11,770 2043 $1,324 $3,983 $5,878 $12,107 2044 $1,373 $4,097 $6,029 $12,452 2045 $1,422 $4,212 $6,180 $12,796 2046 $1,473 $4,328 $6,333 $13,145 2047 $1,524 $4,446 $6,488 $13,500 2048 $1,575 $4,566 $6,644 $ 13,858 2049 $1,628 $4,687 $6,802 $14,220 2050 $1,682 $4,810 $6,963 $14,590 NPVb $9,045 $46,070 $78,037 $140,432 a The SCC values are dollar - ye ar and emissions - year specific . b Note that net present value of reduced GHG emissions is calculated differently than other benefits. The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. Refer to SCC TSD for more detail. We also conducted a separate anal ys is of the CO 2 benefits over the model year lifetimes of the 201 4 through 201 8 model year vehicles. In contrast to the calendar year anal ys is, the model year lifetime analy sis shows the lifetime impacts of the program on each of these MY fleets over the course of its lifetime. Full details of the inputs to this analy sis can be found in RIA chapter 5. The CO 2 benefits of the full l ife of each of the five model years from 2014 through 2018 are shown in Table 9 - 6 through Table 9 - 9 for each of the four different social cost of carbon values. The CO 2 benefits are shown for each ye ar in the model year life and in net present value. The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net pres ent value of SCC for internal consistency . **Table 9**  **- 6**  **: Upstr**  **eam and Downstream** **CO 2**  **Benefits for the 5% (Average**  **SCC )**  **Value, Model Year Analysis a**  **(M illions of 2009**  **dollars)** YEAR MY 2014 MY 2015 MY 2016 MY 2017 MY 2018 SUM 2012 $0 $0 $0 $0 $0 $0 2013 $0 $0 $0 $0 $0 $0 2014 $34 $0 $0 $0 $0 $34 2015 $32 $31 $0 $0 $0 $63 2016 $30 $29 $31 $0 $0 $91 2017 $28 $28 $30 $47 $0 $133 2018 $26 $26 $28 $44 $54 $178 2019 $24 $24 $26 $41 $51 $166***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 23 2020 $22 $22 $24 $39 $48 $155 2021 $20 $20 $23 $36 $46 $145 2022 $18 $19 $21 $33 $43 $134 2023 $17 $17 $19 $31 $40 $123 2024 $15 $15 $17 $28 $37 $112 2025 $13 $14 $16 $26 $33 $102 2026 $12 $12 $14 $23 $31 $92 2027 $11 $11 $13 $21 $28 $83 2028 $9 $10 $11 $19 $25 $74 2029 $8 $9 $10 $17 $23 $66 2030 $7 $7 $9 $15 $20 $57 2031 $6 $6 $8 $13 $18 $51 2032 $5 $6 $7 $11 $15 $44 2033 $4 $5 $6 $10 $14 $39 2034 $4 $4 $5 $9 $12 $33 2035 $3 $4 $4 $7 $10 $29 2036 $3 $3 $4 $6 $9 $25 2037 $2 $3 $3 $5 $8 $21 2038 $2 $2 $3 $5 $7 $18 2039 $2 $2 $ 2 $4 $6 $15 2040 $1 $1 $2 $3 $5 $13 2041 $1 $1 $2 $3 $4 $11 2042 $1 $1 $1 $2 $3 $9 2043 $1 $1 $1 $2 $3 $8 2044 $1 $1 $1 $2 $2 $7 2045 $0 $1 $1 $1 $2 $5 2046 $0 $0 $1 $1 $2 $4 2047 $0 $0 $0 $1 $2 $3 2048 $0 $0 $0 $0 $2 $2 2049 $0 $0 $0 $0 $0 $0 2050 $0 $0 $0 $0 $0 $0 NPV, 5 % $200 $20 0 $200 $300 $300 $1,200

a The SCC values are dollar - ye ar and emissions - year specific . **Table 9**  **- 7**  **: Upstream** **and Downstream CO**  **2 Benefits for the**  **3% (Average SCC) SCC**  **Value, Model Year Analysis**  **a (Millions of 2009**  **dollars)** YEAR MY 2014 MY 2015 MY 2016 MY 2017 MY 2018 SUM 2012 $0 $0 $ 0 $0 $0 $0 2013 $0 $0 $0 $0 $0 $0 2014 $143 $0 $0 $0 $0 $143 2015 $131 $129 $0 $0 $0 $261 2016 $123 $120 $128 $0 $0 $371 2017 $114 $112 $12 0 $188 $0 $534 2018 $104 $104 $111 $174 $215 $708 2019 $94 $94 $102 $161 $199 $651 2020 $85 $86 $94 $149 $186 $600 2021 $76 $78 $86 $138 $173 $551**Regulatory Impact Analysis**

9 - 24 2022 $69 $70 $78 $125 $160 $501 2023 $61 $62 $70 $113 $145 $452 2024 $54 $56 $63 $102 $132 $406 2025 $48 $49 $56 $92 $119 $3 64 2026 $42 $44 $50 $82 $108 $325 2027 $37 $38 $44 $73 $97 $289 2028 $32 $33 $39 $65 $86 $255 2029 $28 $29 $34 $57 $77 $225 2030 $23 $25 $29 $49 $66 $193 2031 $20 $21 $26 $43 $58 $168 2032 $17 $18 $22 $37 $51 $146 2033 $15 $16 $19 $32 $44 $126 2034 $12 $13 $16 $28 $38 $108 2035 $10 $11 $14 $24 $33 $92 2036 $9 $10 $12 $20 $28 $78 2037 $7 $8 $10 $17 $24 $66 2038 $6 $7 $8 $15 $20 $56 2039 $5 $6 $7 $12 $17 $47 2 040 $4 $5 $6 $10 $15 $40 2041 $3 $4 $5 $9 $12 $33 2042 $3 $3 $4 $7 $10 $28 2043 $2 $3 $3 $6 $9 $23 2044 $3 $2 $3 $5 $7 $20 2045 $0 $3 $2 $4 $6 $15 2046 $0 $0 $3 $4 $5 $12 2047 $0 $0 $0 $4 $4 $9 2048 $0 $0 $0 $0 $5 $5 2049 $0 $0 $0 $0 $0 $0 2050 $0 $0 $0 $0 $0 $0 NPV, 3% $1,100 $900 $900 $1,300 $1,500 $5,700

a The SCC values are dollar - ye ar and emissions - year specific . **Table 9**  **- 8**  **: Upstream** **and Downstream CO**  **2 Benefits for the from** **2.5% (Average SCC) SCC**  **Value, Model Year Analysis**  **a (Millions of 200**  **9 dollars)**  YEAR MY 2014 MY 2015 MY 2016 MY 2017 MY 2018 SUM 2012 $0 $0 $0 $0 $0 $0 2013 $0 $0 $0 $0 $0 $0 2014 $230 $0 $0 $0 $0 $230 2015 $212 $208 $0 $0 $0 $420 2016 $197 $193 $205 $0 $0 $595 2017 $183 $180 $191 $300 $0 $853 2018 $166 $165 $177 $278 $343 $1,129 2019 $150 $150 $163 $256 $317 $1,035 2020 $135 $136 $149 $237 $294 $951 2021 $1 21 $123 $136 $217 $273 $870 2022 $108 $109 $123 $197 $251 $787 2023 $96 $98 $109 $177 $228 $708 2024 $84 $87 $98 $159 $206 $634 2025 $74 $77 $88 $142 $185 $566***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 25 2026 $65 $68 $78 $127 $167 $504 2027 $57 $59 $69 $113 $149 $446 2028 $49 $52 $60 $100 $132 $393 2029 $43 $45 $53 $87 $117 $345 2030 $36 $38 $45 $75 $101 $295 2031 $31 $33 $39 $65 $89 $257 2032 $26 $28 $34 $57 $77 $222 2033 $22 $24 $29 $49 $67 $190 2034 $19 $20 $25 $42 $58 $163 2035 $16 $17 $21 $36 $50 $139 2036 $13 $14 $18 $30 $43 $118 2037 $11 $12 $15 $26 $36 $100 2038 $9 $10 $13 $ 22 $31 $84 2039 $8 $8 $11 $18 $26 $71 2040 $6 $7 $9 $15 $22 $59 2041 $5 $6 $7 $13 $18 $49 2042 $4 $5 $6 $11 $15 $41 2043 $3 $4 $5 $9 $13 $ 34 2044 $4 $3 $4 $7 $11 $30 2045 $0 $4 $3 $6 $9 $23 2046 $0 $0 $4 $5 $8 $17 2047 $0 $0 $0 $6 $6 $13 2048 $0 $0 $0 $0 $8 $8 2049 $0 $0 $0 $0 $0 $0 2050 $0 $0 $0 $0 $0 $0 NPV, 2.5 % $1,800 $1,600 $1,500 $2,100 $2,400 $9,400

a The SCC values are dollar - ye ar and emissions - year specific . **Table 9**  **- 9**  **: Up stream** **and Downstream CO**  **2 Benefits for the 3%**  **(95 th**  **Percentile) SCC Value, Model Year**  **Anal ysis**  **a (Millions of 2009**  **dollars)** YEAR MY 2014 MY 2015 MY 2016 MY 2017 MY 2018 SUM 2012 $0 $0 $0 $0 $0 $0 2013 $0 $0 $0 $0 $0 $0 2014 $435 $0 $0 $0 $0 $435 2015 $401 $394 $0 $0 $0 $795 2016 $375 $368 $391 $0 $0 $1,133 2017 $349 $343 $366 $574 $0 $1,632 2018 $320 $318 $340 $533 $659 $2,168 2019 $288 $290 $314 $493 $611 $1,996 2020 $261 $263 $289 $458 $570 $1,841 2021 $235 $239 $263 $422 $531 $1,690 2022 $210 $213 $239 $383 $489 $1,535 2023 $187 $191 $214 $347 $445 $1,385 2024 $165 $171 $192 $311 $405 $1,244 2025 $146 $151 $173 $280 $365 $1,115 2026 $128 $134 $153 $251 $329 $995**Regulatory Impact Analysis**

9 - 26

2027 $112 $117 $136 $223 $296 $884 2028 $98 $102 $119 $198 $263 $781 2029 $85 $89 $105 $174 $234 $687 2030 $72 $76 $90 $150 $202 $589 2 031 $62 $65 $78 $131 $178 $514 2032 $53 $56 $67 $114 $156 $445 2033 $44 $48 $58 $98 $135 $383 2034 $38 $41 $50 $84 $117 $329 2035 $32 $34 $42 $72 $101 $281 2036 $27 $29 $36 $62 $86 $239 2037 $22 $24 $30 $52 $73 $202 2038 $19 $20 $25 $44 $62 $171 2039 $15 $17 $21 $37 $53 $144 2040 $13 $14 $18 $31 $44 $120 2041 $10 $12 $15 $26 $37 $101 2042 $9 $10 $12 $22 $32 $84 2043 $7 $8 $10 $18 $27 $70 2044 $8 $6 $9 $15 $22 $61 2045 $0 $8 $7 $13 $19 $47 2046 $0 $0 $9 $11 $16 $35 2047 $0 $0 $0 $13 $13 $26 2048 $0 $0 $0 $0 $16 $16 2049 $0 $0 $0 $0 $0 $0 2050 $0 $ 0 $0 $0 $0 $0 NPV, 3% $3,300 $2,900 $2,800 $4,000 $4,500 $17,000 a The SCC values are dollar- ye ar and emissions - year specific . 

