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EO12866_GHG+Fuel Eff Stds for Med-Heavy-Duty Engines+Vehicles 2060-AP61 FRM 

RIA_20110722_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, 

• refueling savings,

• energy security impacts,

• increased driving due to the “rebound” effect.

The benefits and costs of this rule are analyzed 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 by reducing consumption and imports of petroleumbased 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 VIII.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. For some discount rates and some projected social costs of 

carbon, however, the reductions in these external costs are less than the costs of new fuel saving 

technologies needed to meet the standards. (See Tables 9-18 and 9-19 in the RIA.) Nevertheless,

this 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

considerably shorter than vehicles’ expected lifetimes under the assumptions used for this 

analysis (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 that 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.”

Standard economic theory suggests that in normally functioning competitive markets, 

interactions between vehicle buyers and producers would lead producers to incorporate all costeffective 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 heavy-duty

truck market are purchased and operated by 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, yield 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 

significantly 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 savings.

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 trucking industry – market 

power, externalities, or asymmetric 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.), whereby participants collectively do not 

minimize costs; or the engineering estimates of fuel savings and costs for these technologies 

might overstate their benefits or understate their costs in real-world applications.

To try to understand why trucking companies have not adopted these seemingly costeffective fuel-saving technologies, the agencies surveyed published literature about the energy

paradox, and held discussions with numerous truck market participants. The proposal discussed 

five categories of possible explanations derived from these sources. Collectively, these five 
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hypotheses may explain the apparent inconsistency between the engineering analysis, 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 fuelsaving 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 imply additional costs of regulating truck fuel efficiency that are not accounted for in this 

analysis. 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 hypotheses 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 analysis or data related 

to this question.

The agencies invited 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 specifically 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 not to 

invest because of warranted skepticism about these technologies. The agencies also requested

comment and information regarding any other hypotheses that could explain the appearance that 

cost-effective fuel-saving technologies have not been widely 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 

understandably be reluctant to spend additional money to purchase vehicles equipped with 

unproven technologies. 

This lack of information can manifest itself in multiple ways. For instance, the problem 

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 by participants in SmartWay; non-participants are unlikely to 

get all the benefits of participants. Other explanations, 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 adequately reward the addition of fuel-saving technology to vehicles to 

ensure their original purchase by new truck buyers. This inadequate payback 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 of 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 styles 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 originally designed, and the fuel-saving technology is 

therefore of less value. 
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It is possible, though, that the fuel savings experienced by the secondary purchasers may

not match those experienced by 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-saving 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 optimize use in the 

secondary market may not be readily available or reliable, however, buyers 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 less 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 

compensation 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 

payback periods required for new technologies in the new vehicle market.

9.2.3 Split Incentives in the Medium- and Heavy-Duty Truck Industry

A third hypothesis explaining the energy paradox as applied to trucking involves split

incentives. When markets work effectively, 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 system, changes in the expected 

future price of fuel should be transmitted rapidly to those who purchase trucks, 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 not decide which truck characteristics to purchase, 

then those price signals may not be transmitted effectively, 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 freely among the trucks they

drive, competition among truck owners to employ operators would encourage owners to invest in 

fuel-saving technology. However, if truck 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 cost 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 prefer 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 should 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 purchases is often different from that responsible for 

purchasing fuel. Therefore, the employees who purchase trucks may have strong incentives to 

lower their 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 About Future Cost Savings

Another hypothesis for the lack of adoption of seemingly 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. In this situation, 

both the expected return on capital investments in higher fuel efficiency and potential variance 

about its expected rate may play a role in a firm’s calculation of its payback 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 quickly may make them worse off by

imposing additional risk on them.

Greene et al (2009) also finds 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 fuelefficient car.

5

Supporting this hypothesis is a finding by Dasgupta et. al (2007) that consumers 
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are more likely to lease than buy a vehicle with higher maintenance 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 heavy-duty vehicles.

Purchasers’ 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 costs does not by itself assure that regulations requiring 

improved fuel efficiency will necessarily 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 costs 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 buyers to adopt new technologies slowly, then successful new technologies are likely 

to be adopted over time without market intervention, but with potentially significant delays 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 by 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 typically 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 rulemaking. 

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 by others. If trucks confer status on their 

owners or operators, and if that status depends on easily observable 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: by increasing the requirements for non-positional fuel efficiency, regulation could 

reduce expenditures made purely 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 role in the heavyduty 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 payback. 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 purchase 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 costeffective fuel-saving technologies. On the other hand, the short payback period required by 

buyers of new trucks is a symptom that suggests some combination of uncertainty about future 

cost savings, transactions costs, and imperfectly functioning markets. In addition, widespread 

use of tractor-trailer combinations introduces the possibility that owners of trailers may have 

weaker incentives than truck owners or operators to adopt fuel-saving technology for their 

trailers. The market for medium- and heavy-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 likely to avoid 
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new technologies out of concerns over the possibility of inadequate returns on the investment, or 

with other adverse impacts. Competitive pressures 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 

technical 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 substantially 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 lightduty 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 sectors. One of the major differences between the HD rebound effect and the 

light-duty rebound effect is that HD vehicles are used primarily for business purposes. Since 

these businesses are profit driven, decision makers are highly likely to be aware of the costs and 

benefits of different 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 and shipping decisions that affect truck usage. 

Another difference from the light-duty case is that, as discussed in the recent NAS 

Report, when calculating the change in trucking costs that causes the rebound effect, all 

components of truck operating costs should be considered. The cost of labor and fuel generally

constitute the two largest shares of truck operating costs, depending on the price of petroleum,

distance traveled, type of truck, and commodity.

89

 See Figure 9-1. In addition, the equipment 

depreciation costs associated with the purchase or lease 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 these 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 

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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. Conversely, if the upfront vehicle costs 

outweighed future cost savings and total costs increased, shipping costs would rise, which would 

likely result in a decrease in truck VMT. In theory, other cost changes resulting from any 

requirement to achieve higher fuel economy, such as changes in maintenance costs or insurance 

rates, should also be taken into account, although information on potential changes in these 

elements of truck operating costs is extremely limited. 

$0.634

$0.062 $0.092 $0.206

$0.060

$0.030

$0.024

$0.019

$0.441

$0.126

$0.036

Cost Per Mile Summary 

Source: ATRI, 2008 

Fuel-Oil Costs* ($/mile)

Fuel Taxes ($/mile)

Truck/Trailer Lease or Purchase 

Payments ($/mile)

Repair and Maintenance ($/mile)

Truck Insurance Premiums ($/mile)

Tires ($/mile)

Licensing and Overweight-Oversize 

Permits ($/mile)

Tolls ($/mile)

Driver Pay ($/mile)

Driver Benefits ($/mile)

Driver Bonus Payments ($/mile)

Total Cost Per Mile: $1.73

* Based on $4.79/gallon diesel in 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 
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rebound effect to date. According to the NAS study, it is “not possible to provide a confident 

measure of the rebound effect,” yet NAS concluded that a rebound effect likely exists and that 

“estimates of fuel savings from regulatory standards will be somewhat misestimated if the 

rebound effect is not considered.” While we believe the HD rebound effect needs to be studied 

in more detail, we have attempted to capture the potential impact of the rebound effect in our 

analysis. 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, respectively. For this rule, we do not discern between short-run and longrun 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 

delivered prices for products 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 distribution networks to take 

advantage of lower truck shipping costs. For example, shippers may shift away from other 

modes of shipping such as rail, barge, or air. In addition, shippers may also choose to reduce the 

number of warehouses, reduce load rates, and make smaller, more frequent shipments, all of 

which could also lead to an increase in HD VMT. Finally, the benefits of the fuel savings could 

ripple through the economy, which could in turn increase overall demand for goods and services

shipped by trucks, and therefore increase HD VMT. 

Conversely, if a fuel efficiency regulation leads to net increases in the cost of trucking 

because fuel savings do not fully offset the increase in upfront vehicle costs, then the price of 

trucking services could rise, spurring a decrease in HD VMT and a shift to alternative shipping

modes. These effects would also ripple through the economy.

As discussed in Section 8 of the preamble, the magnitude of the rebound effect is likely

to be determined by 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 has been conducted on the medium-duty truck rebound effect. In this rule, 

we discuss four options for quantifying the rebound effect. 
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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 percentage change in freight rates. It is important to note that most of the aggregate 

estimates measure the change in freight demanded (tons or ton-miles), rather than a change in 

fuel consumption or VMT. The change in tons or ton-miles is more accurately characterized as a 

freight elasticity. Therefore, it may not be entirely appropriate to interpret these freight 

elasticities as measures of the rebound effect, although these terms are sometimes used 

interchangeably in the literature.10 Given these caveats, freight elasticity estimates rely on 

estimates of aggregate price elasticity of demand for trucking services, given a percentage 

change in trucking prices, which is generally referred to as an “own price elasticity.” Estimates 

of trucking own-price elasticities vary widely (from positive 1.72 to negative 7.92), though a

2004 literature survey found aggregate elasticity estimates generally 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 freight elasticities is that these 

values appear to vary substantially based on the demand elasticity measure (e.g., ton or tonmile), the model specification (e.g., linear functional form or log linear), the length of the trip,

and the type of cargo. In general, elasticity estimates of longer trips tend to be larger than 

elasticity estimates for shorter trips. In addition, elasticities tend to be larger for lower-value 

commodities compared to higher-value commodities. Although these factors explain some of 

the differences in estimates, much of the observed variation cannot be explained quantitatively. 

For example, a recent study that controlled for these variables only accounted for about half of 

the observed variation.

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 by economic activity, there is often the choice of shipping freight on modes other 

than truck. This is because the United States has extensive rail, waterway and air transport 

networks in addition to an extensive highway network; these networks often closely parallel 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 by another mode (e.g., rail) given a percentage change 

in the price of trucking. Aggregate estimates of cross-price elasticities also vary widely, and 

there is no general consensus on the most appropriate value to use for analytical purposes. The 

NAS report cites values ranging from 0.35 to 0.59.13 Other reports provide significantly

different cross-price elasticities, ranging from 0.114 to 2.0.

15
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

9-13

Author/Date Evaluation based 

on ...

Least 

Elastic

Description of Least 

Elastic Value

Most 

Elastic

Description of Most Elastic 

Value Region

Demand Units of

Measurement

Commodity

Abdelwahab (1998) Commodity -0.75 Construction materials -1.40 Textile products U.S. ICC 

Official

Probability of

mode choice

Varies

Spady & Friedlaender 

(1980) Commodity -0.15 Wood and wood products -5.06 Electrical Machinery

U.S. ICC 

Southern ton-miles Varies

Spady & Friedlaender 

(1980) Commodity -1.00 Food Products -3.55 Electrical Machinery

U.S. ICC 

Average

ton-miles Varies

Oum (1980) Commodity -0.41 Metallic products -1.07 Fuel oil except gasoline Canada ton-miles Varies

Winston (1981) Commodity -0.14 Lumber, wood and 

Furniture

-2.96 Transport Equipment USA tons Varies

Li et al. (2011) Commodity -1.09 Other (Commodity

Category) -1.30 Natural Resources

USA, Italy & 

India

tonne-km Varies

Campisi and Gastaldi 

(1996) Commodity -0.27 Petroleum products -1.37 Minerals Italy tonnes Various

Friedlaender and Spady

(1981) Commodity -0.59 Petroleum products -1.72 Wood USA tonne-km Various

Bonilla (2008) Commodity -0.43 Oil and Coal -1.75 Building materials Denmark tonne-km Varies

Rich et al (2011) Competition -0.08 All O-D pairs -0.11 O-D Pairs where competing

modes available Scandinavia tonne-km Agricultural products

Beuthe et al. (2001) Demand measure -0.58 tonnes -1.06 tonne-km Belgium tonne, tonne-km Aggregate

Li et al. (2011) Demand measure -1.02 tonnes -1.30 tonne-km USA, Italy & 

India

tonne, tonne-km Natural 

Resources

Beuthe et al. (2001) Distance -1.06 <300 km -1.31 >300 km Belgium tonne-km Aggregate

Winston (1981) Distance -0.34 <900 miles (average) -1.56 >900 miles (average) USA tons Varies

Christidis and Leduc 

(2009) Distance -0.21 < 800 km -1.15 > 1500 km EU tons All

Oum (1992) Model form -0.69 Translog -1.34 Log-linear Various ton, ton-miles Aggregate

Graham and Glaister 

(2004) Model form -0.048 Linear -1.34 Log-linear Various Various Various

Li et al. (2011) Panel vs. CS -0.93 Panel Translog -1.30 CS Translog

USA, Italy & 

India

tonne-km Natural 

Resources

Abdelwahab (1998) Region -0.80 U.S. ICC Official -2.18 U.S. ICC Southwestern Various U.S. Probability of

mode choice

Metal products

Spady & Friedlaender 

(1980) Region -1.66 US ICC MountainPacific

-5.06 U.S. ICC Southern Various U.S. ton-miles

Electrical 

Machinery

Li et al. (2011) Region -0.86 Canada -1.96 Australia Various tonne-km Natural 

Resources

Graham and Glaister 

(2004) All elasticity 1.72

Least elastic end of

range

-7.92 Most elastic end of range Various Various Various

Figure 9-2: Elasticity of Demand Estimates for Truck Freight Showing Low/High Elasticity Values Based on 

Various Study Elements

When considering intermodal shift, one of the most relevant kinds of shipments are those 

that are competitive between rail and truck modes. These trips generally include long-haul 

shipments greater than 500 miles, which weigh between 50,000 and 80,000 pounds (the legal 

road limit in many states). Special kinds of cargo like coal and short-haul deliveries are of less 

interest because they are generally not economically transferable between truck and rail modes, 

and they would not be expected to shift modes except under an extreme price change. However, 

the total amount of freight that could potentially be subject to mode shifting has also not been 

studied extensively. 

9.3.2.2 Sector-Specific Estimates

Given the limited data available regarding the HD rebound effect, the aggregate approach 

greatly simplifies many of the assumptions associated with calculations of the rebound effect. In 
Regulatory Impact Analysis 

9-14

reality, however, responses to changes in fuel efficiency and new vehicle costs will vary

significantly based on the commodities affected. A detailed, sector specific approach, would be 

expected to more accurately reflect changes in the trucking market in response to the standards in 

this rule. For example, input-output tables could be used to determine the trucking cost share of 

the total delivered price of a commodity. Using the change in trucking prices described in the 

aggregate approach, the product-specific demand elasticities could be used to calculate the 

change in sales and shipments for each product. The change in shipment increases could then be 

weighted by the share of the trucking industry total, and then summed to get the total increase in 

trucking output. A simplifying assumption could then be made that the increase in output results 

in an increase in VMT. To the best of our knowledge, this type of detailed 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 econometrically by estimating truck demand as a function of 

economic activity (e.g., GDP) and different input prices (e.g., vehicle prices, driver wages, and 

fuel costs per mile). This type of econometric model could be estimated for either truck VMT or 

ton-miles as a measure of demand. The resulting elasticity estimates could then be used to 

determine the change in trucking demand, given the change in fuel cost and truck prices per mile 

from these standards. One of the challenges associated with an econometric analysis is the 

potential for omitted variable bias, which could either overstate or understate the potential 

rebound effect if the omitted variable is correlated with the controlled variables.

9.3.2.4 Other Modeling Approaches

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 analysis considered by the EC relies on country-specific 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 types of analysis 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 potential 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 

calculations in the model. Within this intermodal network, the model assigns various economic, 

time-of-delivery, energy, and environmental attributes to real-world goods movement routes. 