### 9.5 **Other Economic Impacts 9.5.1**  **Noise, Congestion**  **, and Accidents** Section 9.2 discus ses 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 th at are already heavily traveled during peak periods. These added delay s impose higher costs 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. Increased vehicle use due to a positive rebound effect may also increase the costs associated with traffic accidents . Drivers may take account of the potential costs they (and their passengers) face from the possibility of being involved in an accident when they decide to make additional trips. However, they probabl y do not consider all of the potential costs they imp ose on occupants of other vehicles and on pedestrians when accidents occur, so any increase in these “external” accident costs must be considered as another cost of additional rebound - effect driving.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 27 Like increased delay

costs, any

increase in external ac cident costs caused by added driving is likely to depend on the traffic conditions under which it takes place, since accidents are more frequent in heavier traffic (although their severity may be reduced b y the slower speeds at which heavier traffic ty

pically moves). Finally , added vehicle use associated with a positive rebound effect may also increase traffic noise. Noise generated b y vehicles causes inconvenience, irritation, and potentially even discomfort to occupants of other vehicles, to pedestrians and other b ys tanders, and to residents or occupants of surrounding property. Because these effects are unlikely to be taken into account by the drivers whose vehicles contribute to traffic noise, they represent additional externalities associated with motor vehicle use. Although there is considerable uncertainty in measuring their value, any increase in the economic costs of traffic noise resulting from added vehicle use should be included together with other increased external costs from the rebound effe ct. 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 caus ed by added driving due to the rebound effect. 27 The FHWA estimates are intended to measure the increases in costs from added congestion, property damages and injuries in traffic accidents, and noise levels caused b y various classes of trucks that are born e 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 GHG final rule . The agencies continue to find them appropriate for this anal ysi s after reviewing the procedures used b y 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 possible. FHWA ’s congestion cost estimate s focus on freeway s becau se 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 increase, 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 congestions costs associated with increased truck use, and thus l ead to a conservative estimat e 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 cost s and 85 percent of the FHWA estimate for single unit truck cost s to reflect the make - up of this segment. The low, mid, and high cost estimates from FHWA updated to 200 9 dollars are included in Table 9 - 3 .**Regulatory Impact Analysis**

9 - 28 **Table 9**  **- 10**  **Low -**  **Mid -**  **High Cost Estimates ( 2009**  **$/mile)**

**Noise High**  **Middle Low**  Pickup Truck, Van $0.002 $0.001 $0.000 Vocational Vehicle $0.02 5 $0.009 $0.003 Combinati on Tractor $0.052 $0.020 $0.006 **Accidents High**  **Middle Low**  Pickup Truck, Van $0.083 $0.027 $0.014 Vocational Vehicle $0.059 $0.019 $0.010 Combination Tractor $0.070 $0.022 $0.010 **Congestion High**  **Middle Low**  Pickup Truck, Van $0.145 $0.049 $0.013 V ocational Vehicle $0.327 $0.111 $0.029 Combination Tractor $0.319 $0.108 $0.029 The agencies are using FHWA’s “Middle” estimates for marginal congestion, accident, and noise costs caused by increased travel from trucks . 28 This approach is consistent with the current methodology

used in the Light - Duty GHG rulemaking anal ys is. These costs are multiplied 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. EPA and NHTSA use the aggregate per mile costs, as shown in Table 9 - 4 in 2009 dollars . Table 9 - 5 presents total monetized estimates of external costs associated with noise, accidents, and congestion.

**Table 9**  **- 11**  **Combined Costs of Congestion, Accidents and Noise (**  **2009 $**  **per m ile**  **)**

Pickup Truck, Van $0. 0 77 Vocational Vehicle $0. 140 Combination Tractor $0. 150***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 29 **Table 9**  **- 12**  **: Annual**  **External Costs Associated with the Heavy**  **- Duty Vehicle**  **Program (M**  **illions of 2009**  **dollars)** YEAR Class 2b&3 Vocational Com bination Total 2012 $0 $0 $0 $0 2013 $0 $0 $0 $0 2014 $8 $21 $18 $46 2015 $15 $38 $31 $84 2016 $22 $55 $43 $120 2017 $29 $71 $54 $153 2018 $36 $85 $64 $186 2019 $43 $99 $74 $217 2020 $51 $112 $83 $246 2021 $58 $123 $91 $272 2022 $65 $134 $98 $297 2023 $71 $144 $105 $320 2024 $77 $152 $111 $341 2025 $83 $161 $117 $361 2026 $88 $1 68 $122 $378 2027 $93 $175 $127 $394 2028 $97 $182 $131 $409 2029 $101 $188 $134 $424 2030 $105 $195 $138 $437 2031 $108 $203 $142 $453 2032 $111 $210 $145 $466 2033 $114 $217 $147 $478 2034 $117 $223 $150 $490 2035 $119 $229 $153 $501 2036 $122 $235 $156 $512 2037 $124 $241 $158 $523 2038 $126 $246 $161 $533 2039 $128 $251 $163 $542 2040 $130 $256 $166 $551 2041 $132 $260 $168 $561 2042 $134 $265 $171 $569 2043 $136 $269 $173 $578 2044 $138 $274 $176 $587 2045 $140 $278 $178 $596 2046 $ 141 $282 $181 $604 2047 $143 $286 $183 $612 2048 $145 $290 $186 $621 2049 $146 $294 $188 $629 2050 $148 $298 $191 $638 NPV, 3% $1,818 $3,620 $2,492 $7,929 NPV, 7% $832 $1,680 $1,184 $3,695**Regulatory Impact Analysis**

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### 9.5.2 **Savings due to Reduced Refueling Time** Reducing the fuel con sumption 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 e the frequency

with which drivers ty

picall y refuel their vehicles , by 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 redu cing 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 p reamble VIII.A and RIA Section 9.1: does the apparent existence of these savings reflect fai lures in the market for fuel economy, or does it reflect costs that are not addressed in this analysis? The response to these questions could vary across truck segment. See those sections for further anal ys is of this question. 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 will spen d filling its fuel tank; this reduced time could result e ither from fewer refueling events, if new trucks’ fuel tank s stay the same size, or from less time spent filling the tank during each refueling stop , if new trucks’ fuel s tank are made proportionatel y smaller. As discussed in Section 9.2 in this RIA , the average number of miles each type of truck is driven annuall y will increase under the regulation, as truck operators respond to lower fuel costs (the “rebound effect”). The estimates of refueling time with the regulation in effect allow for this increase in truck use. However, EPA’s 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 small relative to the value 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 will save each ye ar as a consequence of pumping less fuel into the vehicle ’s tank . The calculation does not include any reduction in time spent searching for a fuel ing statio n or other time spent at the station; it is assumed that time savings occur onl y when truck operators are actually refueling their vehicles . The calculation uses the reduced number of gallons consumed by truck type and divides that value b y the fuel di spense rate (shown in Table 9 - 13 ) to determine the number of hours saved in a given ye ar. The calculation then applies DOT - recommended values of travel time savings to convert the resulting time savings to their economic value. The DOT - recommended value of travel time per vehicle - hour for truck drivers is $22. 36 in 2009 $ (converted from $ 18.10 in 2000$). 29 The inputs used in the anal ys is are included Table 9 - 13 . The savings associated with reduced refueling time for trucks of each ty pe throughout it li f

etime are shown in Table***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 31 9 - 14 . The aggregate savings associated with reduced refueling time are shown in Table 9 - 15 for vehicles sold in 2014 through 2050 . **Table 9**  **- 13**  **: Inputs to Calculate Refueling Time Saving s**  HD PI CKUP TRUCK AND VAN VOCATIONAL VEHICLE TRACTOR Fuel Dispensing Rate (gallon/minute) 30 10 10 20

**Table 9**  **- 14**  **: Lifetim**  **e Refueling Savings**  **for a 2018MY Truck of Each Type (**  **2009 $)** 

PI CKUP TRUCKS AND VANS VOCATIONAL

VEHICLES TRACTORS 3% Discount Rate $ 31 $ 34 $ 341 7% Discount Rate $ 19 $ 22 $ 223 The aggr egate savings of the vehicles sold in 2014 through 2050 are listed in Table 9 - 15 . **Table 9**  **- 15**  **Annual Refueling Savings (dollar values in Millions of**  **2009 dollars)**  CLASS 2B&3 VOCATIONAL COMBINATION Year Hours Saved Savings Hours Saved Savings Hours Saved Savings Total Savings 2012 0 $0 0 $0 0 $0 $0 2013 0 $0 0 $0 0 $0 $0 2014 7,876 $0 63,732 $1 359,414 $8 $10 2015 20,994 $0 116,078 $3 638,836 $14 $17 2016 57,718 $1 168,357 $4 874,937 $20 $25 2017 118,936 $3 278,409 $6 1,195,891 $27 $36 2018 231,532 $5 382,156 $9 1,510,573 $34 $47 2020 467,693 $ 10 568,156 $13 2,065,797 $46 $69**Regulatory Impact Analysis**

9 - 32

2030 1,460,256 $33 1,154,155 $26 3,708,140 $83 $141

2040 1,939,991 $43 1,567,826 $ 35 4,495,207 $101 $179

2050 2,239,008 $50 1,845,231 $41 5,190,689 $116 $207 NPV, 3% $541 $468 $1,467 $2,476 NPV, 7% $231 $210 $685 $1,126

**9.6 The Effect**  **of Safe ty 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 and CO 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 will 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 provided, 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. 

### 9.6.1 **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***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 33 will examine the potential impact on MD/HD vehicle weights for 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 

### 9.6.1.1 **FMVSS 119, 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 F R 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 propo rtionately 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 targeted F, G, H, J and L load range tires. 31 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. 

### 9.6.1.2 **FMVSS No. 121, Airbrake Systems Stopping Distance** FMVSS No. 121 contains performance and equipment requirements for braking s ys tems on v ehicles 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 August 1, 2013 (MY 2013). NHTSA determined that there are several brake s y stems 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 manuf acturer (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,**Regulatory Impact Analysis**

9 - 34

then, the total w eight could increase b y 212 pounds or could decrease b y 134 pounds compared to 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. 

### 9.6.1.3 **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 pre liminary results from the agency ’s cost/weight teardown studies of motor coach seats, 32 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 the 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. 

### 9.6.2 **Electronic Stability Control 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 individual 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. FMVSS No. 105, Hydraulic and electric brake s ys tems, requires 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 p ressure sensor along with a brake solenoid valve. According to data provided by 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

– 45.5) pounds. 

### 9.6.3 **Summary –**  **Overview of Anticipated Weight Increases Error! Reference source not found.**  **Error! Reference source not found.** Table 9 - 16 s ummarizes estimates made by NHTSA regarding the weight added b y the above discussed standards or likel y 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***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 35 increase motor coach vehicle weight b y 171 - 279 pounds and will increase other heavy- duty truck weights b y 10 pounds. **Table 9**  **- 16**  **: Wei**  **ght 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 (?) 0 (?) 208 Motor coaches only 16 1 - 269 73 - 122 MD/HD Vehicle Electronic Stability Control Sy stems 10 4.5 Total Motor coaches 171 - 279 77.5 - 126.5 Total All other MD/HD vehicles 10 4.5



### 9.6.4 **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 2012 - 2016 final rule 33 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 would 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 m edium - duty trucks, and even larger difference between light - 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 currently on - going to address this issue for the light - duty vehicle 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 rulem aking 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**Regulatory Impact Analysis**

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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 added structural integrity requirements increase weight of school buses, and thus decrease fuel economy . They asked that if there are safety and fuel eco nomy 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. 