The model can then calculate different network optimization scenarios, based on changes in 

prices and policies.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, or Release During Review ***

9-15

9.3.3 Estimates of the Rebound Effect

The aggregate methodology was used by Cambridge Systematics, Inc. (CSI) to show 

several examples of the magnitude of the rebound effect.19 In their 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 (ICCT) 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, CSI assumed 142,706 million miles of truck 

VMT and 1,852 billion ton-miles were affected. The truck VMT was based on the Bureau of 

Transportation Statistics (BTS) highway miles for combination tractors in 2006, and the rail tonmiles were based on the 2006 BTS total railroad miles. This assumption may overstate the 

potential rebound effect, 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 mode shifting. 

For CSI’s calculations, all costs except fuel costs and vehicle costs were taken from the 

2008 ATRI study. It is not clear from the report how the new vehicle costs were incorporated 

into the per mile operating costs calculations. For example, in both the ATRI report and the CSI 

report, assumptions about depreciation, useful life, and the opportunity cost of capital are not 

explicitly discussed. 

Based on these two scenarios, CSI found a rebound effect of 11-31% for Scenario 1 and 

5-16% for Scenario 2 when the fuel savings from reduced rail usage were not taken into account 

(“First rebound effect”). When the fuel savings from reduced rail usage were included in the 

calculations, the overall rebound effect was between 9-13% for Scenario 1 and 3-15% for 

Scenario 2 (“Second Rebound Effect”). See Table 9-1. 

CSI included a number of caveats associated with these calculations. Namely, the 

elasticity estimates derived from the literature are “heavily reliant on factors including the type 

of demand measures analyzed (vehicle-miles of travel, ton-miles, or tons), geography, trip 

lengths, markets served, and commodities transported.” Furthermore, the CSI example only

focused on Class 8 trucks and did not attempt to quantify the potential rebound effect for any 

other truck classes. Finally, these scenarios were characterized as “sketches” and were not 

included in the final NAS report. In fact, the NAS report asserted that it is “not possible to 

provide a confident measure of the rebound effect”, yet concluded that a rebound effect likely

exists and that “estimates of fuel savings from regulatory standards will be somewhat 

misestimated if the rebound effect is not considered.”
Regulatory Impact Analysis 

9-16

Table 9-1 Range of Rebound Effect Estimates from Cambridge Systematics Aggregate Assessment

Scenario 1 

(6.8 mpg, $22,930)

Scenario 2

(9.1 mpg, $71,630)

“First Rebound Effect” 

(increase in truck VMT 

resulting from decrease in 

operating costs)

11-31% 5-16%

“Second Rebound Effect” 

(net fuel savings when 

decreases from rail are 

taken into account)

9-13 % 3-15%

As an alternative, using the econometric approach, NHTSA has estimated the rebound 

effect in the short-run and long run for single unit (Class 4-7) and combination (Class 8) trucks. 

As shown in Table 9-2, the estimates for the long-run rebound effect are larger than the estimates 

in the short run, which is consistent with the theory that shippers have more flexibility to change 

their behavior (e.g., restructure contracts or logistics) when they are given more time. In 

addition, the estimates derived from the national data also showed larger rebound effects 

compared to the state data. 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 

increases in fuel prices are highly correlated with economic downturns, and there may not be 

enough variation in the national data to differentiate the impact of fuel price changes from 

changes in economic activity. In contrast, some states may see an increase in output when 

energy prices increase (e.g., large oil producing states such as Texas and Alaska), therefore the 

state data may be more accurately isolating the impact of fuel price changes from that of changes 

in economic activity. It is important to note 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 

increases in new vehicle costs that are likely 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 effective 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 Type National Data State Data

Short Run Long Run Short Run Long Run

Single Unit 13-22% 28-45% 3-8% 12-21%

Combination N/A 12-14% N/A 4-5%

As discussed throughout this section, there are multiple methodologies for quantifying 

the rebound effect, and these different methodologies produce a large range of potential values of 

the rebound effect. However, for the purposes of quantifying the rebound effect for this 

program, we have used a rebound effect with respect to changes in fuel costs per mile on the 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or 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 pickup 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 

heavy-duty vehicles, it does not necessarily seem appropriate to apply one of the heavy-duty

estimates to the HD pickup trucks and vans. These vehicles are more similar in use to large 

light-duty vehicles, so for the purposes of our analysis, we have chosen to apply the light-duty

rebound effect of 10% 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 analysis 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 apply to total operating costs. For example, we assumed the elasticity of Class 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 45% of total truck operating costs;

therefore, the elasticity of truck use with respect to total operating costs is -0.05/0.45 = -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%/45%) larger 

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 21% of total operating costs, and for vocational 

vehicles assuming fuel costs are on average 25% 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 Determine 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 

9-18

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 applying the overall rebound effect 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 by 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 CSI examples. Furthermore, this assumption implies that the new vehicle buyer and the 

used vehicle buyer 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 analysis. 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 usage, 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 

sectors due to mode shifting. However, we have provided CSI’s example calculations in Table 

9-1. The rebound effect values used in the cost and benefit analysis fall within the range of the 

“second rebound effect” identified in the CSI analysis, 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 existence and types of market failures affecting the fuel 

economy of the trucking fleet. If firms are already accurately accounting for the costs and 

benefits of these technologies and fuel savings, then these regulations would increase their net 

costs, because trucks would already include all cost-effective fuel saving technologies. As a 

result, the rebound effect would actually 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 preamble Section VIII.A. for further discussion), it is more likely that 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or 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 along to their customers, then the rebound effect would increase 

truck VMT. This response assumes that trucking rates include both truck purchase costs and fuel 

costs, and that the truck purchase costs included in the rates spread those costs over the full 

expected lifetime of the trucks. If those costs are spread over a shorter period, as the expected 

short payback period implies, then those purchase costs will inhibit reduction of freight rates, and 

to the extent that they do so the rebound effect will be proportionally smaller. 

As discussed in more detail in preamble Section VIII.A, if there are market failures such 

as split incentives, estimating the rebound effect may depend on the nature of the failures. For 

example, if the original purchaser cannot fully recoup the higher upfront costs through fuel 

savings before selling the vehicle nor pass those costs onto the resale buyer, the firm would be 

expected to raise shipping rates. A firm purchasing the truck second-hand might lower shipping 

rates if the firm recognizes the cost savings after operating the vehicle, leading to an increase in 

VMT. Similarly, if there are split incentives and the vehicle buyer is not the same entity that 

purchases the fuel, than there would theoretically be a positive rebound effect. In this scenario, 

fuel savings would lower the net costs to the fuel purchaser, which would result in a larger 

increase in truck VMT. 

If all of these scenarios occur in the marketplace, their consequences for the rebound 

effect will depend on the extent and magnitude of their relative effects, which are also likely to 

vary across truck classes (for instance, split incentives may be a much larger problem for Class 7 

and 8 combination tractor than they are for heavy-duty pickup trucks). 

9.4 Monetized CO2 Impacts

We assigned a dollar value to reductions in carbon dioxide (CO2) emissions using recent 

estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the monetized 

damages associated with an incremental increase in carbon emissions in a given year. It is 

intended to include (but is not limited to) changes in net agricultural productivity, human health, 

property damages from increased flood risk, and the value of ecosystem services due to climate 

change. The SCC estimates used in this analysis were developed through an interagency process 

that included EPA, DOT/NHTSA, and other executive branch entities, and concluded in 

February 2010. We first used these SCC estimates in the benefits analysis for the final joint 

EPA/DOT Rulemaking to establish Light-Duty Vehicle Greenhouse Gas Emission Standards and 

Corporate Average Fuel Economy Standards; see that 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 analyses, which we 

have applied in this analysis: $5, $22, $36, and $67 per metric ton of CO2 emissions 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 

9-20

use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all 

three models at a 3 percent discount rate. It is included to represent higher-than-expected 

impacts from temperature change further out in the tails of the SCC distribution. Low 

probability, high impact events are incorporated into all of the SCC values through explicit 

consideration of their effects in two of the three models as well as the use of a probability density 

function for equilibrium climate sensitivity. Treating climate sensitivity probabilistically results 

in more high temperature outcomes, which in turn lead to higher projections of damages.

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that the interagency group estimated the growth rate of the SCC 

directly using the three integrated assessment models rather than assuming a constant annual 

growth rate. This helps to ensure that the estimates are internally consistent with other modeling 

assumptions. Table VIII.G.1-1 presents the SCC estimates used in this analysis.

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (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 system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages.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 noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. The limited amount of research linking climate impacts to economic damages makes 

the interagency modeling exercise even more difficult. The interagency group hopes that over 

time researchers and modelers will work to fill these gaps and that the SCC estimates used for 

regulatory analysis by the Federal government will continue to evolve with improvements in 

modeling. Additional details on these limitations are discussed in the SCC TSD.

In light of these limitations, the interagency group has committed to updating the current 

estimates as the science and economic understanding of climate change and its impacts on 

society improves over time. Specifically, 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 CO2 emissions for the final program, we estimate the dollar value of the climate related 

benefits for each analysis year. For internal consistency, the annual benefits are discounted back 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or 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 CO2

benefit estimates for each calendar year are shown in Table 9-5.

Table 9-4: Social Cost of CO2, 2012 – 2050a(in 2009 dollars per Metric Ton)

Year Discount Rate and Statistic

5% Average 3% Average 2.5% Average 3% 

95th percentile

2012 $5.284 $23.06 $37.53 $70.14

2015 $5.93 $24. 58 $39.57 $74.03

2020 $7.01 $27.10 $42.98 $83.17

2025 $8. 53 $30.43 $47.28 $93.11

2030 $10.05 $33.75 $51.58 $103.06

2035 $11.57 $37.08 $55.88 $113.00

2040 $13.09 $40.40 $60.19 $122.95

2045 $14.63 $43. 34 $63.59 $131.66

2050 $16.18 $46.27 $66.99 $140.37

a The SCC values are dollar-year and emissions-year specific.

Table 9-5: Upstream and Downstream CO2 Benefits for the Given SCC Value, Calendar Year Analysisa

(Millions of 2009 dollars) 

YEAR

5%

(AVERAGE SCC = 

$5 IN 2012)

3% 

(AVERAGE SCC = 

$23 IN 2012)

2.5% 

(AVERAGE SCC =

$38 IN 2012)

3%

(95TH 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 

2021 $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 $2,331 $3,573 $7,119 

 

A

It is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be discounted at 

rates that differ from those used to develop the SCC estimates.
Regulatory Impact Analysis 

9-22

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-year 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 analysis of the CO2 benefits over the model year lifetimes 

of the 2014 through 2018 model year vehicles. In contrast to the calendar year analysis, the 

model year lifetime analysis 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 analysis can be found in 

RIA chapter 5. The CO2 benefits of the full life 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 CO2 benefits are shown for each year 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 present value of SCC for internal 

consistency.

Table 9-6: Upstream and Downstream CO2 Benefits for the 5% (Average SCC) Value, Model Year Analysisa

(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 $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, or 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 $200 $200 $300 $300 $1,200

a The SCC values are dollar-year and emissions-year specific. 

Table 9-7: Upstream and Downstream CO2 Benefits for the 3% (Average SCC) SCC Value, Model Year 

Analysisa(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 $120 $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 $364 

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 

2040 $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-year and emissions-year specific. 

Table 9-8: Upstream and Downstream CO2 Benefits for the from 2.5% (Average SCC) SCC Value, Model 

Year Analysisa(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 $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 $121 $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, or Release During Review ***

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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-year and emissions-year specific. 

Table 9-9: Upstream and Downstream CO2 Benefits for the 3% (95th Percentile) SCC Value, Model Year 

Analysisa(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 

2031 $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-year and emissions-year specific.

9.5 Other Economic Impacts

9.5.1 Noise, Congestion, and Accidents

Section 9.2 discusses the likely sign of the rebound effect. If net operating costs of the 

vehicle decline, then we expect a positive rebound effect. Increased vehicle use associated with 

a positive rebound effect also contributes to increased traffic congestion, motor vehicle 

accidents, and highway noise. Depending on how the additional travel is distributed throughout

the day and on where it takes place, additional vehicle use can contribute to traffic congestion 

and delays by increasing traffic volumes on facilities that are already heavily traveled during 

peak periods. These added delays 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 probably do not consider all of the potential costs they impose 

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, or Release During Review ***

9-27

Like increased delay costs, any increase in external accident 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 by the slower speeds at which 

heavier traffic typically moves).

Finally, added vehicle use associated with a positive rebound effect may also increase 

traffic noise. Noise generated by vehicles causes inconvenience, irritation, and potentially even 

discomfort to occupants of other vehicles, to pedestrians and other bystanders, 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 effect.

EPA and NHTSA rely on estimates of congestion, accident, and noise costs caused by

pickup trucks and vans, single unit trucks, buses, and combination tractors developed by the 

Federal Highway Administration to estimate the increased external costs caused 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 by various classes of trucks that are borne by persons other than their drivers (or 

“marginal” external costs). EPA and NHTSA employed estimates from this source previously in 

the analysis accompanying the Light-Duty GHG final rule. The agencies continue to find them 

appropriate for this analysis after reviewing the procedures used by FHWA to develop them and 

considering other available estimates of these values. 

FHWA’s congestion cost estimates for trucks, which are weighted averages based on the 

estimated fractions of peak and off-peak freeway travel for each class of trucks, already account 

for the fact that trucks make up a smaller fraction of peak period traffic on congested roads 

because they try to avoid peak periods when possible. FHWA’s congestion cost estimates focus 

on freeways because non-freeway effects are less serious due to lower traffic volumes and 

opportunities to re-route around the congestion. The agencies, however, applied the congestion 

cost to the overall VMT increase, though the fraction of VMT on each road type used in MOVES 

range from 27 to 29 percent of the vehicle miles on freeways for vocational vehicles and 53 

percent for combination tractors. The results of this analysis potentially overestimate the 

congestions costs associated with increased truck use, and thus lead to a conservative estimate of 

benefits. 

EPA and NHTSA estimated the costs of additional vocational vehicle travel using a 

weighted average of 15 percent of the FHWA estimate for bus costs and 85 percent of the 

FHWA estimate for single unit truck costs to reflect the make-up of this segment. The low, mid, 

and high cost estimates from FHWA updated to 2009 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 $0.025 $0.009 $0.003

Combination 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

Vocational 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 analysis. 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 mile)

Pickup Truck, Van $0.077

Vocational Vehicle $0.140

Combination Tractor $0.150
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

9-29

Table 9-12: Annual External Costs Associated with the Heavy-Duty Vehicle Program (Millions of 2009 

dollars)

YEAR Class 2b&3 Vocational Combination 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 $168 $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 

9-30

9.5.2 Savings due to Reduced Refueling Time

Reducing the fuel consumption of heavy-duty trucks will either increase their driving 

range before they require refueling, or lead truck manufacturers to offer, and truck purchasers to 

buy, smaller fuel tanks. Keeping the fuel tank the same size will allow truck operators to reduce

the frequency with which drivers typically refuel their vehicles, by extending the upper limit on

the distance they can travel before requiring refueling. Alternatively, if truck purchasers and 

manufacturers respond to improved fuel economy by reducing the size of fuel tanks, the smaller 

tank will require less time to fill during each refueling stop. 

Because refueling time represents a time cost of truck operation, these time savings 

should be incorporated into truck purchasers’ decisions about how much fuel-saving technology

they purchase as part of their choices of new vehicles. The savings calculated here thus raise the 

same questions discussed in preamble VIII.A and RIA Section 9.1: does the apparent existence 

of these savings reflect failures in the market for fuel economy, or does it reflect costs that are 

not addressed in this analysis? The response to these questions could vary across truck segment. 

See those sections for further analysis of this question.