### 9.7 **Petroleum ,**  **Energy and National**  **Security impact 9.7.1**  

### Security **Impacts** Th e HD National Program is designed to reduce fuel consumption and GHG emissions in medium - and heav y- duty (HD) vehicles, which will result in improved fuel efficiency and , in turn, 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 is a measure of improved U.S. energy security. This section summarizes the agencies’ estimates of U.S. oil import reductions and energy security benefits of the final HD National Program . Additional discussion of this issue can be found in Chapter VIII of the preambl e. The agencies recognize that potential national and energy security risks exist due to the possibility

of tension over oil supplies. Much of the world’s oil and gas supplies are located in countries facing social, economic, and demographic challenges , thus making them even more vulnerable to potential local instability . For example, in 2010 just over 40 percent of world oil supply came from OPEC nations, and this share is not expected to decline in the AEO 2011 projections through 203 0. Approximately 28 percent of global suppl y is from Persian Gulf countries alone. As another measure of concentration, of the 137 countries/principalities that export either crude oil or refined petroleum product, the top 12 have recently accounted for over 55 percent of exports. B Eight of these countries are members of OPEC, and a 9 th is Russia. C Ina mar ket where even a 1 - 2 percent suppl y loss raises prices noticeably , and where a 10 percent supply loss could lead to a significant price shock, this regional concentration is of concern. ” Historically , the countries of the Middl e East have been the source of eight of the ten major world oil disruptions 34 , with the 9 th originating in Venezuela, an OPEC member. Because of U.S. dependence on oil, the military could be called on to prote ct energy resources through such measures as securing shipping lanes from foreign oil fields. To maintain such military

effectiveness and flexibility, the Department of Defense identified in the Quadrennial Defense Review that it is “increasing its use of renewable energy supplies and

B Based on data from the CI A, combining various recent years, https://www.cia.gov/library/publications/the - wo rld - factbook/rankorder/2176rank.html . C The other three are Norway, Canada, and the EU, an exporter of product.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 37 reducing energy

demand to improve operational effectiveness, reduce greenhouse gas emissions in support of U.S. climate change initiatives, and protect the Department from energy price fluctuations.” (DOD 2010) The Department of the Navy has also stated that the Navy and Marine Corps rel y far too much on petroleum, which “degrades the strategic position of our country and the tactical performance of our forces. The global supply of oil is finite, it is becoming increasingl y difficult to find and exploit, and over time cost continues to rise . ” D In remarks given to the White House Energy Security Summit on April 26, 2011, Deputy Security of Defense William J. Lynn, III noted the direct impact of energy security on military

readiness and flexibility . According to Deputy Security Lynn, “ Today , energy technology

remains a critical element of our military superiority . Addressing energy needs must be a fundamental part of our military

planning.” E Thus, to the degree to which the final rule reduces reliance upon imported energy supplies or promotes the development of technologies that can be deployed by either consumers or the nation’s defense forces, the United States could expect benefits related to national security, reduced energy

costs, and increased energy supply . Th e s e benefit s are why President Obama has identified this rule as a key component for improving energy efficiency

and putting America on a path to reducing oil imports in the Blueprint for a Secure Energy Future . F Although the agencies recognize that there clearl y is a benefit to the United States from reducing dependence on foreign oil, the agencies have been unable to calculate the monetary benefit that the United States will receive from the improvements in na tional security

expected to result from this rule. In contrast, the other portion of the energy security premium, the U.S. macroeconomic disruption and adjustment cost that arises from U.S. petroleum imports is included in the energy

security benefits estimated for this program. To summarize, the agencies have included onl y the macroeconomic disruption portion of the energy security benefits to estimate the monetary

value of the total energy

security benefits of this program. The agencies have calculated energy

security in very specific terms, as the reduction of both financial and strategic risks caused b y potential sudden disruptions in the supply of imported petroleum to the U.S. Reducing the amount of oil imported reduces those risks, and thus increa ses the nation’s energy security.

D The Department of the Navy’s Energy Goals (http://www.navy.mil/features/Navy_EnergySecurity.pdf ) (Last accessed May 31, 2011). E U.S. Department of Defense, Speech: Remarks at the White House Energy Security Summit. Tuesday, April 26, 2011. (http://www.defense.gov/speeches/speech.aspx?speechid=1556 ) (Last accessed May 31, 2011). F The White House, Blueprint for a Secure Energy Future (March 30, 20 11) (http://www.whitehouse.gov/sites/default/files/blueprint_secure_energy_future.pdf ) (Last accessed May 27, 2011).**Regulatory Impact Analysis**

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**9.7.2 Impact on U.S.**  **P etroleum**  **I mports**  In 200 8 , U.S. petroleum import expenditures represented 21 percent of total U.S. imports of all goods and services. G In200 8 , the United States imported 66 percent of the petroleum it con sumed , and the transportation sector accounted for 70 percent of total U.S. petroleum consumption . This compares roughl y to 3 7 percent of petroleum from imports and 55 percent consumption of petroleum in the transportation sector in 1975. H Itis clear th at petroleum imports have a significant impact on the U.S. economy . Requiring lower GHG - emitting heavyduty vehicles and improved fuel economy in the U.S. is expected to lower U.S. petroleum imports. EPA used the MOVES model to estimate the reduced consumption in fuel due to this rule. A detailed explanation of the MOVES model can be found in Chapter 5 o f this RIA. Based on a detailed anal ys is of differences in fuel consumption, petroleum imports, and imports of refined petroleum products and crude oil among the Reference Case, High Economic Growth, and L ow Economic Growth Scenarios presented in AEO 2011 , NHTSA and EPA estimate that approximately

50 percent of the reduction in fuel consumption resulting from adopting improved fuel GHG standards and fuel economy standards is likely to be reflected in reduced U.S. imports of refined fuel, while the remaining 50 percent would be expected to be reflected in reduced domestic fuel refining . Of this latter figure, 90 percent is anticipated to reduce U.S. import s of crude petroleum for use as a refinery feedstock, while the remaining 10 percent is expected to reduce U.S. domestic production of crude petroleum. Thus , on balance, each gallon of fuel saved as a consequence of improved fuel heav y- duty GHG standards and fuel economy standards is anticipated to reduce total U.S. imports of crude petroleum or refined fuel by 0.95 gallons. I Based upon the fuel savings estimated b y the MOVES model and the 95 percent oil import factor, the reduction in U.S. oil imports f rom this rule are estimated for the ye ars 2020, 2030, 2040, and 2050 (in millions of barrels per day (MMBD)) in below. **Table 9**  **- 17**  U.S. Oil Import Reductions Resulting from the Heavy- Duty

Vehicle Rule in 2020, 2030, 2040, and 2050

(in MMBD)

G U.S. Bureau of Economic Analysis, U.S. International Transactions Accounts Data, as shown on June 14 , 2010 . H U.S. Department of Energy, Annual Energy Review 2008, Report No. DOE/EIA - 0384(2008), Tables 5.1 and 5.13c, June 26, 2009. I This figure is calculated as 0.50 + 0.50*0.9 = 0.50 + 0.45 = 0.95.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 39 Year mmbd 2020 0.20 2 2030 0.39 3 2040 0.4 89 2050 0.5 66 For comparison purposes, Table 9 - 18 shows the U.S. imports of crude oil in 2020 and 2030 as projected b y DOE in the Annual Energy Outlook 2011 Early

Release Reference Case. J **Table 9**  **- 18**  Projected U.S. Imports of Crude Oil in 2020 and 2030 (in MMBD) 2020 8.38 2030 8.32 **9.7.3 Background on U.S.**  **E nergy**  **S ecurity**  U.S. energy

security is broadly

defined as protecting the U.S. economy against circumstances that result in significant short - and long - term increases in energy costs. Most discussion of U.S. energy security revolves around the topic of the economic costs of U.S. dependence on o il imports. 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 b y exerting monopol y power through the formation of a cartel, the Organization of Petroleum Exporting Countries (OPEC). Finally , these factors contribute to the vulnerability of the U.S. economy to episodic oil supply shocks and price spikes. In 2008, U.S. net expenditures for imports of crude oil and petroleum pro ducts were $326 billion (in 2007$, see Figure 9 - 3).

J AEO 2011, EIA, Imported Liquids by Source.**Regulatory Impact Analysis**

9 - 40 **U.S . E**  **xpe ndit**  **ur**

**es o**  **n Cr ude**  **Oi l** 

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

**1970 1975**  **1980 1985**  **1990 1995**  **2000 2005**  **Ye ar** **( 2** **0 0** **7 $** **B i** **l l** **i o** **n s** **) Dom** 

**est ic**  **Im po**  **rt ed** 

**Figure 9 -**  **3: U.S. Expenditures on Crude Oil from** **1970 through 2008 K**  One effe ct of the 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 imports, 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 is a measure of improved U.S. energy security. For this rule, an “oil premium” approach is utilized to identify those energy security related impacts 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. 

### 9.7.3.1 **M ethodology**  **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 with Oak Ridge National Laboratory (ORNL), which has developed approaches for evaluating the soc ial 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. This study

is included as part of the docket for this rulemaking. L Th is ORNLstudy is an update d version of the approach used for estimating the energy security benefits of U.S. oil

K For historical data through 2006: EIA Annual Energy Review, various editions. For data 2006 - 2008: EIA Annual Energy Outlook (AEO) 2009 (Update Reference (Stimulus) Base Case). See file "aeostimtab_11.xls" available at http://www.eia.doe.gov/oiaf/servicerpt/stimulus/aeostim.html L Leiby, Paul N. "E stimating the Energy Security Benefits of Reduced U.S. Oil Imports," Oak Ridge National Laboratory, ORNL/TM - 2007/028, Final Report, 2008.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 41 import reductions developed in a 1997 ORNL Report by L eib y, Paul N ., Donald W. Jones, T. Randall Curlee, and Russell L ee, entitled “ Oil Imports: An Assessment of Benefits and Costs. ” M When conducting this analy sis, OR NL considered the full cost of importing petroleum into the U.S. The f

ull economic cost is define d to include two components in addition to the purchase price of petroleum itself. These are: (1) the higher costs for oil imports resulting from the effect of U.S. import demand on the world oil price and on OPEC market power ( 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). Maintaining a U.S. military presence to help secure stable oil supply from potentiall y vulnerable regions of the world was not included in this analy sis because its attribution to particular missions or activities is difficult (as discussed further below). The literature on the ene rgy 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: h ow should the energy security premium be used when some benefits from the rule, such as the benefits of reducing greenhouse gas emissions, are calculated using a global value? Monopsony benefits represent avoided pay ments by the U.S. to oil producers in foreign countries that result from a decrease in the world oil price as the U.S. decreases its consumption of imported oil. Although there is clearl y a 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 other countries. Given the redistributive nature of this monopsony effect from a global perspective, it is excluded in the energy

security benefits calculations for this pro gram. In contrast, the other portion of the energy

security premium, the U.S. macroeconomic disruption and adjustment cost that arises from 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 program. To summarize , the agencies have included only the macroeconomic disruption portion of the energy security benefits to estimate the monetary value of the total energy

security benefits of this program . S ection VIII.I of the preamble contains more discussion of how the monopsony and macroeconomic disruption/adjustment components are treated for this analy sis. As part of the process for developing the ORNL energy security estimates, EPA sponsored an independent , expert peer review of th e 2008 ORNL study . A report compiling the peer reviewers’ comments is provided in the docket. N Inaddition, EPA worked with ORNLto

M Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and Cost s , ORNL - 6851, Oak Ridge National Laboratory, November, 1997. N Peer Review Report Summary: Estimating the Energy Security Benefits of Reduced U.S. Oil Imports , ICF, Inc., September 2007.**Regulatory Impact Analysis**

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address comments raised in the peer review and to develop estimates of the energy security benef its associated with a reduction in U.S. oil imports. In response 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. For this rulemaking , ORNLfurther updated the energy security premium by incorporating the most recent oil price forecast and energy market trends from the AEO 2011 (Earl y Release) into its model. In order for the energy security premium to be used in EPA’s MOVES model, ORNLdeveloped energy security premium estimates for a number of different ye ars ( i.e., 2020, 2030, and 2035 ) , Table 19 - 9 provides estimates for energy security premium for the ye ars 2020, 2030 and 2035, O as well as a breakdown of the components of the energy security premium for each year. The components of the energy security premium and their values are discussed below.