No direct estimates of the value of extended vehicle range or reduced fuel tank size are 

readily available. Instead, this analysis calculates the reduction in the annual amount of time a 

driver of each type of truck will spend filling its fuel tank; this reduced time could result either 

from fewer refueling events, if new trucks’ fuel tanks stay the same size, or from less time spent 

filling the tank during each refueling stop, if new trucks’ fuels tank are made proportionately 

smaller. As discussed in Section 9.2 in this RIA, the average number of miles each type of truck 

is driven annually 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 

typical truck in each class will save each year 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 

fueling station or other time spent at the station; it is assumed that time savings occur only when 

truck operators are actually refueling their vehicles. 

The calculation uses the reduced number of gallons consumed by truck type and divides 

that value by the fuel dispense rate (shown in Table 9-13) to determine the number of hours 

saved in a given year. The calculation then applies DOT-recommended values of travel time 

savings to convert the resulting time savings to 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 analysis are included Table 9-13. The savings associated 

with reduced refueling time for trucks of each type throughout it lifetime are shown in Table 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or 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 Savings

HD PICKUP TRUCK 

AND VAN

VOCATIONAL 

VEHICLE TRACTOR

Fuel Dispensing Rate 

(gallon/minute)30 10 10 20

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

PICKUP 

TRUCKS AND 

VANS

VOCATIONAL 

VEHICLES TRACTORS

3% Discount Rate $31 $34 $341

7% Discount Rate $19 $22 $223

The aggregate 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 Safety Standards and Voluntary Safety Improvements on Vehicle 

Weight

Safety standards developed by NHTSA in previous rulemakings may make compliance 

with the fuel efficiency and CO2 emissions standards more difficult or may reduce the projected 

benefits of the program. The primary way that safety regulations can impact fuel efficiency and 

CO2 emissions is through increased vehicle weight, which reduces the fuel efficiency (and thus 

increases the CO2 emissions) of the vehicle. Using MY 2010 as a baseline, this section discusses 

the effects of other government regulations on MYs 2014-2016 medium and heavy-duty vehicle 

fuel efficiency and CO2 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 analysis of the impact of safety standards that might affect the 

MYs 2014-2016 fleets into three parts: 1) those NHTSA final rules with known effective dates, 

2) proposed rules or soon-to-be proposed rules by NHTSA with or without final effective dates, 

and 3) currently voluntary safety improvements planned by 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 heavy-duty (MD/HD) vehicles between MY 2014 and MY 2016. We 
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

9-33

will examine the potential impact on MD/HD vehicle weights for MYs 2014-2016 using MY 

2010 as a baseline. 

1. FMVSS 119, Heavy Truck Tires Endurance and High Speed Tests

2. FMVSS 121, Air Brake Systems Stopping Distance 

3. FMVSS 214, Motor Coach Lap/Shoulder Belts

4. MD/HD Vehicle Electronic Stability Control Systems

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 FR 60036). It proposed to increase significantly the stringency of the endurance test and to 

add a new high speed test. The data in the large truck crash causation study (LTCCS) that 

preceded that NPRM found that J and L load range tires were having proportionately more 

problems than the other sizes and the agency’s test results indicate that H, J, and L load range 

tires are more likely 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 systems 

on vehicles with air brake systems. 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 heavy truck tractors (approximately 99 percent of the 

fleet) to achieve a 30 percent reduction in stopping distance compared to currently required 

levels. Three-axle 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 twoaxle tractors and tractors with a GVWR above 59,600 pounds must meet the reduced stopping 

distance requirements by the later date of August 1, 2013 (MY 2013). NHTSA determined that 

there are several brake systems that can meet the requirements established in the final rule,

including installation of larger S-cam drum brakes or disc brake systems at all positions, or 

hybrid disc and larger rear S-cam drum brake systems. 

According to data provided by a manufacturer (Bendix) in response to the NPRM, the 

heaviest drum brakes weigh more than the lightest disc brakes, while the heaviest disc brakes 

weigh more than the lightest drum brakes. For a three-axle tractor equipped with all disc brakes, 
Regulatory Impact Analysis 

9-34

then, the total weight could increase by 212 pounds or could decrease by 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 vehicles for MYs 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 preliminary results from the 

agency’s cost/weight teardown studies of motor coach seats,32 NHTSA estimates that the weight 

added by 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 assembly 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 by 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 currently required in 

MD/HD vehicles, but could be proposed to be required in these vehicles by NHTSA. FMVSS 

No. 105, Hydraulic and electric brake systems, 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 system (ABS). All MD/HD vehicles having a GVWR of more than 10,000 pounds, are 

required to have ABS installed by that standard.

In addition to the existing ABS functionality, ESC requires sensors including a yaw rate 

sensor, lateral acceleration sensor, steering angle sensor and brake pressure sensor along with a 

brake solenoid valve. According to data provided by Meritor WABCO, the weight of an ESC 

system 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 

summarizes estimates made by NHTSA regarding the weight added by the above discussed 

standards or likely rulemakings. NHTSA estimates that weight additions required by 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, or Release During Review ***

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increase motor coach vehicle weight by 171-279 pounds and will increase other heavy-duty truck 

weights by 10 pounds. 

Table 9-16: Weight 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

161-269 73-122

MD/HD Vehicle Electronic 

Stability Control Systems

10 4.5

Total

Motor coaches

171- 279 77.5-126.5

Total

All other MD/HD vehicles

10 4.5

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 analysis by NHTSA for the MYs 

2012-2016 final rule33 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 similarly would, if anything, 

have a positive impact on safety. However, given the large difference in weight between lightduty vehicles and medium-duty trucks, and even larger difference between light-duty vehicles 

and heavy-duty vehicles with loads, the agencies believe that the impact of weight reductions of 

medium- and heavy-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 

rulemaking if it appears that the MD/HD fuel efficiency and GHG standards have unforeseen 

safety consequences. The American Chemistry Council stated in comments to the agencies that 
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 heavy-duty vehicles. They submitted supporting analyses 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 economy trade offs manufacturers should be able to receive a waiver 

from the regulation requirements. Since no weight reduction is required for school buses – or 

any other vocational vehicle – the agencies do not believe this is an issue with the current 

regulation.

9.7 Petroleum, Energy and National Security impact

9.7.1 Security Impacts

The HD National Program is designed to reduce fuel consumption and GHG emissions in 

medium- and heavy-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 by 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 preamble.

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 2030. Approximately 28 percent of global supply 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.BEight of these countries are members of OPEC, and a 9th is Russia.C In a 

market where even a 1-2 percent supply 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 Middle East have been the source of eight of the ten major 

world oil disruptions34, with the 9th originating in Venezuela, an OPEC member.

Because of U.S. dependence on oil, the military could be called on to protect 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 CIA, combining various recent years,

https://www.cia.gov/library/publications/the-world-factbook/rankorder/2176rank.html.

C The other three are Norway, Canada, and the EU, an exporter of product.
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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 rely 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 increasingly 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. These benefits 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 clearly 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 national 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 only 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 by potential sudden disruptions in the supply of imported petroleum to the 

U.S. Reducing the amount of oil imported reduces those risks, and thus increases 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, 2011) 

(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. Petroleum Imports

In 2008, U.S. petroleum import expenditures represented 21 percent of total U.S. imports 

of all goods and services.G In 2008, the United States imported 66 percent of the petroleum it 

consumed, and the transportation sector accounted for 70 percent of total U.S. petroleum 

consumption. This compares roughly to 37 percent of petroleum from imports and 55 percent 

consumption of petroleum in the transportation sector in 1975.H It is clear that 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 of this RIA. Based 

on a detailed analysis of differences in fuel consumption, petroleum imports, and imports of 

refined petroleum products and crude oil among the Reference Case, High Economic Growth, 

and Low 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. imports 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 heavy-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 by the MOVES model and the 95 percent oil 

import factor, the reduction in U.S. oil imports from this rule are estimated for the years 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.
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9-39

Year mmbd

2020 0.202

2030 0.393

2040 0.489

2050 0.566

For comparison purposes, Table 9-18 shows the U.S. imports of crude oil in 2020 and 

2030 as projected by 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. Energy Security

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 oil 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 by exerting monopoly 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 products were $326 billion (in 2007$, see

Figure 9-3).