**Table 9**  **- 19**  Energy

Security Premium in 2020, 2030 and 2035 (2009$/Barrel) Year (range) Monopsony

(Range) Macroeconomic

Disruption/Adjustment Costs

(Range) Total Mid - Point (Range) 2020 $11.29

($3.86 - $21.32) $7.11 ($3.50 - $11.40) $18.41 ($9.70 - $28.94) 203 0 $11.17

($3.92 - $20.58) $8.32 ($4.04 - $13.33) $19.49 ($10.49 - $29.63) 20 35 $10.56

($3.69 - $19.62) $8.71 ($3.86 - $14.35) $19.27 ($10.32 - $29.13)

**9.7.3.2 Effect of Oil Use on**  **the Long**  **- Run Oil Price**  The first component of the full economic costs of importin g 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 foreign oil supplies , its purchases can affect the world oil price. This monopsony pow er 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

O AEO 20 11 forecasts energy market trends and values only to 203 5 . T he post - 203 5 energy security premium values are assumed to be equal to the 203 5 estimate.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 43 the world price of crude oil. Thus , one benefit of decreasing U.S. oil purchases , due to the inc reased availability

and use of other transportation fuels, 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 $50 per barrel, its total daily bill for oil imports is $500 million. If a decrease in U.S. imports to 9 million barrels per day causes the world oil price to drop to $49 per barrel, the dail y U.S. oil import bill drops to $441 million (9 million barrels times $49 per barrel). While the world oil price only declines $1, the resulting decrease in oil purchase pay ments of $59 million per day ($500 million minus $441 million) is equivalent to an incremental benefit of $59 per barrel of oil imports reduced, or $10 more than the newl y- decreased world price of $49 per barrel. This additional $10 per barrel “import cost premium” represents the incremental external benefits to the U.S. for avoided import costs beyond the price paid oil purchases. This additional benefit arises only to the extent that reduction in U.S. oil imports affects the world oil price. ORNL estimates this component of the energy security benefit in 2020 to be $1 1.29 /barrel, with a range of $ 3.86 /barrel to $ 2 1.32 /barrel of imported oil reduced. 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 emissio ns outside of the United States. This increase in global fuel consumption could offset some portion of the GHG reduction benefits associated with the rule. The agencies have not quantified this increase in global GHG emissions in the RIA. 

### 9.7.4 **Macroeconomic Disruption Adjustment Costs**  The second component of the oil import premium, “macroeconomic disruption/adjustment costs ,” arises from the effect of oil imports on the expected cost of disruptions. 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. ORNL estimates the composite estimate of these two factors that comprise the macroeconomic disruption/adjustment costs premium to be $7. 11 /barrel in 2020, with a range of $ 3.50 /barrel to $ 11.40 /barrel of imported oil reduced. There are two main effects of macroeconomic disruption/adjustment costs. The first is the short - run price increase 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. Unli ke 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 very high costs. Businesses’ and households’ emerg ency responses to suppl y disruptions and rapid price increases consume real economic resources.**Regulatory Impact Analysis**

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While households and businesses can reduce their petroleum consumption, invest in fuel switching technologies, or use futures markets to insulate themselves in advance against the potential costs of rapid increases in oil prices, when deciding how extensively to do so, they

are unlikely to account for the effect of their petroleum consumption on the magnitude of costs that supply interruptions and accompanying p rice 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 from the real costs of rapid increases in energy prices and outlay s that usuall y accompany oil supply interruptions. Th erefore, the ORNLestimate of macroeconomic disruption/adjustment costs that the agencies use to value energy security benefits includes the increased oil import costs stemming from oil price shocks that are unanticipated and not internalized by advance actions of U.S. consumers. The second main effect of macroeconomic disruption/adjustment costs is the macroeconomic losses during price shocks that reflect both aggregate output losses and “allocative” losses. The former are a reduction in the leve l of output that the U.S. economy can produce full y using its available resources; and the latter stem from temporary dislocation and underutilization of available resources due to the shock, such as labor unemploy ment and idle plant capacity . The aggregate output effect, a reduction in “potential” economic output, will last so long as the 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 increase in prices for petroleum products, as well as whether and how rapidl y these prices return to their pre disruption levels. In addition to the aggregate contraction, there are “allocative” or “adjustment” costs associated with dislocated energy markets. Because supply disruptions and resulting price increases occur suddenl y, empirical evidence shows they also impose additional costs on businesses and households which 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 unemplo ym ent of workers and other resources during the time needed for their intersectoral or interregional reallocation, and pauses in capital investment due to uncertainty. These adjustments temporaril y 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 impo sed 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 smoothly 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 “expected value” of thes e 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, wh en assessing the energy security value of a policy to reduce oil use, it is only

the change in the expected costs of disruption that results from the policy that is relevant. The expected costs of disruption may change from lowering the normal (i.e., pre - disruption) level of domestic petroleum use and imports, from any induced alteration in***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 45 the likelihood or size of disruption, or from altering the short - run flexibility (e.g., elasticity) of petroleum use. In summary , the steps needed to calculate the disr uption or security

premium are: 1) determine the likelihood of an oil supply disruption in the future; 2) assess the likely impacts of a potential oil supply

disruption on the world oil price; 3) assess the impact of the oil price shock on the U.S. economy (in terms of import costs and macroeconomic losses); and 4) determine how these costs change with oil imports. The value of price spike costs avoided by reducing oil imports becomes the oil security portion of the premium. **9.7.4.1.1 Cost of Existing U.S. Energy Se**  **curity 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 largest stockpile of government - owned emergency crude oil in the world. Established 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 also allows the U.S. to meet part of its International Energy Agency obligation to maintain emergency oil stocks, an d 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, whi le SPR is factored into the ORNL anal ys is, the cost of maintaining the SPR is excluded. U.S. military

costs are excluded from the anal ys is performed b y ORNL because their attribution to particular missions or activities is difficult. 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 variations in U.S. oil imports . 

### 9.7.4.2 **Energy S**  **ecurity B**  **enefits of this Program**  Using the same methodology as the peer- reviewed model, but updating the anal ys is using AEO 2011 world oil price values and the estimated fuel savings from the rule using the MOVES model, EPA has calculated the energy security benefits of the rule for the years 2020, 2030, 2040, and 2050 R . Since the Agency is taking a global perspective with respect to valuing greenhouse gas benefits from the r ule, onl y the macroeconomic adjustment/disruption portion of

R In order to determine the energy security benefits for beyond 2035, world oil prices were extrapolated from an average grow

th rate for the years 2017 to 2035. This is shown in the spreadsheet labeled "AEO2011 Price Projects_with_final_release.xlsx", which is in the Docket for this rule.**Regulatory Impact Analysis**

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the energy

security premium is used in the energy security benefits estimates present below. These results are shown below in Table 9 - 20 .

**Table 9**  **- 20**  U.S. Energy

Security Benefits of the Heavy- Duty Vehicle Rulemaking in 2020, 2 03 0, 2040 and 2050

(in millions of 2009$) Year Benefits 2020 $ 499 2030 $1, 132 2040 $1, 477 2050 $1, 710 

### 9.8 **Summary of Benefits and Costs** In this section, the agencies present a summary of costs, benefits, and net benefits of the pro gram . Table 9 - 21 shows the estimated annual monetized costs of the program for the indicated calendar ye ars. The table also shows the net present values of those costs for the calendar years 2012 - 2050 using both 3 percent and 7 perc ent discount rate s . S Inthis table, the aggregate value of fuel savings is calculated using pre - tax fuel prices since savings in fuel taxes do not represent a reduction in the value of economic resources utilized in producing and consuming fuel. Note that fuel savings shown here result from reductions in fleet - wide fuel use. Thus , they

grow over time as an increasing fraction of the fleet meets the 2018 standards . **Table 9**  **- 21**  **Estim ated**  **Monetized Costs of the Program** **(Millions of 200**  **9 dollars)**  **a** 2020 2030 2040 2050 NPV , YEARS 2012 - 2050 , 3% DISCOUNT RATE NPV , YEARS

2012 - 2050 , 7% DISCOUNT RATE Truck/Tractor Costs $2,000 $2,200 $2,700 $3,300 $47,400 $24,700 Fuel Savings (pre - tax) - $9,600 - $20,600 - $28,000 - $36,500 - $375,300 - $166,500 Quantified Annual Costs - $7,600 - $18,400 - $25,300 - $33,200 - $327,900 - $141,800

a Technology c osts and fuel saving s for separate truck segments can be found in Chapter 7. Table 9 - 22 presents estimated annual monetized benefits for the indicated calendar years. The table also shows the net present values of those benefits for the calendar years 2012 - 2050

S For the estimation of the stream of costs and benefits, we assume that after implementation of the MY 2014 - 2017 standards, the 2017 standards apply to each year out to 2050 .***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 47 using both 3 percent and 7 percent discount rate s . The table shows the b enefits of reduced CO 2 emissions

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

—for each of four SCC values estimated by the interagency

working group . As discussed in Section 8.5, there are some limitations to the SCC analy sis, in cluding 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. Inaddition, 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, the value of these reductions should not be interpreted as zero. Rather, 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 .

**Table 9**  **- 22**  **Monetized Benefits Associated with the Program** **(Millions of 200**  **9 dollars**  **)** 2020 2030 2040 2050 NPV, YEARS 2012 - 2050, 3% DISCOUNT RATE A NPV, YEARS

2012 - 2050, 3% DISCOUNT RATE A Reduced CO2 Emissions at each assumed SCC value b 5% (avg SCC) $300 $700 $1,200 $1,700 $9,000 $9,000 3% (avg SCC) $1,000 $2,5 00 $3,600 $4,800 $46,100 $46,100 2.5% (avg SCC) $1,600 $3,800 $5,400 $7,000 $78,000 $78,000 3% (95th percentile) $3,100 $7,500 $11,100 $14,600 $140,400 $140,400 Energy Security Im pacts (price shock) $500 $1,100 $1,500 $1,700 $19,800 $8,800 Accidents, Noise, Congestion - $200 - $400 - $600 - $600 - $7,900 - $3,700 Refueling Savings $100 $100 $200 $200 $2,500 $1,100 Non - GHG Im pacts c,d B $2,800 $2,800 $2,800 $25,300 $9,100 Non - CO 2 GHG Im pacts e n/a n/a n/a n/a n/a n/a Total Annual Benefits at each assumed SCC value b 5% (avg SCC) $7 00 $4,300 $5,100 $5,800 $48,700 $24,300 3% (avg SCC) $1,400 $6,100 $7,500 $8,900 $85,800 $61,400 2.5% (avg SCC) $2,000 $7,400 $9,300 $11,100 $117,700 $93,300**Regulatory Impact Analysis**

9 - 48

3% (95th percentile) $3,500 $11,100 $15,000 $18,700 $180,100 $155,700 a 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 (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. Refer to the SCC TSD for more detail. b Section 8.5 of the RIA notes that SCC increases over time. Corresponding to the years in this table (2020 - 2050) , the SCC estimates range as follows: for Average SCC at 5%: $ 7 - $16; for Average SCC at 3%: $ 2 7 - $46 ; f or Average SCC at 2.5%: $ 43 - $ 67 ; and for 95 th percentile SCC at 3%: $ 83 - $1 40 . Section VIII .F also presents these SCC estimates. c Note that “B” indicates unquantified criteria pollutant benefits in the year 2020. For the analysis of the final program, we only modeled the rule’s PM 2.5 - and ozone - related impacts in the calendar year 2030. For the purposes of estimating a stream of future - year criteria pollutant benefits, we assume that the benefits out to 2050 are equal to, and no less than, those modeled in 2030 as reflected by the stream of estimated future emission reductions. The NPV of cri teria pollutant - related benefits should therefore be considered a conservative estimate of the potential benefits associated with the final program. d Non - GHG - related health and welfare impacts (related to PM 2.5 and ozone exposure) range between $110 and $ 340 million in 2030, 2040, and 2050. $200 was chosen as the mid -point of this range for the purposes of estimating total benefits across all monetized categories. e The monetized GHG benefits presented in this analysis exclude the value of changes in non - CO 2 GHG emissions expected under this program (see RIA Chapter 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. Table 9 - 23 presents estimated annual net benefits for the indicated calendar years. The table also shows the net present values of those net benefits for the calendar years 2012 - 2050 using both 3 percent and 7 percent discount rate s . The table includes the benefits of reduced CO 2 em issions (and consequently the annual net benefits) for each of four SCC values considered by EPA. **Table 9**  **- 23**  **Monetized Net Benefits Associated with the Program** **(Millions of 200**  **9 dollars)** 