 

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

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U.S. Expenditures on Crude Oil

0

100

200

300

400

500

600

700

1970 1975 1980 1985 1990 1995 2000 2005

Year

(2007$ Billions)

Domestic

Imported

Figure 9-3: U.S. Expenditures on Crude Oil from 1970 through 2008K

One effect 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 Methodology Used to Estimate U.S. Energy Security Benefits

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 social costs and energy security implications of oil use. The energy security 

estimates provided 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 This ORNL study is an 

updated 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. "Estimating the Energy Security Benefits of Reduced U.S. Oil Imports," Oak Ridge National 

Laboratory, ORNL/TM-2007/028, Final Report, 2008.
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9-41

import reductions developed in a 1997 ORNL Report by Leiby, Paul N., Donald W. Jones, T. 

Randall Curlee, and Russell Lee, entitled “Oil Imports: An Assessment of Benefits and Costs.”

M

When conducting this analysis, ORNL considered the full cost of importing petroleum 

into the U.S. The full economic cost is defined 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 by 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 potentially vulnerable regions of the world was not included in

this analysis because its attribution to particular missions or activities is difficult (as discussed 

further below). 

The literature on the energy security for the last two decades has routinely combined the 

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

the energy security premium. However, in the context of using a global value for the Social Cost 

of Carbon (SCC) the question arises: how should the energy security premium be used when 

some benefits from the rule, such as the benefits of reducing greenhouse gas emissions, are 

calculated using a global value? Monopsony benefits represent avoided payments 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 clearly 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 program. 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. 

Section VIII.I of the preamble contains more discussion of how the monopsony and 

macroeconomic disruption/adjustment components are treated for this analysis. 

As part of the process for developing the ORNL energy security estimates, EPA 

sponsored an independent, expert peer review of the 2008 ORNL study. A report compiling the 

peer reviewers’ comments is provided in the docket.N In addition, EPA worked with ORNL to 

 

M Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and 

Costs, ORNL-6851, Oak Ridge National Laboratory, November, 1997.

N Peer Review Report Summary: Estimating the Energy Security Benefits of Reduced U.S. Oil Imports, ICF, Inc., 

September 2007.
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address comments raised in the peer review and to develop estimates of the energy security 

benefits associated with a reduction in U.S. oil imports. In response to peer reviewer comments, 

ORNL modified its model by changing several key parameters involving OPEC supply behavior, 

the responsiveness of oil demand and supply 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, ORNL further updated the energy security premium by

incorporating the most recent oil price forecast and energy market trends from the AEO 2011 

(Early Release) into its model. In order for the energy security premium to be used in EPA’s 

MOVES model, ORNL developed energy security premium estimates for a number of different 

years (i.e., 2020, 2030, and 2035), Table 19-9 provides estimates for energy security premium 

for the years 2020, 2030 and 2035,Oas 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)

2030 $11.17

($3.92 - $20.58) 

$8.32

($4.04 - $13.33)

$19.49

($10.49 -$29.63)

2035 $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 importing petroleum into the U.S. 

follows from the effect of U.S. import demand on the world oil price over the long-run. Because 

the U.S. is a sufficiently large purchaser of foreign oil supplies, its purchases can affect the world 

oil price. This monopsony power means that increases in U.S. petroleum demand can cause the 

world price of crude oil to rise, and conversely, that reduced U.S. petroleum demand can reduce 

 

O AEO 2011 forecasts energy market trends and values only to 2035. The post-2035 energy security premium 

values are assumed to be equal to the 2035 estimate.
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9-43

the world price of crude oil. Thus, one benefit of decreasing U.S. oil purchases, due to the 

increased 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 daily 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 payments 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 newly-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 $11.29/barrel, with a range of $3.86/barrel to $21.32/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 emissions 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 by 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. Unlike pure transfers, the root cause of 

the disruption price increase is a real resource supply reduction due, for example, to disaster or 

war. Regions where supplies are disrupted, such as the U.S., suffer very high costs. Businesses’ 

and households’ emergency responses to supply 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 price shocks impose on others. As a consequence, the 

U.S. economy as a whole will not make sufficient use of these mechanisms to insulate itself from 

the real costs of rapid increases in energy prices and outlays that usually accompany oil supply 

interruptions. Therefore, the ORNL estimate 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 level of output that the U.S. economy can 

produce fully using its available resources; and the latter stem from temporary dislocation and 

underutilization of available resources due to the shock, such as labor unemployment 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 rapidly these prices return to their predisruption 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 suddenly, empirical evidence shows they also impose additional costs on 

businesses and households which must adjust their use of petroleum and other productive factors 

more rapidly than if the same price increase had occurred gradually. Dislocational effects 

include the unemployment 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 temporarily reduce the level of economic output that can be achieved even 

below the “potential” output level that would ultimately be reached once the economy’s 

adaptation to higher petroleum prices is complete. The additional costs imposed on businesses 

and households for making these adjustments reflect their limited ability to adjust prices, output 

levels, and their use of energy, labor and other inputs quickly and 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 by the probability that the supply of petroleum to 

the U.S. will actually be disrupted. Thus, the “expected value” of these costs – the product of the 

probability that a supply disruption will occur and the sum of costs from reduced economic 

output and the economy’s abrupt adjustment to sharply higher petroleum prices – is the relevant 

measure of their magnitude. Further, when assessing the energy security value of a policy to 

reduce oil use, it is only the change in the expected costs of disruption that results from the 

policy that is relevant. The expected costs of disruption may change from lowering the normal 

(i.e., pre-disruption) level of domestic petroleum use and imports, from any induced alteration in 
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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 disruption 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 Security Policies

The last often-identified component of the full economic costs of U.S. oil imports 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 potentially 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 provides the U.S. with a response option should a 

disruption in commercial oil supplies threaten the U.S. economy. It also allows the U.S. to meet 

part of its International Energy Agency obligation to maintain emergency oil stocks, and it 

provides a national defense fuel reserve. While the costs for building and maintaining the SPR

are more clearly related to U.S. oil use and imports, historically these costs have not varied in 

response to changes in U.S. oil import levels. Thus, while SPR is factored into the ORNL

analysis, the cost of maintaining the SPR is excluded.

U.S. military costs are excluded from the analysis performed by 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 by the need to estimate how those costs might vary 

with incremental variations in U.S. oil imports.

9.7.4.2 Energy Security Benefits of this Program 

Using the same methodology as the peer-reviewed model, but updating the analysis 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 2050R. Since the Agency is taking a global perspective with respect to valuing 

greenhouse gas benefits from the rule, only 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 growth 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, 2030, 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 

program. Table 9-21 shows the estimated annual monetized costs of the program for the 

indicated calendar years. The table also shows the net present values of those costs for the 

calendar years 2012-2050 using both 3 percent and 7 percent discount rates.

S

 In this 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 Estimated Monetized Costs of the Program (Millions of 2009 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 costs and fuel savings 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, or Release During Review ***

9-47

using both 3 percent and 7 percent discount rates. The table shows the benefits of reduced CO2

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 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. 

 In addition, these monetized GHG benefits exclude the value of reductions in non-CO2

GHG emissions (CH4, N2O, HFC) expected under this program. Although EPA has not 

monetized the benefits of reductions in non-CO2 GHGs, the value of these reductions should not 

be interpreted as zero. Rather, the net reductions in non-CO2 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 2009 dollars)

2020 2030 2040 2050

NPV, YEARS 

2012-2050, 3% 

DISCOUNT 

RATEA

NPV, YEARS 

2012-2050, 3% 

DISCOUNT 

RATEA

Reduced CO2 Emissions at each assumed SCC valueb

5% (avg 

SCC) $300 $700 $1,200 $1,700 $9,000 $9,000

3% (avg 

SCC) $1,000 $2,500 $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 

Impacts 

(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 

Impactsc,d B$2,800 $2,800 $2,800 $25,300 $9,100

Non-CO2

GHG 

Impactse

n/a n/a n/a n/a n/a n/a

Total Annual Benefits at each assumed SCC valueb

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
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 CO2 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%: $27-$46; for 

Average SCC at 2.5%: $43-$67; and for 95th percentile SCC at 3%: $83-$140. 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 PM2.5- 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 criteria 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 PM2.5 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-CO2 GHG emissions 

expected under this program (see RIA Chapter 5). Although EPA has not monetized changes in non-CO2 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 rates. The table includes the benefits of reduced CO2

emissions (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 2009 dollars)

2020 2030 2040 2050 NPV, 3% NPV, 7%

Annual 

Costsa-$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 rather than costs.
*** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

9-49

EPA also conducted a separate analysis 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 analysis shows the impacts 

of the program on vehicles produced during each of the model years 2014 through 2018 over the 

course of their expected lifetimes. The net societal benefits over the full lifetimes of vehicles 

produced during each of the five model years from 2014 through 2018 are shown in Table 9-24

and Table 9-25 at both 3 percent and 7 percent discount rates, respectively. 