2020 2030 2040 2050 NPV, 3% NPV, 7% Annual Costs a - $7,600 - $18,400 - $25,300 - $33,200 - $327 ,900 - $141,800 Monetized Annual Benefits at each assumed SCC value 5% (avg SCC) $700 $4,300 $5,100 $5,800 $48,700 $24,300 3% (avg SCC) $1,400 $6,100 $7,500 $8,900 $85,800 $61,400 2.5% (avg SCC) $2,000 $7,400 $9,300 $11,100 $117,700 $93,300 3% (95th percentile) $3,500 $11,100 $15,000 $18,700 $180,100 $155,700 Monetized Net Benefits at each assumed SCC value 5% (avg SCC) $8,300 $22,700 $30,400 $39,000 $376,600 $166,100 3% (avg SCC) $9,000 $24,500 $32,800 $42,100 $413,700 $203,200 2.5% (avg SCC) $9, 600 $25,800 $34,600 $44,300 $445,600 $235,100 3% (95th percentile) $11,100 $29,500 $40,300 $51,900 $508,000 $297,500

a Note that negative costs represent savings rathe r than costs.***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 49 EPA also conducted a separate anal ys is of the total benefits over the model year lifetimes of the 2014 through 2018 model year trucks /tractors . In contrast to the calendar year analysis presented in Table 9 - 21 through Table 9 - 23 , the model year lifetime anal ys is shows the impacts of the program on vehicles produced during each of the model years 2014 through 2018 over the course of their expected lifetime s . The net societal benefits over th e full life times of vehicles produced during each of the five model years from 2014 through 2018 are shown in Ta ble 9 - 24 and Table 9 - 25 at both 3 percent and 7 percent discount rate s , respectivel y. **Ta ble**  **9 -**  **24 Monetized**  **Costs, Benefits, and Net Benefits Ass ociated with the Lifetim**  **es of 2014 -**  **2018 Model Year Trucks (Millions of**  **200 9**  **dollars; 3% Discount Rate)** 2014MY 2015MY 2016MY 2017MY 2018MY SUM Monetized Costs Truck Program Costs - $1,600 - $1,400 - $1,500 - $1,600 - $2,000 - $8,100 Monetized Benefits at each assumed SCC value Pre - tax Fuel Savings $9,300 $8,300 $8,100 $11,500 $12,900 $50,100 Energy Security $500 $400 $400 $600 $700 $2,700 Accidents, No ise, Congestion - $300 - $300 - $300 - $300 - $300 - $1,500 Refueling Savings $60 $60 $60 $80 $100 $400 Non - CO 2 GHG Impacts and Non - GHG Im pacts a,b n/a n/a n/a n/a n/a n/a Reduced CO2 emissions at each a ssumed SCC value 5% (avg SCC) $200 $200 $200 $300 $300 $1,200 3% (avg SCC) $1,100 $900 $900 $1,300 $1,500 $5,700 2.5% (avg SCC) $1,800 $1,600 $1,500 $2,100 $2,400 $9,400 3% (95th percentile) $3,300 $2,900 $2,800 $4,000 $4,500 $17,000 Monetized Net Benefits at each assumed SCC value 5% (avg SCC) $8,200 $7,300 $7,000 $10,600 $11,700 $44,800 3% (avg SCC) $9,100 $8,000 $7,700 $11,600 $12,900 $49,300 2.5% (avg SCC) $9,800 $8,700 $8,300 $12,400 $13,800 $53,000 3% (95th percentile) $11,300 $10,000 $9,600 $14,300 $15,900 $60,600 a The monetized GHG benefits presented in this analysis exclude the value of changes in non - CO 2 GHG emissions expected under this program (see RIA Chapter 5) . Although EPA has not monetized chang es in non - CO 2 GHGs, the value of any increases or reductions should not be interpreted as zero. b Due to analytical and resource limitations, MY non - GHG emissions (direct PM, VOCs, NO 2 and SO 2 ) were not estimated for this analysis.**Regulatory Impact Analysis**

9 - 50

**Table 9**  **- 25**  **Monetized Costs, Benefits, and Net Benefits Associated with the Li**  **fetim es of 2014**  **- 2018 Model**  **Year Trucks (Millions of 200**  **9 dollars;**  **7 % Discount Rate)**  2014MY 2015MY 2016MY 2017MY 2018MY SUM

Monetized Costs Truck Program Costs - $1,600 - $1,400 - $1,500 - $1,600 - $2,000 - $8,100 Mon etized Benefits at each assumed SCC value Pre - tax Fuel Savings $6,900 $5,900 $5,600 $7,600 $8,300 $34,400 Energy Security $400 $300 $300 $400 $400 $1,800 Accidents, Noise, Congestion - $200 - $200 - $200 - $200 - $200 - $1,000 Refueling Savings $50 $40 $40 $60 $60 $200 Non - CO 2 GHG Im

pacts and Non - GHG Im

pacts a,b n/a n/a n/a n/a n/a n/a

Reduced CO2 emissions at each assumed SCC value 5% (avg SCC) $200 $200 $200 $300 $300 $1,200 3% (avg SCC) $1,100 $900 $900 $1,300 $1,500 $5,700 2.5% (avg SCC) $1,800 $1,600 $1,500 $2,100 $2,400 $9,400 3% (95th percentile) $3,300 $2,900 $2,800 $4,000 $4,500 $17,000 Monetized Net Benefits at each assumed SCC value 5% (avg SCC) $5,800 $4,800 $4,400 $6,600 $6,900 $28,500 3% (avg SCC) $6,700 $5,500 $5,100 $7,600 $8,100 $33,000 2.5% (avg SCC) $7,400 $6,200 $5,700 $8,400 $9,000 $36,700 3% (95th percentile) $8,900 $7,500 $7,000 $10,300 $11,100 $4 4,300 a The monetized GHG benefits presented in this analysis exclude the value of changes in non - CO 2 GHG emissions expected under this program (see RIA Chapter 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. b Due to analytical and resource limitations, MY non - GHG emissions (direct PM, VOCs, NO 2 and SO 2 ) were not estimated for this analysis. The agencies have also conducted an uncertainty anal ys is that consi ders the impacts on the net benefits of the rule if certain costs are actually lower or higher than estimated in the primary anal ys is . The cost elements altered and how are shown in Table 9 - 26 . **Table 9**  **- 26**  **Uncertainty Analysis Cost Ele**  **ments Considered**  **and How They Were Altered** COST ELEMENT LOW END HI GH END HOW APPLIED Fuel prices AEO2011

Low oil price case AEO2011

High oil price case All fuel savings Indirect cost multipliers - 20% +20% Calculating all indirect costs associated with adding new technology Aero costs - 10% +10% All direct manufacturing costs ( DMC ) associated with aero technology APU costs - 10% +10% DMC for APU units on combination tractors***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 51 Turbo/compounding costs - 10% +10% DMC for turbo/compounding on combination tractors The results of this uncertainty anal ys is for the Low End Case are shown in Table 9 - 27 and Table 9 - 28 for a 3% and 7% discount rate, respectivel y, and for the High End Case in Table 9 - 29 and Table 9 - 30 for a 3% and 7% discount rate, respectively . **Table 9**  **- 27**  **Monetized Costs, Benefits, and Net Benefits Associated with the Lifetim**  **es of 2014 -**  **2018 Model Year Trucks**  **for the Low End Uncertainty Analysis (Millions of 200**  **9 dollars; 3% Discount Rate)**  2014MY 2015MY 2016MY 2017MY 2018MY SUM Monetized Costs Truck Program Costs - $1,500 - $1,300 - $1,400 - $1,500 - $1,800 - $7,500 Monetized Benefits at each assumed SCC value Pre - tax Fuel Savings $5,900 $5,100 $4,900 $6,800 $7,600 $30,200 Energy Security $500 $400 $400 $600 $700 $2,700 Accidents, Noise, Congestion - $300 - $300 - $300 - $300 - $300 - $1,500 Refueling Savings $60 $60 $60 $80 $100 $360 Non - CO 2 GHG Im

pacts and Non - GHG Im

pacts a,b n/a n/a n/a n/a n/a n/a

Reduced CO2 emissions at each assumed SCC value 5% (avg SCC) $200 $200 $200 $300 $300 $1,200 3% (avg SCC) $1,100 $900 $900 $1,300 $1,500 $5,700 2.5% (avg SCC) $1,800 $1,600 $1,500 $2,100 $2,400 $9,400 3% (95th percentile) $3,300 $2,900 $2,800 $4,000 $4,500 $17,000 Monetized Net Benefits at each assumed SCC value 5% (avg SCC) $4,860 $4,160 $3,860 $5,980 $6,600 $25,460 3% (avg SCC) $5,760 $4,860 $4,560 $6,980 $7,800 $29,960 2.5% (avg SCC) $6,460 $5,560 $5,160 $7,780 $8,700 $33,660 3% (95th percentile) $7,960 $6,860 $6,460 $9,680 $10,800 $41,260 **Table 9**  **- 28**  **Monetized Costs, Benefits, and Net Benefits Associated with the Lifetim**  **es of 2014 -**  **2018 Model Year Trucks**  **for the Low**  **End Uncertainty Analysis (Millions of**  **200 9**  **dollars; 7**  **% Discount Rate)** 2014MY 2015MY 2016MY 2017MY 2018MY SUM Monetized Costs Truck Program Costs - $1,500 - $1,300 - $1,400 - $1,500 - $1,800 - $7,500 Monetized Benefits at each assumed SCC value Pre - tax Fuel Savings $4,400 $3,700 $3,400 $4,600 $4 ,900 $20,900 Energy Security $400 $300 $300 $400 $400 $1,800 Accidents, Noise, Congestion - $200 - $200 - $200 - $200 - $200 - $1,000 Refueling Savings $50 $40 $40 $60 $60 $250 Non - CO 2 GHG Im

pacts and Non - GHG Im

pacts a,b n/a n/a n/a n/a n/a n/a

Reduced CO2 emissions at each assumed SCC value 5% (avg SCC) $200 $200 $200 $300 $300 $1,200 3% (avg SCC) $1,100 $900 $900 $1,300 $1,500 $5,700 2.5% (avg SCC) $1,800 $1,600 $1,500 $2,100 $2,400 $9,400 3% (95th percentile) $3,300 $2,900 $2,800 $4,000 $4,500 $17,000 Monetized Net Benefits at each assumed SCC value**Regulatory Impact Analysis**