Table 9-24 Monetized Costs, Benefits, and Net Benefits Associated with the Lifetimes of 2014-2018 Model 

Year Trucks (Millions of 2009 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, 

Noise, 

Congestion -$300 -$300 -$300 -$300 -$300 -$1,500

Refueling 

Savings $60 $60 $60 $80 $100 $400

Non-CO2

GHG Impacts 

and

Non-GHG 

Impacts 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) $8,200 $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-CO2 GHG emissions 

expected under this program (see RIA Chapter 5). Although EPA has not monetized changes in non-CO2 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, NO2 and SO2) were not 

estimated for this analysis.
Regulatory Impact Analysis 

9-50

Table 9-25 Monetized Costs, Benefits, and Net Benefits Associated with the Lifetimes of 2014-2018 Model 

Year Trucks (Millions of 2009 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

Monetized 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-CO2 GHG Impacts and

Non-GHG Impacts 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 $44,300

a The monetized GHG benefits presented in this analysis exclude the value of changes in non-CO2 GHG emissions 

expected under this program (see RIA Chapter 5). Although EPA has not monetized changes in non-CO2 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, NO2 and SO2) were not 

estimated for this analysis.

The agencies have also conducted an uncertainty analysis that considers the impacts on 

the net benefits of the rule if certain costs are actually lower or higher than estimated in the 

primary analysis. The cost elements altered and how are shown in Table 9-26.

Table 9-26 Uncertainty Analysis Cost Elements Considered and How They Were Altered

COST ELEMENT LOW END HIGH 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, or Release During Review ***

9-51

Turbo/compounding 

costs -10% +10% DMC for turbo/compounding on combination tractors

The results of this uncertainty analysis for the Low End Case are shown in Table 9-27

and Table 9-28 for a 3% and 7% discount rate, respectively, 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 Lifetimes of 2014-2018 Model 

Year Trucks for the Low End Uncertainty Analysis (Millions of 2009 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-CO2 GHG Impacts and

Non-GHG Impacts 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 Lifetimes of 2014-2018 Model 

Year Trucks for the Low End Uncertainty Analysis (Millions of 2009 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-CO2 GHG Impacts and

Non-GHG Impacts 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 

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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 Lifetimes of 2014-2018 Model 

Year Trucks for the High End Uncertainty Analysis (Millions of 2009 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-CO2 GHG Impacts and

Non-GHG Impacts 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) $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 Benefits Associated with the Lifetimes of 2014-2018 Model 

Year Trucks for the High End Uncertainty Analysis (Millions of 2009 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

Refueling Savings $50 $40 $40 $60 $60 $250

Non-CO2 GHG Impacts and

Non-GHG Impacts 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, or 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 from 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 will 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 estimates 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 inclusion of combination tractors from MY 2016 forward, and 

the exclusion of RVs, which are not regulated by NHTSA.

Table 9-31: Monetized Costs, Benefits, and Net Benefits Associated with the Lifetimes of 2016-2018 Model 

Year Trucks (Millions, 2009$; 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-CO2 GHG Impacts and NonGHG Impacts c,dn/a n/a n/a n/a

Reduced CO2 Emissions at each assumed SCC 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) $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 Net present value of reduced CO2 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 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-CO2 GHG emissions 

expected under this proposal (see RIA Chapter 5). Although EPA has not monetized changes in non-CO2 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, NO2 and SO2) were not 

estimated for this analysis.

Table 9-32 Monetized Costs, Benefits, and Net Benefits Associated with the Lifetimes of 2016-2018 Model 

Year Trucks (Millions, 2009$; 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-CO2 GHG Impacts and NonGHG Impacts c,dn/a n/a n/a n/a

Reduced CO2 Emissions at each assumed SCC 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) $2,400 $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, or Release During Review ***

9-55

Notes: 

a Net present value of reduced CO2 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 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-CO2 GHG emissions 

expected under this proposal (see RIA Chapter 5). Although EPA has not monetized changes in non-CO2 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, NO2 and SO2) were not 

estimated for this analysis.

9.10Employment Impacts

(1) Introduction

Although analysis of employment impacts is not part of a cost-benefit analysis (except to 

the extent that labor costs contribute to costs), employment impacts of federal rules are of 

particular concern in the current economic climate of sizeable unemployment. The recently

issued Executive Order 13563, “Improving Regulation and Regulatory Review” (January 18, 

2011), states, “Our regulatory system 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 analysis of the 

proposed rules, several commenters suggested that we undertake an employment analysis for the 

final rulemaking. Therefore, we have provided a qualitative discussion of the potential 

employment impacts of the Heavy-Duty National Program. 

When the economy is at full employment, an environmental regulation is unlikely to have 

much impact on net overall U.S. employment; instead, labor would primarily be shifted from one 

sector to another. These shifts in employment impose an opportunity cost on society, 

approximated by the wages of the employees, as regulation diverts workers from other activities 

in the economy.

35

 In this situation, any effects on net employment are likely 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 

unemployment, a change in labor demand due to regulation may affect net overall U.S. 

employment because the labor market is not in equilibrium. Schmalansee and Stavins36 point out

that net positive employment effects are possible in the near term when the economy is at less 

than full employment due to the potential hiring of idle labor resources by the regulated sector to 

meet new requirements (e.g., to install new equipment) and new economic activity in sectors 

related to the regulated sector. In the longer run, the net effect on employment 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 

employment could vary over time, region, and sector, and positive effects on employment in 

some regions or sectors could be offset by negative effects in other regions or sectors. For this 
Regulatory Impact Analysis 

9-56

reason, they urge caution in reporting partial employment effects since it can “paint an inaccurate 

picture of net employment impacts if not placed in the broader economic context.” 

This rulemaking is expected to have a relatively small effect on net employment in the 

United States through the regulated sector – the truck and engine manufacturer industry – and 

several related sectors, specifically, industries that supply the truck and engine manufacturing 

industry (e.g., truck parts), the trucking industry 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 people in the U.S. in 2009, the most 

recent year that employment estimates are available for this sector.38 Finally, since the net 

reduction in cost associated with these rules is expected to lead to lower transportation and 

shipping costs, in a competitive market a substantial portion of those cost savings 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

employment in affected sectors by offering the potential for new employment opportunities in 

the design and production of new vehicle technologies. Also, these commenters suggested 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 referred to an assessment by the Union of 

Concerned Scientists (UCS) and CalStart of the economic and employment 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 employment 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, these may at best be considered an 

imprecise upper bound on actual employment impacts.40

Determining the direction of employment effects even in the regulated industry may be 

difficult due to the presence of competing effects that lead to an ambiguous adjustment in 

employment as a result of environmental regulation. Morgenstern, Pizer and Shih41 identify three 

separate ways employment 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, 

post-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 
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9-57

a negative effect on employment, the “cost effect” to have a positive effect on employment, and 

the “factor-shift effect” has an ambiguous effect on employment. 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 employment levels in a regulated sector.