9 - 52

5% (avg SCC) $3,350 $2,740 $2,340 $3,660 $3,660 $15,650 3% (avg SCC) $4,250 $3,440 $3,040 $4,660 $4,860 $20,150 2.5% (avg SCC) $4,950 $4,140 $3,640 $5,460 $5,760 $23,850 3% (95th percentile) $6,450 $5, 440 $4,940 $7,360 $7,860 $31,450 **Table 9**  **- 29**  **Monetized Costs, Benefits, and Net Benefits Associated with the Lifetim**  **es of 2014 -**  **2018 Model Year Trucks**  **for the High**  **End Uncertainty Analysis (Millions of 200**  **9 dollars;**  **3 % Discount Rate)**  2014MY 2015MY 2016MY 2017MY 2018MY SUM Monetized Costs Truck Program Costs - $1,700 - $1,500 - $1,600 - $1,700 - $2,100 - $8,600 Monetized Benefits at each assumed SCC value Pre - tax Fuel Savings $13,600 $12,000 $11,800 $16,700 $18,800 $72,900 Energy Security $500 $400 $400 $600 $700 $2,700 Accidents, Noise, Congestion - $300 - $300 - $300 - $300 - $300 - $1,500 Refueling Savings $60 $60 $60 $80 $100 $360 Non - CO 2 GHG Im

pacts and Non - GHG Im

pacts a,b n/a n/a n/a n/a n/a n/a

Reduced CO2 emissions at each assumed SCC value 5% (avg SCC) $200 $200 $200 $300 $300 $1,200 3% (avg SCC) $1,100 $900 $900 $1,300 $1,500 $5,700 2.5% (avg SCC) $1,800 $1,600 $1,500 $2,100 $2,400 $9,400 3% (95th perc entile) $3,300 $2,900 $2,800 $4,000 $4,500 $17,000 Monetized Net Benefits at each assumed SCC value 5% (avg SCC) $12,360 $10,860 $10,560 $15,680 $17,500 $67,060 3% (avg SCC) $13,260 $11,560 $11,260 $16,680 $18,700 $71,560 2.5% (avg SCC) $13,960 $12,260 $11,860 $17,480 $19,600 $75,260 3% (95th percentile) $15,460 $13,560 $13,160 $19,380 $21,700 $82,860

**Table 9**  **- 30**  **Monetized Costs, Benefits, and Net Benefit**  **s Associated with the Lifetim es of 2014**  **- 2018 Model**  **Year Trucks for the High End Uncertainty Analysis**  **(Millions of 200 9**  **dollars; 7**  **% Discount Rate)** 2014MY 2015MY 2016MY 2017MY 2018MY SUM Monetized Costs Truck Program Costs - $1,700 - $1,500 - $1,600 - $1,700 - $2,100 - $8,600 Monetized Benefits at each assumed SCC value Pre - tax Fuel Savings $10,100 $8,600 $8,100 $11,100 $12,000 $49,900 Energy Security $400 $300 $300 $400 $400 $1,800 Accidents, Noise, Congestion - $200 - $200 - $200 - $200 - $200 - $1,000 Refuelin g Savings $50 $40 $40 $60 $60 $250 Non - CO 2 GHG Im

pacts and Non - GHG Im

pacts a,b n/a n/a n/a n/a n/a n/a

Reduced CO2 emissions at each assumed SCC value 5% (avg SCC) $200 $200 $200 $300 $300 $1,200 3% (avg SCC) $1,100 $900 $900 $1,300 $1,500 $5,700 2.5% (avg SCC) $1,800 $1,600 $1,500 $2,100 $2,400 $9,400 3% (95th percentile) $3,300 $2,900 $2,800 $4,000 $4,500 $17,000 Monetized Net Benefits at each assumed SCC value 5% (avg SCC) $8,850 $7,440 $6,840 $9,960 $10,460 $43,550 3% (avg SCC) $9,750 $8,140 $7 ,540 $10,960 $11,660 $48,050***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 53 2.5% (avg SCC) $10,450 $8,840 $8,140 $11,760 $12,560 $51,750 3% (95th percentile) $11,950 $10,140 $9,440 $13,660 $14,660 $59,350 

### 9.9 **Summary of**  **Costs and**  **Benefits f**  **rom the Fuel Efficiency Perspective** The purpose of a program to regulate fuel efficiency is primarily to save fuel, as compared to the purpose of a program to regulate GHG emissions, which is primarily to reduce the impact of climate change. Considering costs and benefits from a fuel efficiency perspective, technology costs occur when the vehicle is purchased, just as they do from a GHG emissions perspective, but fuel savings w ill be counted as benefits that occur over the lifetime of the vehicle as it consumes less fuel, rather than as negative costs experienced either at the time of purchase or over the lifetime of the vehicle. and show similar model year estimates to those provided above in and above in Section VIII.L , but with the categories relabeled to illustrate the fuel efficiency

perspective . These estimat es reflect specific differences in the NHTSA HD program. These include no HD diesel engine impacts prior to MY 2017, assumption of the NHTSA phase - in schedule which achieves 3 year phase - in stability (67% - 67% - 67% - 100% in MY 2016 - 2019 respectively , the inc lusion of combination tractors from MY 2016 forward, and the exclusion of RVs, which are not regulated b y NHTSA. **Table 9**  **- 31**  **: Monetized**  **Costs, Benefits, and Net Benefits Associated with the Lifetim es of 201**  **6 -**  **20 18 Model**  **Year Trucks (Millions ,**  **200 9$**  **; 3**  **% Discount Rate)**

2016 MY 2017 MY 2018 MY Sum Technology Costs - $1,500 - $1,600 - $1,700 - $5,200 Benefits Fuel Savings (pre - tax) $5,500 $10,900 $11,500 $27,900 Energy Security Impacts (price shock) $300 $600 $600 $ 1,500 Accidents, Congestion, Noise - $300 - $300 - $300 - $900 Refueling Savings $40 $80 $80 $200 Non - CO 2 GHG Impacts and Non GHG Impacts c,d n/a n/a n/a n/a Reduced CO 2 Emissions at each assumed SCC value a, b 5% (avg SCC) $100 $300 $300 $700 3% (avg SC C) $600 $1,200 $1,300 $3,100 2.5% (avg SCC) $1,000 $2,000 $2,200 $5,200 3% (95th percentile) $1,900 $3,800 $4,000 $9,700 Monetized Net Benefits at each assumed SCC value a, b 5% (avg SCC) $4,100 $10,000 $10,500 $24,200 3% (avg SCC) $4,600 $10,900 $11, 500 $26,600 2.5% (avg SCC) $5,000 $11,700 $12,400 $28,700 3% (95th percentile) $5,900 $13,500 $14,200 $33,200**Regulatory Impact Analysis**

9 - 54

Notes: a N et present value of reduced CO 2 emissions is calculated differently than other benefits. The same discount rate used to discount th e value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. Refer to the SCC TSD for more detail. b Section VIII. G notes that SCC increases over time. Corresponding to the years in this table, the SCC estimates range as follows: for Average SCC at 5%: $5 - $16; for Average SCC at 3%: $22 - $46; for Average SCC at 2.5%: $36 - $66; and for 95th percentile SCC at 3%: $66 - $139. Section VIII. G also presents these SCC estimates. c The monetized GHG benefits presented in this analysis exclude the value of changes in non - CO 2 GHG emissions expected under this proposal (see RIA Chapter 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. **d** Due to analytical and resource limitations, MY non - GHG emissions (direct PM, VOCs, NO 2 and SO 2 ) were not estimated for this analysis.

**Table 9**  **- 32**  **Monetized Costs, Benefi** **ts, and Net Benefits Associated with the Lifetim es of 201**  **6 -**  **2018 Model Year Trucks (Millions**  **, 200**  **9$ ; 7% Discount Rate)** 

2016 MY 2017 MY 2018 MY Sum Technology Costs - $1,500 - $1,600 - $1,700 - $5,200 Benefits Fuel Savings (pre - tax) $3,800 $7,200 $7,300 $18, 300 Energy Security Impacts (price shock) $200 $400 $400 $1,000 Accidents, Congestion, Noise - $200 - $200 - $200 - $600 Refueling Savings $30 $50 $50 $130 Non - CO 2 GHG Impacts and Non GHG Impacts c,d n/a n/a n/a n/a Reduced CO 2 Emissions at each assumed S CC value a, b 5% (avg SCC) $100 $300 $300 $700 3% (avg SCC) $600 $1,200 $1,300 $3,100 2.5% (avg SCC) $1,000 $2,000 $2,200 $5,200 3% (95th percentile) $1,900 $3,800 $4,000 $9,700 Monetized Net Benefits at each assumed SCC value a, b 5% (avg SCC) $2,40 0 $6,200 $6,200 $14,300 3% (avg SCC) $2,900 $7,100 $7,200 $16,700 2.5% (avg SCC) $3,300 $7,900 $8,100 $18,800 3% (95th percentile) $4,200 $9,700 $9,900 $23,300***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 55 Notes: a Net present value of reduced CO 2 emissions is calculated differently than other be nefits. The same discount rate used to discount the value of damages from future emissions (SCC at 5, 3, 2.5 percent) is used to calculate net present value of SCC for internal consistency. Refer to the SCC TSD for more detail. b Section VIII.G notes that SCC increases over time. Corresponding to the years in this table, the SCC estimates range as follows: for Average SCC at 5%: $5 - $16; for Average SCC at 3%: $22 - $46; for Average SCC at 2.5%: $36 - $66; and for 95 th percentile SCC at 3%: $66 - $139. Section VIII.G also presents these SCC estimates. c The monetized GHG benefits presented in this analysis exclude the value of changes in non - CO 2 GHG emissions expected under this proposal (see RIA Chapter 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. d Due to analytical and resource limitations, MY non - GHG emissions (direct PM, VOCs, NO 2 and SO 2 ) were not estimated for this analysis. 

### 9.10 **Employment Impacts**  **(1)** 

### Introduct **ion** Although analy sis of employ ment impacts is not part of a cost - benefit analy sis (except to the extent that labor costs contribute to costs), employ ment impacts of federal rules are of particular concern in the current economic climate of sizeable unempl oy ment. The recently issued Executive Order 13563, “Improving Regulation and Regulatory Review” (January

18, 2011), states, “ Our regulatory s ys tem must protect public health, welfare, safety, and our environment while promoting economic growth, innovation , competitiveness, and job creation ” (emphasis added). Although EPA and NHTSA did not undertake an employment anal ys is of the proposed rules, several commenters suggested that we undertake an employ ment anal ys is for the final rulemaking. Therefore, we have provided a qualitative discussion of the potential employ ment impacts of the Heav y- Duty National Program . When the economy is at full employment, an environmental regulation is unlikely to have much impact on net overall U.S. employ ment ; instead, labor would primarily be shifted from one sector to another. These shifts in employ ment impose an opportunity cost on society , approximated by

the wages of the employees, as regulation diverts workers from other activities in the economy. 35 Inthis situ ation, any effects on net employ

ment are likel y to be transitory as workers change jobs (e.g., some workers may need to be retrained or require time to search for new jobs, while shortages in some sectors or regions could bid up wages to attract workers). On the other hand, if a regulation comes into effect during a period of high unemploy ment, a change in labor demand due to regulation may affect net overall U.S. employ ment because the labor market is not in equilibrium. Schmalansee and Stavins 36 point out that net positive employment effects are possible in the near term when the economy is at less than full employ

ment due to the potential hiring of idle labor resources b y the regulated sector to meet new requirements (e.g., to install new equipment) and n ew economic activity in sectors related to the regulated sector. In the longer run, the net effect on employ ment is more difficult to predict and will depend on the way in which the related industries respond to the regulatory requirements. As Schmalansee and Stavins note, it is possible that the magnitude of the effect on employ ment could vary

over time, region, and sector, and positive effects on employ ment in some regions or sectors could be offset b y negative effects in other regions or sectors. For this**Regulatory Impact Analysis**

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reason, they

urge caution in reporting partial employ ment effects since it can “paint an inaccurate picture of net employ

ment impacts if not placed in the broader economic context.” This rulemaking is expected to have a relativel y s mall effect on net employ ment in the United States through the regulated sector

– the truck and engine manufacturer industry

– and several related sectors, specificall y, industries that supply the truck and engine manufacturing industry (e.g., truck parts), the trucking in dustry itself, other industries involved in transporting goods (e.g., rail and shipping); the petroleum refining sector, and the retail sector. According to the U.S. Bureau of Labor Statistics, about 1.25 million people were employed in the truck transportation industry

and about 675,000 people were employed in the motor vehicle parts industry between 2010 and 2011. 37 Although heavy- duty vehicles (HD) account for approximately

4% of the vehicles on the road, these vehicles consume more than 20% of on - road gasoline and diesel fuel use. As discussed in Chapter 5 of this RIA, this rulemaking is predicted to reduce the amount of fuel these vehicles use, and thus affect the petroleum refinery industry . The petroleum refinery

industry employed about 65,000 peop le in the U.S. in 2009, the most recent ye ar that employ

ment estimates are available for this sector. 38 Finall y, since the net reduction in cost associated with th e s e 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). Several commenters suggested that the HD vehicle rules would lead to an increase in employ ment in affected sectors by offering the potential for new employ ment opportunities in the design and production of new vehicle technologies. Also, these commenters su ggested that since the U.S. manufacturers and suppliers are leaders in certain advanced truck technologies, this program has the potential to help them consolidate their leadership and thrive in a global market. In this context, several commenters referre d to an assessment by the Union of Concerned Scientists (UCS) and CalStart of the economic and emplo ym ent benefits of the improved efficiency

in HD vehicles . 39 The study predicts an increase in 63,000 jobs in 2020 and 124,000 in 2030, as result of higher fuel efficiency for HD vehicles . These quantitative estimates were derived using a standard input - output model, though the estimates themselves have not yet been peer reviewed. Input - output (I/O) models do not account for opportunity costs of labor

– that is, all employ

ment needs due to the regulatory change are assumed to be met by unemployed workers. In addition, I/O models assume no changes in the average use of labor per dollar of output in the affected sectors. For these and other reasons, the se may at best be considered an imprecise upper bound on actual emplo ym ent impacts. 40 Determining the direction of employ ment effects even in the regulated industry may be difficult due to the presence of competing effects that lead to an ambiguous adjustm ent in employ ment as a result of environmental regulation. Morgenstern, Pizer and Shih41 identify three separate way s employ

ment levels may

change in the regulated industry in response to a new (or more stringent) regulation. First, higher production costs due to the regulation will lead to higher market prices ; higher prices in turn reduce demand for the good, reducing the demand for labor to make that good (“demand effect”) . Second, costs rise because of increased use of inputs, including labor, in order to comply with the new regulatory

requirements (“cost effect”) . Third, p ost - regulation production technologies may be more or less labor- intensive (i.e., more/less labor is required per dollar of output) (“factor- shift effect”). The “demand effect” is expected to have***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 57 a negative effect on employ ment, the “cost effect” to have a positive effect on employ ment, and the “factor- shift effect” has an ambiguous effect on employ ment. Without more information as to the magnitudes of these competing effects, it is not possible to predict the total effect environmental regulation will have on employ ment levels in a regulated sector . Morgenstern et al. estimate d the effects on emplo yment of spending on pollution abatement for four highly polluting/regulated industrie s (pulp and paper, plastics, steel, and petroleum refining). They conclude that increased abatement expenditures generall y have not caused a significant change in employ ment in those sectors. More specifically , their results show that, on average across the industries studied, each additional $1 million spent on pollution abatement results in a (statistically insignificant) net increase of 1.5 jobs. While the specific sectors Morgenstern et al. examined are different that the sectors considered here, the m ethodology

that Morgenstern et al. developed is still useful in this context. **(2)** 

### Overview of Affected Sectors The above discussion focuses on employ ment changes in the regulated sector, but the regulated sector is not the only source of changes in employ ment. In these rules, the regulated sectors are truck and engine manufacturers; they are responsible for meeting the standards set in these rules. The effects of these rules are also likely to have impacts beyond the directl y regulated sector. The related se ctors which these rules are also likely to impact include: motor vehicle parts producers, to the extent that the truck and engine industries purchase components rather than manufacture them in - house; shipping and transport, because many companies in this sector purchase trucks and their operating costs will be affected by both higher truck prices and fuel savings; oil refineries due to reduced demand for petroleum - based fuels; and the final retail market, which is where any net cost reductions due to fuel savings are ultimately expected to be experienced. The foll owing discussion describes the direction of impacts on employ ment in these industries. The effects of the HD rule on net U.S. employ ment depend, not only

on their relative magnitudes, but also on employ ment levels in the overall economy. As previously discussed, in a full - employ ment economy these sector- specific impacts will be mostly offset by employ ment changes elsewhere in the economy and would not be expected to result in a net change in jobs . However, in an economy with significant unemployment these changes may affect net emplo ym ent in the U.S. 

### (a) **Truck and Engine Manufacturers** The regulated sector consists of truck and engine manufacturers. Emplo yment associated with manufacturing trucks and engines may be affected b y the demand, cost, and factor - shift effects. The demand effect depends on the effects of this rulemaking on HD vehicle sales. If vehicle sales increase, then more people will be required to assemble trucks and their components . If vehicle sales decrease, employ ment associated with these activities will decrease. The effects of this rulemaking on HD vehicle sales depend on the perceived desirability

of the new vehicles. On one hand, this rulemaking will increase vehicle costs ; by**Regulatory Impact Analysis**

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itself, this effect would reduce vehicle sales. On the other hand, this rulemaking will reduce the fuel costs of operating the vehicle; b y itself, this effect would increase vehicle sales, especiall y if potential buyers have an expectation of higher fuel prices. The agencies have not made an estimate of the potential change in vehicle sales. However , as discussed in Preamble Section VIII.E.5 , the agencies have estimated an increase in vehicle miles traveled (i.e., VMT rebound) due to the reduced ope rating costs of trucks meeting these new 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 emplo ym ent (recognizing that these increases may

be partially

offset by a decrease in manufacturing and sales for equipment of other modes of transportation such as rail cars or barges). As discussed in Preamble Section VIII.A., the agencies find that the reduction in fuel costs asso ciated with this rulemaking outweigh the increase in vehicle cost. This finding is puzzling insofar as market forces should lead truck manufacturers and buyers to install all cost effective fuel - saving technology , but the agencies find that they have not . Preamble Section VIII.A discusses various hy potheses that have been suggested to explain this phenomenon. Some of the explanations suggest that vehicle manufacturers and buye rs will benefit from the rulemaking, and vehicle sales will increase; others s uggest that the opposite might occur. The agencies do not have strong evidence supporting one specific explanation over another. As a result, the agencies do not suggest a direction for changes in employ ment due to changes in vehicle sales . However, som e in the heav y- duty i ndustry indicate the potential for an increase in jobs. As stated by Tom Linebarger (President and Chief Operatin g Officer of Cummins ) and Fred Krupp (President of the Environmental Defense Fund), "Finally , strong environmental stand ards play

a crucial role in getting innovations to market that will create economic opportunity

for American companies and jobs for American workers. . . . It helps that Cummins and other forward - thinking businesses view this as an opportunity to innovate and increase international market share." 42 One commenter raised the issue of whether there could be a loss of recreation vehicle (RV) industry

jobs due to a reduction in the sales of motor homes and towable RVs. As mentioned above, the effects of this rulemaking on HD vehicle sales depend on the desirability of the new vehicles. The truck and engine manufacturing sector has great flexibility in how to respond to the requirement for reduced greenhouse gases and increasing fuel efficiency , with a broad suite of technologies being available to achieve the standards . These technologies are described in detail in Chapter 2 of this RIA. Among these technologies, a distinction can be made between technologies that can be “added on” to conventional trucks versus those that replace features of a conventional truck. “Added on” features require additional labor to install the technologies on trucks, thus clearl y increasing labor demand (the “cost effect”). For “replacement” technologies, the predicted impact o n labor demand from regulation depends on the change in the amount of labor used to install one type of technology compared to another. In many cases, the new technologies are predicted to be more complex than the existing technologies and are therefore l ikely to require additional labor installation inputs. However, most of the technologies that are expected to be used to meet these standards are replacement technologies, therefore overall changes in the labor intensity are expected to be relatively small . For example, almost all of the engine improvements involve replacement technologies that are not expected to significantl y***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 59 change the labor requirements. Similarly, regulations of the chassis on vocational vehicles will only require the installation of a different type of tire, which is also not expected to have large labor intensity

impacts. Therefore, the potential magnitude of the factor shift effect is expected to be relativel y small, though slightly positive due to the additional labor needed to install more complex technologies. For the truck and engine manufacturing sector, the demand effect may result in either increased or decreased employ ment; the cost effect is expected to increase employ ment; and the factor- shift effect may

have a slightl y positive effect on employment in this sector. 

### (b) **Motor Vehicle Parts**  **Manufacturing Sector**  Some vehicle parts are made in - house and would be included directly in the regulated sector; alternatively , some are made by independent suppliers and are not direc tly regulated, but they will be affected by

the rules as well. The parts manufacturing sector will be involved primarily in providing “add - on” parts, or components for replacement parts built internally . Because demand for these parts is expected to increase, emplo ym ent effects in this sector are expected to be positive. 

### (c) **Transport and Shipping Sectors** Although not directl y regulated by these rules, employ ment effects in the transport and shipping sector are likel y to result from these regulations. I f 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 perfectl y competitive industry these costs savings 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 therefore an increase in employ

ment . In addition, if the relative cost of shipping freight via trucks become s cheaper than shipping b y other modes (e.g., rail or barge), then emplo ym ent in the truck transport industry

is likely to increase. To the extent that 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 shipping sectors. 

### (d) **Fuel Suppliers** In addition to the effects on the trucking industry and related truck parts sector, these rules will result in reductions in fuel use that lower GHG emissions. Fuel s aving, principally reductions in liquid fuels such as diesel and gasoline, will affect emplo ym ent in the fuel suppliers industry sectors, principally

the P etroleum Refinery

sector. 43 Expected fuel consumption reductions b y fuel typ e, and b y heav y- duty veh icle ty pe, can be found in Table VIII - 7. These reductions reflect impacts from the new fuel efficiency and GHG standards and include increased consumption from the rebound effect. These fuel savings are monetized in Table VIII - 8 by multiplying the reduce d fuel consumption in each year b y the corresponding estimated average fuel price in that year, using the Reference Case from the AEO 2011. In 2014, the pre - tax fuel savings is $1.2 billion (2009$). While these figures represent a**Regulatory Impact Analysis**

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level of fuel savings f

or purchasers of fuel, it also represents a loss in value of output for the petroleum refinery

industry . Since 50 percent of the fuel would have been refined in the U.S., the loss in output to the U.S. Petroleum Refinery sector is $600 million (2009$), wh ich will result in reduced sectoral employ ment. 44 Because this sector is very capital - intensive, the employ ment effect is not expected to be large. 