Morgenstern et al. estimated the effects on employment of spending on pollution 

abatement for four highly polluting/regulated industries (pulp and paper, plastics, steel, and 

petroleum refining). They conclude that increased abatement expenditures generally have not 

caused a significant change in employment 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 

methodology that Morgenstern et al. developed is still useful in this context.

(2) Overview of Affected Sectors

The above discussion focuses on employment changes in the regulated sector, but the 

regulated sector is not the only source of changes in employment. 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 directly 

regulated sector. The related sectors 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 following discussion describes the direction of impacts on employment in 

these industries. The effects of the HD rule on net U.S. employment depend, not only on their 

relative magnitudes, but also on employment levels in the overall economy. As previously 

discussed, in a full-employment economy these sector-specific impacts will be mostly offset by

employment 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 employment in the U.S.

(a) Truck and Engine Manufacturers

The regulated sector consists of truck and engine manufacturers. Employment associated 

with manufacturing trucks and engines may be affected by 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, employment 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 

9-58

itself, this effect would reduce vehicle sales. On the other hand, this rulemaking will reduce the 

fuel costs of operating the vehicle; by itself, this effect would increase vehicle sales, especially 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 operating 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 employment (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 associated 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 costeffective fuel-saving technology, but the agencies find that they have not. Preamble Section 

VIII.A discusses various hypotheses that have been suggested to explain this phenomenon. 

Some of the explanations suggest that vehicle manufacturers and buyers will benefit from the 

rulemaking, and vehicle sales will increase; others suggest 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 employment due to changes in 

vehicle sales. However, some in the heavy-duty industry indicate the potential for an increase in 

jobs. As stated by Tom Linebarger (President and Chief Operating Officer of Cummins) and 

Fred Krupp (President of the Environmental Defense Fund), "Finally, strong environmental 

standards 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 clearly increasing labor demand (the “cost effect”). For “replacement” technologies, 

the predicted impact on 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 

likely 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 significantly 
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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 relatively 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 employment; the cost effect is expected to increase employment; and the 

factor-shift effect may have a slightly 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 directly 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, employment effects in this sector are 

expected to be positive.

(c) Transport and Shipping Sectors

Although not directly regulated by these rules, employment effects in the transport and 

shipping sector are likely to result from these regulations. If the overall cost of shipping a ton of 

freight decreases because of increased fuel efficiency (taking into account the increase in upfront 

purchasing costs), in a perfectly competitive industry these costs savings 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 analysis) and therefore an 

increase in employment. In addition, if the relative cost of shipping freight via trucks becomes 

cheaper than shipping by other modes (e.g., rail or barge), then employment 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 

employment 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 saving, principally

reductions in liquid fuels such as diesel and gasoline, will affect employment in the fuel suppliers 

industry sectors, principally the Petroleum Refinery sector.43 

Expected fuel consumption reductions by fuel type, and by heavy-duty vehicle type, 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 reduced fuel consumption in each year by 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 

9-60

level of fuel savings for 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 $600 million (2009$), which will 

result in reduced sectoral employment.44 Because this sector is very capital-intensive, the 

employment 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 

savings. 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 both 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 projected 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 employment in the 

economy through those expenditures. If the fuel savings accrue primarily to firm owners, they

may either reinvest the money or take it as profit. Reinvesting the money in firm operations 

would increase employment directly. 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 employment impacts would be smaller than if the savings went to the public.

(3) Summary

The net employment effects of this rulemaking are expected to be found throughout 

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 employment, depending on the net effect on HD vehicle 

sales. The cost effect is expected to increase employment in the regulated sector; and the factorshift effect may have a slightly positive effect on employment, 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 employment, though this effect may be 

offset somewhat by changes in employment in other shipping sectors. Reduced fuel production 

implies less employment in the fuel provision sectors. Finally, 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 employment through their 

expenditures. Given the job creation as a result of the $1.2B (2009$) in fuel savings in 2014 and 

the possible employment increases in the manufacturing and parts sectors, we find it highly 

unlikely that there would be significant net job losses related to this policy.
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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 

would benefit from trade.

3

See NAS 2010, Note Error! Bookmark 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 57 (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 (2009). “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 of 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-acgoperatingcost2005-2005-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 Reviews Volume 24, 3, 

pp. 261-274, 2004 (Docket ID: EPA-HQ-OAR-2010-0162-0005). 

12 Li, Z., D.A. Hensher, and J.M. Rose, Identifying sources of systematic variation in direct price elasticities from 

revealed preference studies of inter-city freight demand. Transport Policy, 2011

13 Committee to Assess Fuel Economy Technologies for Medium- and Heavy-Duty Vehicles; National Research 

Council; Transportation Research Board (2010). Technologies and Approaches to Reducing the Fuel Consumption 

of Medium- and Heavy-Duty Vehicles. ("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 Systematics, Inc., Draft Final Paper commissioned by the NAS in support of the medium-duty and 

heavy-duty report. Assessment of Fuel Economy Technologies for Medium and Heavy Duty Vehicles: 

Commissioned Paper on Indirect Costs and Alternative Approaches (Docket ID: EPA-HQ-OAR-2010-0162-0009).

14 Friedlaender, A. and Spady, R. (1980) A derived demand function for freight transportation, Review of

Economics and Statistics, 62, pp. 432–441 (Docket ID: EPA-HQ-OAR-2010-0162-0004).

15 Christidis and Leduc, “Longer and Heavier Vehicles for freight transport,” European Commission Joint Research 

Center’s Institute for Prospective Technology Studies, 2009 (Docket ID: EPA-HQ-OAR-2010-0162-0010). 

16 Christidis and Leduc, 2009.
Regulatory Impact Analysis 

9-62

17 Winebrake, James and James J. Corbett (2010). “Improving the Energy Efficiency and Environmental 

Performance of Goods Movement,” in Sperling, Daniel and James S. Cannon (2010) Climate and Transportation 

Solutions: Findings from the 2009 Asilomar Conference on Transportation and Energy Policy. See 

http://www.its.ucdavis.edu/events/2009book/Chapter13.pdf (Docket ID: EPA-HQ-OAR-2010-0162-0011)

18 Winebrake, J. J.; Corbett, J. J.; Falzarano, A.; Hawker, J. S.; Korfmacher, K.; Ketha, S.; Zilora, S., Assessing 

Energy, Environmental, and Economic Tradeoffs in Intermodal Freight Transportation, Journal of the Air & Waste 

Management Association, 58(8), 2008 (Docket ID: EPA-HQ-OAR-2010-0162-0008).

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 and 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 divided by 

average fuel economy of the truck fleet during that same year.) These estimates are derived from time-series 

regression of annual national aggregate VMT for the period 1970-2008 on measures of nationwide economic 

activity, including aggregate GDP, the value of durable and nondurable goods production, and the volume of U.S. 

exports and imports of goods, and variables affecting the price of trucking services (driver wage rates, truck 

purchase prices, and fuel costs), and from regression of VMT for each individual state over the period 1994-2008 on 

similar variables measured at the state level.

22 Memo from Energy and Environmental Research Associates, LLC Regarding HDV Rebound Effect, dated June 8, 

2011. 

23 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, 

with participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, 

Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, 

National Economic Council, Office of Energy and Climate Change, Office of Management and Budget, Office of 

Science and Technology Policy, and Department of Treasury (February 2010). Also available at 

http://epa.gov/otaq/climate/regulations.htm

24 The interagency group decided that these estimates apply only to CO2 emissions. Given that warming profiles and 

impacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group concluded 

“transforming gases into CO2-equivalents using GWP, and then multiplying the carbon-equivalents by the SCC,

would not result in accurate estimates of the social costs of non-CO2 gases” (SCC TSD, pg 13).

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 Consequences 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, or 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 likely this value will increase due to income growth in the future.

30 Passenger vehicle fuel dispensing rate per EPA regulations, last viewed 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 Associates, 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 Emergencies.”

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 Employment 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 IndustryLevel Perspective.” Journal of Environmental Economics and Management 43 (2002): 412-436.

42 Tom Linebarger (President and Chief Operating 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 EPA-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 consumption 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 

9-64

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 impacts from the rule.