### (e) **Fuel Savings** As a result of this rulemaking, it is anticipated that trucking firms will experience fuel sav ings. Fuel savings lower the costs of transportation goods and services and, as a result, could increase the profitability

of trucking firms operating in the U.S. In a competitive market, the fuel saving that initially

accrue to trucking firms are likely to be passed along as lower transportation costs that, in turn, could result in lower prices for final goods and services. In either case, the savings will accrue to some segment of consumers: either owners of trucking firms or the general public. In bo th cases, the effect will be increased spending by consumers in other sectors of the economy, creating jobs in a diverse set of sectors, including retail and service industries. As mentioned above, the value of fuel savings from this rulemaking is projec ted to be $1.2 billion (2009$) in 2014, according to Table VIII - 8. If all those savings are spent by consumers or trucking firm owners, the fuel savings will stimulate increased employ ment in the economy through those expenditures. I f the fuel savings a ccrue primaril y to firm owners , they may either reinvest the money or take it as profit. Reinvesting the money in firm operations would increase emplo ym ent directl y. If they take the money as profit, to the extent that these owners are wealthier than the general public, they may spend less of the savings, and the resulting emplo ym ent impacts would be smaller than if the savings went to the public . **(3)** 

### Summary The net emplo ym ent effects of this rulemaking are expected to be found through out several key

sectors : truck and engine manufacturers, the trucking industry , truck parts manufacturing, fuel production, and consumers. For the regulated sector, the demand effect may result in either increased or decreased employ ment, depending on the net effect on HD vehic le sales. The cost effect is expected to increase employ ment in the regulated sector; and the factor shift effect may

have a slightly

positive effect on employ ment, though we cannot definitively say this is the case without quantification. Increased expenditures by truck and engine manufacturers are expected to require increased labor to build parts , though this effect also depends on any changes in the labor intensity of production ; increased complexity of technologies may

imply increased labor inputs. Lower prices for shipping are expected to lead to an increase in demand for truck shipping services and, therefore, an increase in emplo ym ent, though this effect may be offset somewhat b y changes in emplo ym ent in other shipping sectors . Reduced fuel produ ction 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 empl oy ment through their expenditures . Given the job creation as a result of the $1.2B (2009$) in fuel savings in 2014 and the possible employ

ment increases in the manufacturing and parts sectors, we find it highl y unlikely that there would be significant net job losses related to this policy .***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 61 Reference

1 See NAS 2010 at p. 188. 2 Akerlof, George A. “The Market for ‘Lemons’” Quality Uncertainty and the Market Mechanism,” Quarterly Journal of Economics 84(3) (1970): 488 - 500 points out that asymmetric information

– the seller has better information than the buyer

– can potentially lead to complete failure of a market, even when both buyers and sellers wo uld benefit from trade. 3 See NAS 2010, Note **Error! Bookm ark not defined.**  **Error! Bookmark not defined.** , at p. 182. 4 Metcalf, G., and D. Rosenthal (1995). “ The ‘New’ View of Investment Decisions and Public Policy Analysis: An Application to Green Lights and Cold Refrigerators, ” Journal of Policy Analysis and Management 14: 517

– 531. Hassett and Metcalf (1995). “ Energy Tax Credits and Residential Conservation Investment: Evidence from Panel Data ” Journal of Public Economics 5 7 (1995): 201 - 217. Metcalf, G., and K. Hassett (1999). “ Measuring the Energy Savings from Home Improvement Investments: Evidence from Monthly Billing Data. ” The Review of Economics and Statistics 81(3): 516 - 528. 5 Greene, D., J. German, and M. Delucchi (20 09). “Fuel Economy: The Case for Market Failure” in Reducing Climate Impacts in the Transportation Sector, Sperling, D., and J. Cannon, eds. Springer Science. 6 Dasgupta, S., S. Siddarth, and J. Silva - Risso (2007). “To Lease or to Buy? A Structural Model o f a Consumer’s Vehicle and Contract Choice Decisions.” Journal of Marketing Research 44: 490

– 502. 8 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking , December 2008 (Docket ID: EPA - HQ - OAR - 2010 - 0162 - 0007). 9 Transport Canada, Operating Cost of Trucks, 2005. See http://www.tc.gc.ca/eng/policy/report - acg operatingcost2005 - 2005 - e - 2 - 1727.htm , accessed on July 16, 2010 (Docket ID: EPA - HQ - OAR - 2010 - 0162 - 0006 ). 10 Memo from Energy and Environmental Research Associates , LLC Regarding HDV Rebound Effect, dated June 8, 2011. 11 Graham and Glaister, “Road Traffic Demand Elasticity Estimates: A Review,” Transport Revie ws Volume 24, 3, pp. 261 - 274, 2004 (Docket ID: EPA - HQ - OAR - 2010 - 0162 - 000 5). 12 Li, Z., D.A. Hensher, and J.M. Rose, Identifying sources of systematic variation in direct price elasticities from revealed pre

ference studies of inter- city freight demand. Transport Policy, 2011 13 Committee to Assess Fuel Economy Technologies for Medium - and Heavy - Duty Vehicles; National Research Council; Transportation Research Board (2010). Technologies and Approaches to Reduci ng the Fuel Consumption of Medium - and Heavy - Duty Vehicles. ("The NAS Report") Washington, D.C. **,** The National Academies Press. Available electronically from the National Academy Press Website at http://www.nap.edu/catalog. See also 2009 Cambridge System atics, Inc., Draft Final Paper commissioned by the NAS in support of the medium - duty and heavy - duty report. Assessment of Fuel Economy Technologies for Medium and Heavy Duty Vehicles: Commissioned Paper on Indirect Costs and Alternative Approaches (Docket ID: EPA - HQ - OAR- 2010 - 0162 - 0009). 14 Friedlaender, A. and Spady, R. (1980) A derived demand function for freight transportation, Revie w of Economics and Statistics, 62, pp. 432

–441 (Docket ID: EPA - HQ - OAR - 2010 - 0162 - 0004). 15 Christidis and Leduc, “Longer and H eavier Vehicles for freight transport,” European Commission Joint Research Center’s Institute for Prospective Technology Studies, 2009 (Docket ID: EPA - HQ - OAR - 2010 - 0162 - 0010). 16 Christidis and Leduc, 2009.**Regulatory Impact Analysis**

9 - 62 17 Winebrake, James and James J. Corbett (2010). “I mp roving the Energy Efficiency and Environmental Performance of Goods Movement,” in Sperling, Daniel and James S. Cannon (2010) Climate and Transportation Solutions: Findings from the 2009 Asilomar Conference on Transportation and Energy Policy. See **http://www.its.ucdavis.edu/events/2009book/Chapter13.pdf** (Docket ID: EPA - HQ - OAR- 2010 - 0162 - 0011) 18 Winebrake, J. J.; Corbe tt, J. J.; Falzarano, A.; Ha wker, J. S.; Korfmacher, K.; Ketha, S.; Zilora, S., Assessing Energy, Environmental, and Economic Tradeoffs in Intermodal Freight Transportation, Journal of the Air &W aste Management Association , 58(8), 2008 (Docket ID: EPA - HQ - OAR - 2010 - 0162 - 0008) . 19 Cambridge Systematics, Inc.. 2009. 20 Northeast States Center for a Clean Air Future, Southeast Research Institute, TIAX, LLC., and International Council on Clean Transportation, Reducing Heavy -Duty Long Haul Truck Fuel Consumption an d CO2 Emissions, September 2009. See http://www.nescaum.org/documents/heavy - duty - truck - ghg_report_final - 200910.pdf 21 NHTSA’s estimates of the rebound effect are derived from econometric analysis of national and state VMT data reported in Federal Highway Administration, Highway Statistics , various editions, Tables VM - 1 and VM - 4. Specifically, the estimates of the rebound effect reported in Table 9 - 2 are ranges of the estimated short - run and long - run elasticities of annual VMT by single - unit and combination trucks with respect to fuel cost per mile driven. (Fuel cost per mile driven during each year is equal to average fuel price per gallon during that year divide d by average fuel economy of the truck fleet during that same year.) These estimates are derived from time - series regression of annual national aggregate VMT for the period 1970 - 2008 on measures of nationwide economic activity , including aggregate GDP, the value of durable and nondurable goods production, and the volume of U.S. exports and imports of goods, and variables affecting the price of trucking services (driver wage rates, truck purchase prices, and fuel costs), and from regression of VMT for each individual state over the period 1994 - 2008 on similar variables measured at the state level. 22 Memo from Energy and Environmental Research Associates , LLC Regarding HDV Rebound Effect, dated June 8, 2011 . 23 Docket ID EPA - HQ - OAR - 2009 - 04 72 - 114577, Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon , with participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Also available at http://epa.gov/otaq/climate/regulations.htm 24 The interagency group decided that these estimates apply only to CO 2 emissions. Given that warming profiles and im pacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group concluded “transforming gases into CO 2 - equivalents using GWP, and then multiplying the carbon - equivalents by the SCC, wo uld not result in accurate estimates of the social costs of non - CO 2 gases” (SCC TSD, pg 13). 25 The SCC estimates were converted from 2007 dollars to 2008 dollars using a GDP price deflator (1.021) and again to 2009 dollars using a GDP price deflator (1. 009) obtained from the Bureau of Economic Analysis, National Income and Product Accounts Table 1.1.4, Prices Indexes for Gross Domestic Product . 26 National Research Council (2009). Hidden Costs of Energy: Unpriced Co nsequences of Energy Production and Use . National Academies Press. See docket ID EPA - HQ - OAR - 2009 - 0472 - 11486. 27 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 ) .***** E.O. 12866 Review –**  **Draft –**  **Do Not Cite, Quote, o r Release During Review ** ***

9 - 63 28 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 21, 2010 ). 29 See Table 4. Last viewed on September 9, 2010 at http://ostpxweb.dot.gov/policy/Data/VOTrevision1_2 - 11 - 03.pdf . Note that we assume the value of travel time is constant out to 2050, which is a conservative assumption since it is likel y this value will increase due to income growth in the future. 30 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 31 “Preliminary Regulatory Impact Analysis, FMVSS No. 119, New Pneumatic Tires for Motor Vehicles with a GVWR of More Than 4,536 kg (10,000 pounds), June 2010. 32 Cost and Weight Analysis of Two Motorcoach Seating Systems: One With and One Without Three - Point Lap/Shoulder Belt Restraints, Ludtke and A ssociates, July 2010. 33 “Final Regulatory Impact Analysis, Corporate Average Fuel Economy for MY 2012 - MY 2016 Passenger Cars and Light Trucks”, NHTSA, March 2010, (Docket No. NHTSA - 2009 - 0059 - 0344.1). 34 IEA 2011 “IEA Response System for Oil Supply Emerg encies.” 35 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. 36 Ibid. 37 U.S. Bureau of Labor Statistics seasonally - adjusted Current Em ployment Statistics Survey for the Truck Transportation Industry (NAICS 484) and the Motor Vehicle Parts Manufacturing Industry (NAICS 3363). 38 U.S. Census Bureau, 2009 Annual Survey of Manufactures, Published December 3, 2010. 39 Union of Concerned Scientists and CalStart, Delivering Jobs: The Economic Costs and Benefits of Improving Fuel Economy of Heavy Duty Vehicles , July, 2010. http://**www.ucsusa.org/deliveringjobs .**  40 Berck, Peter, and Sandra Hoffman. “Assessing the Employment Impacts of Environmental and Natural Resource Policy.” Environmental and Resource Economics 22 (2002): 133 - 156. 41 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. 42 Tom Linebarger (President and Chief Operatin g Officer of Cummins ) and Fred Krupp (President of the Environmental Defense Fund), "Clear rules can create better engines, clean air," Indianapolis Star, October 28, 2010, p . 19; included as part of Cummins' comments on the rule, Docket Number EP A - HQ - OAR - 2010 - 0162 - 1765.1[1] . 43 North American Industry Classification System (NAICS) Code 32411. 44 EPA and NHTSA estimate that approximately 50 percent of the reduction in fuel cons um ption resulting from adopting improved fuel GHG standards and fuel efficiency standards is likely to be reflected in reduced U.S. imports of refined fuel, while the remaining 50 percent is expected to be reflected in reduced domestic fuel refining. Of this latter figure, 90 percent is anticipated to reduce U.S. imports of crude petroleum for use as a refinery feedstock,**Regulatory Impact Analysis**

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while the remaining 10 percent is expected to reduce U.S. domestic production of crude petroleum. Because we do not expect to see a significant reduction in crude oil production in the U.S., we do not expect this rule to have a significant impact on the Oil and Gas Extraction industry sector in the U.S. (NAICS 211000). For more information, refer to Section VIII - I on the energy security im pacts from the rule.